https://bou.de/u/api.php?action=feedcontributions&feedformat=atom&user=Chen+HuiniChina Studies Wiki - User contributions [en]2026-04-04T11:34:14ZUser contributionsMediaWiki 1.35.14https://bou.de/u/index.php?title=20211124_homework&diff=13468020211124 homework2021-12-29T08:48:03Z<p>Chen Huini: /* 陈惠妮 Chén Huìnī 英语语言文学（英美文学） 女 202120081479 */</p>
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==陈静 Chén Jìng 国别 女 202020080595==<br />
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我很纳闷：《不自弃文》是篇名，《姬子》是书名，应该同等对待，要么都予注释，要么都不注释，为什么一注一不注呢？难道前者生僻而需要注释，后者人所共知而不必注释吗？显然不是，只能说是避难就易，这与注释的宗旨完全背道而驰。<br />
I wonder that since No Self-surrender is the title of the passage and Jizi is the title of the book, which should be treated equally, why did the situation happened that one annotated while one did not? Did the former need to be annotated while the latter is known to all without having to be annotated? Obviously, it is for choosing the easier way, which is completely contrary to the purpose of the annotation.<br />
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I wonder: "don't abandon yourself" is the title, and "Ji Zi" is the title of the book. It should be treated equally, either annotated or not annotated. Why not annotate one note at a time? Is it true that the former is remote and needs annotation, while the latter is well known and does not need annotation? Obviously not, it can only be said that it is easy to avoid difficulties, which is completely contrary to the purpose of the notes. <br />
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--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 07:01, 24 November 2021 (UTC)<br />
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==蔡珠凤 Cài Zhūfèng 法语语言文学 女 202120081477==<br />
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那怕注为“《姬子》不详”，也还不失为态度诚实。老实说，起初我对《姬子》也一头雾水，因为见所未见，闻所未闻。但根据我自定的注释原则，我不能回避。<br />
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Even if it is noted as "Ji Zi" unknown ", it can be regarded as an honest attitude. To be honest, at first I was confused about Ji Zi, because I had never seen or heard of it. But according to my own annotation principle, I can't avoid it.<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 11:20, 21 November 2021 (UTC)<br />
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Even if it is noted as "unknown Ji Zi" , it can still be regarded as an honest attitude. To be honest, at first I was confused about ''Ji Zi'', because I had never seen or heard of it. But according to my own annotation principle, I can't avoid it.--[[User:Chen Huini|Chen Huini]] ([[User talk:Chen Huini|talk]]) 14:36, 22 November 2021 (UTC)Chen Huini<br />
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==陈惠妮 Chén Huìnī 英语语言文学（英美文学） 女 202120081479==<br />
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于是我首先求助于《中国古典数字工程》，肯定了中国根本不存在《姬子》这么一本书，完全是曹雪芹所杜撰，正如《古今人物通考》、《中国历代文选》都是曹雪芹杜撰一样。其次，我记得俞平伯先生有一篇专门解释《姬子》的文章，但文章的题目、发表时间以及文章内容却不记得了。经过两天的翻箱倒柜，我终于找到了这篇文章，它的题目是《读〈红楼梦〉随笔》第九节《姬子》，初载于《文汇报》1954年1月25日；又收入《红楼梦研究参考资料选辑》第二辑，人民文学出版社1973年11月出版。<br />
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Therefore, I first made reference htiw "Chinese Classical Digital Engineering" and confirmed that there was no such a book called "Ji Zi" in China, which was completely written by Cao Xueqin, just as Cao Xueqin wrote "General Examination of Ancient and Modern Characters" and "Selected Chinese Writings in Past Dynasties". Secondly, I remember that Mr. Yu Pingbo had a special article explaining ''JiZi'', but I do not remember the title, publication time and content of the article. After two days of searching, I finally found this article. Its title is ''Ji Zi'', section 9 of Essays on Reading ''A Dream of Red Mansion''. It was first published in Wenhui Daily on January 25, 1954. It was also included in the second series of ''Selected Research Reference Materials on A Dream of Red Mansion'', published by People's Literature Publishing House in November 1973.--[[User:Chen Huini|Chen Huini]] ([[User talk:Chen Huini|talk]]) 14:46, 22 November 2021 (UTC)Chen Huini<br />
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So, I firstly searched ''Chinese Classical Digital Engineering'' and confirmed there was no such a book called "Ji Zi" in China. This book was completely made up by Cao Xueqin like the same thing he did to ''General Examination of Ancient and Modern Characters'' and ''Selected Chinese Writings in Past Dynasties''.Then, I recalled that Mr. Yu Pingbo especially wrote an article to explain ''Ji Zi'', but I didn't remember the title, publication time and content.After two days of searching, I finally found it. The title of it was ''Ji Zi'', section 9 of Essays on Reading ''A Dream of Red Mansion'' which was first published in Wenhui Daily on January 25, 1954. and then was included in the second series of ''Selected Research Reference Materials on A Dream of Red Mansion'', published by People's Literature Publishing House in November 1973.--[[User:Chen Xiangqiong|Chen Xiangqiong]] ([[User talk:Chen Xiangqiong|talk]]) 00:20, 24 November 2021 (UTC)<br />
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==陈湘琼 Chén Xiāngqióng 外国语言学及应用语言学 女 202120081480==<br />
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据俞先生在文章中说：姬子书到底是部什么书呢，谁也说不上来。特别前些日子把这一回书选为高中国文的教材，教员讲解时碰到问题，每来信相询，我亦不能对。但经过研究，他还是写了这篇文章，作为回答。<br />
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According to Mr.Yu in his article: nobody can tell what book ''Ji Zi'' really is. Moreover, this chapter of ''A Dream in Red Mansions'' with the name ''Ji Zhi'' has been selected as the reading material of the high school,and I can't say anything when the teacher who failed to explain it in the classroom come to me.But after careful research, he still write an article to reply this question.<br />
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According to Mr. Yu in his article: nobody can tell what book ''Ji Zi'' really is. In particular, this chapter was selected as a reading material for the Chinese language in high school some days ago, the teachers encountered problems when explaining it, and they wrote to me every time to ask about it, but I couldn't get it right. But after researching, he wrote this article as an answer.--[[User:Chen Xinyi|Chen Xinyi]] ([[User talk:Chen Xinyi|talk]]) 05:37, 23 November 2021 (UTC)<br />
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==陈心怡 Chén Xīnyí 翻译学 女 202120081481==<br />
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他的结论有三点：其一，《姬子》是“作者杜撰”，并以第三回的《古今人物通考》也是杜撰而作为佐证。其二，“这原来是一个笑话”，是探春“拿姬子来抵制”宝钗用以压人的朱子和孔子，而“比朱子孔子再大，只好是姬子了。殆以周公姓姬，作为顽笑”。<br />
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There are three points in his conclusion: one, ''Ji Zi'' is "the author fabricated", and to the third round of " the general examination of ancient and modern characters" is also fabricated and as proof. Second, "this turns out to be a joke", is Tanchun " take ''Ji Zi'' to resist" Baochai and used to press people by citing Zhu Zi and Confucius, and "higher level than Zhu Zi and Confucius can only be ''Ji Zi''. Probably Zhou Gong was surnamed Ji as a joke.--[[User:Chen Xinyi|Chen Xinyi]] ([[User talk:Chen Xinyi|talk]]) 05:35, 23 November 2021 (UTC)<br />
There are three points in his conclusion: first, ''Ji Zi'' is "fabricated by the author", and can be proved by the fabrication of the third round of " the general examination of ancient and modern characters". Second, "this turns out to be a joke", which Tanchun " held ''Ji Zi'' to resist” Baochai who used to press her by citing Zhu Zi and Confucius, but “requiring higher level than Zhu Zi and Confucius, there’s only be ''Ji Zi''. Probably Zhou Gong was surnamed Ji as a joke.”--[[User:Cheng Yang|Cheng Yang]] ([[User talk:Cheng Yang|talk]]) 12:25, 23 November 2021 (UTC)<br />
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==程杨 Chéng Yáng 英语语言文学（英美文学） 女 202120081482==<br />
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其三，“有人或者要问为什么净瞎捣乱，造书名？我回答：这是小说。”《中国古典数字工程》可以证明俞先生的“杜撰说”是正确的，因此我把俞先生的意见用以注释《姬子》。<br />
Thirdly, "Someone may ask why messing around and making a title? I replied: This is a novel. "It can be proved by ''The Chinese Classical Digital Engineering'' that Mr. Yu’s “theory of fabrication” is correct. Therefore, I applied Mr. Gong's opinion to annotate ''Jizi''.--[[User:Cheng Yang|Cheng Yang]] ([[User talk:Cheng Yang|talk]]) 04:56, 23 November 2021 (UTC)<br />
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Thirdly, "Someone may ask why to mess around and make a title? I replied: This is a novel. "It can be proved by ''The Chinese Classical Digital Engineering'' that Mr. Yu’s ''Theory of Fabrication'' is correct. Therefore, I applied Mr. Gong's opinion to annotate ''Ji Tzu''.--[[User:Ding Xuan|Ding Xuan]] ([[User talk:Ding Xuan|talk]]) 11:01, 26 November 2021 (UTC)<br />
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==丁旋 Dīng Xuán 英语语言文学（英美文学） 女 202120081483==<br />
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据我所知，现在有人搜集了周公的几篇佚文，将其编为集子，按照《老子》、《庄子》、《孟子》之类的惯例，即命名为《姬子》，但这与曹雪芹毫不相干，《红楼梦》中的《姬子》书名绝对是杜撰。此外，有的注本虽然对《不自弃文》作了注释，却只是简述该文的大意，而没有注出曹雪芹的深刻用意。原来朱熹的徒子徒孙认为此文格调低下，有失朱夫子的身份，故将此文排除在众多朱熹文集之外，只有明·朱培编《文公大全集补遗》卷八从抄本《朱熹家谱》中引录，另有《朱子文集大全类编·卷二一·庭训》亦予收录。<br />
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As far as I know, someone collected several lost articles of Duke of Zhou, edited them into an anthology and named it ''Ji Tzu'' according to the routine of ''Lao Tzu'', ''Chang Tzu'' and ''Mencius'' and so on. However, this thing is irrelevant to Cao Xueqin, so the title of ''Ji Tzu'' in ''A Dream in Red Mansions'' is absolutely fabricated. Besides, although some books with annotations made interpretation to ''No Self-surrender'', they just told the main idea of this article rather than annotating the deep meaning made by Cao Xueqin. In fact, disciples and followers of Zhu Xi thought the style of this passage beneath his dignity is very low, so they excluded it out of many anthologies of Zhu Xi. Only when Zhu Pei(Ming dynasty) edited the eighth roll of ''Supplement to Collected Works of Duke Wen'', he incited it from transcript of ''Zhu Xi’s Genealogy''. In addition, it is also included in ''Complete Works of Zhu Tzua•Roll Twenty-one•Home Hearing''.--[[User:Ding Xuan|Ding Xuan]] ([[User talk:Ding Xuan|talk]]) 03:47, 21 November 2021 (UTC)<br />
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As far as I know, now someone collected several lost articles of Duke of Zhou and edited them into a collection that was named ''Ji Tzu'' according to the routine of ''Lao Tzu'', ''Chang Tzu'' and ''Mencius'' and so on. However, this thing is irrelevant to Cao Xueqin, so the title of ''Ji Tzu'' in ''A Dream in Red Mansions'' is absolutely fabricated. Besides, although some books with annotations made interpretations to ''No Self-surrender'', they just briefly described the main idea of this article rather than annotating the deep meaning of Cao Xueqin. In fact, disciples and followers of Zhu Xi thought this passage was low in style and demeaned Zhu Xi, so they excluded it out of many anthologies of Zhu Xi. Only when Zhu Pei(Ming dynasty) edited the Book Eight of ''Supplement to Collected Works of Duke Wen'', he incited it from transcript of Zhu Xi’s Genealogy. In addition, ''Complete Works of Zhu Tzua• Book Twenty-one•Home Hearing'' was also included.--[[User:Du Lina|Du Lina]] ([[User talk:Du Lina|talk]]) 03:07, 22 November 2021 (UTC)<br />
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==杜莉娜 Dù Lìna 英语语言文学（语言学） 女 202120081484==<br />
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曹雪芹借宝钗之口说出这篇少见的文章，一则以显示宝钗无书不读，再者也暗示自己博览群籍，同时也对那些自封的朱熹卫士予以调侃。可见曹雪芹即使开玩笑，也非闲笔，总有一定的用意。（详见注释）鉴于所要注释的词语性质不同，因此对注文的要求也有所不同。<br />
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Saying this rare writing through Precious Hairpin Marshgrass, on the one hand Cao Xueqin showed her strong love of reading as well as implied own extensive reading, and on the other, he played off those self-appointed guards of Zhu Xi. Obviously, his joking is not casual but absolutely with some profound meaning.(see annotations) The nature of words annotated is different, so the requirements for explanatory notes are different as well.--[[User:Du Lina|Du Lina]] ([[User talk:Du Lina|talk]]) 02:41, 22 November 2021 (UTC)<br />
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==付红岩 Fù Hóngyán 英语语言文学（英美文学） 女 202120081485==<br />
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其一，对于一般的疑难词语，重在疏通文意，多不引经据典，追根溯源。其二，对于成语、典故，则既要注明其出典，又要解释其本义，还要说明其引申义或比喻义。其三，对于各种名物（如建筑、服饰、官署、官职、琴棋书画、医卜星相等），则力求变专门术语为通俗语言，以利读者理解。<br />
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Firstly, as for normally confusing words, the emphasis is on clearing up the meaning of the text, as much quoting scripture and tracing the roots as possible. Secondly, for idioms and allusions, it is necessary to indicate their origins, explain their original meanings, and also include their derivative meanings or metaphorical meanings. Thirdly, for all kinds of physical objects (such as architecture, costumes, official offices, official positions, Four Arts( qin, chess, calligraphy and painting), medicine, divination and astrology, etc.), the attempt is to turn specialized terms into common language in order to facilitate readers’ understanding.--[[User:Fu Hongyan|Fu Hongyan]] ([[User talk:Fu Hongyan|talk]]) 11:24, 24 November 2021 (UTC)<br />
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Firstly, as for common confusing words, it should emphasize on clearing up the meaning of the text, as much quoting scripture and tracing the roots as possible. Secondly, for idioms and allusions, it is necessary to indicate their origins, explain their original meanings, and also include their derivative meanings or metaphorical meanings. Thirdly, for various technical terms of objects (such as architecture, costumes, official offices, official positions, Four Arts( qin, chess, calligraphy and painting), medicine, divination and astrology, etc.), the attempt is to turn specialized terms into common language in order to facilitate readers’ understanding.--[[User:Fu Shiyu|Fu Shiyu]] ([[User talk:Fu Shiyu|talk]]) 12:17, 28 November 2021 (UTC)<br />
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==付诗雨 Fù Shīyǔ 日语语言文学 女 202120081486==<br />
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其四，对于历史名人，则注明其所在朝代、简历及突出事迹。对于传说人物，则注明其出处及相关故事。其五，对于珍禽异兽、奇花异卉等，则注明其出处来历、奇异之处及相关故事。<br />
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Fourthly, as for historical celebrities, their dynasties, resumes and outstanding deeds should be indicated. As for legendary figures, their sources and related stories should be indicated. Fifthly, as for rare birds and animals, unusual flowers and different plants, etc., their origins and histories, peculiar places and related stories should be indicated.--[[User:Fu Shiyu|Fu Shiyu]] ([[User talk:Fu Shiyu|talk]]) 14:36, 23 November 2021 (UTC)<br />
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Fourthly, as for historical celebrities, their dynasties, resumes and outstanding deeds should be indicated. As for legendary figures, their sources and related stories should be indicated. Fifthly, as for rare birds and fabulous beasts, unusual flowers and different plants, etc., their origins and histories, peculiarities and related stories should be indicated.--[[User:Gao Mi|Gao Mi]] ([[User talk:Gao Mi|talk]]) 15:50, 23 November 2021 (UTC)<br />
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==高蜜 Gāo Mì 翻译学 女 202120081487==<br />
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其六，对于风俗、礼仪、节气等，则注明其形成沿革、具体内容。其七，对于谜语，则既要揭出谜底，又要解释谜语中的疑难词语、成语典故，还要说明谜底的根据。对于酒令，则要参照令谱，详述酒令的玩法及过程。<br />
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Sixthly, in terms of customs, etiquette and solar terms, its formation, development and content should be indicated. Seventhly, in terms of riddles, answers should be uncovered and an explanation is expected to be given to the answer as well as to difficult words, idioms and allusions in the riddle. Finally, in terms of drinking games, elaboration should be given on the rules and the process according to the instruction manual.--[[User:Gao Mi|Gao Mi]] ([[User talk:Gao Mi|talk]]) 15:51, 23 November 2021 (UTC)<br />
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Sixthly, in terms of customs, etiquette and solar terms, its formation, development and content should be indicated. Seventhly, in terms of riddles, answers should be uncovered and it is necessary to explain the difficult words and idioms in the riddle, and to explain the basis of the answer.Finally, in terms of drinking games, elaboration should be given on the rules and the process according to the instruction manual.--[[User:Gong Boya|Gong Boya]] ([[User talk:Gong Boya|talk]]) 05:45, 27 November 2021 (UTC)<br />
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==宫博雅 Gōng Bóyǎ 俄语语言文学 女 202120081488==<br />
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其八，对于所引前人诗、词、曲、文等，皆要注明出处；诗、词、曲照录全文，文则节录相关的文字。其九，对于具有隐寓或暗示意味的诗、词、曲、文、成语、典故、谜语、酒令等，因其关系到故事情节的发展和人物性格、运命的描写，故除了作注释之外，还要揭示其隐藏的含义。总而言之，注文以释难为易、释疑为明为宗旨，以释义准确、释文简炼为目标。<br />
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Eighthly, reference to predecessors' poems, Ci, Qu, essay, etc., must indicate the source; Poems, Ci, Qu are transcribed without changing the original words, and the essay takes the relevant words. Ninthly, for poems, Ci, Qu, essay, idioms, allusions, riddles, drinkers’ wager game and so on with implicit or suggestive meaning, because they are related to the development of the story plot and the description of the character and fate, so in addition to making annotations, but also to reveal his hidden meaning. In a word, the annotations aim to explain the difficulty as easy, to explain the doubt as clear. Aim to explain the meaning accurately and explain the text concisely.--[[User:Gong Boya|Gong Boya]] ([[User talk:Gong Boya|talk]]) 05:32, 27 November 2021 (UTC)<br />
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Eight, all quotations from poems, lyrics, songs and essays should be attributed to the source; the poems, lyrics and songs should be reproduced in their entirety, while the essays should be excerpted from the relevant texts. Nine, for poems, lyrics, songs, texts, idioms, allusions, riddles, wine orders, etc., which have an implicit or suggestive meaning, as they relate to the development of the storyline and the description of the characters' personalities and fortunes. The commentary should reveal their hidden meanings in addition to annotations. All in all, the aim of the commentary is to explain the difficult for the easy and the doubtful for the clear, and to explain the meaning accurately and to explain the text concisely.--[[User:He Qin|He Qin]] ([[User talk:He Qin|talk]]) 12:32, 28 November 2021 (UTC)<br />
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==何芩 Hé Qín 翻译学 女 202120081489==<br />
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愿望虽然如此，但学力有限，经验欠缺，愿望能否实现，毫无把握。诚望方家指教，读者检验。<br />
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第一回 甄士隐梦幻识通灵 贾雨村风尘怀闺秀<br />
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Although I wish to do so, I am not sure whether my wish can be realized because of my limited learning and lack of experience.I hope that the readers will test it.<br />
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Chapter 1 Hidden Turth--[[User:He Qin|He Qin]] ([[User talk:He Qin|talk]]) 12:33, 28 November 2021 (UTC)<br />
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Although I wish to do so, I am not sure whether the wish can come true with my limited ability and experience. Sincerely hope that other authors teach something and readers check it.<br />
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Chapter 1 Zhen Shiyin, in a vision, apprehends spirituality. Jia Yucun, in the windy and dusty world, cherishes fond thoughts of a beautiful maiden. --[[User:Hu Shuqing|Hu Shuqing]] ([[User talk:Hu Shuqing|talk]]) 07:21, 24 November 2021 (UTC)<br />
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==胡舒情 Hú Shūqíng 英语语言文学（语言学） 女 202120081490==<br />
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此开卷第一回也。作者自云：曾历过一番梦幻之后，故将真事隐去，而借“通灵”之说，撰此《石头记》一书也，故曰“甄士隐”云云。但书中所记何事何人<br />
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This is the first chapter of the book. The author said that after going through the illusion, he prefered covering some truth and in virtue of mysticism wrote the novel ''The Story of the Stone''，so instead he used the name of Zhen Shiyin as a major speaker. But things and people noted in it--[[User:Hu Shuqing|Hu Shuqing]] ([[User talk:Hu Shuqing|talk]]) 03:59, 23 November 2021 (UTC)<br />
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This is the first chapter of the book.Subsequent to the visions of a dream which he had，on some previous occasion，experienced，the writer personally relates，he designedly concealed the true circumstances，and borrowed the attributes of perception and spirituality to relate this story of the Record of the Stone. With this purpose，he made use of such designations as Chen Shih-yin and the like. What are，however，the events recorded in this work？--[[User:Huang Jinyun|Huang Jinyun]] ([[User talk:Huang Jinyun|talk]]) 10:19, 24 November 2021 (UTC)<br />
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==黄锦云 Huáng Jǐnyún 英语语言文学（语言学） 女 202120081491==<br />
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自己又云：“今风尘碌碌，一事无成。忽念及当日所有之女子，一一细考较去，觉其行止见识，皆出我之上；我堂堂须眉，诚不若彼裙钗：我实愧则有馀，悔又无益，大无可如何之日也。当此日，欲将已往所赖天恩祖德，锦衣纨袴之时，饫甘餍肥之日，背父兄教育之恩，负师友规训之德，以致今日一技无成、半生潦倒之罪，编述一集，以告天下：知我之负罪固多，然闺阁中历历有人，万不可因我之不肖，自护己短，一并使其泯灭也。<br />
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The author speaking for himself, goes on to explain, with the lack of success which attended every single concern, I suddenly bethought myself of the womankind of past ages. Passing one by one under a minute scrutiny, I felt that in action and in lore, one and all were far above me; that in spite of the majesty of my manliness, I could not, in point of fact, compare with these characters of the gentle sex. And my shame forsooth then knew no bounds; while regret, on the other hand, was of no avail, as there was not even a remote possibility of a day of remedy.On this very day it was that I became desirous to compile, in a connected form, for publication throughout the world, with a view to (universal) information, how that I bear inexorable and manifold retribution; inasmuch as what time, by the sustenance of the benevolence of Heaven, and the virtue of my ancestors, my apparel was rich and fine, and as what days my fare was savory and sumptuous, I disregarded the bounty of education and nurture of father and mother, and paid no heed to the virtue of precept and injunction of teachers and friends, with the result that I incurred the punishment, of failure recently in the least trifle, and the reckless waste of half my lifetime. There have been meanwhile, generation after generation, those in the inner chambers, the whole mass of whom could not, on any account, be, through my influence, allowed to fall into extinction, in order that I, unfilial as I have been, may have the means to screen my own shortcomings.--[[User:Huang Jinyun|Huang Jinyun]] ([[User talk:Huang Jinyun|talk]]) 12:42, 22 November 2021 (UTC)<br />
<br />
The author goes on to explain, with the lack of success which attended every single concern, I suddenly bethought myself of the womankind of past ages. Thinking of them one by one under a minute scrutiny, I felt that in action and in lore, one and all were far above me; that in spite of the majesty of my manliness, I could not, in point of fact, compare with these characters of the gentle sex. And my shame forsooth then knew no bounds; while regret, on the other hand, was of no avail, as there was not even a remote possibility of a day of remedy.On this very day it was that I became desirous to compile, in a connected form, for publication throughout the world, with a view to (universal) information, how that I bear inexorable and manifold retribution; inasmuch as what time, by the sustenance of the benevolence of Heaven, and the virtue of my ancestors, my apparel was rich and fine, and as what days my fare was savory and sumptuous, I disregarded the bounty of education and nurture of father and mother, and paid no heed to the virtue of precept and injunction of teachers and friends, with the result that I incurred the punishment, of failure recently in the least trifle, and the reckless waste of half my lifetime. There had been meanwhile, generation after generation, those in the inner chambers, the whole mass of whom could not, on any account, be, through my influence, allowed to fall into extinction, in order that I, unfilial as I have been, may have the means to hide my own shortcomings.--[[User:Huang Yiyan1|Huang Yiyan1]] ([[User talk:Huang Yiyan1|talk]]) 14:34, 22 November 2021 (UTC)<br />
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==黄逸妍 Huáng Yìyán 外国语言学及应用语言学 女 202120081492==<br />
<br />
所以蓬牖茅椽，绳床瓦灶，并不足妨我襟怀；况那晨风夕月，阶柳庭花，更觉得润人笔墨。我虽不学无文，又何妨用假语村言敷演出来，亦可使闺阁昭传，复可破一时之闷，醒同人之目，不亦宜乎？”故曰“贾雨村”云云。<br />
<br />
"Though my home is now a thatched cottage in which there are shabby windows, bed made of rope and earthen stove, all of these can not change my being broad and level. Besides, the morning breeze, the dew of night, the willows by me steps and the flowers in the yard inspired me to wield my pen. Though I have little learning and literary talent, it doesn't matter if I tell a tale in rustic language to record those lovely girls. This should help readers distract them from their worries. And that's the reason why I use the name Rainvillage Merchant."--[[User:Huang Yiyan1|Huang Yiyan1]] ([[User talk:Huang Yiyan1|talk]]) 13:27, 21 November 2021 (UTC)<br />
<br />
"Though my home is now a thatched cottage in which there are shabby windows, bed made of rope and earthen stove, all of these can not change my being broad and level. Besides, the morning breeze, the dew of night, the willows by me steps and the flowers in the yard inspired me to wield my pen. Though I have little learning and literary talent, it doesn't matter if I tell a tale in rustic language to record those lovely girls. This should help readers distract them from their worries. And that's the reason why I use the name Jia Yucun."--[[User:Zeng Junlin|Zeng Junlin]] ([[User talk:Zeng Junlin|talk]]) 14:19, 23 November 2021 (UTC)<br />
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==曾俊霖 Zēng Jùnlín 国别 男 202120081478==<br />
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更于篇中间用“梦”、“幻”等字，却是此书本旨，兼寓提醒阅者之意。看官：你道此书从何而起？说来虽近荒唐，细玩颇有趣味。<br />
<br />
In the middle of the article, the words "dream" and "fantasy" are the purpose of the book and the meaning of reminding readers. Reader: where did you start this book? Although it's absurd, it's fun to play.--[[User:Zeng Junlin|Zeng Junlin]] ([[User talk:Zeng Junlin|talk]]) 13:21, 23 November 2021 (UTC)<br />
In the middle of the article, the words "dream" and "fantasy" are the purpose of the book and the meaning of reminding readers. Reader: where did you start this book? Although it's absurd, it's fun to play.--[[User:Huang Zhuliang|Huang Zhuliang]] ([[User talk:Huang Zhuliang|talk]]) 13:33, 24 November 2021 (UTC)Huang Zhuliang<br />
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==黄柱梁 Huáng Zhùliáng 国别 男 202120081493==<br />
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却说那女娲氏炼石补天之时，于大荒山无稽崖，炼成高十二丈、见方二十四丈大的顽石三万六千五百零一块，那娲皇只用了三万六千五百块，单单剩下一块未用，弃在青埂峰下。谁知此石自经锻炼之后，灵性已通，自去自来，可大可小。因见众石俱得补天，独自己无才，不得入选，遂自怨自愧，日夜悲哀。一日，正当嗟悼之际，俄见一僧一道远远而来，生得骨格不凡，丰神迥异，来到这青埂峰下，席地坐谈。It is said that, once upon a time, when Nuwa was refining stones to mend the sky, she refined them into 36,501 pieces of hard stones 12-feet high and 24-feet square on the Wuji Cliff of the Da Huangshan Mountain. Numa, the creator of human beings in Chinese myth, only used 36,500 pieces, leaving only one unused and abandoned it under the Qinggeng Peak.Who knows, after the stone has been refined and created, its spirit has been passed. It can be big or small.Seeing that all the stones were able to mend the sky, he had no talent and could not be selected, so he complained and felt ashamed and mourned day and night. One day, at the time of mourning, he suddenly saw a monk and a Taoism priest with extraordinary personality coming from afar. They came to the Qinggeng Peak and sat on the ground to talk.--[[User:Huang Zhuliang|Huang Zhuliang]] ([[User talk:Huang Zhuliang|talk]]) 13:34, 24 November 2021 (UTC)Huang Zhuliang<br />
<br />
It is said that, once upon a time, when Nuwa(Goddesses of Sky-patching) was refining stones to mend the sky, she refined them into 36,501 pieces of hard stones 12-feet high and 24-feet square on the Wuji Cliff of the Da Huangshan Mountain. Numa, the creator of human beings in Chinese myth, only used 36,500 pieces, leaving only one unused and abandoned it under the Qinggeng Peak.Who knows, after the stone has been refined and created, it had its spirit, it moved freely and could be big or small.Seeing that all the stones were able to mend the sky, he had no talent and could not be selected, so he complained and felt ashamed and mourned day and night. One day, at the time of mourning, he suddenly saw a monk and a Taoism priest with extraordinary personality coming from afar. They came to the Qinggeng Peak and sat on the ground to talk.--[[User:Jin Xiaotong|Jin Xiaotong]] ([[User talk:Jin Xiaotong|talk]]) 05:57, 26 November 2021 (UTC)<br />
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==金晓童 Jīn Xiǎotóng 202120081494==<br />
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见着这块鲜莹明洁的石头，且又缩成扇坠一般，甚属可爱。那僧托于掌上，笑道：“形体倒也是个灵物了，只是没有实在的好处。须得再镌上几个字，使人人见了，便知你是件奇物。<br />
<br />
It was lovely to see this bright and clean stone shrinking like a fan. Resting on his palm, the monk smiled and said, "The body is a spiritual being, but it has no real benefit. Words had to be engraved so that everyone could see you and know that you were a wonder.--[[User:Jin Xiaotong|Jin Xiaotong]] ([[User talk:Jin Xiaotong|talk]]) 15:01, 20 November 2021 (UTC)<br />
Looking at this bright and clean stone shrinking like a fan, which is so lovely, with the stone on his palm the monk smiled and said, "Your body is a spiritual being, but ihas no real benefits. Words should be engraved so that everyone could see you and know that you are a wonder.--[[User:Kuang Yanli|Kuang Yanli]] ([[User talk:Kuang Yanli|talk]]) 11:42, 24 November 2021 (UTC)<br />
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==邝艳丽 Kuàng Yànl 英语语言文学（语言学） 女 202120081495==<br />
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然后携你到那昌明隆盛之邦、诗礼簪缨之族、花柳繁华地、温柔富贵乡那里去走一遭。”石头听了大喜，因问：“不知可镌何字？携到何方？望乞明示。”那僧笑道：“你且莫问，日后自然明白。”<br />
Then take you there, a city-state of prosperity, a family of scholar, a place of flowers and willows. After listening, Stone asked rejoicingly: “I do not know what word I can write? Where I will be taken to? I hope get your instruction.” The monk smiled, “You do not rush into answer, and you will know it some day.”--[[User:Kuang Yanli|Kuang Yanli]] ([[User talk:Kuang Yanli|talk]]) 11:24, 24 November 2021 (UTC)<br />
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Then I'll take you for a walk to the prosperous country, the family of poems, gifts and tassels, the prosperous place of flowers and willows, and the gentle and rich township. " The stone was overjoyed when he asked, "I don't know what word to engrave? Where to carry it? I hope to beg clearly." the monk smiled and said, “You do not rush into answer, and you will know it some day.”--[[User:Li Aixuan|Li Aixuan]] ([[User talk:Li Aixuan|talk]]) 14:54, 28 November 2021 (UTC)<br />
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==李爱璇 Lǐ Àixuán 英语语言文学（语言学） 女 202120081496==<br />
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说毕，便袖了，同那道人飘然而去，竟不知投向何方。又不知过了几世几劫，因有个空空道人访道求仙，从这大荒山无稽崖青埂峰下经过，忽见一块大石，上面字迹分明，编述历历。空空道人乃从头一看，原来是无才补天，幻形入世，被那茫茫大士、渺渺真人携入红尘、引登彼岸的一块顽石：上面叙着堕落之乡、投胎之处，以及家庭琐事、闺阁闲情、诗词谜语，倒还全备。<br />
<br />
Having concluded these words, he put the stone in his sleeve, and proceeded on his journey, in company with the Taoist priest. No one knows where he took the stone. Nor can it be known how many centuries and ages elapsed, before a Taoist priest, named K'ung K'ung, passed, during his researches after the eternal reason and his quest after immortality, by these Ta Huang Hills, Wu Ch'i cave and Ch'ing Keng Peak. Suddenly seeing a large stone, on the surface of which the handwriting on it is clear and the calendar is compiled, K'ung K'ung examined them from first to last. They, in fact, explained how that this stone had originally been devoid of the properties essential for the repairs to the heavens, how it would be transmuted into human form and introduced by Mang Mang the High Lord, and Miao Miao, the Divine, into the world of mortals, and how it would be led over the other bank (across the San Sara). On the surface, it describes the land of degeneration, the place of reincarnation, as well as family trivia, boudoir leisure, poetry, riddles, which could not be ascertained.--[[User:Li Aixuan|Li Aixuan]] ([[User talk:Li Aixuan|talk]]) 04:54, 24 November 2021 (UTC)<br />
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Having concluded these words, he put the stone in his sleeve, and proceeded leisurely on his journey, in company with the Taoist priest. However, no one knew where he went. Nor can it be known how many centuries and ages elapsed, before a Taoist priest, K'ung K'ung by name, passed, during his researches after the eternal reason and his quest after immortality, by these Da Huang Hills, Wu Ch'i cave and Ch'ing Keng Peak. Suddenly perceiving a large block of stone, on the surface of which the traces of characters giving in a connected form, the various incidents of its fate could be clearly predicted. K'ung K'ung examined them from beginning to end. In fact, they explained how this block of worthless stone which had originally been devoid of the properties essential for the mending to the heavens, would be transmuted into human form and introduced by Mang Mang the High Lord, and Miao Miao, the Divine, into the world of mortals, and how it would be led over the other world (across the San Sara). On the surface, it recorded the spot of its degeneration and the place of its birth. The complete recording also included various family trifles, trivial affairs of young ladies, verses and riddles.--[[User:Li Ruiyang|Li Ruiyang]] ([[User talk:Li Ruiyang|talk]]) 06:17, 28 November 2021 (UTC)<br />
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==李瑞洋 Lǐ Ruìyáng 英语语言文学（英美文学） 女 202120081497==<br />
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只是朝代年纪，失落无考。后面又有一偈云：无才可去补苍天，枉入红尘若许年。此系身前身后事，倩谁记去作奇传？<br />
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But the name of the dynasty and the year of the reign were obliterated and could not be confirmed. There was also a Buddhist verse following behind:<br />
Lacking in virtues to mend the azure skies, <br />
in vain I have been into the mortal world for many years. <br />
These facts are of a former and after life,<br />
but who will record a strange legend for me?--[[User:Li Ruiyang|Li Ruiyang]] ([[User talk:Li Ruiyang|talk]]) 03:57, 24 November 2021 (UTC)<br />
<br />
<br />
But the name of the dynasty and the year of the reign were obliterated and could not be confirmed. There was also a Buddhist verse following behind:<br />
Lacking in virtues to mend the azure sky, nothing have I gained within the years spent in the secular world. All of these about my present life and afterlife, who would record them for me?--[[User:Li Shan|Li Shan]] ([[User talk:Li Shan|talk]]) 14:59, 26 November 2021 (UTC)<br />
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==李姗 Lǐ Shān 英语语言文学（英美文学） 女 202120081498==<br />
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空空道人看了一回，晓得这石头有些来历，遂向石头说道：“石兄，你这一段故事，据你自己说来，有些趣味，故镌写在此，意欲闻世传奇。据我看来：第一件，无朝代年纪可考；第二件，并无大贤大忠理朝廷、治风俗的善政，其中只不过几个异样女子，或情或痴，或小才微善。我纵然抄去，也算不得一种奇书。”<br />
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As the Taoist priest named Kongkong once has examined the stone, he had some idea about the story on it, and then said to the stone, "Brother, maybe in your opinion, the story inscribed on you is of some interest so as to be kept here to win a fame throughout the world. But to my mind, it is far from a legend book to be transcribed. Firstly, there are hardly any clues about the time period of the background; secondly, no outstanding governance regarding politics and costumes has been achieved by great talents or loyal officials, and what it mainly narrates are merely several unusual women, some stuck in love, some boasting subtle intelligence and benevolence."--[[User:Li Shan|Li Shan]] ([[User talk:Li Shan|talk]]) 09:02, 21 November 2021 (UTC)<br />
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As the Taoist priest named Kongkong once has examined the stone, he knew that the stone had some history, and then said to the stone, "Brother, maybe in your opinion, the story inscribed on you is of some interest so as to be kept here to win a fame throughout the world. But to my mind, it is far from a legend book to be transcribed. Firstly, there are hardly any clues about the time period of the background; secondly, no outstanding governance regarding politics and costumes has been achieved by great talents or loyal officials, and what it mainly narrates are merely several unusual women, some stuck in love, some boasting subtle intelligence and benevolence."--[[User:Li Shuang|Li Shuang]] ([[User talk:Li Shuang|talk]]) 03:05, 27 November 2021 (UTC)<br />
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==李双 Lǐ Shuāng 翻译学 女 202120081499==<br />
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石头果然答道：“我师何必太痴？我想历来野史的朝代，无非假借汉、唐的名色；莫如我这石头所记，不借此套，只按自己的事体情理，反倒新鲜别致。况且那野史中，或讪谤君相，或贬人妻女，奸淫凶恶，不可胜数；更有一种风月笔墨，其淫秽污臭，最易坏人子弟。<br />
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The stone answered: “Why are you so stubborn? I think the dynasties of unofficial history are nothing more than under the guise of Han, Tang. They are not as good as the stories recorded by me, a stone, which don’t follow the convention but according to the real facts and therefore are more novel on the contrary. Moreover, those unofficial histories are either slandering the emperor and his subjects, or belittling other people’s wives and children. There are countless descriptions of ferocity and adultery which are the most likely to have a bad influence on the younger generation.”--[[User:Li Shuang|Li Shuang]] ([[User talk:Li Shuang|talk]]) 10:54, 24 November 2021 (UTC)<br />
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The stone answered: “Why are you so stubborn? I think the dynasties recorded in the unofficial histories are nothing more than under the guise of Han and Tang Dynasty. They are not as good as the stories recorded by me, a stone, which don’t follow the convention but according to the real facts and therefore are more novel on the contrary. Besides, those unofficial histories are either slandering the emperor and his subjects, or belittling other people’s wives and children. There are countless descriptions of ferocity and adultery which are most likely to have a bad influence on the younger generation.” --[[User:Li Wenxuan|Li Wenxuan]] ([[User talk:Li Wenxuan|talk]]) 11:10, 24 November 2021 (UTC)<br />
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==李文璇 Lǐ Wénxuán 英语语言文学（英美文学） 女 202120081500==<br />
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至于才子佳人等书，则又开口文君，满篇子建，千部一腔，千人一面，且终不能不涉淫滥。在作者不过要写出自己的两首情诗艳赋来，故假捏出男女二人名姓；又必旁添一小人拨乱其间，如戏中小丑一般。更可厌者，之乎者也，非理即文，大不近情，自相矛盾。<br />
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As for the books about the talented and the beauties, they talked about Wen and Jun, the pages were also full of Zi and Jian. A thousand volumes present the same thing, and a thousand person are also in the same character. Moreover, they cannot avoid to some licentious things. The authors, who had to write several sentimental odes and elegant ballads, had falsely invented the names of both men and women, and also some bad guys who like a clown in a play made some troubles in there. As for the annoying men, they had nothing in their minds and talked about Li and Wen, which had no link with the targeted things and paradoxical in the whole.--[[User:Li Wenxuan|Li Wenxuan]] ([[User talk:Li Wenxuan|talk]]) 00:53, 22 November 2021 (UTC)<br />
As for books related to talented scholars and beautiful ladies, he also talked about Wenjun, who is good at articles and is capricious, but he could not avoid prostitution in the end. The author just wants to write two of his own love poems, so he falsely pinches out the names of men and women; he must add a little person to make trouble in the meantime, like a clown in a play. The more annoying, the more it is, the unreasonable is the literary, the most unkind, self-contradictory.--[[User:Li Wen|Li Wen]] ([[User talk:Li Wen|talk]]) 03:24, 24 November 2021 (UTC)<br />
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==李雯 Lǐ Wén 英语语言文学（英美文学） 女 202120081501==<br />
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竟不如我这半世亲见亲闻的几个女子，虽不敢说强似前代书中所有之人，但观其事迹原委，亦可消愁破闷；至于几首歪诗，也可以喷饭供酒。其间离合悲欢，兴衰际遇，俱是按迹循踪，不敢稍加穿凿，至失其真。只愿世人当那醉馀睡醒之时，或避事消愁之际，把此一玩，不但是洗旧翻新，却也省了些寿命筋力，不更去谋虚逐妄了。<br />
It’s not as good as the few women I’ve seen and heard about in this half of my life. Although I dare not say that she is better than all the people in the books of the previous generations. Looking at their deeds, you can also relieve your sorrow and boredom. As for a few poor poems, you can also taste them while eating and drinking.The joys and sorrows, the ups and downs all follow the traces, daring not to lose the truth. I only hope that when the world is awake, or when avoiding troubles and sorrows ,they can enjoy it, not only to renovate, but also to save some lifespan and energy, not to seek falsehood.--[[User:Li Wen|Li Wen]] ([[User talk:Li Wen|talk]]) 03:15, 24 November 2021 (UTC)<br />
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I am not as good as the women I have seen and heard in my life. Though I cannot say that they are as good as all the people in the books of previous generations, I can relieve my sorrow and despair by watching their deeds. As for a few crooked poems, you can also spray rice for wine. During the separation of joys and sorrows, ups and downs, are all traced, dare not slightly cut, to lose its true. I only hope that when people wake up from their drunkenness, or when they are relieved of their sorrow, they will not only wash the old and renew it, but also save some strength of life, so as not to seek for false things.--[[User:Li Xinxing|Li Xinxing]] ([[User talk:Li Xinxing|talk]]) 04:39, 24 November 2021 (UTC)<br />
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==李新星 Lǐ Xīnxīng 亚非语言文学 女 202120081503==<br />
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我师意为如何？”空空道人听如此说，思忖半晌，将这《石头记》再检阅一遍。因见上面大旨不过谈情，亦只是实录其事，绝无伤时诲淫之病，方从头至尾抄写回来，闻世传奇。<br />
What do I mean?" Empty Taoist listen to say so, ponder a long time, will this "stone" review again. Seeing that the message above was only a talk of love, and only a record of it, without suffering from the disease of lewdness, I copied it back from beginning to end and heard the legend of the world.--[[User:Li Xinxing|Li Xinxing]] ([[User talk:Li Xinxing|talk]]) 04:38, 24 November 2021 (UTC)<br />
<br />
What do I mean? " After hearing this, Taoist Kongkong thought for a long time and reviewed the stone story again. Seeing that the above general purpose is nothing but romance, it is only a factual record of its affairs, and there is no disease of obscenity at the time of injury, so I copied it back from beginning to end and heard the legend of the world.--[[User:Li Yi|Li Yi]] ([[User talk:Li Yi|talk]]) 04:35, 24 November 2021 (UTC)<br />
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==李怡 Lǐ Yí 法语语言文学 女 202120081504==<br />
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从此空空道人因空见色，由色生情，传情入色，自色悟空，遂改名情僧，改《石头记》为《情僧录》。东鲁孔梅溪题曰《风月宝鉴》。后因曹雪芹于悼红轩中披阅十载，增删五次，纂成目录，分出章回，又题曰《金陵十二钗》，并题一绝。<br />
Since then empty Taoist empty because of empty see color, from color feeling, feeling into color, since color wukong, then changed the name of the monk, "stone" for "love monk record". Kong Meixi of The Eastern Lu dynasty wrote the book Fengyue Bao Jian. After cao Xueqin in mourning red xuan read ten years, add and delete five times, compiled into a directory, a chapter back, and the title yue ''Jinling twelve Hairpin'', and a must.--[[User:Li Yi|Li Yi]] ([[User talk:Li Yi|talk]]) 04:31, 24 November 2021 (UTC)<br />
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Since then Empty Taoist saw form through emptiness, generated emotions due to form, into which emptiness was stilled and epiphany was revealed, he then changed his epithet into Monk in Love, and changed ''The story of the Stone'' into ''Record of Monk in Love'', which was called ''Catalogue of Chinese Ancient Romance'' by Kong Meixi of The Eastern Lu dynasty. Afterwards Cao Xueqin read and amended it for ten years, revised and polished it for five times, and then compiled it into a directory with chapters and sections. Finally he entitled it ''The Twelve Flowers in Jinlin'' attached with a Chinese quatrain.--[[User:Liu Peiting|Liu Peiting]] ([[User talk:Liu Peiting|talk]]) 04:55, 24 November 2021 (UTC)<br />
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==刘沛婷 Liú Pèitíng 英语语言文学（英美文学） 女 202120081505==<br />
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即此便是《石头记》的缘起。诗云：满纸荒唐言，一把辛酸泪。都云作者痴，谁解其中味?<br />
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This was the origin of ''The Story of The Stone''. A poem once said, “the whole novel is full of absurd words, as well as bitter tears. People all consider the author crazy, but is there anyone who knows its true meaning？--[[User:Liu Peiting|Liu Peiting]] ([[User talk:Liu Peiting|talk]]) 07:22, 23 November 2021 (UTC)<br />
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This is the origin of ''The Story of the Stone''. The poem says: The pages were full of idle words which was penned with hot and bitter tears; All men call the author fool, but no one understood his secret message.--[[User:Liu Shengnan|Liu Shengnan]] ([[User talk:Liu Shengnan|talk]]) 08:25, 23 November 2021 (UTC)<br />
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==刘胜楠 Liú Shèngnán 翻译学 女 202120081506==<br />
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《石头记》缘起既明，正不知那石头上面记着何人何事？看官请听。按那石上书云：当日地陷东南，这东南有个姑苏城，城中阊门最是红尘中一二等富贵风流之地。<br />
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Now that the origin of the stone is clear, let us see what was written on the stone. Dear readers, please listen. Long ago, the earth dipped downwards in the southeast where there was a city named Gusu; and the quarter around Changmen Gate of Gusu was one of the most fashionable centres of wealth and nobility in the world of men. --[[User:Liu Shengnan|Liu Shengnan]] ([[User talk:Liu Shengnan|talk]]) 02:10, 23 November 2021 (UTC)<br />
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The origin of "The Story of the Stone" was clear, but did you know who or what was written on the stone? Please listen to me and go on. According to the record on the stone: One day, there was a subsidence in southeast and there was Gusu City. In the city, the quarter around Changmen Gate was one of the most fashionable centres of wealth and nobility in the world of men. --[[User:Liu Wei|Liu Wei]] ([[User talk:Liu Wei|talk]]) 03:13, 23 November 2021 (UTC)Liu Wei<br />
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==刘薇 Liú Wēi 国别 女 202120081507==<br />
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这阊门外有个十里街，街内有个仁清巷，巷内有个古庙，因地方狭窄，人皆呼作“葫芦庙”。庙旁住着一家乡宦，姓甄名费，字士隐；嫡妻封氏，性情贤淑，深明礼义。家中虽不甚富贵，然本地也推他为望族了。<br />
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Outside the city of Changmen gate, there was a Shili street and a Renqing lane was on the street. In the Renqing lane, there was an ancient temple called "Hulu temple" owing to it`s narrow location. Next to the temple lived a hometown official named Zhen Fei, courtesy named Shi Yin; his legal wife, surnamed Feng, was a virtuous person with a deep sense of courtesy and righteousness. Although the family was not very rich, the local people also thought that he was a prominent family. --[[User:Liu Wei|Liu Wei]] ([[User talk:Liu Wei|talk]]) 14:02, 21 November 2021 (UTC)Liu Wei<br />
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Outside the Changmen gate was the Shili street and the Renqing lane, inside which was an ancient temple, called the "Gourd temple" for its narrow space. Next to the temple lived a retired official named Zhen Fei, whose courtesy name was Shi Yin; his legal wife Mrs. Feng was a virtuous person with a deep awareness of courtesy and righteousness. Although the family was not very rich, the locals also regarded him as a noble man.--[[User:Liu Xiao|Liu Xiao]] ([[User talk:Liu Xiao|talk]]) 15:19, 22 November 2021 (UTC)<br />
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==刘晓 Liú Xiǎo 英语语言文学（英美文学） 女 202120081508==<br />
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因这甄士隐禀性恬淡，不以功名为念，每日只以观花种竹、酌酒吟诗为乐，倒是神仙一流人物。只是一件不足：年过半百，膝下无儿；只有一女，乳名英莲，年方三岁。一日炎夏永昼，士隐于书房闲坐，手倦抛书，伏几盹睡，不觉矇眬中走至一处，不辨是何地方。<br />
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Hidden Truth had a tranquil mind, indifferent to fame or gain. The only fun in every day life was enjoying beautiful flowers and planting bamboo, drinking nectared wine and reciting poetry. What a fairy-like figure! There was only one pity, that is, though in his fifty years old, he had no son nut only one three-year-old daughter, named Pity Zhen. One day in the hot summer, Hidden Truth was sitting idly in his study. Tired, he threw away his book and fell asleep at his desk, drifting to a place he could not tell.--[[User:Liu Xiao|Liu Xiao]] ([[User talk:Liu Xiao|talk]]) 03:19, 21 November 2021 (UTC)<br />
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Zhen Shiyin had a tranquil mind, indifferent to fame or gain. The only fun in every day life was enjoying beautiful flowers and planting bamboo, drinking nectared wine and reciting poetry. What a fairy-like figure! But there was one pity, that is, though in his fifty years old, he had no son nut only one three-year-old daughter, named Yinglian. One day in the hot summer, Shenyin was sitting idly in his study. He was so tired that he threw away his book and fell asleep at his desk, drifting to a place he could not tell.--[[User:Liu Yue|Liu Yue]] ([[User talk:Liu Yue|talk]]) 05:12, 23 November 2021 (UTC)<br />
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==刘越 Liú Yuè 亚非语言文学 女 202120081509==<br />
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忽见那厢来了一僧一道，且行且谈。只听道人问道：“你携了此物，意欲何往？”那僧笑道：“你放心。如今现有一段风流公案，正该了结，这一干风流冤家，尚未投胎人世。<br />
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Unexpectedly he espied， in the opposite direction， two priests coming towards him： the one a Buddhist， the other a Taoist. As they advanced they kept up the conversation in which they were engaged. "Whither do you purpose taking the object you have brought away？" he heard the Taoist inquire. To this question the Buddhist replied with a smile： "Set your mind at ease，" he said； "there's now in maturity a plot of a general character involving mundane pleasures， which will presently come to a denouement. The whole number of the votaries of voluptuousness have， as yet， not been quickened or entered the world.--[[User:Liu Yue|Liu Yue]] ([[User talk:Liu Yue|talk]]) 05:07, 23 November 2021 (UTC)<br />
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Unexpectedly he espied, from the opposite direction, A monk and a Taoist coming up to him. As they advanced, they kept up the conversation in which they were engaged. "Whither do you purpose taking the thing you have brought away？" He heard the Taoist inquire. The Buddhist replied with a smile: "Set your mind at ease. There's now a case of romantic affairs, which should presently come to a denouement. The whole number of the votaries of voluptuousness involved in have not been reincarnated.--[[User:Liu Yunxin|Liu Yunxin]] ([[User talk:Liu Yunxin|talk]]) 12:31, 24 November 2021 (UTC)<br />
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==刘运心 Liú Yùnxīn 英语语言文学（英美文学） 女 202120081510==<br />
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趁此机会，就将此物夹带于中，使他去经历经历。”那道人道：“原来近日风流冤家又将造劫历世，但不知起于何处，落于何方？”那僧道：“此事说来好笑。<br />
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Taking this opportunity, we can mingle it in them and let it experience the life on earth." The Taoist said: "So those debtors of love affairs will be reincarnated and then suffer on earth. But from which place will the reincarnation start and in which direction will them be placed still remain unsettled." The monk said: "It's a funny story. --[[User:Liu Yunxin|Liu Yunxin]] ([[User talk:Liu Yunxin|talk]]) 12:28, 24 November 2021 (UTC)<br />
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Taking this opportunity, we can carry it away and let it experience the life on earth. ”The Taoist priest said: "The debtors of love affairs will be reincarnated and then suffer on earth recently. But it' s unknown that from when the story started and to where will it go.“ The monk said: "It's a funny story....-- --[[User:Luo Anyi|Luo Anyi]] ([[User talk:Luo Anyi|talk]]) 08:23, 28 November 2021 (UTC)<br />
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==罗安怡 Luó Ānyí 英语语言文学（英美文学） 女 202120081511==<br />
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只因当年这个石头，娲皇未用，自己却也落得逍遥自在，各处去游玩。一日来到警幻仙子处，那仙子知他有些来历，因留他在赤霞宫中，名他为赤霞宫神瑛侍者。他却常在西方灵河岸上行走，看见那灵河岸上三生石畔有棵绛珠仙草，十分娇娜可爱，遂日以甘露灌溉，这绛珠草始得久延岁月。<br />
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This stone have not been used by The Empress Nu Wo. Thus It used to be free to roam on the heavens until one day he came to the Fairy of Wonders, who knew his special background. So she kept him in her palace and gave him the name by the Divine Eunuch of the Palace. He often walked along the bank of the Spirit River in the West where he saw a delicate and lovely flower on the bank of the Three Living Stones. Being struck with the great beauty of this flower, the stone remained there, tending its protegee with the most loving care, and daily moistening its roots with the choicest nectar of the sky. Yielding to the influence of disinterested love, the flower lived a longer life. --[[User:Luo Anyi|Luo Anyi]] ([[User talk:Luo Anyi|talk]]) 07:32, 28 November 2021 (UTC)<br />
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Because this stone in that age hasn‘t been used by a goddess in Chinese mythology，he could be careless and can go to visit many places for fun.One day，he come to the Fairy of Wonders who knew his special background. So she kept him in her palace and gave him the name by the Divine Eunuch of the Palace.But he always walks by the bank of the Spirit River.One day，he saw a fairy grass beside the Three Living Stones on the bank of the river，which is cute and delicate，so he irrigated it day by day，making it living longer.--[[User:Luo Xi|Luo Xi]] ([[User talk:Luo Xi|talk]]) 15:31, 28 November 2021 (UTC)<br />
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==罗曦 Luó Xī 英语语言文学（英美文学） 女 202120081512==<br />
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后来既受天地精华，复得甘露滋养，遂脱了草木之胎，幻化人形，仅仅修成女体，终日游于离恨天外，饥餐秘情果，渴饮灌愁水。只因尚未酬报灌溉之德，故甚至五内郁结着一段缠绵不尽之意。常说：‘自己受了他雨露之惠，我并无此水可还。<br />
Afterwards，because of the essence of the nature and the nutrients of the dew，it gradually got rid of itself from the trees and become a human-being，but only can become a female，meandering outside all day long，when feeling hungry，she would eat fruits，and when feeling thirsty，she would drink water.The reason for her lingering emotions is that she haven‘t showed her gratitude to her benefactors.She always said that：“I was benefited from his dew，but I can‘t bring back a report.”--[[User:Luo Xi|Luo Xi]] ([[User talk:Luo Xi|talk]]) 15:21, 28 November 2021 (UTC)--[[User:Luo Xi|Luo Xi]] ([[User talk:Luo Xi|talk]]) 15:21, 28 November 2021 (UTC)<br />
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==马新 Mǎ Xīn 外国语言学及应用语言学 女 202120081513==<br />
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他若下世为人，我也同去走一遭，但把我一生所有的眼泪还他，也还得过了。’因此一事，就勾出多少风流冤家都要下凡，造历幻缘，那绛珠仙草也在其中。今日这石正该下世，我来特地将他仍带到警幻仙子案前，给他挂了号，同这些情鬼下凡，一了此案。”<br />
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If he could be reincarnated as human in the next life, I would also go with him but only in this time return all the sorrows to him, which can let me go through the life. “For this reason, how many pretty teases have to descend to the world suffering the illusory fates, and the Crimson Pearl Flower is also among them. Today, this stone is about to be born, so I comes here specially to bring him to the court of Fairy Maiden Jinhuan, registering him and letting him go down to the earth with ghosts in order to settle the case.”--[[User:Ma Xin|Ma Xin]] ([[User talk:Ma Xin|talk]]) 12:37, 24 November 2021 (UTC)<br />
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If he could be reincarnated as human in the next life, I would go with him, but I should also return all the sorrows in this time to him, which can enable me to go through the life. "For this reason, lots of pretty teases have to descend to the world suffering the illusory destiny, and that Crimson Pearl Flower is also amomg them. Today this jade is about to be born, so I come here specially to take him to the court of Fairy Maiden Jinhuan, endowing him with a registration and letting him go down to the earth with those sentimental ghosts so as to settle the case.--[[User:Mao Yawen|Mao Yawen]] ([[User talk:Mao Yawen|talk]]) 15:16, 27 November 2021 (UTC)<br />
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==毛雅文 Máo Yǎwén 英语语言文学（英美文学） 女 202120081514==<br />
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那道人道：“果是好笑，从来不闻有‘还泪’之说。趁此，你我何不也下世度脱几个，岂不是一场功德？”那僧道：“正合吾意。你且同我到警幻仙子宫中，将这蠢物交割清楚，待这一干风流孽鬼下世，你我再去。如今有一半落尘，然犹未全集。”<br />
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The Taoist priest says:"It's really ridiculous. I have never heard of the saying of 'returning tears'. We can also take this opportunity to release several souls from purgatory (help several souls of the decease get rid of worldly sufferings). Isn't it a merit?" The monk replies:"It's exactly what I am hoping for. You and I are going to the palace of the fairy maiden Jing Huan, and to deliver such a jade and figure it out. When these dissolute and sinful evils all pass away, we will go to the afterlife. Half of them have fallen into the earthly world, but they have not yet gathered completely."--[[User:Mao Yawen|Mao Yawen]] ([[User talk:Mao Yawen|talk]]) 15:04, 27 November 2021 (UTC)<br />
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The Taoist priest says:"It's really ridiculous. I have never heard of the saying of 'returning tears'. We can also take this opportunity to release several souls from purgatory (help several souls of the decease get rid of worldly sufferings). Isn't it a merit?" The monk replies:"It's exactly what I am hoping for. You and I will go the palace of the fairy maiden Jing Huan, and to deliver such a jade and figure it out. When these dissolute and sinful evils all pass away, we will go to the afterlife. Half of them have fallen into the earthly world, but they have not yet gathered completely."--[[User:Mao You|Mao You]] ([[User talk:Mao You|talk]]) 06:56, 23 November 2021 (UTC)<br />
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==毛优 Máo Yōu 俄语语言文学 女 202120081515==<br />
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道人道：“既如此，便随你去来。”却说甄士隐俱听得明白，遂不禁上前施礼，笑问道：“二位仙师请了。”那僧、道也忙答礼相问。<br />
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The Taoist said, "In that case, let's go with you." Then Hidden Truth heard and understood, so he could not help but go forward to salute, smiling and saying, " Please, distinguished masters." The monk and the Taoist also replied with manners.<br />
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The Taoist said, "In that case, let's go with you." Then Hidden Truth heard and understood, so he could not help but go forward to salute, smiling and saying, " Please, distinguished masters." The monk and the Taoist also immediately replied with manners.--[[User:Mou Yixin|Mou Yixin]] ([[User talk:Mou Yixin|talk]]) 10:50, 24 November 2021 (UTC)<br />
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==牟一心 Móu Yīxīn 英语语言文学（英美文学） 女 202120081516==<br />
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士隐因说道：“适闻仙师所谈因果，实人世罕闻者。但弟子愚拙，不能洞悉明白。若蒙大开痴顽，备细一闻，弟子洗耳谛听，稍能警省，亦可免沉沦之苦了。”<br />
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Hidden Truth said: "What you master talked about the cause and effect is definitely rare in the world. But I am stupid and can't fully understand it. If you can explain it for me to get rid of infatuation and stubbornness, I will listen to you carefully and then take warning from it, avoiding the suffering of enthrallment."--[[User:Mou Yixin|Mou Yixin]] ([[User talk:Mou Yixin|talk]]) 08:01, 21 November 2021 (UTC)<br />
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Hidden Truth then said, "I have just heard you master's words about karma, a truly rare insight in the world. But I am too ignorant to understand it. If I could be enlightened by you two to get rid of infatuation and stubbornness, I would certainly listen carefully to all that you say and then take warning from it, avoiding the suffering of enthrallment."--[[User:Peng Ruixue|Peng Ruixue]] ([[User talk:Peng Ruixue|talk]]) 07:54, 22 November 2021 (UTC)<br />
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==彭瑞雪 Péng Ruìxuě 法语语言文学 女 202120081517==<br />
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二仙笑道：“此乃玄机，不可预泄。到那时只不要忘了我二人，便可跳出火坑矣。”士隐听了，不便再问，因笑道：“玄机固不可泄露，但适云‘蠢物’，不知为何？或可得见否？”那僧说：“若问此物，倒有一面之缘。”<br />
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The two immortals laughed and said, "It is something metaphysical and cannot be divulged in advance. At that time, just don't forget the two of us, and you will be free from your predicament." When Shi Yin heard this, he stopped pursuing the matter. He laughed and said, "Of course the mystery must not be divulged, but I don't quite understand what the 'stupid thing' is that you just mentioned. Perhaps I have a chance to see it?" The monk said, "This thing you are asking about, you do have the fortune to see it.--[[User:Peng Ruixue|Peng Ruixue]] ([[User talk:Peng Ruixue|talk]]) 07:39, 22 November 2021 (UTC)<br />
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The two immortals laughed and said, "It is metaphysical and cannot be divulged in advance. At that time, if you don't forget two of us, and you will be free from your predicament." When Shi Yin heard this, he stopped pursuing the matter. He laughed and said, "Of course the mystery must not be divulged, but I don't quite understand what the 'stupid thing' is that you just mentioned. Perhaps I have a chance to see it?" The monk said, "This thing you are asking about, you do have the fortune to see it.--[[User:Qing Jianan|Qing Jianan]] ([[User talk:Qing Jianan|talk]]) 06:17, 29 November 2021 (UTC)<br />
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==秦建安 Qín Jiànān 外国语言学及应用语言学 女 202120081518==<br />
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说着取出，递与士隐。士隐接了看时，原来是块鲜明美玉，上面字迹分明，镌着“通灵宝玉”四字，后面还有几行小字。正欲细看时，那僧便说已到幻境，就强从手中夺了去。<br />
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He said and took it out to Hidden Truth. ShiYin received it and found it a bright beautiful jade in which there were four clear characters:Tong Ling Bao Yu followes by several lines of words.When Hidden Truth craved for a careful look, that monk said that he had reached the illusion, so he snatched it from ShiYin's hand.--[[User:Qing Jianan|Qing Jianan]] ([[User talk:Qing Jianan|talk]]) 02:57, 21 November 2021 (UTC)<br />
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Then he said as he took it out and handed it to Hidden Truth. Hidden Truth took a look, and it turned out to be a piece of bright beautiful jade, with clear writing above, engraved with the “Tongling jade”. There were a few lines of small characters behind. When he was about to take a closer look, the monk said he had reached the Illusory Land and snatched the jade from his hands.--[[User:Qiu Tingting|Qiu Tingting]] ([[User talk:Qiu Tingting|talk]]) 01:47, 22 November 2021 (UTC)<br />
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==邱婷婷 Qiū Tíngtíng 英语语言文学（语言学） 女 202120081519==<br />
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和那道人竟过了一座大石牌坊，上面大书四字，乃是“太虚幻境”。两边又有一副对联道：假作真时真亦假，无为有处有还无。士隐意欲也跟着过去，方举步时，忽听一声霹雳，若山崩地陷。<br />
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And with that Taoist priest actually passed a large stone archway, above which was engraved four big words, is “ Illusory Land of Great Void ”.On both sides there was a pair of couplets: If false is taken as the truth, then truth is said to be lieing , when nothing is taken as being, then being itself is turned into nothing. Hidden Truth also wanted to pass the big stone archway, but the moment he was about to raise his foot, he heard a crack of thunder which sounded as if the hills were rending asunder and the earth falling in.--[[User:Qiu Tingting|Qiu Tingting]] ([[User talk:Qiu Tingting|talk]]) 02:52, 21 November 2021 (UTC)<br />
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And that Taoist passed a large stone pagoda, written on it four big words, is "Taixu fantasy realm". On both sides, there is a couplet saying: "Falsehood is true when it is true, and there is nothing where there is nothing". Hidden Truth also wanted to follow, when just ready to raise his feet, he heard a thunderbolt all of a sudden, as if a landslide happened.--[[User:Rao Jinying|Rao Jinying]] ([[User talk:Rao Jinying|talk]]) 02:48, 21 November 2021 (UTC)<br />
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==饶金盈 Ráo Jīnyíng 英语语言文学（语言学） 女 202120081520==<br />
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士隐大叫一声，定睛看时，只见烈日炎炎，芭蕉冉冉，梦中之事便忘了一半。又见奶母抱了英莲走来。士隐见女儿越发生得粉装玉琢，乖觉可喜，便伸手接来，抱在怀中，斗他玩耍一会。<br />
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Hidden Truth cried out, and when he fixed his eyes, only seeing the sun is shining, the weather is bright, and the plantains are flourishing, and then he forgot half of his dream. Later, the lactating mother coming with Pity Zhen in her arms. When Hidden Truth noted that his daughter was becoming more and more beautiful and cute, he reached out and took her in his arms, and teased her for a while.--[[User:Rao Jinying|Rao Jinying]] ([[User talk:Rao Jinying|talk]]) 02:43, 21 November 2021 (UTC)<br />
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Hidden Truth cried out, fixing his eyes on the blazing sun and supplely drooping banana leaves, only to be oblivious to half of his dream. Then the wet nurse came over with Pity Zhen in her arms. Hidden Truth perceived that his daughter became so fair and lovely that he couldn’t wait to cradle her in his arms to amuse her.--[[User:Shi Liqing|Shi Liqing]] ([[User talk:Shi Liqing|talk]]) 05:48, 21 November 2021 (UTC)<br />
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== Headline text ==<br />
==石丽青 Shí Lìqīng 英语语言文学（英美文学） 女 202120081521==<br />
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又带至街前，看那过会的热闹。方欲进来时，只见从那边来了一僧一道：那僧癞头跣足，那道跛足蓬头，疯疯癫癫，挥霍谈笑而至。及到了他门前，看见士隐抱着英莲，那僧便大哭起来，又向士隐道：“施主，你把这有命无运、累及爹娘之物抱在怀内作甚？”<br />
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Hidden Truth then took his lovely daughter out into the street to see the lively meeting. When he was about to enter the door, he saw a monk and a Taoist coming from the other side: the monk had ringworm on his head and no shoes or socks on his feet; the Taoist priest was characterized by lameness and untidy hair. They came over, crazy, talking and laughing. When they got to Hidden Truth’s door, seeing him holding Pity Zhen in his arms. The monk began to cry and said to Hidden Truth, “ Benefactor, why did you cradle such an ill-fated and encumbering child in your arms？”--[[User:Shi Liqing|Shi Liqing]] ([[User talk:Shi Liqing|talk]]) 01:35, 21 November 2021 (UTC)<br />
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Shiyin then took his lovely daughter to the street to see the lively agora. When he was about to enter the door, he saw a monk and a Taoist priest coming from the other side: the monk had ringworm on his head and no shoes or socks on his feet; the Taoist priest was characterized by lameness and untidy hair. They acted like a lunatic and came over,talking and laughing. When they got to Shiyin’s door, seeing him holding Yinglian in his arms. The monk began to cry and said to Shiyin, “ Benefactor, why did you cradle such an ill-fated and encumbering child in your arms？”--[[User:Sun Yashi|Sun Yashi]] ([[User talk:Sun Yashi|talk]]) 06:06, 21 November 2021 (UTC)<br />
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==孙雅诗 Sūn Yǎshī 外国语言学及应用语言学 女 202120081522==<br />
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士隐听了，知是疯话，也不睬他。那僧还说：“舍我罢，舍我罢。”士隐不耐烦，便抱着女儿转身。<br />
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After listening to him,Shiyin knew that it's crazy words and ignored him.But the monk also said:"Give her to me,give her to me." Shiyin was impatient,so he held his daughter and turned to leave.--[[User:Sun Yashi|Sun Yashi]] ([[User talk:Sun Yashi|talk]]) 05:59, 21 November 2021 (UTC)<br />
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After listening to him, Hidden Truth knew that it was lunatic ravings and ignored him. But the monk complemented:"Give her to me, give her to me." Hidden Truth got impatient, so he embraced his daughter and turned around. --[[User:Wang Lifei|Wang Lifei]] ([[User talk:Wang Lifei|talk]]) 12:14, 23 November 2021 (UTC)<br />
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==王李菲 Wáng Lǐfēi 英语语言文学（英美文学） 女 202120081523==<br />
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才要进去，那僧乃指着他大笑，口内念了四句言词，道是：惯养娇生笑你痴，菱花空对雪澌澌。好防佳节元宵后，便是烟消火灭时。<br />
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When he was about to get in, the monk pointed at him and laughed, mumbling four sentences, which mean “how crazy that you pamper your daughter like this, (see you embrace Yinglian), just like the summer lotus are exposed to the winter snow. Beware of the days after the Lantern Festival, then there is a fire to vanish everything.”--[[User:Wang Lifei|Wang Lifei]] ([[User talk:Wang Lifei|talk]]) 03:03, 21 November 2021 (UTC)<br />
When he wanted to go in, the monk pointed at him and laughed, saying…--[[User:Wang Yifan21|Wang Yifan21]] ([[User talk:Wang Yifan21|talk]]) 06:56, 24 November 2021 (UTC)<br />
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==王逸凡 Wáng Yìfán 亚非语言文学 女 202120081524==<br />
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士隐听得明白，心下犹豫，意欲问他来历，只听道人说道：“你我不必同行，就此分手，各干营生去罢。三劫后，我在北邙山等你，会齐了，同往太虚幻境销号。”那僧道：“最妙，最妙。”<br />
Shi Yin understood and hesitated, intending to ask him where he came from. The Taoist said, "You and I don't need to go together. Three days later, I wait for you in north mangshan, meet together, with the imaginary land sales number." The monk said, "The best, the best."--[[User:Wang Yifan21|Wang Yifan21]] ([[User talk:Wang Yifan21|talk]]) 06:44, 24 November 2021 (UTC)<br />
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Shiyin understood,hesitated in his heart,and wanted to ask him where he came from.He only heard the Taoist say: "You and I don't have to go together, just break up and go to work. After the Three Tribulations, I will wait for you in Beimanshan,nnd go to the Tai Unreal Realm to sell the number." The monk said: "The most wonderful, the most wonderful."--[[User:Wang Zhenlong|Wang Zhenlong]] ([[User talk:Wang Zhenlong|talk]]) 13:40, 28 November 2021 (UTC)<br />
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==王镇隆 Wáng Zhènlóng 英语语言文学（英美文学） 男 202120081525==<br />
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说毕，二人一去，再不见个踪影了。士隐心中此时自忖：“这两个人必有来历，很该问他一问，如今后悔却已晚了。”这士隐正在痴想，忽见隔壁葫芦庙内寄居的一个穷儒，姓贾名化、表字时飞、别号雨村的走来。<br />
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After that,the two went away,and they were nowhere to be seen.Shiyin thought to himself at this moment: "These two people must have a history.It's time to ask him,but now it's too late to regret." Shiyin was thinking about it,but suddenly saw a poor scholar living in the Hulu temple next door whose first name is Jia,last name hua,Courtesy name Shifei,and another name Yucun came.--[[User:Wang Zhenlong|Wang Zhenlong]] ([[User talk:Wang Zhenlong|talk]]) 13:37, 28 November 2021 (UTC)<br />
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After saying that, the two went away, and there was no sign of them anymore. Shiyin thought to himself at this moment: "These two people surely had some backgrounds. I should have asked him, but it was too late to regret now." Shiyin was daydreaming, but suddenly saw a poor scholar living in the Hulu temple next door coming up. His first name is hua, last name is jia, secondary personal name is Shifei, and another name is Yucun.--[[User:Wei Yiwen|Wei Yiwen]] ([[User talk:Wei Yiwen|talk]]) 14:46, 28 November 2021 (UTC)<br />
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==卫怡雯 Wèi Yíwén 英语语言文学（英美文学） 女 202120081526==<br />
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这贾雨村原系湖州人氏，也是诗书仕宦之族。因他生于末世，父母祖宗根基已尽，人口衰丧，只剩得他一身一口。在家乡无益，因进京求取功名，再整基业。<br />
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Jia Yucun was born in Huzhou and came from a family of Confucian scholars and officials. Because he was born in last phase of age, the roots of his ancestors had died out. Family declined, and left him alone. He found no benefit in hometown, so he went to Beijing to strive for fame and tried to make another solid foundation for family.--[[User:Wei Yiwen|Wei Yiwen]] ([[User talk:Wei Yiwen|talk]]) 03:10, 21 November 2021 (UTC)<br />
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Jia Yucun came from Huzhou and was born in a family of scholars and officials. However, because he was born in the last phase of the age, the root of his ancestors had died out and family declined, leaving him alone in the world. He found no benefit in hometown, so he went to Beijing to strive for success and fame and tried to make the revitalization of his family.--[[User:Wei Chuxuan|Wei Chuxuan]] ([[User talk:Wei Chuxuan|talk]]) 04:46, 21 November 2021 (UTC)<br />
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==魏楚璇 Wèi Chǔxuán 英语语言文学（英美文学） 女 202120081527==<br />
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自前岁来此，又淹蹇住了，暂寄庙中安身，每日卖文作字为生，故士隐常与他交接。当下雨村见了士隐，忙施礼陪笑道：“老先生倚门伫望，敢街市上有甚新闻么？”士隐笑道：“非也。适因小女啼哭，引他出来作耍。正是无聊的很，贾兄来得正好，请入小斋，彼此俱可消此永昼。” <br />
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Since arriving in Beijing the year before last, Jia Yuchun had led a hard life, living only in a temple. He wrote poems and articles in exchange for money every day, so Shiyin often often met with him. Once Yucun saw Shiyin, hurriedly saluted and said with a smile, " Sir, you are leaning on the door and looking at something. Is there any news in the market?" Shiyin smiled and said, "No. Just because my little girl cried, so I took her out to play. I am so bored now and you are so nice to appear in time. Please come into my study with me, so that we can both kill the boring time." --[[User:Wei Chuxuan|Wei Chuxuan]] ([[User talk:Wei Chuxuan|talk]]) 03:35, 21 November 2021 (UTC)<br />
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Since arriving in Beijing the year before last, Jia Yuchun found himself in difficult conditions and desperate straits. He lived only in a temple and made a living by writing in exchange for money every day, so Shiyin often met with him. At that moment, Yucun saw Shiyin, hurriedly saluted and said with smile, " An old gentleman as you, leaning on the door and looking at something, I wander that is there any news in the street?" Shiyin smiled and said, "Hardly, just because my little girl cried, so I take her out to play. I am so bored now and you‘ve come just at the right moment. Please come into my study, so that we can spend the long day together."--[[User:Wei Zhaoyan|Wei Zhaoyan]] ([[User talk:Wei Zhaoyan|talk]]) 14:13, 23 November 2021 (UTC)<br />
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==魏兆妍 Wèi Zhàoyán 英语语言文学（英美文学） 女 202120081528==<br />
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说着，便令人送女儿进去。自携了雨村来至书房中，小童献茶。方谈得三五句话，忽家人飞报：“严老爷来拜。”<br />
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While he was talking, he asked someone to take his daughter back to her room. Then he took Yucun to his study, and a child offered a cup of tea for each of them. But just said a few words, suddenly the family member came quickly to say that "Master Yan came to visit."--[[User:Wei Zhaoyan|Wei Zhaoyan]] ([[User talk:Wei Zhaoyan|talk]]) 07:30, 23 November 2021 (UTC)<br />
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While talking, he asked someone to take his daughter back to her room. Then he took Yucun to his study, and a child offered a cup of tea for each of them. But just said a few words, suddenly the family member came quickly to say that "Master Yan came to visit." --[[User:Wu Jingyue|Wu Jingyue]] ([[User talk:Wu Jingyue|talk]]) 14:01, 24 November 2021 (UTC)<br />
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==吴婧悦 Wú Jìngyuè 俄语语言文学 女 202120081529==<br />
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士隐慌忙起身谢道：“恕诓驾之罪。且请略坐，弟即来奉陪。”雨村起身也让道：“老先生请便。晚生乃常造之客，稍候何妨！”说着，士隐已出前厅去了。<br />
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Shiying stood up hurriedly and said, " Excuse me.Please sit for a moment first, and I will entertain you at once." Yucun also stood up and answered:" Old gentleman, you go. I often come to you here as a guest, wait a little while is not the matter!" Said, Shiying had walked out of the guest room.--[[User:Wu Jingyue|Wu Jingyue]] ([[User talk:Wu Jingyue|talk]]) 02:51, 21 November 2021 (UTC)<br />
Shiyin hurriedly got up and thanked, "excuse the crime of cheating driving. Please sit down and my brother will accompany you." Yucun got up and said, "please help yourself, sir. My late life is a regular guest. Why not wait a minute!" said Shiyin, who had left the front hall.--[[User:Wu Yinghong|Wu Yinghong]] ([[User talk:Wu Yinghong|talk]]) 13:44, 22 November 2021 (UTC)<br />
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==吴映红 Wú Yìnghóng 日语语言文学 女 202120081530==<br />
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这里雨村且翻弄诗籍解闷，忽听得窗外有女子嗽声。雨村遂起身往外一看，原来是一个丫鬟在那里掐花儿：生的仪容不俗，眉目清秀，虽无十分姿色，却也有动人之处。雨村不觉看得呆了。那甄家丫鬟掐了花儿，方欲走时，猛抬头见窗内有人：敝巾旧服，虽是贫窘，然生得腰圆背厚，面阔口方，更兼剑眉星眼，直鼻方腮。<br />
Here in the rain village, I turned to poetry books to relieve my boredom. Suddenly I heard a woman coughing outside the window. Yucun then got up and looked out. It turned out that it was a servant girl pinching flowers there: Sheng's appearance was not vulgar and his eyebrows were beautiful. Although he was not very beautiful, he was also moving. Yucun was stunned. The Zhen servant girl pinched the flowers. When Fang was about to leave, she suddenly looked up and saw someone in the window: Although I was poor and embarrassed, I had a round waist, thick back, wide face and square mouth. I also had sword eyebrows, star eyes, straight nose and square cheeks.<br />
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Yuncun was reading poems to relieve his boredom, suddenly hearing a girl outside the window coughing. Yucun stood up and found a housemaid picking flowers: she was of good appearance and pretty features. Although she was not perfect, she had something touching. Yucun felt stunned. The girl had pinched the flowers and was about to leave when she suddenly raised her head and saw someone in the window. In rags, he had a round waist and a thick back, a wide face and a square mouth, with a sword eyebrow and star eyes, a straight nose and a square cheek.--[[User:Xiao Yiyao|Xiao Yiyao]] ([[User talk:Xiao Yiyao|talk]]) 10:14, 24 November 2021 (UTC)<br />
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==肖毅瑶 Xiāo Yìyáo 英语语言文学（英美文学） 女 202120081531==<br />
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这丫鬟忙转身回避，心下自想：“这人生的这样雄壮，却又这样褴褛。我家并无这样贫窘亲友，想他定是主人常说的什么贾雨村了。怪道又说他必非久困之人，每每有意帮助周济他，只是没什么机会。”如此一想，不免又回头一两次。雨村见他回头，便以为这女子心中有意于他，遂狂喜不禁，自谓此女子必是个巨眼英豪，风尘中之知己。<br />
The housemaid turned away quickly and said to herself:” the man is so grand and ragged. I don’t have such deprived friends and relatives, thus he must be Jia Yucun that the master has mentioned frequently. It’s said that he will not be trapped in poverty for a long time. The master has meant to help him but doesn’t find a proper chance.” At the thought of this, she looked back for several times, which misled Yu Cun to think the girl was attracted by him and felt very excited. He believed that the girl must have a pair of wisdom eye and was his true friend in difficulty.--[[User:Xiao Yiyao|Xiao Yiyao]] ([[User talk:Xiao Yiyao|talk]]) 02:16, 24 November 2021 (UTC)<br />
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The housemaid turned away hurriedly and thought to herself:” the man is so strong, but his clothes are shabby. I don’t have such deprived friends and relatives, thus he must be Jia Yucun that the master has mentioned frequently. It’s said that he will not be trapped in poverty for a long time. The master has always meant to help him but doesn’t find a proper chance.” At the thought of this, she looked back for several times, which misled Yu Cun to think the girl was attracted by him and felt very excited. He believed that the girl must have a good taste and was his true friend in difficulty.--[[User:Xie Jiafen|Xie Jiafen]] ([[User talk:Xie Jiafen|talk]]) 11:27, 24 November 2021 (UTC)<br />
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==谢佳芬 Xiè Jiāfēn 英语语言文学（英美文学） 女 202120081532==<br />
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一时小童进来，雨村打听得前面留饭，不可久待，遂从夹道中，自便门出去了。士隐待客既散，知雨村已去，便也不去再邀。一日，到了中秋佳节。<br />
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When a child came in, yucun heard that the host entertained the guests meal. Therefore, he knew that he couldn't stay long, so he went out through the lane and went out by himself. Later, Shiyin had already served guests, knowing that yucun had gone, so he didn't invite again. One day, it was the Mid Autumn Festival.<br />
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When a child came in, Yucun heard that the host entertained the guests meal. Therefore, he knew that he couldn't stay long, so he went out through the lane and went out by himself. Later, Shiyin had already served guests, knowing that yucun had gone, so he didn't invite again. One day, it was the Mid Autumn Festival.--[[User:Xie Qinglin|Xie Qinglin]] ([[User talk:Xie Qinglin|talk]]) 07:53, 26 November 2021 (UTC)<br />
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==谢庆琳 Xiè Qìnglín 俄语语言文学 女 202120081533==<br />
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士隐家宴已毕，又另具一席于书房，自己步至庙中来邀雨村。原来雨村自那日见了甄家丫鬟曾回顾他两次，自谓是个知己，便时刻放在心上。今又正值中秋，不免对月有怀，因而口占五言一律云：<br />
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Shiiyin‘s family banquet has been completed, and another seat in the study, he came to the temple to invite Yucun. It turns out that since that day Yucun saw the Zhen family maid had looked back at him twice, since he said he was a confidant, so he always put on his heart. Now it was the mid-autumn festival, so I couldn't help but feel nostalgic for the moon, so I took five words from the mouth and said.--[[User:Xie Qinglin|Xie Qinglin]] ([[User talk:Xie Qinglin|talk]]) 07:51, 26 November 2021 (UTC)<br />
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Shiiyin‘s family banquet has been completed, and another seat in the study, he came to the temple to invite Yucun. It turns out that since that day Yucun saw the Zhen family maid had looked back at him twice, since he said he was a confidant, so he always put on his heart. Now it was the mid-autumn festival, so I couldn't help but feel nostalgic for the moon, so I took five words from the mouth and said.--[[User:Xiong Min|Xiong Min]] ([[User talk:Xiong Min|talk]]) 14:30, 28 November 2021 (UTC)<br />
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==熊敏 Xióng Mǐn 英语语言文学（英美文学） 女 202120081534==<br />
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未卜三生愿，频添一段愁。闷来时敛额，行去几回头。自顾风前影，谁堪月下俦？蟾光如有意，先上玉人楼。<br />
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I haven’t realized my dream yet, but had sorrowful experience. I often frown when I feel depressed and look back repeatedly when I farewell. With the wind blowing, I look at my shadow. Who can be my partner? If the moon helps,please shed light on the girl’s window and show her my love.<br />
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I haven’t realized my dream yet, but had sorrowful experience. I often frown when I feel depressed and look back repeatedly when I farewell. With the wind blowing, I look at my shadow. Who can be my partner? If the moon helps,please shed light on the girl’s window and show her my love.--[[User:Xu Minyun|Xu Minyun]] ([[User talk:Xu Minyun|talk]]) 12:02, 28 November 2021 (UTC)<br />
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==徐敏赟 Xú Mǐnyūn 语言智能与跨文化传播研究 男 202120081535==<br />
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雨村吟罢，因又思及平生抱负，苦未逢时，乃又搔首对天长叹，复高吟一联云：玉在椟中求善价，钗于奁内待时飞。恰值士隐走来听见，笑道：“雨村兄真抱负不凡也！”<br />
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After Yucun finished reciting the love poems of Jiao Xing, he thought of his great ambitions and thought that he had not met a good time, so he recited a pair of couplets that he created aloud: "Jade and hairpin are all placed in the box, hoping that one day it can realize its value and play its role." Just when Shiyin came to hear it, Shiyin smiled and said, "Brother Yucun is really ambitious!"--[[User:Xu Minyun|Xu Minyun]] ([[User talk:Xu Minyun|talk]]) 11:38, 24 November 2021 (UTC)<br />
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After Yucun finished reciting the love poems of Jiao Xing, he thought of his great ambitions and thought that he had not met a good time, so he recited aloud a pair of couplets that he created: "Jade and hairpin are all placed in the box, hoping that one day it can realize its value and play its role." Just when Shiyin came to hear it, Shiyin smiled and said, "Yucun is really ambitious!"--[[User:Yan Jing|Yan Jing]] ([[User talk:Yan Jing|talk]]) 16:53, 28 November 2021 (UTC)<br />
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==颜静 Yán Jìng 语言智能与跨文化传播研究 女 202120081536==<br />
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雨村忙笑道：“不敢。不过偶吟前人之句，何期过誉如此！”因问：“老先生何兴至此？”士隐笑道：“今夜中秋，俗谓团圆之节。想尊兄旅寄僧房，不无寂寥之感。故特具小酌，邀兄到敝斋一饮。不知可纳芹意否？”<br />
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Yucun laughed and hurriedly said, "No no, I just recite the words of the predecessors. You speak too highly of me!" he asked, "Why did you come here, sir?" Shiyin smiled, "Tonight is the reunion time of Mid Autumn Festival. You lodged with a monk's room alone. So I come to invite you to have a drink with me. What do you think?"--[[User:Yan Jing|Yan Jing]] ([[User talk:Yan Jing|talk]]) 10:51, 21 November 2021 (UTC)<br />
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"Why did you come here, master?"<br />
You are lodged with a monk's room alone. --[[User:Yan Lili|Yan Lili]] ([[User talk:Yan Lili|talk]]) 02:18, 29 December 2021 (UTC)<br />
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==颜莉莉 Yán Lìlì 国别 女 202120081537==<br />
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雨村听了，并不推辞，便笑道：“既蒙谬爱，何敢拂此盛情！”说着，便同士隐复过这边书院中来了。须臾茶毕，早已设下杯盘，那美酒佳肴，自不必说。<br />
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Hearing this, Rainvillage Merchant did not refuse, but said with a smile: "Since I am indebted to you, I dare not live up to this feeling." Then he and Hidden Truth came to the academy here. In a moment they had finished their tea, and a feast had already been set up, with wine and food, in which the delicacy was all.<br />
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They went to the court in front of Shiyin's study. Soon they had fin rished their tea and sat down to a collation of choice wine and delicacies.--[[User:Yan Zihan|Yan Zihan]] ([[User talk:Yan Zihan|talk]]) 11:36, 28 November 2021 (UTC)<br />
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==颜子涵 Yán Zǐhán 国别 女 202120081538==<br />
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二人归坐，先是款酌慢饮；渐次谈至兴浓，不觉飞觥献斝起来。当时街坊上家家箫管，户户笙歌；当头一轮明月，飞彩凝辉。二人愈添豪兴，酒到杯干。<br />
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At first, they drank slowly,but their spirits rose as they talked and they began to drink more recklessly. At that time, Flutes and strings can be heard everywhere and every family in the neighborhood was singing; When a bright moon rises, The two became more and more cheerful, and the wine dried cup up.--[[User:Yan Zihan|Yan Zihan]] ([[User talk:Yan Zihan|talk]]) 14:26, 27 November 2021 (UTC)<br />
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At first, they drank slowly,but their spirits rose as they talked in depth， they began to drink more recklessly. At that time, the sound of flutes and strings can be heard everywhere and every family in the neighborhood was playing and singing; When a bright moon rises, The two became more and more cheerful and drained cup after cup.--[[User:Yang Jiaying|Yang Jiaying]] ([[User talk:Yang Jiaying|talk]]) 11:39, 28 November 2021 (UTC)<br />
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==阳佳颖 Yáng Jiāyǐng 国别 女 202120081540==<br />
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雨村此时已有七八分酒意，狂兴不禁，乃对月寓怀，口占一绝云：时逢三五便团圆，满把清光护玉栏。天上一轮才捧出，人间万姓仰头看。士隐听了，大叫：“妙极！弟每谓兄必非久居人下者，今所吟之句，飞腾之兆已现，不日可接履于云霄之上了。可贺，可贺！”<br />
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WE Rainvillage Merchant, eight-tenths drunk, cannot suppress his high spirits. As he gazed at the moon, he fostered thoughts, to which he gave vent by the recital of a double couplet.<br />
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"On the fifteenth the moon is full, Her pure rays fill the court; As her bright orb sails up the sky, All men on earth gaze upwards at the sight." <br />
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"Excellent!" cried Hidden Truth with a loud voice, after he had heard these lines; "I have repeatedly maintained that it was impossible for you to remain in a subordinate position for a long period, and now the verses are a prognostic of your rapid advancement. In a few days you will extend your footsteps far above the clouds! Let me congratulate you."！<br />
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WE Rainvillage Merchant, eight-tenths drunk, cannot suppress his elation. As he gazed at the moon, he improvised a poetry to the moon and declaimed it:<br />
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"On the fifteenth the moon is full, Her pure rays fill the court; As her bright orb sails up the sky, All men on earth gaze upwards at the sight." <br />
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"Excellent!" cried Hidden Truth with a loud voice, after he had heard these lines; "I have repeatedly maintained that it was impossible for you to remain in a subordinate position for a long period, and now the verses reveals your rapid advancement. In a few days you will extend your footsteps far above the clouds! Let me congratulate you."！--[[User:Yang Aijiang|Yang Aijiang]] ([[User talk:Yang Aijiang|talk]]) 04:36, 28 November 2021 (UTC)<br />
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==杨爱江 Yáng Àijiāng 英语语言文学（语言学） 女 202120081541==<br />
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乃亲斟一斗为贺。雨村饮干，忽叹道：“非晚生酒后狂言，若论时尚之学，晚生也或可去充数挂名。只是如今行李路费，一概无措，神京路远，非赖卖字撰文，即能到得。”士隐不待说完，便道：“兄何不早言？弟已久有此意，但每遇兄时，并未谈及，故未敢唐突。今既如此，弟虽不才，‘义利’二字，却还识得。且喜明岁正当大比，兄宜作速入都，春闱一捷，方不负兄之所学。其盘费馀事，弟自代为处置，亦不枉兄之谬识矣。”<br />
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Shiyin filled another large cup of alcohol. Yucun tossed it off and then signed. "Don't think this is just a talk after being drunk," he said, "I'm sure I could acquit myself quite creditably in the examinations, but I have no money in my wallet for my travelling expenses and the capital is far away. I can't raise enough money by selling my words and articles ...." "Why didn't you say so before?" interjected Shiyin. "I've always thought about this, but since you never mentioned it it is inappropriate for me to mention this subject. If that's how things are, dull as I am at least I know what's due to a firend. Luckily the Metropolitan Examinations are coming up next year. You must go as fast as you can to the capital and prove your learning in the Spring Test. I shall take it an honor to take care of the travelling expenses and other business for you."--[[User:Yang Aijiang|Yang Aijiang]] ([[User talk:Yang Aijiang|talk]]) 04:28, 28 November 2021 (UTC)<br />
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Hidden Truth filled another large cup of alcohol for congratulation. Rainvillage Merchant tossed it off and then sighed: "Don't think this is just a talk after drinking. I'm sure I could acquit myself quite creditably in the examinations, but I have no money in my wallet for my travelling expenses and the capital is far away. I can't raise enough money only by selling my words and articles ...." "Why didn't you say that before?" interjected Hidden Truth. "I've always thought about this, but since you never mentioned it.It is inappropriate for me to mention this subject. If that's how things are, dull as I am at least I know what's due to a real friend. Luckily the Metropolitan Examinations are coming up next year. You must go as fast as you can to the capital and prove your learning in the Spring Test. I shall take it an honor to take care of the travelling expenses and other business for you."--[[User:Yang Kun|Yang Kun]] ([[User talk:Yang Kun|talk]]) 11:43, 28 November 2021 (UTC)<br />
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==杨堃 Yáng Kūn 法语语言文学 女 202120081542==<br />
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当下即命小童进去，速封五十两白银并两套冬衣。又云：“十九日乃黄道之期，兄可即买舟西上。待雄飞高举，明冬再晤，岂非大快之事！”雨村收了银、衣，不过略谢一语，并不介意，仍是吃酒谈笑。<br />
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Hidden Truth immediately ordered the child to go in and quickly seal fifty liang silver and two sets of winter clothes. And he also said, "the 19th of March is the time of the zodiac, and you can buy a boat to the west. Isn't it a great pleasure to wait for triumph of the war[1] and meet again the next winter?" Rainvillage Merchant received the silver and clothes, but he thanked Hidden Truth a little. He didn't mind and was still drinking wine, talking and laughing.<br />
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[1]The war between Li Zicheng and the emperor Chongzhen. On March 19 of the lunar calendar in 1644, Emperor Chongzhen of the Ming Dynasty hanged himself. Then, Li Zicheng entered Beijing to overthrow the Ming Dynasty.--[[User:Yang Kun|Yang Kun]] ([[User talk:Yang Kun|talk]]) 03:23, 21 November 2021 (UTC)<br />
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Shiyin immediately ordered the child in and seal fifty liang silver and two suits of winter clothes quickly. Then he said, "the 19th of March is favorable time, and you can buy a boat to the west. Isn't it a great pleasure to wait for triumph of the war[1] and meet again the next winter?" Yucun received the silver and clothes, but he thanked Shiyin a little. He didn't mind and was still drinking wine, talking and laughing.<br />
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[1]The war between Li Zicheng and the emperor Chongzhen. On March 19 of the lunar calendar in 1644, Emperor Chongzhen of the Ming Dynasty hanged himself. Then, Li Zicheng entered Beijing to overthrow the Ming Dynasty.--[[User:Yang Liuqing|Yang Liuqing]] ([[User talk:Yang Liuqing|talk]]) 12:15, 23 November 2021 (UTC)<br />
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==杨柳青 Yáng Liǔqīng 英语语言文学（英美文学） 女 202120081543==<br />
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那天已交三鼓，二人方散。士隐送雨村去后，回房一觉，直至红日三竿方醒。因思昨夜之事，意欲写荐书两封与雨村，带至都中去，使雨村投谒个仕宦之家，为寄身之地。<br />
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Jia Yunchun and Zhen Shiyin drank until midnight and then dispersed. Zhen Shiyin sent Jia Yuchun bedchamber and went back his room to sleep. He didn't wake up until the late morning. Considering Zhen Shiyin's bad conditions, Zhen Shiyin intended to write two recommendation letters for Jia Yuchun, so Jia Yuchun could take and deliver it to a family of dignities in Qi Zhou city to find a place to stay.--[[User:Yang Liuqing|Yang Liuqing]] ([[User talk:Yang Liuqing|talk]]) 06:10, 25 November 2021 (UTC)<br />
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These two friends drank until midnight and then left. Zhen Shiyin sent Jia Yuchun back to his bedchamber and headed back to sleep. He didn't wake up until the noon time. Zhen Shiyin intended to write two recommendation letters for Jia Yuchun considering his bad condition, so Jia Yuchun could take and deliver it to some family of dignities in Qi Zhou city to find a fine place to settle.--[[User:Ye Weijie|Ye Weijie]] ([[User talk:Ye Weijie|talk]]) 05:11, 29 November 2021 (UTC)<br />
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==叶维杰 Yè Wéijié 国别 男 202120081544==<br />
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因使人过去请时，那家人回来说：“和尚说：贾爷今日五鼓已进京去了，也曾留下话与和尚转达老爷，说：‘读书人不在黄道黑道，总以事理为要，不及面辞了。’”士隐听了，也只得罢了。真是闲处光阴易过，倏忽又是元宵佳节。<br />
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The family came back and said, “The monk said: The Five Drums of Master Jia has entered Beijing today, and he also left a message with the monk to convey to the master, saying:'The scholar is not in the zodiacal and underworld, and he always takes affair as the priority. , It's too late to resign.'" Shiyin listened and had no choice but to leave. It's really easy to spend leisure time, and suddenly it is the Lantern Festival.--[[User:Ye Weijie|Ye Weijie]] ([[User talk:Ye Weijie|talk]]) 13:21, 28 November 2021 (UTC)<br />
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When the family came back, they said, "The monk said that Master Merchant had gone to the capital between three and five o'clock today, and had left a message for the monk to convey to you, saying, 'No matter what the background of a scholar is, it is always important to take care of things, so it is too late to say goodbye.'" When Hidden Truth heard this, he had no choice but to do nothing. It was easy to spend time at leisure, and suddenly it was the Lantern Festival again.--[[User:Yi Yangfan|Yi Yangfan]] ([[User talk:Yi Yangfan|talk]]) 13:36, 28 November 2021 (UTC)Yi Yangfan<br />
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==易扬帆 Yì Yángfān 英语语言文学（英美文学） 女 202120081545==<br />
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士隐令家人霍启抱了英莲，去看社火花灯。半夜中霍启因要小解，便将英莲放在一家门槛上坐着。待他小解完了来抱时，那有英莲的踪影。<br />
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Zhen Shiyin asked his family member Huo Qi to carry Yinglian and go to see the lanterns. In the middle of the night, Huo Qi had to take a piss, so he left Yinglian sitting on the threshold of a door. When he came to carry her after taking a piss, there was no sign of Yinglian.<br />
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Hidden Truth asked his family member trouble beginner to carry Pity Zhen and go to see the lanterns. In the middle of the night, trouble beginner left Pity Zhen alone sitting on the threshold of a door because of the urgency of urinating. When he came back, Pity Zhen disappeared.--[[User:Yin Huizhen|Yin Huizhen]] ([[User talk:Yin Huizhen|talk]]) 09:11, 27 November 2021 (UTC)<br />
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==殷慧珍 Yīn Huìzhēn 英语语言文学（英美文学） 女 202120081546==<br />
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急的霍启直寻了半夜，至天明不见。那霍启也不敢回来见主人，便逃往他乡去了。那士隐夫妇见女儿一夜不归，便知有些不好。再使几人去找寻，回来皆云影响全无。<br />
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Huo Qi was so anxious that he looked for her all night and did not find her by dawn. So he did not dare to return to meet the host and he fled to his hometown. Mr. and Mrs. Shi Yin felt something is about to go wrong when they found their daughter didn't go home all night. They sent more people to look for her, but when they came back they said they didn't have any trace of her.--[[User:Yin Huizhen|Yin Huizhen]] ([[User talk:Yin Huizhen|talk]]) 14:22, 21 November 2021 (UTC)<br />
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Being so anxious, Huo Qi looked for her all night but in vain till dawn. Dare not to return to his master, he fled to another place. Mr. and Mrs. Shi Yin felt something wrong when their daughter didn't go home all night. More people were sent to look for her, but only to find no trace of her.--[[User:Yin Meida|Yin Meida]] ([[User talk:Yin Meida|talk]]) 14:11, 22 November 2021 (UTC)<br />
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==殷美达 Yīn Měidá 英语语言文学（语言学） 女 202120081547==<br />
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夫妻二人半世只生此女，一旦失去，何等烦恼，因此昼夜啼哭，几乎不顾性命。看看一月，士隐已先得病，夫人封氏也因思女搆疾，日日请医问卦。不想这日三月十五，葫芦庙中炸供，那和尚不小心，油锅火逸，便烧着窗纸。<br />
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The couple, having spent half of their lifetime, couldn't bear the thought of losing their only child. They wept day and night, almost risking their lives. In January, Shi Yin was already sick, and his wife Feng shi also fell ill for missing her daughter excessively and had to see doctors and fortunetellers everyday. Unfortunately, on March 15, when frying tributes in the calabash Temple, the careless monk let the fire escape from the oil boiler, which set the window paper on fire.--[[User:Yin Meida|Yin Meida]] ([[User talk:Yin Meida|talk]]) 14:57, 22 November 2021 (UTC)<br />
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The couple, having spent half of their lifetime, couldn't bear the annoyance of losing their only daughter. They wept all days and nights, almost risking their lives. In January, Hidden Truth was already sick, and his wife Feng also got ill for missing her daughter excessively and had to call the doctors and fortunetellers everyday. Unfortunately, on March 15, when frying tributes in the calabash Temple, the careless monk let the fire escape from the oil boiler, which set the window paper on fire.--[[User:Yin Yuan|Yin Yuan]] ([[User talk:Yin Yuan|talk]]) 12:11, 28 November 2021 (UTC)<br />
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==尹媛 Yǐn Yuán 英语语言文学（英美文学） 女 202120081548==<br />
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此方人家俱用竹篱木壁，也是劫数应当如此，于是接二连三，牵五挂四，将一条街烧得如火焰山一般。彼时虽有军民来救，那火已成了势了，如何救得下，直烧了一夜方熄，也不知烧了多少人家。只可怜甄家在隔壁，早成了一堆瓦砾场了，只有他夫妇并几个家人的性命不曾伤了，急的士隐惟跌足长叹而已。<br />
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This family deserves this fate for using bamboos and wood as hedge. One by one, the whole street burnt like the Mountain of Flames. At that time, though the army and people came for rescue, the fire had already been too large to put out. It didn't burn until the morning. It cannot be estimated that how many houses had been destroyed. Poor Hiden Truth's house next door had turned into a pile of rubble, only the couple and the families unhurt, which made Hidden Truth anxious and sigh deeply.--[[User:Yin Yuan|Yin Yuan]] ([[User talk:Yin Yuan|talk]]) 14:44, 22 November 2021 (UTC)<br />
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This family deserved this fate for using bamboos and wood as hedge. One house by one house, the whole street burnt like the Mountain of Flames. At that time, though the army and people came for rescue, the fire had already been too large to put out. It didn't burn until the morning. It cannot be estimated that how many houses had been destroyed. The fire had turned Poor Zhen's house next door into a pile of rubble, only the couple and several families unhurt, which made Zhen Shiyin anxious and sigh deeply.--[[User:Zhan Ruoxuan|Zhan Ruoxuan]] ([[User talk:Zhan Ruoxuan|talk]]) 10:47, 24 November 2021 (UTC)<br />
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==詹若萱 Zhān Ruòxuān 英语语言文学（英美文学） 女 202120081549==<br />
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与妻子商议，且到田庄上去住。偏值近年水旱不收，贼盗蜂起，官兵剿捕，田庄上又难以安身。只得将田地都折变了，携了妻子与两个丫鬟，投他岳丈家去。<br />
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He discussed with his wife, and went to live on the farm. However, in recent years, harvests have been ruined by flood and drought, and thieves and robbers have been rising. It was difficult to settle down on the farm. He had to sell his lands at a discount and took his wife and two maids to seek his father-in-law's help.--[[User:Zhan Ruoxuan|Zhan Ruoxuan]] ([[User talk:Zhan Ruoxuan|talk]]) 10:42, 24 November 2021 (UTC)<br />
Take counsel with his wife, and come to live at the Grange. Partial value in recent years flood and drought do not harvest, thieves bee, officers and soldiers suppression, the grange and difficult to live. He had to change the land and went to his husband's house with his wife and two maids.--[[User:Zhang Qiuyi|Zhang Qiuyi]] ([[User talk:Zhang Qiuyi|talk]]) 12:22, 28 November 2021 (UTC)<br />
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==张秋怡 Zhāng Qiūyí 亚非语言文学 女 202120081550==<br />
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他岳丈名唤封肃，本贯大如州人氏，虽是务农，家中却还殷实。今见女婿这等狼狈而来，心中便有些不乐。幸而士隐还有折变田产的银子在身边，拿出来托他随便置买些房地，以为后日衣食之计。<br />
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His father-in-law's name was Feng Su. His native place was Such State. Although he was a farmer, his family was well off. Now, seeing her son-in-law come in such a discomfiture, I felt unhappy. Fortunately, there are hidden converted field of silver in the side, took out to entrust him to buy some premises, that the day after the means of food and clothing.--[[User:Zhang Qiuyi|Zhang Qiuyi]] ([[User talk:Zhang Qiuyi|talk]]) 10:19, 21 November 2021 (UTC)<br />
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His father-in-law's name was Feng Su, who was originally from Daru State. Although he was a farmer, his family was well off. Now, seeing his son-in-law come in such a discomfiture, he felt unpleasant inside. Fortunately, there are hidden silver of converted field by his side, so he took it out to entrust him to purchase some premises for buying food and clothing in the future.--[[User:Zhang Yang|Zhang Yang]] ([[User talk:Zhang Yang|talk]]) 11:40, 28 November 2021 (UTC)<br />
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==张扬 Zhāng Yáng 国别 男 202120081551==<br />
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那封肃便半用半赚的，略与他些薄田破屋。士隐乃读书之人，不惯生理稼穑等事，勉强支持了一二年，越发穷了。封肃见面时，便说些现成话儿；且人前人后，又怨他不会过，只一味好吃懒做。<br />
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Feng Su made money while using some of it, and provided small fields and a shabby house with him. Shi Yin was a scholar and was not used to farming and other handworks. He reluctantly struggled for a year or two and became poorer and poorer. When Feng Su met him, he said something ready-made. But behind Shiyin's back, he complained that he couldn't live his life well by just being lazy.--[[User:Zhang Yang|Zhang Yang]] ([[User talk:Zhang Yang|talk]]) 11:34, 28 November 2021 (UTC)<br />
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Feng Su made money while using some of it, and provided small fields and a shabby house with him. Hidden Truth was a scholar and was not used to farming and other handworks. He reluctantly struggled for a year or two and became poorer and poorer. When Feng Su met him, he said something ready-made. But behind Shiyin's back, he complained that he couldn't live his life well by just being lazy.--[[User:Zhang Yiran|Zhang Yiran]] ([[User talk:Zhang Yiran|talk]]) 12:15, 28 November 2021 (UTC)<br />
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==张怡然 Zhāng Yírán 俄语语言文学 女 202120081552==<br />
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士隐知道了，心中未免悔恨；再兼上年惊唬，急忿怨痛：暮年之人，那禁得贫病交攻，竟渐渐的露出那下世的光景来。可巧这日拄了拐，扎挣到街前散散心时，忽见那边来了一个跛足道人，疯狂落拓，麻鞋鹑衣，口内念着几句言词道：<br />
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When Hidden Truth found out about this, he felt remorse in his heart; he was also frightened of the previous year, and he felt angry and resentful: a man in his twilight years, who could not help being attacked by poverty and illness, was gradually revealing the scene of his next life. It happened that when he was on crutches, he went to the street for a walk, and suddenly he saw a crippled Taoist, crazy and untidy, with sackcloth shoes and quails, reciting a few words under his breath.--[[User:Zhang Yiran|Zhang Yiran]] ([[User talk:Zhang Yiran|talk]]) 02:26, 22 November 2021 (UTC)<br />
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When Hidden Truth found out this, he felt remorse in his heart; he was also frightened of the previous year, angry and resentful: a man in his twilight， who could not be able to be attacked by poverty and illness, was gradually revealing the scene of his next life. It happened that when he was on crutches, he went to the street for a walk, and suddenly he saw a crippled Taoist, crazy and untidy, with ragged shoes and clothes，reciting a few words under his breath.--[[User:Zhong Yifei|Zhong Yifei]] ([[User talk:Zhong Yifei|talk]]) 07:00, 22 November 2021 (UTC)<br />
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==钟义菲 Zhōng Yìfēi 英语语言文学（英美文学） 女 202120081553==<br />
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世人都晓神仙好，惟有功名忘不了。古今将相在何方？荒冢一堆草没了。世人都晓神仙好，只有金银忘不了。终朝只恨聚无多，及到多时眼闭了。<br />
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The common people know that immortals are good, but they can't forget their achievements and fame. Where are the generals and prime ministers from ancient times to the present？What we can see are just deserted graves full of grass. The common people know that immortals are good, but they can‘t forget gold and silver. Till the end of life，they would regret their inability to create as much wealth as possible when they are alive and regret they are going to the heaven after they have accumulated plenty of wealth.--[[User:Zhong Yifei|Zhong Yifei]] ([[User talk:Zhong Yifei|talk]]) 02:42, 21 November 2021 (UTC)<br />
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All the common people know that immortals are good, but they can't forget their achievements and ambitions. Where are the great ones of old？They are just deserted graves full of grass. All the common people know that immortals are good, but they can‘t forget gold and silver. They grub for money all their lives until death seals up their eyes.--[[User:Zhong Yulu|Zhong Yulu]] ([[User talk:Zhong Yulu|talk]]) 07:24, 21 November 2021 (UTC)<br />
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==钟雨露 Zhōng Yǔlù 英语语言文学（英美文学） 女 202120081554==<br />
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世人都晓神仙好，只有姣妻忘不了。君生日日说恩情，君死又随人去了。世人都晓神仙好，只有儿孙忘不了。痴心父母古来多，孝顺子孙谁见了？<br />
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All men want to be immortals, but dote on the wives they’ve married. Those who swear to love their husband forever, but remarry as soon as he’s dead. All men want to be immortals, but dote on the sons they’ve gotten. Although infatuated parents are numerous, who ever saw really filial sons or daughters?--[[User:Zhong Yulu|Zhong Yulu]] ([[User talk:Zhong Yulu|talk]]) 02:05, 21 November 2021 (UTC)<br />
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Everyone in the world knows that the gods are good, but only the pretty wife can't forget. You swear to remember your husband’s kindness, but when your husband die, you go away with others. Everyone knows that the gods are good, but only the children and grandchildren cannot be forgotten. There are many loving parents in the past, but who has seen the filial children?--[[User:Zhou Jiu|Zhou Jiu]] ([[User talk:Zhou Jiu|talk]]) 09:04, 22 November 2021 (UTC)<br />
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==周玖 Zhōu Jiǔ 英语语言文学（英美文学） 女 202120081555==<br />
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士隐听了，便迎上来道：“你满口说些什么？只听见些‘好’、‘了’，‘好’、‘了’。”那道人笑道：“你若果听见‘好’、‘了’二字，还算你明白。可知世上万般，好便是了，了便是好：若不了，便不好；若要好，须是了。我这歌儿便叫《好了歌》。<br />
When the hermit heard it, he came up and said, "What are you talking about ?" I just hear 'Hao’ (means good), 'Liao' (means end) 'Hao’, ‘Liao'. The man laughed, "If you hear the words 'Hao' and 'Liao', you understand it. In this world, good is end, and the end is good. If there is no end, there is no good, and vice versa. My song is called ‘The Song of ‘Hao’ and ‘ Liao’. ”--[[User:Zhou Jiu|Zhou Jiu]] ([[User talk:Zhou Jiu|talk]]) 08:54, 22 November 2021 (UTC)<br />
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Hidden Truth came over after heard this and said: “ What are you talking about? I just hear the words ‘good’ and ‘end’.” That man laughed, “ You heard the words ‘good’ and ‘end’, that means you got a few things going for you. In this world, good is end, and end is good. If there is no end, there is no good, and vice versa. My song is called ''All Dood Things Must End''.”--[[User:Zhou Junhui|Zhou Junhui]] ([[User talk:Zhou Junhui|talk]]) 12:19, 22 November 2021 (UTC)<br />
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==周俊辉 Zhōu Jùnhuī 法语语言文学 女 202120081556==<br />
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士隐本是有夙慧的，一闻此言，心中早已悟彻，因笑道：“且住，待我将你这《好了歌》注解出来何如？”道人笑道：“你就请解。”士隐乃说道：陋室空堂，当年笏满床。<br />
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So intelligent, Hidden Truth understood the essence of the song entirely in his head as soon as he heard it, and said: “ Wait a minute. How about I explain your song ''All Good Things Must End'' ？” The Taoist priest said, laughing : “ Would you please explain.” Hidden Truth then explain: “ The empty and dilapidated rattraps we see today, were the grand mansions full of beds and boards used by dignitaries at that time.”--[[User:Zhou Junhui|Zhou Junhui]] ([[User talk:Zhou Junhui|talk]]) 08:01, 22 November 2021 (UTC)<br />
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So intelligent as Hidden Truth is, he understood the essence of the song entirely in his head as soon as once hearing it, and said: “ Wait a minute. How about I explain your song ''All Good Things Must End'' ？” The Taoist priest said, laughing : “ Would you please explain.” Hidden Truth then explain: “ The empty and dilapidated rattraps we see today, were the grand mansions full of boards used by courtiers at that time.”--[[User:Zhou Qiao1|Zhou Qiao1]] ([[User talk:Zhou Qiao1|talk]]) 13:23, 23 November 2021 (UTC)<br />
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==周巧 Zhōu Qiǎo 英语语言文学（语言学） 女 202120081557==<br />
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衰草枯杨，曾为歌舞场。蛛丝儿结满雕梁，绿纱今又在蓬窗上。说甚么脂正浓，粉正香，如何两鬓又成霜？<br />
Humble hovels and abandoned halls where courtiers once paid daily calls；Bleak places where weeds and trees scarcely thrive<br />
were once with a show of peace and prosperity．When cobwebs cover the mansion’s gilded beams，and collage casement with choice muslin gleams．Would you of perfumed elegance recite? Even as you speak, the raven locks turn white．--[[User:Zhou Qiao1|Zhou Qiao1]] ([[User talk:Zhou Qiao1|talk]]) 13:06, 23 November 2021 (UTC)<br />
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Withered grass and withered poplar, This used to be a place where people sang and danced. The spider silk is full of carved beams, but the green gauze is on the simple window. The fat is thick and the powder is fragrant, how the temples become the color of frost?--[[User:Zhou Qing|Zhou Qing]] ([[User talk:Zhou Qing|talk]]) 15:20, 25 November 2021 (UTC)<br />
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==周清 Zhōu Qīng 法语语言文学 女 202120081558==<br />
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昨日黄土陇头埋白骨，今宵红绡帐底卧鸳鸯。金满箱，银满箱，转眼乞丐人皆谤。正叹他人命不长，那知自己归来丧。<br />
<br />
Yesterday the bones were buried in the loess, and the mandarin ducks(allusions to couples) lie under the red silk tent tonight. Boxes full of gold, boxes full of silver, everyone yelled and insulted beggars in a blink of an eye. I'm sighing that the lives of others are not long, and I know I'm back to be mourned.<br />
<br />
Yesterday the bones were buried in the loess, then tonight it is the time for a newly married couple to sleep behind bed curtains with burning red candle lights.Boxes full of gold, boxes full of silver, everyone yelled and insulted beggars in a blink of an eye. I'm sighed that the lives of others are not long, but failed to predict my own future after return from the funeral.--[[User:Zhou Xiaoxue|Zhou Xiaoxue]] ([[User talk:Zhou Xiaoxue|talk]]) 11:36, 28 November 2021 (UTC)<br />
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==周小雪 Zhōu Xiǎoxuě 日语语言文学 女 202120081559==<br />
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训有方，保不定日后作强梁；择膏粱，谁承望流落在烟花巷。因嫌纱帽小，致使锁枷扛；昨怜破袄寒，今嫌紫蟒长。乱烘烘，你方唱罢我登场，反认他乡是故乡。<br />
<br />
Though she was well educated according to her parent's plan, one can become a bandit later. She tried her best to marry into a rich family. However, she ended up in a (red-light)? district beyond everyone's expectation. People who are not satisfied with their positions have to spend the rest of their life in a prison in chains. People who used to be very poor and used worn coats to resist the cold were not satisfied with fancy clothes after they became rich. I come on the stage as soon as you have finished your singing in a chaotic manner. Taking other's hometown as my own. --[[User:Zhou Xiaoxue|Zhou Xiaoxue]] ([[User talk:Zhou Xiaoxue|talk]]) 11:42, 22 November 2021 (UTC)--[[User:Mahzad Heydarian|Mahzad Heydarian]] ([[User talk:Mahzad Heydarian|talk]]) 16:56, 23 November 2021 (UTC)<br />
<br />
If properly educated under parents' guidance, one can probabaly become a bandit later. Although trying her best to marry a rich man, she ends up with a driftage in the red-light district beyond everyone's expectation. People's unsatisfication with their positions leads to their miserable life in prison with chains on their bodies. People who used to be very poor and used worn coats to resist the cold were not satisfied with gorgeous clothes any more when they became rich. In noisy disorder, you just finished and I come on the scene.Instead, taking other's hometown as my own.--[[User:Zhu Suzhen|Zhu Suzhen]] ([[User talk:Zhu Suzhen|talk]]) 15:17, 25 November 2021 (UTC)<br />
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==朱素珍 Zhū Sùzhēn 英语语言文学（语言学） 女 202120081561==<br />
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甚荒唐，到头来，都是为他人作嫁衣裳。那疯跛道人听了，拍掌大笑道：“解得切，解得切！”士隐便说一声：“走罢。”<br />
<br />
It's so ridiculous. In the end, they made wedding clothes for others. Hearing this, the crazy lame Taoist clapped his hands and laughed, saying "All right, all right !" Then Shiyin said, "let's go."<br />
<br />
<br />
It's ridiculous. In the end, they make wedding clothes for others. The crazy lame Taoist listened, clapped his hands and laughed and said, "it's right, it's right!" Shiyin said, "let's go."--[[User:Zou Yueli|Zou Yueli]] ([[User talk:Zou Yueli|talk]]) 11:43, 21 November 2021 (UTC)<br />
<br />
== Headline text ==<br />
==邹岳丽 Zōu Yuèlí 日语语言文学 女 202120081562==<br />
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将道人肩上的搭裢抢过来背上，竟不回家，同着疯道人飘飘而去。当下哄动街坊，众人当作一件新闻传说。封氏闻知此信，哭个死去活来。<br />
<br />
He grabbed the lap on the Taoist's shoulder and carried it on his back. He didn't go home and floated away with the crazy Taoist. At that moment, the neighborhood was stirred up and everyone regarded it as a news legend. When Feng heard this letter, he cried to death.--[[User:Zou Yueli|Zou Yueli]] ([[User talk:Zou Yueli|talk]]) 11:34, 21 November 2021 (UTC)<br />
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==Nadia 202011080004==<br />
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只得与父亲商议，遣人各处访寻，那讨音信。<br />
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==Mahzad Heydarian 玛莎 202021080004==<br />
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无奈何，只得依靠着他父母度日。<br />
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He was so helpless that he had to rely on his parents to survive.<br />
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==Mariam toure 2020GBJ002301==<br />
<br />
幸而身边还有两个旧日的丫鬟伏侍，主仆三人，日夜作些针线，帮着父亲用度。<br />
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==Rouabah Soumaya 202121080001==<br />
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那封肃虽然每日抱怨，也无可奈何了。<br />
Although Feng Su complained every day, he was helpless<br />
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==Muhammad Numan 202121080002==<br />
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这日那甄家的大丫鬟在门前买线，忽听得街上喝道之声。<br />
The other day, the eldest maid of the Chen family was buying thread at the door when she heard a shout from the street.--[[User:Atta Ur Rahman|Atta Ur Rahman]] ([[User talk:Atta Ur Rahman|talk]]) 14:50, 23 November 2021 (UTC)<br />
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==Atta Ur Rahman 202121080003==<br />
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众人都说：“新太爷到任了。”<br />
Everyone said: "The new grandfather has arrived"<br />
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==Muhammad Saqib Mehran 202121080004==<br />
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丫鬟隐在门内看时，只见军牢、快手一对一对过去，俄而大轿内抬着一个乌帽猩袍的官府来了。<br />
When the maid concealed in the door, she saw the military jail and quick hands passing one by one, and the official mansion carrying a black hat and ape robe came in the big sedan chair.<br />
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==Zohaib Chand 202121080005==<br />
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那丫鬟倒发了个怔，自思：“这官儿好面善，倒像在那里见过的。”<br />
<br />
<nowiki>Insert non-formatted text here</nowiki><br />
The maid was startled, and thought to herself: "This official is so good-natured, but it looks like someone I've seen there."<br />
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==Jawad Ahmad 202121080006==<br />
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于是进入房中，也就丢过，不在心上。<br />
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Then she went into the room and laid the matter aside ，without taking it to heart.<br />
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==Nizam Uddin 202121080007==<br />
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至晚间正待歇息之时，忽听一片声打的门响，许多人乱嚷，说：“本县太爷的差人来传人问话！”<br />
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English:<br />
When I was about to rest in the evening, I heard a bang on the door suddenly, and many people shouted in disorder, saying, "The county's grandfather's messenger is here for questioning!"<br />
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==Öncü 202121080008==<br />
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封肃听了，唬得目瞪口呆。<br />
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Fengsu hear it,he gaped in consternation --[[User:AkiraJantarat|AkiraJantarat]] ([[User talk:AkiraJantarat|talk]]) 13:28, 22 November 2021 (UTC)<br />
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==Akira Jantarat 202121080009==<br />
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不知有何祸事，且听下回分解。<br />
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Don't know something calamity happened, will describe in the ensuing chapter.--[[User:AkiraJantarat|AkiraJantarat]] ([[User talk:AkiraJantarat|talk]]) 06:36, 21 November 2021 (UTC)<br />
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If you don't know what calamity took place, listen to the break down given in the next chapter.--[[User:Benjamin Wellsand|Benjamin Wellsand]] ([[User talk:Benjamin Wellsand|talk]]) 12:54, 21 November 2021 (UTC)<br />
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==Benjamin Wellsand 202111080118==<br />
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通灵──“通灵宝玉”的简称。Psychic--short for ''Psychic Treasure.--[[User:Benjamin Wellsand|Benjamin Wellsand]] ([[User talk:Benjamin Wellsand|talk]]) 12:48, 21 November 2021 (UTC)<br />
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Psychic -- the abbreviation for "Psychic Treasure". --[[User:Asep Budiman|Asep Budiman]] ([[User talk:Asep Budiman|talk]]) 03:35, 24 November 2021 (UTC)<br />
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==Asep Budiman 202111080020==<br />
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亦即下文所说女娲炼石补天所剩的那块“顽石”，因其历经锻炼而“灵性已通”，并能幻化为贾宝玉，故称。<br />
<br />
That is to say, the "stubborn stone" left by Nüwa's refining stone to replenish the sky. As it has undergone training, it has "spiritual achievement" and can be transformed into Jia Baoyu, so it is called. --[[User:Asep Budiman|Asep Budiman]] ([[User talk:Asep Budiman|talk]]) 03:48, 24 November 2021 (UTC)<br />
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That is to say, the "stubborn stone" left by Nüwa's refining stone to replenish the sky, because it has undergone training, has "spiritually achieved" and can be transformed into Jia Baoyu, so it is called.--[[User:EIMONKYAW|EIMONKYAW]] ([[User talk:EIMONKYAW|talk]]) 06:10, 24 November 2021 (UTC)Ei Mon Kyaw<br />
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==Ei Mon Kyaw 202111080021==<br />
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《石头记》──此书的本名。<br />
"Story of the Stone" - the original name of the book. --[[User:EIMONKYAW|EIMONKYAW]] ([[User talk:EIMONKYAW|talk]]) 06:26, 24 November 2021 (UTC)Ei Mon Kyaw</div>Chen Huinihttps://bou.de/u/index.php?title=20210929_homework&diff=13463820210929 homework2021-12-29T08:00:47Z<p>Chen Huini: /* 英语语言文学（英美文学） 202120081479 陈惠妮 女 */</p>
<hr />
<div>Quicklinks: [[Introduction_to_Translation_Studies_2021|Back to course homepage]] [https://bou.de/u/wiki/uvu:Community_Portal#Frequently_asked_questions_FAQ FAQ] [https://bou.de/u/wiki/uvu:Community_Portal Manual] [[20210926_homework|Back to all homework webpages overview]] [[20220112_final_exam|final exam page]]<br />
<br />
[[20210929_homework|homework of session 1 for session 2 Sep 29]]<br />
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IN PREPARATION<br />
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专业班级名称 学号 姓 名 性别<br />
==语言智能与跨文化传播研究 202120081535 徐敏赟 男==<br />
This is the homework of 徐敏赟.--[[User:Root|Root]] ([[User talk:Root|talk]]) 12:41, 26 September 2021 (UTC)<br />
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清初时期的汉籍（书）翻译及其文化沟通意涵<br />
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摘要：清初之际，统治者虽以武力立国，但社会的主导意识形态尚未形成，国家的“治统”与“道统”尚未确立。为了匡扶社稷，教化臣民，探求君主治术，建构符合国家需求的集体价值观，统治者积极组织汉书翻译，促进文化交流，增进民族和解。<br />
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<br />
The Translation of Chinese Books in the Early Qing Dynasty and its Cultural Communication Implications<br />
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Abstract: At the beginning of the Qing Dynasty, the ruler established the country by force, but the dominant social ideology has not yet been formed. Besides, the country's "ruling" and "moral orthodox" have not yet been established either. In order to help the community, educate the people, explore the rules of the monarchy, and construct collective values that meet the needs of the country, the ruler organized the translation of Chinese books actively, which helped promote cultural exchanges and enhance national reconciliation.<br />
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The Translation of the Han Classics in Early Qing Dynasty and its Implications for Cultural Communication<br />
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Abstract: At the beginning of Qing Dynasty, the ruler established the country by force, but the dominant social ideology has not yet been formed. Besides, the country's "governance" and "moral orthodoxy" have not yet been established neither. In order to help the community, educate the people, explore the rules of the monarchy, and construct collective values that meet the needs of the country, the ruler organized the translation of Chinese books actively, which helped promote cultural exchanges and enhance national reconciliation.--[[User:Root|Root]] ([[User talk:Root|talk]]) 12:44, 29 September 2021 (UTC)<br />
<br />
The Translation of the Han Classics in Early Qing Dynasty and its Implications for Cultural Communication<br />
<br />
Abstract: At the beginning of the Qing Dynasty, the ruler established the country by force, but the dominant social ideology has not yet been formed. Besides, the country's "governance" and "moral orthodox" have not yet been established either. In order to rectify and sustain the society, educate the subjects, explore the rules of the monarchy, and construct collective values that meet the needs of the country, the ruler organized the translation of Han Classics actively, which helped promote cultural exchanges and enhance national reconciliation.--[[User:Yan Jing|Yan Jing]] ([[User talk:Yan Jing|talk]]) 08:31, 8 October 2021 (UTC)<br />
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==语言智能与跨文化传播研究 202120081536 颜静 女==<br />
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作为清初文化事业的重要组成部分，汉书翻译有着明确的选择标准，实用主义色彩浓厚，主要关注汉族的文治教化与典章制度，将翻译与政要相关联，反对浮华藻饰的翻译。组织上，汉书翻译以官方为主，以民间为辅，译书者既有兼通满、汉双语之旗人，又有八旗科举考试之及第者，这些人既是文化交流的管理者，又是实践者，代表了统治阶级意欲沟通满汉的主观愿望。成效上，汉书翻译不仅促进了新生政权的制度建设，而且为统治者建构政权合法性做出了贡献。<br />
As an important part of cutural undertakings in early Qing dynasty, the translation of Han Shu had clear selection criteria and strong pragmatism. It focused on cultural education and institutions of Han nationality, associated the translation with politics and opposed flashly one. Organizationally,Han Shu was translated mainly by officials, and then public people. The translators contained not only the Eight Banners' People who mastered both Manchu and Chinese language, but also those who passed the Eight Banners imperial examination. These people were administrators of cultural exchanges, and also practicers, representing that the ruling class was willing to communicate the Manchu and Han people. In effect, the translating of Han Shu promoted the institutional construction of new regime, and also contributed to the rulers for constructing the regime legitimacy.<br />
<br />
As an important part of cutural undertakings in early Qing dynasty, the translation of Han Shu had clear selection criteria and strong pragmatism. It (mainly) focused on cultural education and institutions of Han nationality, associated the translation with politics and opposed flashly one(embellishments). Organizationally,Han Shu was translated mainly by officials, and then (by) public people. The translators contained not only the Eight Banners' People who mastered both Manchu and Chinese language, but also those who passed the Eight Banners imperial examination. These people were administrators of cultural exchanges, and also practicers, representing that the ruling class was willing to (intended to) communicate the Manchu and Han people. In effect(As a result), the translating of Han Shu promoted the institutional construction of new regime, and also contributed to the rulers for constructing the regime legitimacy.--[[User:Du Lina|Du Lina]] ([[User talk:Du Lina|talk]]) 07:06, 29 September 2021 (UTC)<br />
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==英语语言文学（语言学） 202120081484 杜莉娜 女==<br />
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导论：凡国家之建立，必有立国精神和主导意识形态，以及相应之文化政策。<br />
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早在天聪年间，清太宗便为新政权提出文武并用的战略构想，既强调以武功勘祸乱，又主张以文教佐太平，提出了文化统制与文化建设的独到见解。顺治十年，世祖章皇帝订定崇儒重道之政策，并以此为基础构建了兴文教、崇经术的治国理念。<br />
Introduction: The establishment of any countries must need the national spirit ,dominant ideology and appropriate cultural policies.<br />
As early as the year of Tiancong, Hong Taiji put forward the strategy of combining education and force for the new regime. He emphasized to calm down the chaos by force and to keep the peace by civilian. And he came up with unique insights into cultural unification and cultural development as well. During the first decade of Shunzhi, the emperor made the policies of respecting Confucianism and Taoism, and from this the concepts of governing like prospering education and worshipping scriptures were built.<br />
--[[User:Du Lina|Du Lina]] ([[User talk:Du Lina|talk]]) 16:07, 28 September 2021 (UTC)<br />
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1、“文武并用”应该更侧重于“文化”，译为culture；<br />
2、“以文教佐太平”中的文教可译为cultural education；<br />
3、“and from this the concepts of governing like prospering education and worshipping scriptures were built.”此句可用定从，which served as a basis of …<br />
--[[User:Hu Shuqing|Hu Shuqing]] ([[User talk:Hu Shuqing|talk]]) 02:25, 30 September 2021 (UTC)<br />
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==英语语言文学（语言学） 202120081490 胡舒情 女==<br />
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自此以后，虽然清代历朝统治者在关注满、汉文化交流时，不免对汉人进行打压，但也同时对汉族文化进行宣扬与推广。<br />
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清初统治者在构建“治统”与“道统”的过程中，围绕“崇儒重道”，衍生出众多文化政策实举，如科举取士、博学鸿词等，而汉籍经史的翻译与编纂也是其中重要内容。汉籍翻译是清代文化事业的重要组成部分，是清代民族关系与文化政策的重要载体，它和满清政权的其它文化活动一样，在功能上相互关联，彼此补充，为促进民族之间的相互了解，维护王朝体系的稳定发展，做出了重要贡献。<br />
Since then, although all the dominators of Qing dynasty would squash the Han people when focusing on cultural exchange between Manchu and Han, they also propagated and promoted the Han culture at the same time. The dominators of early Qing dynasty implemented numerous cultural policies around the idea of “respecting and emphasizing Confucianism” during the progress of constructing Monarchism and statesmanship, which included imperial examinations, erudite and an important part - compilation and translation of Han classics and history. Its translation formed Qing’s cultural undertakings as necessary parts and served as a carrier of Qing’s ethnic relations and cultural policies. It was the same as other cultural activities of Qing dynasty. They related to and complemented each other, which made a contribution to promoting mutual understanding among peoples and maintaining the stable development of the dynastic system.<br />
--[[User:Hu Shuqing|Hu Shuqing]] ([[User talk:Hu Shuqing|talk]]) 02:26, 30 September 2021 (UTC)<br />
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<br />
Since then when it comes to Man and Han culture communication, all the dominants of Qing dynasty would freeze Han people but also promoted their culture at the same time.<br />
During the process of constructing Monarchism and statesmanship, the dominators of early Qing dynasty implemented numerous cultural policies around the idea of “respecting and emphasizing Confucianism”,which included imperial examinations, erudite etc. and also compilation and translation of Han classics and history as an important content. Translating Han classic is an important part of Qing culture and the critical carrier of its ethnic relationship and cultural policies. Like other cultural activities it related to each other functionally and made great contributions to ethnic communication and the solid development of Qing dynasty.--[[User:Huang Jinyun|Huang Jinyun]] ([[User talk:Huang Jinyun|talk]]) 13:18, 11 October 2021 (UTC)<br />
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==英语语言文学（语言学） 202120081491 黄锦云 女==<br />
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作为汉籍翻译的管理者和实践者，译者们承担了沟通满、汉文化的历史使命，其所翻译的汉族书籍不仅有效增进了旗人对于汉文化的了解，而且为新生政权进行制度建设，以及合法性的建构发挥了历史性作用。<br />
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一 满洲前身时期的汉籍翻译<br />
<br />
满洲的前身系女真，语言文字上属阿尔泰语系。<br />
As the manager and practicer of translating Han's books, translators take charge of the historial mission to combine Manchu cultrue and Han cultrue. Their translations not only enhance the Bannermen to know Han cultrue, but also play a historical role in forming a new regime and conlidating its validity.--Translation of Han's books before Manchu period<br />
Manchurians originates from Nvzhen race, and their language and character belongs to Altaic languages.<br />
<br />
As managers and practitioners of translation of Han's books, the translators undertake the historical mission of cultural communication between the Manchu nationality and the Han nationality. Their translations of Han's books not only efficiently improve the Manchu's understanding of Han culture, but also play a historical role in system construction for new regime and legal construction. <br />
-- Translation of Han's books before the Manchu period<br />
Manchurians originate from Nvzhen race, and their language belongs to Altaic language.--[[User:Kuang Yanli|Kuang Yanli]] ([[User talk:Kuang Yanli|talk]]) 14:11, 9 October 2021 (UTC)<br />
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==英语语言文学（语言学） 202120081495 邝艳丽 女==<br />
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满族和历史上的女真族一样，原本没有自己的语言，记事时需要借用其他民族文字。《金史》中说：“初无文字，国势日强，与邻国交好，迺用契丹字。”[ 杨家骆：《金史》，台北：鼎文书局，1985年，第1684页。] 金朝立国后，初期的内、外公文几乎都用契丹文书写，金太祖本人也擅长契丹语。<br />
As Jurchen in the history, the Manchu nationality did not have its own language, and they needed to use other national characters when making a memorandum. "The Jin dynasty did not have their own characters, but with its increasing development and its need to deal with the relation with neighbouring country, then used Khitan characters" said in ''The History of Jin Dynasty''[Yang Jialuo: ''The History of Jin Dynasty'',Taipei: Dingwen Publishing House, 1985, p.16884] After Jin Dynasty was established, the official documents inside and outside the court were nearly written with Khitan characters, even the Emperor Taizu of Jin Dynasty was skilled in using Khitan language.--[[User:Kuang Yanli|Kuang Yanli]] ([[User talk:Li Aixuan|talk]])09:41, 29 September 2021 (UTC)<br />
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<br />
As Jurchen in the history, the Manchu nationality did not have its own language. Therefore, keeping a record of events depended on the characters of other nation. Said in The History of Jin Dynasty, "the Jin dynasty did not have their own characters, but with its increasing development and its need to deal with the relation with neighbouring country, then used Khitan characters". [Yang Jialuo: The History of Jin Dynasty,Taipei: Dingwen Publishing House, 1985, p.16884] After the establishment of Jin Dynasty, the official documents at home and abroad were nearly written in Khitan characters, even the Emperor Taizu of Jin was skilled in using Khitan characters.--[[User:Li Aixuan|Li Aixuan]] ([[User talk:Li Aixuan|talk]]) 13:34, 29 September 2021 (UTC)<br />
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==英语语言文学（语言学） 202120081496 李爱璇 女==<br />
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然而，契丹语与金人女真语差距较大，因而金太祖命完颜希尹、叶鲁依据汉人楷字，并参照契丹字制度，创制适合本族的语言文字“女直文”。天辅（金太祖年号）三年，女直文依诏令颁行，称“女直大字”。二十年后，即1138年，金熙宗完颜亶又命人参照契丹字，创制并颁布另一种女直文字，即“女直小字”。<br />
However, there was a huge gap between the Khitan language and the Jurchen language. Therefore, the Emperor Taizu of Jin ordered Wanyan Xiyin and Ye Lu to create a language suitable for their own people, Jurchen script, based on the Han regular script and referring to the Khitan script system. In the third year of the period of Tianfu (the reign title of Emperor Taizu of Jin), according to the edict, the Jurchen script was enacted as "Jurchen Large Script". In 1138, twenty years later, Wanyan Dan, the Emperor of Xizong, ordered someone to create and enact another kind of Jurchen script, namely "Jurchen Little Script", referring to the Khitan script system.--[[User:Li Aixuan|Li Aixuan]] ([[User talk:Li Aixuan|talk]]) 01:15, 29 September 2021 (UTC)<br />
However, there was a big gap between Khitan language and Jin Nuzhen language. Therefore, Jin Taizu ordered Wanyan Xiyin and Ye Lu to create a language suitable for their own nationality "Jurchen script", according to the regular script of Han people and with reference to the Khitan character system. In the third year of Tianfu (the year of emperor Taizu of Jin Dynasty), Jurchen script was issued in accordance with the imperial edict, known as "Jurchen Large Script". Twenty years later, in 1138, Jin Xizong, Wanyan Dan ordered people to create and promulgate another Jurchen script, namely “Jurchen Little Script", according to the Khitan character.— Li Xichang<br />
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==英语语言文学（语言学） 202120081502 李习长 男==<br />
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金世宗继位后，在推动女直文字的使用上，力度更大，举措更丰。如大定（金世宗年号）四年，金世宗令翰林侍讲学士徒单子温等用女直大、小字，翻译经书。女直文字的创制，对金朝翻译汉书影响巨大，而汉书翻译又影响了金朝的政治制度与国家治理。<br />
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After Jin Shizong succeeded to the throne, he made greater efforts and took more measures to promote the use of nvzhi characters. For example, in the fourth year of Dading (the year of Jin Shizong), Jin Shizong ordered the Imperial College to teach the bachelor's Apprentice Shan Ziwen to translate scriptures in large and small characters. The creation of nvzhi characters had a great impact on the translation of Chinese books in the Jin Dynasty, which in turn affected the political system and national governance of the Jin Dynasty.<br />
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After Jin Shizong succeeded to the throne, he made greater efforts and took more measures to promote the use of nvzhi characters. For example, in the fourth year of Dading (the year of the region of emperor Jin Shizong), he ordered his disciple Shan Ziwen and other Shijiang academicians of Hanlin to translate Confucian Classics in Jurchen large and small scripts. the creation of Jurchen script had a great impact on the translation of Chinese books in the Jin Dynasty, and that, in turn, the Chinese books affected the political system and national governance of the Jin Dynasty.—[[User:Qiu Tingting|Qiu Tingting]] ([[User talk:Qiu Tingting|talk]]) 03:39, 29 September 2021 (UTC)<br />
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==英语语言文学（语言学） 202120081519 邱婷婷 女==<br />
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如大定五年，金世宗命徒单镒翻译《贞观政要》和《白氏策林》等书，次年徒单子温将译本进呈皇帝。大定七年，《史记》和《西汉书》等翻译成书，金世宗敕令刊刻颁行。大定十五年，金世宗再令翻译各部经书，由温迪罕缔达（著作佐郎）、宗璧（编修官）、阿鲁（尚书省译史）、杨克忠（史部令史）等负责对译本进行注解。<br />
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For example, in the fifth year of Dading (the year of the region of emperor Jin Shizong), the emperor ordered his disciple Shan Yi to translate The Political Program of Zhen Guan and Bai Shi Ce Lin, etc. Then emperor Shizong received the translations submitted by another disciple named Shan Ziwen in the next year. In the seventh year of Dading, the Records of the Grand Historian and the book of the Western Han Dynasty were translated into books, which were printed and issued by the royal decree of emperor Shizong. What’s more, he released an order again translating several Confucian classics which were annotated by Wendihan Dida （assistant of Zhu Zuolang）， Zong Bi（ BianxiuOfficer), A Lu( translator of history of Department of State Affairs ), Yang Kezhong( Lingshi of the Ministry of Official Personnel Affairs ) etc.<br />
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Annotation:<br />
1.Zhu Zuolang: A person whose responsibility is to compile historical works.<br />
2.Bianxiu: The official name, first placed in the Song Dynasty, is mainly responsible for the revision and compilation of documents.<br />
3.Lingshi: Official name; the general name of petty officials in the government since the song and Yuan Dynasties.—[[User:Qiu Tingting|Qiu Tingting]] ([[User talk:Qiu Tingting|talk]]) 16:58, 28 September 2021 (UTC)<br />
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For example, in the fifth year of Dading (the year of the region of emperor Jin Shizong), the emperor ordered his disciple Shan Yi to translate ''The Political Program of Zhen Guan'' and ''Bai Shi Ce Lin'', etc. Then emperor Shizong received the translations submitted by another disciple named Shan Ziwen in the next year. One year later,''the Records of the Grand Historian'' and ''the book of the Western Han Dynasty'' were translated into books, which were printed and issued under the royal decree of emperor Shizong. What’s more, he released an order again translating various kinds of Confucian classics, and put Wendihan Dida （assistant of Zhu Zuolang）， Zong Bi（ BianxiuOfficer), A Lu( translator of history of Department of State Affairs ), Yang Kezhong( Lingshi of the Ministry of Official Personnel Affairs ) etc.in chagrge of the annotation of the translations of these books.<br />
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Annotation:<br />
1.Zhu Zuolang: A person whose responsibility is to compile historical works.<br />
2.Bianxiu: The official name, first placed in the Song Dynasty, is mainly responsible for the revision and compilation of documents.<br />
3.Lingshi: Official name; the general name of petty officials in the government since the song and Yuan Dynasties.--[[User:Rao Jinying|Rao Jinying]] ([[User talk:Rao Jinying|talk]]) 13:00, 29 September 2021 (UTC)<br />
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==英语语言文学（语言学） 202120081520 饶金盈 女==<br />
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金世宗在位期间，朝廷设立译经所，专司汉文经史的翻译。期间，所译汉书除上述几种之外，另有《易》、《书》、《论语》、《老子》、《孟子》、《扬子》、《文中子》、《刘子》以及《新唐书》等。之所以翻译这些书籍，是因为金世宗希望女直人了解汉人的仁义道德，以利于治国安邦。<br />
During the reign of Wan Yanyong, the fifth emperor of the Jin Dynasty, the imperial court set up a sutra translation office to specialize in the translation of scriptures and historical materials written in classical Chinese. Meantime, in addition to the above-mentioned Chinese books, there were also ''Yi'', ''Shu'', ''the Analects of Confucius'', ''the Laozi'', ''the Mencius'', ''the Yangzi'', ''the Wenzhongzi'', ''the Liuzi'' and ''the New Book of Tang Dynasty''. The reason why these books were translated was that Wan Yanyong hoped that the Jurchen people would get some knowledge of the humanity, justice, and morality of Han people by reading these books, so as to develop a prosperous and stable country.--[[User:Rao Jinying|Rao Jinying]] ([[User talk:Rao Jinying|talk]]) 13:05, 28 September 2021 (UTC)<br />
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During the reign of Emperor Jin Shizong,the imperial court set up a classics translation office concentrating on the translation of Chinese classics. During this period,apart from translating the above-mentioned Chinese classics, they also translated " Yi", "Shu", "the Analects of Confucius", "Laozi", "Mencius", "Yangzi", "Wenzhongzi", "Liuzi" and "New Book of Tang", etc. The reason why Jin Shizong chose these classics was that he hoped that Nuzhi people could know the virtue and morality of Chinese by reading these classics. And then he could rule the nation better and build a stable society. -- Yang Aijiang (talk) 21.57. 11 October 2021<br />
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==英语语言文学（语言学） 202120081541 杨爱江 女==<br />
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然而，当时的女直字与汉字不能直接对译，中间需要经过转译为契丹字。为解决这一问题，金章宗在位期间，诏设弘文院，命人译写儒家经典并讲解。旋即，又废止契丹字，要求嗣后汉文典籍直接译为女直字，以省去须经契丹字转译的中间环节。<br />
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However, it was not feasible to translate Nvzhi characters into Chinese characters at that time. And it needed to be translated into Khitan characters first. During the reign of Emperor Jin Zhangzong, he set up Hongwen Academy and commanded his ministers to translate and explain Confucian classics in order to figure out the language problem. Besides, Jin Zhangzong abolished its use of Khitan characters and demanded that the Chinese classics should be translated in Nvzhi characters directly, omitting taking advantage of the translation of Khitan characters as a bridge.<br />
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However, Nvzhi characters could not be directly translated into Chinese without being rendered into Khitan ones first. During the reign of Emperor Jin Zhangzong, he set up Hongwen Academy and commanded his ministers to translate and explain Confucian classics in order to figure out the language problem. Soon after, Jin Zhangzong abolished the use of Khitan characters and demanded that the Chinese classics should be translated into Nvzhi characters directly, avoiding the intermediate step of using Khitan characters.--[[User:Yin Meida|Yin Meida]] ([[User talk:Yin Meida|talk]]) 13:37, 11 October 2021 (UTC)<br />
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==英语语言文学（语言学） 202120081547 殷美达 女==<br />
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金朝政权覆灭之后，虽然留居东北故地的少数女直上层人士尚能娴习女直文，但女直字作为一种语言逐渐失传。明朝政府设置“四夷馆”后，又延人专习女直字，以应付中央与地方政府，或中央与藩属地之间的通译需要。虽然如此，女直语的凋落已成定势。<br />
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二 太祖时期汉籍（书）翻译之“始”<br />
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After the collapse of Jin Dynasty, a small number of upper class Nuzhen people who still lived in northeastern China were proficient in Nuzhen language, but its words were being lost gradually. The Ming Dynasty, when setting up "Si Yi Academy ", hired particular people to study Nuzhen language to meet the needs of communication and translation between the central and local governments or its dependent territories. Nevertheless, the decline of Nuzhen language had become a foregone conclusion. <br />
Second, the initial translation of Chinese books in the Emperor Taizu period<br />
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After the collapse of the Jin Dynasty, although a small number of Nuzhen upper class people living in Northeast China were very proficient in Nuzhen language, Nuzhen characters have been gradually lost. Having set up the Si-yi-guan, the Ming Dynasty hired people to specially learn Nuzhen characters to meet the needs of translation between the central government and local governments or its affiliated places. Nevertheless, the decline of Nuzhen language is inevitable.--[[User:Zhou Qiao1|Zhou Qiao1]] ([[User talk:Zhou Qiao1|talk]]) 01:18, 29 September 2021 (UTC)<br />
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==英语语言文学（语言学） 202120081557 周巧 女==<br />
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明朝万历年间，努尔哈赤率部崛起之时，作为女直后裔的满族并无文字。那时，文移往来必须靠蒙古语的学习与翻译才能完成。为满足文移往来，记注政事的需要，并解决“今我国之语，必译为蒙古语读之，则未习蒙古语者，不能知也”的问题，清太祖努尔哈赤于明万历二十七年，命额尔德尼、噶（gá）盖等改制国书（即，国家的语言文字），以改变满人说女真语却写蒙古字的尴尬局面。[ 明珠等奉敕修：《清实录·太祖高皇帝实录》，北京：中华书局，1986年，第2页。]<br />
In the wanli period of Ming Dynasty, when Nurhachi led the rise of the Manchu, as a straight descendant of Jurchen, it had no written words. At that time, the exchange of letters had to rely on Mongolian learning and translation to complete. For recording the political affairs and solving the problem that "Nowadays, the language of our country have to be translated into Mongolian to read, otherwise people can’t understand it without the learning of it. In the wanli 27 years of Ming Dynasty, Emperor Nurhachi（the first founder of Ming Dynasty） ordered Erdeni, Gagai and etc to reform credentials (namely the national language), which is to change the embarrassing situation of Manchu speaking Jurchen language while writing Mongolian. Beijing: Zhonghua Book Company, 1986, p. 2.<br />
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During the Wanli period of Ming Dynasty, when Nurhachi led the rise of the Manchu, the Manchu, as the straight descendant of Jurchen, had no written words . At that time, the exchange of letters had to rely on Mongolian learning and translation. In order to meet the need of exchange of letters, recording and annotating the political affairs as well as solving the problem that "Nowadays, the language of our country have to be translated into Mongolian to read, and the people who do not learn Mobolian can't understand it.", in the Wanli 27 years of Ming Dynasty, Emperor Nurhachi（the first founder of Ming Dynasty） ordered Erdeni, Gagai and etc to reform credentials (namely the national language), which aimed to change the embarrassing situation that Manchu speaking Jurchen language while writing in Mongolian. Beijing: Zhonghua Book Company, 1986, p. 2.--[[User:Zhu Suzhen|Zhu Suzhen]] ([[User talk:Zhu Suzhen|talk]]) 06:16, 30 September 2021 (UTC)<br />
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==英语语言文学（语言学） 202120081561 朱素珍 女==<br />
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自此，满洲的语言和文字渐趋一致。然而问题在于，此时创制的满洲语言（老满文）系参照畏兀儿体老蒙文字母，而蒙古与女真语音原本存在差异，借用的蒙文字母未必能充分传达女真语言的意义。如太宗皇太极在评价老满文时所说，“书中寻常语言，视其文义，易于通晓”，但“至于人名、地名，必致错误。”[ 中国第一历史档案馆、中国社科院历史研究所译注：《满文老档》，北京：中华书局，1990年，第1196页。]<br />
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From then on, the languange and the forms in Manchurian language are gradually consistant. However, the problem is that the Manchurian language(Old Manchu) was created with the guidance of Uighur Old Mongolia alphabets, and there exist original difference between Mongolia and Jurchen pronunciation. Therefore, the borrowed Mongolian alphabets could not explicitly express the meaning of Jurchen language. Just as what the great emperor Taizong Hoang Taiji said when he evaluated Old Manchu: "You can easily understand the meaning of the ordinary language in a book written in Old Manchu. However, in terms of the name of people and places, the Old Manchu would lead you to misunderstanding without any doubt." (Translated by The First Historical Archives of China and Institute of Chinese Social History :''Manchu Old File'',Peking:Zhonghua Book Company,1990, page 1196.) <br />
Annotation:<br />
1. Jurchen: an ancient nationality in China<br />
2.Uighur(畏兀儿体/古维吾尔语): the earliest Mongolian Chinese characters<br />
3. Old Manchu:the language used by ancient Machurian<br />
4. Manchurian: the founder of Qing Dynasty<br />
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From then on, the languange and the characters in Manchurian language are gradually consistant. However, the problem is that the Manchurian language(Old Manchu) was created with the guidance of Uighur Old Mongolia alphabets, and there existed original difference between Mongolia and Jurchen pronunciation. Therefore, the borrowed Mongolian alphabets could not explicitly express the true meaning of Jurchen language. Just as what the great emperor Taizong Hoang Taiji said when he evaluated Old Manchu: "You can easily understand the meaning of the ordinary language in a book written in Old Manchu. However, in terms of the name of people and places, the Old Manchu would lead you to misunderstanding without any doubt." (Translated by The First Historical Archives of China and Institute of Chinese Social History :“Manchu Old File'',Peking:Zhonghua Book Company,1990, page 1196.) --[[User:Chen Huini|Chen Huini]] ([[User talk:Chen Huini|talk]]) 05:10, 3 October 2021 (UTC)Chen Huini<br />
Annotation:<br />
1. Jurchen: an ancient nationality in China<br />
2.Uighur(畏兀儿体/古维吾尔语): the earliest Mongolian Chinese characters<br />
3. Old Manchu:the language used by ancient Machurian<br />
4. Manchurian: the founder of Qing Dynasty<br />
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==英语语言文学（英美文学） 202120081479 陈惠妮 女==<br />
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为此，皇太极命“巴克什”达海对老满文加以改进，使其音、义明晓，有助于学习，形成了所谓“新满文”。[ 鄂尔泰等奉敕修：《清实录·太宗文皇帝实录》，北京：中华书局，1985年，第13页。]文字虽已形成，但满人一时间几乎无书可读，原因有二：其一，此时的满文尚属草创，旗人尚不能以满语编纂书籍；其二，汉字书籍的获取极为不易。<br />
面对上述情况，太祖努尔哈赤敕令在旗人中延请师傅，教子弟读书，并令达海等人以满文翻译汉文典籍。<br />
Therefore, the King asked Baks Dahai to develop the Old Manchu to make its sound and meaning more clear, which maked it easier to learn. It is in this way that the so-called New Manchu was formed. [E'ertai et al. Fengxiu: "Records of the Qing Dynasty·Records of Emperor Taizongwen", Beijing: Zhonghua Book Company, 1985, p. 13. ] Although the characters and words have been developed, the Manchu can hardly find bookes to read for two reasons. One is that the Manchu language at that time is still an initial creation. The other is that it's so hard to get the books in Chineses charaters.On the face of this situation, Taizu Nurha Chi invited masters among the bannermen to teach children to read, and ordered Dahai and others to translate Chinese classics in Manchu. --[[User:Chen Huini|Chen Huini]] ([[User talk:Chen Huini|talk]]) 07:59, 29 December 2021 (UTC)Chen Huini<br />
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Therefore, Huang Taiji (an emperor of Qing Dynasty) instructed "Bakshi (a title of a scholar of Qing Dynasty)" Da Hai to develop the Old Manchu to the so-called "New Manchu", which made the sound and meaning more clear and then make it easier to learn. [E'ertai et al.: Factual Record of Qing Dynasty·Factual Records of Emperor Taizongwen, Beijing: Zhonghua Book Company, 1985, p. 13. ] Although the characters and words have been formed, the Manchu for a time almost no books to read for two reasons. One is that the Manchu language at that time was still an initial creation, and the Bannermen couldn't compile with it. The other was that the acquisition of books in Chineses charaters was extremely hard.On the face of this situation, Taizu Nurha Chi invited masters among the bannermen to teach children to read, and ordered Dahai and others to translate Chinese classics into Manchu.--[[User:Cheng Yang|Cheng Yang]] ([[User talk:Cheng Yang|talk]]) 10:16, 29 September 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081482 程杨 女==<br />
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《清史列传》中对此的记载如下：弱冠，太祖高皇帝召直文馆，凡国家与明及蒙古、朝鲜词命，悉出其手。有诏旨应兼汉文者，亦承命传宣，悉当上意。旋奉命译《明会典》及《素书》、《三略》。[ 王钟翰点校：《清史列传》，北京：中华书局，1987年，第187页。]<br />
The records of Da Hai in ''the Biographies of the Qing Dynasty'' as follows: at the age of twenty, he was called into the imperial palace and served in Wen Guan (the bureaucratic ministry for translating Chinese books in the early Qing Dynasty). The naming of new words that related to the Ming Dynasty, Mongolia and North Korea were all by him to complete. He was also instructed to convey some of the Chainese-related orders totally reflecting the will of the emperor. Soon, he was asked to translate ''the Code of Ming Dynasty'', ''Su Shu''(written in Qing Dynasty), and ''San Lue''(written by Huang Shigong).[''The Biographies of the Qing Dynasty'', edited by Wang Zhonghan, Zhong Hua Book Company, 1987, pp.187.]--[[User:Cheng Yang|Cheng Yang]] ([[User talk:Cheng Yang|talk]]) 03:52, 29 September 2021 (UTC)<br />
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The records of Da Hai in ''the Biographies of Qing Dynasty'' as follows: at the age of twenty, he was appointed by Nurhachi①as a translator in Wen Guan②. The naming of new words that related to Ming Dynasty, Mongolia and North Korea were all by him to complete. He was also instructed to convey some of the Han's language-related orders totally reflecting the will of the emperor. Soon, he was asked to translate ''the Code of Ming Dynasty'', ''Su Shu''③, and ''San Lue''④.[''The Biographies of the Qing Dynasty'', edited by Wang Zhonghan, Zhong Hua Book Company, 1987, pp.187.<br />
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Annotation:①Nurhachi:the founder of Qing Dynasty, 1559–1626. ②Wen Guan: the bureaucratic ministry for translating Chinese books in the early Qing Dynasty. ③''Su Shu'': moral principles written in Western Han Dynasty by Huang Shigong. ④''San Lue'':military monograph written in Western Han Dynasty by Huang Shigong.--[[User:Ding Xuan|Ding Xuan]] ([[User talk:Ding Xuan|talk]]) 13:10, 29 September 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081483 丁旋 女==<br />
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上文中所谓《明会典》，又称《大明会典》，系明代典章制度史书，内容涉及汉族文教、历法、习俗等。与《明会典》不同，《素书》成书于西汉，并非典章著作，而是哲理之学，道家思想的智慧之作。《三略》又名《黄石公三略》，既是军事战略专论，又糅合了诸子百家思想。<br />
''Code of Ming Dynasty''① mentioned above, also known as ''Code of Great Ming Dynasty'', is one historical book about laws and regulations in the Ming Dynasty, covering Education of Han②, the Chinese calendar, and custom and so on. Different from Code of Ming Dynasty, ''Su Shu''③ written in the Western Han Dynasty is a wisdom work full of philosophy and Taoism rather than law and regulations. ''Sun-Lue''④, also called Sun-Lue of Huang Shigong, is not only a military strategy monograph but a mixture of the hundred schools of thought. <br />
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Annotation: <br />
①''Code of Ming Dynasty'': One code about Ming’s laws and regulations in many aspects started to write in 1393 and finished in 1578.<br />
②Education of Han: The education policy initiated by emperors of Ming Dynasty in order to transmit Confucianism thoughts and consolidate their reign.<br />
③''Su Shu'': It is one book full of principles and truth written by Huang Shigong in the western Han Dynasty. It is used for the reign of country because of its instructive and moral function.<br />
④''Sun-Lue'': It is one famous ancient military book including three parts written by Huang Shigong in the Western Han Dynasty. The military thoughts in it are critically useful and different from others because it discusses military strategies from political perspective.--[[User:Ding Xuan|Ding Xuan]] ([[User talk:Ding Xuan|talk]]) 06:43, 29 September 2021 (UTC)<br />
''Code of Ming Dynasty''① mentioned above, also known as ''Code of Great Ming Dynasty'', is one historical book about laws and regulations in the Ming Dynasty, covering the range of Education of Han②, the Chinese calendar, and customs and so on. Different from Code of Ming Dynasty, ''Su Shu''③ written in the Western Han Dynasty is a work full of philosophical wisdom and Taoism rather than law and regulations. ''Sun-Lue''④, also called Sun-Lue of Huang Shigong, is not only a military strategy monograph but a mixture of the hundred schools of thought.<br />
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==英语语言文学（英美文学） 202120081485 付红岩 女==<br />
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以上三者皆是重要的汉文典籍，清太祖令达海翻译它们，开启了清代翻译汉籍（书）之先河，其政治策略上的深刻用意不言而喻。<br />
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三 太宗时期汉籍（书）翻译之“兴”<br />
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太宗皇太极即位后，为鼓励旗人读书，多措并举：一方面，要求“十五岁以下，八岁以上者，俱令读书”，惩处不愿教子读书者；另一方面，为改善“无书可读”的情况，又致函朝鲜，索求汉文典籍。<br />
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Since the three ancient books just mentioned above were all significant Chinese classics, Nurhaci, the Emperor in Qing Dynasty requested Dahai, the official should translate the classics into the mongolian, whose profound meaning in the aspect of political layout was very explicit.<br />
Third, the interest of translating Chinese classics has reached its climax during the power of Nurhaci.<br />
After NUrhaci’s coming into the power, many decrees has been enacted in attempt to encourage the people to read. On the one hand, the teenagers between 8 and 15 were allowed to read. In addition , the parents who were reluctant to support their offspring to read would be punished. On the other hand, Qing Dynasty sent a letter to Korea, in which the former asked the later to denote more Chinese classics in order to improve the predicament that the books were not enough to read.<br />
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批改：译者译文总体流畅，对原文理解总体得当。以下为细节处理上的建议 <br />
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1.“清太祖”一词为中国专有名词，应先查后译。清太祖指的是努尔哈赤（1616-1626），是后金第一位大汗，清朝的奠基者，太祖在位时，“大清国”还没有定鼎中原，故此时的“清”并不能算是中国的一个朝代。译者将其处理为“the Emperor in Qing Dynasty”不够严谨。<br />
建议改为：Nurhaci, the Founder of Qing Dynasty<br />
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2.“三 太宗时期汉籍（书）翻译之‘兴’”此处为论文小标题，首先，应当注意标题格式。结构上，原文为名词短语，译者翻译时使用英语句子的SVO（A）结构，虽然意思完整，但失去了原文的简洁。<br />
词语上，“兴”强调翻译之风的兴起、盛行，译者将其处理为“reached its climax”缺乏严谨性，太宗时期不一定是汉籍翻译的高潮时期。<br />
建议改为：Third The Prosperity of Chinese Classics Translation During the Power of Nurhaci<br />
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3.“十五岁以下，八岁以上者，俱令读书”<br />
词语上，“令”是主动要求的意思，译者将其处理为“be allowed”，即被允许读书的可能性，读与不读似乎都是可以接受的。结合后文“惩处不愿教子读书者”，说明“令”是强制的。“be allowed”不能精准传达出原文清政府对旗人读书的鼓励与强制性。<br />
结构上，原句省略了主语（清政府），原意为：清政府要求十五岁以下，八岁以上的人都得读书。译者将主动结构改为了被动结构，突出强调了实施对象。<br />
建议改为：The teenagers between 8 and 15 were asked to read.--[[User:Li Ruiyang|Li Ruiyang]] ([[User talk:Li Ruiyang|talk]]) 04:20, 29 September 2021 (UTC)<br />
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All the above three were important Han classics, so Nurhaci, the founder of Qing Dynasty, commanded Dahai to translate them into Manchu language, which opened the floodgates to the large-scale translations of Han classics and its profound political meaning was explicit.<br />
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Third The Prosperity of Chinese Classics Translation During the Power of Nurhaci<br />
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After Abahai, the son of Nurhaci, coming into power, many decrees has been issued to encourage people to read and study. On the one hand, hand, teenagers between 8 and 15 were asked to study, and their parents would be punished if they were unwilling to educate their children; On the other hand, in order to avoid the scarcity of Han classics, Qing Dynasty sent a written request to Korea for a generous lending.--[[User:Li Ruiyang|Li Ruiyang]] ([[User talk:Li Ruiyang|talk]]) 14:43, 29 September 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081497 李瑞洋 女==<br />
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《朝鲜王朝实录·仁祖实录》中写道：“闻贵国有金、元所译《书》、《诗》等经及《四书》，敬求一览，惟冀慨然。”[ 国史编纂委员会：《朝鲜王朝实录》，汉城：国史编纂委员会，1973年，第38页。]可见，皇太极此时想要借阅的并非汉文原书，而是汉籍的金、元译本，即女真语和蒙古文译本。对此，朝鲜政府方面虽然进行了回应，但态度并不热忱：见索《诗》、《书》、《四书》等书籍，此意甚善，深嘉贵国尊信圣贤，慕悦礼义之盛意。<br />
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As mentioned in ''King Injo the Great'', one of the volumes in ''Annals of the Korean Dynasty'' “We heard your country have ''the Four Books'' and other classics including ''The Book of Poetry'' and T''he Book of History'', which were translated in Jin Dynasty and Yuan Dynasty, so we sincerely hope your generous lending of these books.” [National History Compilation Committee: ''Annals of the Korean Dynasty'', Seoul: National History Compilation Committee, 1973,P38] Obviously, at this time what Huang Taiji wanted to borrow was not the original Chinese book, but the Jin and Yuan translations, namely the Jurchen and Mongolian versions. Although Korean officials responded to this request, they were not very willing: It is very nice of you to ask for the Four Books and other classics including ''The Book of Poetry'' and ''The Book of History'', and we really appreciate your country’s popularity of respecting men of virtue and advocating courtesy and justice.--[[User:Li Ruiyang|Li Ruiyang]] ([[User talk:Li Ruiyang|talk]]) 02:19, 29 September 2021 (UTC)<br />
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It was recorded in ''King Injo the Great'', a volume of ''Annals of the Korean Dynasty'', that “We have heard your country have classics like ''The Book of Songs'', ''The Book of History'' and ''the Four Books'' , which were translated in Jin and Yuan Dynasties, so we sincerely hope for your generous lending of these books to us.” [National History Compilation Committee: ''Annals of the Korean Dynasty'', Seoul: National History Compilation Committee, 1973, 38.]<br />
Obviously, what Huang Taiji wanted to borrow at that time were not the original Chinese books, but the translated versions of Jin and Yuan periods, namely the Jurchen and Mongolian versions. Although Korean officials respondedt, they didn't give a active response: It is glad to receive your borrowing request of these books, and we do appreciate your country’s deeds of respecting men of virtues and advocating courtesy and justice.--[[User:Li Shan|Li Shan]] ([[User talk:Li Shan|talk]]) 01:28, 30 September 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081498 李姗 女==<br />
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第国中所有，只是天下通行本，而金、元所译，则曾未得见，兹未能奉副，无任愧歉。[ 国史编纂委员会：《朝鲜王朝实录》，汉城：国史编纂委员会，1973年，第62页。]<br />
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由于未能索得属意书籍，皇太极遂令达海继续翻译，后者于天聪六年开始翻译《通鉴》、《六韬》、《孟子》、《三国志（通俗演义）》，以及《大乘经》等。只可惜，由于达海英年早逝，上述书籍未能译竟。<br />
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The one that existed in the great empire was just a general version that circulated all over the country.（* But the one that existed in the whole country was just a general version.--[[User:Li Wenxuan|Li Wenxuan]] ([[User talk:Li Wenxuan|talk]]) 02:37, 29 September 2021 (UTC)） As the translating versions of Jin and Yuan were not publicized yet, it's a pity that their works were not able to be offered to the emperor.（* However, the translated versions in Jing Dynasty and Yuan Dynasty hadn't published yet.It's a pity that their works were not able to be offered to the emperor.--[[User:Li Wenxuan|Li Wenxuan]] ([[User talk:Li Wenxuan|talk]]) 02:37, 29 September 2021 (UTC)) [National History Compilation Committee: ''Records of the Korean Dynasty'', Seoul: National History Compilation Committee, 1973, 63.]<br />
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Failing to obtain desired translation versions, Huangtaiji,the first emperor of Qing dynasty, ordered Dahai to continue his translation work. And in the sixth year of Huangtaiji's rule (the year of 1632), Dahai began to translate ''History as a Mirror'', ''The Six Arts of War'', ''The Records of Three Kingdoms'' as well as ''Mahayana Sutra'' and so forth. [* ''Tong Jian'' ( a history book), ''Liu Tao'' ( a military book), ''Mencius''，''Records of the Three Kingdoms'' and ''Dacheng Jing'' ( a book about Buddhism) --[[User:Li Wenxuan|Li Wenxuan]] ([[User talk:Li Wenxuan|talk]]) 02:37, 29 September 2021 (UTC)] Unfortunately, the translation of these books was not finished as a result of Dahai's early death.--[[User:Li Shan|Li Shan]] ([[User talk:Li Shan|talk]]) 12:46, 11 October 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081500 李文璇 女==<br />
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清初之际，达海被满洲“群推为圣人”，他翻译的汉文书籍对于拓展满族的知识范围，甚为关键。[ 叶高树：《清朝前期的文化政策》，台北：稻乡出版社，2002年，第58页。]《清实录·太宗文皇帝实录》中说：其平日所译汉书，有《刑部会典》、《素书》、《三略》、《万宝全书》俱成帙。（天聪六年）时方译《通鉴》、《六韬》、《孟子》、《三国志（通俗演义）》及《大乘经》，未竣而卒。<br />
In the early Qing Dynasty, Dahai was regarded as a sage by the people of Manchuria. The Chinese books translated by him was significant for extending the knowledge. In the book ''Memoir of the Qing Dynasty'' for the Emperor Taizong, it recorded that the books Dahai had translated, including ''Records of Ministry of Punishment'', ''Su Shu'' ( a book about Taoism), ''San Lue'' ( a military book), ''Wan Bao Quan Shu'' ( a book about daily life), all of which had been compiled into volumes. In 1632, he translated ''Tong Jian'' ( a history book), ''Liu Tao'' ( a military book), ''Mencius''，''Records of the Three Kingdoms'' and ''Dacheng Jing'' ( a book about Buddhism). However, he died without finishing these books. --[[User:Li Wenxuan|Li Wenxuan]] ([[User talk:Li Wenxuan|talk]]) 23:26, 28 September 2021 (UTC)--[[User:Li Wen|Li Wen]] ([[User talk:Li Wen|talk]]) 02:38, 29 September 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081501 李雯 女==<br />
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初我国未深谙典故，诸事皆以意创行，达海始用满文，译历代史书，颁行国中，人尽通晓。惟我太祖天纵聪明，因心肇造，所行皆与古圣贤同符默契。达海与额尔德尼应运而生，实佐一代文明之治云。[ 鄂尔泰等奉敕修：《清实录·太宗文皇帝实录》，北京：中华书局，1985年，第10页。]<br />
In the beginning, our country was not familiar with the ancient books and stories, so everything began by "thoughts". Since Dai Hai began to use Man Character to translate historical books of the past dynasties and promulgated it in our country, it has become universal among public. Only my great Grandfather, gifted and wise, created from the heart, and acted in accordance with the ancient sages. So the Dai Hai and E Erdeni came into being following the tendecy, who help to create this generation of civilization.[E ertai,ect compiling by order of the Emperor;The record of Emperor Taizong in Qing Danasty,Beijing,Zhonghua Book Company,1985,Page 10.--[[User:Li Wen|Li Wen]] ([[User talk:Li Wen|talk]]) 02:36, 29 September 2021 (UTC)<br />
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In the beginning, our country established things at will without the study of ancient classics. They were not well-known to the public until Da Hai translated historical books of the past dynasties in Manchu and promulated them in the country. Only the Emperor Taizu, who founded the dynasty on his own way, was so gifted and wise that what he had done was exactly accordance with the ancient sages.In reasponse to the call of the times, Da Hai and Erdeni came into being and helped to create this generation of civilization.[E ertai,ect compiling by order of the Emperor;''The Record of Emperor Taizong in Qing Danasty'',Beijing,Zhonghua Book Company,1985,Page 10.--[[User:Liu Peiting|Liu Peiting]] ([[User talk:Liu Peiting|talk]]) 04:49, 29 September 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081505 刘沛婷 女==<br />
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如前所述，《明会典》、《素书》、《三略》等书的翻译始于太祖时期，但成书于天聪四年，其余书籍的翻译则为时稍晚。综观达海译书，既有《孟子》之类所谓“知正心、修身、齐家、治国”者，又有《素书》、《三略》和《六韬》等“益聪明智识，选练战攻的机权”者，以及《通鉴》等“知古来兴废事迹”者，他的翻译“实佐一代文明之治”。<br />
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以《刑部会典》（又称《明会典》）为例，达海译本既是天聪年间国家创制法律的依据，也是太宗推行政治改革的蓝本。<br />
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As mentioned above, the translation of ''Code of Great Ming Dynasty'', ''Su Shu'' and ''Three Policies'' began in the reign of Taizu but was completed in 1630. The other books were translated a little later. Da Hai's translations covered abundant eminent men, such as those in ''Mencius'' who make their minds just, morality cultivated, families regulated and the country governed orderly, and those with extrodinary intelligence and knowledge of training and combat in ''Su Shu'', ''Three Policies'' and ''Six Strategies'', as well as the men knowing the rise and fall of ancient times in ''Comprehensive Mirror for Aid in Government''. Therefore, his translations did promote the rule of civilization.<br />
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Taking ''Code of Ministry of Penalty'' (also called ''Code of Great Ming Dynasty'')as an example,Da Hai's translations were not only the basis for establishing laws during the period of Tiancong, but also the blueprint for the political reforms carried out by Taizong.--[[User:Liu Peiting|Liu Peiting]] ([[User talk:Liu Peiting|talk]]) 15:25, 28 September 2021 (UTC)<br />
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As mentioned above, the translating of ''Code of Great Ming Dynasty'', ''Su Shu'' and ''Three Policies'' began during the reign of Taizu but was completed in 1630, the fourth year under the reign of Taizong. Other books were translated at a later time. Given that in Da Hai's translations exist abundant eminent men, such as those in ''Mencius'' who make their minds just, morality cultivated, families regulated and the country governed orderly, and those with extrodinary intelligence and knowledge of training the military in ''Su Shu'', ''Three Policies'' and ''Six Strategies'', as well as the men comprehending the ups and downs experienced by dynasties in the past in ''Comprehensive Mirror for Aid in Government'', his translations did promote the governance under civilization.<br />
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Taking ''Code of Ministry of Penalty'' (also called ''Code of Great Ming Dynasty'')as an example,Da Hai's translations were not only the basis for establishing laws during the period of Tiancong, but also the blueprint for the political reform carried out by Taizong.--[[User:Liu Xiao|Liu Xiao]] ([[User talk:Liu Xiao|talk]]) 02:46, 29 September 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081508 刘晓 女==<br />
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太宗素有“振兴文治”的愿望，不仅创制了作为清代考试制度之滥觞的生员和举人考试，而且使巴克什、笔帖式制度臻于成熟，二者合力使得汉籍翻译的风气渐开：一方面，作为“巴克什”或“笔帖式”，希福、尼堪、刚林、苏开等人先后奉敕“（翻）译汉字书籍”或“记注国政”；另一方面，太宗以自古国家“以文教佐太平”为由，令满人争相读书，从生员中选取“文艺明通者优奖之”。[ 鄂尔泰等奉敕修：《清实录·太宗文皇帝实录》，北京：中华书局，1985年，第14页。]<br />
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在对待前朝即明代的问题上，太宗采取“讲和”与“自固”并行的政策，但其本人向往中原汉族文化，所谓“性嗜典籍，披览弗卷”即是这一情况的体现。<br />
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Holding the idea to "revitalize the administration by education and culture", Taizong not only created examinations from which the examination system of Qing Dynasty originated, for xiucai and juren, those who have passed in the exam at the county and provincial level respectively, but also made the Baksh and Bithesi system reach a mature state. Together, the two measures promoted the translation of books written in Chinese. On one hand, Xifu, Nikan, Ganglin and Su Kai, as "Bakshs" or"Bithesis", successively "translated Chinese books" or "annotated state affairs" by the order of the emperor. On the other hand, by saying that countries applied the education and culture to safeguarding the stablization since ancient times, Taizong urged people to scramble to study, and then awarded those from xiucai "who mastered literature and arts". (''Records of Qing Dynasty· Emperor Taizong'', edited by Ertai, officials in the Qing Dynasty, Beijing: China Publishing House, 1985, P14.)<br />
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In dealing with the treatment of the previous dynasty, that is Ming Dynasty, Taizong adopted the policy of "settling a dispute" and "self-consolidation". But he himself yearned for the Chinese culture of the Central Plains, which is embodied by the so called expression "Loving Chinese works and reading Buddha volumes."<br />
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Revised version：<br />
Holding to the idea of "revitalizing the empire by education and cultural development", Taizong not only established shengyuan and juren exams（exams at the county and provincial level perspectively） initiating the imperial examination system of Qing Dynasty, but also brought the Baksh and Bithesi system to a mature state. Together, the two measures brought about the upsurge in translation of Han books. On the one hand, Xifu, Nikan, Ganglin and Su Kai, as "Bakshs" or"Bithesis", successively "translated Han books" or "annotated state affairs" by the order of the emperor. On the other hand, in the name of the national tradition of ensuring stabilization by education and cultural development, Taizong urged Man people to read and those shengyuan who stands out for their familiarity with literature and arts were awarded. (The Memoir of Qing Dynasty· Emperor Taizong, edited by Ertai, officials in the Qing Dynasty, Beijing: China Publishing House, 1985, P14.)<br />
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In dealing with the treatment of the former dynasty, namely Ming Dynasty, Taizong resorted to the policy of “peace negotiation” and " self-consolidation" in parallel. However, Taizong himself yearned for the Central Plains culture of Han, and the expression “a voracious reader of Han and Buddhist classics” can properly manifests it. --[[User:Liu Yunxin|Liu Yunxin]] ([[User talk:Liu Yunxin|talk]]) 07:53, 29 September 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081510 刘运心 女==<br />
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于是，太宗诏令儒臣翻译汉书，并命金、汉之人阅读。[ 同上，第2页。]太宗深知，汉文典籍言微而义大，其精要者不仅涉及帝王治平之道，而且涉及正心、修身、齐家之理。但汉文典籍数量庞杂，翻译时必须有所选择。<br />
Therefore, Taizong issued an order asking the civilian official to translate Han books and all the Han and Jin people are required read those. (Ibid., p.2) Taizong knew well how brief and profound Han classics were. Their essence not only involves the governess of the country and its people, but also discusses inner integrity, decent behavior and family harmony. Nevertheless, a great number and variety of Han classics means what to be translated needs selection.<br />
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Therefore, Taizong ordered Confucian officials to translate Han books, while all the Han and Jin people were required to read those. (Ibid., p.2) Taizong knew well that Han classics were sublime works with deep meaning, whose essentials included not only the method of statecraft, but also self-cultivation and family regulation. Nevertheless, among a great number of Han classics, there must be an extraction.<br />
--[[User:Luo Anyi|Luo Anyi]] ([[User talk:Luo Anyi|talk]]) 02:14, 29 September 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081511 罗安怡 女==<br />
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如天聪六年九月，王文奎奏请从读书笔帖式内，选取“伶俐通文者”一、二人，并从秀才内选取“老成明察者”一、二人，令其“讲解翻写”。天聪九年三月二十一日，仇震向太宗谏言，要求从汉人中选取精通经、史者二、三人，并从金人中选取熟悉字法者三、四人，将各经史典籍及《通鉴》（即《资治通鉴》）中“有裨君道”的精要部分“集为一部”，日日讲解，以便统治者在翻译、日讲中学习汉族文化，以及治世之道。[ 罗振玉：《天聪朝臣工奏议》，北京：中国人民大学出版社，1989年，第24-25、115页。]固然，对于汉文典籍与汉族文化，太宗并非全盘接受，而是辩证地加以吸收。<br />
For instance, in September, 1632, the sixth year of T'ien-ts'ung (the first reign title of Emperor Taizong of Qing Dynasty), Wang Wenkui wrote to His Majesty, advising that they should elect one or two "literates who were skilled at writing and translation" from the bithesi, and one or two "scholars who were learned and perspicacious", for "teaching, interpretation, and translation" of Han classics. On March 21st, 1635, the ninth year of T’ien-ts'ung, Chou Zhen advised Emperor Taizong to elect two or three Han people who were proficient in lections and historical records, and three or four Jin people who were familiar with 字法. These scholars should choose essentials from classics and ''The Zizhi'' (''The Zizhi Tongjian'') , which benefits the ideas of ruling power of feudal emperors, and compile them into one book. Also they should discourse the compiled thoughts frequently, in which the ruler could learn the Han culture and method of statecraft. [Zhenyu, Luo .(罗振玉),《天聪朝臣工奏议》，Beijing：China Renmin University Press, 1989, P24-25, P115.] Of course, Taizong did accepted Chinese classics and han culture critically and dialectically.<br />
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For instance, in September, the sixth year of Tian Cong(the first reign title of Emperor Taizong of Qing Dynasty), Wang Wenkui wrote to His Majesty to pick up one or two who are "talented in literature” and pick up one or two who are "sophisticated and insightful"from the scholars for " interpretation, and translation" of Han classics. On March 21st, 1635, the ninth year of T’ien-ts'ung,Qiu Zhen advised Emperor Taizong to pick up two or three Han people who were proficient in lections and historical records, and three or four Jin people who were familiar with grammer to let them choose the essence which are benefical to the reign from classics and ''The Zizhi'' (''The Zizhi Tongjian'') and compile them into one book.After that,they should illuminate the compiled thoughts every day, which is beneficial to the study of the ruler about the Han culture and ruling ways. [Zhenyu, Luo .(罗振玉),《天聪朝臣工奏议》，Beijing：China Renmin University Press, 1989, P24-25, P115.] Definitely speaking, Taizong did accepted Chinese classics and han culture critically and dialectically.--[[User:Luo Xi|Luo Xi]] ([[User talk:Luo Xi|talk]]) 03:24, 29 September 2021 (UTC)(Luo Xi罗曦）<br />
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==英语语言文学（英美文学） 202120081512 罗曦 女==<br />
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如天聪九年五月，上谕文馆诸臣：朕观汉文史书，殊多饰辞，虽全览无益也。今宜于《辽》、《宋》、《金》、《元》四史内，择其勤于求治而国祚昌隆，或所行悖道而统绪废坠，与夫用兵行师之方略，以及佐理之忠良、乱国之奸佞，有关政要者，汇纂翻译成书，用备观览。至汉文正史之外，野史所载，如交战几合，逞施法术之语，皆系妄诞，此等书籍，传之国中，恐无知之人信以为真，当停其翻译。[ 鄂尔泰等奉敕修：《清实录·太宗文皇帝实录》，北京：中华书局，1985年，第9页。]<br />
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For instance，in May，the ninth year of Tian Cong，the emperor informed all the offcials in the cultural center that：I have read all those historical records in Han Dynasty filled with various ornaments，only to find that I have gotten nothing。Now you had better to pick up some references among <Liao>、<Song>、<Jin>、<Yuan> about some cases like dilligence making a prosperous country or idleness making a weak one,also,you can pick up some military stratedies,and some information about those noble and talented citizens or traitors,and than you ought to translate all those relevence and compile them into a book prepared to be read.As for those unofficial history in Han excluded,like how many rounds did soldiers battle or the spell spoken by wizards,are all the nonsense,which will bewilder the ignorant,deserving to be prohibited from translation.{E Ertai：《清实录、太宗文皇帝实录》，Beijing：Chinese bookstore，1985，p9.}<br />
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A modified version: For instance, in May 1653, the Emperor said to the ministers in Literature Institution(that is, the later Cabinet in Qing Dynasty), "I have leafed through the historical works in Chinese language with various ornamental rhetorics, but the complete reading of them is not beneficial. Now it is appropriate to select salient examples referred from the four historical recrods of ''Liao'', ''Song'', ''Jin'' and ''Yuan'', which will be compiled into books for later reading. These examples include those who were dedicated in governing the country thereby with a promising national development, or those who deviated from the correct path with weak administration, and the generals adept at warfare, loyal and honest servants assisting the sovereign to handle state affairs, the treacherous ones rendering the nation disorderly and chaotic as well as other relevant political workers. Meanwhile, apart from the official history, the unofficial historical works that record untrue events, like the conditions of battles, are all fabricated. If those unfavorable books are spread to the mainland, they may bewilder the ignorant and gullible individuals, thus deserving to be prohibited from translation. (E Eratai: ''Records of Qing Dynasty·Emperor Taizong'', Beijing: China Publishing House, 1985, Page9.)--[[User:Mao Yawen|Mao Yawen]] ([[User talk:Mao Yawen|talk]]) 04:47, 29 September 2021 (UTC)毛雅文<br />
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==英语语言文学（英美文学） 202120081514 毛雅文 女==<br />
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由上可知，在汉籍翻译的问题上，太宗讲求的是实用，希望将翻译与政要相关联，反对翻译浮华藻饰的汉文书籍，或者野史中所载不足为信者。以《刑部会典》的翻译为例，该译本的刊刻与颁行逐渐成为太宗朝的临政规范。《天聪朝臣工奏议》中说：“近奉上谕，凡事都照《大明会典》行，极为得策。”[ 罗振玉：《天聪朝臣工奏议》，北京：中国人民大学出版社，1989年，第2页。]<br />
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From here it can be seen that the Emperor Taizong put an emphasis on practicality with regard to the translation of Chinese works, hoping to associate translation with politics. He objected to the translation of Chinese books with flashy embellishments, or that of unofficial historical works recording unconvincing events. Take the translation of ''The Code of the Ministry of Penalty'' as an example. After its publication and promulgation, the tranlation of this code gradually became the norm of handling state affairs. ''The Petition of Ministers during the Reign of Tiancong'' states, "Recently, by the order of the Emperor, everything should be conducted in accordance with ''The Code of Ming Dynasty'', which is a desirable policy." (Luo Zhenyu: ''The Petition of Ministers during the Reign of Tiancong'', Beijing: China Remin University Press, 1989, Page2.)<br />
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From above it can be seen that the Emperor Taizong put an emphasis on practicality with regard to the translation of Chinese works, hoping to associate translation with politics. He objected to the translation of Chinese books with flashy embellishments, or that of unofficial historical works recording unconvincing events. Take the translation of The Code of the Ministry of Penalty as an example. After its publication and promulgation, the tranlation of this code gradually became the norm of handling state affairs. The Petition of Ministers during the Reign of Tiancong states, "Recently, by the order of the Emperor, everything has been conducted in accordance with The Code of Ming Dynasty, which is a desirable policy." (Luo Zhenyu: The Petition of Ministers during the Reign of Tiancong, Beijing: China Renmin University Press, 1989, Page2.)--[[User:Mou Yixin|Mou Yixin]] ([[User talk:Mou Yixin|talk]]) 10:19, 29 September 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081516 牟一心 女==<br />
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宁完我也说，我国六部的设立原是照“蛮子家立的”，因而金官对于部中当举事宜原本并不知情，而今翻译《会典》（即《明会典》），参汉酌金，加以“打动”，必将使其“去因循之习”而“渐就中国之制”。[ 同上，第82页。]<br />
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四 世祖时期汉籍（书）翻译之发展<br />
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清初儒臣中，除额尔德尼、噶盖和达海之外，通满、蒙、汉字者不乏他人，如伊成额、希福、刚林等，便是其中姣姣者。<br />
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Ning Wanwo also said that the establishment of the six ministries in feudal China was based “on that of minority nationalities”, so the officers of Jin Dynasty had no idea about the affairs in the ministries. Now the translation of Code(a record of laws and systems of a dynasty)(namely, Code of Great Ming Dynasty) must refer to both the precedents of Central China and Jin Dynasty and amend them to make it get rid of rigid traditions and turn to the conventions of Central China.[ibidem, Page 82]<br />
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FOURTH The development of Chinese literature (books) in Qing Dynasty Shunzhi period<br />
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Among the civilian officials in the early Qing Dynasty, besides Erdeni, Gagai, Daher, there is no lack of masters of Manchu language, Mongolian and Chinese, such as Icher, Hifo, Galin, who were the strong performers among them.<br />
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Ning Wanwo also said that the establishment of the six ministries of feudal China was based on “that of the southerners”, so Jin Guan had no knowledge of the affairs concerning the appointment in the ministries. Now the translation of Code( a record of laws and systems of a dynasty)( namely, Code of Great Ming Dynasty) must refer to both the precedents of Han and Jin Dynasty and then amend them to make it get rid of rigid traditions and gradually conform to the conventions of Central China. [ibidem, Page 82.]<br />
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FOURTH The development of Chinese literature (books) in Shizu Period <br />
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Among the civilian officials in the early Qing Dynasty, besides Erdeni, Gagai, Daher, there is no lack of masters of Manchu language, Mongolian and Chinese, such as Icher, Hifo, Galin, who were the best performers of them.--[[User:Shi Liqing|Shi Liqing]] ([[User talk:Shi Liqing|talk]]) 12:58, 29 September 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081521 石丽青 女==<br />
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据传，伊成额不仅将《太祖高皇帝实录》译成汉文，而且翻译了朝鲜所奏表章，以及《礼部会典》等书。[ 清高宗敕纂：《八旗满洲氏族通谱》，沈阳：辽沈书社，1989年，第10页。]希福兼通满、蒙、汉三种语言，他的翻译有别于伊成额，不是将满文译成汉语，也不是将朝鲜文译成满语，而是主要翻译汉书、汉典，所翻译的汉文书籍包括《辽》、《金》、《元》三史等。希福的上述译书于顺治元年进呈皇帝，获世祖恩赉。<br />
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It is said that Yi Cheng'e translated not only the Annals of Emperor Taizu Gao into Chinese, but also the seals played by North Korea and the Book of the Ministry of Ceremonies. [ Qing Gaozong's Royal Compilation: "Eight Banners Manzhou Clan Genealogy", Shenyang: Liaoshen Publishing House, 1989, page10.] Xifu was fluent in Manchu, Mongolian and Chinese. His translation was different from Yicheng'e. He did not translate Manchu into Chinese or Korean into Manchu, but mainly translated Chinese books and Chinese classics, including Liao, Jin and Yuan. Xifu's translation was presented to the emperor in the first year of Shunzhi and won the honor of Shizu. --[[User:Shi Liqing|Shi Liqing]] ([[User talk:Shi Liqing|talk]]) 05:50, 29 December 2021 (UTC)<br />
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It is said that Yi Cheng'e translated not only the Factual Record of Taizu, but also the memorials presented to the emperor by North Korea, Records of the Board of Rites, and other books. [ Qing Gaozong's Royal Compilation: "Eight Banners Manzhou Clan Genealogy", Shenyang: Liaoshen Publishing House, 1989, page10.] Xi Fu was proficient in Manchu, Mongolian and Chinese, whose translation was different from Yi Cheng’e’s. Neither did he translate Manchu into Chineses nor Korean into Manchu, he mainly translated Chinese books and classics, including Liao, Jin and Yuan. Xi Fu’s translation was presented to the emperor in the first year of Shunzhi and won the honor of Shizu.--[[User:Wang Lifei|Wang Lifei]] ([[User talk:Wang Lifei|talk]]) 02:14, 29 September 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081523 王李菲 女==<br />
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《清实录·世祖章皇帝实录》中，曾详细记载了希福进呈译本时的情形：窃稽自古史册所载，政治之得失，民生之休戚，国家之治乱，无不详悉具备，其事虽往，而可以诏今；其人虽亡，而足以镜世。故《语》云：“善者吾师，不善者亦吾师。”从来嬗继之圣王，未有不法此而行者也。<br />
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In the “Records of Qing Dynasty· Emperor Fu Lin”, there was a detailed record of the situation when Xi Fu presented the translation: in the ancient records, whatever gain and loss in politics, weal and woe of people’s livelihood, or governance and chaos of countries, they’ve all been documented in detail. Although things have passed, they can still enlighten us. The ancestors have passed away, they can also provide references. Therefore, the “Language” said, “The heroes are my teacher, and so are the villains .” Virtuous emperors from ancient times to present have all followed this rule.--[[User:Wang Lifei|Wang Lifei]] ([[User talk:Wang Lifei|talk]]) 16:01, 28 September 2021 (UTC)<br />
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The “Records of Qing Dynasty· Emperor Fu Lin”, which once recorded in detail the situation when Xi Fu presented the translation: As far as I am concerned, from the ancient records, the gains and losses of politics, the welfare and woe of people’s livelihood, and the governance and chaos in the country, all of which have been documented in detail. Although things have passed, they can still enlighten us. Although the ancestors passed away, they are enough to mirror the world. Therefore, the “Language” says, “The success is my teacher, and so is the failure.” Virtuous emperors from ancient times have all followed this rule.--[[User:Wang Lifei|Wang Lifei]] ([[User talk:Wang Lifei|talk]]) 16:01, 28 September 2021 (UTC) Edited by Wang Zhenlong.<br />
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==英语语言文学（英美文学） 202120081525 王镇隆 男==<br />
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辽、金虽未混一，而辽已得天下之半，金亦得天下之大半，至元则混一寰区，奄有天下，其法令政教皆有可观者焉。我先帝鉴古之心，永怀不释，特命臣等将《辽》、《金》、《元》三史，芟（shān）削繁冗，惟取其善足为法，恶足为戒，及征伐畋（tián）猎之事，译以满语，缮写呈书。臣等敬奉纶音，将《辽史》自高祖至西辽耶律大石末年，凡十四帝，共三百七年；《金》凡九帝，共一百十九年；《元》凡十四帝，共一百六十二年，详录其有裨益者，……伏乞皇上万几之暇，时赐省览，懋稽古之德，弘无前之烈，臣等不胜幸甚。[ 鄂尔泰等奉敕修：《清实录·世祖章皇帝实录》，北京：中华书局，1985年，第15-16页。]<br />
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Although Liao and Jin did not mix together, Liao had already won half of the world, and Jin had also won the other half. Until the Yuan Dynasty, they were combined into one whole world,and there were considerable and available laws, politics and religions. My first emperor’s desire to learn from the ancients will never be released. I and other ministers were ordered to edit and slash the three histories of "Liao", "Jin" and "Yuan", but take the good part as the law, and the evil part as the precepts. And the matters of conquering and hunting, were translated into Manchu,written and presented in a book. I and others followed respectfully my Lord ‘s orders,took the history of Liao from Gaozu to the last year of Yelu Dashi of Xi Liao, where there were 14 emperors, a total of 370 years; Jin, the nine Emperors, lasted one hundred and nineteen years;Yuan recorded all the fourteen emperors for 162 years, and had careful records of those who have benefited us, ... I beg my Lord to have occasional reviews when my Lord is not dealing with country’s affairs,and to encourage your people to live up to ancient virtues and promote the unparalleled huge achievement. I and other would greatly appreciate it. [E'ertai et al. revised with imperial edict "Records of the Qing Dynasty·Records of Emperor Shizuzhang", Beijing: Zhonghua Book Company, 1985, pp. 15-16. ]<br />
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Although Liao and Jin did not unite, Liao had already occupied half of the world, so had Jin. Until Yuan Dynasty, they combined and occupied the whole country, and there were considerable and available laws, politics and religions. My previous emperor’s desire to learn from the ancients will never be released. Other ministers and I were ordered to edit and slash ''the History of Liao, Jin and Yuan Dynasty'', take merits as the principle, and learn lessons from demerits. And the matters of conquering and hunting, were translated into Manchu, written and presented in a book. We followed respectfully my Majesty ‘s orders, took ''The History of Liao Dynasty'' from Gaozu to the last year of Yelu Dashi of Xi Liao, where there were 14 emperors, a total of 370 years; ''Jin'', nine Emperors, lasted one hundred and nineteen years; ''Yuan'' recorded all the fourteen emperors for 162 years, and had careful records of those who have benefited us, ... I beg my Majesty to have occasional reviews when he is not dealing with country’s affairs, and to encourage people to observe traditional virtues and advocate the unprecedent huge achievement. We all would greatly appreciate it. [E'ertai et al. revised with imperial edict ''Records of Emperor Shizuzhang in the Qing Dynasty'', Beijing: China Book Company, 1985, pp. 15-16. ]--[[User:Wei Yiwen|Wei Yiwen]] ([[User talk:Wei Yiwen|talk]]) 09:35, 29 September 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081526 卫怡雯 女==<br />
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上文中，不仅明确论及汉文典籍的历史作用，而且阐述了翻译这些典籍的必要性、作用与目的等，其总目标是“懋稽古之德”，以翻译佐文教，治太平。换言之，希福希望通过翻译《辽》、《金》、《元》三史等，从中原汉族王朝中借鉴国家治理经验，以接续太宗皇太极以来以经世致用为核心的翻译思想，并为嗣后翻译汉籍树立原则与典范。据《国立故宫博物院善本旧籍总目》、《世界满文文献目录》、《全国满文图书资料联合目录》，以及《清代内府刻书目录解题》等综合统计，顺治年间，由官方刊刻的汉书满文译本共九种，分别是《辽史》、《金史》、《元史》、《洪武要训》、《三国志（通俗演义）》、《诗经》、《表忠录》、《孝经》（阿什坦译）和《六韬三略》。其中，顺治七年译成的《三国志（通俗演义）》既有满文本，又有满汉合璧本。<br />
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As described in the above, it not only discussed the historical role of Chinese classics, but also illustrated the necessity, the function and the goal of translating these ancient books and records. The overall goal is to encourage people to observe traditional virtues, using translation to assist the country to develop culture and education. In other words, Xifu wanted to draw on the experience of state governance from Han Dynasty in the central China through translating the history of Liao, Jin, and Yuan Dynasty in order to succeed the thought of translation of administering state affairs and applying theory to practice as the core since the emperor Taizong Huangtaiji, and set up the principle and model of translating Chinese books for later generation. According to general statistics of The General Catalogue of The Best Edition and the Ancient Books and Records of Taipei's National Palace Museum, The Catalogue of World’s Manchu Literature and The Union Directory of National Manchu Books and Materials and Solving Problems in the Catalogue of Engraved Books in the Qing Dynasty, during the period of Tongzhi, there are nine Manchu translated versions of the official published Chinese books by blocking print, they are: The History of Liao Dynasty, The History of Jin Dynasty, The History of Yuan Dynasty, Hongwu Yaoxun, The Romance of Three Kingdoms, Book of Songs, The Record of Loyalty, Classic of Filial Piety(translated by Ashtan), Liu Tao and San Lue. Among them, The Romance of Three Kingdoms translated in the seventh year of Tongzhi had both Manchu version and the combined one of Chinese and Manchu.--[[User:Wei Yiwen|Wei Yiwen]] ([[User talk:Wei Yiwen|talk]]) 03:23, 29 September 2021 (UTC)<br />
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Thereinbefore, it not only discussed the historic role of Chinese classics, but also illustrated the necessity, the function and the goal of translating these books. The overall goal is to encourage people to observe traditional virtues, using translation to develop culture and education and bring social prosperity. In other words, Xifu wanted to draw on the experience of state governance from Chinese dynasty in the central plains through translating the histories of ''Liao'', ''Jin'', and ''Yuan''. In this way, the translation idea originated from the dynasty of Huangtaiji that knowledge should benefit national affairs can be succeeded and set up the principle and model of translating Chinese books for later generation. According to the general statistics of ''The General Catalogue of the Publications and Classics of National Palace Museum'', ''The Catalogue of World’s Manchu Literature'' and ''The Union Catalogue of National Manchu Books and Materials'' and ''Solving Problems in the Catalogue of Engraved Books in Qing Dynasty'', during the period of Shunzhi dynasty, there are nine official Manchu translations of Chinese books.They are ''History of Liao Dynasty'', ''History of Jin Dynasty'', ''History of Yuan Dynasty'', ''Hongwuyaoxun'', ''Records of the Three Kingdoms'', ''Classic of Poetry'', ''Record of Loyalty'', ''Classic of Filial Piety''(translated by Ashtan), ''Liutaosanlue''. Among them, ''Records of the Three Kingdoms'' translated in the seventh year of Shunzhi dynasty has both Manchu version and the combined one of Chinese and Manchu.(revised by Wei Chuxuan)--[[User:Wei Chuxuan|Wei Chuxuan]] ([[User talk:Wei Chuxuan|talk]]) 07:11, 29 September 2021 (UTC)--[[User:Wei Chuxuan|Wei Chuxuan]] ([[User talk:Wei Chuxuan|talk]]) 08:59, 29 September 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081527 魏楚璇 女==<br />
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另外，《孝经》曾于顺治、康熙、雍正朝刊刻三次，但版本明显不同：顺治年间的刊行本为阿什坦译本，康熙年间的刊行本为和素译校本，雍正时期的版本则译者未明，仅注明“雍正皇帝敕译”。令人好奇的是，虽然世祖笃信佛教，论佛谈法，但从相关文献看，未见有顺治朝翻译的汉文经书，其中原因，不得而知。<br />
What's more, ''Filial Piety'' has different publications of translation in different dynasties. In Shunzhi dynasty, the publication is Ashtan's translation. In Kangxi dynasty, the publication is Hesu's translation. In Yongzheng dynasty, the translator is unknown. It is only indicated in the publication that the translation is asked by Yongzheng emperor. Curiously according to relevant literature though Shunzhi emperor believed in Buddhism, there is no translation of Buddhist classics made in his dynasty. And the reason of this remains a mystery.--[[User:Wei Chuxuan|Wei Chuxuan]] ([[User talk:Wei Chuxuan|talk]]) 02:12, 29 September 2021 (UTC)--[[User:Wei Chuxuan|Wei Chuxuan]] ([[User talk:Wei Chuxuan|talk]]) 09:05, 29 September 2021 (UTC)<br />
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What's more, ''Filial Piety'' has been published and printed three times in the dynasty of Shunzhi, Kangxi and Yongzheng respectively but with three obvious different versions. During the Shunzhi dynasty was the translation of Ashtan, during the dynasty of Kangxi was of Hesu and during the Yongzheng dynasty was of the unknown writer, only indicating "translating under the order of the emperor Yongzheng". What made people curious was that although Shizu sincerely believed in Buddhism, talking about Buddhism and Buddhist doctrine, there was no Chinese Confucian classics translated during the Shunzhi Dynasty according to the relevant references. And the reason of this remains unknown.--[[User:Wei Zhaoyan|Wei Zhaoyan]] ([[User talk:Wei Zhaoyan|talk]]) 08:14, 29 September 2021 (UTC)(Wei Zhaoyan 魏兆妍）<br />
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==英语语言文学（英美文学） 202120081528 魏兆妍 女==<br />
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世祖年间翻译刊行的九种汉文书籍中，《六韬》、《三国志（通俗演义）》和《大乘经》等三种原系达海于天聪六年开始翻译，但由于达海早逝未能译成。三部作品中，尤以《三国志（通俗演义）》的翻译所获世祖认可为甚。《清实录·世祖章皇帝实录》中说：<br />
以翻译《三国志（通俗演义）》告成，赏大学士范文程、刚林、祁充格、宁完我、洪承畴、冯铨、宋权、学士查布海、苏纳海、王文奎、伊图、胡理、刘清泰、来袞（gǔn）、马尔笃、蒋赫德等鞍马、银两有差。[ 鄂尔泰等奉敕修：《清实录·世祖章皇帝实录》，北京：中华书局，1985年，第13页。]<br />
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Nine kinds of Chinese literary books has been translated and published during the year of Shizu, among which Da Hai began translating three kinds of the original system of ''Liu Tao'', ''The Popular Romance of the Three Kingdoms'' and ''Mahayana Sutra'' during the sixth year of Tian Cong. However, Da Hai failed to translate them all successfully due to his early death. Among these three works, especially the translation of ''The Popular Romance of the Three Kingdoms'' has received the most approval of Shizu. Said in ''The Real Record of the Qing Dynasty · Real Record of the Emperor Shizu Zhang'': With the completed translation of ''The Popular Romance of the Three Kingdoms'', Shizu would award some side horses and silver to the Grand Master Fan Wenching, Gang Lin, Qi Chongge, Ning Wanwo, Hong Chengchou, Feng Quan, Song Quan and Bachelor Zha Buhai, Su Nahai, Wang Wenkui, Yi Tu, Hu Li, Liu Qingtai, Lai Gun, Ma Erdu, Jiang Hede. [ Ertai and others were ordered by the emperor to modify: ''The Real Record of Qing Dynasty · Real Record of the Emperor Shizu Zhang'', Beijing: China Publishing House, 1985, Page 13. ]--[[User:Wei Zhaoyan|Wei Zhaoyan]] ([[User talk:Wei Zhaoyan|talk]]) 07:52, 29 September 2021 (UTC)<br />
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Among the nine kinds of Chinese books translated and published during the reign of Shizu emperor , three were first translated by Da Hai in 1632, including "Liu Tao", “The Popular Romance of the Three Kingdoms" and "Mahayana Sutra”. Yet they failed to be finished due to his early death. Of the three works, the translation of "The Popular Romance of the Three Kingdoms" received the most recognition of Shizu emperor. According to "The Real Record of the Qing Dynasty · Real Record of the Emperor Shizu Zhang": Given the successful translation of "The Popular Romance of the Three Kingdoms", Shizu emperor awarded some side horses and silver to the Grand Master Fan Wenching, Gang Lin, Qi Chongge, Ning Wanwo, Hong Chengchou, Feng Quan, Song Quan and Bachelor Zha Buhai, Su Nahai, Wang Wenkui, Yi Tu, Hu Li, Liu Qingtai, Lai Gun, Ma Erdu, Jiang Hede, and the amount of awards depended on their position.[ Ertai and others ordered by the emperor to modify: ''The Real Record of Qing Dynasty · Real Record of the Emperor Shizu Zhang'', Beijing: China Publishing House, 1985, Page 13. ]--[[User:Xiao Yiyao|Xiao Yiyao]] ([[User talk:Xiao Yiyao|talk]]) 07:32, 29 September 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081531 肖毅瑶 女==<br />
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此次获得赏赐者共计十六人，从大学士到学士不等，既赏鞍马，又赏银两，可见其对于该书翻译的重视。值得特别注意的是，关于《三国志（通俗演义）》一书的翻译，《清初内国史院满文档案译编》中也有记载，不仅更加详实，而且内容上也与《清实录》中的记载有较大出入。为便于比较，现一并摘录如下：<br />
A total number of 16 officers, varying from Grand Masters to Bachelors, were awarded saddled horses and silver, which indicated that the emperor had attached great importance to the translation of this book. Besides, what deserves a speacial attention was that ''Translation and Compilation of Manchu Archives of the National Institute of History in the Early Qing Dynasty'' also documented some facts about the translation of the ''Romance of Three Kingdoms''. The records were not only more detailed but also quite different from that of the ''Factual Record of Qing dynasty''. For the convenience of comparison, both were excerpted as follows:<br />
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A total number of 16 officers, varying from Grand Academcians to Bachelors, were awarded saddled horses and silver, which indicated that the emperor had attached great importance to the translation of this book. Besides, it is worth paying speacial attention that ''Translation and Compilation of Manchu Archives of the National Institute of History in the Early Qing Dynasty'' also documented some facts about the translation of the ''Romance of Three Kingdoms''. The records were not only more detailed but also quite different from that of the ''Factual Record of Qing dynasty''. For the convenience of comparison, both were excerpted as follows:----[[User:Xie Jiafen|Xie Jiafen]] ([[User talk:Xie Jiafen|talk]]) 13:33, 11 October 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081532 谢佳芬 女==<br />
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以翻译《三国志（通俗演义）》告成，赏赐内翰林院大臣。赏予大学士范文程巴克什、刚林巴克什、祁充格、宁完我、洪承畴、冯铨、宋权七大臣彩鞍、雕辔（pèi）、……头等马各一匹、银各五十两。赏学士查布海、苏纳海、王文奎、伊图、胡理、清泰、来袞、马迩都、赫德九人无鞍二等马各一匹、银各四十两。<br />
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When ''Romance of the Three Kingdoms’’ having been completed，all the ministers in Hanlin Academcian were awarded. The grand secretaries Fan Wencheng, Gang Lin, Qi Chongge, Ning Wanwo, Hong Chengchou, Feng Quan, Song Quan were rewarded color saddle, bridle, one first-class horse and fifty liang of silver respectively. The nine scholars, Cha Buhai, Su Nahai, Wang Wenkui, Yitu, Huli, Qingtai, laigon, Ma Youdu and Hede, each have one second-class bareback horse and forty liang of silver<br />
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After the completion of “Records of the Three Kingdoms”, ministers of Hanlin Academy were awarded. Maesters including Fan Wencheng, Gang Lin, Qi Chongge, Ning Wanwo, Hong Chengchou, Feng Quan, Song Quan were awarded with colorized saddle, bridle, a first-class horse and fifty Liang of silver respectively. Nine scholars like Cha Buhai, Su Nahai, Wang Wenkui, Yitu, Huli, Qingtai, laigon, Ma Youdu and Hede, each of them has one second-class bareback horse and forty liang of silver respectively.--[[User:Xiong Min|Xiong Min]] ([[User talk:Xiong Min|talk]]) 13:45, 11 October 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081534 熊敏 女==<br />
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赏内弘文院主事能图、叶成格、曹皮、铿特依、杜当、布尔凯、侍讲学士吕宗烈、侍读学士张皮机、典籍官王丛庞九人银各四十两。赏博士科尔科岱、霍斯霍利、尼曼、苏和、奇同格、芒色、霍托、穆成格、周有德、必利科图、国史院博士图巴海、秘书院秦达浑臣十二人银各二十两。赏笔帖式翁国顺、额斯黑、高利、马齐蘭、乌勒扈、穆成格、必利科图、严楚蘭、阿希图、国史院笔帖式朱臣十人银各二十两。<br />
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Chiefs of Hongwen Academy like Neng Tu,Ye Chengge,Cao Pi,Qiang Teyi,Du Dang, But Erkai,teacher like Lv Zonglie, attendant like Zhang Piji, manager of ancient books like Wang Congpang were rewarded forty pounds of silver respectively.Doctor Me Erkedai, Huh Sihuoli,Niman,Su He,Qi Tongge, Mang Se, Huo Tuo,Mu Chengge,Zhou Youde, Bi Liketu,Tu Bahai,Qin Dahun were all awarded 20 pounds of silver. Weng Guoshun,E Sihei,Golly, Ma Qilan,Wu Leba,Mu Chengge, Bi Liketu,Yan Chulan,A Xitu,Zhu Chen were all rewarded with 20 pounds of silver.<br />
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Chief cadres of Hongwen Academy including Neng Tu,Ye Chengge,Cao Pi,Qiang Teyi,Du Dang,Bu Erkai,Teacher like Lv Zonglie, Attendant like Zhang Piji, Manager of ancient books Wang Congpang were rewarded forty silver pounds respectively. Doctor Me Erkedai, Huh Sihuoli,Niman,Su He,Qi Tongge, Mang Se, Huo Tuo,Mu Chengge,Zhou Youde, Bi Liketu,Tu Bahai,Qin Dahun were all awarded 20 silver pounds. Weng Guoshun,E Sihei,Golly, Ma Qilan,Wu Leba,Mu Chengge, Bi Liketu,Yan Chulan,A Xitu,Zhu Chen were all rewarded with 20 silver pounds.--[[User:Yang Ye|Yang Ye]] ([[User talk:Yang Ye|talk]]) 12:23, 30 September 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081539 羊叶 女==<br />
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以上送礼部一一宣名，跪受赏。[ 中国第一历史档案馆编：《清初内国史院满文档案译编·顺治朝》，北京：光明日报出版社，1989年，第80页。]<br />
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显然，上述两种文献提到的系同一件事情，但内容上有明显出入：首先，封赏的人数不同。<br />
The officials above give gifts to the Ministry of Rites one by one, being declared the name, and kneeling to be rewarded. [Editor of China's First Historical Archives: Translation of manchu archives of the National Historical Institute of the early Qing Dynasty, Shunzhi Dynasty, Beijing: Guangming Daily Press, 1989, p. 80.] Obviously,the two documents mention the same thing, but there are obvious differences in content: first, the number of people who are rewarded is quite different.<br />
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All these officials on the list will be named in recognition and rewarded on their knees at the Ministry of Rites. [ Edited by the First Historical Archive of China: "Translation and compilation of Manchu archives in the Early Qing Dynasty•Shunzhi Dynasty", GuangMing Daily Press, 1989, P80]<br />
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Appartently, the two literatures mentioned above refer to the same thing, but they differ in content: Firstly, the number of rewarded people is different. --[[User:Yang Liuqing|Yang Liuqing]] ([[User talk:Yang Liuqing|talk]]) 03:34, 29 September 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081543 杨柳青 女==<br />
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例如，《清实录》中只有十六人，《清初内国史院满文档案译编》则多达四十七人，二者之间的出入主要在于弘文院主事、侍讲与侍读学士、典籍官、博士，以及笔帖式等职官群体名单。其次，在赏赐的物件问题上，《清初内国史院满文档案译编》的记载较之《清实录》明显更加详实、具体，可信度更高。再次，在个别封赏对象的称呼上，两种文献之间也有差异。<br />
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For example, only 16 civil officials are rewarded according to the "Records of the Qing Dynasty" while officials who are rewarded according to the "Translation and Compilation of Manchu Archives of the Chinese Academy in the Early Qing Dynasty" are as many as over 40. This difference mainly lies in the list of official groups such as the director of the Hong Arts Institute, official bachelors, classics officers, learned scholars and clerks handling paperwork. Secondly, the records about rewarded items in the "Translation of Manchu Archives of the Chinese Academy in the Early Qing Dynasty" are obviously more detailed, accurate and credible than those in the "Records of the Qing Dynasty". Thirdly, there are also some differences between the two literatures in terms of the appellation of the rewarded officials.--[[User:Yang Liuqing|Yang Liuqing]] ([[User talk:Yang Liuqing|talk]]) 03:30, 29 September 2021 (UTC)<br />
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For example, only 16 civil officials were rewarded in the The Records of the Qing Dynasty while officials rewarded in the Manchu Archives Translation and Compilation of the Inner State History Academy in the Early Qing Dynasty were as many as over 40. This difference mainly lay in the list of official groups such as the director of the Hong Arts Institute, official bachelors, classics officers, learned scholars and clerks handling paperwork. Secondly, the records about rewarded items in the Manchu Archives Translation and Compilation of the Inner State History Academy in the Early Qing Dynasty were obviously more detailed, accurate and credible than those in the The Records of the Qing Dynasty. Thirdly, there were also some differences between the two literatures in terms of the appellation of the rewarded officials.--[[User:Yi Yangfan|Yi Yangfan]] ([[User talk:Yi Yangfan|talk]]) 05:11, 29 September 2021 (UTC)--[[User:Yi Yangfan|Yi Yangfan]] ([[User talk:Yi Yangfan|talk]]) 05:11, 29 September 2021 (UTC)Yi Yangfan<br />
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==英语语言文学（英美文学） 202120081545 易扬帆 女==<br />
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例如，《清实录》中的“刘清泰”被写作《清初内国史院满文档案译编》中的“清泰”，“马尔笃”被写作“马迩都”，“蒋赫德”被写作“赫德”，等等。<br />
世祖年间翻译的汉文书籍中，《洪武宝训》、《表忠录》、《诗经》与《孝经》等也各具代表性。《洪武宝训》系明朝皇权政治的象征，反映了明太祖朱元璋治国理政的理念和方针政策。<br />
For example, Liu Qingtai in Factual Record of Qing Dynasty was written as Qingtai in Manchu Archives Translation and Compilation of the Inner State History Academy in the Early Qing Dynasty, Mar Du was written as Ma Erdu, and Jiang Hede was written as Hurd, etc.<br />
The translated Chinese books in the certain era of Fulin years, such as HongWu Baoxun, The Record of Loyalty, Books of Songs and Classic of Filial Piety have had their own representatives. HongWu Baoxun was a symbol of the imperial power politics of Ming Dynasty, which reflected the ideas and policies of Zhu Yuanzhang, the emperor Taizu of Ming Dynasty.--[[User:Yi Yangfan|Yi Yangfan]] ([[User talk:Yi Yangfan|talk]]) 16:03, 28 September 2021 (UTC)Yi Yangfan<br />
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For example, “Liu Qingtai” in Factual Record of Qing Dynasty was written as “Qingtai” in Manchu Archives Translation and Compilation of the Inner State History Academy in the Early Qing Dynasty, "Mar Du" was written as "Ma Erdu", "Jiang Hede" was written as "Hede", and so on. The translated Chinese books in Fulin years, such as HongWu Baoxun, The Record of Loyalty, Books of Songs and Classic of Filial Piety all were of representative. Hongwu Baoxun was a symbol of imperial power politics in the Ming Dynasty and reflected the ideas and policies of Zhu Yuanzhang, the emperor of the Ming Dynasty.--[[User:Yin Huizhen|Yin Huizhen]] ([[User talk:Yin Huizhen|talk]]) 02:46, 29 September 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081546 殷慧珍 女==<br />
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顺治三年三月，《洪武宝训》的满文翻译完成，成为清朝入关后的首部汉籍译作。世祖对于该部译作极为重视，不仅赏赐了刚林、宁完我、范文程等译者，而且由摄政王多尔衮钦命汉官代笔，以世祖名义为译作制作序文，颁行全国。世祖对明太祖推崇备至，尤其是后者制定的条例章程，认为历代贤君莫如洪武，因而本书的翻译目的性极强。<br />
In the March of the third year of Shunzhi, the Manchu translation of Hongwu Baoxun was completed, becoming the first translation written by Chinese writer after the Qing Dynasty was in power. The emperor Shizu attached great importance to this translation. He not only rewarded the translators such as Gang Lin,Ning Wanwo, and Fan Wencheng, ect., but also the regent Dorgon appointed Han officals to write a preface in the name of Shizu, which was issued throughout the country. The emperor Shizu revered the emperor Taizu of Ming Dynasty, especially the regulations and articles he formulated, and believed that there were no virtuous monarchs of all dynasties like Hongwu, so the translation purpose of this book was very strong.--[[User:Yin Huizhen|Yin Huizhen]] ([[User talk:Yin Huizhen|talk]]) 01:17, 29 September 2021 (UTC)<br />
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In the March of the third year of Shunzhi, the Manchu translation of Hongwu Baoxun was completed, becoming the first translation done by Chinese translators since the Qing Dynasty was established. The Emperor Shizu attached great importance to this translation. Not only he rewarded the translators such as Gang Lin,Ning Wanwo, and Fan Wencheng, ect., but also the regent Dorgon appointed the officals of Han Dynasty to write a preface, which was issued throughout the country, in the name of Shizu. The Emperor Shizu praised the emperor Taizu of Ming Dynasty highly, especially the regulations and articles he formulated, and he believed that there were no more virtuous monarchs of all dynasties than Hongwu, so the translation purpose of this book was very strong.--[[User:Yin Yuan|Yin Yuan]] ([[User talk:Yin Yuan|talk]]) 02:21, 29 September 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081548 尹媛 女==<br />
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换言之，世祖希望借由此书的翻译，以中原汉族王朝的所谓“正统”巩固满清统治，粉饰民族征服和民族压迫。事实上，以翻译汉书，尤其是汉文典章制度书籍的翻译，作为统治的工具和手段，这一点在太宗时期即已存在。作为国家治理的重要手段，太宗不仅令达海改进老满文，而且钦定翻译了不少汉文书籍，如明太祖颁发的《大诰三编》和《三国演义》等，用作出谋划策和军事征讨的参考。<br />
In other words, Shizu hoped to consolidate the rule of Qing Dynasty with the so-called "orthodox" of Han Dynasty in the Central Plains to deny national conquest and oppression by translating this book. Actually, in the reign of Emperor Taizong, it had existed that the translation of Chinese books, especially the translation of Chinese laws and regulations, was regarded as the tools and means of ruling. As the important mean of national governance, the old manchu scripts were developed by Da Hai and not a few Chinese books, such as ''The third Edition of Penal Code'' and ''The Romance of the Three Kingdoms''issued by Emperor Hongwu were translated under the decree of Emperor Taizong, which were used as a reference for planning and advising and military campaigns.--[[User:Yin Yuan|Yin Yuan]] ([[User talk:Yin Yuan|talk]]) 15:44, 28 September 2021 (UTC)<br />
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In other words, Shizu hoped to consolidate the rule of Qing Dynasty with the so-called "orthodox" of Han Dynasty in the Central Plains to whitewash national conquests and oppressions by translating this book. Actually, in the reign of Emperor Taizong, it had existed that the translation of Chinese books, especially the translation of Chinese laws and regulations, was regarded as the tools and means of ruling. As an important mean of national governance, the old manchu scripts were developed by Da Hai and not a few Chinese books, such as ''The third Edition of Penal Code'' and ''The Romance of the Three Kingdoms''issued by Emperor Hongwu were translated under the decree of Emperor Taizong, which were used as a reference for planning and military campaigns.--[[User:Zhan Ruoxuan|Zhan Ruoxuan]] ([[User talk:Zhan Ruoxuan|talk]]) 03:01, 29 September 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081549 詹若萱 女==<br />
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《表忠录》同样辑自明朝嘉靖年间，实为杨继盛的两部奏疏，即《请诛贼臣疏》和《请罢马市疏》，由汪宗伊撰于明朝万历年间，属吏部传记类。顺治十三年前后，世祖降旨将杨继盛事迹写成《忠愍记》。所谓“忠愍”，即明穆宗因念及杨继盛参劾严嵩之功，誉其为“直谏诸臣之首”，而追赠给后者的谥号。<br />
The Record of Loyalty was also written from the JiaJing peroid of Ming Dynasty. It was actually consisted of two memorials to throne of Yang JIsheng, namely memorial on killing traitors and memorial on closing the horse market written by Wang Zongyi during the Wanli period of Ming Dynasty. It belonged to official biography. About ShunZhi 13 years, the Emperor Shizu made a decree to write Yang Jisheng’s good deeds into The Record of Zhong Min. The so-called “Zhong Min” referred to the posthumous title given to Yang Jisheng after his death, because Emperor Muzong praised him as “the head of the ministers” for his contribution to impeaching Yan Song.--[[User:Zhan Ruoxuan|Zhan Ruoxuan]] ([[User talk:Zhan Ruoxuan|talk]]) 02:27, 29 September 2021 (UTC)<br />
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The Record of Loyalty was also written in the JiaJing peroid of Ming Dynasty. It was actually consisted of two memorials to throne of Yang JIsheng, namely memorial on killing traitors and memorial on closing the horse market written by Wang Zongyi during the Wanli period of Ming Dynasty. It is a kind of official biography. About ShunZhi 13 years, the Emperor Shizu made a decree to write Yang Jisheng’s good deeds into The Record of Zhong Min. The so-called “Zhong Min” referred to the posthumous title given to Yang Jisheng after his death, because Emperor Muzong praised him as “the head of the ministers” for his contribution to impeaching Yan Song.--[[User:Zhong Yifei|Zhong Yifei]] ([[User talk:Zhong Yifei|talk]]) 13:16, 11 October 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081553 钟义菲 女==<br />
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《忠愍记》成书后，世祖御制《表忠录·序》以表彰杨继盛，其中写道：自古贤臣正士效力王家，率授命致身，捐生赴义。迹其所遭，若无厚幸然。<br />
After the record of Zhong Min was written, the emperor Shizu personally wrote a preface to the record of loyalty to commend Yang Jisheng, which wrote: since ancient times, virtuous officials have devotedly served the emperor family, sacrificing their lives for justice. From what happened, there was no luck.<br />
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After the Record of Zhong Min was finished, the emperor Shizu personally wrote the Preface to the Record of Loyalty to commend Yang Jisheng, which wrote: since ancient times, virtuous officials and soldiers have devotedly served the loyal family, sacrificing their lives for justice. From what happened to them, there was no luck.--[[User:Zhong Yulu|Zhong Yulu]] ([[User talk:Zhong Yulu|talk]]) 12:40, 1 October 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081554 钟雨露 女==<br />
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而时过论定，声称振杨，及于代远风遥，流徽弥茂，留连曩迹，如遘其人。是以孟轲有言：“奋乎百世之上，百世之下闻者莫不兴起也”。……顾竭志尽忠者，人臣之谊；善善恶恶者，大道之公。<br />
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But as time went by, the verdict on Yang Jisheng’s exploits had been reached and he gained considerable fame. When the years have passed, the fame of him spread all around. When people recalled his deeds, it was like meeting him in person. Just as Mencius said, “Those who made themselves distinguished a hundred generations before, and after a hundred generations, some people who heard of them are all aroused in this manner.” ……..Those who were intensely loyal could be harmonious with the ministers; And it was fair to punish the bad and to be kind to the good. --[[User:Zhong Yulu|Zhong Yulu]] ([[User talk:Zhong Yulu|talk]]) 01:33, 2 October 2021 (UTC)<br />
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But as time went by, Yang Jisheng’s exploits have been recognized, and he gained considerable fame. The older the years, the more his fame spread. When people recalled his deeds, it was like meeting him in person. Just as Mencius said, “Those who made themselves distinguished a hundred generations before, and after a hundred generations, some people who heard of them are all aroused in this manner.” ……..Those who were intensely loyal could be harmonious with the ministers. And it was fair to punish the bad and to be kind to the good. --[[User:Zhou Jiu|Zhou Jiu]] ([[User talk:Zhou Jiu|talk]]) 13:09, 11 October 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081555 周玖 女==<br />
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循省往哲，爱结于中，诚有不能自己者也。朕万机之暇，绎载籍，每览忠孝节义之事，未尝不反复三致意焉。[ 阎崇年校注：《康熙顺天府志》，北京：中华书局，2009年，第482页。]<br />
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一方面，世祖赞誉杨继盛为忠臣之典范；另一方面，世祖又斥责严嵩为逆臣，认为正是严嵩父子威福专擅，浊乱王家，致使纪纲废断。<br />
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Reflecting on predecessors, despite the disloyal, the love to nation was condensed together. I was bombarded with numerous affairs. But in my spare time, I translated and recorded many books through dictation. When I read books about loyalty, I often analyzed them repeatedly to express my regard. [ Yan Chongnian: Kang Xi Shun Tianfu, Beijing: China Publishing House, 2009, Page 482. ]--[[User:Zhou Jiu|Zhou Jiu]] ([[User talk:Zhou Jiu|talk]]) 13:12, 11 October 2021 (UTC)<br />
On the one hand, the emperor Shizu acclaimed Yang Jisheng as the paragon of loyal minister. On the other hand, he denounced Yan Song as a traitor, and believing（believed--Chen Xiangqiong(talk) )the fact that Yan Song and his son misused their authorities to domineer and disturb the loyal family so that the law and regulations became slacked.（were broken--Chen Xiangqiong(talk)）<br />
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==外国语言学及应用语言学 202120081480 陈湘琼 女==<br />
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世祖敕令翻译此书，目的是为了激励官员学做忠谏之臣，劝勉意味浓厚。毋庸置疑，世祖时期的汉籍翻译秉承的原则也是“实用主义”原则，这一点太祖、太宗时期的汉籍翻译并无本质区别。例如，世祖敕译《诗经》，即有显著的现实意义与政治考量。<br />
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The Emperor Shunzhi commanded to translate this book for the purpose that officials could be encouraged to become people who dare to tell the truth and give right suggestions to the emperor, which had persuasive and warning meanings by itself. In this period, translation of books from Han dynasty was in fact abiding to the “utilization” principle, which had no difference to translations in the period of Qianlong Emperor and Huang Taiji Emperor. For example , practical significance and political meditation had been considered for Emperor Shunzhi commanding the translation of “Book of Songs”.<br />
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The Emperor Shunzhi commanded to translate this book to encourage officials to tell the truth and put forward reasonable suggestions, which was of great persuasive meaning.In this period, translation of canons from Han dynasty actually followed the “utilitarianism” principle, which had no difference to those during the reign of Qianlong Emperor and Huang Taiji Emperor. For example , practical significance and political meditation had been considered for Emperor Shunzhi commanding the translation of “Book of Songs”. --[[User:Huang Yiyan1|Huang Yiyan1]] ([[User talk:Huang Yiyan1|talk]]) 14:00, 30 September 2021 (UTC)<br />
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==外国语言学及应用语言学 202120081492 黄逸妍 女==<br />
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《清实录·世祖章皇帝实录》中说，顺治十年二月，“上幸内院，披阅翻译《五经》，谕诸臣曰：‘天德王道，备载于书，真万世不易之理也’”。[ 鄂尔泰等奉敕修：《清实录·世祖章皇帝实录》，北京：中华书局，1985年，第9页。]可见，世祖饬令翻译《诗经》之时，其它各经的翻译也在进行，但《五经》中仅有《诗经》一部付梓。《诗经》译毕，世祖也为其御制序文，认为该部作品能使人明性意，崇礼义，“其言之深者，可用于庙堂；言之浅者，可用于身家。以之事君，必忠；以之事父，必孝。<br />
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According to Records of Qing Dynasty •Records of the Emperor Shizu, in February of the tenth year during the reign of the emperor Shunzhi, "After reading the Five Classics in palace, the emperor contended that morals and natural laws are recorded in these books in extenso, which are tough to reach." [Revised by Eertai: Records of Qing Dynasty •Records of the Emperor Shizu, Beijing: Zhonghua Publishing House, 1985:9 ] It was clear that the translating of other classics was also proceeding when the emperor Shizu commanded the translation of The Book of Songs while only the latter had been finished among the five classics. When the translation of The Book of Songs came to an end, the emperor Shizu wrote the preface to it himself and remained steadfast in the belief that that work helped to behave with propriety and righteousness. "The book can be used in imperial court from its profound aspect and in average families from its unadorned aspect. Instilled with the belief, the officials would be faithful and the children filial.<br />
--[[User:Huang Yiyan1|Huang Yiyan1]] ([[User talk:Huang Yiyan1|talk]]) 00:44, 29 September 2021 (UTC)<br />
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According to Records of Qing Dynasty •Records of the Emperor Shizu, in February of the tenth year during the reign of the emperor Shunzhi, "After having a careful perusal of the Five Classics in adytum, the emperor proclaimed all ministers that morals and natural laws are recorded in these books in extenso, which are tough to reach for all ages." [Revised by Eertai et al: Records of Qing Dynasty •Records of the Emperor Shizu, Beijing: Zhonghua Publishing House, 1985:9 ] It could be seen that the translating of other classics was also proceeding when the emperor Shizu commanded to translate The Book of Songs, while only the latter had been finished among the five classics. When the translation of The Book of Songs came to an end, the emperor Shizu wrote the preface to it himself and believed that reading this work would help us to behave with propriety and righteousness. "It can be used in imperial court from its profound aspect and in average families from its unadorned aspect. Instilled with the belief, the officials would be faithful and the children filial.edit--[[User:Ma Xin|Ma Xin]] ([[User talk:Ma Xin|talk]]) 14:12, 30 September 2021 (UTC)<br />
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==外国语言学及应用语言学 202120081513 马新 女==<br />
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更可以敦厚人伦，端正教化。”[ 叶高树：《清朝前期的文化政策》，台北：稻乡出版社，2002年，第72-73页。]<br />
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从“教化”的角度思考《诗经》的翻译，既是世祖本人的自觉认识，也是以他为首的统治者在面对稗（bài）官小说盛行，而满洲人竞相翻译时所做的一种调整。正如顺治九年进士、刑科给事中阿什坦指出的那样：学者立志，宜以圣贤为期，读书务以经史为中。<br />
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"It could also give depth to moral relations, straighten out educational ideas and transform people." [Ye Shugao: Cultural Policy in the Early Qing Dynasty, Taipei: Daoxiang Publishing House, 2002: 72-73.]<br />
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It is not only the conscious understanding of Qingshizhu Emperor himself to think about the translation of The Book of Songs from an "Enlightenment" perspective, but also an adjustment made by the rulers at his head faced with the prevalence of Bai-guan novels and Manchurians competing for translation. As pointed out by Ashtan, an advanced scholar in the ninth year of Shunzhi and a supervising censor of Justice, scholars should aspire to be saints and their reading must focus on classical and historical books. --[[User:Ma Xin|Ma Xin]] ([[User talk:Ma Xin|talk]]) 13:11, 30 September 2021 (UTC)<br />
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It could also give depth to moral relations, straighten out educational ideas and transform people." [Ye Shugao: Cultural Policy in the Early Qing Dynasty, Taipei: Daoxiang Publishing House, 2002: 72-73.]<br />
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Thinking about the translation of The Book of Songs from the "Teaching" perspectives is not only the personal understanding of Qingshizhu Emperor himself but also an adjustment made by the rulers at his head faced with the prevalence of Bai-guan novels and Manchurians competing for translation. As pointed out by Ashtan, an advanced scholar in the ninth year of Shunzhi and a supervising censor of Justice, the scholars should aspire to be saints and focus on classical and historical books.--[[User:Qing Jianan|Qing Jianan]] ([[User talk:Qing Jianan|talk]]) 15:38, 30 September 2021 (UTC)<br />
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==外国语言学及应用语言学 202120081518 秦建安 女==<br />
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此外杂书无益之言，必概废之而不睹。则庶乎学业日隆，而邪慝（tè）之心无由而入。近见满洲译书内，多有小说秽言，非惟无益，恐流行渐染，则人心易致于邪慝 。<br />
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Besides, there is no beneficial words in assorted books which should not be allowed to be published and be viewed.(So they shouldn't be allowed to be published and viewed--[[User:Sun Yashi|Sun Yashi]] ([[User talk:Sun Yashi|talk]]) 07:26, 29 September 2021 (UTC)) Then the level of study approximately can be uplifted day by day. Meanwhile, the minds of people will also not be degenerate. Recently I found that the translation of some Manchurian books was full of obscene words which personally was of futility. I am afraid that such transition will be so popular that easily erode people’s thoughts.<br />
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Revised version:--[[User:Sun Yashi|Sun Yashi]] ([[User talk:Sun Yashi|talk]]) 13:08, 11 October 2021 (UTC)<br />
Besides, there is no beneficial words in assorted books.So they shouldn't be allowed to be published and viewed.Then the level of study approximately can be uplifted day by day. Meanwhile, the minds of people will also not be degenerate. Recently I found that the translation of some Manchurian books was full of obscene words which personally was of futility. I am afraid that such transition will be so popular that easily erode people’s thoughts.<br />
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==外国语言学及应用语言学 202120081522 孙雅诗 女==<br />
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况圣贤古训，日详究之，犹恐不及，何暇费日时于无用之地？[ 鄂尔泰等修，李洵、赵德贵等点校：《八旗通志·初集》，长春：东北师范大学出版社，1989年，第5339页。]<br />
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阿什坦的奏章开宗明义，指出读书必以经史为要，必须摒弃杂书，尤其是污言秽语之书，以免贾祸人心。为此，他奏请皇帝对旗人读书严加限制，要求嗣后翻译书籍也应针对“关圣贤义理，古今治乱之书”，对于其它书籍则“概为禁饬，不许翻译”。[ 同上。]<br />
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What's more,there are many sages and wisdom need to be researched.We are afraid that we don't have enough time to do it in details day by day.So why do we waste our time translating those things?[Revised by Eertai ect.,proofread by Li Xun，Zhao De ect.:''Baqitongzhi·chuji'',Changchun:Northeast Normal University Press,1989,p.5339.]<br />
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The advice of Ashitan is very clear from the very begining--[[User:Wu Yinghong|Wu Yinghong]] ([[User talk:Wu Yinghong|talk]]) 15:33, 28 September 2021 (UTC),which points that reading should focus on the Confusion classic and history --[[User:Wu Yinghong|Wu Yinghong]] ([[User talk:Wu Yinghong|talk]]) 15:39, 28 September 2021 (UTC)and abandon those irrelevant books,especially those of dirty words,to get people rid of the obscene thoughts.In order to do this,he advised the emperor be more strict with the Qi people's readings.flag people's learning.--[[User:Wu Yinghong|Wu Yinghong]] ([[User talk:Wu Yinghong|talk]]) 15:30, 28 September 2021 (UTC)And he also required his offspring translate books about sages,principles and those can help to govern the society.Besides these books,other books are all forbidden and mustn't be translated.--[[User:Sun Yashi|Sun Yashi]] ([[User talk:Sun Yashi|talk]]) 11:44, 28 September 2021 (UTC)<br />
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==日语语言文学 202120081530 吴映红 女==<br />
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这一观点既是他翻译《大学》、《中庸》、《孝经》、《潘氏（通鉴）总论》、《太公家教》的原则与标准，也是太祖朝以来一以贯之的译书宗旨。<br />
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五 汉籍（书）翻译的文化沟通意涵<br />
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清初的汉书翻译主要由两部分人员构成：兼通满、汉的旗人（满洲、蒙古、汉军），以及八旗文科举中的举人、进士及第者，特别是顺治朝以来从新科进士中拣选出来学习满文的汉籍士子。<br />
The opinion is not only the principle and standard of his translation about ‘’University‘’, ‘’the doctrine of the mean‘’,‘ ‘’ the book of filial piety‘’, ‘’the general theory of pan (Tongjian) ‘’and ‘’Taigong family education‘’, but also the consistent purpose of translation from the Taizu Dynasty. <br />
FIFTH Cultural communication of Han nationality.<br />
《大学》''The Great Learning'' --[[User:He Qin|He Qin]] ([[User talk:He Qin|talk]]) 14:01, 28 September 2021 (UTC)<br />
In the early Qing Dynasty, the translation of Chinese traditional book was mostly composed of two parts: the flag people who knew Manchu and Han (Manchuria, Mongolia and Han Army), as well as the candidates, Jinshi and others in the eight flag liberal arts test, especially the Chinese bechelors who was selected from the new Jinshi to study Manchu from the Shunzhi Dynasty.<br />
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The opinion is not only the principle and standard of his translation about ‘’Daxue‘’, ‘’Zhongyong‘’,‘ ‘’ Xiaojing‘’, ‘’Genneral Theory(Tongjian) of Pan's Family ‘’and ‘’Taigong Family Education‘’, but also the consistent purpose of translation from the Taizu Dynasty. <br />
FIFTH Cultural communication of Han nationality.<br />
《大学》''The Great Learning'' --[[User:He Qin|He Qin]] ([[User talk:He Qin|talk]]) 14:01, 28 September 2021 (UTC)<br />
In the early Qing Dynasty, the translation of Chinese traditional book was mostly composed of two parts: the flag people who knew Manchu and Han (Manchuria, Mongolia and Han Army), as well as the candidates, Jinshi and others in the eight flag liberal arts test, especially the Chinese bechelors who was selected from the new Jinshi to study Manchu from the Shunzhi Dynasty.<br />
--[[User:Zhu Renduo|Zhu Renduo]] ([[User talk:Zhu Renduo|talk]]) 12:51, 29 September 2021 (UTC)<br />
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==日语语言文学 202120081560 朱壬铎 男==<br />
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这些人员既是翻译专才，又是文化接触与交流的实践者，代表了满洲统治阶级想要与汉族之间进行文化沟通的意愿。如顺治六年四月，礼科给事中姚文然以“以满汉同心合力为念。窃思满汉一家，咸思报主”为由，奏请从新科进士内广选庶吉士，令其肄习清书，待精熟之后即授以科、道等官。[ 鄂尔泰等奉敕修：《清实录·世祖章皇帝实录》，北京：中华书局，1985年，第11页。]顺治十年，世祖降旨，对此前所选三科庶吉士进行考试，从中选取“通满洲文义者三人”，“以应升之缺用”，并选取“其次可造者十二人”，“仍照原衔，责令勉力学习，俟再试分别。”[ 同上，第7-8页。]<br />
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These persons are not only experts in translation,but also practicer in cultral contact and communication,which represented the will,which is to cultrally communicate with the Han people,of the ruling class of Manchu.<br />
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For example,in April of the sixth year of Shunzhi,Yao Wenran,the inspectorof the department of rites,with the reason that "with the purpose that is combining the Man ethnic and the Han ethnic,I secretly came up with an idea about the Man-Han family but with my whole life for the emperor",made a request that is to widely select Shujishi from newly selected Jinshi and order them to sutdy the books of Qing Dynasty to get well-learned then teach the bureaucrats from other departments and circuits.[E Ertai et al.write by imperial command:Veritable Records of Qing Dynasty-Records of Shizu Zhang Emperor,Beijing,China Book Bureau,1985,p.11]<br />
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In the tenth year of Shunzhi,Shizong emperor declared an imperial order about examining the selected Shujishi from all three subjects,from which select three persons who are "familiar with the texts of Manchu",to"fill the vacancy caused by the former Jinshi selection",and select "another 12 gifted persons""remain the former title,make them study hard for the further tests and selection."[E Ertai et al.write by imperial command:Veritable Records of Qing Dynasty-Records of Shizu Zhang Emperor,Beijing,China Book Bureau,1985,p.7-8]<br />
--[[User:Zhu Renduo|Zhu Renduo]] ([[User talk:Zhu Renduo|talk]]) 02:47, 29 September 2021 (UTC)<br />
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These person are not only experts in translation,but also practicers in cultural contact and communication. They represent the will to culturally communicate with the Han people,of the ruling class of Manchu.<br />
For example,in April of the sixth year of Shunzhi,Yao Wenran,the inspector of the Department of Rites,with the reason that "with the purpose that is combining the Manchu and the Han ethnic,I think both Manchu and Han should hold the thought to requite favours to the emperor,made a request to widely select Hanlin bachelor from newly selected Jinshi(a successful candidate in the highest imperial examinations) and order them to sutdy the books of Qing Dynasty to get well-learned then grant them official positions.[E Ertai et al.write by imperial command:Veritable Records of <br />
Qing Dynasty-Records of Shizu Zhang Emperor,Beijing,China Book Bureau,1985,p.11]<br />
In the tenth year of Shunzhi, the emperor declared an imperial order about examining the selected Han bachelor of all three subjects,from which select three persons who are "familiar with the texts of Manchu",to"fill the vacancy caused by the former Jinshi selection",and select "another 12 gifted persons""remain the former title,make them study hard for the further tests and selection."[Idem, p.7-8]<br />
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--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 06:10, 29 September 2021 (UTC)<br />
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==日语语言文学 202120081477 蔡珠凤 女==<br />
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雍、乾、嘉年间，从新科进士中选取习满文者的做法得到延续，但每次选取者人数不一，整体上渐呈下降之势，至道光二十年前后废止，为汉书的满文翻译提供了人力来源，同时也为沟通满、汉两族文化提供了桥梁。<br />
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事实上，早在太宗时期，由于满人尚居关外，不识汉字，又罔知政体，遂敕达海等翻译汉书，使“满洲臣民未习汉文者，亦能兼通汉书”，而太宗自己也以它们作为“临政规范”，学习汉族的国家治理模式。[ 鄂尔泰等修，李洵、赵德贵等点校：《八旗通志·初集》，长春：东北师范大学出版社，1989年，第5325页。]顺治年间，阿什坦译成《大学》、《中庸》等书后，世祖又以它们作为倡导礼义教化的工具，希望通过此举使“满洲人知崇正学、尚经术”，令“邪说不得行”，而“风俗丕变”。[ 同上。]<br />
In the years of Yong Zheng , Qian Long and Jia Qing , the practice of selecting Manchu scholars from new scholars continued, but the number of candidates selected each time varied, and the overall trend gradually decreased. It was abolished around the 20th year of Dao Guang, which not only provided a source of manpower resources for Manchu translation of Hanshu, but also provided a bridge for communication between Manchu and Han cultures.<br />
In fact, as early as the Taizong period, because the Manchus still lived outside the frontier fortress, did not know Chinese characters and did not know about the regime, they ordered Dahai and other people to translate Chinese books, so that "Manchus who did not learn Chinese could also understand the contents of Chinese books", and Taizong himself used them as "administration norms" to learn the national governance model of the Han nationality. [Writed by Ertai etc.Checked by Li Xun, Zhao Degui etc.:"Ba Qi Tong Zhi I", Changchun: Northeast Normal University Press, 1989, P. 5325.] during the reign of Shunzhi, after Ashitan translated the 《University》《Moderate》and other books, Shizu used them as a tool to advocate etiquette and righteousness education, hoping to make "Manchus know and worship orthodox learning and classics" and "heresy can not be carried out" And "Customs change". [ibid.]<br />
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In the years of Yong Zheng , Qian Long and Jia Qing , the practice of selecting Manchu scholars from new scholars was continued, but the number of candidates selected each time varied, and the overall trend gradually tended to decrease. It was abolished around the second decade of Dao Guang, which not only provided human resources for Manchu translation of the Han books, but also builded the bridge of cultural communication between Manchu and Han.<br />
In fact, as early as the the Emperor Taizong period, because the Manchu who still lived outside shanhaiguan pass, did not know Chinese characters and the regime,Taizong ordered Dahai and others to translate the Han books, so that "Manchus who did not learn Chinese could also understand the Han books", and Taizong himself also used them as " the administration norms" to learn the national governance model of the Han nationality. [Written by Ertai etc.Checked by Li Xun, Zhao Degui etc.:"Ba Qi Tong Zhi I", Changchun: Northeast Normal University Press, 1989, P. 5325.] During the reign of Shunzhi, after Ashitan translated the "University""Moderate"and other books,Hong Taiji used them as the tool to advocate confucian code of ethics , hoping to make "Manchus know and worship orthodox learning and classics" and "heresy can not be carried out" and "Customs change". [ibid.]--[[User:Fu Shiyu|Fu Shiyu]] ([[User talk:Fu Shiyu|talk]]) 02:40, 29 September 2021 (UTC)<br />
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==日语语言文学 202120081486 付诗雨 女==<br />
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显然，汉文经、史的译印有助于端正满洲的人心风俗，使满、汉文化互通有无，即便是《诗经》中提到的各种花草树木、鸟兽虫鱼，也对拓展满人见闻甚有助益。[ 叶高树：《<诗经>满文译本比较研究——以<周南>、<召南>为例》，《国立台湾师范大学历史学报》1992年第20期。]有清一代，并非只有官方组织汉书的翻译，私人译书也很盛行。但官方译书与私人译书不同，无论是在译书的取材上，还是在译书的组织管理上，抑或是译书的颁行上，都有其特殊的考量。<br />
Obviously,the translation and printing of the classics and history of Han are conductive to correcting the humanity and customs of Manchuria,and exchanging of needed culture between Manchu and Han.Even different kinds of flowers and trees, insects and fish, which were mentioned in ''the Book of Songs'', also contribute to enrich their knowledge.[Ye Gaoshu:"''Comparative Study of the Manchu translation for 'the book of songs'--Taking 'Zhounan' 'Shannan' as example","The Histotrical Journal of National Taiwan Normal University''<br />
"1992;No.20.]In the Qing Dynasty, not only the official organization of the Han Books' translation, private translation was also very popular. However, unlike the private translation, the official translation was considered specially, whether in the materials of translation, the organization and management of the translation, or the publishment of the translation.--[[User:Fu Shiyu|Fu Shiyu]] ([[User talk:Fu Shiyu|talk]]) 15:16, 28 September 2021 (UTC)<br />
Obviously,the translation and printing of the classics and history of chinese are conductive to correcting the morality and customs of Manchuria,and exchanging of needed culture between Manchu and Han.Even different kinds of flowers and trees, insects and fish, which were mentioned in "the Book of Songs", also contribute to enrich their knowledge.[Ye Gaoshu:"Comparative Study of the Manchu translation for 'the book of songs'--Taking 'Zhounan' 'Shannan' as the example","The Histotrical Journal of National Taiwan Normal University "1992;No.20.]In the Qing Dynasty, not only the official organization of the Han Books' translation, private translation was also very popular. However, unlike the private translation, the official translation was considered specially, whether in the materials,the organization,the management or the publishment of the translation.--[[User:Zhou Xiaoxue|Zhou Xiaoxue]] ([[User talk:Zhou Xiaoxue|talk]]) 14:05, 29 September 2021 (UTC)<br />
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==日语语言文学 202120081559 周小雪 女==<br />
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即便是《三国志（通俗演义）》这样的通俗文学作品，译成满文后也被赋予了严肃的兵法与战略意义。以满文遍译汉书，尤其是经、史、子、集等书，并不是为了显示满语语文系统的优越性，所谓“精微巧妙，实小学家所未有”，而是为了“表章经学，天下从风”，通过汉籍的翻译“研究微言，讲求古义”，进行文化沟通。[ 叶高树：《清朝前期的文化政策》，台北：稻乡出版社，2002年，第91页。]藉由汉文典籍的翻译，满洲统治者不仅了解了汉族文化，而且在接触与学习中获得了“统制”汉民的重要经验，使满洲政权在性质上逐渐向“中原政权”转化，并实现“治统”与“道统”的和谐统一。<br />
Even popular literature such as The History of the Three Kingdoms was given serious military and strategic significance when translated into Manchu script. The purpose of translating Chinese books with Manchu script ,especially Canon ,History,Philosophy and Literature such book is not to show the superiority of the Manchu language system,but to show the confucian classics。Through the translation of Chinese classics ,study small points ,emphasize the ancient significance and carry out cultural communication.Ye Gaoshu.Cultural Policy in the early Qing Dynasty.Taipei:Rice Village Press,2002,p.91.Through the translation of Chinese classics,Manchu rulers not only understood the Han culture,but also gained important experience of controlling the Han people in the process of contact and learning,so that Manchu regime gradually transformed into central plains regime in nature and realized the unity of regnant orthodoxy and confucian orthodoxy.--[[User:Zhou Xiaoxue|Zhou Xiaoxue]] ([[User talk:Zhou Xiaoxue|talk]]) 14:08, 29 September 2021 (UTC)<br />
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Modifying canon, history, philosophy and literature to Confusion classics, history, pre Qin hundred works, religion and classical writings.因为经：经书，是指儒家经典著作；史：史书，即正史；子：先秦百家著作，宗教；集：文集，即诗词汇编。泛指我国古代典籍。Canon, history, philosophy and literature did not express the exact meaning.<br />
--[[User:Zou Yueli|Zou Yueli]] ([[User talk:Zou Yueli|talk]]) 01:41, 29 September 2021 (UTC)"表章经学，天下从风”The first half of the sentence is not translated accuratelyand the second half of the sentence has not been translated，so I think it should be translated into "Commend Confucian classics and let people all over the world follow"<br />
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==日语语言文学 202120081562 邹岳丽 女==<br />
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结语<br />
清初之际，虽然国家尚未实现从“征服王朝”向“中原王朝”的转变，但统治者在致力于武力开拓的同时，也开始关注文化活动。一方面，统治者念念不忘“国语骑射”的满洲旧制，将其视作立国精神；另一方面，又积极组织汉书翻译，倡导汉文化精神，从中原儒学中探求君主治术，构建国家的治统与道统。汉书翻译不仅让统治者得以接触汉族思想精粹和政治观念，而且让其在了解汉文经典与汉族文化的过程中，学习历代帝王的执政得失，以及古往今来的兴废事迹，从中汲取治国经验。<br />
Conclusion At the beginning of the Qing Dynasty, although the country has not yet realized the transformation from "conquering Dynasty" to "Central Plains Dynasty"，however, while the rulers were committed to the development of force, they also began to pay attention to cultural activities. On the one hand, the rulers never forget the old Manchu system of "national language riding and shooting" and regarded it as the spirit of founding the country;On the other hand, --[[User:Zhu Suzhen|Zhu Suzhen]] ([[User talk:Zhu Suzhen|talk]]) 06:46, 28 September 2021 (UTC)he actively organized the translation of Chinese calligraphy, advocated the spirit of Chinese culture, explored the rule of monarchy from the Confucianism of the Central Plains, and constructed the rule and orthodoxy of the country.Chinese translation not only allows the rulers to get in touch with the <br />
ideological essence and political concepts of the Han nationality, but also allows them to learn from the ruling gains and losses of emperors and the rise and fall deeds from ancient to modern times in the process of understanding Chinese classics and Han<br />
culture, so as to learn from the experience of governing the country. --[[User:Zhu Suzhen|Zhu Suzhen]] ([[User talk:Zhu Suzhen|talk]]) 06:46, 28 September 2021 (UTC)from the experience of governing the country. “here the preposition from should be deleted--[[User:Zhu Suzhen|Zhu Suzhen]] ([[User talk:Zhu Suzhen|talk]]) 06:46, 28 September 2021 (UTC)<br />
<br />
however, while the rulers were committed to expansion by military force.<br />
国语骑射：Manchu language, horse-riding and archery--[[User:Zeng Junlin|Zeng Junlin]] ([[User talk:Zeng Junlin|talk]]) 01:49, 3 October 2021 (UTC)<br />
<br />
<br />
--[[User:Zhu Suzhen|Zhu Suzhen]] ([[User talk:Zhu Suzhen|talk]]) 06:46, 28 September 2021 (UTC) ( here "he" is not consistant with the subjective "the rulers", so "he" should be changed in to "they" )<br />
<br />
==国别 202120081478 曾俊霖 男==<br />
<br />
概言之，汉书的翻译既匡扶了社稷，又教化了臣民，令满、汉文化之间的交流得以开启并加深。虽然清初三朝期间，汉书翻译的规模不一，统治者对于汉族文化的具体态度存在差异，但翻译的原则与标准基本未变，那便是以文治教化和典章制度为主，通过翻译汉族典籍，凝聚符合国家需求的集体价值观，并将汉族传统文化落实为国家治理的大政方针，实现兴文教、崇经术、开太平的治国理念。<br />
<br />
In short, the translation of books of Han nationality not only helped the whole country, but also educated his people so that the cultural exchange between Manchu and Han can be opened and deepened. During the three dynasties of the early Qing Dynasty, the scale of books of Han nationality translation was different, and the rulers' specific attitudes towards Han culture were different, but the principles and standards of translation remained basically unchanged, which focused on cultural education and the system of laws and regulations, condensed the collective values in line with the national needs through the translation of Han classics, and implemened the Han traditional culture as the major policy of national governance, realized the governing concept of promoting culture and education, advocating studies of Confucian classics and opening up peace.<br />
<br />
In short, the translation of Books of Han not only gave great help to the whole country, but also educated its(the Qing Dynasty) people so that it began and then deepened the cultural exchange between Manchu and Han. During the first three emperors' rulings of the early Qing Dynasty, though the scales of translation was different, and the rulers' specific attitudes towards Han culture differed from each other, the principles and standards of translation remained basically unchanged, that is to say, it will focus on cultural education and the system of laws and regulations, condense the collective values in line with the national needs through the translation of Han classics, and implement the Han traditional culture as the major policy of national governance to realize the governing concept of promoting culture and education, advocating studies of Confucian classics and opening up peace.<br />
--[[User:Huang Zhuliang|Huang Zhuliang]] ([[User talk:Huang Zhuliang|talk]]) 08:19, 29 September 2021 (UTC)Huang Zhuliang<br />
<br />
==国别 202120081493 黄柱梁 男==<br />
Footnotes and References<br />
<br />
1 杨家骆：《金史》，台北：鼎文书局，1985年，第1684页。<br />
<br />
2 明珠等奉敕修：《清实录·太祖高皇帝实录》，北京：中华书局，1986年，第2页。<br />
<br />
3 中国第一历史档案馆、中国社科院历史研究所译注：《满文老档》，北京：中华书局，1990年，第1196页。<br />
<br />
4 鄂尔泰等奉敕修：《清实录·太宗文皇帝实录》，北京：中华书局，1985年，第13页。<br />
<br />
5 王钟翰点校：《清史列传》，北京：中华书局，1987年，第187页。<br />
<br />
6 国史编纂委员会：《朝鲜王朝实录》，汉城：国史编纂委员会，1973年，第38页。<br />
<br />
1. Yang Jialuo, ''History of the Jin Dynasty''(1115-1234), Taipei, Dingwen Book Company, 1985, pp.1684.<br />
<br />
2. Ming Zhu et al. (compiled under the order of Emperor Kangxi ), ''the Imperial Archives of Emperor Taichu(1559-1626, posthumous titled Gao Huang Di) of the Qing Dynasty'', Peking, Zhonghua Book Company, 1986, pp.2. <br />
<br />
3. The First Historical Archives of China, Translated and Noted by the Institute of History in Chinese Academy of Social Sciences, ''Old Documents of Manchu Script'', Peking, Zhonghua Book Company, 1990, pp.1196.<br />
<br />
4. E Ertai et al. (compiled under the order of Emperor Yongzheng ), ''the Imperial Archives of Emperor Taizong(1592-1643, posthumous titled Wen Huang Di) of the Qing Dynasty'', Peking, Zhonghua Book Company, 1985, pp.13.<br />
<br />
5. Proofread by Wang Zhongshan, ''Biographies of the Qing Dynasty'', Peking, Zhonghua Book Company, 1987, pp.187.<br />
<br />
6. Committee of National History Compilation, ''the Imperial Archives of Joseon Dynasty'', Seoul, Committee of National History Compilation, 1973, pp.38.<br />
--[[User:Huang Zhuliang|Huang Zhuliang]] ([[User talk:Huang Zhuliang|talk]]) 08:18, 29 September 2021 (UTC)Huang Zhuliang<br />
<br />
1 页码标记应为p.1684.<br />
<br />
2 posthumous titled Gao Huang Di应改为posthumously titled Gao Huang Di,页码标记为p.2.<br />
<br />
3“老满文”指的是清太祖努尔哈赤时期创制的满文，以文字中没有圈和点为特点。《满文老档》即是用老满文写成的档案汇编 ，所以《满文老档》应为 ''Manchu Archives written in Fore Manwen'',页码标记为p.1196.<br />
<br />
4 同第二句，posthumous titled Wen Huang Di改为posthumously titled Wen Huang Di, 页码标记为p.13.<br />
<br />
5 页码标记为p.187.<br />
<br />
6 页码标记为p.38.<br />
--[[User:Liu Wei|Liu Wei]] ([[User talk:Liu Wei|talk]]) 05:20, 1 October 2021 (UTC)Liu Wei<br />
<br />
==国别 202120081507 刘薇 女==<br />
7 国史编纂委员会：《朝鲜王朝实录》，汉城：国史编纂委员会，1973年，第62页。<br />
<br />
8 叶高树：《清朝前期的文化政策》，台北：稻乡出版社，2002年，第58页。<br />
<br />
9 鄂尔泰等奉敕修：《清实录·太宗文皇帝实录》，北京：中华书局，1985年，第10页。<br />
<br />
10 罗振玉：《天聪朝臣工奏议》，北京：中国人民大学出版社，1989年，第2页。<br />
<br />
11 鄂尔泰等奉敕修：《清实录·太宗文皇帝实录》，北京：中华书局，1985年，第14页。<br />
<br />
12 同上，第2页。<br />
<br />
7 National History Compilation Committee, ''Records of the Korean Dynasty'', Seoul: National History Compilation Committee, 1973, p.62.<br />
<br />
8 Ye Gaoshu,'' Cultural policise in the early Qing Dynasty'', Taipei: Daoxiang press, 2002, p.58.<br />
<br />
9 E Ertai et al.(compiled by order of the emperor),''Record of the Emperor Taizong Wen in the Qing Dynasty'', Beijing: Zhonghua press, 1985,p.10.<br />
<br />
10 Luo Zhenyu, ''Collections of secretary's memorial to the throne during the reign of Tencong'', Beijing: Renmin University of China Press, 1989, p.2.<br />
<br />
11 E Ertai et al.(compiled by order of the emperor),''Record of the Emperor Taizong Wen in the Qing Dynasty'', Beijing: Zhonghua press, 1985,p.14.<br />
<br />
12 Ertai et al.(compiled by order of the emperor),''Record of the Emperor Taizong Wen in the Qing Dynasty'', Beijing: Zhonghua press, 1985,p.2. <br />
--[[User:Liu Wei|Liu Wei]] ([[User talk:Liu Wei|talk]]) 15:23, 28 September 2021 (UTC)Liu wei<br />
<br />
7 National Institute of Korean History, ''Records of the Korean Dynasty'', Seoul: National Institute of Korean History, 1973, p.62.--[[User:Yan Lili|Yan Lili]] ([[User talk:Yan Lili|talk]]) 13:30, 11 October 2021 (UTC)<br />
<br />
==国别 202120081537 颜莉莉 女==<br />
13 罗振玉：《天聪朝臣工奏议》，北京：中国人民大学出版社，1989年，第24-25、115页。<br />
<br />
14 鄂尔泰等奉敕修：《清实录·太宗文皇帝实录》，北京：中华书局，1985年，第9页。<br />
<br />
15 罗振玉：《天聪朝臣工奏议》，北京：中国人民大学出版社，1989年，第2页。<br />
<br />
16 同上，第82页。<br />
<br />
17 清高宗敕纂：《八旗满洲氏族通谱》，沈阳：辽沈书社，1989年，第10页。<br />
<br />
18 鄂尔泰等奉敕修：《清实录·世祖章皇帝实录》，北京：中华书局，1985年，第15-16页。<br />
<br />
13th. Lou Zhenyu: ''Tiancongchao Chengong Zouyi'' , Beijing: China People's University Press, 1989, pp.24-25,115.<br />
<br />
14th. E Ertai eat al revised under order of emperor: ''Factual Record Of Qing Dynasty• Actual Record Of TaiZu'', Beijing: Zhonghua Book Company, 1985, p.9<br />
<br />
15th. Lou Zhenyu: ''Tiancongchao Chengong Zouyi'' , Beijing: China People's University Press, 1989, p.2<br />
<br />
16th. Idem<br />
<br />
17th. Qing emperor Gaozong ordered to write: ''General spectrum of Manchu clan in eight banners'', Shenyang: Liaoshen Book Company, 1989,p.10<br />
<br />
18th. E Ertai eat al revised under order of emperor: ''Factual Record Of Qing Dynasty• Actual Record Of TaiZu'', Beijing: Zhonghua Book Company, 1985, pp.15-16<br />
<br />
<br />
14. E Ertai eat al （eds. on the order of emperor): ''Factual Record Of Qing Dynasty• Actual Record Of TaiZu'', Beijing: Zhonghua Book Company, 1985, p.9<br />
<br />
16. Idem, p.82<br />
<br />
17. Qing emperor Gaozong ordered to write: ''Genealogy of Manchu clan in eight banners'', Shenyang: Liaoshen Book Company, 1989,p.10<br />
<br />
18. E Ertai eat al （eds. on the order of emperor): ''Factual Record Of Qing Dynasty• Actual Record Of TaiZu'', Beijing: Zhonghua Book Company, 1985, pp.15-16<br />
<br />
==国别 202120081538 颜子涵 女==<br />
19 鄂尔泰等奉敕修：《清实录·世祖章皇帝实录》，北京：中华书局，1985年，第13页。<br />
<br />
20 中国第一历史档案馆编：《清初内国史院满文档案译编·顺治朝》，北京：光明日报出版社，1989年，第80页。<br />
<br />
21 阎崇年校注：《康熙顺天府志》，北京：中华书局，2009年，第482页。<br />
<br />
22 鄂尔泰等奉敕修：《清实录·世祖章皇帝实录》，北京：中华书局，1985年，第9页。<br />
<br />
23 叶高树：《清朝前期的文化政策》，台北：稻乡出版社，2002年，第72-73页。<br />
<br />
24 鄂尔泰等修，李洵、赵德贵等点校：《八旗通志·初集》，长春：东北师范大学出版社，1989年，第5339页。<br />
<br />
19. Ortai and some people compiled it on the orders of the emperor: ''Records of emperor shizuzhang in the Qing Dynasty'', Beijing: China Book Company, 1989, p.13.<br />
<br />
20. Editor of China's First Historical Archives: ''Translation of manchu archives of the National Historical Institute of the early Qing Dynasty, Shunzhi Dynasty'', Beijing: Guangming Daily Press, 1989, p.80.<br />
<br />
21. Yan Chongnian made proofreading: ''Kangxi Shuntian Fuzhi'', Beijing: China Book Company, 2009, p.482.<br />
<br />
22. Ortai and some people compiled it on the orders of the emperor: ''Records of emperor shizuzhang in the Qing Dynasty'', Beijing: China Book Company ,1989,p.9.<br />
<br />
23.Kao-Shu Yeh, ''Cultural policy in the early Qing Dynasty'', Taipei:Daoxiang Press, 2002, pp. 72-73.<br />
<br />
24. Ortai and some people compiled，Li Wei, Zhao Degui and others proofread: ''Journal of the eight banners •First Episode'', Changchun: Northeast Normal University Press, 1989, p. 5339.<br />
<br />
<br />
19. E,Ertai et al. (eds. under the order of the Emperor). ''An Actual Record of ShiZu Zhang in Factual Record of Qing Dynasty'', Beijing:Zhonghua Book Company,1985,p.13.<br />
<br />
20. The First Historical Archives of China(ed.). ''A translation of Manchu archives of the Imperial Academy of National History in the Shunzhi Period of the early Qing Dynasty'', Beijing: Guangming Daily Press, 1989, p.80.<br />
<br />
21. Yan Chongnian(proofread). ''The History of Shuntian of Emperor Kangxi'', Beijing: China Book Company, 2009, p.482.<br />
<br />
22. E,Ertai et al. (eds. under the order of the Emperor). ''An Actual Record of ShiZu Zhang in Factual Record of Qing Dynasty'', Beijing:Zhonghua Book Company,1985,p.9.<br />
<br />
23. Kao-Shu Yeh, ''The Cultural Policies of the Early Qing Dynasty'', Taipei:Daoxiang Press, 2002, pp. 72-73.<br />
<br />
24. E,Ertai et al.(eds.), Li,Xun&Zhao Degui et al.(proofread). ''The First Collectanea in Ba Qi Tong Zhi'', Changchun:Northeast Normal University Press,1989, p.5339.<br />
<br />
==国别 202120081540 阳佳颖 女==<br />
25 同上。<br />
<br />
26 鄂尔泰等奉敕修：《清实录·世祖章皇帝实录》，北京：中华书局，1985年，第11页。<br />
<br />
27 同上，第7-8页。<br />
<br />
28 鄂尔泰等修，李洵、赵德贵等点校：《八旗通志·初集》，长春：东北师范大学出版社，1989年，第5325页。<br />
<br />
29 同上。<br />
<br />
30 叶高树：《<诗经>满文译本比较研究——以<周南>、<召南>为例》，《国立台湾师范大学历史学报》1992年第20期。<br />
<br />
31 叶高树：《清朝前期的文化政策》，台北：稻乡出版社，2002年，第91页。<br />
<br />
<br />
<br />
[25] Idem<br />
<br />
[26] Ertai et al. (eds. under the order of the Emperor). "Actual Record of ShiZu Zhang in Factual Record of Qing Dynasty", Beijing:Zhonghua Book Company,1985,p.11.<br />
<br />
[27] Idem,pp.7-8.<br />
<br />
[28] E,Ertai et al.(eds.), Li,Xun&Zhao Degui et al.(proofread). ''The First Collectanea in Ba Qi Tong Zhi'', Changchun:Northeast Normal University Press,1989, p.5325.<br />
<br />
[29] Idem<br />
<br />
[30] Ye,Gaoshu. “The Comparative Study of the Manchu Translation On The Book of Songs---Cases Study of Zhounan and Zhaonan”.''Historical Inquiry of the National Taiwan Normal University'',no.20(1992).<br />
<br />
[31] Ye,Gaoshu. ''The Cultural Policies of the Early Qing Dynasty''. Taipei:Daoxiang Press,2002,p.91.--[[User:Yang Jiaying|Yang Jiaying]] ([[User talk:Yang Jiaying|talk]]) 07:16, 29 December 2021 (UTC)<br />
<br />
<br />
[25] Idem<br />
<br />
[26] E,Ertai et al. (eds. under the order of the Emperor). "''Actual Record of Shih Tsu Fu Lin in Factual Record of Tsing Dynasty''", Beijing: China publishing house,1985,p.11.<br />
<br />
[27] Idem,p.7-8.<br />
<br />
[28] Ertai et al.(eds.), Li,Xun & Zhao Degui et al.(proofread). "''General History of the Eight Banners''", Changchun: Northeast Normal University Press,1989, p.5325.<br />
<br />
[29] Idem<br />
<br />
[30] Ye,Gaoshu. "''The Comparative Study of the Manchu Translation On The Book of Songs---Cases Study of Zhounan and Zhaonan.''", Bulletin of Historical Research of National Taiwan Normal University, No.20(1992).<br />
<br />
[31] Ye,Gaoshu. "''The Cultural Policies of the Early Tsing Dynasty''". Taipei: Daoxiang Publishing House,2002,p.91. --Ye Weijie<br />
<br />
=Hongloumeng=<br />
HERE STARTS A NEW TRANSLATION: REST OF CHAPTER 19 OF HONGLOUMENG<br />
<br />
PLEASE READ [[Joint_translation_terms|Joint translation terms]] <br />
<br />
PLEASE ALSO READ THE PREVIOUS PARTS, AT LEAST THE SENTENCES BEFORE YOUR OWN PART IN CHAPTER 19 [[20210303_culture|1, Mar 3 Chapters 1-4]], [[20210310_culture|2, Mar 10 Chapters 6-7]], [[20210317_culture|3, Mar 17 Chapters 11-13]], [[20210324_culture|4, Mar 24 Chapters 15-17]], [[20210331_culture|5, Mar 31 Chapters 4-7]], [[20210407_culture|6, Apr 7 Chapters 8-10]], [[20210414_culture|7, Apr 14 Chapters 13-15]] , [[20210519_culture|12, May 19 Chapters 17-19]], [[20210929_homework#Hongloumeng|for Sep 29 - rest of HLM Chapter 19]] [[20211013_homework|for Oct 13 - HLM Chapters 20-21]] etc.<br />
<br />
==国别 202120081544 叶维杰 男==<br />
<br />
第十九回 ... 这些丫头们明知宝玉不讲究这些；二则李嬷嬷已是告老解事出去的了，如今管不着他们：因此只顾玩笑，并不理他。那李嬷嬷还只管问：“宝玉如今一顿吃多少饭？什么时候睡觉？”丫头们总胡乱答应。有的说：“好个讨厌的老货！” 李嬷嬷又问道：“这盖碗里是酪，怎么不送给我吃？”说毕，拿起就吃。一个丫头道：“快别动，那是说了给袭人留着的，回来又惹气了。你老人家自己承认，别带累我们受气。”李嬷嬷听了，又气又愧，便说道：“我不信他这么坏了肠子。别说我吃了一碗牛奶，就是再比这个值钱的，也是应该的。难道待袭人比我还重？难道他不想想怎么长大了？我的血变了奶，吃的长这么大，如今我吃他碗牛奶，他就生气了？我偏吃了，看他怎么着！你们看袭人不知怎么样，那是我手里调理出来的毛丫头，什么阿物儿！”一面说，一面赌气把酪全吃了。<br />
<br />
Chapter XIX...These girls' busy joking with each other and not much cared about Nanny Li as they previously knew Pao'yue was not particular about these, and there's no room left for Nanny Li to discipline them for she's already be dismissed. While Nanny Li kept asking:“ How's Pao-yue's appetite these day? And the time he make rest?” Always with so less careness girls reply. Some complained:“Aye! Such an old nosy lady!” Still Nanny Li asked：“A bowl of cheeze here! Why don't you bring it to me?”Then she directly grabbed some to her mouth. One girl hurriedly said:“Quit it! That's what Pao'yue reserved for Xiren! You take the blame yourself if he gets discontented, don't get us involved.” Angry but ashamed also Nanny Li went, and said:“I don't believe it！I even deserve something more valuable, let alone a bowl of milk! Is Xi'ren more important than me? Pao'yue can't be this heartless! Think about it, I myself raised him up this good step by step heart and soul with my own blood! How can he get discontented merely because of a bowl of milk? Still I'm gonna take it, even if he did! And Xi'ren? I taught her everything! Mad at me? Don't be ridiculous!”Nagging, Nanny Li emptied the whole bowl.<br />
<br />
Chapter XIX...These maids knew clearly that Baoyu didn't care the trifles. Furthermore, Mammy Li was already retired and she had no control over them. Therefor she just ignored him in her teasing. Mammy Li always asked:"How many meals did Baoyu eat recently? When did he go to bed?" The maids'answers always were irrelevant. Some of them said:"What a nasty old woman!" While Mammy Li kept asking :"There is some cheese in the bowl. Why don't you give me?" As soon as the voice fell down, she grabbed the cheese and had it. One maid hurriedly said:“Quit it! That's what Baoyu reserved for Xiren! You take the blame yourself and don't get us involved.” Angry but ashamed also Mammy Li was, and said:“I don't believe he has such a bad temper！I even deserve something more valuable, not to mention a bowl of milk! Is Xi'ren more important than me? Think about it, I raised him up by my breast nursing with my own blood! How can he get discontented merely because of a bowl of milk? Still I'm gonna take it, even if he would! And Xiren? That's me who taught her everything! Mad at me? Don't be ridiculous!”Nagging, Mammy Li emptied the whole bowl.--[[User:Zhou Junhui|Zhou Junhui]] ([[User talk:Zhou Junhui|talk]]) 01:29, 2 October 2021 (UTC)<br />
<br />
Chapter XIX ... these servant-girls were well aware that Precious Jade was not particular in these respects, and that in the next place Nanny Plum，having pleaded old age, resigned her place and gone home，had nowadays no control over them，so that they simply gave their minds to romping and joking，and paid no heed whatsoever to her. Nanny Plum however still kept on asking about Precious Jade，"How much rice do you now eat at one meal? And at what time do you go to sleep?" to which questions the servant-girls replied quite at random；some of those being there observed: "What a dreadful despicable old thing she is!" - "In this covered bowl," she continued to inquire, "is cream, and why not give it to me to eat?" and having concluded these words，she took it up there and then began eating it." Be quick，and leave it alone!" a servant-girl expostulated，"that，she said, was kept in order to be given to Aroma, and on his return，when he again gets into a huff，you，old lady，must，on your own motion，confess to having eaten it，and not involve us in any way as to have to bear his resentment." Nanny Plum，at these words，felt both angry and ashamed. "I can't believe，" she forthwith remarked，"that he has become so bad at heart！Not to speak of the milk I've had. I have，in fact every right to even something more expensive than this；for is it likely that he holds Aroma dearer than myself？ It can't forsooth be that he doesn't bear in mind how that I've brought him up to be a big man，and how that he has eaten my blood transformed into milk and grown up to this age！and will be because I'm now having a bowl of milk of his be angry on that score！I will，yes，eat it，and we'll see what he'll do！I don't know what you people think of Aroma，but she was a lowbred girl，whom I've with my own hands raised up! And what fine object indeed was she！"As she spoke，she flew into a temper, and taking the cream, she drank the whole of it.--[[User:Zhang Yang|Zhang Yang]] ([[User talk:Zhang Yang|talk]]) 13:02, 11 October 2021 (UTC)Zhang Yang<br />
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==国别 202120081551 张扬 男==<br />
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又一个丫头笑道：“他们不会说话，怨不得你老人家生气。宝玉还送东西给你老人家去，岂有为这个不自在的？”李嬷嬷道：“你也不必装狐媚子哄我，打量上次为茶撵茜雪的事我不知道呢！明儿有了不是，我再来领。”说着，赌气去了。<br />
少时，宝玉回来，命人去接袭人。只见晴雯躺在床上不动，宝玉因问：“可是病了？还是输了呢？”秋纹道：“他倒是赢的，谁知李老太太来了，混输了，他气的睡去了。”宝玉笑道：“你们别和他一般见识，由他去就是了。”<br />
说着，袭人已来，彼此相见。袭人又问宝玉何处吃饭，多早晚回来；又代母、妹问诸同伴姊妹好。一时换衣卸妆。宝玉命取酥酪来，丫鬟们回说：“李奶奶吃了。”宝玉才要说话，袭人便忙笑说道：“原来留的是这个，多谢费心。前儿我因为好吃，吃多了，好肚子疼，闹的吐了，才好了。他吃了倒好，搁在这里白糟蹋了。我只想风干栗子吃，你替我剥栗子，我去铺炕。”<br />
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Another servant-girl grinned:“They don’t know how to speak properly, and it’s no wonder you old lady should get angry. Precious Jade still sends you great things, and it’s impossible that he will feel uncomfortable for a thing like this.” “You don’t have to act like a vixen to cajole me!” Nanny Plum said, “You think I’m not aware that you pushed Snow Alizarin away on account of a cup of tea the other day? And if I did make a mistake, I’ll come by and admit it!” Having said this, she went off, pissed off.<br />
Soon Precious Jade came back and gave orders to go and fetch Aroma. Seeing Sunny Cloud Formation lying perfectly still on bed, Precious Jade asked:“ Is she ill? Or has she lost at cards?” “She had been a winner,” Autumn Vein answered,“but Nanny Plum came and muddled her so that she lost, and angry at that she rushed off to sleep,” Precious Jade smiled:“Don’t place yourselves on the same footing as nanny Plum. Leave her alone.”Aroma came as Precious Jade was saying his words. After the mutual salutations, Aroma went on to ask of Precious Jade:“ Where did you have your dinner? And when did you come back?” and to present likewise on behalf of her mother and sisters her salutations to all the girls, who were her companions. In a short while, she changed her costume and washed off her make up. When Precious Jade bade them fetch the cream, the servant-girls answered:“ Nanny Plum has eaten it.” And as Precious Jade was on the point of making some remarks Aroma hastened to interfere, laughing:“ Is it really this that you have kept for me? Many thanks for troubling. The other day when I ate some of it, I found it very tasty and had a lot of it, then I got a pain in the stomach. I was so upset that it was only after I had thrown it all up that I feel right. So it's fine that she has had it. If it had been kept there, it would have been wasted all for no use. What I want are dry chestnuts, and if you can clean a few for me, I'll go and lay the bed."<br />
--[[User:Zhang Yang|Zhang Yang]] ([[User talk:Zhang Yang|talk]]) 10:58, 29 September 2021 (UTC)Zhang Yang<br />
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Another servant-girl grinned, “They don’t know how to talk properly, and no wonder you, old lady, get angry. Precious Jade still sends you great things, and it’s no need to be unpleased with it.” “You don’t have to cheat me!” Nanny Plum said, “You think I’m not aware that you sent Snow Alizarin away just on account of a cup of tea the other day? I will get it when it is prepared well tomorrow!” Having said this, she went off with sulks. Soon Precious Jade came back and asked someone to pick up Aroma. Seeing Sunny Cloud Formation lying still on bed, Precious Jade asked, “ Is she ill? Or has she lost at cards?” “She had been a winner,” Autumn Vein answered,“but Nanny Plum came and muddled her, so she lost the game and rushed off to sleep for the anger.” Precious Jade smiled, “Don’t take it serious and just leave her alone.”Aroma came as Precious Jade was saying his words. After the mutual salutations, Aroma went on to ask Precious Jade about the place for dinner and the time he would come back, then greet everyone on behalf of her mother and sisters. In a short while, she changed her costume and washed off her make-up. When Precious Jade asked one to fetch the cream, the servant-girls answered,“ Nanny Plum has taken it.” As Precious Jade was on the point of saying something, Aroma laughing, “ Is it this that you have kept for me? Thanks for troubling. The other day when I ate some of it, I found it very tasty and had a lot of it, then I got a pain in the stomach. It was better that she did so, for at least it is not wasted before getting bad. I just want some drying chestnuts. Could you please peel them while I will make the beds.”--[[User:Chen Jing|Chen Jing]] ([[User talk:Chen Jing|talk]]) 11:23, 29 September 2021 (UTC)<br />
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==国别 202020080595 陈静 女==<br />
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宝玉听了，信以为真，方把酥酪丢开，取了栗子来，自向灯下检剥。一面见众人不在房中，乃笑问袭人道：“今儿那个穿红的是你什么人？”袭人道：“那是我两姨姐姐。”宝玉听了，赞叹了两声。袭人道：“叹什么？我知道你心里的缘故，想是说他那里配穿红的？”宝玉笑道：“不是，不是。那样的人不配穿红的，谁还敢穿？我因为见他实在好的很，怎么也得他在咱们家就好了。”袭人冷笑道：“我一个人是奴才命罢了，难道连我的亲戚都是奴才命不成，定还要拣实在好的丫头才往你们家来？”宝玉听了，忙笑道：“你又多心了。我说往咱们家来，必定是奴才不成，说亲戚就使不得？”袭人道：“那也般配不上。”<br />
宝玉便不肯再说，只是剥栗子。袭人笑道：“怎么不言语了？想是我才冒撞冲犯了你？明儿赌气花几两银子，买进他们来就是了。”<br />
宝玉笑道：“你说的话，怎么叫人答言呢？我不过是赞他好，正配生在这深宅大院里，没的我们这宗浊物倒生在这里。”<br />
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Hearing this, Master Bao fell for it and throw the curds away, taking some chestnuts and then peeling them under the lantern. Finding others not in the room except Aroma, Master quipped: “Who is the the person in red today?” Aroma answered, “Two of my cousins”. Knowing it, Master Bao couldn’t repress a sigh of admiration. “What do you sigh for? I know it is because they could not wear red.” Aroma said. Master Bao replied with smile: “No! Who dares to wear red if such persons doesn't deserve it? I just admire them and hope if they can stay in our home.” Aroma sneered, “I am just a slave. So all of my families are slaves and we should pick up the best girls to be slaves in your home?” Master Bao said with smile, “Don’t be touchy. It doesn’t mean to be the slave but to be our relatives in our house.” Aroma replied, “It doesn’t match, either.” Master Bao refused to say any more, but just peeled chestnuts. Aroma smiled and said, “Why are you silent? I just offended you. You could spend some money and buy them if you want.” Master smiled and said, “How to respond to your words. I just show my admiration and think they should be in such circumstance where the persons like me live.”--[[User:Chen Jing|Chen Jing]] ([[User talk:Chen Jing|talk]]) 11:26, 29 September 2021 (UTC)<br />
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Hearing this, Master Bao fell for it, threw the curds away, took some chestnuts and then peeled them under the lantern. Finding others not in the room except Aroma, Master quipped: “What’s the relationship between you and the person in red today?” Aroma answered, “Two of my cousins”. Knowing it, Master Bao couldn’t repress a sigh of admiration. “What do you sigh for? I know what you are thinking. You must think they don’t deserve to wear red.” Aroma said. Master Bao replied with smile: “No! Who dares to wear red if such persons don’t deserve it? I just admire them and hope if they can stay in our house.” Aroma sneered, “I live as a maid. So all of my families should be the same as me? And we should pick up the best girls to be maids in your home?” Master Bao said with smile, “Don’t be touchy. It doesn’t mean to be the maid but to be our relatives in our house.” Aroma replied, “It doesn’t match, either.” Master Bao refused to say any more, but just peeled chestnuts. Aroma smiled and said, “Why are you silent? I think I just offended you. You could spend some money and buy them if you want.” Master smiled and said, “How to respond to your words. I just show my admiration and think they are born to be in such circumstance where the persons like me live. ”--[[User:Chen Xinyi|Chen Xinyi]] ([[User talk:Chen Xinyi|talk]]) 10:34, 29 September 2021 (UTC)<br />
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==翻译学 202120081481 陈心怡 女==<br />
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袭人道：“他虽没这样造化，倒也是娇生惯养的，我姨父、姨娘的宝贝儿似的。如今十七岁，各样的嫁妆都齐备了，明年就出嫁。”宝玉听了“出嫁”二字，不禁又嗐了两声。正不自在，又听袭人叹道：“我这几年，姊妹们都不大见；如今我要回去了，他们又都去了。”<br />
宝玉听这话里有文章，不觉吃了一惊，忙扔下栗子，问道：“怎么着，你如今要回去？”袭人道：“我今儿听见我妈和哥哥商量，教我再耐一年，明年他们上来，就赎出我去呢。”宝玉听了这话，越发忙了，因问：“为什么赎你呢？”袭人道：“这话奇了。我又比不得是这里的家生子儿，我们一家子都在别处，独我一个人在这里，怎么是个了局呢？”宝玉道：“我不叫你去，也难哪。”袭人道：“从来没这个理。就是朝廷宫里，也有定例：几年一挑，几年一放，没有长远留下人的理，别说你们家。” 宝玉想一想，果然有理。又道：“老太太要不放你呢？”<br />
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Aroma said, “Although she has few achievements, she is pampered and the apple of my uncle’s, aunt’s eyes. She is now 17. Since all kinds of dowries had been prepared, she could get married next year. ” Hearing “get married”, Precious Jade Merchant signed spontaneously. He was still ill at ease and then heard Aroma said, “I have rarely met with my sisters in the past few years. Now I’m going back, however, they came.” Precious Jade Merchant thought that there’s more to it than what is said. He was amazed, threw chestnuts at once and asked, “what’s wrong? You are going back now?” Aroma said, “I heard my mother and brother talking about it today. They want me to remain patient for another year and they will come here to redeem me next year. ” Precious Jade heard it, felt anxious and asked, “Why they want to redeem you?” Aroma said, “What you said is pretty strange. I’m not a daughter of maids here. My family is elsewhere, and I’m the only one here. How can it be like this?” Precious Jade said, “I’m afraid I can’t let you go.” Aroma said, “It makes no sense. There are routines even in the imperial palace: palace maids are selected once every few years and then set free. There is no reason to keep a person for a long time in the imperial palace, let alone in your house.” Precious Jade thought about it for a while and thought it made sense. Then he said, “What if my grandmothers doesn’t let you go?”--[[User:Chen Xinyi|Chen Xinyi]] ([[User talk:Chen Xinyi|talk]]) 10:02, 29 September 2021 (UTC)<br />
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Aroma said, “Though not born as good, she is also spoilt and pampered, being the apple of my aunt and uncles’ eyes. She is now 17 and dowries of all sorts have been prepared for her marriage next year.” At the hearing of the word “marriage”, Precious Jade Merchant gave a sigh. As he was feeling ill at ease, Aroma sighed and continued, “I rarely met my sisters in the past few years. While I’m going back home, they are all about to leave.” Precious Jade Merchant felt surprised at what she said. He threw away chestnuts at once and asked, “why, you are going back home?” Aroma answered, ““I heard my mother and my brother talking today. They told me to stay here for another year and they will come here to ransom me next year.” Hearing that, Precious Jade got more anxious and asked, “Why do they want to ransom you?” Aroma replied, “what a strange question! I’m no daughter of maids here. My family lives elsewhere and I’m all alone here. How can we be together?” Precious Jade sighed, “If I won’t let you go, I suppose it’s difficult.” Aroma answered, “It makes no sense. There are routines even in the imperial palace: maids are selected once every few years and then set free. There is no reason to keep a servant for a long time in the imperial palace, let alone in your house.”Precious Jade thought for a while and considered it reasonable. Then he said, “What if my grandmothers won’t let you go?”--[[User:Gao Mi|Gao Mi]] ([[User talk:Gao Mi|talk]]) 09:47, 29 September 2021 (UTC)<br />
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==翻译学 202120081487 高蜜 女==<br />
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袭人道：“为什么不放呢？我果然是个难得的，或者感动了老太太、太太，不肯放我出去，再多给我们家几两银子留下，也还有的；其实我又不过是个最平常的人，比我强的多而且多。我从小儿跟着老太太，先伏侍了史大姑娘几年，这会子又伏侍了你几年。我们家要来赎我，正是该叫去的，只怕连身价不要，就开恩放我去呢。要说为伏侍的你好，不叫我去，断然没有的事。那伏侍的好，是分内应当的，不是什么奇功；我去了，仍旧又有好的了，不是没了我就使不得的。”<br />
宝玉听了这些话，竟是有去的理，无留的理，心里越发急了。因又道：“虽然如此说，我只一心要留下你，不怕老太太不和你母亲说，多多给你母亲些银子，他也不好意思接你了。”<br />
袭人道：“我妈自然不敢强：且慢说和他好说，又多给银子；就便不好好和他说，一个钱也不给，安心要强留下我，他也不敢不依。但只是咱们家从没干过这倚势仗贵霸道的事。这比不得别的东西，因为喜欢，加十倍利，弄了来给你，那卖的人不吃亏，就可以行得的；如今无故平空留下我，于你又无益，反教我们骨肉分离，这件事，老太太、太太肯行吗？”<br />
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Aroma said, “Why won’t she let me leave? I should be so important, or I have somehow moved Grandma Merchant and Lady King so that they won’t let go of me and give some more money to my family so as to make me stay. Actually, I am a most ordinary person. A whole lot people are better than me. Bought in by Grandma Merchant since I was a child, I had served Lady History for the first several years, and these several years I have been serving you. Now my family are about to pay the ransom. When I ask for a leave, I’m thinking that you could have mercy on me and allow my leaving without the ransom money. I certainly don’t buy it that you stop me from leaving just because I have served you well. Even if it’s true, I’m just doing what I’m supposed to do, which is really no big deal. Nothing will go wrong without me as there are still good servants who will take my place when I leave.” Hearing that, Precious Jade Merchant became all the more anxious because she had a reason to leave and no reason to stay. Therefore, he continued, “Since all I want is to keep you here, I might as well tell you that I wish Grandma Merchant to talk to your mother and give her a lot more money so that she has no reason to come and take you home. Aroma replied, “My mother certainly dares not to ask for my leaving, not to mention that you talk to her in a mild and polite way and give her extra money. Even if you force her without a penny, she dares not to defy. It’s only that your family has never done such a thing as to throw your weight about. It’s feasible when you buy something with ten times of its original price out of love, for the seller suffers no loss. Unlike anything else, it does you no good to keep me for no reason, which instead separates me from my mother. Do you think Grandma Merchant and Lady King will let that happen?”--[[User:Gao Mi|Gao Mi]] ([[User talk:Gao Mi|talk]]) 16:09, 28 September 2021 (UTC)<br />
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Aroma said, “Why won’t she let me leave? I should be so important, or I have somehow moved Grandma Merchant and Lady King so that they won’t let go of me and give some more money to my family so as to make me stay. Actually, I can't be more ordinary, and too many maids out there are better than me. Bought in by Grandma Merchant when I was a child, I had served Lady History for the first several years, and these years I have been serving you. Now my families are about to pay the ransom. When I ask for leaving, I’m thinking that you could have mercy on me and allow my leaving without the ransom money. I certainly don’t buy it that you stop me from leaving just because I have taken good care of you. Even if it’s true, I’m just doing what I’m supposed to do, which is really no big deal. Nothing will go wrong without me as there are still good maids who will take my place when I leave.” Hearing that, Precious Jade Merchant became more anxious because she had every reason to leave but no reason to stay. Therefore, he continued, “Since all I want is to keep you here, I might as well tell you that I may beg Grandma to talk to your mother and give her extra money so that she has no reason to come and take you home.” Aroma replied, “My mother certainly dares not to ask, not to mention that you talk to her in a mild and polite way and give her extra money. Even if you force her without a penny, she dares not to defy. It’s only that your family has never done such a thing as to throw your weight about. It’s feasible when you buy something with ten times of its original price out of love, for the seller suffers no loss. Unlike anything else, it does you no good to keep me for no reason, which instead separates me from my family. Do you think Grandma Merchant and Lady King will let that happen?”--[[User:He Qin|He Qin]] ([[User talk:He Qin|talk]]) 10:02, 29 September 2021 (UTC)<br />
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==翻译学 202120081489 何芩 女==<br />
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宝玉听了，思忖半晌，乃说道：“依你说来说去，是去定了？”袭人道：“去定了。”宝玉听了，自思道：“谁知这样一个人，这样薄情无义呢！”乃叹道：“早知道都是要去的，我就不该弄了来，临了剩我一个孤鬼儿。”说着，便赌气上床睡了。原来袭人在家，听见他母、兄要赎他回去，他就说：“至死也不回去。”又说：“当日原是你们没饭吃，就剩了我还值几两银子，要不叫你们卖，没有个看着老子娘饿死的理；如今幸而卖到这个地方儿，吃穿和主子一样，又不朝打暮骂。况如今爹虽没了，你们却又整理的家成业就，复了元气；若果然还艰难，把我赎出来，再多掏摸几个钱，也还罢了。其实又不难了，这会子又赎我做什么？权当我死了，再不必起赎我的念头了。”因此哭了一阵。他母、兄见他这般坚执，自然必不出来的了；况且原是卖倒的死契。明仗着贾宅是慈善宽厚人家儿，不过求求，只怕连身价银一并赏了，还是有的事呢。<br />
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After hearing this, Precious Jade Merchant thought for a while and said, “According to you, you have to go?” “I have to.” Aroma confirmed. Hearing this, Precious Jade thought to himself, “Who knows how heartless you are.” “I should not have had you here, since you are doomed to go, leaving me alone as a ghost.” Precious Jade sighed and went to bed with complaints. However, during her stay at home, Aroma heard her mother and elder brother wanted to redeem her back. “I won’t go back until I die.” Aroma railed, “At that time, you were starved to death. Nothing but I was worth some money. If I had refused to be sold, you would have been dead. I was fortunately to be sold to the Merchant’s and treated as a lady, free from abuses. Though my father has passed away, you have recollected yourselves and established new lives. If you were still in trouble, it would make sense that you wanted to reap some profit by redeeming me out. Since you are not, why are you bothering to do this? Don’t ever think about it! Just pretend I’m dead.” Seeing Aroma’s tears and insistence, her mother and elder brother knew it was impossible for her to leave the Merchant’s, besides, it was a sold-out death indenture. In fact, it was not impossible for the Merchant’s, who was a charitable and generous family, to set Aroma free with compensation money, if Aroma pleaded.--[[User:He Qin|He Qin]] ([[User talk:He Qin|talk]]) 13:03, 28 September 2021 (UTC)<br />
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After hearing this, Precious Jade Merchant thought for a while and said, “According to you, you have to go?” “Yes, I have to.” Aroma confirmed. Hearing this, Precious Jade thought, “Who knows how heartless she is.” “I should not have had you here, since you are doomed to go, leaving me alone as a ghost.” Precious Jade sighed and went to bed with complaints. However, during her stay at home, Aroma heard her mother and elder brother want to redeem her back. “I won’t return home until I die.” Aroma railed, “At that time, you were starved to death. Nothing but I was worth some money. If I had refused to be sold, you would have been dead. I was fortunately to be sold to the Merchant’s and treated as a lady, free from abuses. Though my father has passed away, you have reorganized the family and established new lives. If you were still in trouble, it would make sense that you wanted to reap some profit by redeeming me out. Since you are not, why are you bothering to do this? Don’t think about it anymore! Just pretend I’m dead.” Seeing Aroma’s tears and insistence, her mother and elder brother knew it was impossible for Aroma to leave the Merchant’s, besides, it was a sold-out lifetime indenture. In fact, it was possible for the Merchant’s, who was a charitable and generous family, to set Aroma free with compensation money, if Aroma pleaded.--[[User:Li Shuang|Li Shuang]] ([[User talk:Li Shuang|talk]]) 09:41, 29 September 2021 (UTC)<br />
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==翻译学 202120081499 李双 女==<br />
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二则，贾府中从不曾作践下人，只有恩多威少的；且凡老少房中所有亲侍的女孩子们，更比待家下众人不同，平常寒薄人家的女孩儿也不能那么尊重。因此他母子两个就死心不赎了。<br />
次后忽然宝玉去了，他两个又是那个光景儿，母子二人心中更明白了，越发一块石头落了地，而且是意外之想，彼此放心，再无别意了。<br />
且说袭人自幼儿见宝玉性格异常，其淘气憨顽出于众小儿之外，更有几件千奇百怪、口不能言的毛病儿。近来仗着祖母溺爱，父母亦不能十分严紧拘管，更觉放纵弛荡，任情恣性，最不喜务正。每欲劝时，谅不能听。今日可巧有赎身之论，故先用骗词以探其情，以压其气，然后好下箴规。今见宝玉默默睡去，知其情有不忍，气已馁堕。自己原不想栗子吃，只因怕为酥酪生事，又像那茜雪之茶，是以假要栗子为由，混过宝玉不提就完了。<br />
What’s more, the Merchant’s was very good to the servants, and never treated them cruelly. All the girls who served the old or the young received more respect than the others, even more than the girls who lived in poor families. Consequently Aroma’s mother and brother made their minds not to redeem her. Later the sudden arrival of Precious Jade and his meeting with Aroma still further reassured them and put down their stone in heart. The unexpected situation dispelled their other thoughts. When Precious Jade Merchant was young, Aroma found that he was different from ordinary children and that he was naughtier and had some foibles which can’t be told to others. Recently, because Grandma Merchant spoiled Precious Jade too much, his parents couldn’t discipline him too. He was therefore more indulgent and willful, and hated doing the right thing. Whenever others persuaded him, he was stubborn. Today it happened to talk about redemption, so Aroma deliberately lied to Precious Jade to test his attitude and to restrain his anger, and then made the rules. She saw Precious Jade go to sleep silently. She knew the truth, therefore she couldn’t help feeling some guilt, and her anger also subsided. Aroma hadn’t wanted to eat the chestnuts, but was just afraid of the problems that would result from the milk, just like Snow Alizarin’s tea. For this reason, she tricked Precious Jade into not mentioning milk by pretending she wanted to eat chestnuts.--[[User:Li Shuang|Li Shuang]] ([[User talk:Li Shuang|talk]]) 04:50, 29 September 2021 (UTC)<br />
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What’s more, the Merchant’s was very nice to the servants, and never treated them cruelly. All the girls who served as maids of the family members, old or young, were generally treated more kindly than the servants in other position, and were even better off than daughters of ordinary families. Consequently Aroma’s mother and brother made their minds not to buy her freedom. Then the sudden arrival of Precious Jade and the acquaintance between Aroma and her master showed the true situation of Aroma, which made them reassure. The unexpected situation dispelled their other thoughts. These years had shown Aroma that Baoyu with some indescribably odd habits, was no ordinary youth and was more willful than others. Recently, Precious Jade was so indulged by his grandma that his parents couldn’t discipline him strictly. Therefore, he became more indulgent, headstrong and impatient at conventions. Whenever she wanted to exhort him to clean up his act, she was convinced he would not listen to her. Luckily, using the incident as a convenient excuse, Aroma enabled to sound him out, pacify his mood, and give him a good lecture. She saw Precious Jade go to sleep silently. She knew his sadness about her departure, so she didn’t have the heart to give a lecture to him. As for chestnuts, Aroma hadn’t wanted to eat but she had pretended to be eager for them, for fear that the junket would creat a disturbance, just like Snow Alizarin’s tea. She made Precious Jade forget the junket by pretending to hanker after chestnuts.--[[User:Liu Shengnan|Liu Shengnan]] ([[User talk:Liu Shengnan|talk]]) 08:53, 1 October 2021 (UTC)<br />
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==翻译学 202120081506 刘胜楠 女==<br />
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于是命小丫头子们将栗子拿去吃了，自己来推宝玉，只见宝玉泪痕满面。<br />
袭人便笑道：“这有什么伤心的？你果然留我，我自然不肯出去。”宝玉见这话头儿活动了，便道：“你说说，我还要怎么留你？我自己也难说了。”<br />
袭人笑道：“咱们两个的好，是不用说了。但你要安心留我，不在这上头。我另说出三件事来，你果然依了，那就是真心留我了；刀搁在脖子上，我也不出去了。”<br />
宝玉忙笑道：“你说那几件？我都依你。好姐姐，好亲姐姐，别说两三件，就是两三百件，我也依的。只求你们看守着我，等我有一日化成了飞灰，——飞灰还不好，灰还有形有迹，还有知识的。——等我化成一股轻烟，风一吹就散了的时候儿，你们也管不得我，我也顾不得你们了，凭你们爱那里去，那里去就完了。”<br />
<br />
Aroma thereupon gave the chestnuts to the other maids and nudged Precious Jade gently. She found his face is tear-strained .“Why you so sad about that?”she cajoled. “If you really want me to remind here, I won’t leave of course.” Reading between the lines, Precious Jade quickly replied , “Just tell me what I can do to keep you. I don't know how to persuade you.” She laughed, “We needn’t mention how well we get along with each other. If you really want to keep me, that’s a whole other story. If you promise me two or three things I come up with, I’ll assume that you are truly want me to stay. Then even a knife at my throat could not make me get out of here.”Precious Jade merrily said, “Well, what are these three conditions? I will agree them all, dear sister, my nice sister. I’d agree to two or three hundred conditions, let alone two or three. I merely implore you all to stay and take care of me until the day that I turn into flying ashes. No, I don’t want to turn into ashes because ashes have a trace of form and awareness . I’d like to let you all stay until I’ve turned into a wisp of smoke and been blown away by the wind. Then you will not be able to watch over me, and l will not be able to care about you. I will let you go wherever you please as well.”--[[User:Liu Shengnan|Liu Shengnan]] ([[User talk:Liu Shengnan|talk]]) 04:48, 29 September 2021 (UTC) <br />
<br />
Xi Ren thereupon gave the chestnuts to the other maids and nudged Baoyu gently. She found his face tear-strained .<br />
“Why are you so sad about that?”she cajoled . “If you really want me to remind in Jia’s mansion, I won’t leave naturally.” Reading between the lines, Baoyu quickly replied , “Just tell me what else I can do to keep you. I don't know.” <br />
She laughed, “We needn’t mention how well we get along with each other. If you really want to keep me, that’s a whole other story. If you promise me two or three things I come up with, I‘ll assume that you are truly want me to stay. Then even a knife at my throat could not make me get out of here.” Baoyu merrily said, “Well, what are these three conditions? I will agree them all, good sister, my dear sister. I’d agree to two or three hundred conditions, let alone two or three. I merely implore you all to stay and take care of me until the day that I turn into flying ashes. No, I don’t want to turn into ashes because ashes have a trace of form and awareness . I’d like to let you all stay until I’ve turned into a wisp of smoke and been blown away by the wind. Then you will not be able to take care of me, and l will not be able to care about you. I will let you go wherever you want as well.”--[[User:Jin Xiaotong|Jin Xiaotong]] ([[User talk:Jin Xiaotong|talk]]) 13:38, 29 September 2021 (UTC)<br />
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==法语语言文学 202120021494 金晓童 女==<br />
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急的袭人忙捂他的嘴道：“好爷，我正为劝你这些个，更说的狠了。”宝玉忙说道：“再不说这话了。”袭人道：“这是头一件要改的。”宝玉道：“改了，再说你就拧嘴。还有什么？”<br />
袭人道：“第二件，你真爱念书也罢，假爱也罢，只在老爷跟前，或在别人跟前，你别只管嘴里混批，只作出个爱念书的样儿来，也叫老爷少生点儿气，在人跟前也好说嘴。老爷心里想着：我家代代念书，只从有了你，不承望不但不爱念书(已经他心里又气又恼了)，而且背前面后混批评：凡读书上进的人，你就起个外号儿，叫人家‘禄蠹’；又说只除了什么‘明明德’外就没书了，都是前人自己混编纂出来的。这些话，你怎么怨得老爷不气，不时时刻刻的要打你呢？”<br />
宝玉笑道：“再不说了。那是我小时候儿不知天多高地多厚，信口胡说的，如今再不敢说了。还有什么呢？”<br />
Aroma covered his mouth in a hurry and said:"my dear lord, I'm trying to persuade you, but you said these even harder." Precious Jade Merchant quickly said:"I will not say these again.""This is the first thing you need to change."Aroma replied.Precious Jade Merchant said:"OK,if I say it again,you can twist my mouth.Anything else?＂<br />
Aroma said:” The second thing, whether you like studying or not, except for nonsense you just need to pretend that you like reading when talking to milord or other people, which can make milord less angry so that he have something to talk. Milord will say to himself:“Everyone in my family have studied from generation to generation except you. And I can’t imagine that not only do you hate reading(already he was angry and bitter), but also making criticism everywhere----if someone are motivated and love reading, you will call him ‘greedy man’; or you will say every book is fabricated but ‘li Ji’Because of these above, why did you complain that milord are usually annoyed and beat your ass?” “I will never be like that before. When I was a kid, I didn’t understand bragging and talk whatever I want. My dare not say now, and what else?” Precious Jade Merchant said with a smile.--[[User:Jin Xiaotong|Jin Xiaotong]] ([[User talk:Jin Xiaotong|talk]]) 14:49, 28 September 2021 (UTC)<br />
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Xiren covered his mouth in a hurry and said: "my dear lord, I'm trying to persuade you, but you're exaggerating these."Baoyu quickly said: "I will not say these again. " "This is the first thing you need to change. "Xiren replied.Baoyu said: "OK, if I say it again,you can twist my mouth. Anything else? "Xiren said: "The second thing, whether you like studying or not, you can't just talk nonsense, you just need to pretend that you like reading when talking to milord or other people, which can make milord less angry so that he have something to talk with others. Milord will say to himself: "Everyone in my family have studied from generation to generation except you .And I can't imagine that not only do you hate reading (already he was angry and bitter), but also making criticism everywhere casually----if someone are motivated and love reading, you will call him 'greedy man'; you also said there were no other books in the world except 'li Ji', that all other books were made up groundlessly by predecessors. Because of these above, why did you complain that milord are usually annoyed and beat your ass? " <br />
"I will never be like that before. When I was a kid, I didn't understand bragging and talk whatever I want. My dare not say now, and what else? "BaoYu said with a smile.--[[User:Li Yi|Li Yi]] ([[User talk:Li Yi|talk]]) 02:19, 2 October 2021 (UTC)<br />
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==法语语言文学 202120081504 李怡 女==<br />
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袭人道：“再不许谤僧毁道的了。还有更要紧的一件事：再不许弄花儿，弄粉儿，偷着吃人嘴上擦的胭脂和那个爱红的毛病儿了。”宝玉道：“都改，都改。再有什么，快说罢。”<br />
袭人道：“也没有了，只是百事检点些，不任意任性的就是了。你要果然都依了，就拿八人轿也抬不出我去了。”宝玉笑道：“你在这里长远了，不怕没八人轿你坐。”袭人冷笑道：“这我可不稀罕的！有那个福气，没有那个道理，纵坐了也没趣儿。”<br />
二人正说着，只见秋纹走进来说：“三更天了，该睡了。方才老太太打发嬷嬷来问，我答应睡了。”宝玉命取表来看时，果然针已指到子初二刻了。方从新盥漱，宽衣安歇，不在话下。<br />
至次日清晨，袭人起来，便觉身体发重，头疼目胀，四肢火热。先时还扎挣的住，次后挨不住，只要睡，因而和衣躺在炕上。<br />
Aroma said :Stop denigrating monks and dhamma.There is a more important things : no more indulging in flowers and rouge and powder,no more touching lipstick on other people's lips secretly ,and give up the habit of applying makeup. Precious Jade Merchant said :i will change it all .And anything else ? Aroma said : Nothing,you must be careful of everything and stop being capricious. If you change them all, I won't leave even if you lift me in a huge Jiao. Precious Jade Merchant laughed and said : If you stay here long enough, you'll be able to ride in a big Jiao. Aroma sneered and said :I don't desire it, and even if I were lucky enough to ride in the big Jiao, it would be against the rules and boring.<br />
While the two were talking, Autumn Vein came in and said : It's early in the morning and it's time to go to bed. Grandma Merchant sent her maid to ask after you, and I said you were asleep. Precious Jade Merchant took a watch and checked the time, and it was already midnight, he cleaned up again and then undressed and went to bed and stopped talking.<br />
When Aroma got up early the next morning, she felt heavy and dizzy,and she felt her body was burning. At first she managed to stand up, then she couldn't hold on and felt sleepy, and finally she lay down in bed with her clothes on.--[[User:Li Yi|Li Yi]] ([[User talk:Li Yi|talk]]) 08:35, 29 September 2021 (UTC)<br />
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Aroma said, “Stop denigrating monks and dhamma. There is a more important things : no more indulging in flowers and rouge and powder, no more eating lipstick on other people’s lips secretly, and give up the habit of applying makeup.” Precious Jade Merchant said, “I I will rectify all these addictions. And anything else ? ” Aroma said: “No, however, you must be careful of everything and stop being capricious. If you were a kind man, I won’t leave even if you had an eight-man palanquin. Precious Jade Merchant laughed and said: “If you stay here long enough, you’ll be able to in an eight-person palanquin. Aroma sneered and said: “I don’t desire it, and even if I were lucky enough to be in the palanquin, it would be against the rules. That’s boring.” While the two were talking, Autumn Vein came in and said, “It's almost dawn and it’s time to go to bed. Grandma Merchant sent her maid to ask after you, and I said you were asleep.” Precious Jade Merchant asked his servant to bring the watch and checked the time, and it was already midnight. He cleaned up again, undressed and went to bed promptly. When Aroma got up the next morning, she felt heavy and dizzy with a body burning. At first, she was able to stand up, but couldn’t hold on after a short while, and felt extremely sleepy. Thus, she lay down in bed with her clothes on.--[[User:Peng Ruixue|Peng Ruixue]] ([[User talk:Peng Ruixue|talk]]) 12:59, 29 September 2021 (UTC)彭瑞雪<br />
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==法语语言文学 202120081517 彭瑞雪 女==<br />
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宝玉忙回了贾母，传医诊视，说道：“不过偶感风寒，吃一两剂药，疏散疏散就好了。”开方去后，令人取药来煎好。刚服下去，命他盖上被窝焐汗。宝玉自去黛玉房中来看视。<br />
彼时黛玉自在床上歇午，丫鬟们皆出去自便，满屋内静悄悄的。宝玉揭起绣线软帘，进入里间，只见黛玉睡在那里，忙上来推他道：“好妹妹，才吃了饭，又睡觉。”将黛玉唤醒。黛玉见是宝玉，因说道：“你且出去逛逛。我前儿闹了一夜，今儿还没歇过来，浑身酸疼。”宝玉道：“酸疼事小，睡出来的病大。我替你解闷儿，混过困去就好了。”黛玉只合着眼，说道：“我不困，只略歇歇儿。你且别处去闹会子再来。”宝玉推他道：“我往那里去呢？见了别人就怪腻的。”<br />
After hastily greeting Mother Jia, Baoyu invited a doctor to see Xiren at home. The doctor said,“This young lady has only caught a cold by chance, take a few pills, the illness will slowly fade away.” According to the prescription prescribed by the doctor, the servant was ordered to pick out the medicine in the pharmacy and to cook it. As soon as Xiren finished the soup, the doctor asked her to cover herself with a quilt in order to sweat and get rid of the cold in her body. Then, Baoyu went alone to Daiyu’s room to visit her. At that time, Daiyu was lying alone in bed, taking a nap, and the maids had all gone off to do their own things, and silence filled the whole room. Baoyu gently lifted the curtain and entered the room, only to see Daiyu sleeping there, and hurriedly went up to her, nudged her and said to her: “Lovely sister, you just went to bed after eating, how can you do that?” Baoyu tried to wake Daiyu up. When she was awakened, she saw that the person who had woken her up was Bao Yu, she saisd, “Could you go out for a walk for the time being. I was up all night the night before, and to this day I still have not recovered my energy. I feel sick.” Baoyu answered, “Feeling sick is not a big problem. But if you keep sleeping, you will become really very ill. I will relieve your boredom so that the sleepiness is dispelled, and you will be refreshed.” Daiyu just closed her eyes and said,“I’m not sleepy, I just want to rest for a while. Go and play somewhere else for a while, after that, you can come back to me.” Baoyu nudged her and said, “Where can I go? I’m tired of others.”<br />
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After hastily greeting Grandma Merchant, Precious Jade Merchant invited a doctor to see Aroma at home. The doctor said,“This young lady has only caught a cold by chance, take a few pills and she will be recovered.” According to the prescription prescribed by the doctor, the servant was ordered to pick out the medicine in the pharmacy and to decoct it. As soon as Aroma finished the medicine, the doctor asked her to cover herself with a quilt in order to sweat and get rid of the cold in her body. Then, Precious Jade Merchant went alone to Mascara Jade Forest’s room to visit her. At that time, Mascara Jade Forest was lying alone in bed, taking a nap, and the maids had all gone off to do their own things, and silence filled the whole room. Precious Jade Merchant gently lifted the curtain and entered the room, only to see Daiyu sleeping there, and hurriedly went up to her, nudged her and said to her: “Lovely sister, you just went to bed after eating, how can you do that?” Precious Jade Merchant tried to wake Mascara Jade Forest up. When she was awakened, she saw that the person who had woken her up was Precious Jade Merchant, she saisd, “Could you go out for a walk for the time being. I was up all night the night before, and to this day I still have not recovered my energy. I feel sick.” Precious Jade Merchant answered, “Feeling sick is not a big problem. But if you keep sleeping, you will become really very sick. I will relieve your boredom so that the sleepiness will be dispelled, and you will be refreshed.” Mascara Jade Forest just closed her eyes and said,“I’m not sleepy, I just want to rest for a while. Go and play somewhere else for a while, after that, you can come back to me.” Precious Jade Merchant nudged her and said, “Where can I go? I’m tired of others.”--Yang Kun(talk) 21;22, 29 September 2021 (UTC)<br />
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==法语语言文学 202120081542 杨堃 女==<br />
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黛玉听了，嗤的一笑道：“你既要在这里，那边去老老实实的坐着，咱们说话儿。”宝玉道：“我也歪着。”黛玉道：“你就歪着。”宝玉道：“没有枕头，咱们在一个枕头上罢。”黛玉道：“放屁！外头不是枕头？拿一个来枕着。”<br />
宝玉出至外间，看了一看，回来笑道：“那个我不要，也不知是那个腌臜老婆子的。”黛玉听了，睁开眼，起身笑道：“真真你就是我命中的魔星！请枕这一个。”说着，将自己枕的推给宝玉，又起身将自己的再拿了一个来枕上，二人对着脸儿躺下。<br />
黛玉一回眼，看见宝玉左边腮上有钮扣大小的一块血迹，便欠身凑近前来，以手抚之细看道：“这又是谁的指甲划破了？”宝玉倒身，一面躲，一面笑道：“不是划的，只怕是才刚替他们淘澄胭脂膏子，溅上了一点儿。”说着，便找绢子要擦。<br />
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When Mascara Jade heard this, she giggled and said, "Since you want to sit here, sit honestly there and let's talk." Precious Jade Merchant said, "I want to lie down here,too." "You can do it!" said Mascara Jade. " Precious Jade said, "There isn't other pillow. Let's be on the same pillow." Mascara Jade said, "Bullshit! It's not a pillow outside? Take one !" <br />
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Precious Jade went out to the outer room, looked at it, came back and smiled, "I don't want that, and I don't know if it belongs to a certain pickled old woman." Hearing this, Mascara Jade opened her eyes, got up and smiled, " You are the magic star I hit! Please rest with this one. " Then, she pushed her pillow to Precious Jade, got up and fetched another one .After that,the two lay down face-to-face. <br />
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As soon as Mascara Jade looked back and saw a piece of blood ,the size of a button, on Precious Jade's left cheek. She leaned forward and looked closely with her hand, saying, "whose nails cut your face?" Precious Jade turned back, hiding, and said with a smile, "It's not a wound. I think it's just spattered with a little blusher when I washed it for them just now." With that, he looked for the handkerchief to wipe.--Yang Kun (talk) 23:50, 28 September 2021 (UTC)<br />
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When Mascara Jade heard this, she giggled and said, "Since you want to stay here, sit there quietly and let's have a talk." Precious Jade said, "I want to lie down, too." "Do as you like" said Mascara Jade. " Precious Jade said, "There isn't other pillow. Let's share the pillow." Mascara Jade said, "Don’t talk rot! It's not a pillow outside? Take one !" Precious Jade went out to the outer room, looked at it, came back and smiled, "I don't want that, and I don't know if it belongs to a certain pickled old woman." Hearing this, Mascara Jade opened her eyes, got up and smiled, " You are the magic star I hit! Please rest with this one. " Then, she pushed her pillow to Precious Jade, got up and fetched another one. After that, the two lay down face-to-face. As soon as Mascara Jade looked back and saw a piece of blood ,the size of a button, on Precious Jade’s left cheek. She leaned forward and looked closely with her hand, saying, "whose nails cut your face?" Precious Jade turned back, hiding, and said with a smile, "It's not a wound. I think it's just spattered with a little blusher when I washed it for them just now." With that, he looked for the handkerchief to wipe.--[[User:Zhou Junhui|Zhou Junhui]] ([[User talk:Zhou Junhui|talk]]) 05:12, 29 December 2021 (UTC)<br />
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==法语语言文学 202120081556 周俊辉 女==<br />
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黛玉便用自己的绢子替他擦了，咂着嘴儿说道：“你又干这些事了；干也罢了，必定还要带出幌子来。就是舅舅看不见，别人看见了，又当作奇怪事，新鲜话儿，去学舌讨好儿，吹到舅舅耳朵里，大家又该不得心净了。”<br />
宝玉总没听见这些话，只闻见一股幽香，却是从黛玉袖中发出，闻之令人醉魂酥骨。<br />
宝玉一把便将黛玉的衣袖拉住，要瞧瞧笼着何物。黛玉笑道：“这时候谁带什么香呢？”宝玉笑道：“那么着，这香是那里来的？”黛玉道：“连我也不知道，想必是柜子里头的香气熏染的，也未可知。”宝玉摇头道：“未必。这香的气味奇怪，不是那些香饼子、香球子、香袋儿的香。”黛玉冷笑道：“难道我也有什么罗汉、真人给我些奇香不成？就是得了奇香，也没有亲哥哥亲兄弟弄了花儿、朵儿、霜儿、雪儿替我炮制。我有的是那些俗香罢了。”<br />
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Mascara Jade wiped his cheek with her handkerchief. Smacking her mouth, she said:“ You did it again. You always make excuses to do the trifles. You’re lucky that my uncle doesn’t know. But if other people saw it, they would take it as an anecdote, an opportunity to play up to my uncle. As soon as my uncle hears about it, there will be no peace in the house.”<br />
The words went in one his ear and out the other. At that moment, he smelled a faint fragrance coming from Mascara Jade’s sleeve, which was intoxicating. <br />
Precious Jade grabbed Mascara Jade’s sleeve to see what it was. Mascara Jade laughed and said, “Who brings balsam at this time?” He laughed: “Then where does the fragrance come from?” “Even I don’t know. Maybe it came out of the closet.” Precious Jade shook his head:“Not necessarily, the smell is very strange, not like the fragrance of incense cake, of incense ball, of incense bag.” Mascara Jade sneered: “ Will some god give me some magic spice? Even if I really get it, there is no brothers to help me find flowers, buds, frost and snow, then help me make incense. All I have is mediocre spices.”--[[User:Zhou Junhui|Zhou Junhui]] ([[User talk:Zhou Junhui|talk]]) 01:04, 2 October 2021 (UTC)<br />
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Daiyu wiped his mouth with her mocket,she pouted and said:“You did it again, but you also make trouble to us. Although uncle couldn't see it, other people could and would take it as a anecdote. Mabye he heard of it beacause of someone's flattery, there won't be quiet.”<br />
The words went in one of his ears and out the other. At that moment, he smelled a faint fragrance coming from Daiyu’s sleeve,which was intoxicating. <br />
Baoyu grabbed Daiyu’s sleeve to see what stuff hide im. Daiyu smiled and said, “Who brings balsam at this time?” He laughed: “Then where does the fragrance come from?” “Even I don’t know. Maybe it's fumigated by the fragrance of the closet.” Baoyu shook his head:“Not necessarily, the smell is very strange, not like the fragrance of incense cake, of incense ball, of incense bag.” Daiyu sneered: “ Will some god give me some magic spice? Even if I really get it, there is no brothers to help me find flowers, buds, frost and snow, then help me make incense. All I have is mediocre spices.”--[[User:Zhou Qing|Zhou Qing]] ([[User talk:Zhou Qing|talk]]) 13:13, 29 September 2021 (UTC)<br />
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==法语语言文学 202120081558 周清 女==<br />
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宝玉笑道：“凡我说一句，你就拉上这些。不给你个利害也不知道，从今儿可不饶你了。”说着，翻身起来，将两只手呵了两口，便伸向黛玉膈肢窝内两胁下乱挠。黛玉素性触痒不禁，见宝玉两手伸来乱挠，便笑的喘不过气来。口里说：“宝玉，你再闹，我就恼了。”宝玉方住了手，笑问道：“你还说这些不说了？”黛玉笑道：“再不敢了。”一面理鬓，笑道：“我有奇香，你有‘暖香’没有？”<br />
宝玉见问，一时解不来，因问：“什么‘暖香’？”黛玉点头笑叹道：“蠢才，蠢才！你有‘玉’，人家就有‘金’来配你；人家有‘冷香’，你就没有‘暖香’去配他？”宝玉方听出来，因笑道：“方才告饶，如今更说狠了。”说着又要伸手。黛玉忙笑道：“好哥哥，我可不敢了。”宝玉笑道：“饶你不难，只把袖子我闻一闻。”说着便拉了袖子，笼在面上，闻个不住。黛玉夺了手道：“这可该去了。”宝玉笑道：“要去不能。咱们斯斯文文的躺着说话儿。”说着，复又躺下。黛玉也躺下，用绢子盖上脸。<br />
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Baoyu smiled and said:"if i say a word,you will do something like this. You don't know how to constrain, from now on, i won't forgive you anymore if you won't change." As he said, he stood up and exhaled two warm breaths to his both hands, and then put them into the two flanks of Daiyu's diaphragm and scratched randomly. Daiyu still couldn't help itching, so she couldn't breathe with a smile. She said:"Baoyu, if you make a disturbance,i will bw anoyed." Baoyu stopped, he smiled and said:"Will you still say this?" "i'll never do it again. But i have a special perfume, do you have the 'warm perfume'." Daiyu smiled and said.<br />
Baoyu couldn't solve it for a while, so he asked:"What's the "warm perfume?" Daiyu nodded and signed with a smile:"stupd, stupid!You have "jade", people will have "gold"to match you;i have "cold perfume", but you don't have "warm perfume?" Baoyu undstood:"You just surrendered, and now it's even more excesive." He was about to stretch out his hand again. Daiyu hurriedly smiled: "Good brother, I don't dare anymore."Baoyu smiled: "It's not difficult to spare you. I just smell your sleeves." As he said, he pulled up his sleeves, caged on his face, and couldn't help smelling it.Daiyu grabbed his hand and said, "This is the time to go." Baoyu smiled and said, "I can't go. Let's lie down and talk quietly." As he said, he lay down again. Daiyu also lay down and covered her face with silk.--[[User:Zhou Qing|Zhou Qing]] ([[User talk:Zhou Qing|talk]]) 12:54, 11 October 2021 (UTC)<br />
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Baoyu smiled and said:"if i say a word,you will do something like this. You don't know what happened if I'm not serious. I won't let you off from this day forward. " As he said, he stood up and exhaled two warm breaths to his both hands, and then put them into the two flanks of Daiyu's diaphragm and scratched randomly. Daiyu still couldn't help itching, so she couldn't breathe with a smile. She said:"Baoyu, if you make a disturbance,i will bw anoyed." Baoyu stopped, he smiled and said:"Will you still say this?" "i'll never do it again. But i have a special perfume, do you have the 'warm perfume'." Daiyu smiled and said.<br />
Baoyu couldn't solve it for a while, so he asked:"What's the "warm perfume?" Daiyu nodded and signed with a smile:"stupd, stupid!You have "jade", people will have "gold"to match you;i have "cold perfume", but you don't have "warm perfume?" Baoyu undstood:"You just surrendered, and now it's even more excesive." He was about to stretch out his hand again. Daiyu hurriedly smiled: "Good brother, I don't dare anymore."Baoyu smiled: "It's not difficult to spare you. I just smell your sleeves." As he said, he pulled up his sleeves, caged on his face, and couldn't help smelling it.Daiyu grabbed his hand and said, "This is the time to go." Baoyu smiled and said, "I don't go. Let's lie down and talk quietly." As he said, he lay down again. Daiyu also lay down and covered her face with silk.--[[User:Gong Boya|Gong Boya]] ([[User talk:Gong Boya|talk]]) 13:56, 29 September 2021 (UTC)<br />
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==俄语语言文学 202120081488 宫博雅 女==<br />
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宝玉有一搭没一搭的说些鬼话，黛玉总不理。宝玉问他几岁上京，路上见何景致，扬州有何古迹，土俗民风如何。黛玉不答。宝玉只怕他睡出病来，便哄他道：“嗳哟！你们扬州衙门里有一件大故事，你可知道么？”黛玉见他说的郑重，又且正言厉色，只当是真事，因问：“什么事？”宝玉见问，便忍着笑顺口诌道：“扬州有一座黛山，山上有个林子洞。”黛玉笑道：“这就扯谎，自来也没听见这山。”宝玉道：“天下山水多着呢，你那里都知道？等我说完了，你再批评。”黛玉道：“你说。”<br />
宝玉又诌道：“林子洞里原来有一群耗子精。那一年腊月初七，老耗子升座议事，说：‘明儿是腊八儿了，世上的人都熬腊八粥。如今我们洞里果品短少，须得趁此打劫些个来才好。’乃拔令箭一枝，遣了个能干小耗子去打听。小耗子回报：‘各处都打听了，惟有山下庙里果、米最多。’老耗子便问：‘米有几样？果有几品？’小耗子道：‘米、豆成仓。果品却只有五样：一是红枣，二是栗子，三是落花生，四是菱角，五是香芋。’<br />
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Precious Jade Merchant chattered by fits and starts, Mascara Jade Forest kept silent. Precious Jade Merchant asked, “How old were you when you went to the captical? What did you see along the road? Are there any historical sites in Yangzhou? How about the folk customs?” She didn’t answer. Precious Jade Merchant was worried she will get ill for sleeping too long , and coaxed her, “ Ah! There is an important event in yamen of Yangzhou, you know that? ” Mascara Jade Forest saw what he said with a stern look,so she aslo took it seriously. Then she asked, “What envent? ” Upon seeing this, Precious Jade Merchant concealed a smile and kept cooking up, “in Yangzhou there is a mountain called Daishan and a forest cave upon there. ” Mascara Jade Forest smiled, “It is nonsense, I have not heard of it at all. ”Precious Jade Merchant replied, “There are so many landscapes in the world, how do you know all of them? Keeping your comments until i finish my words. ” She said, “Say it. ” Precious Jade Merchant talked recklessly again, “There used to be a bunch of ratspirits in the forest cave. On the seventh day of the twelfth lunar month, the elder ratspirit held a meeting. On the meeting, he said, ‘Tomorrow is the eighth day of the twelfth lunar month. People will all make laba rice porridge. Now that we are short of grain in the cave, we must seize the chance to rob some. ’ Cosequently he threw a command arrow and sent an able young ratspirit to inquire. Young ratspirit returned and reported, ‘I have made inquiries everywhere. The temple at the mountain foot stores most fruits and rice. ’ The old ratspirit asked, ‘ How many kinds of grain? How many kinds of tribute? ’ The young ratspirit said, ‘There's a whole warehouse of rice and bean. There are only five kinds of cereal: one is red dates, two is chestnuts, three is peanuts, four is water chestnut, five is taro. ’--[[User:Gong Boya|Gong Boya]] ([[User talk:Gong Boya|talk]]) 01:55, 13 October 2021 (UTC)<br />
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Chinese names can be translated directly in pinyin.(宝玉—Baoyu；黛玉—Daiyu);Bao Yu was talking nonsense, but Dai Yu always ignored it. She asked him how old he was when he went to Beijing, what sights he saw on the way, what are the monuments in Yangzhou, and what are the customs and folklore. Daiyu did not answer. She was afraid that he would fall asleep and get sick, so she coaxed him, "Oh! There is a big story in your Yangzhou government office, do you know it?" The first time I saw him, I thought he was telling the truth, so I asked, "What is it?" When Baoyu saw the question, he stifled a laugh and said smoothly, "There is a Dai Mountain in Yangzhou and there is a Lin Zi Cave on the mountain." Daiyu laughed and said, "That's a lie, I haven't heard of this mountain since." The world is full of mountains and rivers, where do you know them all? When I've finished, you can criticise again." Daiyu said, "You say it." Bao Yu said, "There was a group of rat spirits in the forest cave. That year, on the seventh day of the lunar month, the old rats rose to their seats and said: 'Tomorrow is the eighth day of the lunar month, and everyone in the world is making lunar porridge. Now we are short of fruits in the cave, so we must take advantage of this to rob some of them.' He drew an arrow and sent a competent little rat to inquire. The little rat reported, 'I have asked everywhere, but the temple at the bottom of the hill has the most fruit and rice.' The old rat then asked, 'How many kinds of rice are there? How many kinds of fruit are there?' The little rat said, 'Rice and beans are in the warehouse. But there are only five kinds of fruits: first, red dates, second, chestnuts, third, groundnuts, fourth, lozenges, and fifth, taro.--[[User:Mao You|Mao You]] ([[User talk:Mao You|talk]]) 06:46, 29 September 2021 (UTC)<br />
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==俄语语言文学 202120081515 毛优 女==<br />
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“老耗子听了大喜，即时拔了一枝令箭，问：‘谁去偷米？’一个耗子便接令去偷米。又拔令箭问：‘谁去偷豆？’又一个耗子接令去偷豆。然后一一的都各领令去了。只剩下香芋，因又拔令箭问：‘谁去偷香芋？’只见一个极小极弱的小耗子应道：‘我愿去偷香芋。’<br />
“老耗子和众耗子见他这样，恐他不谙练，又怯懦无力，不准他去。小耗子道：‘我虽年小身弱，却是法术无边，口齿伶俐，机谋深远。这一去，管比他们偷的还巧呢。’众耗子忙问：‘怎么比他们巧呢？’小耗子道：‘我不学他们直偷；我只摇身一变，也变成个香芋，滚在香芋堆里，叫人瞧不出来，却暗暗儿的搬运，渐渐的就搬运尽了。这不比直偷硬取的巧吗？’众耗子听了，都说：‘妙却妙，只是不知怎么变？你先变个我们瞧瞧。’小耗子听了，笑道：‘这个不难，等我变来。’说毕，摇身说：‘变！’竟变了一个最标致美貌的一位小姐。众耗子忙笑说：‘错了，错了。原说变果子，怎么变出个小姐来了呢？’小耗子现了形，笑道：‘我说你们没见世面，只认得这果子是香芋，却不知盐课林老爷的小姐才是真正的香玉呢。’”<br />
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The old mouse was overjoyed and immediately drew an arrow and asked:"Who is going to steal the rice?" A mouse took the order to steal the rice. Then he drew /pulled another arrow and asked again :"Who is going to steal the beans?" Another mouse took the order to steal the beans. All the mouses One by one received the orders, only the order of taro was left. So the old mouse drew this left arrow and asked:"Who is going to steal the taro?" At this time a very small and weak mouse responded:"I am willing to steal the taro." The old mouse and all the mice saw him like this, and they would not allow him to go because they were afraid that he would be unskilled and cowardly. But the little mouse said: ‘Although I am young and weak, I have boundless power, skillful tongue and foresight. I will steal more cleverly than others. "The mice hurriedly asked: "How can you do that?" The little mouse said: "I won't learn from them to steal directy. I just changed my body and turned into a taro, rolled in the taro pile. In that way people can't see me. Then I will secretly carry the taros, and gradually they were exhausted. Isn't this more clever than stealing directly? All the mice heard this, and they all said, "It's indeed a wonderful way, but how can you change yourself into a taro? Can you show us now? let's see!"The little mouse listened and said with a smile: "This is not difficult, wait for me!" After speaking, he said: "Change! "She has turned into the most beautiful girl in this world. The mice laughed and said:"it’s wrong, it’s wrong. Originally you said that you would turn into fruit, why did you turn into a girl?" The little mouse appeared, and smiled: "I said you hadn't seen the world, because you only recognized that it was a taro, but you didn't know that master Lin's daughter was the real fragrant jade. "(In Chinese pronounciation of the word "taro" is as the same as the word "fragrant jade")--[[User:Mao You|Mao You]] ([[User talk:Mao You|talk]]) 14:28, 28 September 2021 (UTC)Sept. 28<br />
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The old mouse was overjoyed and immediately drew an arrow and asked:"Who is going to steal the rice?" A mouse took the order to steal the rice. Then he pulled another arrow and asked again :"Who is going to steal the beans?" Another mouse took the order to steal the beans. All the mouses One by one received the orders, only the order of taro was left. So the old mouse pulled this left arrow and asked:"Who is going to steal the taro?" At this time a very small and weak mouse responded:"I am willing to steal the taro." The old mouse and all the mice saw him like this, and they would not allow him to go because they were afraid that he would be unskilled and cowardly. But the little mouse said: ‘Although I am young and weak, I have boundless supernatural power, skillful tongue and foresight. I will steal more cleverly than others. "The mice hurriedly asked: "How can you do that?" The little mouse said: "I won't learn from them to steal directy. I just changed my body and turned into a taro, rolled in the taro pile. In that way people can't see me. Then I will secretly carry the taros, and gradually they were exhausted. Isn't this more clever than stealing directly? All the mice heard this, and they all said, "It's indeed a wonderful way, but how can you change yourself into a taro? Can you show us now? let's see!"The little mouse listened and said with a smile: "This is not difficult, wait for me!" After speaking, he said: "Change! "She has turned into the most beautiful girl in this world. The mice laughed and said:"it’s wrong, it’s wrong. Originally you said that you would turn into fruit, why did you turn into a girl?" The little mouse appeared, and smiled: "I said you hadn't seen the world, because you only recognized that it was a taro, but you didn't know that master Lin's daughter was the real fragrant jade. "--[[User:Wu Jingyue|Wu Jingyue]] ([[User talk:Wu Jingyue|talk]]) 13:00, 11 October 2021 (UTC)<br />
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==俄语语言文学 202120081529 吴婧悦 女==<br />
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黛玉听了，翻身爬起来，按着宝玉笑道：“我把你这个烂了嘴的！我就知道你是编派我呢。”说着便拧。宝玉连连央告：“好妹妹，饶了我罢，再不敢了。我因为闻见你的香气，忽然想起这个故典来。”黛玉笑道：“饶骂了人，你还说是故典呢。” 一语未了，只见宝钗走来，笑问：“谁说故典呢？我也听听。”黛玉忙让坐，笑道：“你瞧瞧，还有谁？他饶骂了，还说是故典。”宝钗笑道：“哦！是宝兄弟哟，怪不得他，他肚子里的故典本来多么。就只是可惜一件：该用故典的时候儿，他就偏忘了。有今儿记得的，前儿夜里的芭蕉诗就该记得呀，眼面前儿的倒想不起来。别人冷的了不得，他只是出汗。这会子偏又有了记性了。”黛玉听了，笑道：“阿弥陀佛！到底是我的好姐姐。你一般也遇见对子了。可知一还一报，不爽不错的。”<br />
刚说到这里，只听宝玉房中一片声吵嚷起来。<br />
未知何事，下回分解。<br />
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Daiyu listened, turned over and got up, she laughed at Baoyu and said: “ Why you love to gossip so much? I knew that you were making fun of me.” She said and tweaked his ear. Baoyu hastened to say that: “ my dear sister, forgive me please, I dare not do it again. Because I smelt your scent, and suddenly remembered this literary allusion.” Daiyu smiled and added: “ You spout insults but said that it is an allusion.” Didn’t finish saying, while Baochai came in, she also smiled and said: “ Who is telling allusions? I want to know, too.” Daiyu offered her seat to Baochai, and said: “ Look! Who else? He spout insults, but said they are allusion.” Baochai added with a smile: “ Oh! It is brother Baoyu, it’s none of his business, because he knew a lot of allusions, but it is a pity that he didn’t remember the allusion when it needed. He should have remembered the Plantain poem, but didn’t remember the allusion before him. The others were so cold, but he only sweated, and this time he unexpectedly had a good memory.” Daiyu listened to her, laughing: “ God! You exactly my good sister. Now you also meet with your opponent. It is true that measure for measure.” By this time, Baoyu’s room rang out a noise. Unknown what had happened, it will be told in the next chapter.<br />
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Daiyu heard, turned over and got up, she laughed at Baoyu and said: “ Why you love to gossip so much? I know that you are making fun of me.” She said and tweaked his ear. Baoyu hastened to say that: “ my dear sister, forgive me please, I dare not do it again. Because I smelt your scent, and suddenly remembered this literary allusion.” Daiyu smiled and added: “ You spout insults but said that it is an allusion.” Didn’t finish saying, while Baochai came in, she also smiled and said: “ Who is telling allusions? I want to know, too.” Daiyu offered her seat to Baochai, and said: “ Look! Who else? He spout insults, but said they are allusion.” Baochai added with a smile: “ Oh! It is brother Baoyu, it’s none of his business, because he knew a lot of allusions, but it is a pity that he didn’t remember the allusion when it needed. He should have remembered the Plantain poem, but didn’t remember the allusion before him. The others were so cold, but he only sweated, and this time he unexpectedly had a good memory.” Daiyu listened to her, laughing: “ God! You are exactly my good sister. Now you also meet with your opponent. It is true that measure for measure.” By this time, Baoyu’s room rang out a noise. Unknown what had happened, it will be told in the next chapter. --[[User:Xie Qinglin|Xie Qinglin]] ([[User talk:Xie Qinglin|talk]]) 02:20, 29 September 2021 (UTC)<br />
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==俄语语言文学 202120081533 谢庆琳 女==<br />
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花解语──解：理解，领会。 语本“解语花”，出自五代·王仁裕《开元天宝遗事·卷下·解语花》：“秋八月，太液池有千叶白莲，数枝盛开，帝(唐玄宗)与贵戚宴赏焉。左右皆叹羡。久之，帝指贵妃示于左右曰：‘争如我解语花！’”(争：怎。)原指唐玄宗把杨贵妃比做善解人意的鲜花。引申以比喻善解人意的美女。曹雪芹化用为“花解语”，则变为美女善解人意；而“花”又为袭人之姓，则“花解语”就是花袭人善解人意。<br />
玉生香──玉：指林黛玉。 生香：散发出香气。语或本“活色生香”，出自唐·薛能《杏花》诗：“活色生香第一枝，手中移得近青楼。”原形容杏花呈现出生机盎然的美丽颜色，散发出沁人心脾的香气。引申以形容女子的天生美貌和芳香气息。这里用以形容林黛玉的天生美貌和芳香气息。<br />
“Hua Jie Yu” - " Jie": to understand, to comprehend. The phrase is from Wang Renyu's "The Legacy of Kaiyuan Tianbao - Volume 2 - The Flower of Explanation": "In the eighth month of autumn, there were a thousand leaves of white lotus in full bloom at the Taiyan Pond. The Emperor (Tang Xuanzong) and his noble relatives enjoyed the feast, and all the people around him admired them. After a long time, the emperor pointed to the noble princess and showed her to the left and right, saying: 'Compete with me to interpret the flowers!'" (Strive: how.) Originally, Emperor Tang Xuanzong compared Yang Guifei to an understanding flower. This is a metaphor for a beautiful woman who understands people. Cao Xueqin's use of the word "Hua Jie Yu" is a metaphor for a beautiful woman who understands people's feelings; and since "Hua" is also the surname of Assailant, "Hua Jie Yu" means that Hua Assailant understands people's feelings. “Jade is fragrant” - “Jade” refers to Lin Daiyu. The name of the poem is "Jade". The phrase is derived from the poem 'Apricot Blossoms' by Xue Neng: "The first branch of apricot blossoms is in living colour and fragrance, and the hand has moved close to the green tower." The original description is that the apricot blossoms are of a vibrant and beautiful colour, emitting a refreshing fragrance. It is also used to describe the natural beauty and fragrance of a woman. Here it is used to describe the natural beauty and fragrance of Lin Daiyu.<br />
--[[User:Xie Qinglin|Xie Qinglin]] ([[User talk:Xie Qinglin|talk]]) 02:02, 29 September 2021 (UTC)<br />
“Hua Jie Yu” - " Jie": to understand, to comprehend. The phrase is from five dynasties and ten years ·Wang Renyu's "The Legacy of Kaiyuan Tianbao - Volume 2 - The Flower of Explanation": "In the eighth month of autumn, there were a thousand leaves of white lotus in full bloom at the Taiye Pond. The Emperor (Tang Xuanzong) and his relatives enjoyed the beautiful scenery, and all the people around him admired them. After a long time, the emperor pointed to Yang Guifei（his wife）and showed her to the left and right, saying: 'Compete with me to interpret the flowers!'" (Strive: how.) Originally, Emperor Tang Xuanzong compared Yang Guifei to an understanding flower. This is a metaphor for a beautiful woman who understands people. Cao Xueqin used the word "Hua Jie Yu" as a metaphor for a beautiful woman who understands people's feelings; and since “Hua” is also the surname of Aroma, “Hua Jie Yu” means that “Aroma Hua” understands people's feelings. “Yu Sheng Xiang” - “Yu”(Jade) refers to Mascara Jade Forest. “Sheng Xiang”- exude fragrance. Or it could be called ''Huo Se Sheng Xiang''.The phrase is derived from the poem 'Apricot Blossoms' by Xue Neng: "The first branch of apricot blossoms is in living colour and fragrance, when got it in hand has moved close to the brothel." The original description is that the apricot blossoms are of a vibrant and beautiful colour, emitting a refreshing fragrance. It is also used to describe the natural beauty and fragrance of a woman. Here it is used to describe the natural beauty and fragrance of Mascara Jade Forest.--[[User:Zhang Yiran|Zhang Yiran]] ([[User talk:Zhang Yiran|talk]]) 03:24, 29 September 2021 (UTC)<br />
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==俄语语言文学 202120081552 张怡然 女==<br />
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掷骰(t óu投)子——是一种游戏，即互掷骰子，以点数多少决输赢。 骰子：游戏或赌博用具。多用兽骨制成，为立体小方块，六面分别刻以一、二、三、四、五、六点，一、四点涂以红色，其馀涂以黑色，故又称“色子”。相传为曹操之子曹植发明。<br />
《丁郎认父》──取材于小说《升仙记》的弋阳腔剧目。写明代杜文学因受奸相严嵩迫害，流落湖广，入赘胡丞相府，与前妻所生子丁郎曲折相认的故事。<br />
《黄伯央大摆阴魂阵》──或作《黄伯英大摆阴兵阵》。是由《七国春秋平话》改编的地方戏目。写燕国大将乐毅请师父黄伯央(《七国春秋平话》作“黄伯杨”)下山摆设阴魂阵围困齐将孙膑，最后两国讲和的故事。<br />
Cast the dice - is a game in which two people cast in turn, using the size of the dice to determine the winner. Dice: a game or gambling device.Most of them are small cubes made of animal bones, six sides are engraved on one, two, three, four, five, six points, one and four coated into red, and the rest is also black, so the dice is called again ''Shai zi''.It is said to be invented by Cao Zhi, the son of Cao Cao. <br />
''Ding lang got acquainted with his father''-- based on the novel ''Sheng Xian Ji'' yiyang repertoire. It tells the story of a man named Du Wenxue in the Ming Dynasty, who was forced to live in Huguang area because of the persecution of Yan Song, the adulterous phase. He entered the residence of Prime Minister Hu and met ding Lang, the son of his ex-wife with twists and turns.<br />
''Huang Boyang's Great display of ghosts'' ─ or ''Huang Boying's great display of died soldiers''. It is a local opera adapted from ”The story collection of the Seven Kingdoms In the Spring and Autumn Period”. It tells the story of the state of Yan general Yue Yi please master Huang Boyang (''The story collection of the Seven Kingdoms In the Spring and Autumn Period'' for ''Huang Boyang'' ) Down the mountain set up the ghost array besiege Qi general Sun Bin， finally the last two countries peace story.--[[User:Zhang Yiran|Zhang Yiran]] ([[User talk:Zhang Yiran|talk]]) 15:47, 28 September 2021 (UTC)<br />
Cast the dice - is a game in which two people cast in turn, using the size of the dice to determine the winner. Dice: a game or gambling device.Most of them are small cubes made of animal bones, six sides are engraved on one, two, three, four, five, six points, one and four coated into red, and the rest is coated into black, so the dice is also called 'Shai zi''.It is said to be invented by Cao Zhi, the son of Cao Cao. <br />
''Ding lang got acquainted with his father''-- based on yiyang repertoire of the novel ''Sheng Xian Ji''. It tells the story of a man named Du Wenxue in the Ming Dynasty, who was forced to live in Huguang area because of the persecution of Yan Song, the adulterous phase. He entered the residence of Prime Minister Hu and met ding Lang, the son of his ex-wife with twists and turns.<br />
''Huang Boyang's Great display of ghosts'' ─ or ''Huang Boying's great display of died soldiers''. It is a local opera adapted from ”The story collection of the Seven Kingdoms In the Spring and Autumn Period”. It tells the story of the state of Yan general Yue Yi please master Huang Boyang (''The story collection of the Seven Kingdoms In the Spring and Autumn Period'' for ''Huang Boyang'' ) Down the mountain set up the ghost array besiege Qi general Sun Bin， finally the last make peace between the two countries.--[[User:Liu Yue|Liu Yue]] ([[User talk:Liu Yue|talk]]) 10:26, 29 September 2021 (UTC)<br />
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==亚非语言文学 202120081509 刘越 女==<br />
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香玉──典出唐·温庭筠《晚归曲》：“弯堤弱柳遥相瞩，雀扇团圆掩香玉。”原比喻美女散发的香气和洁白如玉的肌肤。这里是把玉石的“玉”和林黛玉的“玉”合为一体，以比喻美女林黛玉。<br />
《孙行者大闹天宫》──京剧和地方戏均有此剧目，取材于小说《西游记》。写孙悟空跟随唐僧前大闹天宫的故事。<br />
《姜太公斩将封神》──京剧和地方戏均有此剧目，取材于小说《封神演义》。写姜太公助周灭商后斩将封神的故事。 按：以上四剧皆为闹剧，隐寓宁国府子弟粗俗不堪。<br />
献酬──亦作“献醻”。指酒席上主宾互相敬酒。《诗经·小雅·楚茨》：“献醻交错，礼仪卒度，笑语卒获。”郑玄笺：“始主人酌宾为献，宾既酌主人，主人又自饮酌宾曰醻。”<br />
行令──即行酒令。是一种宴会中助兴的游戏。其方法是：由一人任令官，按一定规矩行令，违令或按令该饮者都要饮酒。<br />
Sweet jade -- classic tang · Wen Tingjun late homing song : "Bent dike weak willow distant look, bird fan reunion mask sweet jade." Originally used as a metaphor for beauty to send out fragrance and white jade skin. Here, the "jade" of jade and the "jade" of Lin Daiyu are combined as a whole to compare the beauty Lin Daiyu. <br />
Monkey King Makes Havoc in Heaven-- a play in Both Beijing and local operas, based on the novel 'Journey to the West.' It tells the story of Sun Wukong who caused havoc in heaven before he followed Tang Priest. <br />
Jiang Taigong beheaded a General and canonized a God. This play is used in Both Beijing Opera and local opera, and is based on the novel Creation of the Gods(Fengshen Yanyi). Write the story of Jiang Taigong, who helped Zhou destroy the Shang dynasty and then beheaded the generals and sealed the gods. The author explains: the above four plays are farce, alluding to the vulgar children of the Ningguo mansion.<br />
献酬（Make toasts）─ can also be said 献醻. Refers to the banquet guests toasting each other. The Book of Songs· xiaoya ·chuets : "The host and the guest toast each other, the etiquette completely conforms to the law, then a word and a smile are appropriate natural." Zheng Xuanjian: "First, the host toasts to the guests. After the guests drink the wine revered by the host, the host drinks it, which is called making toasts ." <br />
Order -- that is, order to drink. It's a fun game at a banquet. Its method is: by a person as an officer, according to certain rules of the order, the violation or according to the order of the drinker to drink.--[[User:Liu Yue|Liu Yue]] ([[User talk:Liu Yue|talk]]) 10:26, 29 September 2021 (UTC)<br />
Here is the "jade" of Jade and Lin Daiyu "jade" into one--[[User:Zhang Qiuyi|Zhang Qiuyi]] ([[User talk:Zhang Qiuyi|talk]]) 10:38, 29 September 2021 (UTC)<br />
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==亚非语言文学 202120081550 张秋怡 女==<br />
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家生子儿——奴仆在主子家生养的子女。清代规定家奴之子女必须为奴，世代如此。清·赵翼《陔馀丛考·家生子儿》：“奴仆在主家所生子，俗谓家生子。按《法苑珠林》记庸岭有大蛇为患，都尉令长求人家生婢子及有罪家女祭之，‘家生’之名见此。”(按：《法苑珠林》为唐·释道世撰。)又《汉书·陈胜传》“秦令少府章邯免骊山徒、人奴产子”唐·颜师古注：“奴产子，犹今人云家生奴也。”“家生奴”即“家生子”。可知“家生子”或“家生奴”之称至少在唐代已有。<br />
卖倒的死契——指双方约定卖出后不能赎身、必须终生为奴的卖身契。 卖倒：犹“卖定”、“卖死”。不可变更或反悔之意。<br />
禄蠹——禄：身居官爵，享受俸禄。 蠹：蛀虫。 “禄蠹”或本“国蠧”，出自《左传·襄公二十二年》：“不可使也，而傲使人，国之蠧也。”意思是本无治国之才而身居高位，便是国家的蛀虫。“禄蠹”与“国蠧” 的意思基本上一样，也是指身居高位、坐享国家俸禄而不干实事的官吏。<br />
Offspring - The offspring of a servant in his master's house.The Qing Dynasty stipulated that the children of domestic slaves must be slaves,from generation to generation so.Qing · Zhaoyi “Gai Yu Cong Kao·The offspring of a servant in his master's house”：“A son born to a slave in his master's house is called a son of the family. according to the story in the Pearl forest of Fayuan that there was a snake in yongling, the commander ordered the family to give birth to maidservants and guilty family female sacrifice, the name of ‘Jia Sheng’ can be seen here.”(according to: the Pearl forest of Fayuan was written for Tang· Shi Daoshi.) Also in The Book of Han · Biography of Chen Sheng, “The Qin Dynasty sent Zhang Han to pardon those who had served in mount Lishan for crimes and the sons born to house slaves”Tang · Yan Shigu notes: “Slaves give birth to children, and is also slaves”.“family born slave” is “family born child”. It can be known that “family born son” or “family born slave” had been known at least in the Tang Dynasty.Dead deed of sale refers to the deed of sale in which both parties agree that they cannot redeem themselves and must be slaves for life. Sell down: “sell it” and “sell it to death”.The meaning of not changing or repentance.“LU DU”-Lu：official status, enjoy the salary. Du：moth. “Ludu” or “Guodu” from “Zuo Zhuan · Xianggong 22 years”: “do not make, but proud to make people the worm of the nation.It means that if you are in a high position without the ability to govern a country, you are the moth of the country. “Ludu” and “Guodu” basically the same meaning, also refers to a high position, enjoy the state salary and do not work officials.--[[User:Zhang Qiuyi|Zhang Qiuyi]] ([[User talk:Zhang Qiuyi|talk]]) 01:29, 29 September 2021 (UTC)<br />
--[[User:Wang Yifan21|Wang Yifan21]] ([[User talk:Wang Yifan21|talk]]) 13:47, 11 October 2021 (UTC)<br />
Annotation:<br />
First:The common term for a son born to a slave in his master’s house is a family son.<br />
Second:It means that if you are in a high position without the ability to govern a country,you are the moth of the country.<br />
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==亚非语言文学 202120081524 王逸凡 女==<br />
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明明德——头一个“明”为动词。彰明、弘扬之意。 明德：美德，至德，完美的道德。 语出《礼记·大学》：“大学之道，在明明德，在亲民，在止于至善。”意思是弘扬完美的道德。这里是指贾宝玉只肯定包括《大学》在内的《四书》(《论语》、《大学》、《中庸》、《孟子》)是正经书，其他书都要不得。<br />
魔星——曹雪芹的原作为“天魔星”，显然是借用了佛家和道家的“天魔”之说。佛家说“天魔”为欲界第六天主。如《楞严经》卷九说：“或汝阴魔，或复天魔。”又《百喻经·小儿得大龟喻》说：“邪见外道，天魔波旬，及恶知识，而语之言，汝但极意六尘，恣情五欲，如我语者，必得解脱。”道家则说“天魔”为天上的魔怪。如《云笈七签》卷四说：“有经无符，则天魔害人。”这里的“魔星”则为冤家之意。<br />
--[[User:Wang Yifan21|Wang Yifan21]] ([[User talk:Wang Yifan21|talk]]) 13:55, 11 October 2021 (UTC)<br />
Mingmingde-The first Ming is a verb which means obviousness, clearness or carrying forward and the word Mingde means virtue,supreme virtue,perfect morality.TheBook of Rites·Great learningsaid that The way of a university lies in being clear and virtuous,being close to the people and being perfect.It means to promote perfect morality.Jiabaoyu only affirmed The Four Books including Great learning.(Analects,Great Learning,The Doctrine of the Mean,Mencius) are the proper scriptures and the other books are not acceptable.<br />
Devil star—Cao Xueqin's original as heaven devil star apparently borrowed Buddhism and Taoism’s doctrine of heaven devil.Buddhists say that demons are the sixth god of desire.For example,volume 9 of the Surangama Sutra says, Either you cast shadows or you recover demons from heaven.And Buddhist parables said Evil sees the outside world,the sky is full of demons,and evil knowledge.But as you speak,you will be free from your desires.Taoism says that demons of heaven are demons in the sky.Such as Cloud gupta seven signs volume 4 said there is no sign,then the devil harm people.The devil star here is the meaning of enemy.<br />
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Laba Porridge-Originated from what Buddhism calls "Buddha Bathing Day". According to legend, Sakyamuni was born on the eighth day of the fourth month of the lunar calendar. Therefore, since the Han Dynasty, every Buddhist temple has held commemorative activities on this day. , "Buddhist Bathing Festival" or "Buddha Day". "The Book of the Later Han Dynasty·Tao Qian Biography" said:" Every time Buddha baths, to provid alms and rice to the road."In the Southern Dynasties Liang Zongmo's "Jingchu Sui Shi Ji" said: "April 8th, all monasteries Set up a fast, bath the Buddha with five-color perfume, and make Longhua Hui together. According to "The Biography of the Eminent Monk": ‘On April 8th, to bathe the Buddha, use Duliang incense as blue water, tulip as red water, Qiulong incense as white water, aconite incense as yellow water, benzoin as black water, to infuse the top of the Buddha. ’"In the Song Dynasty, the day of Sakyamuni’s Buddhahood, the eighth day of the twelfth lunar month, was the "Buddha Bathing Day." Every time this day, all the temples held commemorative activities, not only followed the alms, bathing Buddha and other items, but also added laba porridge, known as "Buddha Bathing Day." "On the eighth day of the lunar New Year, three or five monks and nuns in the streets chanted Buddha in teams. They would sit on a gold, bronze or wooden Buddha statue in a silver or bronze saro or a good basin. They would soak it in perfume and shower it with the branches of a tree and intended for the edification of the masses. The great monasteries served as bathing buddhas, and gave seven treasures and five flavors porridge and disciples, called "laba porridge". Since then every families also cooks porridge with fruit and miscellaneous ingredients. "Since then, the phase has become a common practice, and it has not faded to this day.--[[User:Li Xinxing|Li Xinxing]] ([[User talk:Li Xinxing|talk]]) 12:47, 11 October 2021 (UTC)<br />
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Laba congee - Originated from a festival which calls “Buddha Bathing Day” in Buddhism. According to legend, Sakyamuni was born on the eighth of forth lunar month. Therefore, on this day, every Buddhist temple will hold activities since Han Dynasty. People will practice alms all over the place, and wash Buddha statues with water full of spices, which is called “Buddha Bathing Day”, “Buddha Bathing Festival” or “Buddha Buddhism Day”. “Book of Later Han Dynasty · Tao Qian Biography” said: “Every time Buddha baths, there are more drinking and rice to the homeless people. And Liang Zongmo, the author of “Jingchu Sui Shi Ji”, said: “April 8th, all monasteries set up a fast, bath the Buddha with five-color perfume, and make Longhua Hui together.”<br />
According to “The Biography of the Eminent Monk”: “On April 8th, people use Duliang incense as blue water, Yujin incense as red water, Qiulong incense as white water, Fuzi incense as yellow water, and Anxi incense as black water, to infuse the top of the Buddha”. In Song Dynasty, the day when Sakyamuni became a Buddha, the eighth of twelfth lunar month, was the "Buddha Bathing Day." On that day, all Buddhist temples held the activities which called “Buddha Bathing Activities”, not only followed the traditional items, but also added Laba congee. The folks also follow the example and eat Laba congee. We can see these things from “DongJingMengHualu·Volume Ten·December”, a book written by Meng in Song Dynasty: three or five monks and nuns in the streets chanted Buddha in teams. They would sit on a gold, bronze or wooden Buddha statue in a silver or bronze saro or a good basin. They would soak it in perfume and shower it with the branches of a tree and intended for the edification of the masses. The great monasteries served as bathing buddhas, and gave seven treasures and five flavors porridge and disciples, called ‘Laba congee’. Since then every families also cooks porridge with fruit and miscellaneous ingredients.” Since then, the phase has become a common practice, and it has not faded to this day.--[[User:Xu Minyun|Xu Minyun]] ([[User talk:Xu Minyun|talk]]) 13:55, 11 October 2021 (UTC)</div>Chen Huinihttps://bou.de/u/index.php?title=20210929_homework&diff=13463520210929 homework2021-12-29T07:59:12Z<p>Chen Huini: /* 英语语言文学（英美文学） 202120081479 陈惠妮 女 */</p>
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<div>Quicklinks: [[Introduction_to_Translation_Studies_2021|Back to course homepage]] [https://bou.de/u/wiki/uvu:Community_Portal#Frequently_asked_questions_FAQ FAQ] [https://bou.de/u/wiki/uvu:Community_Portal Manual] [[20210926_homework|Back to all homework webpages overview]] [[20220112_final_exam|final exam page]]<br />
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[[20210929_homework|homework of session 1 for session 2 Sep 29]]<br />
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IN PREPARATION<br />
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专业班级名称 学号 姓 名 性别<br />
==语言智能与跨文化传播研究 202120081535 徐敏赟 男==<br />
This is the homework of 徐敏赟.--[[User:Root|Root]] ([[User talk:Root|talk]]) 12:41, 26 September 2021 (UTC)<br />
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清初时期的汉籍（书）翻译及其文化沟通意涵<br />
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摘要：清初之际，统治者虽以武力立国，但社会的主导意识形态尚未形成，国家的“治统”与“道统”尚未确立。为了匡扶社稷，教化臣民，探求君主治术，建构符合国家需求的集体价值观，统治者积极组织汉书翻译，促进文化交流，增进民族和解。<br />
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The Translation of Chinese Books in the Early Qing Dynasty and its Cultural Communication Implications<br />
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Abstract: At the beginning of the Qing Dynasty, the ruler established the country by force, but the dominant social ideology has not yet been formed. Besides, the country's "ruling" and "moral orthodox" have not yet been established either. In order to help the community, educate the people, explore the rules of the monarchy, and construct collective values that meet the needs of the country, the ruler organized the translation of Chinese books actively, which helped promote cultural exchanges and enhance national reconciliation.<br />
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The Translation of the Han Classics in Early Qing Dynasty and its Implications for Cultural Communication<br />
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Abstract: At the beginning of Qing Dynasty, the ruler established the country by force, but the dominant social ideology has not yet been formed. Besides, the country's "governance" and "moral orthodoxy" have not yet been established neither. In order to help the community, educate the people, explore the rules of the monarchy, and construct collective values that meet the needs of the country, the ruler organized the translation of Chinese books actively, which helped promote cultural exchanges and enhance national reconciliation.--[[User:Root|Root]] ([[User talk:Root|talk]]) 12:44, 29 September 2021 (UTC)<br />
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The Translation of the Han Classics in Early Qing Dynasty and its Implications for Cultural Communication<br />
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Abstract: At the beginning of the Qing Dynasty, the ruler established the country by force, but the dominant social ideology has not yet been formed. Besides, the country's "governance" and "moral orthodox" have not yet been established either. In order to rectify and sustain the society, educate the subjects, explore the rules of the monarchy, and construct collective values that meet the needs of the country, the ruler organized the translation of Han Classics actively, which helped promote cultural exchanges and enhance national reconciliation.--[[User:Yan Jing|Yan Jing]] ([[User talk:Yan Jing|talk]]) 08:31, 8 October 2021 (UTC)<br />
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==语言智能与跨文化传播研究 202120081536 颜静 女==<br />
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作为清初文化事业的重要组成部分，汉书翻译有着明确的选择标准，实用主义色彩浓厚，主要关注汉族的文治教化与典章制度，将翻译与政要相关联，反对浮华藻饰的翻译。组织上，汉书翻译以官方为主，以民间为辅，译书者既有兼通满、汉双语之旗人，又有八旗科举考试之及第者，这些人既是文化交流的管理者，又是实践者，代表了统治阶级意欲沟通满汉的主观愿望。成效上，汉书翻译不仅促进了新生政权的制度建设，而且为统治者建构政权合法性做出了贡献。<br />
As an important part of cutural undertakings in early Qing dynasty, the translation of Han Shu had clear selection criteria and strong pragmatism. It focused on cultural education and institutions of Han nationality, associated the translation with politics and opposed flashly one. Organizationally,Han Shu was translated mainly by officials, and then public people. The translators contained not only the Eight Banners' People who mastered both Manchu and Chinese language, but also those who passed the Eight Banners imperial examination. These people were administrators of cultural exchanges, and also practicers, representing that the ruling class was willing to communicate the Manchu and Han people. In effect, the translating of Han Shu promoted the institutional construction of new regime, and also contributed to the rulers for constructing the regime legitimacy.<br />
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As an important part of cutural undertakings in early Qing dynasty, the translation of Han Shu had clear selection criteria and strong pragmatism. It (mainly) focused on cultural education and institutions of Han nationality, associated the translation with politics and opposed flashly one(embellishments). Organizationally,Han Shu was translated mainly by officials, and then (by) public people. The translators contained not only the Eight Banners' People who mastered both Manchu and Chinese language, but also those who passed the Eight Banners imperial examination. These people were administrators of cultural exchanges, and also practicers, representing that the ruling class was willing to (intended to) communicate the Manchu and Han people. In effect(As a result), the translating of Han Shu promoted the institutional construction of new regime, and also contributed to the rulers for constructing the regime legitimacy.--[[User:Du Lina|Du Lina]] ([[User talk:Du Lina|talk]]) 07:06, 29 September 2021 (UTC)<br />
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==英语语言文学（语言学） 202120081484 杜莉娜 女==<br />
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导论：凡国家之建立，必有立国精神和主导意识形态，以及相应之文化政策。<br />
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早在天聪年间，清太宗便为新政权提出文武并用的战略构想，既强调以武功勘祸乱，又主张以文教佐太平，提出了文化统制与文化建设的独到见解。顺治十年，世祖章皇帝订定崇儒重道之政策，并以此为基础构建了兴文教、崇经术的治国理念。<br />
Introduction: The establishment of any countries must need the national spirit ,dominant ideology and appropriate cultural policies.<br />
As early as the year of Tiancong, Hong Taiji put forward the strategy of combining education and force for the new regime. He emphasized to calm down the chaos by force and to keep the peace by civilian. And he came up with unique insights into cultural unification and cultural development as well. During the first decade of Shunzhi, the emperor made the policies of respecting Confucianism and Taoism, and from this the concepts of governing like prospering education and worshipping scriptures were built.<br />
--[[User:Du Lina|Du Lina]] ([[User talk:Du Lina|talk]]) 16:07, 28 September 2021 (UTC)<br />
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1、“文武并用”应该更侧重于“文化”，译为culture；<br />
2、“以文教佐太平”中的文教可译为cultural education；<br />
3、“and from this the concepts of governing like prospering education and worshipping scriptures were built.”此句可用定从，which served as a basis of …<br />
--[[User:Hu Shuqing|Hu Shuqing]] ([[User talk:Hu Shuqing|talk]]) 02:25, 30 September 2021 (UTC)<br />
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==英语语言文学（语言学） 202120081490 胡舒情 女==<br />
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自此以后，虽然清代历朝统治者在关注满、汉文化交流时，不免对汉人进行打压，但也同时对汉族文化进行宣扬与推广。<br />
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清初统治者在构建“治统”与“道统”的过程中，围绕“崇儒重道”，衍生出众多文化政策实举，如科举取士、博学鸿词等，而汉籍经史的翻译与编纂也是其中重要内容。汉籍翻译是清代文化事业的重要组成部分，是清代民族关系与文化政策的重要载体，它和满清政权的其它文化活动一样，在功能上相互关联，彼此补充，为促进民族之间的相互了解，维护王朝体系的稳定发展，做出了重要贡献。<br />
Since then, although all the dominators of Qing dynasty would squash the Han people when focusing on cultural exchange between Manchu and Han, they also propagated and promoted the Han culture at the same time. The dominators of early Qing dynasty implemented numerous cultural policies around the idea of “respecting and emphasizing Confucianism” during the progress of constructing Monarchism and statesmanship, which included imperial examinations, erudite and an important part - compilation and translation of Han classics and history. Its translation formed Qing’s cultural undertakings as necessary parts and served as a carrier of Qing’s ethnic relations and cultural policies. It was the same as other cultural activities of Qing dynasty. They related to and complemented each other, which made a contribution to promoting mutual understanding among peoples and maintaining the stable development of the dynastic system.<br />
--[[User:Hu Shuqing|Hu Shuqing]] ([[User talk:Hu Shuqing|talk]]) 02:26, 30 September 2021 (UTC)<br />
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Since then when it comes to Man and Han culture communication, all the dominants of Qing dynasty would freeze Han people but also promoted their culture at the same time.<br />
During the process of constructing Monarchism and statesmanship, the dominators of early Qing dynasty implemented numerous cultural policies around the idea of “respecting and emphasizing Confucianism”,which included imperial examinations, erudite etc. and also compilation and translation of Han classics and history as an important content. Translating Han classic is an important part of Qing culture and the critical carrier of its ethnic relationship and cultural policies. Like other cultural activities it related to each other functionally and made great contributions to ethnic communication and the solid development of Qing dynasty.--[[User:Huang Jinyun|Huang Jinyun]] ([[User talk:Huang Jinyun|talk]]) 13:18, 11 October 2021 (UTC)<br />
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==英语语言文学（语言学） 202120081491 黄锦云 女==<br />
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作为汉籍翻译的管理者和实践者，译者们承担了沟通满、汉文化的历史使命，其所翻译的汉族书籍不仅有效增进了旗人对于汉文化的了解，而且为新生政权进行制度建设，以及合法性的建构发挥了历史性作用。<br />
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一 满洲前身时期的汉籍翻译<br />
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满洲的前身系女真，语言文字上属阿尔泰语系。<br />
As the manager and practicer of translating Han's books, translators take charge of the historial mission to combine Manchu cultrue and Han cultrue. Their translations not only enhance the Bannermen to know Han cultrue, but also play a historical role in forming a new regime and conlidating its validity.--Translation of Han's books before Manchu period<br />
Manchurians originates from Nvzhen race, and their language and character belongs to Altaic languages.<br />
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As managers and practitioners of translation of Han's books, the translators undertake the historical mission of cultural communication between the Manchu nationality and the Han nationality. Their translations of Han's books not only efficiently improve the Manchu's understanding of Han culture, but also play a historical role in system construction for new regime and legal construction. <br />
-- Translation of Han's books before the Manchu period<br />
Manchurians originate from Nvzhen race, and their language belongs to Altaic language.--[[User:Kuang Yanli|Kuang Yanli]] ([[User talk:Kuang Yanli|talk]]) 14:11, 9 October 2021 (UTC)<br />
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==英语语言文学（语言学） 202120081495 邝艳丽 女==<br />
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满族和历史上的女真族一样，原本没有自己的语言，记事时需要借用其他民族文字。《金史》中说：“初无文字，国势日强，与邻国交好，迺用契丹字。”[ 杨家骆：《金史》，台北：鼎文书局，1985年，第1684页。] 金朝立国后，初期的内、外公文几乎都用契丹文书写，金太祖本人也擅长契丹语。<br />
As Jurchen in the history, the Manchu nationality did not have its own language, and they needed to use other national characters when making a memorandum. "The Jin dynasty did not have their own characters, but with its increasing development and its need to deal with the relation with neighbouring country, then used Khitan characters" said in ''The History of Jin Dynasty''[Yang Jialuo: ''The History of Jin Dynasty'',Taipei: Dingwen Publishing House, 1985, p.16884] After Jin Dynasty was established, the official documents inside and outside the court were nearly written with Khitan characters, even the Emperor Taizu of Jin Dynasty was skilled in using Khitan language.--[[User:Kuang Yanli|Kuang Yanli]] ([[User talk:Li Aixuan|talk]])09:41, 29 September 2021 (UTC)<br />
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As Jurchen in the history, the Manchu nationality did not have its own language. Therefore, keeping a record of events depended on the characters of other nation. Said in The History of Jin Dynasty, "the Jin dynasty did not have their own characters, but with its increasing development and its need to deal with the relation with neighbouring country, then used Khitan characters". [Yang Jialuo: The History of Jin Dynasty,Taipei: Dingwen Publishing House, 1985, p.16884] After the establishment of Jin Dynasty, the official documents at home and abroad were nearly written in Khitan characters, even the Emperor Taizu of Jin was skilled in using Khitan characters.--[[User:Li Aixuan|Li Aixuan]] ([[User talk:Li Aixuan|talk]]) 13:34, 29 September 2021 (UTC)<br />
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==英语语言文学（语言学） 202120081496 李爱璇 女==<br />
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然而，契丹语与金人女真语差距较大，因而金太祖命完颜希尹、叶鲁依据汉人楷字，并参照契丹字制度，创制适合本族的语言文字“女直文”。天辅（金太祖年号）三年，女直文依诏令颁行，称“女直大字”。二十年后，即1138年，金熙宗完颜亶又命人参照契丹字，创制并颁布另一种女直文字，即“女直小字”。<br />
However, there was a huge gap between the Khitan language and the Jurchen language. Therefore, the Emperor Taizu of Jin ordered Wanyan Xiyin and Ye Lu to create a language suitable for their own people, Jurchen script, based on the Han regular script and referring to the Khitan script system. In the third year of the period of Tianfu (the reign title of Emperor Taizu of Jin), according to the edict, the Jurchen script was enacted as "Jurchen Large Script". In 1138, twenty years later, Wanyan Dan, the Emperor of Xizong, ordered someone to create and enact another kind of Jurchen script, namely "Jurchen Little Script", referring to the Khitan script system.--[[User:Li Aixuan|Li Aixuan]] ([[User talk:Li Aixuan|talk]]) 01:15, 29 September 2021 (UTC)<br />
However, there was a big gap between Khitan language and Jin Nuzhen language. Therefore, Jin Taizu ordered Wanyan Xiyin and Ye Lu to create a language suitable for their own nationality "Jurchen script", according to the regular script of Han people and with reference to the Khitan character system. In the third year of Tianfu (the year of emperor Taizu of Jin Dynasty), Jurchen script was issued in accordance with the imperial edict, known as "Jurchen Large Script". Twenty years later, in 1138, Jin Xizong, Wanyan Dan ordered people to create and promulgate another Jurchen script, namely “Jurchen Little Script", according to the Khitan character.— Li Xichang<br />
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==英语语言文学（语言学） 202120081502 李习长 男==<br />
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金世宗继位后，在推动女直文字的使用上，力度更大，举措更丰。如大定（金世宗年号）四年，金世宗令翰林侍讲学士徒单子温等用女直大、小字，翻译经书。女直文字的创制，对金朝翻译汉书影响巨大，而汉书翻译又影响了金朝的政治制度与国家治理。<br />
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After Jin Shizong succeeded to the throne, he made greater efforts and took more measures to promote the use of nvzhi characters. For example, in the fourth year of Dading (the year of Jin Shizong), Jin Shizong ordered the Imperial College to teach the bachelor's Apprentice Shan Ziwen to translate scriptures in large and small characters. The creation of nvzhi characters had a great impact on the translation of Chinese books in the Jin Dynasty, which in turn affected the political system and national governance of the Jin Dynasty.<br />
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After Jin Shizong succeeded to the throne, he made greater efforts and took more measures to promote the use of nvzhi characters. For example, in the fourth year of Dading (the year of the region of emperor Jin Shizong), he ordered his disciple Shan Ziwen and other Shijiang academicians of Hanlin to translate Confucian Classics in Jurchen large and small scripts. the creation of Jurchen script had a great impact on the translation of Chinese books in the Jin Dynasty, and that, in turn, the Chinese books affected the political system and national governance of the Jin Dynasty.—[[User:Qiu Tingting|Qiu Tingting]] ([[User talk:Qiu Tingting|talk]]) 03:39, 29 September 2021 (UTC)<br />
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==英语语言文学（语言学） 202120081519 邱婷婷 女==<br />
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如大定五年，金世宗命徒单镒翻译《贞观政要》和《白氏策林》等书，次年徒单子温将译本进呈皇帝。大定七年，《史记》和《西汉书》等翻译成书，金世宗敕令刊刻颁行。大定十五年，金世宗再令翻译各部经书，由温迪罕缔达（著作佐郎）、宗璧（编修官）、阿鲁（尚书省译史）、杨克忠（史部令史）等负责对译本进行注解。<br />
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For example, in the fifth year of Dading (the year of the region of emperor Jin Shizong), the emperor ordered his disciple Shan Yi to translate The Political Program of Zhen Guan and Bai Shi Ce Lin, etc. Then emperor Shizong received the translations submitted by another disciple named Shan Ziwen in the next year. In the seventh year of Dading, the Records of the Grand Historian and the book of the Western Han Dynasty were translated into books, which were printed and issued by the royal decree of emperor Shizong. What’s more, he released an order again translating several Confucian classics which were annotated by Wendihan Dida （assistant of Zhu Zuolang）， Zong Bi（ BianxiuOfficer), A Lu( translator of history of Department of State Affairs ), Yang Kezhong( Lingshi of the Ministry of Official Personnel Affairs ) etc.<br />
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Annotation:<br />
1.Zhu Zuolang: A person whose responsibility is to compile historical works.<br />
2.Bianxiu: The official name, first placed in the Song Dynasty, is mainly responsible for the revision and compilation of documents.<br />
3.Lingshi: Official name; the general name of petty officials in the government since the song and Yuan Dynasties.—[[User:Qiu Tingting|Qiu Tingting]] ([[User talk:Qiu Tingting|talk]]) 16:58, 28 September 2021 (UTC)<br />
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For example, in the fifth year of Dading (the year of the region of emperor Jin Shizong), the emperor ordered his disciple Shan Yi to translate ''The Political Program of Zhen Guan'' and ''Bai Shi Ce Lin'', etc. Then emperor Shizong received the translations submitted by another disciple named Shan Ziwen in the next year. One year later,''the Records of the Grand Historian'' and ''the book of the Western Han Dynasty'' were translated into books, which were printed and issued under the royal decree of emperor Shizong. What’s more, he released an order again translating various kinds of Confucian classics, and put Wendihan Dida （assistant of Zhu Zuolang）， Zong Bi（ BianxiuOfficer), A Lu( translator of history of Department of State Affairs ), Yang Kezhong( Lingshi of the Ministry of Official Personnel Affairs ) etc.in chagrge of the annotation of the translations of these books.<br />
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Annotation:<br />
1.Zhu Zuolang: A person whose responsibility is to compile historical works.<br />
2.Bianxiu: The official name, first placed in the Song Dynasty, is mainly responsible for the revision and compilation of documents.<br />
3.Lingshi: Official name; the general name of petty officials in the government since the song and Yuan Dynasties.--[[User:Rao Jinying|Rao Jinying]] ([[User talk:Rao Jinying|talk]]) 13:00, 29 September 2021 (UTC)<br />
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==英语语言文学（语言学） 202120081520 饶金盈 女==<br />
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金世宗在位期间，朝廷设立译经所，专司汉文经史的翻译。期间，所译汉书除上述几种之外，另有《易》、《书》、《论语》、《老子》、《孟子》、《扬子》、《文中子》、《刘子》以及《新唐书》等。之所以翻译这些书籍，是因为金世宗希望女直人了解汉人的仁义道德，以利于治国安邦。<br />
During the reign of Wan Yanyong, the fifth emperor of the Jin Dynasty, the imperial court set up a sutra translation office to specialize in the translation of scriptures and historical materials written in classical Chinese. Meantime, in addition to the above-mentioned Chinese books, there were also ''Yi'', ''Shu'', ''the Analects of Confucius'', ''the Laozi'', ''the Mencius'', ''the Yangzi'', ''the Wenzhongzi'', ''the Liuzi'' and ''the New Book of Tang Dynasty''. The reason why these books were translated was that Wan Yanyong hoped that the Jurchen people would get some knowledge of the humanity, justice, and morality of Han people by reading these books, so as to develop a prosperous and stable country.--[[User:Rao Jinying|Rao Jinying]] ([[User talk:Rao Jinying|talk]]) 13:05, 28 September 2021 (UTC)<br />
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During the reign of Emperor Jin Shizong,the imperial court set up a classics translation office concentrating on the translation of Chinese classics. During this period,apart from translating the above-mentioned Chinese classics, they also translated " Yi", "Shu", "the Analects of Confucius", "Laozi", "Mencius", "Yangzi", "Wenzhongzi", "Liuzi" and "New Book of Tang", etc. The reason why Jin Shizong chose these classics was that he hoped that Nuzhi people could know the virtue and morality of Chinese by reading these classics. And then he could rule the nation better and build a stable society. -- Yang Aijiang (talk) 21.57. 11 October 2021<br />
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==英语语言文学（语言学） 202120081541 杨爱江 女==<br />
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然而，当时的女直字与汉字不能直接对译，中间需要经过转译为契丹字。为解决这一问题，金章宗在位期间，诏设弘文院，命人译写儒家经典并讲解。旋即，又废止契丹字，要求嗣后汉文典籍直接译为女直字，以省去须经契丹字转译的中间环节。<br />
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However, it was not feasible to translate Nvzhi characters into Chinese characters at that time. And it needed to be translated into Khitan characters first. During the reign of Emperor Jin Zhangzong, he set up Hongwen Academy and commanded his ministers to translate and explain Confucian classics in order to figure out the language problem. Besides, Jin Zhangzong abolished its use of Khitan characters and demanded that the Chinese classics should be translated in Nvzhi characters directly, omitting taking advantage of the translation of Khitan characters as a bridge.<br />
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However, Nvzhi characters could not be directly translated into Chinese without being rendered into Khitan ones first. During the reign of Emperor Jin Zhangzong, he set up Hongwen Academy and commanded his ministers to translate and explain Confucian classics in order to figure out the language problem. Soon after, Jin Zhangzong abolished the use of Khitan characters and demanded that the Chinese classics should be translated into Nvzhi characters directly, avoiding the intermediate step of using Khitan characters.--[[User:Yin Meida|Yin Meida]] ([[User talk:Yin Meida|talk]]) 13:37, 11 October 2021 (UTC)<br />
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==英语语言文学（语言学） 202120081547 殷美达 女==<br />
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金朝政权覆灭之后，虽然留居东北故地的少数女直上层人士尚能娴习女直文，但女直字作为一种语言逐渐失传。明朝政府设置“四夷馆”后，又延人专习女直字，以应付中央与地方政府，或中央与藩属地之间的通译需要。虽然如此，女直语的凋落已成定势。<br />
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二 太祖时期汉籍（书）翻译之“始”<br />
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After the collapse of Jin Dynasty, a small number of upper class Nuzhen people who still lived in northeastern China were proficient in Nuzhen language, but its words were being lost gradually. The Ming Dynasty, when setting up "Si Yi Academy ", hired particular people to study Nuzhen language to meet the needs of communication and translation between the central and local governments or its dependent territories. Nevertheless, the decline of Nuzhen language had become a foregone conclusion. <br />
Second, the initial translation of Chinese books in the Emperor Taizu period<br />
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After the collapse of the Jin Dynasty, although a small number of Nuzhen upper class people living in Northeast China were very proficient in Nuzhen language, Nuzhen characters have been gradually lost. Having set up the Si-yi-guan, the Ming Dynasty hired people to specially learn Nuzhen characters to meet the needs of translation between the central government and local governments or its affiliated places. Nevertheless, the decline of Nuzhen language is inevitable.--[[User:Zhou Qiao1|Zhou Qiao1]] ([[User talk:Zhou Qiao1|talk]]) 01:18, 29 September 2021 (UTC)<br />
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==英语语言文学（语言学） 202120081557 周巧 女==<br />
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明朝万历年间，努尔哈赤率部崛起之时，作为女直后裔的满族并无文字。那时，文移往来必须靠蒙古语的学习与翻译才能完成。为满足文移往来，记注政事的需要，并解决“今我国之语，必译为蒙古语读之，则未习蒙古语者，不能知也”的问题，清太祖努尔哈赤于明万历二十七年，命额尔德尼、噶（gá）盖等改制国书（即，国家的语言文字），以改变满人说女真语却写蒙古字的尴尬局面。[ 明珠等奉敕修：《清实录·太祖高皇帝实录》，北京：中华书局，1986年，第2页。]<br />
In the wanli period of Ming Dynasty, when Nurhachi led the rise of the Manchu, as a straight descendant of Jurchen, it had no written words. At that time, the exchange of letters had to rely on Mongolian learning and translation to complete. For recording the political affairs and solving the problem that "Nowadays, the language of our country have to be translated into Mongolian to read, otherwise people can’t understand it without the learning of it. In the wanli 27 years of Ming Dynasty, Emperor Nurhachi（the first founder of Ming Dynasty） ordered Erdeni, Gagai and etc to reform credentials (namely the national language), which is to change the embarrassing situation of Manchu speaking Jurchen language while writing Mongolian. Beijing: Zhonghua Book Company, 1986, p. 2.<br />
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During the Wanli period of Ming Dynasty, when Nurhachi led the rise of the Manchu, the Manchu, as the straight descendant of Jurchen, had no written words . At that time, the exchange of letters had to rely on Mongolian learning and translation. In order to meet the need of exchange of letters, recording and annotating the political affairs as well as solving the problem that "Nowadays, the language of our country have to be translated into Mongolian to read, and the people who do not learn Mobolian can't understand it.", in the Wanli 27 years of Ming Dynasty, Emperor Nurhachi（the first founder of Ming Dynasty） ordered Erdeni, Gagai and etc to reform credentials (namely the national language), which aimed to change the embarrassing situation that Manchu speaking Jurchen language while writing in Mongolian. Beijing: Zhonghua Book Company, 1986, p. 2.--[[User:Zhu Suzhen|Zhu Suzhen]] ([[User talk:Zhu Suzhen|talk]]) 06:16, 30 September 2021 (UTC)<br />
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==英语语言文学（语言学） 202120081561 朱素珍 女==<br />
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自此，满洲的语言和文字渐趋一致。然而问题在于，此时创制的满洲语言（老满文）系参照畏兀儿体老蒙文字母，而蒙古与女真语音原本存在差异，借用的蒙文字母未必能充分传达女真语言的意义。如太宗皇太极在评价老满文时所说，“书中寻常语言，视其文义，易于通晓”，但“至于人名、地名，必致错误。”[ 中国第一历史档案馆、中国社科院历史研究所译注：《满文老档》，北京：中华书局，1990年，第1196页。]<br />
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From then on, the languange and the forms in Manchurian language are gradually consistant. However, the problem is that the Manchurian language(Old Manchu) was created with the guidance of Uighur Old Mongolia alphabets, and there exist original difference between Mongolia and Jurchen pronunciation. Therefore, the borrowed Mongolian alphabets could not explicitly express the meaning of Jurchen language. Just as what the great emperor Taizong Hoang Taiji said when he evaluated Old Manchu: "You can easily understand the meaning of the ordinary language in a book written in Old Manchu. However, in terms of the name of people and places, the Old Manchu would lead you to misunderstanding without any doubt." (Translated by The First Historical Archives of China and Institute of Chinese Social History :''Manchu Old File'',Peking:Zhonghua Book Company,1990, page 1196.) <br />
Annotation:<br />
1. Jurchen: an ancient nationality in China<br />
2.Uighur(畏兀儿体/古维吾尔语): the earliest Mongolian Chinese characters<br />
3. Old Manchu:the language used by ancient Machurian<br />
4. Manchurian: the founder of Qing Dynasty<br />
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From then on, the languange and the characters in Manchurian language are gradually consistant. However, the problem is that the Manchurian language(Old Manchu) was created with the guidance of Uighur Old Mongolia alphabets, and there existed original difference between Mongolia and Jurchen pronunciation. Therefore, the borrowed Mongolian alphabets could not explicitly express the true meaning of Jurchen language. Just as what the great emperor Taizong Hoang Taiji said when he evaluated Old Manchu: "You can easily understand the meaning of the ordinary language in a book written in Old Manchu. However, in terms of the name of people and places, the Old Manchu would lead you to misunderstanding without any doubt." (Translated by The First Historical Archives of China and Institute of Chinese Social History :“Manchu Old File'',Peking:Zhonghua Book Company,1990, page 1196.) --[[User:Chen Huini|Chen Huini]] ([[User talk:Chen Huini|talk]]) 05:10, 3 October 2021 (UTC)Chen Huini<br />
Annotation:<br />
1. Jurchen: an ancient nationality in China<br />
2.Uighur(畏兀儿体/古维吾尔语): the earliest Mongolian Chinese characters<br />
3. Old Manchu:the language used by ancient Machurian<br />
4. Manchurian: the founder of Qing Dynasty<br />
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==英语语言文学（英美文学） 202120081479 陈惠妮 女==<br />
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为此，皇太极命“巴克什”达海对老满文加以改进，使其音、义明晓，有助于学习，形成了所谓“新满文”。[ 鄂尔泰等奉敕修：《清实录·太宗文皇帝实录》，北京：中华书局，1985年，第13页。]文字虽已形成，但满人一时间几乎无书可读，原因有二：其一，此时的满文尚属草创，旗人尚不能以满语编纂书籍；其二，汉字书籍的获取极为不易。<br />
面对上述情况，太祖努尔哈赤敕令在旗人中延请师傅，教子弟读书，并令达海等人以满文翻译汉文典籍。<br />
Therefore, the King asked Baks Dahai to develop the Old Manchu to make its sound and meaning more clear, which maked it easier to learn. It is in this way that the so-called New Manchu was formed. [E'ertai et al. Fengxiu: "Records of the Qing Dynasty·Records of Emperor Taizongwen", Beijing: Zhonghua Book Company, 1985, p. 13. ] Although the characters and words have been developed, the Manchu can hardly find bookes to read for two reasons. One is that the Manchu language at that time is still an initial creation. The other is that it's so hard to get the books in Chineses charaters.On the face of this situation, Taizu Nurha Chi invited masters among the bannermen to teach children to read, and ordered Dahai and others to translate Chinese classics in Manchu. --[[User:Chen Huini|Chen Huini]] ([[User talk:Chen Huini|talk]]) 07:59, 29 December 2021 (UTC)Chen Huini<br />
Therefore, Huang Taiji (an emperor of Qing Dynasty) instructed "Bakshi (a title of a scholar of Qing Dynasty)" Da Hai to develop the Old Manchu to the so-called "New Manchu", which made the sound and meaning more clear and then make it easier to learn. [E'ertai et al.: Factual Record of Qing Dynasty·Factual Records of Emperor Taizongwen, Beijing: Zhonghua Book Company, 1985, p. 13. ] Although the characters and words have been formed, the Manchu for a time almost no books to read for two reasons. One is that the Manchu language at that time was still an initial creation, and the Bannermen couldn't compile with it. The other was that the acquisition of books in Chineses charaters was extremely hard.On the face of this situation, Taizu Nurha Chi invited masters among the bannermen to teach children to read, and ordered Dahai and others to translate Chinese classics into Manchu.--[[User:Cheng Yang|Cheng Yang]] ([[User talk:Cheng Yang|talk]]) 10:16, 29 September 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081482 程杨 女==<br />
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《清史列传》中对此的记载如下：弱冠，太祖高皇帝召直文馆，凡国家与明及蒙古、朝鲜词命，悉出其手。有诏旨应兼汉文者，亦承命传宣，悉当上意。旋奉命译《明会典》及《素书》、《三略》。[ 王钟翰点校：《清史列传》，北京：中华书局，1987年，第187页。]<br />
The records of Da Hai in ''the Biographies of the Qing Dynasty'' as follows: at the age of twenty, he was called into the imperial palace and served in Wen Guan (the bureaucratic ministry for translating Chinese books in the early Qing Dynasty). The naming of new words that related to the Ming Dynasty, Mongolia and North Korea were all by him to complete. He was also instructed to convey some of the Chainese-related orders totally reflecting the will of the emperor. Soon, he was asked to translate ''the Code of Ming Dynasty'', ''Su Shu''(written in Qing Dynasty), and ''San Lue''(written by Huang Shigong).[''The Biographies of the Qing Dynasty'', edited by Wang Zhonghan, Zhong Hua Book Company, 1987, pp.187.]--[[User:Cheng Yang|Cheng Yang]] ([[User talk:Cheng Yang|talk]]) 03:52, 29 September 2021 (UTC)<br />
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The records of Da Hai in ''the Biographies of Qing Dynasty'' as follows: at the age of twenty, he was appointed by Nurhachi①as a translator in Wen Guan②. The naming of new words that related to Ming Dynasty, Mongolia and North Korea were all by him to complete. He was also instructed to convey some of the Han's language-related orders totally reflecting the will of the emperor. Soon, he was asked to translate ''the Code of Ming Dynasty'', ''Su Shu''③, and ''San Lue''④.[''The Biographies of the Qing Dynasty'', edited by Wang Zhonghan, Zhong Hua Book Company, 1987, pp.187.<br />
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Annotation:①Nurhachi:the founder of Qing Dynasty, 1559–1626. ②Wen Guan: the bureaucratic ministry for translating Chinese books in the early Qing Dynasty. ③''Su Shu'': moral principles written in Western Han Dynasty by Huang Shigong. ④''San Lue'':military monograph written in Western Han Dynasty by Huang Shigong.--[[User:Ding Xuan|Ding Xuan]] ([[User talk:Ding Xuan|talk]]) 13:10, 29 September 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081483 丁旋 女==<br />
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上文中所谓《明会典》，又称《大明会典》，系明代典章制度史书，内容涉及汉族文教、历法、习俗等。与《明会典》不同，《素书》成书于西汉，并非典章著作，而是哲理之学，道家思想的智慧之作。《三略》又名《黄石公三略》，既是军事战略专论，又糅合了诸子百家思想。<br />
''Code of Ming Dynasty''① mentioned above, also known as ''Code of Great Ming Dynasty'', is one historical book about laws and regulations in the Ming Dynasty, covering Education of Han②, the Chinese calendar, and custom and so on. Different from Code of Ming Dynasty, ''Su Shu''③ written in the Western Han Dynasty is a wisdom work full of philosophy and Taoism rather than law and regulations. ''Sun-Lue''④, also called Sun-Lue of Huang Shigong, is not only a military strategy monograph but a mixture of the hundred schools of thought. <br />
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Annotation: <br />
①''Code of Ming Dynasty'': One code about Ming’s laws and regulations in many aspects started to write in 1393 and finished in 1578.<br />
②Education of Han: The education policy initiated by emperors of Ming Dynasty in order to transmit Confucianism thoughts and consolidate their reign.<br />
③''Su Shu'': It is one book full of principles and truth written by Huang Shigong in the western Han Dynasty. It is used for the reign of country because of its instructive and moral function.<br />
④''Sun-Lue'': It is one famous ancient military book including three parts written by Huang Shigong in the Western Han Dynasty. The military thoughts in it are critically useful and different from others because it discusses military strategies from political perspective.--[[User:Ding Xuan|Ding Xuan]] ([[User talk:Ding Xuan|talk]]) 06:43, 29 September 2021 (UTC)<br />
''Code of Ming Dynasty''① mentioned above, also known as ''Code of Great Ming Dynasty'', is one historical book about laws and regulations in the Ming Dynasty, covering the range of Education of Han②, the Chinese calendar, and customs and so on. Different from Code of Ming Dynasty, ''Su Shu''③ written in the Western Han Dynasty is a work full of philosophical wisdom and Taoism rather than law and regulations. ''Sun-Lue''④, also called Sun-Lue of Huang Shigong, is not only a military strategy monograph but a mixture of the hundred schools of thought.<br />
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==英语语言文学（英美文学） 202120081485 付红岩 女==<br />
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以上三者皆是重要的汉文典籍，清太祖令达海翻译它们，开启了清代翻译汉籍（书）之先河，其政治策略上的深刻用意不言而喻。<br />
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三 太宗时期汉籍（书）翻译之“兴”<br />
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太宗皇太极即位后，为鼓励旗人读书，多措并举：一方面，要求“十五岁以下，八岁以上者，俱令读书”，惩处不愿教子读书者；另一方面，为改善“无书可读”的情况，又致函朝鲜，索求汉文典籍。<br />
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Since the three ancient books just mentioned above were all significant Chinese classics, Nurhaci, the Emperor in Qing Dynasty requested Dahai, the official should translate the classics into the mongolian, whose profound meaning in the aspect of political layout was very explicit.<br />
Third, the interest of translating Chinese classics has reached its climax during the power of Nurhaci.<br />
After NUrhaci’s coming into the power, many decrees has been enacted in attempt to encourage the people to read. On the one hand, the teenagers between 8 and 15 were allowed to read. In addition , the parents who were reluctant to support their offspring to read would be punished. On the other hand, Qing Dynasty sent a letter to Korea, in which the former asked the later to denote more Chinese classics in order to improve the predicament that the books were not enough to read.<br />
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批改：译者译文总体流畅，对原文理解总体得当。以下为细节处理上的建议 <br />
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1.“清太祖”一词为中国专有名词，应先查后译。清太祖指的是努尔哈赤（1616-1626），是后金第一位大汗，清朝的奠基者，太祖在位时，“大清国”还没有定鼎中原，故此时的“清”并不能算是中国的一个朝代。译者将其处理为“the Emperor in Qing Dynasty”不够严谨。<br />
建议改为：Nurhaci, the Founder of Qing Dynasty<br />
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2.“三 太宗时期汉籍（书）翻译之‘兴’”此处为论文小标题，首先，应当注意标题格式。结构上，原文为名词短语，译者翻译时使用英语句子的SVO（A）结构，虽然意思完整，但失去了原文的简洁。<br />
词语上，“兴”强调翻译之风的兴起、盛行，译者将其处理为“reached its climax”缺乏严谨性，太宗时期不一定是汉籍翻译的高潮时期。<br />
建议改为：Third The Prosperity of Chinese Classics Translation During the Power of Nurhaci<br />
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3.“十五岁以下，八岁以上者，俱令读书”<br />
词语上，“令”是主动要求的意思，译者将其处理为“be allowed”，即被允许读书的可能性，读与不读似乎都是可以接受的。结合后文“惩处不愿教子读书者”，说明“令”是强制的。“be allowed”不能精准传达出原文清政府对旗人读书的鼓励与强制性。<br />
结构上，原句省略了主语（清政府），原意为：清政府要求十五岁以下，八岁以上的人都得读书。译者将主动结构改为了被动结构，突出强调了实施对象。<br />
建议改为：The teenagers between 8 and 15 were asked to read.--[[User:Li Ruiyang|Li Ruiyang]] ([[User talk:Li Ruiyang|talk]]) 04:20, 29 September 2021 (UTC)<br />
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All the above three were important Han classics, so Nurhaci, the founder of Qing Dynasty, commanded Dahai to translate them into Manchu language, which opened the floodgates to the large-scale translations of Han classics and its profound political meaning was explicit.<br />
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Third The Prosperity of Chinese Classics Translation During the Power of Nurhaci<br />
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After Abahai, the son of Nurhaci, coming into power, many decrees has been issued to encourage people to read and study. On the one hand, hand, teenagers between 8 and 15 were asked to study, and their parents would be punished if they were unwilling to educate their children; On the other hand, in order to avoid the scarcity of Han classics, Qing Dynasty sent a written request to Korea for a generous lending.--[[User:Li Ruiyang|Li Ruiyang]] ([[User talk:Li Ruiyang|talk]]) 14:43, 29 September 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081497 李瑞洋 女==<br />
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《朝鲜王朝实录·仁祖实录》中写道：“闻贵国有金、元所译《书》、《诗》等经及《四书》，敬求一览，惟冀慨然。”[ 国史编纂委员会：《朝鲜王朝实录》，汉城：国史编纂委员会，1973年，第38页。]可见，皇太极此时想要借阅的并非汉文原书，而是汉籍的金、元译本，即女真语和蒙古文译本。对此，朝鲜政府方面虽然进行了回应，但态度并不热忱：见索《诗》、《书》、《四书》等书籍，此意甚善，深嘉贵国尊信圣贤，慕悦礼义之盛意。<br />
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As mentioned in ''King Injo the Great'', one of the volumes in ''Annals of the Korean Dynasty'' “We heard your country have ''the Four Books'' and other classics including ''The Book of Poetry'' and T''he Book of History'', which were translated in Jin Dynasty and Yuan Dynasty, so we sincerely hope your generous lending of these books.” [National History Compilation Committee: ''Annals of the Korean Dynasty'', Seoul: National History Compilation Committee, 1973,P38] Obviously, at this time what Huang Taiji wanted to borrow was not the original Chinese book, but the Jin and Yuan translations, namely the Jurchen and Mongolian versions. Although Korean officials responded to this request, they were not very willing: It is very nice of you to ask for the Four Books and other classics including ''The Book of Poetry'' and ''The Book of History'', and we really appreciate your country’s popularity of respecting men of virtue and advocating courtesy and justice.--[[User:Li Ruiyang|Li Ruiyang]] ([[User talk:Li Ruiyang|talk]]) 02:19, 29 September 2021 (UTC)<br />
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It was recorded in ''King Injo the Great'', a volume of ''Annals of the Korean Dynasty'', that “We have heard your country have classics like ''The Book of Songs'', ''The Book of History'' and ''the Four Books'' , which were translated in Jin and Yuan Dynasties, so we sincerely hope for your generous lending of these books to us.” [National History Compilation Committee: ''Annals of the Korean Dynasty'', Seoul: National History Compilation Committee, 1973, 38.]<br />
Obviously, what Huang Taiji wanted to borrow at that time were not the original Chinese books, but the translated versions of Jin and Yuan periods, namely the Jurchen and Mongolian versions. Although Korean officials respondedt, they didn't give a active response: It is glad to receive your borrowing request of these books, and we do appreciate your country’s deeds of respecting men of virtues and advocating courtesy and justice.--[[User:Li Shan|Li Shan]] ([[User talk:Li Shan|talk]]) 01:28, 30 September 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081498 李姗 女==<br />
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第国中所有，只是天下通行本，而金、元所译，则曾未得见，兹未能奉副，无任愧歉。[ 国史编纂委员会：《朝鲜王朝实录》，汉城：国史编纂委员会，1973年，第62页。]<br />
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由于未能索得属意书籍，皇太极遂令达海继续翻译，后者于天聪六年开始翻译《通鉴》、《六韬》、《孟子》、《三国志（通俗演义）》，以及《大乘经》等。只可惜，由于达海英年早逝，上述书籍未能译竟。<br />
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The one that existed in the great empire was just a general version that circulated all over the country.（* But the one that existed in the whole country was just a general version.--[[User:Li Wenxuan|Li Wenxuan]] ([[User talk:Li Wenxuan|talk]]) 02:37, 29 September 2021 (UTC)） As the translating versions of Jin and Yuan were not publicized yet, it's a pity that their works were not able to be offered to the emperor.（* However, the translated versions in Jing Dynasty and Yuan Dynasty hadn't published yet.It's a pity that their works were not able to be offered to the emperor.--[[User:Li Wenxuan|Li Wenxuan]] ([[User talk:Li Wenxuan|talk]]) 02:37, 29 September 2021 (UTC)) [National History Compilation Committee: ''Records of the Korean Dynasty'', Seoul: National History Compilation Committee, 1973, 63.]<br />
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Failing to obtain desired translation versions, Huangtaiji,the first emperor of Qing dynasty, ordered Dahai to continue his translation work. And in the sixth year of Huangtaiji's rule (the year of 1632), Dahai began to translate ''History as a Mirror'', ''The Six Arts of War'', ''The Records of Three Kingdoms'' as well as ''Mahayana Sutra'' and so forth. [* ''Tong Jian'' ( a history book), ''Liu Tao'' ( a military book), ''Mencius''，''Records of the Three Kingdoms'' and ''Dacheng Jing'' ( a book about Buddhism) --[[User:Li Wenxuan|Li Wenxuan]] ([[User talk:Li Wenxuan|talk]]) 02:37, 29 September 2021 (UTC)] Unfortunately, the translation of these books was not finished as a result of Dahai's early death.--[[User:Li Shan|Li Shan]] ([[User talk:Li Shan|talk]]) 12:46, 11 October 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081500 李文璇 女==<br />
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清初之际，达海被满洲“群推为圣人”，他翻译的汉文书籍对于拓展满族的知识范围，甚为关键。[ 叶高树：《清朝前期的文化政策》，台北：稻乡出版社，2002年，第58页。]《清实录·太宗文皇帝实录》中说：其平日所译汉书，有《刑部会典》、《素书》、《三略》、《万宝全书》俱成帙。（天聪六年）时方译《通鉴》、《六韬》、《孟子》、《三国志（通俗演义）》及《大乘经》，未竣而卒。<br />
In the early Qing Dynasty, Dahai was regarded as a sage by the people of Manchuria. The Chinese books translated by him was significant for extending the knowledge. In the book ''Memoir of the Qing Dynasty'' for the Emperor Taizong, it recorded that the books Dahai had translated, including ''Records of Ministry of Punishment'', ''Su Shu'' ( a book about Taoism), ''San Lue'' ( a military book), ''Wan Bao Quan Shu'' ( a book about daily life), all of which had been compiled into volumes. In 1632, he translated ''Tong Jian'' ( a history book), ''Liu Tao'' ( a military book), ''Mencius''，''Records of the Three Kingdoms'' and ''Dacheng Jing'' ( a book about Buddhism). However, he died without finishing these books. --[[User:Li Wenxuan|Li Wenxuan]] ([[User talk:Li Wenxuan|talk]]) 23:26, 28 September 2021 (UTC)--[[User:Li Wen|Li Wen]] ([[User talk:Li Wen|talk]]) 02:38, 29 September 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081501 李雯 女==<br />
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初我国未深谙典故，诸事皆以意创行，达海始用满文，译历代史书，颁行国中，人尽通晓。惟我太祖天纵聪明，因心肇造，所行皆与古圣贤同符默契。达海与额尔德尼应运而生，实佐一代文明之治云。[ 鄂尔泰等奉敕修：《清实录·太宗文皇帝实录》，北京：中华书局，1985年，第10页。]<br />
In the beginning, our country was not familiar with the ancient books and stories, so everything began by "thoughts". Since Dai Hai began to use Man Character to translate historical books of the past dynasties and promulgated it in our country, it has become universal among public. Only my great Grandfather, gifted and wise, created from the heart, and acted in accordance with the ancient sages. So the Dai Hai and E Erdeni came into being following the tendecy, who help to create this generation of civilization.[E ertai,ect compiling by order of the Emperor;The record of Emperor Taizong in Qing Danasty,Beijing,Zhonghua Book Company,1985,Page 10.--[[User:Li Wen|Li Wen]] ([[User talk:Li Wen|talk]]) 02:36, 29 September 2021 (UTC)<br />
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In the beginning, our country established things at will without the study of ancient classics. They were not well-known to the public until Da Hai translated historical books of the past dynasties in Manchu and promulated them in the country. Only the Emperor Taizu, who founded the dynasty on his own way, was so gifted and wise that what he had done was exactly accordance with the ancient sages.In reasponse to the call of the times, Da Hai and Erdeni came into being and helped to create this generation of civilization.[E ertai,ect compiling by order of the Emperor;''The Record of Emperor Taizong in Qing Danasty'',Beijing,Zhonghua Book Company,1985,Page 10.--[[User:Liu Peiting|Liu Peiting]] ([[User talk:Liu Peiting|talk]]) 04:49, 29 September 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081505 刘沛婷 女==<br />
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如前所述，《明会典》、《素书》、《三略》等书的翻译始于太祖时期，但成书于天聪四年，其余书籍的翻译则为时稍晚。综观达海译书，既有《孟子》之类所谓“知正心、修身、齐家、治国”者，又有《素书》、《三略》和《六韬》等“益聪明智识，选练战攻的机权”者，以及《通鉴》等“知古来兴废事迹”者，他的翻译“实佐一代文明之治”。<br />
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以《刑部会典》（又称《明会典》）为例，达海译本既是天聪年间国家创制法律的依据，也是太宗推行政治改革的蓝本。<br />
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As mentioned above, the translation of ''Code of Great Ming Dynasty'', ''Su Shu'' and ''Three Policies'' began in the reign of Taizu but was completed in 1630. The other books were translated a little later. Da Hai's translations covered abundant eminent men, such as those in ''Mencius'' who make their minds just, morality cultivated, families regulated and the country governed orderly, and those with extrodinary intelligence and knowledge of training and combat in ''Su Shu'', ''Three Policies'' and ''Six Strategies'', as well as the men knowing the rise and fall of ancient times in ''Comprehensive Mirror for Aid in Government''. Therefore, his translations did promote the rule of civilization.<br />
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Taking ''Code of Ministry of Penalty'' (also called ''Code of Great Ming Dynasty'')as an example,Da Hai's translations were not only the basis for establishing laws during the period of Tiancong, but also the blueprint for the political reforms carried out by Taizong.--[[User:Liu Peiting|Liu Peiting]] ([[User talk:Liu Peiting|talk]]) 15:25, 28 September 2021 (UTC)<br />
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As mentioned above, the translating of ''Code of Great Ming Dynasty'', ''Su Shu'' and ''Three Policies'' began during the reign of Taizu but was completed in 1630, the fourth year under the reign of Taizong. Other books were translated at a later time. Given that in Da Hai's translations exist abundant eminent men, such as those in ''Mencius'' who make their minds just, morality cultivated, families regulated and the country governed orderly, and those with extrodinary intelligence and knowledge of training the military in ''Su Shu'', ''Three Policies'' and ''Six Strategies'', as well as the men comprehending the ups and downs experienced by dynasties in the past in ''Comprehensive Mirror for Aid in Government'', his translations did promote the governance under civilization.<br />
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Taking ''Code of Ministry of Penalty'' (also called ''Code of Great Ming Dynasty'')as an example,Da Hai's translations were not only the basis for establishing laws during the period of Tiancong, but also the blueprint for the political reform carried out by Taizong.--[[User:Liu Xiao|Liu Xiao]] ([[User talk:Liu Xiao|talk]]) 02:46, 29 September 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081508 刘晓 女==<br />
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太宗素有“振兴文治”的愿望，不仅创制了作为清代考试制度之滥觞的生员和举人考试，而且使巴克什、笔帖式制度臻于成熟，二者合力使得汉籍翻译的风气渐开：一方面，作为“巴克什”或“笔帖式”，希福、尼堪、刚林、苏开等人先后奉敕“（翻）译汉字书籍”或“记注国政”；另一方面，太宗以自古国家“以文教佐太平”为由，令满人争相读书，从生员中选取“文艺明通者优奖之”。[ 鄂尔泰等奉敕修：《清实录·太宗文皇帝实录》，北京：中华书局，1985年，第14页。]<br />
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在对待前朝即明代的问题上，太宗采取“讲和”与“自固”并行的政策，但其本人向往中原汉族文化，所谓“性嗜典籍，披览弗卷”即是这一情况的体现。<br />
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Holding the idea to "revitalize the administration by education and culture", Taizong not only created examinations from which the examination system of Qing Dynasty originated, for xiucai and juren, those who have passed in the exam at the county and provincial level respectively, but also made the Baksh and Bithesi system reach a mature state. Together, the two measures promoted the translation of books written in Chinese. On one hand, Xifu, Nikan, Ganglin and Su Kai, as "Bakshs" or"Bithesis", successively "translated Chinese books" or "annotated state affairs" by the order of the emperor. On the other hand, by saying that countries applied the education and culture to safeguarding the stablization since ancient times, Taizong urged people to scramble to study, and then awarded those from xiucai "who mastered literature and arts". (''Records of Qing Dynasty· Emperor Taizong'', edited by Ertai, officials in the Qing Dynasty, Beijing: China Publishing House, 1985, P14.)<br />
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In dealing with the treatment of the previous dynasty, that is Ming Dynasty, Taizong adopted the policy of "settling a dispute" and "self-consolidation". But he himself yearned for the Chinese culture of the Central Plains, which is embodied by the so called expression "Loving Chinese works and reading Buddha volumes."<br />
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Revised version：<br />
Holding to the idea of "revitalizing the empire by education and cultural development", Taizong not only established shengyuan and juren exams（exams at the county and provincial level perspectively） initiating the imperial examination system of Qing Dynasty, but also brought the Baksh and Bithesi system to a mature state. Together, the two measures brought about the upsurge in translation of Han books. On the one hand, Xifu, Nikan, Ganglin and Su Kai, as "Bakshs" or"Bithesis", successively "translated Han books" or "annotated state affairs" by the order of the emperor. On the other hand, in the name of the national tradition of ensuring stabilization by education and cultural development, Taizong urged Man people to read and those shengyuan who stands out for their familiarity with literature and arts were awarded. (The Memoir of Qing Dynasty· Emperor Taizong, edited by Ertai, officials in the Qing Dynasty, Beijing: China Publishing House, 1985, P14.)<br />
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In dealing with the treatment of the former dynasty, namely Ming Dynasty, Taizong resorted to the policy of “peace negotiation” and " self-consolidation" in parallel. However, Taizong himself yearned for the Central Plains culture of Han, and the expression “a voracious reader of Han and Buddhist classics” can properly manifests it. --[[User:Liu Yunxin|Liu Yunxin]] ([[User talk:Liu Yunxin|talk]]) 07:53, 29 September 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081510 刘运心 女==<br />
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于是，太宗诏令儒臣翻译汉书，并命金、汉之人阅读。[ 同上，第2页。]太宗深知，汉文典籍言微而义大，其精要者不仅涉及帝王治平之道，而且涉及正心、修身、齐家之理。但汉文典籍数量庞杂，翻译时必须有所选择。<br />
Therefore, Taizong issued an order asking the civilian official to translate Han books and all the Han and Jin people are required read those. (Ibid., p.2) Taizong knew well how brief and profound Han classics were. Their essence not only involves the governess of the country and its people, but also discusses inner integrity, decent behavior and family harmony. Nevertheless, a great number and variety of Han classics means what to be translated needs selection.<br />
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Therefore, Taizong ordered Confucian officials to translate Han books, while all the Han and Jin people were required to read those. (Ibid., p.2) Taizong knew well that Han classics were sublime works with deep meaning, whose essentials included not only the method of statecraft, but also self-cultivation and family regulation. Nevertheless, among a great number of Han classics, there must be an extraction.<br />
--[[User:Luo Anyi|Luo Anyi]] ([[User talk:Luo Anyi|talk]]) 02:14, 29 September 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081511 罗安怡 女==<br />
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如天聪六年九月，王文奎奏请从读书笔帖式内，选取“伶俐通文者”一、二人，并从秀才内选取“老成明察者”一、二人，令其“讲解翻写”。天聪九年三月二十一日，仇震向太宗谏言，要求从汉人中选取精通经、史者二、三人，并从金人中选取熟悉字法者三、四人，将各经史典籍及《通鉴》（即《资治通鉴》）中“有裨君道”的精要部分“集为一部”，日日讲解，以便统治者在翻译、日讲中学习汉族文化，以及治世之道。[ 罗振玉：《天聪朝臣工奏议》，北京：中国人民大学出版社，1989年，第24-25、115页。]固然，对于汉文典籍与汉族文化，太宗并非全盘接受，而是辩证地加以吸收。<br />
For instance, in September, 1632, the sixth year of T'ien-ts'ung (the first reign title of Emperor Taizong of Qing Dynasty), Wang Wenkui wrote to His Majesty, advising that they should elect one or two "literates who were skilled at writing and translation" from the bithesi, and one or two "scholars who were learned and perspicacious", for "teaching, interpretation, and translation" of Han classics. On March 21st, 1635, the ninth year of T’ien-ts'ung, Chou Zhen advised Emperor Taizong to elect two or three Han people who were proficient in lections and historical records, and three or four Jin people who were familiar with 字法. These scholars should choose essentials from classics and ''The Zizhi'' (''The Zizhi Tongjian'') , which benefits the ideas of ruling power of feudal emperors, and compile them into one book. Also they should discourse the compiled thoughts frequently, in which the ruler could learn the Han culture and method of statecraft. [Zhenyu, Luo .(罗振玉),《天聪朝臣工奏议》，Beijing：China Renmin University Press, 1989, P24-25, P115.] Of course, Taizong did accepted Chinese classics and han culture critically and dialectically.<br />
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For instance, in September, the sixth year of Tian Cong(the first reign title of Emperor Taizong of Qing Dynasty), Wang Wenkui wrote to His Majesty to pick up one or two who are "talented in literature” and pick up one or two who are "sophisticated and insightful"from the scholars for " interpretation, and translation" of Han classics. On March 21st, 1635, the ninth year of T’ien-ts'ung,Qiu Zhen advised Emperor Taizong to pick up two or three Han people who were proficient in lections and historical records, and three or four Jin people who were familiar with grammer to let them choose the essence which are benefical to the reign from classics and ''The Zizhi'' (''The Zizhi Tongjian'') and compile them into one book.After that,they should illuminate the compiled thoughts every day, which is beneficial to the study of the ruler about the Han culture and ruling ways. [Zhenyu, Luo .(罗振玉),《天聪朝臣工奏议》，Beijing：China Renmin University Press, 1989, P24-25, P115.] Definitely speaking, Taizong did accepted Chinese classics and han culture critically and dialectically.--[[User:Luo Xi|Luo Xi]] ([[User talk:Luo Xi|talk]]) 03:24, 29 September 2021 (UTC)(Luo Xi罗曦）<br />
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==英语语言文学（英美文学） 202120081512 罗曦 女==<br />
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如天聪九年五月，上谕文馆诸臣：朕观汉文史书，殊多饰辞，虽全览无益也。今宜于《辽》、《宋》、《金》、《元》四史内，择其勤于求治而国祚昌隆，或所行悖道而统绪废坠，与夫用兵行师之方略，以及佐理之忠良、乱国之奸佞，有关政要者，汇纂翻译成书，用备观览。至汉文正史之外，野史所载，如交战几合，逞施法术之语，皆系妄诞，此等书籍，传之国中，恐无知之人信以为真，当停其翻译。[ 鄂尔泰等奉敕修：《清实录·太宗文皇帝实录》，北京：中华书局，1985年，第9页。]<br />
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For instance，in May，the ninth year of Tian Cong，the emperor informed all the offcials in the cultural center that：I have read all those historical records in Han Dynasty filled with various ornaments，only to find that I have gotten nothing。Now you had better to pick up some references among <Liao>、<Song>、<Jin>、<Yuan> about some cases like dilligence making a prosperous country or idleness making a weak one,also,you can pick up some military stratedies,and some information about those noble and talented citizens or traitors,and than you ought to translate all those relevence and compile them into a book prepared to be read.As for those unofficial history in Han excluded,like how many rounds did soldiers battle or the spell spoken by wizards,are all the nonsense,which will bewilder the ignorant,deserving to be prohibited from translation.{E Ertai：《清实录、太宗文皇帝实录》，Beijing：Chinese bookstore，1985，p9.}<br />
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A modified version: For instance, in May 1653, the Emperor said to the ministers in Literature Institution(that is, the later Cabinet in Qing Dynasty), "I have leafed through the historical works in Chinese language with various ornamental rhetorics, but the complete reading of them is not beneficial. Now it is appropriate to select salient examples referred from the four historical recrods of ''Liao'', ''Song'', ''Jin'' and ''Yuan'', which will be compiled into books for later reading. These examples include those who were dedicated in governing the country thereby with a promising national development, or those who deviated from the correct path with weak administration, and the generals adept at warfare, loyal and honest servants assisting the sovereign to handle state affairs, the treacherous ones rendering the nation disorderly and chaotic as well as other relevant political workers. Meanwhile, apart from the official history, the unofficial historical works that record untrue events, like the conditions of battles, are all fabricated. If those unfavorable books are spread to the mainland, they may bewilder the ignorant and gullible individuals, thus deserving to be prohibited from translation. (E Eratai: ''Records of Qing Dynasty·Emperor Taizong'', Beijing: China Publishing House, 1985, Page9.)--[[User:Mao Yawen|Mao Yawen]] ([[User talk:Mao Yawen|talk]]) 04:47, 29 September 2021 (UTC)毛雅文<br />
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==英语语言文学（英美文学） 202120081514 毛雅文 女==<br />
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由上可知，在汉籍翻译的问题上，太宗讲求的是实用，希望将翻译与政要相关联，反对翻译浮华藻饰的汉文书籍，或者野史中所载不足为信者。以《刑部会典》的翻译为例，该译本的刊刻与颁行逐渐成为太宗朝的临政规范。《天聪朝臣工奏议》中说：“近奉上谕，凡事都照《大明会典》行，极为得策。”[ 罗振玉：《天聪朝臣工奏议》，北京：中国人民大学出版社，1989年，第2页。]<br />
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From here it can be seen that the Emperor Taizong put an emphasis on practicality with regard to the translation of Chinese works, hoping to associate translation with politics. He objected to the translation of Chinese books with flashy embellishments, or that of unofficial historical works recording unconvincing events. Take the translation of ''The Code of the Ministry of Penalty'' as an example. After its publication and promulgation, the tranlation of this code gradually became the norm of handling state affairs. ''The Petition of Ministers during the Reign of Tiancong'' states, "Recently, by the order of the Emperor, everything should be conducted in accordance with ''The Code of Ming Dynasty'', which is a desirable policy." (Luo Zhenyu: ''The Petition of Ministers during the Reign of Tiancong'', Beijing: China Remin University Press, 1989, Page2.)<br />
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From above it can be seen that the Emperor Taizong put an emphasis on practicality with regard to the translation of Chinese works, hoping to associate translation with politics. He objected to the translation of Chinese books with flashy embellishments, or that of unofficial historical works recording unconvincing events. Take the translation of The Code of the Ministry of Penalty as an example. After its publication and promulgation, the tranlation of this code gradually became the norm of handling state affairs. The Petition of Ministers during the Reign of Tiancong states, "Recently, by the order of the Emperor, everything has been conducted in accordance with The Code of Ming Dynasty, which is a desirable policy." (Luo Zhenyu: The Petition of Ministers during the Reign of Tiancong, Beijing: China Renmin University Press, 1989, Page2.)--[[User:Mou Yixin|Mou Yixin]] ([[User talk:Mou Yixin|talk]]) 10:19, 29 September 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081516 牟一心 女==<br />
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宁完我也说，我国六部的设立原是照“蛮子家立的”，因而金官对于部中当举事宜原本并不知情，而今翻译《会典》（即《明会典》），参汉酌金，加以“打动”，必将使其“去因循之习”而“渐就中国之制”。[ 同上，第82页。]<br />
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四 世祖时期汉籍（书）翻译之发展<br />
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清初儒臣中，除额尔德尼、噶盖和达海之外，通满、蒙、汉字者不乏他人，如伊成额、希福、刚林等，便是其中姣姣者。<br />
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Ning Wanwo also said that the establishment of the six ministries in feudal China was based “on that of minority nationalities”, so the officers of Jin Dynasty had no idea about the affairs in the ministries. Now the translation of Code(a record of laws and systems of a dynasty)(namely, Code of Great Ming Dynasty) must refer to both the precedents of Central China and Jin Dynasty and amend them to make it get rid of rigid traditions and turn to the conventions of Central China.[ibidem, Page 82]<br />
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FOURTH The development of Chinese literature (books) in Qing Dynasty Shunzhi period<br />
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Among the civilian officials in the early Qing Dynasty, besides Erdeni, Gagai, Daher, there is no lack of masters of Manchu language, Mongolian and Chinese, such as Icher, Hifo, Galin, who were the strong performers among them.<br />
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Ning Wanwo also said that the establishment of the six ministries of feudal China was based on “that of the southerners”, so Jin Guan had no knowledge of the affairs concerning the appointment in the ministries. Now the translation of Code( a record of laws and systems of a dynasty)( namely, Code of Great Ming Dynasty) must refer to both the precedents of Han and Jin Dynasty and then amend them to make it get rid of rigid traditions and gradually conform to the conventions of Central China. [ibidem, Page 82.]<br />
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FOURTH The development of Chinese literature (books) in Shizu Period <br />
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Among the civilian officials in the early Qing Dynasty, besides Erdeni, Gagai, Daher, there is no lack of masters of Manchu language, Mongolian and Chinese, such as Icher, Hifo, Galin, who were the best performers of them.--[[User:Shi Liqing|Shi Liqing]] ([[User talk:Shi Liqing|talk]]) 12:58, 29 September 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081521 石丽青 女==<br />
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据传，伊成额不仅将《太祖高皇帝实录》译成汉文，而且翻译了朝鲜所奏表章，以及《礼部会典》等书。[ 清高宗敕纂：《八旗满洲氏族通谱》，沈阳：辽沈书社，1989年，第10页。]希福兼通满、蒙、汉三种语言，他的翻译有别于伊成额，不是将满文译成汉语，也不是将朝鲜文译成满语，而是主要翻译汉书、汉典，所翻译的汉文书籍包括《辽》、《金》、《元》三史等。希福的上述译书于顺治元年进呈皇帝，获世祖恩赉。<br />
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It is said that Yi Cheng'e translated not only the Annals of Emperor Taizu Gao into Chinese, but also the seals played by North Korea and the Book of the Ministry of Ceremonies. [ Qing Gaozong's Royal Compilation: "Eight Banners Manzhou Clan Genealogy", Shenyang: Liaoshen Publishing House, 1989, page10.] Xifu was fluent in Manchu, Mongolian and Chinese. His translation was different from Yicheng'e. He did not translate Manchu into Chinese or Korean into Manchu, but mainly translated Chinese books and Chinese classics, including Liao, Jin and Yuan. Xifu's translation was presented to the emperor in the first year of Shunzhi and won the honor of Shizu. --[[User:Shi Liqing|Shi Liqing]] ([[User talk:Shi Liqing|talk]]) 05:50, 29 December 2021 (UTC)<br />
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It is said that Yi Cheng'e translated not only the Factual Record of Taizu, but also the memorials presented to the emperor by North Korea, Records of the Board of Rites, and other books. [ Qing Gaozong's Royal Compilation: "Eight Banners Manzhou Clan Genealogy", Shenyang: Liaoshen Publishing House, 1989, page10.] Xi Fu was proficient in Manchu, Mongolian and Chinese, whose translation was different from Yi Cheng’e’s. Neither did he translate Manchu into Chineses nor Korean into Manchu, he mainly translated Chinese books and classics, including Liao, Jin and Yuan. Xi Fu’s translation was presented to the emperor in the first year of Shunzhi and won the honor of Shizu.--[[User:Wang Lifei|Wang Lifei]] ([[User talk:Wang Lifei|talk]]) 02:14, 29 September 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081523 王李菲 女==<br />
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《清实录·世祖章皇帝实录》中，曾详细记载了希福进呈译本时的情形：窃稽自古史册所载，政治之得失，民生之休戚，国家之治乱，无不详悉具备，其事虽往，而可以诏今；其人虽亡，而足以镜世。故《语》云：“善者吾师，不善者亦吾师。”从来嬗继之圣王，未有不法此而行者也。<br />
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In the “Records of Qing Dynasty· Emperor Fu Lin”, there was a detailed record of the situation when Xi Fu presented the translation: in the ancient records, whatever gain and loss in politics, weal and woe of people’s livelihood, or governance and chaos of countries, they’ve all been documented in detail. Although things have passed, they can still enlighten us. The ancestors have passed away, they can also provide references. Therefore, the “Language” said, “The heroes are my teacher, and so are the villains .” Virtuous emperors from ancient times to present have all followed this rule.--[[User:Wang Lifei|Wang Lifei]] ([[User talk:Wang Lifei|talk]]) 16:01, 28 September 2021 (UTC)<br />
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The “Records of Qing Dynasty· Emperor Fu Lin”, which once recorded in detail the situation when Xi Fu presented the translation: As far as I am concerned, from the ancient records, the gains and losses of politics, the welfare and woe of people’s livelihood, and the governance and chaos in the country, all of which have been documented in detail. Although things have passed, they can still enlighten us. Although the ancestors passed away, they are enough to mirror the world. Therefore, the “Language” says, “The success is my teacher, and so is the failure.” Virtuous emperors from ancient times have all followed this rule.--[[User:Wang Lifei|Wang Lifei]] ([[User talk:Wang Lifei|talk]]) 16:01, 28 September 2021 (UTC) Edited by Wang Zhenlong.<br />
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==英语语言文学（英美文学） 202120081525 王镇隆 男==<br />
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辽、金虽未混一，而辽已得天下之半，金亦得天下之大半，至元则混一寰区，奄有天下，其法令政教皆有可观者焉。我先帝鉴古之心，永怀不释，特命臣等将《辽》、《金》、《元》三史，芟（shān）削繁冗，惟取其善足为法，恶足为戒，及征伐畋（tián）猎之事，译以满语，缮写呈书。臣等敬奉纶音，将《辽史》自高祖至西辽耶律大石末年，凡十四帝，共三百七年；《金》凡九帝，共一百十九年；《元》凡十四帝，共一百六十二年，详录其有裨益者，……伏乞皇上万几之暇，时赐省览，懋稽古之德，弘无前之烈，臣等不胜幸甚。[ 鄂尔泰等奉敕修：《清实录·世祖章皇帝实录》，北京：中华书局，1985年，第15-16页。]<br />
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Although Liao and Jin did not mix together, Liao had already won half of the world, and Jin had also won the other half. Until the Yuan Dynasty, they were combined into one whole world,and there were considerable and available laws, politics and religions. My first emperor’s desire to learn from the ancients will never be released. I and other ministers were ordered to edit and slash the three histories of "Liao", "Jin" and "Yuan", but take the good part as the law, and the evil part as the precepts. And the matters of conquering and hunting, were translated into Manchu,written and presented in a book. I and others followed respectfully my Lord ‘s orders,took the history of Liao from Gaozu to the last year of Yelu Dashi of Xi Liao, where there were 14 emperors, a total of 370 years; Jin, the nine Emperors, lasted one hundred and nineteen years;Yuan recorded all the fourteen emperors for 162 years, and had careful records of those who have benefited us, ... I beg my Lord to have occasional reviews when my Lord is not dealing with country’s affairs,and to encourage your people to live up to ancient virtues and promote the unparalleled huge achievement. I and other would greatly appreciate it. [E'ertai et al. revised with imperial edict "Records of the Qing Dynasty·Records of Emperor Shizuzhang", Beijing: Zhonghua Book Company, 1985, pp. 15-16. ]<br />
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Although Liao and Jin did not unite, Liao had already occupied half of the world, so had Jin. Until Yuan Dynasty, they combined and occupied the whole country, and there were considerable and available laws, politics and religions. My previous emperor’s desire to learn from the ancients will never be released. Other ministers and I were ordered to edit and slash ''the History of Liao, Jin and Yuan Dynasty'', take merits as the principle, and learn lessons from demerits. And the matters of conquering and hunting, were translated into Manchu, written and presented in a book. We followed respectfully my Majesty ‘s orders, took ''The History of Liao Dynasty'' from Gaozu to the last year of Yelu Dashi of Xi Liao, where there were 14 emperors, a total of 370 years; ''Jin'', nine Emperors, lasted one hundred and nineteen years; ''Yuan'' recorded all the fourteen emperors for 162 years, and had careful records of those who have benefited us, ... I beg my Majesty to have occasional reviews when he is not dealing with country’s affairs, and to encourage people to observe traditional virtues and advocate the unprecedent huge achievement. We all would greatly appreciate it. [E'ertai et al. revised with imperial edict ''Records of Emperor Shizuzhang in the Qing Dynasty'', Beijing: China Book Company, 1985, pp. 15-16. ]--[[User:Wei Yiwen|Wei Yiwen]] ([[User talk:Wei Yiwen|talk]]) 09:35, 29 September 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081526 卫怡雯 女==<br />
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上文中，不仅明确论及汉文典籍的历史作用，而且阐述了翻译这些典籍的必要性、作用与目的等，其总目标是“懋稽古之德”，以翻译佐文教，治太平。换言之，希福希望通过翻译《辽》、《金》、《元》三史等，从中原汉族王朝中借鉴国家治理经验，以接续太宗皇太极以来以经世致用为核心的翻译思想，并为嗣后翻译汉籍树立原则与典范。据《国立故宫博物院善本旧籍总目》、《世界满文文献目录》、《全国满文图书资料联合目录》，以及《清代内府刻书目录解题》等综合统计，顺治年间，由官方刊刻的汉书满文译本共九种，分别是《辽史》、《金史》、《元史》、《洪武要训》、《三国志（通俗演义）》、《诗经》、《表忠录》、《孝经》（阿什坦译）和《六韬三略》。其中，顺治七年译成的《三国志（通俗演义）》既有满文本，又有满汉合璧本。<br />
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As described in the above, it not only discussed the historical role of Chinese classics, but also illustrated the necessity, the function and the goal of translating these ancient books and records. The overall goal is to encourage people to observe traditional virtues, using translation to assist the country to develop culture and education. In other words, Xifu wanted to draw on the experience of state governance from Han Dynasty in the central China through translating the history of Liao, Jin, and Yuan Dynasty in order to succeed the thought of translation of administering state affairs and applying theory to practice as the core since the emperor Taizong Huangtaiji, and set up the principle and model of translating Chinese books for later generation. According to general statistics of The General Catalogue of The Best Edition and the Ancient Books and Records of Taipei's National Palace Museum, The Catalogue of World’s Manchu Literature and The Union Directory of National Manchu Books and Materials and Solving Problems in the Catalogue of Engraved Books in the Qing Dynasty, during the period of Tongzhi, there are nine Manchu translated versions of the official published Chinese books by blocking print, they are: The History of Liao Dynasty, The History of Jin Dynasty, The History of Yuan Dynasty, Hongwu Yaoxun, The Romance of Three Kingdoms, Book of Songs, The Record of Loyalty, Classic of Filial Piety(translated by Ashtan), Liu Tao and San Lue. Among them, The Romance of Three Kingdoms translated in the seventh year of Tongzhi had both Manchu version and the combined one of Chinese and Manchu.--[[User:Wei Yiwen|Wei Yiwen]] ([[User talk:Wei Yiwen|talk]]) 03:23, 29 September 2021 (UTC)<br />
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Thereinbefore, it not only discussed the historic role of Chinese classics, but also illustrated the necessity, the function and the goal of translating these books. The overall goal is to encourage people to observe traditional virtues, using translation to develop culture and education and bring social prosperity. In other words, Xifu wanted to draw on the experience of state governance from Chinese dynasty in the central plains through translating the histories of ''Liao'', ''Jin'', and ''Yuan''. In this way, the translation idea originated from the dynasty of Huangtaiji that knowledge should benefit national affairs can be succeeded and set up the principle and model of translating Chinese books for later generation. According to the general statistics of ''The General Catalogue of the Publications and Classics of National Palace Museum'', ''The Catalogue of World’s Manchu Literature'' and ''The Union Catalogue of National Manchu Books and Materials'' and ''Solving Problems in the Catalogue of Engraved Books in Qing Dynasty'', during the period of Shunzhi dynasty, there are nine official Manchu translations of Chinese books.They are ''History of Liao Dynasty'', ''History of Jin Dynasty'', ''History of Yuan Dynasty'', ''Hongwuyaoxun'', ''Records of the Three Kingdoms'', ''Classic of Poetry'', ''Record of Loyalty'', ''Classic of Filial Piety''(translated by Ashtan), ''Liutaosanlue''. Among them, ''Records of the Three Kingdoms'' translated in the seventh year of Shunzhi dynasty has both Manchu version and the combined one of Chinese and Manchu.(revised by Wei Chuxuan)--[[User:Wei Chuxuan|Wei Chuxuan]] ([[User talk:Wei Chuxuan|talk]]) 07:11, 29 September 2021 (UTC)--[[User:Wei Chuxuan|Wei Chuxuan]] ([[User talk:Wei Chuxuan|talk]]) 08:59, 29 September 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081527 魏楚璇 女==<br />
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另外，《孝经》曾于顺治、康熙、雍正朝刊刻三次，但版本明显不同：顺治年间的刊行本为阿什坦译本，康熙年间的刊行本为和素译校本，雍正时期的版本则译者未明，仅注明“雍正皇帝敕译”。令人好奇的是，虽然世祖笃信佛教，论佛谈法，但从相关文献看，未见有顺治朝翻译的汉文经书，其中原因，不得而知。<br />
What's more, ''Filial Piety'' has different publications of translation in different dynasties. In Shunzhi dynasty, the publication is Ashtan's translation. In Kangxi dynasty, the publication is Hesu's translation. In Yongzheng dynasty, the translator is unknown. It is only indicated in the publication that the translation is asked by Yongzheng emperor. Curiously according to relevant literature though Shunzhi emperor believed in Buddhism, there is no translation of Buddhist classics made in his dynasty. And the reason of this remains a mystery.--[[User:Wei Chuxuan|Wei Chuxuan]] ([[User talk:Wei Chuxuan|talk]]) 02:12, 29 September 2021 (UTC)--[[User:Wei Chuxuan|Wei Chuxuan]] ([[User talk:Wei Chuxuan|talk]]) 09:05, 29 September 2021 (UTC)<br />
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What's more, ''Filial Piety'' has been published and printed three times in the dynasty of Shunzhi, Kangxi and Yongzheng respectively but with three obvious different versions. During the Shunzhi dynasty was the translation of Ashtan, during the dynasty of Kangxi was of Hesu and during the Yongzheng dynasty was of the unknown writer, only indicating "translating under the order of the emperor Yongzheng". What made people curious was that although Shizu sincerely believed in Buddhism, talking about Buddhism and Buddhist doctrine, there was no Chinese Confucian classics translated during the Shunzhi Dynasty according to the relevant references. And the reason of this remains unknown.--[[User:Wei Zhaoyan|Wei Zhaoyan]] ([[User talk:Wei Zhaoyan|talk]]) 08:14, 29 September 2021 (UTC)(Wei Zhaoyan 魏兆妍）<br />
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==英语语言文学（英美文学） 202120081528 魏兆妍 女==<br />
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世祖年间翻译刊行的九种汉文书籍中，《六韬》、《三国志（通俗演义）》和《大乘经》等三种原系达海于天聪六年开始翻译，但由于达海早逝未能译成。三部作品中，尤以《三国志（通俗演义）》的翻译所获世祖认可为甚。《清实录·世祖章皇帝实录》中说：<br />
以翻译《三国志（通俗演义）》告成，赏大学士范文程、刚林、祁充格、宁完我、洪承畴、冯铨、宋权、学士查布海、苏纳海、王文奎、伊图、胡理、刘清泰、来袞（gǔn）、马尔笃、蒋赫德等鞍马、银两有差。[ 鄂尔泰等奉敕修：《清实录·世祖章皇帝实录》，北京：中华书局，1985年，第13页。]<br />
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Nine kinds of Chinese literary books has been translated and published during the year of Shizu, among which Da Hai began translating three kinds of the original system of ''Liu Tao'', ''The Popular Romance of the Three Kingdoms'' and ''Mahayana Sutra'' during the sixth year of Tian Cong. However, Da Hai failed to translate them all successfully due to his early death. Among these three works, especially the translation of ''The Popular Romance of the Three Kingdoms'' has received the most approval of Shizu. Said in ''The Real Record of the Qing Dynasty · Real Record of the Emperor Shizu Zhang'': With the completed translation of ''The Popular Romance of the Three Kingdoms'', Shizu would award some side horses and silver to the Grand Master Fan Wenching, Gang Lin, Qi Chongge, Ning Wanwo, Hong Chengchou, Feng Quan, Song Quan and Bachelor Zha Buhai, Su Nahai, Wang Wenkui, Yi Tu, Hu Li, Liu Qingtai, Lai Gun, Ma Erdu, Jiang Hede. [ Ertai and others were ordered by the emperor to modify: ''The Real Record of Qing Dynasty · Real Record of the Emperor Shizu Zhang'', Beijing: China Publishing House, 1985, Page 13. ]--[[User:Wei Zhaoyan|Wei Zhaoyan]] ([[User talk:Wei Zhaoyan|talk]]) 07:52, 29 September 2021 (UTC)<br />
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Among the nine kinds of Chinese books translated and published during the reign of Shizu emperor , three were first translated by Da Hai in 1632, including "Liu Tao", “The Popular Romance of the Three Kingdoms" and "Mahayana Sutra”. Yet they failed to be finished due to his early death. Of the three works, the translation of "The Popular Romance of the Three Kingdoms" received the most recognition of Shizu emperor. According to "The Real Record of the Qing Dynasty · Real Record of the Emperor Shizu Zhang": Given the successful translation of "The Popular Romance of the Three Kingdoms", Shizu emperor awarded some side horses and silver to the Grand Master Fan Wenching, Gang Lin, Qi Chongge, Ning Wanwo, Hong Chengchou, Feng Quan, Song Quan and Bachelor Zha Buhai, Su Nahai, Wang Wenkui, Yi Tu, Hu Li, Liu Qingtai, Lai Gun, Ma Erdu, Jiang Hede, and the amount of awards depended on their position.[ Ertai and others ordered by the emperor to modify: ''The Real Record of Qing Dynasty · Real Record of the Emperor Shizu Zhang'', Beijing: China Publishing House, 1985, Page 13. ]--[[User:Xiao Yiyao|Xiao Yiyao]] ([[User talk:Xiao Yiyao|talk]]) 07:32, 29 September 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081531 肖毅瑶 女==<br />
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此次获得赏赐者共计十六人，从大学士到学士不等，既赏鞍马，又赏银两，可见其对于该书翻译的重视。值得特别注意的是，关于《三国志（通俗演义）》一书的翻译，《清初内国史院满文档案译编》中也有记载，不仅更加详实，而且内容上也与《清实录》中的记载有较大出入。为便于比较，现一并摘录如下：<br />
A total number of 16 officers, varying from Grand Masters to Bachelors, were awarded saddled horses and silver, which indicated that the emperor had attached great importance to the translation of this book. Besides, what deserves a speacial attention was that ''Translation and Compilation of Manchu Archives of the National Institute of History in the Early Qing Dynasty'' also documented some facts about the translation of the ''Romance of Three Kingdoms''. The records were not only more detailed but also quite different from that of the ''Factual Record of Qing dynasty''. For the convenience of comparison, both were excerpted as follows:<br />
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A total number of 16 officers, varying from Grand Academcians to Bachelors, were awarded saddled horses and silver, which indicated that the emperor had attached great importance to the translation of this book. Besides, it is worth paying speacial attention that ''Translation and Compilation of Manchu Archives of the National Institute of History in the Early Qing Dynasty'' also documented some facts about the translation of the ''Romance of Three Kingdoms''. The records were not only more detailed but also quite different from that of the ''Factual Record of Qing dynasty''. For the convenience of comparison, both were excerpted as follows:----[[User:Xie Jiafen|Xie Jiafen]] ([[User talk:Xie Jiafen|talk]]) 13:33, 11 October 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081532 谢佳芬 女==<br />
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以翻译《三国志（通俗演义）》告成，赏赐内翰林院大臣。赏予大学士范文程巴克什、刚林巴克什、祁充格、宁完我、洪承畴、冯铨、宋权七大臣彩鞍、雕辔（pèi）、……头等马各一匹、银各五十两。赏学士查布海、苏纳海、王文奎、伊图、胡理、清泰、来袞、马迩都、赫德九人无鞍二等马各一匹、银各四十两。<br />
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When ''Romance of the Three Kingdoms’’ having been completed，all the ministers in Hanlin Academcian were awarded. The grand secretaries Fan Wencheng, Gang Lin, Qi Chongge, Ning Wanwo, Hong Chengchou, Feng Quan, Song Quan were rewarded color saddle, bridle, one first-class horse and fifty liang of silver respectively. The nine scholars, Cha Buhai, Su Nahai, Wang Wenkui, Yitu, Huli, Qingtai, laigon, Ma Youdu and Hede, each have one second-class bareback horse and forty liang of silver<br />
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After the completion of “Records of the Three Kingdoms”, ministers of Hanlin Academy were awarded. Maesters including Fan Wencheng, Gang Lin, Qi Chongge, Ning Wanwo, Hong Chengchou, Feng Quan, Song Quan were awarded with colorized saddle, bridle, a first-class horse and fifty Liang of silver respectively. Nine scholars like Cha Buhai, Su Nahai, Wang Wenkui, Yitu, Huli, Qingtai, laigon, Ma Youdu and Hede, each of them has one second-class bareback horse and forty liang of silver respectively.--[[User:Xiong Min|Xiong Min]] ([[User talk:Xiong Min|talk]]) 13:45, 11 October 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081534 熊敏 女==<br />
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赏内弘文院主事能图、叶成格、曹皮、铿特依、杜当、布尔凯、侍讲学士吕宗烈、侍读学士张皮机、典籍官王丛庞九人银各四十两。赏博士科尔科岱、霍斯霍利、尼曼、苏和、奇同格、芒色、霍托、穆成格、周有德、必利科图、国史院博士图巴海、秘书院秦达浑臣十二人银各二十两。赏笔帖式翁国顺、额斯黑、高利、马齐蘭、乌勒扈、穆成格、必利科图、严楚蘭、阿希图、国史院笔帖式朱臣十人银各二十两。<br />
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Chiefs of Hongwen Academy like Neng Tu,Ye Chengge,Cao Pi,Qiang Teyi,Du Dang, But Erkai,teacher like Lv Zonglie, attendant like Zhang Piji, manager of ancient books like Wang Congpang were rewarded forty pounds of silver respectively.Doctor Me Erkedai, Huh Sihuoli,Niman,Su He,Qi Tongge, Mang Se, Huo Tuo,Mu Chengge,Zhou Youde, Bi Liketu,Tu Bahai,Qin Dahun were all awarded 20 pounds of silver. Weng Guoshun,E Sihei,Golly, Ma Qilan,Wu Leba,Mu Chengge, Bi Liketu,Yan Chulan,A Xitu,Zhu Chen were all rewarded with 20 pounds of silver.<br />
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Chief cadres of Hongwen Academy including Neng Tu,Ye Chengge,Cao Pi,Qiang Teyi,Du Dang,Bu Erkai,Teacher like Lv Zonglie, Attendant like Zhang Piji, Manager of ancient books Wang Congpang were rewarded forty silver pounds respectively. Doctor Me Erkedai, Huh Sihuoli,Niman,Su He,Qi Tongge, Mang Se, Huo Tuo,Mu Chengge,Zhou Youde, Bi Liketu,Tu Bahai,Qin Dahun were all awarded 20 silver pounds. Weng Guoshun,E Sihei,Golly, Ma Qilan,Wu Leba,Mu Chengge, Bi Liketu,Yan Chulan,A Xitu,Zhu Chen were all rewarded with 20 silver pounds.--[[User:Yang Ye|Yang Ye]] ([[User talk:Yang Ye|talk]]) 12:23, 30 September 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081539 羊叶 女==<br />
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以上送礼部一一宣名，跪受赏。[ 中国第一历史档案馆编：《清初内国史院满文档案译编·顺治朝》，北京：光明日报出版社，1989年，第80页。]<br />
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显然，上述两种文献提到的系同一件事情，但内容上有明显出入：首先，封赏的人数不同。<br />
The officials above give gifts to the Ministry of Rites one by one, being declared the name, and kneeling to be rewarded. [Editor of China's First Historical Archives: Translation of manchu archives of the National Historical Institute of the early Qing Dynasty, Shunzhi Dynasty, Beijing: Guangming Daily Press, 1989, p. 80.] Obviously,the two documents mention the same thing, but there are obvious differences in content: first, the number of people who are rewarded is quite different.<br />
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All these officials on the list will be named in recognition and rewarded on their knees at the Ministry of Rites. [ Edited by the First Historical Archive of China: "Translation and compilation of Manchu archives in the Early Qing Dynasty•Shunzhi Dynasty", GuangMing Daily Press, 1989, P80]<br />
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Appartently, the two literatures mentioned above refer to the same thing, but they differ in content: Firstly, the number of rewarded people is different. --[[User:Yang Liuqing|Yang Liuqing]] ([[User talk:Yang Liuqing|talk]]) 03:34, 29 September 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081543 杨柳青 女==<br />
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例如，《清实录》中只有十六人，《清初内国史院满文档案译编》则多达四十七人，二者之间的出入主要在于弘文院主事、侍讲与侍读学士、典籍官、博士，以及笔帖式等职官群体名单。其次，在赏赐的物件问题上，《清初内国史院满文档案译编》的记载较之《清实录》明显更加详实、具体，可信度更高。再次，在个别封赏对象的称呼上，两种文献之间也有差异。<br />
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For example, only 16 civil officials are rewarded according to the "Records of the Qing Dynasty" while officials who are rewarded according to the "Translation and Compilation of Manchu Archives of the Chinese Academy in the Early Qing Dynasty" are as many as over 40. This difference mainly lies in the list of official groups such as the director of the Hong Arts Institute, official bachelors, classics officers, learned scholars and clerks handling paperwork. Secondly, the records about rewarded items in the "Translation of Manchu Archives of the Chinese Academy in the Early Qing Dynasty" are obviously more detailed, accurate and credible than those in the "Records of the Qing Dynasty". Thirdly, there are also some differences between the two literatures in terms of the appellation of the rewarded officials.--[[User:Yang Liuqing|Yang Liuqing]] ([[User talk:Yang Liuqing|talk]]) 03:30, 29 September 2021 (UTC)<br />
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For example, only 16 civil officials were rewarded in the The Records of the Qing Dynasty while officials rewarded in the Manchu Archives Translation and Compilation of the Inner State History Academy in the Early Qing Dynasty were as many as over 40. This difference mainly lay in the list of official groups such as the director of the Hong Arts Institute, official bachelors, classics officers, learned scholars and clerks handling paperwork. Secondly, the records about rewarded items in the Manchu Archives Translation and Compilation of the Inner State History Academy in the Early Qing Dynasty were obviously more detailed, accurate and credible than those in the The Records of the Qing Dynasty. Thirdly, there were also some differences between the two literatures in terms of the appellation of the rewarded officials.--[[User:Yi Yangfan|Yi Yangfan]] ([[User talk:Yi Yangfan|talk]]) 05:11, 29 September 2021 (UTC)--[[User:Yi Yangfan|Yi Yangfan]] ([[User talk:Yi Yangfan|talk]]) 05:11, 29 September 2021 (UTC)Yi Yangfan<br />
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==英语语言文学（英美文学） 202120081545 易扬帆 女==<br />
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例如，《清实录》中的“刘清泰”被写作《清初内国史院满文档案译编》中的“清泰”，“马尔笃”被写作“马迩都”，“蒋赫德”被写作“赫德”，等等。<br />
世祖年间翻译的汉文书籍中，《洪武宝训》、《表忠录》、《诗经》与《孝经》等也各具代表性。《洪武宝训》系明朝皇权政治的象征，反映了明太祖朱元璋治国理政的理念和方针政策。<br />
For example, Liu Qingtai in Factual Record of Qing Dynasty was written as Qingtai in Manchu Archives Translation and Compilation of the Inner State History Academy in the Early Qing Dynasty, Mar Du was written as Ma Erdu, and Jiang Hede was written as Hurd, etc.<br />
The translated Chinese books in the certain era of Fulin years, such as HongWu Baoxun, The Record of Loyalty, Books of Songs and Classic of Filial Piety have had their own representatives. HongWu Baoxun was a symbol of the imperial power politics of Ming Dynasty, which reflected the ideas and policies of Zhu Yuanzhang, the emperor Taizu of Ming Dynasty.--[[User:Yi Yangfan|Yi Yangfan]] ([[User talk:Yi Yangfan|talk]]) 16:03, 28 September 2021 (UTC)Yi Yangfan<br />
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For example, “Liu Qingtai” in Factual Record of Qing Dynasty was written as “Qingtai” in Manchu Archives Translation and Compilation of the Inner State History Academy in the Early Qing Dynasty, "Mar Du" was written as "Ma Erdu", "Jiang Hede" was written as "Hede", and so on. The translated Chinese books in Fulin years, such as HongWu Baoxun, The Record of Loyalty, Books of Songs and Classic of Filial Piety all were of representative. Hongwu Baoxun was a symbol of imperial power politics in the Ming Dynasty and reflected the ideas and policies of Zhu Yuanzhang, the emperor of the Ming Dynasty.--[[User:Yin Huizhen|Yin Huizhen]] ([[User talk:Yin Huizhen|talk]]) 02:46, 29 September 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081546 殷慧珍 女==<br />
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顺治三年三月，《洪武宝训》的满文翻译完成，成为清朝入关后的首部汉籍译作。世祖对于该部译作极为重视，不仅赏赐了刚林、宁完我、范文程等译者，而且由摄政王多尔衮钦命汉官代笔，以世祖名义为译作制作序文，颁行全国。世祖对明太祖推崇备至，尤其是后者制定的条例章程，认为历代贤君莫如洪武，因而本书的翻译目的性极强。<br />
In the March of the third year of Shunzhi, the Manchu translation of Hongwu Baoxun was completed, becoming the first translation written by Chinese writer after the Qing Dynasty was in power. The emperor Shizu attached great importance to this translation. He not only rewarded the translators such as Gang Lin,Ning Wanwo, and Fan Wencheng, ect., but also the regent Dorgon appointed Han officals to write a preface in the name of Shizu, which was issued throughout the country. The emperor Shizu revered the emperor Taizu of Ming Dynasty, especially the regulations and articles he formulated, and believed that there were no virtuous monarchs of all dynasties like Hongwu, so the translation purpose of this book was very strong.--[[User:Yin Huizhen|Yin Huizhen]] ([[User talk:Yin Huizhen|talk]]) 01:17, 29 September 2021 (UTC)<br />
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In the March of the third year of Shunzhi, the Manchu translation of Hongwu Baoxun was completed, becoming the first translation done by Chinese translators since the Qing Dynasty was established. The Emperor Shizu attached great importance to this translation. Not only he rewarded the translators such as Gang Lin,Ning Wanwo, and Fan Wencheng, ect., but also the regent Dorgon appointed the officals of Han Dynasty to write a preface, which was issued throughout the country, in the name of Shizu. The Emperor Shizu praised the emperor Taizu of Ming Dynasty highly, especially the regulations and articles he formulated, and he believed that there were no more virtuous monarchs of all dynasties than Hongwu, so the translation purpose of this book was very strong.--[[User:Yin Yuan|Yin Yuan]] ([[User talk:Yin Yuan|talk]]) 02:21, 29 September 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081548 尹媛 女==<br />
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换言之，世祖希望借由此书的翻译，以中原汉族王朝的所谓“正统”巩固满清统治，粉饰民族征服和民族压迫。事实上，以翻译汉书，尤其是汉文典章制度书籍的翻译，作为统治的工具和手段，这一点在太宗时期即已存在。作为国家治理的重要手段，太宗不仅令达海改进老满文，而且钦定翻译了不少汉文书籍，如明太祖颁发的《大诰三编》和《三国演义》等，用作出谋划策和军事征讨的参考。<br />
In other words, Shizu hoped to consolidate the rule of Qing Dynasty with the so-called "orthodox" of Han Dynasty in the Central Plains to deny national conquest and oppression by translating this book. Actually, in the reign of Emperor Taizong, it had existed that the translation of Chinese books, especially the translation of Chinese laws and regulations, was regarded as the tools and means of ruling. As the important mean of national governance, the old manchu scripts were developed by Da Hai and not a few Chinese books, such as ''The third Edition of Penal Code'' and ''The Romance of the Three Kingdoms''issued by Emperor Hongwu were translated under the decree of Emperor Taizong, which were used as a reference for planning and advising and military campaigns.--[[User:Yin Yuan|Yin Yuan]] ([[User talk:Yin Yuan|talk]]) 15:44, 28 September 2021 (UTC)<br />
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In other words, Shizu hoped to consolidate the rule of Qing Dynasty with the so-called "orthodox" of Han Dynasty in the Central Plains to whitewash national conquests and oppressions by translating this book. Actually, in the reign of Emperor Taizong, it had existed that the translation of Chinese books, especially the translation of Chinese laws and regulations, was regarded as the tools and means of ruling. As an important mean of national governance, the old manchu scripts were developed by Da Hai and not a few Chinese books, such as ''The third Edition of Penal Code'' and ''The Romance of the Three Kingdoms''issued by Emperor Hongwu were translated under the decree of Emperor Taizong, which were used as a reference for planning and military campaigns.--[[User:Zhan Ruoxuan|Zhan Ruoxuan]] ([[User talk:Zhan Ruoxuan|talk]]) 03:01, 29 September 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081549 詹若萱 女==<br />
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《表忠录》同样辑自明朝嘉靖年间，实为杨继盛的两部奏疏，即《请诛贼臣疏》和《请罢马市疏》，由汪宗伊撰于明朝万历年间，属吏部传记类。顺治十三年前后，世祖降旨将杨继盛事迹写成《忠愍记》。所谓“忠愍”，即明穆宗因念及杨继盛参劾严嵩之功，誉其为“直谏诸臣之首”，而追赠给后者的谥号。<br />
The Record of Loyalty was also written from the JiaJing peroid of Ming Dynasty. It was actually consisted of two memorials to throne of Yang JIsheng, namely memorial on killing traitors and memorial on closing the horse market written by Wang Zongyi during the Wanli period of Ming Dynasty. It belonged to official biography. About ShunZhi 13 years, the Emperor Shizu made a decree to write Yang Jisheng’s good deeds into The Record of Zhong Min. The so-called “Zhong Min” referred to the posthumous title given to Yang Jisheng after his death, because Emperor Muzong praised him as “the head of the ministers” for his contribution to impeaching Yan Song.--[[User:Zhan Ruoxuan|Zhan Ruoxuan]] ([[User talk:Zhan Ruoxuan|talk]]) 02:27, 29 September 2021 (UTC)<br />
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The Record of Loyalty was also written in the JiaJing peroid of Ming Dynasty. It was actually consisted of two memorials to throne of Yang JIsheng, namely memorial on killing traitors and memorial on closing the horse market written by Wang Zongyi during the Wanli period of Ming Dynasty. It is a kind of official biography. About ShunZhi 13 years, the Emperor Shizu made a decree to write Yang Jisheng’s good deeds into The Record of Zhong Min. The so-called “Zhong Min” referred to the posthumous title given to Yang Jisheng after his death, because Emperor Muzong praised him as “the head of the ministers” for his contribution to impeaching Yan Song.--[[User:Zhong Yifei|Zhong Yifei]] ([[User talk:Zhong Yifei|talk]]) 13:16, 11 October 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081553 钟义菲 女==<br />
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《忠愍记》成书后，世祖御制《表忠录·序》以表彰杨继盛，其中写道：自古贤臣正士效力王家，率授命致身，捐生赴义。迹其所遭，若无厚幸然。<br />
After the record of Zhong Min was written, the emperor Shizu personally wrote a preface to the record of loyalty to commend Yang Jisheng, which wrote: since ancient times, virtuous officials have devotedly served the emperor family, sacrificing their lives for justice. From what happened, there was no luck.<br />
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After the Record of Zhong Min was finished, the emperor Shizu personally wrote the Preface to the Record of Loyalty to commend Yang Jisheng, which wrote: since ancient times, virtuous officials and soldiers have devotedly served the loyal family, sacrificing their lives for justice. From what happened to them, there was no luck.--[[User:Zhong Yulu|Zhong Yulu]] ([[User talk:Zhong Yulu|talk]]) 12:40, 1 October 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081554 钟雨露 女==<br />
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而时过论定，声称振杨，及于代远风遥，流徽弥茂，留连曩迹，如遘其人。是以孟轲有言：“奋乎百世之上，百世之下闻者莫不兴起也”。……顾竭志尽忠者，人臣之谊；善善恶恶者，大道之公。<br />
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But as time went by, the verdict on Yang Jisheng’s exploits had been reached and he gained considerable fame. When the years have passed, the fame of him spread all around. When people recalled his deeds, it was like meeting him in person. Just as Mencius said, “Those who made themselves distinguished a hundred generations before, and after a hundred generations, some people who heard of them are all aroused in this manner.” ……..Those who were intensely loyal could be harmonious with the ministers; And it was fair to punish the bad and to be kind to the good. --[[User:Zhong Yulu|Zhong Yulu]] ([[User talk:Zhong Yulu|talk]]) 01:33, 2 October 2021 (UTC)<br />
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But as time went by, Yang Jisheng’s exploits have been recognized, and he gained considerable fame. The older the years, the more his fame spread. When people recalled his deeds, it was like meeting him in person. Just as Mencius said, “Those who made themselves distinguished a hundred generations before, and after a hundred generations, some people who heard of them are all aroused in this manner.” ……..Those who were intensely loyal could be harmonious with the ministers. And it was fair to punish the bad and to be kind to the good. --[[User:Zhou Jiu|Zhou Jiu]] ([[User talk:Zhou Jiu|talk]]) 13:09, 11 October 2021 (UTC)<br />
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==英语语言文学（英美文学） 202120081555 周玖 女==<br />
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循省往哲，爱结于中，诚有不能自己者也。朕万机之暇，绎载籍，每览忠孝节义之事，未尝不反复三致意焉。[ 阎崇年校注：《康熙顺天府志》，北京：中华书局，2009年，第482页。]<br />
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一方面，世祖赞誉杨继盛为忠臣之典范；另一方面，世祖又斥责严嵩为逆臣，认为正是严嵩父子威福专擅，浊乱王家，致使纪纲废断。<br />
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Reflecting on predecessors, despite the disloyal, the love to nation was condensed together. I was bombarded with numerous affairs. But in my spare time, I translated and recorded many books through dictation. When I read books about loyalty, I often analyzed them repeatedly to express my regard. [ Yan Chongnian: Kang Xi Shun Tianfu, Beijing: China Publishing House, 2009, Page 482. ]--[[User:Zhou Jiu|Zhou Jiu]] ([[User talk:Zhou Jiu|talk]]) 13:12, 11 October 2021 (UTC)<br />
On the one hand, the emperor Shizu acclaimed Yang Jisheng as the paragon of loyal minister. On the other hand, he denounced Yan Song as a traitor, and believing（believed--Chen Xiangqiong(talk) )the fact that Yan Song and his son misused their authorities to domineer and disturb the loyal family so that the law and regulations became slacked.（were broken--Chen Xiangqiong(talk)）<br />
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==外国语言学及应用语言学 202120081480 陈湘琼 女==<br />
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世祖敕令翻译此书，目的是为了激励官员学做忠谏之臣，劝勉意味浓厚。毋庸置疑，世祖时期的汉籍翻译秉承的原则也是“实用主义”原则，这一点太祖、太宗时期的汉籍翻译并无本质区别。例如，世祖敕译《诗经》，即有显著的现实意义与政治考量。<br />
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The Emperor Shunzhi commanded to translate this book for the purpose that officials could be encouraged to become people who dare to tell the truth and give right suggestions to the emperor, which had persuasive and warning meanings by itself. In this period, translation of books from Han dynasty was in fact abiding to the “utilization” principle, which had no difference to translations in the period of Qianlong Emperor and Huang Taiji Emperor. For example , practical significance and political meditation had been considered for Emperor Shunzhi commanding the translation of “Book of Songs”.<br />
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The Emperor Shunzhi commanded to translate this book to encourage officials to tell the truth and put forward reasonable suggestions, which was of great persuasive meaning.In this period, translation of canons from Han dynasty actually followed the “utilitarianism” principle, which had no difference to those during the reign of Qianlong Emperor and Huang Taiji Emperor. For example , practical significance and political meditation had been considered for Emperor Shunzhi commanding the translation of “Book of Songs”. --[[User:Huang Yiyan1|Huang Yiyan1]] ([[User talk:Huang Yiyan1|talk]]) 14:00, 30 September 2021 (UTC)<br />
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==外国语言学及应用语言学 202120081492 黄逸妍 女==<br />
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《清实录·世祖章皇帝实录》中说，顺治十年二月，“上幸内院，披阅翻译《五经》，谕诸臣曰：‘天德王道，备载于书，真万世不易之理也’”。[ 鄂尔泰等奉敕修：《清实录·世祖章皇帝实录》，北京：中华书局，1985年，第9页。]可见，世祖饬令翻译《诗经》之时，其它各经的翻译也在进行，但《五经》中仅有《诗经》一部付梓。《诗经》译毕，世祖也为其御制序文，认为该部作品能使人明性意，崇礼义，“其言之深者，可用于庙堂；言之浅者，可用于身家。以之事君，必忠；以之事父，必孝。<br />
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According to Records of Qing Dynasty •Records of the Emperor Shizu, in February of the tenth year during the reign of the emperor Shunzhi, "After reading the Five Classics in palace, the emperor contended that morals and natural laws are recorded in these books in extenso, which are tough to reach." [Revised by Eertai: Records of Qing Dynasty •Records of the Emperor Shizu, Beijing: Zhonghua Publishing House, 1985:9 ] It was clear that the translating of other classics was also proceeding when the emperor Shizu commanded the translation of The Book of Songs while only the latter had been finished among the five classics. When the translation of The Book of Songs came to an end, the emperor Shizu wrote the preface to it himself and remained steadfast in the belief that that work helped to behave with propriety and righteousness. "The book can be used in imperial court from its profound aspect and in average families from its unadorned aspect. Instilled with the belief, the officials would be faithful and the children filial.<br />
--[[User:Huang Yiyan1|Huang Yiyan1]] ([[User talk:Huang Yiyan1|talk]]) 00:44, 29 September 2021 (UTC)<br />
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According to Records of Qing Dynasty •Records of the Emperor Shizu, in February of the tenth year during the reign of the emperor Shunzhi, "After having a careful perusal of the Five Classics in adytum, the emperor proclaimed all ministers that morals and natural laws are recorded in these books in extenso, which are tough to reach for all ages." [Revised by Eertai et al: Records of Qing Dynasty •Records of the Emperor Shizu, Beijing: Zhonghua Publishing House, 1985:9 ] It could be seen that the translating of other classics was also proceeding when the emperor Shizu commanded to translate The Book of Songs, while only the latter had been finished among the five classics. When the translation of The Book of Songs came to an end, the emperor Shizu wrote the preface to it himself and believed that reading this work would help us to behave with propriety and righteousness. "It can be used in imperial court from its profound aspect and in average families from its unadorned aspect. Instilled with the belief, the officials would be faithful and the children filial.edit--[[User:Ma Xin|Ma Xin]] ([[User talk:Ma Xin|talk]]) 14:12, 30 September 2021 (UTC)<br />
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==外国语言学及应用语言学 202120081513 马新 女==<br />
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更可以敦厚人伦，端正教化。”[ 叶高树：《清朝前期的文化政策》，台北：稻乡出版社，2002年，第72-73页。]<br />
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从“教化”的角度思考《诗经》的翻译，既是世祖本人的自觉认识，也是以他为首的统治者在面对稗（bài）官小说盛行，而满洲人竞相翻译时所做的一种调整。正如顺治九年进士、刑科给事中阿什坦指出的那样：学者立志，宜以圣贤为期，读书务以经史为中。<br />
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"It could also give depth to moral relations, straighten out educational ideas and transform people." [Ye Shugao: Cultural Policy in the Early Qing Dynasty, Taipei: Daoxiang Publishing House, 2002: 72-73.]<br />
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It is not only the conscious understanding of Qingshizhu Emperor himself to think about the translation of The Book of Songs from an "Enlightenment" perspective, but also an adjustment made by the rulers at his head faced with the prevalence of Bai-guan novels and Manchurians competing for translation. As pointed out by Ashtan, an advanced scholar in the ninth year of Shunzhi and a supervising censor of Justice, scholars should aspire to be saints and their reading must focus on classical and historical books. --[[User:Ma Xin|Ma Xin]] ([[User talk:Ma Xin|talk]]) 13:11, 30 September 2021 (UTC)<br />
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It could also give depth to moral relations, straighten out educational ideas and transform people." [Ye Shugao: Cultural Policy in the Early Qing Dynasty, Taipei: Daoxiang Publishing House, 2002: 72-73.]<br />
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Thinking about the translation of The Book of Songs from the "Teaching" perspectives is not only the personal understanding of Qingshizhu Emperor himself but also an adjustment made by the rulers at his head faced with the prevalence of Bai-guan novels and Manchurians competing for translation. As pointed out by Ashtan, an advanced scholar in the ninth year of Shunzhi and a supervising censor of Justice, the scholars should aspire to be saints and focus on classical and historical books.--[[User:Qing Jianan|Qing Jianan]] ([[User talk:Qing Jianan|talk]]) 15:38, 30 September 2021 (UTC)<br />
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==外国语言学及应用语言学 202120081518 秦建安 女==<br />
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此外杂书无益之言，必概废之而不睹。则庶乎学业日隆，而邪慝（tè）之心无由而入。近见满洲译书内，多有小说秽言，非惟无益，恐流行渐染，则人心易致于邪慝 。<br />
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Besides, there is no beneficial words in assorted books which should not be allowed to be published and be viewed.(So they shouldn't be allowed to be published and viewed--[[User:Sun Yashi|Sun Yashi]] ([[User talk:Sun Yashi|talk]]) 07:26, 29 September 2021 (UTC)) Then the level of study approximately can be uplifted day by day. Meanwhile, the minds of people will also not be degenerate. Recently I found that the translation of some Manchurian books was full of obscene words which personally was of futility. I am afraid that such transition will be so popular that easily erode people’s thoughts.<br />
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Revised version:--[[User:Sun Yashi|Sun Yashi]] ([[User talk:Sun Yashi|talk]]) 13:08, 11 October 2021 (UTC)<br />
Besides, there is no beneficial words in assorted books.So they shouldn't be allowed to be published and viewed.Then the level of study approximately can be uplifted day by day. Meanwhile, the minds of people will also not be degenerate. Recently I found that the translation of some Manchurian books was full of obscene words which personally was of futility. I am afraid that such transition will be so popular that easily erode people’s thoughts.<br />
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==外国语言学及应用语言学 202120081522 孙雅诗 女==<br />
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况圣贤古训，日详究之，犹恐不及，何暇费日时于无用之地？[ 鄂尔泰等修，李洵、赵德贵等点校：《八旗通志·初集》，长春：东北师范大学出版社，1989年，第5339页。]<br />
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阿什坦的奏章开宗明义，指出读书必以经史为要，必须摒弃杂书，尤其是污言秽语之书，以免贾祸人心。为此，他奏请皇帝对旗人读书严加限制，要求嗣后翻译书籍也应针对“关圣贤义理，古今治乱之书”，对于其它书籍则“概为禁饬，不许翻译”。[ 同上。]<br />
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What's more,there are many sages and wisdom need to be researched.We are afraid that we don't have enough time to do it in details day by day.So why do we waste our time translating those things?[Revised by Eertai ect.,proofread by Li Xun，Zhao De ect.:''Baqitongzhi·chuji'',Changchun:Northeast Normal University Press,1989,p.5339.]<br />
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The advice of Ashitan is very clear from the very begining--[[User:Wu Yinghong|Wu Yinghong]] ([[User talk:Wu Yinghong|talk]]) 15:33, 28 September 2021 (UTC),which points that reading should focus on the Confusion classic and history --[[User:Wu Yinghong|Wu Yinghong]] ([[User talk:Wu Yinghong|talk]]) 15:39, 28 September 2021 (UTC)and abandon those irrelevant books,especially those of dirty words,to get people rid of the obscene thoughts.In order to do this,he advised the emperor be more strict with the Qi people's readings.flag people's learning.--[[User:Wu Yinghong|Wu Yinghong]] ([[User talk:Wu Yinghong|talk]]) 15:30, 28 September 2021 (UTC)And he also required his offspring translate books about sages,principles and those can help to govern the society.Besides these books,other books are all forbidden and mustn't be translated.--[[User:Sun Yashi|Sun Yashi]] ([[User talk:Sun Yashi|talk]]) 11:44, 28 September 2021 (UTC)<br />
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==日语语言文学 202120081530 吴映红 女==<br />
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这一观点既是他翻译《大学》、《中庸》、《孝经》、《潘氏（通鉴）总论》、《太公家教》的原则与标准，也是太祖朝以来一以贯之的译书宗旨。<br />
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五 汉籍（书）翻译的文化沟通意涵<br />
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清初的汉书翻译主要由两部分人员构成：兼通满、汉的旗人（满洲、蒙古、汉军），以及八旗文科举中的举人、进士及第者，特别是顺治朝以来从新科进士中拣选出来学习满文的汉籍士子。<br />
The opinion is not only the principle and standard of his translation about ‘’University‘’, ‘’the doctrine of the mean‘’,‘ ‘’ the book of filial piety‘’, ‘’the general theory of pan (Tongjian) ‘’and ‘’Taigong family education‘’, but also the consistent purpose of translation from the Taizu Dynasty. <br />
FIFTH Cultural communication of Han nationality.<br />
《大学》''The Great Learning'' --[[User:He Qin|He Qin]] ([[User talk:He Qin|talk]]) 14:01, 28 September 2021 (UTC)<br />
In the early Qing Dynasty, the translation of Chinese traditional book was mostly composed of two parts: the flag people who knew Manchu and Han (Manchuria, Mongolia and Han Army), as well as the candidates, Jinshi and others in the eight flag liberal arts test, especially the Chinese bechelors who was selected from the new Jinshi to study Manchu from the Shunzhi Dynasty.<br />
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The opinion is not only the principle and standard of his translation about ‘’Daxue‘’, ‘’Zhongyong‘’,‘ ‘’ Xiaojing‘’, ‘’Genneral Theory(Tongjian) of Pan's Family ‘’and ‘’Taigong Family Education‘’, but also the consistent purpose of translation from the Taizu Dynasty. <br />
FIFTH Cultural communication of Han nationality.<br />
《大学》''The Great Learning'' --[[User:He Qin|He Qin]] ([[User talk:He Qin|talk]]) 14:01, 28 September 2021 (UTC)<br />
In the early Qing Dynasty, the translation of Chinese traditional book was mostly composed of two parts: the flag people who knew Manchu and Han (Manchuria, Mongolia and Han Army), as well as the candidates, Jinshi and others in the eight flag liberal arts test, especially the Chinese bechelors who was selected from the new Jinshi to study Manchu from the Shunzhi Dynasty.<br />
--[[User:Zhu Renduo|Zhu Renduo]] ([[User talk:Zhu Renduo|talk]]) 12:51, 29 September 2021 (UTC)<br />
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==日语语言文学 202120081560 朱壬铎 男==<br />
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这些人员既是翻译专才，又是文化接触与交流的实践者，代表了满洲统治阶级想要与汉族之间进行文化沟通的意愿。如顺治六年四月，礼科给事中姚文然以“以满汉同心合力为念。窃思满汉一家，咸思报主”为由，奏请从新科进士内广选庶吉士，令其肄习清书，待精熟之后即授以科、道等官。[ 鄂尔泰等奉敕修：《清实录·世祖章皇帝实录》，北京：中华书局，1985年，第11页。]顺治十年，世祖降旨，对此前所选三科庶吉士进行考试，从中选取“通满洲文义者三人”，“以应升之缺用”，并选取“其次可造者十二人”，“仍照原衔，责令勉力学习，俟再试分别。”[ 同上，第7-8页。]<br />
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These persons are not only experts in translation,but also practicer in cultral contact and communication,which represented the will,which is to cultrally communicate with the Han people,of the ruling class of Manchu.<br />
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For example,in April of the sixth year of Shunzhi,Yao Wenran,the inspectorof the department of rites,with the reason that "with the purpose that is combining the Man ethnic and the Han ethnic,I secretly came up with an idea about the Man-Han family but with my whole life for the emperor",made a request that is to widely select Shujishi from newly selected Jinshi and order them to sutdy the books of Qing Dynasty to get well-learned then teach the bureaucrats from other departments and circuits.[E Ertai et al.write by imperial command:Veritable Records of Qing Dynasty-Records of Shizu Zhang Emperor,Beijing,China Book Bureau,1985,p.11]<br />
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In the tenth year of Shunzhi,Shizong emperor declared an imperial order about examining the selected Shujishi from all three subjects,from which select three persons who are "familiar with the texts of Manchu",to"fill the vacancy caused by the former Jinshi selection",and select "another 12 gifted persons""remain the former title,make them study hard for the further tests and selection."[E Ertai et al.write by imperial command:Veritable Records of Qing Dynasty-Records of Shizu Zhang Emperor,Beijing,China Book Bureau,1985,p.7-8]<br />
--[[User:Zhu Renduo|Zhu Renduo]] ([[User talk:Zhu Renduo|talk]]) 02:47, 29 September 2021 (UTC)<br />
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These person are not only experts in translation,but also practicers in cultural contact and communication. They represent the will to culturally communicate with the Han people,of the ruling class of Manchu.<br />
For example,in April of the sixth year of Shunzhi,Yao Wenran,the inspector of the Department of Rites,with the reason that "with the purpose that is combining the Manchu and the Han ethnic,I think both Manchu and Han should hold the thought to requite favours to the emperor,made a request to widely select Hanlin bachelor from newly selected Jinshi(a successful candidate in the highest imperial examinations) and order them to sutdy the books of Qing Dynasty to get well-learned then grant them official positions.[E Ertai et al.write by imperial command:Veritable Records of <br />
Qing Dynasty-Records of Shizu Zhang Emperor,Beijing,China Book Bureau,1985,p.11]<br />
In the tenth year of Shunzhi, the emperor declared an imperial order about examining the selected Han bachelor of all three subjects,from which select three persons who are "familiar with the texts of Manchu",to"fill the vacancy caused by the former Jinshi selection",and select "another 12 gifted persons""remain the former title,make them study hard for the further tests and selection."[Idem, p.7-8]<br />
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--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 06:10, 29 September 2021 (UTC)<br />
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==日语语言文学 202120081477 蔡珠凤 女==<br />
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雍、乾、嘉年间，从新科进士中选取习满文者的做法得到延续，但每次选取者人数不一，整体上渐呈下降之势，至道光二十年前后废止，为汉书的满文翻译提供了人力来源，同时也为沟通满、汉两族文化提供了桥梁。<br />
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事实上，早在太宗时期，由于满人尚居关外，不识汉字，又罔知政体，遂敕达海等翻译汉书，使“满洲臣民未习汉文者，亦能兼通汉书”，而太宗自己也以它们作为“临政规范”，学习汉族的国家治理模式。[ 鄂尔泰等修，李洵、赵德贵等点校：《八旗通志·初集》，长春：东北师范大学出版社，1989年，第5325页。]顺治年间，阿什坦译成《大学》、《中庸》等书后，世祖又以它们作为倡导礼义教化的工具，希望通过此举使“满洲人知崇正学、尚经术”，令“邪说不得行”，而“风俗丕变”。[ 同上。]<br />
In the years of Yong Zheng , Qian Long and Jia Qing , the practice of selecting Manchu scholars from new scholars continued, but the number of candidates selected each time varied, and the overall trend gradually decreased. It was abolished around the 20th year of Dao Guang, which not only provided a source of manpower resources for Manchu translation of Hanshu, but also provided a bridge for communication between Manchu and Han cultures.<br />
In fact, as early as the Taizong period, because the Manchus still lived outside the frontier fortress, did not know Chinese characters and did not know about the regime, they ordered Dahai and other people to translate Chinese books, so that "Manchus who did not learn Chinese could also understand the contents of Chinese books", and Taizong himself used them as "administration norms" to learn the national governance model of the Han nationality. [Writed by Ertai etc.Checked by Li Xun, Zhao Degui etc.:"Ba Qi Tong Zhi I", Changchun: Northeast Normal University Press, 1989, P. 5325.] during the reign of Shunzhi, after Ashitan translated the 《University》《Moderate》and other books, Shizu used them as a tool to advocate etiquette and righteousness education, hoping to make "Manchus know and worship orthodox learning and classics" and "heresy can not be carried out" And "Customs change". [ibid.]<br />
<br />
In the years of Yong Zheng , Qian Long and Jia Qing , the practice of selecting Manchu scholars from new scholars was continued, but the number of candidates selected each time varied, and the overall trend gradually tended to decrease. It was abolished around the second decade of Dao Guang, which not only provided human resources for Manchu translation of the Han books, but also builded the bridge of cultural communication between Manchu and Han.<br />
In fact, as early as the the Emperor Taizong period, because the Manchu who still lived outside shanhaiguan pass, did not know Chinese characters and the regime,Taizong ordered Dahai and others to translate the Han books, so that "Manchus who did not learn Chinese could also understand the Han books", and Taizong himself also used them as " the administration norms" to learn the national governance model of the Han nationality. [Written by Ertai etc.Checked by Li Xun, Zhao Degui etc.:"Ba Qi Tong Zhi I", Changchun: Northeast Normal University Press, 1989, P. 5325.] During the reign of Shunzhi, after Ashitan translated the "University""Moderate"and other books,Hong Taiji used them as the tool to advocate confucian code of ethics , hoping to make "Manchus know and worship orthodox learning and classics" and "heresy can not be carried out" and "Customs change". [ibid.]--[[User:Fu Shiyu|Fu Shiyu]] ([[User talk:Fu Shiyu|talk]]) 02:40, 29 September 2021 (UTC)<br />
<br />
==日语语言文学 202120081486 付诗雨 女==<br />
<br />
显然，汉文经、史的译印有助于端正满洲的人心风俗，使满、汉文化互通有无，即便是《诗经》中提到的各种花草树木、鸟兽虫鱼，也对拓展满人见闻甚有助益。[ 叶高树：《<诗经>满文译本比较研究——以<周南>、<召南>为例》，《国立台湾师范大学历史学报》1992年第20期。]有清一代，并非只有官方组织汉书的翻译，私人译书也很盛行。但官方译书与私人译书不同，无论是在译书的取材上，还是在译书的组织管理上，抑或是译书的颁行上，都有其特殊的考量。<br />
Obviously,the translation and printing of the classics and history of Han are conductive to correcting the humanity and customs of Manchuria,and exchanging of needed culture between Manchu and Han.Even different kinds of flowers and trees, insects and fish, which were mentioned in ''the Book of Songs'', also contribute to enrich their knowledge.[Ye Gaoshu:"''Comparative Study of the Manchu translation for 'the book of songs'--Taking 'Zhounan' 'Shannan' as example","The Histotrical Journal of National Taiwan Normal University''<br />
"1992;No.20.]In the Qing Dynasty, not only the official organization of the Han Books' translation, private translation was also very popular. However, unlike the private translation, the official translation was considered specially, whether in the materials of translation, the organization and management of the translation, or the publishment of the translation.--[[User:Fu Shiyu|Fu Shiyu]] ([[User talk:Fu Shiyu|talk]]) 15:16, 28 September 2021 (UTC)<br />
Obviously,the translation and printing of the classics and history of chinese are conductive to correcting the morality and customs of Manchuria,and exchanging of needed culture between Manchu and Han.Even different kinds of flowers and trees, insects and fish, which were mentioned in "the Book of Songs", also contribute to enrich their knowledge.[Ye Gaoshu:"Comparative Study of the Manchu translation for 'the book of songs'--Taking 'Zhounan' 'Shannan' as the example","The Histotrical Journal of National Taiwan Normal University "1992;No.20.]In the Qing Dynasty, not only the official organization of the Han Books' translation, private translation was also very popular. However, unlike the private translation, the official translation was considered specially, whether in the materials,the organization,the management or the publishment of the translation.--[[User:Zhou Xiaoxue|Zhou Xiaoxue]] ([[User talk:Zhou Xiaoxue|talk]]) 14:05, 29 September 2021 (UTC)<br />
<br />
==日语语言文学 202120081559 周小雪 女==<br />
<br />
即便是《三国志（通俗演义）》这样的通俗文学作品，译成满文后也被赋予了严肃的兵法与战略意义。以满文遍译汉书，尤其是经、史、子、集等书，并不是为了显示满语语文系统的优越性，所谓“精微巧妙，实小学家所未有”，而是为了“表章经学，天下从风”，通过汉籍的翻译“研究微言，讲求古义”，进行文化沟通。[ 叶高树：《清朝前期的文化政策》，台北：稻乡出版社，2002年，第91页。]藉由汉文典籍的翻译，满洲统治者不仅了解了汉族文化，而且在接触与学习中获得了“统制”汉民的重要经验，使满洲政权在性质上逐渐向“中原政权”转化，并实现“治统”与“道统”的和谐统一。<br />
Even popular literature such as The History of the Three Kingdoms was given serious military and strategic significance when translated into Manchu script. The purpose of translating Chinese books with Manchu script ,especially Canon ,History,Philosophy and Literature such book is not to show the superiority of the Manchu language system,but to show the confucian classics。Through the translation of Chinese classics ,study small points ,emphasize the ancient significance and carry out cultural communication.Ye Gaoshu.Cultural Policy in the early Qing Dynasty.Taipei:Rice Village Press,2002,p.91.Through the translation of Chinese classics,Manchu rulers not only understood the Han culture,but also gained important experience of controlling the Han people in the process of contact and learning,so that Manchu regime gradually transformed into central plains regime in nature and realized the unity of regnant orthodoxy and confucian orthodoxy.--[[User:Zhou Xiaoxue|Zhou Xiaoxue]] ([[User talk:Zhou Xiaoxue|talk]]) 14:08, 29 September 2021 (UTC)<br />
<br />
Modifying canon, history, philosophy and literature to Confusion classics, history, pre Qin hundred works, religion and classical writings.因为经：经书，是指儒家经典著作；史：史书，即正史；子：先秦百家著作，宗教；集：文集，即诗词汇编。泛指我国古代典籍。Canon, history, philosophy and literature did not express the exact meaning.<br />
--[[User:Zou Yueli|Zou Yueli]] ([[User talk:Zou Yueli|talk]]) 01:41, 29 September 2021 (UTC)"表章经学，天下从风”The first half of the sentence is not translated accuratelyand the second half of the sentence has not been translated，so I think it should be translated into "Commend Confucian classics and let people all over the world follow"<br />
<br />
==日语语言文学 202120081562 邹岳丽 女==<br />
<br />
结语<br />
清初之际，虽然国家尚未实现从“征服王朝”向“中原王朝”的转变，但统治者在致力于武力开拓的同时，也开始关注文化活动。一方面，统治者念念不忘“国语骑射”的满洲旧制，将其视作立国精神；另一方面，又积极组织汉书翻译，倡导汉文化精神，从中原儒学中探求君主治术，构建国家的治统与道统。汉书翻译不仅让统治者得以接触汉族思想精粹和政治观念，而且让其在了解汉文经典与汉族文化的过程中，学习历代帝王的执政得失，以及古往今来的兴废事迹，从中汲取治国经验。<br />
Conclusion At the beginning of the Qing Dynasty, although the country has not yet realized the transformation from "conquering Dynasty" to "Central Plains Dynasty"，however, while the rulers were committed to the development of force, they also began to pay attention to cultural activities. On the one hand, the rulers never forget the old Manchu system of "national language riding and shooting" and regarded it as the spirit of founding the country;On the other hand, --[[User:Zhu Suzhen|Zhu Suzhen]] ([[User talk:Zhu Suzhen|talk]]) 06:46, 28 September 2021 (UTC)he actively organized the translation of Chinese calligraphy, advocated the spirit of Chinese culture, explored the rule of monarchy from the Confucianism of the Central Plains, and constructed the rule and orthodoxy of the country.Chinese translation not only allows the rulers to get in touch with the <br />
ideological essence and political concepts of the Han nationality, but also allows them to learn from the ruling gains and losses of emperors and the rise and fall deeds from ancient to modern times in the process of understanding Chinese classics and Han<br />
culture, so as to learn from the experience of governing the country. --[[User:Zhu Suzhen|Zhu Suzhen]] ([[User talk:Zhu Suzhen|talk]]) 06:46, 28 September 2021 (UTC)from the experience of governing the country. “here the preposition from should be deleted--[[User:Zhu Suzhen|Zhu Suzhen]] ([[User talk:Zhu Suzhen|talk]]) 06:46, 28 September 2021 (UTC)<br />
<br />
however, while the rulers were committed to expansion by military force.<br />
国语骑射：Manchu language, horse-riding and archery--[[User:Zeng Junlin|Zeng Junlin]] ([[User talk:Zeng Junlin|talk]]) 01:49, 3 October 2021 (UTC)<br />
<br />
<br />
--[[User:Zhu Suzhen|Zhu Suzhen]] ([[User talk:Zhu Suzhen|talk]]) 06:46, 28 September 2021 (UTC) ( here "he" is not consistant with the subjective "the rulers", so "he" should be changed in to "they" )<br />
<br />
==国别 202120081478 曾俊霖 男==<br />
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概言之，汉书的翻译既匡扶了社稷，又教化了臣民，令满、汉文化之间的交流得以开启并加深。虽然清初三朝期间，汉书翻译的规模不一，统治者对于汉族文化的具体态度存在差异，但翻译的原则与标准基本未变，那便是以文治教化和典章制度为主，通过翻译汉族典籍，凝聚符合国家需求的集体价值观，并将汉族传统文化落实为国家治理的大政方针，实现兴文教、崇经术、开太平的治国理念。<br />
<br />
In short, the translation of books of Han nationality not only helped the whole country, but also educated his people so that the cultural exchange between Manchu and Han can be opened and deepened. During the three dynasties of the early Qing Dynasty, the scale of books of Han nationality translation was different, and the rulers' specific attitudes towards Han culture were different, but the principles and standards of translation remained basically unchanged, which focused on cultural education and the system of laws and regulations, condensed the collective values in line with the national needs through the translation of Han classics, and implemened the Han traditional culture as the major policy of national governance, realized the governing concept of promoting culture and education, advocating studies of Confucian classics and opening up peace.<br />
<br />
In short, the translation of Books of Han not only gave great help to the whole country, but also educated its(the Qing Dynasty) people so that it began and then deepened the cultural exchange between Manchu and Han. During the first three emperors' rulings of the early Qing Dynasty, though the scales of translation was different, and the rulers' specific attitudes towards Han culture differed from each other, the principles and standards of translation remained basically unchanged, that is to say, it will focus on cultural education and the system of laws and regulations, condense the collective values in line with the national needs through the translation of Han classics, and implement the Han traditional culture as the major policy of national governance to realize the governing concept of promoting culture and education, advocating studies of Confucian classics and opening up peace.<br />
--[[User:Huang Zhuliang|Huang Zhuliang]] ([[User talk:Huang Zhuliang|talk]]) 08:19, 29 September 2021 (UTC)Huang Zhuliang<br />
<br />
==国别 202120081493 黄柱梁 男==<br />
Footnotes and References<br />
<br />
1 杨家骆：《金史》，台北：鼎文书局，1985年，第1684页。<br />
<br />
2 明珠等奉敕修：《清实录·太祖高皇帝实录》，北京：中华书局，1986年，第2页。<br />
<br />
3 中国第一历史档案馆、中国社科院历史研究所译注：《满文老档》，北京：中华书局，1990年，第1196页。<br />
<br />
4 鄂尔泰等奉敕修：《清实录·太宗文皇帝实录》，北京：中华书局，1985年，第13页。<br />
<br />
5 王钟翰点校：《清史列传》，北京：中华书局，1987年，第187页。<br />
<br />
6 国史编纂委员会：《朝鲜王朝实录》，汉城：国史编纂委员会，1973年，第38页。<br />
<br />
1. Yang Jialuo, ''History of the Jin Dynasty''(1115-1234), Taipei, Dingwen Book Company, 1985, pp.1684.<br />
<br />
2. Ming Zhu et al. (compiled under the order of Emperor Kangxi ), ''the Imperial Archives of Emperor Taichu(1559-1626, posthumous titled Gao Huang Di) of the Qing Dynasty'', Peking, Zhonghua Book Company, 1986, pp.2. <br />
<br />
3. The First Historical Archives of China, Translated and Noted by the Institute of History in Chinese Academy of Social Sciences, ''Old Documents of Manchu Script'', Peking, Zhonghua Book Company, 1990, pp.1196.<br />
<br />
4. E Ertai et al. (compiled under the order of Emperor Yongzheng ), ''the Imperial Archives of Emperor Taizong(1592-1643, posthumous titled Wen Huang Di) of the Qing Dynasty'', Peking, Zhonghua Book Company, 1985, pp.13.<br />
<br />
5. Proofread by Wang Zhongshan, ''Biographies of the Qing Dynasty'', Peking, Zhonghua Book Company, 1987, pp.187.<br />
<br />
6. Committee of National History Compilation, ''the Imperial Archives of Joseon Dynasty'', Seoul, Committee of National History Compilation, 1973, pp.38.<br />
--[[User:Huang Zhuliang|Huang Zhuliang]] ([[User talk:Huang Zhuliang|talk]]) 08:18, 29 September 2021 (UTC)Huang Zhuliang<br />
<br />
1 页码标记应为p.1684.<br />
<br />
2 posthumous titled Gao Huang Di应改为posthumously titled Gao Huang Di,页码标记为p.2.<br />
<br />
3“老满文”指的是清太祖努尔哈赤时期创制的满文，以文字中没有圈和点为特点。《满文老档》即是用老满文写成的档案汇编 ，所以《满文老档》应为 ''Manchu Archives written in Fore Manwen'',页码标记为p.1196.<br />
<br />
4 同第二句，posthumous titled Wen Huang Di改为posthumously titled Wen Huang Di, 页码标记为p.13.<br />
<br />
5 页码标记为p.187.<br />
<br />
6 页码标记为p.38.<br />
--[[User:Liu Wei|Liu Wei]] ([[User talk:Liu Wei|talk]]) 05:20, 1 October 2021 (UTC)Liu Wei<br />
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==国别 202120081507 刘薇 女==<br />
7 国史编纂委员会：《朝鲜王朝实录》，汉城：国史编纂委员会，1973年，第62页。<br />
<br />
8 叶高树：《清朝前期的文化政策》，台北：稻乡出版社，2002年，第58页。<br />
<br />
9 鄂尔泰等奉敕修：《清实录·太宗文皇帝实录》，北京：中华书局，1985年，第10页。<br />
<br />
10 罗振玉：《天聪朝臣工奏议》，北京：中国人民大学出版社，1989年，第2页。<br />
<br />
11 鄂尔泰等奉敕修：《清实录·太宗文皇帝实录》，北京：中华书局，1985年，第14页。<br />
<br />
12 同上，第2页。<br />
<br />
7 National History Compilation Committee, ''Records of the Korean Dynasty'', Seoul: National History Compilation Committee, 1973, p.62.<br />
<br />
8 Ye Gaoshu,'' Cultural policise in the early Qing Dynasty'', Taipei: Daoxiang press, 2002, p.58.<br />
<br />
9 E Ertai et al.(compiled by order of the emperor),''Record of the Emperor Taizong Wen in the Qing Dynasty'', Beijing: Zhonghua press, 1985,p.10.<br />
<br />
10 Luo Zhenyu, ''Collections of secretary's memorial to the throne during the reign of Tencong'', Beijing: Renmin University of China Press, 1989, p.2.<br />
<br />
11 E Ertai et al.(compiled by order of the emperor),''Record of the Emperor Taizong Wen in the Qing Dynasty'', Beijing: Zhonghua press, 1985,p.14.<br />
<br />
12 Ertai et al.(compiled by order of the emperor),''Record of the Emperor Taizong Wen in the Qing Dynasty'', Beijing: Zhonghua press, 1985,p.2. <br />
--[[User:Liu Wei|Liu Wei]] ([[User talk:Liu Wei|talk]]) 15:23, 28 September 2021 (UTC)Liu wei<br />
<br />
7 National Institute of Korean History, ''Records of the Korean Dynasty'', Seoul: National Institute of Korean History, 1973, p.62.--[[User:Yan Lili|Yan Lili]] ([[User talk:Yan Lili|talk]]) 13:30, 11 October 2021 (UTC)<br />
<br />
==国别 202120081537 颜莉莉 女==<br />
13 罗振玉：《天聪朝臣工奏议》，北京：中国人民大学出版社，1989年，第24-25、115页。<br />
<br />
14 鄂尔泰等奉敕修：《清实录·太宗文皇帝实录》，北京：中华书局，1985年，第9页。<br />
<br />
15 罗振玉：《天聪朝臣工奏议》，北京：中国人民大学出版社，1989年，第2页。<br />
<br />
16 同上，第82页。<br />
<br />
17 清高宗敕纂：《八旗满洲氏族通谱》，沈阳：辽沈书社，1989年，第10页。<br />
<br />
18 鄂尔泰等奉敕修：《清实录·世祖章皇帝实录》，北京：中华书局，1985年，第15-16页。<br />
<br />
13th. Lou Zhenyu: ''Tiancongchao Chengong Zouyi'' , Beijing: China People's University Press, 1989, pp.24-25,115.<br />
<br />
14th. E Ertai eat al revised under order of emperor: ''Factual Record Of Qing Dynasty• Actual Record Of TaiZu'', Beijing: Zhonghua Book Company, 1985, p.9<br />
<br />
15th. Lou Zhenyu: ''Tiancongchao Chengong Zouyi'' , Beijing: China People's University Press, 1989, p.2<br />
<br />
16th. Idem<br />
<br />
17th. Qing emperor Gaozong ordered to write: ''General spectrum of Manchu clan in eight banners'', Shenyang: Liaoshen Book Company, 1989,p.10<br />
<br />
18th. E Ertai eat al revised under order of emperor: ''Factual Record Of Qing Dynasty• Actual Record Of TaiZu'', Beijing: Zhonghua Book Company, 1985, pp.15-16<br />
<br />
<br />
14. E Ertai eat al （eds. on the order of emperor): ''Factual Record Of Qing Dynasty• Actual Record Of TaiZu'', Beijing: Zhonghua Book Company, 1985, p.9<br />
<br />
16. Idem, p.82<br />
<br />
17. Qing emperor Gaozong ordered to write: ''Genealogy of Manchu clan in eight banners'', Shenyang: Liaoshen Book Company, 1989,p.10<br />
<br />
18. E Ertai eat al （eds. on the order of emperor): ''Factual Record Of Qing Dynasty• Actual Record Of TaiZu'', Beijing: Zhonghua Book Company, 1985, pp.15-16<br />
<br />
==国别 202120081538 颜子涵 女==<br />
19 鄂尔泰等奉敕修：《清实录·世祖章皇帝实录》，北京：中华书局，1985年，第13页。<br />
<br />
20 中国第一历史档案馆编：《清初内国史院满文档案译编·顺治朝》，北京：光明日报出版社，1989年，第80页。<br />
<br />
21 阎崇年校注：《康熙顺天府志》，北京：中华书局，2009年，第482页。<br />
<br />
22 鄂尔泰等奉敕修：《清实录·世祖章皇帝实录》，北京：中华书局，1985年，第9页。<br />
<br />
23 叶高树：《清朝前期的文化政策》，台北：稻乡出版社，2002年，第72-73页。<br />
<br />
24 鄂尔泰等修，李洵、赵德贵等点校：《八旗通志·初集》，长春：东北师范大学出版社，1989年，第5339页。<br />
<br />
19. Ortai and some people compiled it on the orders of the emperor: ''Records of emperor shizuzhang in the Qing Dynasty'', Beijing: China Book Company, 1989, p.13.<br />
<br />
20. Editor of China's First Historical Archives: ''Translation of manchu archives of the National Historical Institute of the early Qing Dynasty, Shunzhi Dynasty'', Beijing: Guangming Daily Press, 1989, p.80.<br />
<br />
21. Yan Chongnian made proofreading: ''Kangxi Shuntian Fuzhi'', Beijing: China Book Company, 2009, p.482.<br />
<br />
22. Ortai and some people compiled it on the orders of the emperor: ''Records of emperor shizuzhang in the Qing Dynasty'', Beijing: China Book Company ,1989,p.9.<br />
<br />
23.Kao-Shu Yeh, ''Cultural policy in the early Qing Dynasty'', Taipei:Daoxiang Press, 2002, pp. 72-73.<br />
<br />
24. Ortai and some people compiled，Li Wei, Zhao Degui and others proofread: ''Journal of the eight banners •First Episode'', Changchun: Northeast Normal University Press, 1989, p. 5339.<br />
<br />
<br />
19. E,Ertai et al. (eds. under the order of the Emperor). ''An Actual Record of ShiZu Zhang in Factual Record of Qing Dynasty'', Beijing:Zhonghua Book Company,1985,p.13.<br />
<br />
20. The First Historical Archives of China(ed.). ''A translation of Manchu archives of the Imperial Academy of National History in the Shunzhi Period of the early Qing Dynasty'', Beijing: Guangming Daily Press, 1989, p.80.<br />
<br />
21. Yan Chongnian(proofread). ''The History of Shuntian of Emperor Kangxi'', Beijing: China Book Company, 2009, p.482.<br />
<br />
22. E,Ertai et al. (eds. under the order of the Emperor). ''An Actual Record of ShiZu Zhang in Factual Record of Qing Dynasty'', Beijing:Zhonghua Book Company,1985,p.9.<br />
<br />
23. Kao-Shu Yeh, ''The Cultural Policies of the Early Qing Dynasty'', Taipei:Daoxiang Press, 2002, pp. 72-73.<br />
<br />
24. E,Ertai et al.(eds.), Li,Xun&Zhao Degui et al.(proofread). ''The First Collectanea in Ba Qi Tong Zhi'', Changchun:Northeast Normal University Press,1989, p.5339.<br />
<br />
==国别 202120081540 阳佳颖 女==<br />
25 同上。<br />
<br />
26 鄂尔泰等奉敕修：《清实录·世祖章皇帝实录》，北京：中华书局，1985年，第11页。<br />
<br />
27 同上，第7-8页。<br />
<br />
28 鄂尔泰等修，李洵、赵德贵等点校：《八旗通志·初集》，长春：东北师范大学出版社，1989年，第5325页。<br />
<br />
29 同上。<br />
<br />
30 叶高树：《<诗经>满文译本比较研究——以<周南>、<召南>为例》，《国立台湾师范大学历史学报》1992年第20期。<br />
<br />
31 叶高树：《清朝前期的文化政策》，台北：稻乡出版社，2002年，第91页。<br />
<br />
<br />
<br />
[25] Idem<br />
<br />
[26] Ertai et al. (eds. under the order of the Emperor). "Actual Record of ShiZu Zhang in Factual Record of Qing Dynasty", Beijing:Zhonghua Book Company,1985,p.11.<br />
<br />
[27] Idem,pp.7-8.<br />
<br />
[28] E,Ertai et al.(eds.), Li,Xun&Zhao Degui et al.(proofread). ''The First Collectanea in Ba Qi Tong Zhi'', Changchun:Northeast Normal University Press,1989, p.5325.<br />
<br />
[29] Idem<br />
<br />
[30] Ye,Gaoshu. “The Comparative Study of the Manchu Translation On The Book of Songs---Cases Study of Zhounan and Zhaonan”.''Historical Inquiry of the National Taiwan Normal University'',no.20(1992).<br />
<br />
[31] Ye,Gaoshu. ''The Cultural Policies of the Early Qing Dynasty''. Taipei:Daoxiang Press,2002,p.91.--[[User:Yang Jiaying|Yang Jiaying]] ([[User talk:Yang Jiaying|talk]]) 07:16, 29 December 2021 (UTC)<br />
<br />
<br />
[25] Idem<br />
<br />
[26] E,Ertai et al. (eds. under the order of the Emperor). "''Actual Record of Shih Tsu Fu Lin in Factual Record of Tsing Dynasty''", Beijing: China publishing house,1985,p.11.<br />
<br />
[27] Idem,p.7-8.<br />
<br />
[28] Ertai et al.(eds.), Li,Xun & Zhao Degui et al.(proofread). "''General History of the Eight Banners''", Changchun: Northeast Normal University Press,1989, p.5325.<br />
<br />
[29] Idem<br />
<br />
[30] Ye,Gaoshu. "''The Comparative Study of the Manchu Translation On The Book of Songs---Cases Study of Zhounan and Zhaonan.''", Bulletin of Historical Research of National Taiwan Normal University, No.20(1992).<br />
<br />
[31] Ye,Gaoshu. "''The Cultural Policies of the Early Tsing Dynasty''". Taipei: Daoxiang Publishing House,2002,p.91. --Ye Weijie<br />
<br />
=Hongloumeng=<br />
HERE STARTS A NEW TRANSLATION: REST OF CHAPTER 19 OF HONGLOUMENG<br />
<br />
PLEASE READ [[Joint_translation_terms|Joint translation terms]] <br />
<br />
PLEASE ALSO READ THE PREVIOUS PARTS, AT LEAST THE SENTENCES BEFORE YOUR OWN PART IN CHAPTER 19 [[20210303_culture|1, Mar 3 Chapters 1-4]], [[20210310_culture|2, Mar 10 Chapters 6-7]], [[20210317_culture|3, Mar 17 Chapters 11-13]], [[20210324_culture|4, Mar 24 Chapters 15-17]], [[20210331_culture|5, Mar 31 Chapters 4-7]], [[20210407_culture|6, Apr 7 Chapters 8-10]], [[20210414_culture|7, Apr 14 Chapters 13-15]] , [[20210519_culture|12, May 19 Chapters 17-19]], [[20210929_homework#Hongloumeng|for Sep 29 - rest of HLM Chapter 19]] [[20211013_homework|for Oct 13 - HLM Chapters 20-21]] etc.<br />
<br />
==国别 202120081544 叶维杰 男==<br />
<br />
第十九回 ... 这些丫头们明知宝玉不讲究这些；二则李嬷嬷已是告老解事出去的了，如今管不着他们：因此只顾玩笑，并不理他。那李嬷嬷还只管问：“宝玉如今一顿吃多少饭？什么时候睡觉？”丫头们总胡乱答应。有的说：“好个讨厌的老货！” 李嬷嬷又问道：“这盖碗里是酪，怎么不送给我吃？”说毕，拿起就吃。一个丫头道：“快别动，那是说了给袭人留着的，回来又惹气了。你老人家自己承认，别带累我们受气。”李嬷嬷听了，又气又愧，便说道：“我不信他这么坏了肠子。别说我吃了一碗牛奶，就是再比这个值钱的，也是应该的。难道待袭人比我还重？难道他不想想怎么长大了？我的血变了奶，吃的长这么大，如今我吃他碗牛奶，他就生气了？我偏吃了，看他怎么着！你们看袭人不知怎么样，那是我手里调理出来的毛丫头，什么阿物儿！”一面说，一面赌气把酪全吃了。<br />
<br />
Chapter XIX...These girls' busy joking with each other and not much cared about Nanny Li as they previously knew Pao'yue was not particular about these, and there's no room left for Nanny Li to discipline them for she's already be dismissed. While Nanny Li kept asking:“ How's Pao-yue's appetite these day? And the time he make rest?” Always with so less careness girls reply. Some complained:“Aye! Such an old nosy lady!” Still Nanny Li asked：“A bowl of cheeze here! Why don't you bring it to me?”Then she directly grabbed some to her mouth. One girl hurriedly said:“Quit it! That's what Pao'yue reserved for Xiren! You take the blame yourself if he gets discontented, don't get us involved.” Angry but ashamed also Nanny Li went, and said:“I don't believe it！I even deserve something more valuable, let alone a bowl of milk! Is Xi'ren more important than me? Pao'yue can't be this heartless! Think about it, I myself raised him up this good step by step heart and soul with my own blood! How can he get discontented merely because of a bowl of milk? Still I'm gonna take it, even if he did! And Xi'ren? I taught her everything! Mad at me? Don't be ridiculous!”Nagging, Nanny Li emptied the whole bowl.<br />
<br />
Chapter XIX...These maids knew clearly that Baoyu didn't care the trifles. Furthermore, Mammy Li was already retired and she had no control over them. Therefor she just ignored him in her teasing. Mammy Li always asked:"How many meals did Baoyu eat recently? When did he go to bed?" The maids'answers always were irrelevant. Some of them said:"What a nasty old woman!" While Mammy Li kept asking :"There is some cheese in the bowl. Why don't you give me?" As soon as the voice fell down, she grabbed the cheese and had it. One maid hurriedly said:“Quit it! That's what Baoyu reserved for Xiren! You take the blame yourself and don't get us involved.” Angry but ashamed also Mammy Li was, and said:“I don't believe he has such a bad temper！I even deserve something more valuable, not to mention a bowl of milk! Is Xi'ren more important than me? Think about it, I raised him up by my breast nursing with my own blood! How can he get discontented merely because of a bowl of milk? Still I'm gonna take it, even if he would! And Xiren? That's me who taught her everything! Mad at me? Don't be ridiculous!”Nagging, Mammy Li emptied the whole bowl.--[[User:Zhou Junhui|Zhou Junhui]] ([[User talk:Zhou Junhui|talk]]) 01:29, 2 October 2021 (UTC)<br />
<br />
Chapter XIX ... these servant-girls were well aware that Precious Jade was not particular in these respects, and that in the next place Nanny Plum，having pleaded old age, resigned her place and gone home，had nowadays no control over them，so that they simply gave their minds to romping and joking，and paid no heed whatsoever to her. Nanny Plum however still kept on asking about Precious Jade，"How much rice do you now eat at one meal? And at what time do you go to sleep?" to which questions the servant-girls replied quite at random；some of those being there observed: "What a dreadful despicable old thing she is!" - "In this covered bowl," she continued to inquire, "is cream, and why not give it to me to eat?" and having concluded these words，she took it up there and then began eating it." Be quick，and leave it alone!" a servant-girl expostulated，"that，she said, was kept in order to be given to Aroma, and on his return，when he again gets into a huff，you，old lady，must，on your own motion，confess to having eaten it，and not involve us in any way as to have to bear his resentment." Nanny Plum，at these words，felt both angry and ashamed. "I can't believe，" she forthwith remarked，"that he has become so bad at heart！Not to speak of the milk I've had. I have，in fact every right to even something more expensive than this；for is it likely that he holds Aroma dearer than myself？ It can't forsooth be that he doesn't bear in mind how that I've brought him up to be a big man，and how that he has eaten my blood transformed into milk and grown up to this age！and will be because I'm now having a bowl of milk of his be angry on that score！I will，yes，eat it，and we'll see what he'll do！I don't know what you people think of Aroma，but she was a lowbred girl，whom I've with my own hands raised up! And what fine object indeed was she！"As she spoke，she flew into a temper, and taking the cream, she drank the whole of it.--[[User:Zhang Yang|Zhang Yang]] ([[User talk:Zhang Yang|talk]]) 13:02, 11 October 2021 (UTC)Zhang Yang<br />
<br />
==国别 202120081551 张扬 男==<br />
<br />
又一个丫头笑道：“他们不会说话，怨不得你老人家生气。宝玉还送东西给你老人家去，岂有为这个不自在的？”李嬷嬷道：“你也不必装狐媚子哄我，打量上次为茶撵茜雪的事我不知道呢！明儿有了不是，我再来领。”说着，赌气去了。<br />
少时，宝玉回来，命人去接袭人。只见晴雯躺在床上不动，宝玉因问：“可是病了？还是输了呢？”秋纹道：“他倒是赢的，谁知李老太太来了，混输了，他气的睡去了。”宝玉笑道：“你们别和他一般见识，由他去就是了。”<br />
说着，袭人已来，彼此相见。袭人又问宝玉何处吃饭，多早晚回来；又代母、妹问诸同伴姊妹好。一时换衣卸妆。宝玉命取酥酪来，丫鬟们回说：“李奶奶吃了。”宝玉才要说话，袭人便忙笑说道：“原来留的是这个，多谢费心。前儿我因为好吃，吃多了，好肚子疼，闹的吐了，才好了。他吃了倒好，搁在这里白糟蹋了。我只想风干栗子吃，你替我剥栗子，我去铺炕。”<br />
<br />
<br />
Another servant-girl grinned:“They don’t know how to speak properly, and it’s no wonder you old lady should get angry. Precious Jade still sends you great things, and it’s impossible that he will feel uncomfortable for a thing like this.” “You don’t have to act like a vixen to cajole me!” Nanny Plum said, “You think I’m not aware that you pushed Snow Alizarin away on account of a cup of tea the other day? And if I did make a mistake, I’ll come by and admit it!” Having said this, she went off, pissed off.<br />
Soon Precious Jade came back and gave orders to go and fetch Aroma. Seeing Sunny Cloud Formation lying perfectly still on bed, Precious Jade asked:“ Is she ill? Or has she lost at cards?” “She had been a winner,” Autumn Vein answered,“but Nanny Plum came and muddled her so that she lost, and angry at that she rushed off to sleep,” Precious Jade smiled:“Don’t place yourselves on the same footing as nanny Plum. Leave her alone.”Aroma came as Precious Jade was saying his words. After the mutual salutations, Aroma went on to ask of Precious Jade:“ Where did you have your dinner? And when did you come back?” and to present likewise on behalf of her mother and sisters her salutations to all the girls, who were her companions. In a short while, she changed her costume and washed off her make up. When Precious Jade bade them fetch the cream, the servant-girls answered:“ Nanny Plum has eaten it.” And as Precious Jade was on the point of making some remarks Aroma hastened to interfere, laughing:“ Is it really this that you have kept for me? Many thanks for troubling. The other day when I ate some of it, I found it very tasty and had a lot of it, then I got a pain in the stomach. I was so upset that it was only after I had thrown it all up that I feel right. So it's fine that she has had it. If it had been kept there, it would have been wasted all for no use. What I want are dry chestnuts, and if you can clean a few for me, I'll go and lay the bed."<br />
--[[User:Zhang Yang|Zhang Yang]] ([[User talk:Zhang Yang|talk]]) 10:58, 29 September 2021 (UTC)Zhang Yang<br />
<br />
Another servant-girl grinned, “They don’t know how to talk properly, and no wonder you, old lady, get angry. Precious Jade still sends you great things, and it’s no need to be unpleased with it.” “You don’t have to cheat me!” Nanny Plum said, “You think I’m not aware that you sent Snow Alizarin away just on account of a cup of tea the other day? I will get it when it is prepared well tomorrow!” Having said this, she went off with sulks. Soon Precious Jade came back and asked someone to pick up Aroma. Seeing Sunny Cloud Formation lying still on bed, Precious Jade asked, “ Is she ill? Or has she lost at cards?” “She had been a winner,” Autumn Vein answered,“but Nanny Plum came and muddled her, so she lost the game and rushed off to sleep for the anger.” Precious Jade smiled, “Don’t take it serious and just leave her alone.”Aroma came as Precious Jade was saying his words. After the mutual salutations, Aroma went on to ask Precious Jade about the place for dinner and the time he would come back, then greet everyone on behalf of her mother and sisters. In a short while, she changed her costume and washed off her make-up. When Precious Jade asked one to fetch the cream, the servant-girls answered,“ Nanny Plum has taken it.” As Precious Jade was on the point of saying something, Aroma laughing, “ Is it this that you have kept for me? Thanks for troubling. The other day when I ate some of it, I found it very tasty and had a lot of it, then I got a pain in the stomach. It was better that she did so, for at least it is not wasted before getting bad. I just want some drying chestnuts. Could you please peel them while I will make the beds.”--[[User:Chen Jing|Chen Jing]] ([[User talk:Chen Jing|talk]]) 11:23, 29 September 2021 (UTC)<br />
<br />
==国别 202020080595 陈静 女==<br />
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宝玉听了，信以为真，方把酥酪丢开，取了栗子来，自向灯下检剥。一面见众人不在房中，乃笑问袭人道：“今儿那个穿红的是你什么人？”袭人道：“那是我两姨姐姐。”宝玉听了，赞叹了两声。袭人道：“叹什么？我知道你心里的缘故，想是说他那里配穿红的？”宝玉笑道：“不是，不是。那样的人不配穿红的，谁还敢穿？我因为见他实在好的很，怎么也得他在咱们家就好了。”袭人冷笑道：“我一个人是奴才命罢了，难道连我的亲戚都是奴才命不成，定还要拣实在好的丫头才往你们家来？”宝玉听了，忙笑道：“你又多心了。我说往咱们家来，必定是奴才不成，说亲戚就使不得？”袭人道：“那也般配不上。”<br />
宝玉便不肯再说，只是剥栗子。袭人笑道：“怎么不言语了？想是我才冒撞冲犯了你？明儿赌气花几两银子，买进他们来就是了。”<br />
宝玉笑道：“你说的话，怎么叫人答言呢？我不过是赞他好，正配生在这深宅大院里，没的我们这宗浊物倒生在这里。”<br />
<br />
Hearing this, Master Bao fell for it and throw the curds away, taking some chestnuts and then peeling them under the lantern. Finding others not in the room except Aroma, Master quipped: “Who is the the person in red today?” Aroma answered, “Two of my cousins”. Knowing it, Master Bao couldn’t repress a sigh of admiration. “What do you sigh for? I know it is because they could not wear red.” Aroma said. Master Bao replied with smile: “No! Who dares to wear red if such persons doesn't deserve it? I just admire them and hope if they can stay in our home.” Aroma sneered, “I am just a slave. So all of my families are slaves and we should pick up the best girls to be slaves in your home?” Master Bao said with smile, “Don’t be touchy. It doesn’t mean to be the slave but to be our relatives in our house.” Aroma replied, “It doesn’t match, either.” Master Bao refused to say any more, but just peeled chestnuts. Aroma smiled and said, “Why are you silent? I just offended you. You could spend some money and buy them if you want.” Master smiled and said, “How to respond to your words. I just show my admiration and think they should be in such circumstance where the persons like me live.”--[[User:Chen Jing|Chen Jing]] ([[User talk:Chen Jing|talk]]) 11:26, 29 September 2021 (UTC)<br />
<br />
Hearing this, Master Bao fell for it, threw the curds away, took some chestnuts and then peeled them under the lantern. Finding others not in the room except Aroma, Master quipped: “What’s the relationship between you and the person in red today?” Aroma answered, “Two of my cousins”. Knowing it, Master Bao couldn’t repress a sigh of admiration. “What do you sigh for? I know what you are thinking. You must think they don’t deserve to wear red.” Aroma said. Master Bao replied with smile: “No! Who dares to wear red if such persons don’t deserve it? I just admire them and hope if they can stay in our house.” Aroma sneered, “I live as a maid. So all of my families should be the same as me? And we should pick up the best girls to be maids in your home?” Master Bao said with smile, “Don’t be touchy. It doesn’t mean to be the maid but to be our relatives in our house.” Aroma replied, “It doesn’t match, either.” Master Bao refused to say any more, but just peeled chestnuts. Aroma smiled and said, “Why are you silent? I think I just offended you. You could spend some money and buy them if you want.” Master smiled and said, “How to respond to your words. I just show my admiration and think they are born to be in such circumstance where the persons like me live. ”--[[User:Chen Xinyi|Chen Xinyi]] ([[User talk:Chen Xinyi|talk]]) 10:34, 29 September 2021 (UTC)<br />
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==翻译学 202120081481 陈心怡 女==<br />
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袭人道：“他虽没这样造化，倒也是娇生惯养的，我姨父、姨娘的宝贝儿似的。如今十七岁，各样的嫁妆都齐备了，明年就出嫁。”宝玉听了“出嫁”二字，不禁又嗐了两声。正不自在，又听袭人叹道：“我这几年，姊妹们都不大见；如今我要回去了，他们又都去了。”<br />
宝玉听这话里有文章，不觉吃了一惊，忙扔下栗子，问道：“怎么着，你如今要回去？”袭人道：“我今儿听见我妈和哥哥商量，教我再耐一年，明年他们上来，就赎出我去呢。”宝玉听了这话，越发忙了，因问：“为什么赎你呢？”袭人道：“这话奇了。我又比不得是这里的家生子儿，我们一家子都在别处，独我一个人在这里，怎么是个了局呢？”宝玉道：“我不叫你去，也难哪。”袭人道：“从来没这个理。就是朝廷宫里，也有定例：几年一挑，几年一放，没有长远留下人的理，别说你们家。” 宝玉想一想，果然有理。又道：“老太太要不放你呢？”<br />
<br />
Aroma said, “Although she has few achievements, she is pampered and the apple of my uncle’s, aunt’s eyes. She is now 17. Since all kinds of dowries had been prepared, she could get married next year. ” Hearing “get married”, Precious Jade Merchant signed spontaneously. He was still ill at ease and then heard Aroma said, “I have rarely met with my sisters in the past few years. Now I’m going back, however, they came.” Precious Jade Merchant thought that there’s more to it than what is said. He was amazed, threw chestnuts at once and asked, “what’s wrong? You are going back now?” Aroma said, “I heard my mother and brother talking about it today. They want me to remain patient for another year and they will come here to redeem me next year. ” Precious Jade heard it, felt anxious and asked, “Why they want to redeem you?” Aroma said, “What you said is pretty strange. I’m not a daughter of maids here. My family is elsewhere, and I’m the only one here. How can it be like this?” Precious Jade said, “I’m afraid I can’t let you go.” Aroma said, “It makes no sense. There are routines even in the imperial palace: palace maids are selected once every few years and then set free. There is no reason to keep a person for a long time in the imperial palace, let alone in your house.” Precious Jade thought about it for a while and thought it made sense. Then he said, “What if my grandmothers doesn’t let you go?”--[[User:Chen Xinyi|Chen Xinyi]] ([[User talk:Chen Xinyi|talk]]) 10:02, 29 September 2021 (UTC)<br />
.<br />
<br />
Aroma said, “Though not born as good, she is also spoilt and pampered, being the apple of my aunt and uncles’ eyes. She is now 17 and dowries of all sorts have been prepared for her marriage next year.” At the hearing of the word “marriage”, Precious Jade Merchant gave a sigh. As he was feeling ill at ease, Aroma sighed and continued, “I rarely met my sisters in the past few years. While I’m going back home, they are all about to leave.” Precious Jade Merchant felt surprised at what she said. He threw away chestnuts at once and asked, “why, you are going back home?” Aroma answered, ““I heard my mother and my brother talking today. They told me to stay here for another year and they will come here to ransom me next year.” Hearing that, Precious Jade got more anxious and asked, “Why do they want to ransom you?” Aroma replied, “what a strange question! I’m no daughter of maids here. My family lives elsewhere and I’m all alone here. How can we be together?” Precious Jade sighed, “If I won’t let you go, I suppose it’s difficult.” Aroma answered, “It makes no sense. There are routines even in the imperial palace: maids are selected once every few years and then set free. There is no reason to keep a servant for a long time in the imperial palace, let alone in your house.”Precious Jade thought for a while and considered it reasonable. Then he said, “What if my grandmothers won’t let you go?”--[[User:Gao Mi|Gao Mi]] ([[User talk:Gao Mi|talk]]) 09:47, 29 September 2021 (UTC)<br />
<br />
==翻译学 202120081487 高蜜 女==<br />
<br />
袭人道：“为什么不放呢？我果然是个难得的，或者感动了老太太、太太，不肯放我出去，再多给我们家几两银子留下，也还有的；其实我又不过是个最平常的人，比我强的多而且多。我从小儿跟着老太太，先伏侍了史大姑娘几年，这会子又伏侍了你几年。我们家要来赎我，正是该叫去的，只怕连身价不要，就开恩放我去呢。要说为伏侍的你好，不叫我去，断然没有的事。那伏侍的好，是分内应当的，不是什么奇功；我去了，仍旧又有好的了，不是没了我就使不得的。”<br />
宝玉听了这些话，竟是有去的理，无留的理，心里越发急了。因又道：“虽然如此说，我只一心要留下你，不怕老太太不和你母亲说，多多给你母亲些银子，他也不好意思接你了。”<br />
袭人道：“我妈自然不敢强：且慢说和他好说，又多给银子；就便不好好和他说，一个钱也不给，安心要强留下我，他也不敢不依。但只是咱们家从没干过这倚势仗贵霸道的事。这比不得别的东西，因为喜欢，加十倍利，弄了来给你，那卖的人不吃亏，就可以行得的；如今无故平空留下我，于你又无益，反教我们骨肉分离，这件事，老太太、太太肯行吗？”<br />
<br />
Aroma said, “Why won’t she let me leave? I should be so important, or I have somehow moved Grandma Merchant and Lady King so that they won’t let go of me and give some more money to my family so as to make me stay. Actually, I am a most ordinary person. A whole lot people are better than me. Bought in by Grandma Merchant since I was a child, I had served Lady History for the first several years, and these several years I have been serving you. Now my family are about to pay the ransom. When I ask for a leave, I’m thinking that you could have mercy on me and allow my leaving without the ransom money. I certainly don’t buy it that you stop me from leaving just because I have served you well. Even if it’s true, I’m just doing what I’m supposed to do, which is really no big deal. Nothing will go wrong without me as there are still good servants who will take my place when I leave.” Hearing that, Precious Jade Merchant became all the more anxious because she had a reason to leave and no reason to stay. Therefore, he continued, “Since all I want is to keep you here, I might as well tell you that I wish Grandma Merchant to talk to your mother and give her a lot more money so that she has no reason to come and take you home. Aroma replied, “My mother certainly dares not to ask for my leaving, not to mention that you talk to her in a mild and polite way and give her extra money. Even if you force her without a penny, she dares not to defy. It’s only that your family has never done such a thing as to throw your weight about. It’s feasible when you buy something with ten times of its original price out of love, for the seller suffers no loss. Unlike anything else, it does you no good to keep me for no reason, which instead separates me from my mother. Do you think Grandma Merchant and Lady King will let that happen?”--[[User:Gao Mi|Gao Mi]] ([[User talk:Gao Mi|talk]]) 16:09, 28 September 2021 (UTC)<br />
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Aroma said, “Why won’t she let me leave? I should be so important, or I have somehow moved Grandma Merchant and Lady King so that they won’t let go of me and give some more money to my family so as to make me stay. Actually, I can't be more ordinary, and too many maids out there are better than me. Bought in by Grandma Merchant when I was a child, I had served Lady History for the first several years, and these years I have been serving you. Now my families are about to pay the ransom. When I ask for leaving, I’m thinking that you could have mercy on me and allow my leaving without the ransom money. I certainly don’t buy it that you stop me from leaving just because I have taken good care of you. Even if it’s true, I’m just doing what I’m supposed to do, which is really no big deal. Nothing will go wrong without me as there are still good maids who will take my place when I leave.” Hearing that, Precious Jade Merchant became more anxious because she had every reason to leave but no reason to stay. Therefore, he continued, “Since all I want is to keep you here, I might as well tell you that I may beg Grandma to talk to your mother and give her extra money so that she has no reason to come and take you home.” Aroma replied, “My mother certainly dares not to ask, not to mention that you talk to her in a mild and polite way and give her extra money. Even if you force her without a penny, she dares not to defy. It’s only that your family has never done such a thing as to throw your weight about. It’s feasible when you buy something with ten times of its original price out of love, for the seller suffers no loss. Unlike anything else, it does you no good to keep me for no reason, which instead separates me from my family. Do you think Grandma Merchant and Lady King will let that happen?”--[[User:He Qin|He Qin]] ([[User talk:He Qin|talk]]) 10:02, 29 September 2021 (UTC)<br />
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==翻译学 202120081489 何芩 女==<br />
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宝玉听了，思忖半晌，乃说道：“依你说来说去，是去定了？”袭人道：“去定了。”宝玉听了，自思道：“谁知这样一个人，这样薄情无义呢！”乃叹道：“早知道都是要去的，我就不该弄了来，临了剩我一个孤鬼儿。”说着，便赌气上床睡了。原来袭人在家，听见他母、兄要赎他回去，他就说：“至死也不回去。”又说：“当日原是你们没饭吃，就剩了我还值几两银子，要不叫你们卖，没有个看着老子娘饿死的理；如今幸而卖到这个地方儿，吃穿和主子一样，又不朝打暮骂。况如今爹虽没了，你们却又整理的家成业就，复了元气；若果然还艰难，把我赎出来，再多掏摸几个钱，也还罢了。其实又不难了，这会子又赎我做什么？权当我死了，再不必起赎我的念头了。”因此哭了一阵。他母、兄见他这般坚执，自然必不出来的了；况且原是卖倒的死契。明仗着贾宅是慈善宽厚人家儿，不过求求，只怕连身价银一并赏了，还是有的事呢。<br />
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After hearing this, Precious Jade Merchant thought for a while and said, “According to you, you have to go?” “I have to.” Aroma confirmed. Hearing this, Precious Jade thought to himself, “Who knows how heartless you are.” “I should not have had you here, since you are doomed to go, leaving me alone as a ghost.” Precious Jade sighed and went to bed with complaints. However, during her stay at home, Aroma heard her mother and elder brother wanted to redeem her back. “I won’t go back until I die.” Aroma railed, “At that time, you were starved to death. Nothing but I was worth some money. If I had refused to be sold, you would have been dead. I was fortunately to be sold to the Merchant’s and treated as a lady, free from abuses. Though my father has passed away, you have recollected yourselves and established new lives. If you were still in trouble, it would make sense that you wanted to reap some profit by redeeming me out. Since you are not, why are you bothering to do this? Don’t ever think about it! Just pretend I’m dead.” Seeing Aroma’s tears and insistence, her mother and elder brother knew it was impossible for her to leave the Merchant’s, besides, it was a sold-out death indenture. In fact, it was not impossible for the Merchant’s, who was a charitable and generous family, to set Aroma free with compensation money, if Aroma pleaded.--[[User:He Qin|He Qin]] ([[User talk:He Qin|talk]]) 13:03, 28 September 2021 (UTC)<br />
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After hearing this, Precious Jade Merchant thought for a while and said, “According to you, you have to go?” “Yes, I have to.” Aroma confirmed. Hearing this, Precious Jade thought, “Who knows how heartless she is.” “I should not have had you here, since you are doomed to go, leaving me alone as a ghost.” Precious Jade sighed and went to bed with complaints. However, during her stay at home, Aroma heard her mother and elder brother want to redeem her back. “I won’t return home until I die.” Aroma railed, “At that time, you were starved to death. Nothing but I was worth some money. If I had refused to be sold, you would have been dead. I was fortunately to be sold to the Merchant’s and treated as a lady, free from abuses. Though my father has passed away, you have reorganized the family and established new lives. If you were still in trouble, it would make sense that you wanted to reap some profit by redeeming me out. Since you are not, why are you bothering to do this? Don’t think about it anymore! Just pretend I’m dead.” Seeing Aroma’s tears and insistence, her mother and elder brother knew it was impossible for Aroma to leave the Merchant’s, besides, it was a sold-out lifetime indenture. In fact, it was possible for the Merchant’s, who was a charitable and generous family, to set Aroma free with compensation money, if Aroma pleaded.--[[User:Li Shuang|Li Shuang]] ([[User talk:Li Shuang|talk]]) 09:41, 29 September 2021 (UTC)<br />
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==翻译学 202120081499 李双 女==<br />
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二则，贾府中从不曾作践下人，只有恩多威少的；且凡老少房中所有亲侍的女孩子们，更比待家下众人不同，平常寒薄人家的女孩儿也不能那么尊重。因此他母子两个就死心不赎了。<br />
次后忽然宝玉去了，他两个又是那个光景儿，母子二人心中更明白了，越发一块石头落了地，而且是意外之想，彼此放心，再无别意了。<br />
且说袭人自幼儿见宝玉性格异常，其淘气憨顽出于众小儿之外，更有几件千奇百怪、口不能言的毛病儿。近来仗着祖母溺爱，父母亦不能十分严紧拘管，更觉放纵弛荡，任情恣性，最不喜务正。每欲劝时，谅不能听。今日可巧有赎身之论，故先用骗词以探其情，以压其气，然后好下箴规。今见宝玉默默睡去，知其情有不忍，气已馁堕。自己原不想栗子吃，只因怕为酥酪生事，又像那茜雪之茶，是以假要栗子为由，混过宝玉不提就完了。<br />
What’s more, the Merchant’s was very good to the servants, and never treated them cruelly. All the girls who served the old or the young received more respect than the others, even more than the girls who lived in poor families. Consequently Aroma’s mother and brother made their minds not to redeem her. Later the sudden arrival of Precious Jade and his meeting with Aroma still further reassured them and put down their stone in heart. The unexpected situation dispelled their other thoughts. When Precious Jade Merchant was young, Aroma found that he was different from ordinary children and that he was naughtier and had some foibles which can’t be told to others. Recently, because Grandma Merchant spoiled Precious Jade too much, his parents couldn’t discipline him too. He was therefore more indulgent and willful, and hated doing the right thing. Whenever others persuaded him, he was stubborn. Today it happened to talk about redemption, so Aroma deliberately lied to Precious Jade to test his attitude and to restrain his anger, and then made the rules. She saw Precious Jade go to sleep silently. She knew the truth, therefore she couldn’t help feeling some guilt, and her anger also subsided. Aroma hadn’t wanted to eat the chestnuts, but was just afraid of the problems that would result from the milk, just like Snow Alizarin’s tea. For this reason, she tricked Precious Jade into not mentioning milk by pretending she wanted to eat chestnuts.--[[User:Li Shuang|Li Shuang]] ([[User talk:Li Shuang|talk]]) 04:50, 29 September 2021 (UTC)<br />
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What’s more, the Merchant’s was very nice to the servants, and never treated them cruelly. All the girls who served as maids of the family members, old or young, were generally treated more kindly than the servants in other position, and were even better off than daughters of ordinary families. Consequently Aroma’s mother and brother made their minds not to buy her freedom. Then the sudden arrival of Precious Jade and the acquaintance between Aroma and her master showed the true situation of Aroma, which made them reassure. The unexpected situation dispelled their other thoughts. These years had shown Aroma that Baoyu with some indescribably odd habits, was no ordinary youth and was more willful than others. Recently, Precious Jade was so indulged by his grandma that his parents couldn’t discipline him strictly. Therefore, he became more indulgent, headstrong and impatient at conventions. Whenever she wanted to exhort him to clean up his act, she was convinced he would not listen to her. Luckily, using the incident as a convenient excuse, Aroma enabled to sound him out, pacify his mood, and give him a good lecture. She saw Precious Jade go to sleep silently. She knew his sadness about her departure, so she didn’t have the heart to give a lecture to him. As for chestnuts, Aroma hadn’t wanted to eat but she had pretended to be eager for them, for fear that the junket would creat a disturbance, just like Snow Alizarin’s tea. She made Precious Jade forget the junket by pretending to hanker after chestnuts.--[[User:Liu Shengnan|Liu Shengnan]] ([[User talk:Liu Shengnan|talk]]) 08:53, 1 October 2021 (UTC)<br />
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==翻译学 202120081506 刘胜楠 女==<br />
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于是命小丫头子们将栗子拿去吃了，自己来推宝玉，只见宝玉泪痕满面。<br />
袭人便笑道：“这有什么伤心的？你果然留我，我自然不肯出去。”宝玉见这话头儿活动了，便道：“你说说，我还要怎么留你？我自己也难说了。”<br />
袭人笑道：“咱们两个的好，是不用说了。但你要安心留我，不在这上头。我另说出三件事来，你果然依了，那就是真心留我了；刀搁在脖子上，我也不出去了。”<br />
宝玉忙笑道：“你说那几件？我都依你。好姐姐，好亲姐姐，别说两三件，就是两三百件，我也依的。只求你们看守着我，等我有一日化成了飞灰，——飞灰还不好，灰还有形有迹，还有知识的。——等我化成一股轻烟，风一吹就散了的时候儿，你们也管不得我，我也顾不得你们了，凭你们爱那里去，那里去就完了。”<br />
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Aroma thereupon gave the chestnuts to the other maids and nudged Precious Jade gently. She found his face is tear-strained .“Why you so sad about that?”she cajoled. “If you really want me to remind here, I won’t leave of course.” Reading between the lines, Precious Jade quickly replied , “Just tell me what I can do to keep you. I don't know how to persuade you.” She laughed, “We needn’t mention how well we get along with each other. If you really want to keep me, that’s a whole other story. If you promise me two or three things I come up with, I’ll assume that you are truly want me to stay. Then even a knife at my throat could not make me get out of here.”Precious Jade merrily said, “Well, what are these three conditions? I will agree them all, dear sister, my nice sister. I’d agree to two or three hundred conditions, let alone two or three. I merely implore you all to stay and take care of me until the day that I turn into flying ashes. No, I don’t want to turn into ashes because ashes have a trace of form and awareness . I’d like to let you all stay until I’ve turned into a wisp of smoke and been blown away by the wind. Then you will not be able to watch over me, and l will not be able to care about you. I will let you go wherever you please as well.”--[[User:Liu Shengnan|Liu Shengnan]] ([[User talk:Liu Shengnan|talk]]) 04:48, 29 September 2021 (UTC) <br />
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Xi Ren thereupon gave the chestnuts to the other maids and nudged Baoyu gently. She found his face tear-strained .<br />
“Why are you so sad about that?”she cajoled . “If you really want me to remind in Jia’s mansion, I won’t leave naturally.” Reading between the lines, Baoyu quickly replied , “Just tell me what else I can do to keep you. I don't know.” <br />
She laughed, “We needn’t mention how well we get along with each other. If you really want to keep me, that’s a whole other story. If you promise me two or three things I come up with, I‘ll assume that you are truly want me to stay. Then even a knife at my throat could not make me get out of here.” Baoyu merrily said, “Well, what are these three conditions? I will agree them all, good sister, my dear sister. I’d agree to two or three hundred conditions, let alone two or three. I merely implore you all to stay and take care of me until the day that I turn into flying ashes. No, I don’t want to turn into ashes because ashes have a trace of form and awareness . I’d like to let you all stay until I’ve turned into a wisp of smoke and been blown away by the wind. Then you will not be able to take care of me, and l will not be able to care about you. I will let you go wherever you want as well.”--[[User:Jin Xiaotong|Jin Xiaotong]] ([[User talk:Jin Xiaotong|talk]]) 13:38, 29 September 2021 (UTC)<br />
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==法语语言文学 202120021494 金晓童 女==<br />
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急的袭人忙捂他的嘴道：“好爷，我正为劝你这些个，更说的狠了。”宝玉忙说道：“再不说这话了。”袭人道：“这是头一件要改的。”宝玉道：“改了，再说你就拧嘴。还有什么？”<br />
袭人道：“第二件，你真爱念书也罢，假爱也罢，只在老爷跟前，或在别人跟前，你别只管嘴里混批，只作出个爱念书的样儿来，也叫老爷少生点儿气，在人跟前也好说嘴。老爷心里想着：我家代代念书，只从有了你，不承望不但不爱念书(已经他心里又气又恼了)，而且背前面后混批评：凡读书上进的人，你就起个外号儿，叫人家‘禄蠹’；又说只除了什么‘明明德’外就没书了，都是前人自己混编纂出来的。这些话，你怎么怨得老爷不气，不时时刻刻的要打你呢？”<br />
宝玉笑道：“再不说了。那是我小时候儿不知天多高地多厚，信口胡说的，如今再不敢说了。还有什么呢？”<br />
Aroma covered his mouth in a hurry and said:"my dear lord, I'm trying to persuade you, but you said these even harder." Precious Jade Merchant quickly said:"I will not say these again.""This is the first thing you need to change."Aroma replied.Precious Jade Merchant said:"OK,if I say it again,you can twist my mouth.Anything else?＂<br />
Aroma said:” The second thing, whether you like studying or not, except for nonsense you just need to pretend that you like reading when talking to milord or other people, which can make milord less angry so that he have something to talk. Milord will say to himself:“Everyone in my family have studied from generation to generation except you. And I can’t imagine that not only do you hate reading(already he was angry and bitter), but also making criticism everywhere----if someone are motivated and love reading, you will call him ‘greedy man’; or you will say every book is fabricated but ‘li Ji’Because of these above, why did you complain that milord are usually annoyed and beat your ass?” “I will never be like that before. When I was a kid, I didn’t understand bragging and talk whatever I want. My dare not say now, and what else?” Precious Jade Merchant said with a smile.--[[User:Jin Xiaotong|Jin Xiaotong]] ([[User talk:Jin Xiaotong|talk]]) 14:49, 28 September 2021 (UTC)<br />
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Xiren covered his mouth in a hurry and said: "my dear lord, I'm trying to persuade you, but you're exaggerating these."Baoyu quickly said: "I will not say these again. " "This is the first thing you need to change. "Xiren replied.Baoyu said: "OK, if I say it again,you can twist my mouth. Anything else? "Xiren said: "The second thing, whether you like studying or not, you can't just talk nonsense, you just need to pretend that you like reading when talking to milord or other people, which can make milord less angry so that he have something to talk with others. Milord will say to himself: "Everyone in my family have studied from generation to generation except you .And I can't imagine that not only do you hate reading (already he was angry and bitter), but also making criticism everywhere casually----if someone are motivated and love reading, you will call him 'greedy man'; you also said there were no other books in the world except 'li Ji', that all other books were made up groundlessly by predecessors. Because of these above, why did you complain that milord are usually annoyed and beat your ass? " <br />
"I will never be like that before. When I was a kid, I didn't understand bragging and talk whatever I want. My dare not say now, and what else? "BaoYu said with a smile.--[[User:Li Yi|Li Yi]] ([[User talk:Li Yi|talk]]) 02:19, 2 October 2021 (UTC)<br />
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==法语语言文学 202120081504 李怡 女==<br />
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袭人道：“再不许谤僧毁道的了。还有更要紧的一件事：再不许弄花儿，弄粉儿，偷着吃人嘴上擦的胭脂和那个爱红的毛病儿了。”宝玉道：“都改，都改。再有什么，快说罢。”<br />
袭人道：“也没有了，只是百事检点些，不任意任性的就是了。你要果然都依了，就拿八人轿也抬不出我去了。”宝玉笑道：“你在这里长远了，不怕没八人轿你坐。”袭人冷笑道：“这我可不稀罕的！有那个福气，没有那个道理，纵坐了也没趣儿。”<br />
二人正说着，只见秋纹走进来说：“三更天了，该睡了。方才老太太打发嬷嬷来问，我答应睡了。”宝玉命取表来看时，果然针已指到子初二刻了。方从新盥漱，宽衣安歇，不在话下。<br />
至次日清晨，袭人起来，便觉身体发重，头疼目胀，四肢火热。先时还扎挣的住，次后挨不住，只要睡，因而和衣躺在炕上。<br />
Aroma said :Stop denigrating monks and dhamma.There is a more important things : no more indulging in flowers and rouge and powder,no more touching lipstick on other people's lips secretly ,and give up the habit of applying makeup. Precious Jade Merchant said :i will change it all .And anything else ? Aroma said : Nothing,you must be careful of everything and stop being capricious. If you change them all, I won't leave even if you lift me in a huge Jiao. Precious Jade Merchant laughed and said : If you stay here long enough, you'll be able to ride in a big Jiao. Aroma sneered and said :I don't desire it, and even if I were lucky enough to ride in the big Jiao, it would be against the rules and boring.<br />
While the two were talking, Autumn Vein came in and said : It's early in the morning and it's time to go to bed. Grandma Merchant sent her maid to ask after you, and I said you were asleep. Precious Jade Merchant took a watch and checked the time, and it was already midnight, he cleaned up again and then undressed and went to bed and stopped talking.<br />
When Aroma got up early the next morning, she felt heavy and dizzy,and she felt her body was burning. At first she managed to stand up, then she couldn't hold on and felt sleepy, and finally she lay down in bed with her clothes on.--[[User:Li Yi|Li Yi]] ([[User talk:Li Yi|talk]]) 08:35, 29 September 2021 (UTC)<br />
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Aroma said, “Stop denigrating monks and dhamma. There is a more important things : no more indulging in flowers and rouge and powder, no more eating lipstick on other people’s lips secretly, and give up the habit of applying makeup.” Precious Jade Merchant said, “I I will rectify all these addictions. And anything else ? ” Aroma said: “No, however, you must be careful of everything and stop being capricious. If you were a kind man, I won’t leave even if you had an eight-man palanquin. Precious Jade Merchant laughed and said: “If you stay here long enough, you’ll be able to in an eight-person palanquin. Aroma sneered and said: “I don’t desire it, and even if I were lucky enough to be in the palanquin, it would be against the rules. That’s boring.” While the two were talking, Autumn Vein came in and said, “It's almost dawn and it’s time to go to bed. Grandma Merchant sent her maid to ask after you, and I said you were asleep.” Precious Jade Merchant asked his servant to bring the watch and checked the time, and it was already midnight. He cleaned up again, undressed and went to bed promptly. When Aroma got up the next morning, she felt heavy and dizzy with a body burning. At first, she was able to stand up, but couldn’t hold on after a short while, and felt extremely sleepy. Thus, she lay down in bed with her clothes on.--[[User:Peng Ruixue|Peng Ruixue]] ([[User talk:Peng Ruixue|talk]]) 12:59, 29 September 2021 (UTC)彭瑞雪<br />
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==法语语言文学 202120081517 彭瑞雪 女==<br />
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宝玉忙回了贾母，传医诊视，说道：“不过偶感风寒，吃一两剂药，疏散疏散就好了。”开方去后，令人取药来煎好。刚服下去，命他盖上被窝焐汗。宝玉自去黛玉房中来看视。<br />
彼时黛玉自在床上歇午，丫鬟们皆出去自便，满屋内静悄悄的。宝玉揭起绣线软帘，进入里间，只见黛玉睡在那里，忙上来推他道：“好妹妹，才吃了饭，又睡觉。”将黛玉唤醒。黛玉见是宝玉，因说道：“你且出去逛逛。我前儿闹了一夜，今儿还没歇过来，浑身酸疼。”宝玉道：“酸疼事小，睡出来的病大。我替你解闷儿，混过困去就好了。”黛玉只合着眼，说道：“我不困，只略歇歇儿。你且别处去闹会子再来。”宝玉推他道：“我往那里去呢？见了别人就怪腻的。”<br />
After hastily greeting Mother Jia, Baoyu invited a doctor to see Xiren at home. The doctor said,“This young lady has only caught a cold by chance, take a few pills, the illness will slowly fade away.” According to the prescription prescribed by the doctor, the servant was ordered to pick out the medicine in the pharmacy and to cook it. As soon as Xiren finished the soup, the doctor asked her to cover herself with a quilt in order to sweat and get rid of the cold in her body. Then, Baoyu went alone to Daiyu’s room to visit her. At that time, Daiyu was lying alone in bed, taking a nap, and the maids had all gone off to do their own things, and silence filled the whole room. Baoyu gently lifted the curtain and entered the room, only to see Daiyu sleeping there, and hurriedly went up to her, nudged her and said to her: “Lovely sister, you just went to bed after eating, how can you do that?” Baoyu tried to wake Daiyu up. When she was awakened, she saw that the person who had woken her up was Bao Yu, she saisd, “Could you go out for a walk for the time being. I was up all night the night before, and to this day I still have not recovered my energy. I feel sick.” Baoyu answered, “Feeling sick is not a big problem. But if you keep sleeping, you will become really very ill. I will relieve your boredom so that the sleepiness is dispelled, and you will be refreshed.” Daiyu just closed her eyes and said,“I’m not sleepy, I just want to rest for a while. Go and play somewhere else for a while, after that, you can come back to me.” Baoyu nudged her and said, “Where can I go? I’m tired of others.”<br />
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After hastily greeting Grandma Merchant, Precious Jade Merchant invited a doctor to see Aroma at home. The doctor said,“This young lady has only caught a cold by chance, take a few pills and she will be recovered.” According to the prescription prescribed by the doctor, the servant was ordered to pick out the medicine in the pharmacy and to decoct it. As soon as Aroma finished the medicine, the doctor asked her to cover herself with a quilt in order to sweat and get rid of the cold in her body. Then, Precious Jade Merchant went alone to Mascara Jade Forest’s room to visit her. At that time, Mascara Jade Forest was lying alone in bed, taking a nap, and the maids had all gone off to do their own things, and silence filled the whole room. Precious Jade Merchant gently lifted the curtain and entered the room, only to see Daiyu sleeping there, and hurriedly went up to her, nudged her and said to her: “Lovely sister, you just went to bed after eating, how can you do that?” Precious Jade Merchant tried to wake Mascara Jade Forest up. When she was awakened, she saw that the person who had woken her up was Precious Jade Merchant, she saisd, “Could you go out for a walk for the time being. I was up all night the night before, and to this day I still have not recovered my energy. I feel sick.” Precious Jade Merchant answered, “Feeling sick is not a big problem. But if you keep sleeping, you will become really very sick. I will relieve your boredom so that the sleepiness will be dispelled, and you will be refreshed.” Mascara Jade Forest just closed her eyes and said,“I’m not sleepy, I just want to rest for a while. Go and play somewhere else for a while, after that, you can come back to me.” Precious Jade Merchant nudged her and said, “Where can I go? I’m tired of others.”--Yang Kun(talk) 21;22, 29 September 2021 (UTC)<br />
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==法语语言文学 202120081542 杨堃 女==<br />
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黛玉听了，嗤的一笑道：“你既要在这里，那边去老老实实的坐着，咱们说话儿。”宝玉道：“我也歪着。”黛玉道：“你就歪着。”宝玉道：“没有枕头，咱们在一个枕头上罢。”黛玉道：“放屁！外头不是枕头？拿一个来枕着。”<br />
宝玉出至外间，看了一看，回来笑道：“那个我不要，也不知是那个腌臜老婆子的。”黛玉听了，睁开眼，起身笑道：“真真你就是我命中的魔星！请枕这一个。”说着，将自己枕的推给宝玉，又起身将自己的再拿了一个来枕上，二人对着脸儿躺下。<br />
黛玉一回眼，看见宝玉左边腮上有钮扣大小的一块血迹，便欠身凑近前来，以手抚之细看道：“这又是谁的指甲划破了？”宝玉倒身，一面躲，一面笑道：“不是划的，只怕是才刚替他们淘澄胭脂膏子，溅上了一点儿。”说着，便找绢子要擦。<br />
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When Mascara Jade heard this, she giggled and said, "Since you want to sit here, sit honestly there and let's talk." Precious Jade Merchant said, "I want to lie down here,too." "You can do it!" said Mascara Jade. " Precious Jade said, "There isn't other pillow. Let's be on the same pillow." Mascara Jade said, "Bullshit! It's not a pillow outside? Take one !" <br />
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Precious Jade went out to the outer room, looked at it, came back and smiled, "I don't want that, and I don't know if it belongs to a certain pickled old woman." Hearing this, Mascara Jade opened her eyes, got up and smiled, " You are the magic star I hit! Please rest with this one. " Then, she pushed her pillow to Precious Jade, got up and fetched another one .After that,the two lay down face-to-face. <br />
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As soon as Mascara Jade looked back and saw a piece of blood ,the size of a button, on Precious Jade's left cheek. She leaned forward and looked closely with her hand, saying, "whose nails cut your face?" Precious Jade turned back, hiding, and said with a smile, "It's not a wound. I think it's just spattered with a little blusher when I washed it for them just now." With that, he looked for the handkerchief to wipe.--Yang Kun (talk) 23:50, 28 September 2021 (UTC)<br />
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When Mascara Jade heard this, she giggled and said, "Since you want to stay here, sit there quietly and let's have a talk." Precious Jade said, "I want to lie down, too." "Do as you like" said Mascara Jade. " Precious Jade said, "There isn't other pillow. Let's share the pillow." Mascara Jade said, "Don’t talk rot! It's not a pillow outside? Take one !" Precious Jade went out to the outer room, looked at it, came back and smiled, "I don't want that, and I don't know if it belongs to a certain pickled old woman." Hearing this, Mascara Jade opened her eyes, got up and smiled, " You are the magic star I hit! Please rest with this one. " Then, she pushed her pillow to Precious Jade, got up and fetched another one. After that, the two lay down face-to-face. As soon as Mascara Jade looked back and saw a piece of blood ,the size of a button, on Precious Jade’s left cheek. She leaned forward and looked closely with her hand, saying, "whose nails cut your face?" Precious Jade turned back, hiding, and said with a smile, "It's not a wound. I think it's just spattered with a little blusher when I washed it for them just now." With that, he looked for the handkerchief to wipe.--[[User:Zhou Junhui|Zhou Junhui]] ([[User talk:Zhou Junhui|talk]]) 05:12, 29 December 2021 (UTC)<br />
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==法语语言文学 202120081556 周俊辉 女==<br />
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黛玉便用自己的绢子替他擦了，咂着嘴儿说道：“你又干这些事了；干也罢了，必定还要带出幌子来。就是舅舅看不见，别人看见了，又当作奇怪事，新鲜话儿，去学舌讨好儿，吹到舅舅耳朵里，大家又该不得心净了。”<br />
宝玉总没听见这些话，只闻见一股幽香，却是从黛玉袖中发出，闻之令人醉魂酥骨。<br />
宝玉一把便将黛玉的衣袖拉住，要瞧瞧笼着何物。黛玉笑道：“这时候谁带什么香呢？”宝玉笑道：“那么着，这香是那里来的？”黛玉道：“连我也不知道，想必是柜子里头的香气熏染的，也未可知。”宝玉摇头道：“未必。这香的气味奇怪，不是那些香饼子、香球子、香袋儿的香。”黛玉冷笑道：“难道我也有什么罗汉、真人给我些奇香不成？就是得了奇香，也没有亲哥哥亲兄弟弄了花儿、朵儿、霜儿、雪儿替我炮制。我有的是那些俗香罢了。”<br />
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Mascara Jade wiped his cheek with her handkerchief. Smacking her mouth, she said:“ You did it again. You always make excuses to do the trifles. You’re lucky that my uncle doesn’t know. But if other people saw it, they would take it as an anecdote, an opportunity to play up to my uncle. As soon as my uncle hears about it, there will be no peace in the house.”<br />
The words went in one his ear and out the other. At that moment, he smelled a faint fragrance coming from Mascara Jade’s sleeve, which was intoxicating. <br />
Precious Jade grabbed Mascara Jade’s sleeve to see what it was. Mascara Jade laughed and said, “Who brings balsam at this time?” He laughed: “Then where does the fragrance come from?” “Even I don’t know. Maybe it came out of the closet.” Precious Jade shook his head:“Not necessarily, the smell is very strange, not like the fragrance of incense cake, of incense ball, of incense bag.” Mascara Jade sneered: “ Will some god give me some magic spice? Even if I really get it, there is no brothers to help me find flowers, buds, frost and snow, then help me make incense. All I have is mediocre spices.”--[[User:Zhou Junhui|Zhou Junhui]] ([[User talk:Zhou Junhui|talk]]) 01:04, 2 October 2021 (UTC)<br />
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Daiyu wiped his mouth with her mocket,she pouted and said:“You did it again, but you also make trouble to us. Although uncle couldn't see it, other people could and would take it as a anecdote. Mabye he heard of it beacause of someone's flattery, there won't be quiet.”<br />
The words went in one of his ears and out the other. At that moment, he smelled a faint fragrance coming from Daiyu’s sleeve,which was intoxicating. <br />
Baoyu grabbed Daiyu’s sleeve to see what stuff hide im. Daiyu smiled and said, “Who brings balsam at this time?” He laughed: “Then where does the fragrance come from?” “Even I don’t know. Maybe it's fumigated by the fragrance of the closet.” Baoyu shook his head:“Not necessarily, the smell is very strange, not like the fragrance of incense cake, of incense ball, of incense bag.” Daiyu sneered: “ Will some god give me some magic spice? Even if I really get it, there is no brothers to help me find flowers, buds, frost and snow, then help me make incense. All I have is mediocre spices.”--[[User:Zhou Qing|Zhou Qing]] ([[User talk:Zhou Qing|talk]]) 13:13, 29 September 2021 (UTC)<br />
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==法语语言文学 202120081558 周清 女==<br />
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宝玉笑道：“凡我说一句，你就拉上这些。不给你个利害也不知道，从今儿可不饶你了。”说着，翻身起来，将两只手呵了两口，便伸向黛玉膈肢窝内两胁下乱挠。黛玉素性触痒不禁，见宝玉两手伸来乱挠，便笑的喘不过气来。口里说：“宝玉，你再闹，我就恼了。”宝玉方住了手，笑问道：“你还说这些不说了？”黛玉笑道：“再不敢了。”一面理鬓，笑道：“我有奇香，你有‘暖香’没有？”<br />
宝玉见问，一时解不来，因问：“什么‘暖香’？”黛玉点头笑叹道：“蠢才，蠢才！你有‘玉’，人家就有‘金’来配你；人家有‘冷香’，你就没有‘暖香’去配他？”宝玉方听出来，因笑道：“方才告饶，如今更说狠了。”说着又要伸手。黛玉忙笑道：“好哥哥，我可不敢了。”宝玉笑道：“饶你不难，只把袖子我闻一闻。”说着便拉了袖子，笼在面上，闻个不住。黛玉夺了手道：“这可该去了。”宝玉笑道：“要去不能。咱们斯斯文文的躺着说话儿。”说着，复又躺下。黛玉也躺下，用绢子盖上脸。<br />
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Baoyu smiled and said:"if i say a word,you will do something like this. You don't know how to constrain, from now on, i won't forgive you anymore if you won't change." As he said, he stood up and exhaled two warm breaths to his both hands, and then put them into the two flanks of Daiyu's diaphragm and scratched randomly. Daiyu still couldn't help itching, so she couldn't breathe with a smile. She said:"Baoyu, if you make a disturbance,i will bw anoyed." Baoyu stopped, he smiled and said:"Will you still say this?" "i'll never do it again. But i have a special perfume, do you have the 'warm perfume'." Daiyu smiled and said.<br />
Baoyu couldn't solve it for a while, so he asked:"What's the "warm perfume?" Daiyu nodded and signed with a smile:"stupd, stupid!You have "jade", people will have "gold"to match you;i have "cold perfume", but you don't have "warm perfume?" Baoyu undstood:"You just surrendered, and now it's even more excesive." He was about to stretch out his hand again. Daiyu hurriedly smiled: "Good brother, I don't dare anymore."Baoyu smiled: "It's not difficult to spare you. I just smell your sleeves." As he said, he pulled up his sleeves, caged on his face, and couldn't help smelling it.Daiyu grabbed his hand and said, "This is the time to go." Baoyu smiled and said, "I can't go. Let's lie down and talk quietly." As he said, he lay down again. Daiyu also lay down and covered her face with silk.--[[User:Zhou Qing|Zhou Qing]] ([[User talk:Zhou Qing|talk]]) 12:54, 11 October 2021 (UTC)<br />
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Baoyu smiled and said:"if i say a word,you will do something like this. You don't know what happened if I'm not serious. I won't let you off from this day forward. " As he said, he stood up and exhaled two warm breaths to his both hands, and then put them into the two flanks of Daiyu's diaphragm and scratched randomly. Daiyu still couldn't help itching, so she couldn't breathe with a smile. She said:"Baoyu, if you make a disturbance,i will bw anoyed." Baoyu stopped, he smiled and said:"Will you still say this?" "i'll never do it again. But i have a special perfume, do you have the 'warm perfume'." Daiyu smiled and said.<br />
Baoyu couldn't solve it for a while, so he asked:"What's the "warm perfume?" Daiyu nodded and signed with a smile:"stupd, stupid!You have "jade", people will have "gold"to match you;i have "cold perfume", but you don't have "warm perfume?" Baoyu undstood:"You just surrendered, and now it's even more excesive." He was about to stretch out his hand again. Daiyu hurriedly smiled: "Good brother, I don't dare anymore."Baoyu smiled: "It's not difficult to spare you. I just smell your sleeves." As he said, he pulled up his sleeves, caged on his face, and couldn't help smelling it.Daiyu grabbed his hand and said, "This is the time to go." Baoyu smiled and said, "I don't go. Let's lie down and talk quietly." As he said, he lay down again. Daiyu also lay down and covered her face with silk.--[[User:Gong Boya|Gong Boya]] ([[User talk:Gong Boya|talk]]) 13:56, 29 September 2021 (UTC)<br />
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==俄语语言文学 202120081488 宫博雅 女==<br />
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宝玉有一搭没一搭的说些鬼话，黛玉总不理。宝玉问他几岁上京，路上见何景致，扬州有何古迹，土俗民风如何。黛玉不答。宝玉只怕他睡出病来，便哄他道：“嗳哟！你们扬州衙门里有一件大故事，你可知道么？”黛玉见他说的郑重，又且正言厉色，只当是真事，因问：“什么事？”宝玉见问，便忍着笑顺口诌道：“扬州有一座黛山，山上有个林子洞。”黛玉笑道：“这就扯谎，自来也没听见这山。”宝玉道：“天下山水多着呢，你那里都知道？等我说完了，你再批评。”黛玉道：“你说。”<br />
宝玉又诌道：“林子洞里原来有一群耗子精。那一年腊月初七，老耗子升座议事，说：‘明儿是腊八儿了，世上的人都熬腊八粥。如今我们洞里果品短少，须得趁此打劫些个来才好。’乃拔令箭一枝，遣了个能干小耗子去打听。小耗子回报：‘各处都打听了，惟有山下庙里果、米最多。’老耗子便问：‘米有几样？果有几品？’小耗子道：‘米、豆成仓。果品却只有五样：一是红枣，二是栗子，三是落花生，四是菱角，五是香芋。’<br />
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Precious Jade Merchant chattered by fits and starts, Mascara Jade Forest kept silent. Precious Jade Merchant asked, “How old were you when you went to the captical? What did you see along the road? Are there any historical sites in Yangzhou? How about the folk customs?” She didn’t answer. Precious Jade Merchant was worried she will get ill for sleeping too long , and coaxed her, “ Ah! There is an important event in yamen of Yangzhou, you know that? ” Mascara Jade Forest saw what he said with a stern look,so she aslo took it seriously. Then she asked, “What envent? ” Upon seeing this, Precious Jade Merchant concealed a smile and kept cooking up, “in Yangzhou there is a mountain called Daishan and a forest cave upon there. ” Mascara Jade Forest smiled, “It is nonsense, I have not heard of it at all. ”Precious Jade Merchant replied, “There are so many landscapes in the world, how do you know all of them? Keeping your comments until i finish my words. ” She said, “Say it. ” Precious Jade Merchant talked recklessly again, “There used to be a bunch of ratspirits in the forest cave. On the seventh day of the twelfth lunar month, the elder ratspirit held a meeting. On the meeting, he said, ‘Tomorrow is the eighth day of the twelfth lunar month. People will all make laba rice porridge. Now that we are short of grain in the cave, we must seize the chance to rob some. ’ Cosequently he threw a command arrow and sent an able young ratspirit to inquire. Young ratspirit returned and reported, ‘I have made inquiries everywhere. The temple at the mountain foot stores most fruits and rice. ’ The old ratspirit asked, ‘ How many kinds of grain? How many kinds of tribute? ’ The young ratspirit said, ‘There's a whole warehouse of rice and bean. There are only five kinds of cereal: one is red dates, two is chestnuts, three is peanuts, four is water chestnut, five is taro. ’--[[User:Gong Boya|Gong Boya]] ([[User talk:Gong Boya|talk]]) 01:55, 13 October 2021 (UTC)<br />
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Chinese names can be translated directly in pinyin.(宝玉—Baoyu；黛玉—Daiyu);Bao Yu was talking nonsense, but Dai Yu always ignored it. She asked him how old he was when he went to Beijing, what sights he saw on the way, what are the monuments in Yangzhou, and what are the customs and folklore. Daiyu did not answer. She was afraid that he would fall asleep and get sick, so she coaxed him, "Oh! There is a big story in your Yangzhou government office, do you know it?" The first time I saw him, I thought he was telling the truth, so I asked, "What is it?" When Baoyu saw the question, he stifled a laugh and said smoothly, "There is a Dai Mountain in Yangzhou and there is a Lin Zi Cave on the mountain." Daiyu laughed and said, "That's a lie, I haven't heard of this mountain since." The world is full of mountains and rivers, where do you know them all? When I've finished, you can criticise again." Daiyu said, "You say it." Bao Yu said, "There was a group of rat spirits in the forest cave. That year, on the seventh day of the lunar month, the old rats rose to their seats and said: 'Tomorrow is the eighth day of the lunar month, and everyone in the world is making lunar porridge. Now we are short of fruits in the cave, so we must take advantage of this to rob some of them.' He drew an arrow and sent a competent little rat to inquire. The little rat reported, 'I have asked everywhere, but the temple at the bottom of the hill has the most fruit and rice.' The old rat then asked, 'How many kinds of rice are there? How many kinds of fruit are there?' The little rat said, 'Rice and beans are in the warehouse. But there are only five kinds of fruits: first, red dates, second, chestnuts, third, groundnuts, fourth, lozenges, and fifth, taro.--[[User:Mao You|Mao You]] ([[User talk:Mao You|talk]]) 06:46, 29 September 2021 (UTC)<br />
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==俄语语言文学 202120081515 毛优 女==<br />
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“老耗子听了大喜，即时拔了一枝令箭，问：‘谁去偷米？’一个耗子便接令去偷米。又拔令箭问：‘谁去偷豆？’又一个耗子接令去偷豆。然后一一的都各领令去了。只剩下香芋，因又拔令箭问：‘谁去偷香芋？’只见一个极小极弱的小耗子应道：‘我愿去偷香芋。’<br />
“老耗子和众耗子见他这样，恐他不谙练，又怯懦无力，不准他去。小耗子道：‘我虽年小身弱，却是法术无边，口齿伶俐，机谋深远。这一去，管比他们偷的还巧呢。’众耗子忙问：‘怎么比他们巧呢？’小耗子道：‘我不学他们直偷；我只摇身一变，也变成个香芋，滚在香芋堆里，叫人瞧不出来，却暗暗儿的搬运，渐渐的就搬运尽了。这不比直偷硬取的巧吗？’众耗子听了，都说：‘妙却妙，只是不知怎么变？你先变个我们瞧瞧。’小耗子听了，笑道：‘这个不难，等我变来。’说毕，摇身说：‘变！’竟变了一个最标致美貌的一位小姐。众耗子忙笑说：‘错了，错了。原说变果子，怎么变出个小姐来了呢？’小耗子现了形，笑道：‘我说你们没见世面，只认得这果子是香芋，却不知盐课林老爷的小姐才是真正的香玉呢。’”<br />
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The old mouse was overjoyed and immediately drew an arrow and asked:"Who is going to steal the rice?" A mouse took the order to steal the rice. Then he drew /pulled another arrow and asked again :"Who is going to steal the beans?" Another mouse took the order to steal the beans. All the mouses One by one received the orders, only the order of taro was left. So the old mouse drew this left arrow and asked:"Who is going to steal the taro?" At this time a very small and weak mouse responded:"I am willing to steal the taro." The old mouse and all the mice saw him like this, and they would not allow him to go because they were afraid that he would be unskilled and cowardly. But the little mouse said: ‘Although I am young and weak, I have boundless power, skillful tongue and foresight. I will steal more cleverly than others. "The mice hurriedly asked: "How can you do that?" The little mouse said: "I won't learn from them to steal directy. I just changed my body and turned into a taro, rolled in the taro pile. In that way people can't see me. Then I will secretly carry the taros, and gradually they were exhausted. Isn't this more clever than stealing directly? All the mice heard this, and they all said, "It's indeed a wonderful way, but how can you change yourself into a taro? Can you show us now? let's see!"The little mouse listened and said with a smile: "This is not difficult, wait for me!" After speaking, he said: "Change! "She has turned into the most beautiful girl in this world. The mice laughed and said:"it’s wrong, it’s wrong. Originally you said that you would turn into fruit, why did you turn into a girl?" The little mouse appeared, and smiled: "I said you hadn't seen the world, because you only recognized that it was a taro, but you didn't know that master Lin's daughter was the real fragrant jade. "(In Chinese pronounciation of the word "taro" is as the same as the word "fragrant jade")--[[User:Mao You|Mao You]] ([[User talk:Mao You|talk]]) 14:28, 28 September 2021 (UTC)Sept. 28<br />
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The old mouse was overjoyed and immediately drew an arrow and asked:"Who is going to steal the rice?" A mouse took the order to steal the rice. Then he pulled another arrow and asked again :"Who is going to steal the beans?" Another mouse took the order to steal the beans. All the mouses One by one received the orders, only the order of taro was left. So the old mouse pulled this left arrow and asked:"Who is going to steal the taro?" At this time a very small and weak mouse responded:"I am willing to steal the taro." The old mouse and all the mice saw him like this, and they would not allow him to go because they were afraid that he would be unskilled and cowardly. But the little mouse said: ‘Although I am young and weak, I have boundless supernatural power, skillful tongue and foresight. I will steal more cleverly than others. "The mice hurriedly asked: "How can you do that?" The little mouse said: "I won't learn from them to steal directy. I just changed my body and turned into a taro, rolled in the taro pile. In that way people can't see me. Then I will secretly carry the taros, and gradually they were exhausted. Isn't this more clever than stealing directly? All the mice heard this, and they all said, "It's indeed a wonderful way, but how can you change yourself into a taro? Can you show us now? let's see!"The little mouse listened and said with a smile: "This is not difficult, wait for me!" After speaking, he said: "Change! "She has turned into the most beautiful girl in this world. The mice laughed and said:"it’s wrong, it’s wrong. Originally you said that you would turn into fruit, why did you turn into a girl?" The little mouse appeared, and smiled: "I said you hadn't seen the world, because you only recognized that it was a taro, but you didn't know that master Lin's daughter was the real fragrant jade. "--[[User:Wu Jingyue|Wu Jingyue]] ([[User talk:Wu Jingyue|talk]]) 13:00, 11 October 2021 (UTC)<br />
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==俄语语言文学 202120081529 吴婧悦 女==<br />
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黛玉听了，翻身爬起来，按着宝玉笑道：“我把你这个烂了嘴的！我就知道你是编派我呢。”说着便拧。宝玉连连央告：“好妹妹，饶了我罢，再不敢了。我因为闻见你的香气，忽然想起这个故典来。”黛玉笑道：“饶骂了人，你还说是故典呢。” 一语未了，只见宝钗走来，笑问：“谁说故典呢？我也听听。”黛玉忙让坐，笑道：“你瞧瞧，还有谁？他饶骂了，还说是故典。”宝钗笑道：“哦！是宝兄弟哟，怪不得他，他肚子里的故典本来多么。就只是可惜一件：该用故典的时候儿，他就偏忘了。有今儿记得的，前儿夜里的芭蕉诗就该记得呀，眼面前儿的倒想不起来。别人冷的了不得，他只是出汗。这会子偏又有了记性了。”黛玉听了，笑道：“阿弥陀佛！到底是我的好姐姐。你一般也遇见对子了。可知一还一报，不爽不错的。”<br />
刚说到这里，只听宝玉房中一片声吵嚷起来。<br />
未知何事，下回分解。<br />
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Daiyu listened, turned over and got up, she laughed at Baoyu and said: “ Why you love to gossip so much? I knew that you were making fun of me.” She said and tweaked his ear. Baoyu hastened to say that: “ my dear sister, forgive me please, I dare not do it again. Because I smelt your scent, and suddenly remembered this literary allusion.” Daiyu smiled and added: “ You spout insults but said that it is an allusion.” Didn’t finish saying, while Baochai came in, she also smiled and said: “ Who is telling allusions? I want to know, too.” Daiyu offered her seat to Baochai, and said: “ Look! Who else? He spout insults, but said they are allusion.” Baochai added with a smile: “ Oh! It is brother Baoyu, it’s none of his business, because he knew a lot of allusions, but it is a pity that he didn’t remember the allusion when it needed. He should have remembered the Plantain poem, but didn’t remember the allusion before him. The others were so cold, but he only sweated, and this time he unexpectedly had a good memory.” Daiyu listened to her, laughing: “ God! You exactly my good sister. Now you also meet with your opponent. It is true that measure for measure.” By this time, Baoyu’s room rang out a noise. Unknown what had happened, it will be told in the next chapter.<br />
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Daiyu heard, turned over and got up, she laughed at Baoyu and said: “ Why you love to gossip so much? I know that you are making fun of me.” She said and tweaked his ear. Baoyu hastened to say that: “ my dear sister, forgive me please, I dare not do it again. Because I smelt your scent, and suddenly remembered this literary allusion.” Daiyu smiled and added: “ You spout insults but said that it is an allusion.” Didn’t finish saying, while Baochai came in, she also smiled and said: “ Who is telling allusions? I want to know, too.” Daiyu offered her seat to Baochai, and said: “ Look! Who else? He spout insults, but said they are allusion.” Baochai added with a smile: “ Oh! It is brother Baoyu, it’s none of his business, because he knew a lot of allusions, but it is a pity that he didn’t remember the allusion when it needed. He should have remembered the Plantain poem, but didn’t remember the allusion before him. The others were so cold, but he only sweated, and this time he unexpectedly had a good memory.” Daiyu listened to her, laughing: “ God! You are exactly my good sister. Now you also meet with your opponent. It is true that measure for measure.” By this time, Baoyu’s room rang out a noise. Unknown what had happened, it will be told in the next chapter. --[[User:Xie Qinglin|Xie Qinglin]] ([[User talk:Xie Qinglin|talk]]) 02:20, 29 September 2021 (UTC)<br />
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==俄语语言文学 202120081533 谢庆琳 女==<br />
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花解语──解：理解，领会。 语本“解语花”，出自五代·王仁裕《开元天宝遗事·卷下·解语花》：“秋八月，太液池有千叶白莲，数枝盛开，帝(唐玄宗)与贵戚宴赏焉。左右皆叹羡。久之，帝指贵妃示于左右曰：‘争如我解语花！’”(争：怎。)原指唐玄宗把杨贵妃比做善解人意的鲜花。引申以比喻善解人意的美女。曹雪芹化用为“花解语”，则变为美女善解人意；而“花”又为袭人之姓，则“花解语”就是花袭人善解人意。<br />
玉生香──玉：指林黛玉。 生香：散发出香气。语或本“活色生香”，出自唐·薛能《杏花》诗：“活色生香第一枝，手中移得近青楼。”原形容杏花呈现出生机盎然的美丽颜色，散发出沁人心脾的香气。引申以形容女子的天生美貌和芳香气息。这里用以形容林黛玉的天生美貌和芳香气息。<br />
“Hua Jie Yu” - " Jie": to understand, to comprehend. The phrase is from Wang Renyu's "The Legacy of Kaiyuan Tianbao - Volume 2 - The Flower of Explanation": "In the eighth month of autumn, there were a thousand leaves of white lotus in full bloom at the Taiyan Pond. The Emperor (Tang Xuanzong) and his noble relatives enjoyed the feast, and all the people around him admired them. After a long time, the emperor pointed to the noble princess and showed her to the left and right, saying: 'Compete with me to interpret the flowers!'" (Strive: how.) Originally, Emperor Tang Xuanzong compared Yang Guifei to an understanding flower. This is a metaphor for a beautiful woman who understands people. Cao Xueqin's use of the word "Hua Jie Yu" is a metaphor for a beautiful woman who understands people's feelings; and since "Hua" is also the surname of Assailant, "Hua Jie Yu" means that Hua Assailant understands people's feelings. “Jade is fragrant” - “Jade” refers to Lin Daiyu. The name of the poem is "Jade". The phrase is derived from the poem 'Apricot Blossoms' by Xue Neng: "The first branch of apricot blossoms is in living colour and fragrance, and the hand has moved close to the green tower." The original description is that the apricot blossoms are of a vibrant and beautiful colour, emitting a refreshing fragrance. It is also used to describe the natural beauty and fragrance of a woman. Here it is used to describe the natural beauty and fragrance of Lin Daiyu.<br />
--[[User:Xie Qinglin|Xie Qinglin]] ([[User talk:Xie Qinglin|talk]]) 02:02, 29 September 2021 (UTC)<br />
“Hua Jie Yu” - " Jie": to understand, to comprehend. The phrase is from five dynasties and ten years ·Wang Renyu's "The Legacy of Kaiyuan Tianbao - Volume 2 - The Flower of Explanation": "In the eighth month of autumn, there were a thousand leaves of white lotus in full bloom at the Taiye Pond. The Emperor (Tang Xuanzong) and his relatives enjoyed the beautiful scenery, and all the people around him admired them. After a long time, the emperor pointed to Yang Guifei（his wife）and showed her to the left and right, saying: 'Compete with me to interpret the flowers!'" (Strive: how.) Originally, Emperor Tang Xuanzong compared Yang Guifei to an understanding flower. This is a metaphor for a beautiful woman who understands people. Cao Xueqin used the word "Hua Jie Yu" as a metaphor for a beautiful woman who understands people's feelings; and since “Hua” is also the surname of Aroma, “Hua Jie Yu” means that “Aroma Hua” understands people's feelings. “Yu Sheng Xiang” - “Yu”(Jade) refers to Mascara Jade Forest. “Sheng Xiang”- exude fragrance. Or it could be called ''Huo Se Sheng Xiang''.The phrase is derived from the poem 'Apricot Blossoms' by Xue Neng: "The first branch of apricot blossoms is in living colour and fragrance, when got it in hand has moved close to the brothel." The original description is that the apricot blossoms are of a vibrant and beautiful colour, emitting a refreshing fragrance. It is also used to describe the natural beauty and fragrance of a woman. Here it is used to describe the natural beauty and fragrance of Mascara Jade Forest.--[[User:Zhang Yiran|Zhang Yiran]] ([[User talk:Zhang Yiran|talk]]) 03:24, 29 September 2021 (UTC)<br />
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==俄语语言文学 202120081552 张怡然 女==<br />
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掷骰(t óu投)子——是一种游戏，即互掷骰子，以点数多少决输赢。 骰子：游戏或赌博用具。多用兽骨制成，为立体小方块，六面分别刻以一、二、三、四、五、六点，一、四点涂以红色，其馀涂以黑色，故又称“色子”。相传为曹操之子曹植发明。<br />
《丁郎认父》──取材于小说《升仙记》的弋阳腔剧目。写明代杜文学因受奸相严嵩迫害，流落湖广，入赘胡丞相府，与前妻所生子丁郎曲折相认的故事。<br />
《黄伯央大摆阴魂阵》──或作《黄伯英大摆阴兵阵》。是由《七国春秋平话》改编的地方戏目。写燕国大将乐毅请师父黄伯央(《七国春秋平话》作“黄伯杨”)下山摆设阴魂阵围困齐将孙膑，最后两国讲和的故事。<br />
Cast the dice - is a game in which two people cast in turn, using the size of the dice to determine the winner. Dice: a game or gambling device.Most of them are small cubes made of animal bones, six sides are engraved on one, two, three, four, five, six points, one and four coated into red, and the rest is also black, so the dice is called again ''Shai zi''.It is said to be invented by Cao Zhi, the son of Cao Cao. <br />
''Ding lang got acquainted with his father''-- based on the novel ''Sheng Xian Ji'' yiyang repertoire. It tells the story of a man named Du Wenxue in the Ming Dynasty, who was forced to live in Huguang area because of the persecution of Yan Song, the adulterous phase. He entered the residence of Prime Minister Hu and met ding Lang, the son of his ex-wife with twists and turns.<br />
''Huang Boyang's Great display of ghosts'' ─ or ''Huang Boying's great display of died soldiers''. It is a local opera adapted from ”The story collection of the Seven Kingdoms In the Spring and Autumn Period”. It tells the story of the state of Yan general Yue Yi please master Huang Boyang (''The story collection of the Seven Kingdoms In the Spring and Autumn Period'' for ''Huang Boyang'' ) Down the mountain set up the ghost array besiege Qi general Sun Bin， finally the last two countries peace story.--[[User:Zhang Yiran|Zhang Yiran]] ([[User talk:Zhang Yiran|talk]]) 15:47, 28 September 2021 (UTC)<br />
Cast the dice - is a game in which two people cast in turn, using the size of the dice to determine the winner. Dice: a game or gambling device.Most of them are small cubes made of animal bones, six sides are engraved on one, two, three, four, five, six points, one and four coated into red, and the rest is coated into black, so the dice is also called 'Shai zi''.It is said to be invented by Cao Zhi, the son of Cao Cao. <br />
''Ding lang got acquainted with his father''-- based on yiyang repertoire of the novel ''Sheng Xian Ji''. It tells the story of a man named Du Wenxue in the Ming Dynasty, who was forced to live in Huguang area because of the persecution of Yan Song, the adulterous phase. He entered the residence of Prime Minister Hu and met ding Lang, the son of his ex-wife with twists and turns.<br />
''Huang Boyang's Great display of ghosts'' ─ or ''Huang Boying's great display of died soldiers''. It is a local opera adapted from ”The story collection of the Seven Kingdoms In the Spring and Autumn Period”. It tells the story of the state of Yan general Yue Yi please master Huang Boyang (''The story collection of the Seven Kingdoms In the Spring and Autumn Period'' for ''Huang Boyang'' ) Down the mountain set up the ghost array besiege Qi general Sun Bin， finally the last make peace between the two countries.--[[User:Liu Yue|Liu Yue]] ([[User talk:Liu Yue|talk]]) 10:26, 29 September 2021 (UTC)<br />
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==亚非语言文学 202120081509 刘越 女==<br />
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香玉──典出唐·温庭筠《晚归曲》：“弯堤弱柳遥相瞩，雀扇团圆掩香玉。”原比喻美女散发的香气和洁白如玉的肌肤。这里是把玉石的“玉”和林黛玉的“玉”合为一体，以比喻美女林黛玉。<br />
《孙行者大闹天宫》──京剧和地方戏均有此剧目，取材于小说《西游记》。写孙悟空跟随唐僧前大闹天宫的故事。<br />
《姜太公斩将封神》──京剧和地方戏均有此剧目，取材于小说《封神演义》。写姜太公助周灭商后斩将封神的故事。 按：以上四剧皆为闹剧，隐寓宁国府子弟粗俗不堪。<br />
献酬──亦作“献醻”。指酒席上主宾互相敬酒。《诗经·小雅·楚茨》：“献醻交错，礼仪卒度，笑语卒获。”郑玄笺：“始主人酌宾为献，宾既酌主人，主人又自饮酌宾曰醻。”<br />
行令──即行酒令。是一种宴会中助兴的游戏。其方法是：由一人任令官，按一定规矩行令，违令或按令该饮者都要饮酒。<br />
Sweet jade -- classic tang · Wen Tingjun late homing song : "Bent dike weak willow distant look, bird fan reunion mask sweet jade." Originally used as a metaphor for beauty to send out fragrance and white jade skin. Here, the "jade" of jade and the "jade" of Lin Daiyu are combined as a whole to compare the beauty Lin Daiyu. <br />
Monkey King Makes Havoc in Heaven-- a play in Both Beijing and local operas, based on the novel 'Journey to the West.' It tells the story of Sun Wukong who caused havoc in heaven before he followed Tang Priest. <br />
Jiang Taigong beheaded a General and canonized a God. This play is used in Both Beijing Opera and local opera, and is based on the novel Creation of the Gods(Fengshen Yanyi). Write the story of Jiang Taigong, who helped Zhou destroy the Shang dynasty and then beheaded the generals and sealed the gods. The author explains: the above four plays are farce, alluding to the vulgar children of the Ningguo mansion.<br />
献酬（Make toasts）─ can also be said 献醻. Refers to the banquet guests toasting each other. The Book of Songs· xiaoya ·chuets : "The host and the guest toast each other, the etiquette completely conforms to the law, then a word and a smile are appropriate natural." Zheng Xuanjian: "First, the host toasts to the guests. After the guests drink the wine revered by the host, the host drinks it, which is called making toasts ." <br />
Order -- that is, order to drink. It's a fun game at a banquet. Its method is: by a person as an officer, according to certain rules of the order, the violation or according to the order of the drinker to drink.--[[User:Liu Yue|Liu Yue]] ([[User talk:Liu Yue|talk]]) 10:26, 29 September 2021 (UTC)<br />
Here is the "jade" of Jade and Lin Daiyu "jade" into one--[[User:Zhang Qiuyi|Zhang Qiuyi]] ([[User talk:Zhang Qiuyi|talk]]) 10:38, 29 September 2021 (UTC)<br />
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==亚非语言文学 202120081550 张秋怡 女==<br />
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家生子儿——奴仆在主子家生养的子女。清代规定家奴之子女必须为奴，世代如此。清·赵翼《陔馀丛考·家生子儿》：“奴仆在主家所生子，俗谓家生子。按《法苑珠林》记庸岭有大蛇为患，都尉令长求人家生婢子及有罪家女祭之，‘家生’之名见此。”(按：《法苑珠林》为唐·释道世撰。)又《汉书·陈胜传》“秦令少府章邯免骊山徒、人奴产子”唐·颜师古注：“奴产子，犹今人云家生奴也。”“家生奴”即“家生子”。可知“家生子”或“家生奴”之称至少在唐代已有。<br />
卖倒的死契——指双方约定卖出后不能赎身、必须终生为奴的卖身契。 卖倒：犹“卖定”、“卖死”。不可变更或反悔之意。<br />
禄蠹——禄：身居官爵，享受俸禄。 蠹：蛀虫。 “禄蠹”或本“国蠧”，出自《左传·襄公二十二年》：“不可使也，而傲使人，国之蠧也。”意思是本无治国之才而身居高位，便是国家的蛀虫。“禄蠹”与“国蠧” 的意思基本上一样，也是指身居高位、坐享国家俸禄而不干实事的官吏。<br />
Offspring - The offspring of a servant in his master's house.The Qing Dynasty stipulated that the children of domestic slaves must be slaves,from generation to generation so.Qing · Zhaoyi “Gai Yu Cong Kao·The offspring of a servant in his master's house”：“A son born to a slave in his master's house is called a son of the family. according to the story in the Pearl forest of Fayuan that there was a snake in yongling, the commander ordered the family to give birth to maidservants and guilty family female sacrifice, the name of ‘Jia Sheng’ can be seen here.”(according to: the Pearl forest of Fayuan was written for Tang· Shi Daoshi.) Also in The Book of Han · Biography of Chen Sheng, “The Qin Dynasty sent Zhang Han to pardon those who had served in mount Lishan for crimes and the sons born to house slaves”Tang · Yan Shigu notes: “Slaves give birth to children, and is also slaves”.“family born slave” is “family born child”. It can be known that “family born son” or “family born slave” had been known at least in the Tang Dynasty.Dead deed of sale refers to the deed of sale in which both parties agree that they cannot redeem themselves and must be slaves for life. Sell down: “sell it” and “sell it to death”.The meaning of not changing or repentance.“LU DU”-Lu：official status, enjoy the salary. Du：moth. “Ludu” or “Guodu” from “Zuo Zhuan · Xianggong 22 years”: “do not make, but proud to make people the worm of the nation.It means that if you are in a high position without the ability to govern a country, you are the moth of the country. “Ludu” and “Guodu” basically the same meaning, also refers to a high position, enjoy the state salary and do not work officials.--[[User:Zhang Qiuyi|Zhang Qiuyi]] ([[User talk:Zhang Qiuyi|talk]]) 01:29, 29 September 2021 (UTC)<br />
--[[User:Wang Yifan21|Wang Yifan21]] ([[User talk:Wang Yifan21|talk]]) 13:47, 11 October 2021 (UTC)<br />
Annotation:<br />
First:The common term for a son born to a slave in his master’s house is a family son.<br />
Second:It means that if you are in a high position without the ability to govern a country,you are the moth of the country.<br />
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==亚非语言文学 202120081524 王逸凡 女==<br />
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明明德——头一个“明”为动词。彰明、弘扬之意。 明德：美德，至德，完美的道德。 语出《礼记·大学》：“大学之道，在明明德，在亲民，在止于至善。”意思是弘扬完美的道德。这里是指贾宝玉只肯定包括《大学》在内的《四书》(《论语》、《大学》、《中庸》、《孟子》)是正经书，其他书都要不得。<br />
魔星——曹雪芹的原作为“天魔星”，显然是借用了佛家和道家的“天魔”之说。佛家说“天魔”为欲界第六天主。如《楞严经》卷九说：“或汝阴魔，或复天魔。”又《百喻经·小儿得大龟喻》说：“邪见外道，天魔波旬，及恶知识，而语之言，汝但极意六尘，恣情五欲，如我语者，必得解脱。”道家则说“天魔”为天上的魔怪。如《云笈七签》卷四说：“有经无符，则天魔害人。”这里的“魔星”则为冤家之意。<br />
--[[User:Wang Yifan21|Wang Yifan21]] ([[User talk:Wang Yifan21|talk]]) 13:55, 11 October 2021 (UTC)<br />
Mingmingde-The first Ming is a verb which means obviousness, clearness or carrying forward and the word Mingde means virtue,supreme virtue,perfect morality.TheBook of Rites·Great learningsaid that The way of a university lies in being clear and virtuous,being close to the people and being perfect.It means to promote perfect morality.Jiabaoyu only affirmed The Four Books including Great learning.(Analects,Great Learning,The Doctrine of the Mean,Mencius) are the proper scriptures and the other books are not acceptable.<br />
Devil star—Cao Xueqin's original as heaven devil star apparently borrowed Buddhism and Taoism’s doctrine of heaven devil.Buddhists say that demons are the sixth god of desire.For example,volume 9 of the Surangama Sutra says, Either you cast shadows or you recover demons from heaven.And Buddhist parables said Evil sees the outside world,the sky is full of demons,and evil knowledge.But as you speak,you will be free from your desires.Taoism says that demons of heaven are demons in the sky.Such as Cloud gupta seven signs volume 4 said there is no sign,then the devil harm people.The devil star here is the meaning of enemy.<br />
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Laba Porridge-Originated from what Buddhism calls "Buddha Bathing Day". According to legend, Sakyamuni was born on the eighth day of the fourth month of the lunar calendar. Therefore, since the Han Dynasty, every Buddhist temple has held commemorative activities on this day. , "Buddhist Bathing Festival" or "Buddha Day". "The Book of the Later Han Dynasty·Tao Qian Biography" said:" Every time Buddha baths, to provid alms and rice to the road."In the Southern Dynasties Liang Zongmo's "Jingchu Sui Shi Ji" said: "April 8th, all monasteries Set up a fast, bath the Buddha with five-color perfume, and make Longhua Hui together. According to "The Biography of the Eminent Monk": ‘On April 8th, to bathe the Buddha, use Duliang incense as blue water, tulip as red water, Qiulong incense as white water, aconite incense as yellow water, benzoin as black water, to infuse the top of the Buddha. ’"In the Song Dynasty, the day of Sakyamuni’s Buddhahood, the eighth day of the twelfth lunar month, was the "Buddha Bathing Day." Every time this day, all the temples held commemorative activities, not only followed the alms, bathing Buddha and other items, but also added laba porridge, known as "Buddha Bathing Day." "On the eighth day of the lunar New Year, three or five monks and nuns in the streets chanted Buddha in teams. They would sit on a gold, bronze or wooden Buddha statue in a silver or bronze saro or a good basin. They would soak it in perfume and shower it with the branches of a tree and intended for the edification of the masses. The great monasteries served as bathing buddhas, and gave seven treasures and five flavors porridge and disciples, called "laba porridge". Since then every families also cooks porridge with fruit and miscellaneous ingredients. "Since then, the phase has become a common practice, and it has not faded to this day.--[[User:Li Xinxing|Li Xinxing]] ([[User talk:Li Xinxing|talk]]) 12:47, 11 October 2021 (UTC)<br />
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Laba congee - Originated from a festival which calls “Buddha Bathing Day” in Buddhism. According to legend, Sakyamuni was born on the eighth of forth lunar month. Therefore, on this day, every Buddhist temple will hold activities since Han Dynasty. People will practice alms all over the place, and wash Buddha statues with water full of spices, which is called “Buddha Bathing Day”, “Buddha Bathing Festival” or “Buddha Buddhism Day”. “Book of Later Han Dynasty · Tao Qian Biography” said: “Every time Buddha baths, there are more drinking and rice to the homeless people. And Liang Zongmo, the author of “Jingchu Sui Shi Ji”, said: “April 8th, all monasteries set up a fast, bath the Buddha with five-color perfume, and make Longhua Hui together.”<br />
According to “The Biography of the Eminent Monk”: “On April 8th, people use Duliang incense as blue water, Yujin incense as red water, Qiulong incense as white water, Fuzi incense as yellow water, and Anxi incense as black water, to infuse the top of the Buddha”. In Song Dynasty, the day when Sakyamuni became a Buddha, the eighth of twelfth lunar month, was the "Buddha Bathing Day." On that day, all Buddhist temples held the activities which called “Buddha Bathing Activities”, not only followed the traditional items, but also added Laba congee. The folks also follow the example and eat Laba congee. We can see these things from “DongJingMengHualu·Volume Ten·December”, a book written by Meng in Song Dynasty: three or five monks and nuns in the streets chanted Buddha in teams. They would sit on a gold, bronze or wooden Buddha statue in a silver or bronze saro or a good basin. They would soak it in perfume and shower it with the branches of a tree and intended for the edification of the masses. The great monasteries served as bathing buddhas, and gave seven treasures and five flavors porridge and disciples, called ‘Laba congee’. Since then every families also cooks porridge with fruit and miscellaneous ingredients.” Since then, the phase has become a common practice, and it has not faded to this day.--[[User:Xu Minyun|Xu Minyun]] ([[User talk:Xu Minyun|talk]]) 13:55, 11 October 2021 (UTC)</div>Chen Huinihttps://bou.de/u/index.php?title=20211222_homework&diff=13438220211222 homework2021-12-27T06:13:50Z<p>Chen Huini: /* 曾俊霖 Zēng Jùnlín 国别 男 202120081478 */</p>
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PLEASE READ [[Joint_translation_terms|Joint translation terms]] <br />
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PLEASE ALSO READ THE PREVIOUS PARTS, AT LEAST THE SENTENCES BEFORE YOUR OWN PART IN CHAPTER 19 [[20210303_culture|1, Mar 3 Chapters 1-4]], [[20210310_culture|2, Mar 10 Chapters 6-7]], [[20210317_culture|3, Mar 17 Chapters 11-13]], [[20210324_culture|4, Mar 24 Chapters 15-17]], [[20210331_culture|5, Mar 31 Chapters 4-7]], [[20210407_culture|6, Apr 7 Chapters 8-10]], [[20210414_culture|7, Apr 14 Chapters 13-15]] , [[20210519_culture|12, May 19 Chapters 17-19]], [[20210929_homework#Hongloumeng|for Sep 29 - rest of HLM Chapter 19]] [[20211013_homework|for Oct 13 - HLM Chapters 20-21]] [[20211020_homework|for Oct 20 - HLM Chapters 21-22]] etc.<br />
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==陈静 Chén Jìng 国别 女 202020080595==<br />
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闲静似娇花照水，行动如弱柳扶风。心较比干多一窍，病如西子胜三分。宝玉看罢，笑道：“这个妹妹我曾见过的。”<br />
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She, who is demure as delicate flowers and act like weak willow, is more clever than Bigan(the most clever man in the Legend of Deification) and more beautiful than Xishi(the most beautiful woman in acient China). Precious Jade simled, “I have seen her.”--[[User:Chen Jing|Chen Jing]] ([[User talk:Chen Jing|talk]]) 11:26, 26 December 2021 (UTC)<br />
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Leisure is like a delicate flower shining on the water, and action is like a weak willow supporting the wind. The heart has more than one orifices than the stem, and like a disease wins Xizi. Baoyu said with a smile, "I've seen this sister."<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 11:18, 26 December 2021 (UTC)<br />
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==蔡珠凤 Cài Zhūfèng 日语语言文学 女 202120081477==<br />
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贾母笑道：“又胡说了，你何曾见过？”宝玉笑道：“虽没见过，却看着面善，心里倒像是远别重逢的一般。”贾母笑道：“好，好！这么更相和睦了。”<br />
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Grandma Merchant smiled and said, "nonsense again. Have you ever seen her?" Baoyu said with a smile, "although I haven't seen her, I look good and feel like I'm far from meeting again." Grandma Merchant said with a smile, "OK, OK! It's more harmonious."<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 11:22, 26 December 2021 (UTC)<br />
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Dowager Jia smiled and said, "nonsense again. Have you ever seen it?" Baoyu said with a smile, "although I haven't seen it, I look good and feel like I'm far from meeting again." Dowager Jia said with a smile, "OK, OK! It's more harmonious."--[[User:Zeng Junlin|Zeng Junlin]] ([[User talk:Zeng Junlin|talk]]) 13:18, 21 December 2021 (UTC)<br />
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Grandma Merchant smiled and said, "Nonsense again. Have you ever seen it?" Precious Jade said with a smile, "Although I haven't seen it, I look good and feel like I'm far from meeting again." Grandma Merchant said with a smile, "OK, OK! It's more harmonious."--[[User:Zeng Junlin|Zeng Junlin]] ([[User talk:Zeng Junlin|talk]]) 13:18, 21 December 2021 (UTC)<br />
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==曾俊霖 Zēng Jùnlín 国别 男 202120081478==<br />
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宝玉便走向黛玉身边坐下，又细细打量一番，因问：“妹妹可曾读书？”黛玉道：“不曾读书，只上了一年学，些须认得几个字。”<br />
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Baoyu went to Daiyu and sat down. She looked at her carefully, because she asked, "has your sister ever read?" Daiyu said, "I didn't study. I only studied for a year. I have to recognize a few words."--[[User:Zeng Junlin|Zeng Junlin]] ([[User talk:Zeng Junlin|talk]]) 13:12, 21 December 2021 (UTC)<br />
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Baoyu went to Daiyu and sat down. He looked at her carefully, because he asked, "has you ever went to school?" Daiyu said, "I didn't go to school. I only studied for a year, so I can recognize a few words."--[[User:Chen Huini|Chen Huini]] ([[User talk:Chen Huini|talk]]) 06:13, 27 December 2021 (UTC)Chen Huini<br />
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==陈惠妮 Chén Huìnī 英语语言文学（英美文学） 女 202120081479==<br />
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宝玉又道：“妹妹尊名？”黛玉便说了名。宝玉又道：“表字？”黛玉道：“无字。”<br />
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Baoyuu asked, "What is your name?" Daiyu told her name to him. Baoyu said, "Watch characters?" "No words," Said Daiyu.--[[User:Chen Huini|Chen Huini]] ([[User talk:Chen Huini|talk]]) 06:10, 27 December 2021 (UTC)Chen Huini<br />
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==陈湘琼 Chén Xiāngqióng 外国语言学及应用语言学 女 202120081480==<br />
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宝玉笑道：“我送妹妹一字：莫若‘颦颦’二字极妙。”探春便道：“何处出典？”宝玉道：“《古今人物通考》上说：<br />
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Baoyu smiled and said:" I want to describe you with two words—Ping Ping, and no words are better than them." Tanchun then asked:"In which book did you find them?" Baoyu said:" On ''General Study of Ancient and Modern Characters''"--[[User:Chen Xiangqiong|Chen Xiangqiong]] ([[User talk:Chen Xiangqiong|talk]]) 01:04, 20 December 2021 (UTC)<br />
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Precious Jade smiled and said:" I want to give away two words to you—Pingping, and no words are better than them." Tanchun then asked:"In which book did you find them?" Jade said:"''On General Study of Ancient and Modern Characters''."--[[User:Chen Xinyi|Chen Xinyi]] ([[User talk:Chen Xinyi|talk]]) 06:37, 22 December 2021 (UTC)<br />
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==陈心怡 Chén Xīnyí 翻译学 女 202120081481==<br />
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‘西方有石名黛，可代画眉之墨。’况这妹妹眉尖若蹙，取这个字，岂不甚美？”探春笑道：“只怕又是杜撰。”<br />
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'There is a stone in the west named Dai, it can replace the ink of painting eyebrows.' The sister's eyebrows are frown, if take this word for name, isn’t it very beautiful?" Tanchun laughed: "I'm afraid it's a fabrication again."--[[User:Chen Xinyi|Chen Xinyi]] ([[User talk:Chen Xinyi|talk]]) 06:29, 22 December 2021 (UTC)<br />
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‘There is a stone in the west named Dai, it can replace the ink of drawing eyebrows.' Besides, her eyebrows are frown, if take this word for name, isn’t it very beautiful?" Tanchun laughed: "I'm afraid it's a fabrication again."--[[User:Cheng Yang|Cheng Yang]] ([[User talk:Cheng Yang|talk]]) 11:33, 26 December 2021 (UTC)<br />
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==程杨 Chéng Yáng 英语语言文学（英美文学） 女 202120081482==<br />
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宝玉笑道：“除了《四书》，杜撰的也太多呢。”因又问黛玉：“可有玉没有？”众人都不解。<br />
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Baoyu laughed and said, "Besides the Four Books, there are too many things made up." Again he asked Daiyu, "Do you have any jade?" Everyone was puzzled.--[[User:Cheng Yang|Cheng Yang]] ([[User talk:Cheng Yang|talk]]) 11:35, 26 December 2021 (UTC)<br />
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==丁旋 Dīng Xuán 英语语言文学（英美文学） 女 202120081483==<br />
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黛玉便忖度着：“因他有玉，所以才问我的。”便答道：“我没有玉。你那玉也是件稀罕物儿，岂能人人皆有？”<br />
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Mascara Jade Forest contemplated, “He asked me because he has a jade.” So she answered, “I have no jade. Your jade is a rarity. How could everyone have it?”--[[User:Ding Xuan|Ding Xuan]] ([[User talk:Ding Xuan|talk]]) 13:45, 22 December 2021 (UTC)<br />
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Mascara Jade Forest presumed, “He asked me because he has a jade.” So she answered, “I have no jade. Your jade is a rarity. How could everyone have it?”--[[User:Du Lina|Du Lina]] ([[User talk:Du Lina|talk]]) 08:25, 23 December 2021 (UTC)<br />
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==杜莉娜 Dù Lìnuó 英语语言文学（语言学） 女 202120081484==<br />
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宝玉听了，登时发作起狂病来，摘下那玉就狠命摔去，骂道：“什么罕物！人的高下不识，还说灵不灵呢！我也不要这劳什子！”<br />
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After hearing that,Precious Jade Merchant suddenly went mad. And he took off and dropped the jade with cursing that “What the hell is a rare thing! You all say that it is divine, but it can't tell lowliness or nobleness.I won't have the waste now!” --[[User:Du Lina|Du Lina]] ([[User talk:Du Lina|talk]]) 12:29, 19 December 2021 (UTC)<br />
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After hearing that,Precious Jade Merchant suddenly went mad. And he took off and dropped the jade with cursing that “What the hell is a rare thing! You all say that it is divine, but nobody could tell lowliness or nobleness.I won't have the waste now!” --[[User:Fu Hongyan|Fu Hongyan]] ([[User talk:Fu Hongyan|talk]]) 11:18, 26 December 2021 (UTC)<br />
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--[[User:Fu Hongyan|Fu Hongyan]] ([[User talk:Fu Hongyan|talk]]) 11:19, 26 December 2021 (UTC)==付红岩 Fù Hóngyán 英语语言文学（英美文学） 女 202120081485==<br />
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吓的地下众人一拥争去拾玉。贾母急的搂了宝玉道：“孽障，你生气，要打骂人容易，何苦摔那命根子？”宝玉满面泪痕，哭道：“家里姐姐妹妹都没有，单我有，我说没趣儿；<br />
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The aside servants were scared and then rushed to pick up the jade.Jia's mother anxiously hugged Baoyu and said:" poor kid, if you are angry, why do you bother to fall the jade rahtere to beat and curse."Covered with tears, Baoyu cried:" my beloved elder and litter sisters have no one. I'm ashamed of owning one."--[[User:Fu Hongyan|Fu Hongyan]] ([[User talk:Fu Hongyan|talk]]) 11:19, 26 December 2021 (UTC)<br />
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All those, who stood below, were startled; and in a body they pressed forward, vying with each other as to who should pick up the gem.<br />
Grandma Merchant was so distressed that she clasped Precious Jade in her embrace. "You child of wrath," she exclaimed. "When you get into a passion, it's easy enough for you to beat and abuse people; but what makes you fling away that stem of life?"<br />
Precious Jade’s face was covered with the traces of tears. "All my cousins here, senior as well as junior," he rejoined, as he sobbed, "have no gem, and if it's only I to have one, there's no fun in it, I maintain! “.--[[User:Fu Shiyu|Fu Shiyu]] ([[User talk:Fu Shiyu|talk]]) 06:27, 22 December 2021 (UTC)<br />
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==付诗雨 Fù Shīyǔ 日语语言文学 女 202120081486==<br />
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如今来了这个神仙似的妹妹也没有：可知这不是个好东西。”贾母忙哄他道：“你这妹妹原有玉来着，因你姑妈去世时，舍不得你妹妹，无法可处，遂将他的玉带了去：<br />
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And now comes this angelic sort of cousin, and she too has none, so that it's clear enough that it is no profitable thing." Grandma Merchant hastened to coax him. "This cousin of yours," she explained, "would, under former circumstances, have come here with a jade; and it's because your aunt felt unable, as she lay on her death-bed, to reconcile herself to the separation from your cousin, that in the absence of any remedy, she forthwith took the gem belonging to her (daughter), along with her (in the grave); --[[User:Fu Shiyu|Fu Shiyu]] ([[User talk:Fu Shiyu|talk]]) 12:24, 19 December 2021 (UTC)<br />
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Now the newly arrived cousin who is as lovely as a fairy hasn't got one either, so it can't be any good." "Your cousin did have one once," said Dowager lady Chia to soothe him, "but when your aunt was dying she was unwilling to leave your cousin, the best she could do was to take the jade with her instead. --[[User:Gao Mi|Gao Mi]] ([[User talk:Gao Mi|talk]]) 04:49, 20 December 2021 (UTC)<br />
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==高蜜 Gāo Mì 翻译学 女 202120081487==<br />
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一则全殉葬之礼，尽你妹妹的孝心；二则你姑妈的阴灵儿也可权作见了你妹妹了。因此他说没有，也是不便自己夸张的意思啊。<br />
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In that way, your cousin showed her filial piety by letting the jade be buried with her; in the meantime, your aunt’s spirit could see your cousin through the jade. Therefore, when your cousin said she hadn’t got one, it was because she didn’t want to boast about it. --[[User:Gao Mi|Gao Mi]] ([[User talk:Gao Mi|talk]]) 04:50, 20 December 2021 (UTC)<br />
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In that way, your cousin showed her filial piety by letting the jade be buried with her; in the meantime, your aunt’s spirit could see your cousin through the jade. Therefore, when your cousin said she hadn’t got one, it was because she didn't want to publicise it.--[[User:Gong Boya|Gong Boya]] ([[User talk:Gong Boya|talk]]) 07:00, 22 December 2021 (UTC)<br />
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==宫博雅 Gōng Bóyǎ 俄语语言文学 女 202120081488==<br />
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你还不好生带上，仔细你娘知道。”说着，便向丫鬟手中接来，亲与他带上。宝玉听如此说，想了一想，也就不生别论。<br />
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"If you don't take it with you, be careful your mother knows" As she spoke, she took the jade from the servant girl and adorned him herself. When Precious Jade Merchant heard her say this, he thought for a while and said nothing else.--[[User:Gong Boya|Gong Boya]] ([[User talk:Gong Boya|talk]]) 06:56, 22 December 2021 (UTC)<br />
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"You‘d better keep it well in case your mother notices.” As she spoke, she took the jade from the maid and adorned him herself. When Precious Jade Merchant heard her saying this, he thought for a while and said nothing else. --[[User:He Qin|He Qin]] ([[User talk:He Qin|talk]]) 12:18, 22 December 2021 (UTC)<br />
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==何芩 Hé Qín 翻译学 女 202120081489==<br />
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当下奶娘来问黛玉房舍，贾母便说：“将宝玉挪出来，同我在套间暖阁里，把你林姑娘暂且安置在碧纱厨里。等过了残冬，春天再给他们收拾房屋，另作一番安置罢。”<br />
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When the nanny came to ask where Mascara Jade should stay, Lady Dowager answered, “ Place Precious Jade in the warm house in my suit and settle your Miss Forest in the Green Voile House temporarily until the winter ends. In next spring, you’ll rearrange the room for them.”--[[User:He Qin|He Qin]] ([[User talk:He Qin|talk]]) 12:34, 22 December 2021 (UTC)<br />
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Hardly when the nanny came to ask the room of Mascara Jade, Lady Dowager said, “ Place Precious Jade in the warm house with me and settle your Miss Lin inside the room of the partition door temporarily until the winter ends. In next spring, you’ll rearrange the room for them.”--[[User:Hu Shuqing|Hu Shuqing]] ([[User talk:Hu Shuqing|talk]]) 11:31, 26 December 2021 (UTC)<br />
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==胡舒情 Hú Shūqíng 英语语言文学（语言学） 女 202120081490==<br />
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宝玉道：“好祖宗，我就在碧纱厨外的床上很妥当，又何必出来，闹的老祖宗不得安静呢？”贾母想一想说：“也罢了。<br />
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Baoyu said:” Dear grandma, I would rather stay at the bed outside the partition door, than at your room to bother you.” The Lady Dowager said thoughtfully:”That’s Ok.”--[[User:Hu Shuqing|Hu Shuqing]] ([[User talk:Hu Shuqing|talk]]) 05:38, 21 December 2021 (UTC)<br />
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Presious Jade said:"Dear mother, I would rather stay on the bed outside the partition door rather than at grandma's to bother her." The Lady Dowager said thoughtfully:"That's OK."--[[User:Huang Jinyun|Huang Jinyun]] ([[User talk:Huang Jinyun|talk]]) 09:06, 25 December 2021 (UTC)<br />
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==黄锦云 Huáng Jǐnyún 英语语言文学（语言学） 女 202120081491==<br />
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每人一个奶娘并一个丫头照管，馀者在外间上夜听唤。”一面早有熙凤命人送了一顶藕合色花帐并锦被、缎褥之类。<br />
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But let each one of you have a nurse, as well as a waiting-maid to attend on you; the other servants can remain in the outside rooms and keep night watch and be ready to answer any call." At an early hour, besides, Hsi-feng had sent a servant round with a grey flowered curtain, embroidered coverlets and satin quilts and other such articles.--[[User:Huang Jinyun|Huang Jinyun]] ([[User talk:Huang Jinyun|talk]]) 14:25, 19 December 2021 (UTC)<br />
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But let each one of you have a nurse and a waiting-maid; the other servants can remain in the outside rooms and keep night watch and be ready to answer any call." At an early hour, besides, Hsi-feng had sent a servant round with a grey flowered curtain, embroidered coverlets and satin quilts and other such articles.--[[User:Huang Yiyan1|Huang Yiyan1]] ([[User talk:Huang Yiyan1|talk]]) 14:22, 22 December 2021 (UTC)<br />
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==黄逸妍 Huáng Yìyán 外国语言学及应用语言学 女 202120081492==<br />
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黛玉只带了两个人来：一个是自己的奶娘王嬷嬷；一个是十岁的小丫头，名唤雪雁。贾母见雪雁甚小，一团孩气；<br />
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Daiyu had brought her old Wet-nurse Nanny Wang and ten-year-old Xueyan,who had also attended her since she was a child. The Lady Dowager considered Xueyan too young;--[[User:Huang Yiyan1|Huang Yiyan1]] ([[User talk:Huang Yiyan1|talk]]) 14:17, 22 December 2021 (UTC)<br />
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Daiyu had brought her old Wet-nurse Nanny Wang and ten-year-old Xueyan,who had also attended her since she was a child. The Lady Dowager considered Xueyan too young;--[[User:Huang Zhuliang|Huang Zhuliang]] ([[User talk:Huang Zhuliang|talk]]) 01:41, 26 December 2021 (UTC)Huang Zhuliang<br />
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==黄柱梁 Huáng Zhùliáng 国别 男 202120081493==<br />
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王嬷嬷又极老：料黛玉皆不遂心，将自己身边一个二等小丫头，名唤鹦哥的与了黛玉。亦如迎春等一般：每人除自幼乳母外，另有四个教引嬷嬷；Mammy(Here mammy not means the lady who gives birth to a baby, but a lady who looks after some noble children) Wang is very old: she is not expectd to look after Daiyu well. So,Daiyu's grandmother gave Daiyu to a second-class little page girl named Yingge. Daiyu's arrangement is also like Jia Yingchun who not only has the nursing mother, but also four teaching mothers.--[[User:Huang Zhuliang|Huang Zhuliang]] ([[User talk:Huang Zhuliang|talk]]) 14:02, 19 December 2021 (UTC)Huang Zhuliang<br />
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Mammy(Here mammy not means the lady who gives birth to a baby, but a lady who looks after some noble children) Wang is very old: she is not expectd to look after Daiyu well. So,Daiyu's grandmother gave Daiyu to a second-class little girl named Polly. Daiyu's arrangement is also like Jia Yingchun who not only has the nursing mother, but also four teaching mummys.--[[User:Jin Xiaotong|Jin Xiaotong]] ([[User talk:Jin Xiaotong|talk]]) 14:40, 20 December 2021 (UTC)<br />
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==金晓童 Jīn Xiǎotóng 202120081494==<br />
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除贴身掌管钗钏盥沐两个丫头外，另有四五个洒扫房屋、来往使役的小丫头。当下王嬷嬷与鹦哥陪侍黛玉在碧纱厨内，宝玉乳母李嬷嬷并大丫头名唤袭人的陪侍在外面大床上。<br />
In addition to the two servants who are in charge of jewelry and toiletries, there are four or five little maids who sweep the house and do chores. At the moment King mammy and polly accompany daiyu in green gauze room, Baoyu’s mammy li and big maid Xiren accompany on the big bed outside.--[[User:Jin Xiaotong|Jin Xiaotong]] ([[User talk:Jin Xiaotong|talk]]) 14:37, 20 December 2021 (UTC)<br />
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==邝艳丽 Kuàng Yànl 英语语言文学（语言学） 女 202120081495==<br />
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原来这袭人亦是贾母之婢，本名蕊珠，贾母因溺爱宝玉，恐宝玉之婢不中使，素喜蕊珠心地纯良，遂与宝玉。宝玉因知他本姓花，又曾见旧人诗句有“花气袭人”之句，遂回明贾母，即把蕊珠更名袭人。<br />
This maid Xi Ren, whose real name is Rui Zhu, also belongs to Lady Dowager. Lady Dowager enjoyed Rui Zhu’s purity and kindness then assigned her to Baoyu, for Lady Dowager coddled him and worried that the maids of Baoyu not work well. Baoyu knew her last name was Hua, and saw once poetic sentence “the fragrance of flowers assails noses”, then he talked it with Lady Dowager, and then Rui Zhu was named Xi Ren.--[[User:Kuang Yanli|Kuang Yanli]] ([[User talk:Kuang Yanli|talk]]) 07:12, 20 December 2021 (UTC)<br />
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This maid Xi Ren, whose real name is Rui Zhu, also belongs to Lady Dowager. Lady Dowager enjoyed Rui Zhu’s purity and kindness, then assigned her to serve Jade, for Lady Dowager coddled him and worried that the maids of Jade not professional. Jade knew her last name was Flower, and saw once a poetic sentence “the fragrance of flowers assails noses”, then he talked it with Lady Dowager. And then Rui Zhu was named Xi Ren.--[[User:Li Aixuan|Li Aixuan]] ([[User talk:Li Aixuan|talk]]) 12:33, 20 December 2021 (UTC)<br />
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==李爱璇 Lǐ Àixuán 英语语言文学（语言学） 女 202120081496==<br />
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却说袭人倒有些痴处：伏侍贾母时，心中只有贾母；如今跟了宝玉，心中又只有宝玉了。只因宝玉性情乖僻，每每规谏，见宝玉不听，心中着实忧郁。<br />
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However, it is said that Hsi-jen is crazy: when seving Jia's mother, only Jia's mother is in her heart; now serving Jade, there is only Jade in her heart. Because of Jade's perverse temperament, when Jade doesn't listen to her advise, Hsi-jen is really depressed.--[[User:Li Aixuan|Li Aixuan]] ([[User talk:Li Aixuan|talk]]) 07:11, 25 December 2021 (UTC)<br />
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However, Hsi Jen had several simple traits. While in attendance upon dowager lady Chia, in her heart and her eyes there was no one but her venerable ladyship, and her alone; and now in her attendance upon Pao-yue, her heart and her eyes were again full of Pao-yue, and him alone. But as Pao-yue was of a perverse temperament and did not heed her repeated injunctions, she felt at heart exceedingly grieved.--[[User:Li Ruiyang|Li Ruiyang]] ([[User talk:Li Ruiyang|talk]]) 00:42, 21 December 2021 (UTC)<br />
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==李瑞洋 Lǐ Ruìyáng 英语语言文学（英美文学） 女 202120081497==<br />
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是晚，宝玉、李嬷嬷已睡了，他见里面黛玉、鹦哥犹未安歇，他自卸了妆，悄悄的进来，笑问：“姑娘怎么还不安歇？”黛玉忙笑让：“姐姐请坐。”<br />
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At night, after nurse Li had fallen asleep, seeing that in the inner chambers, Tai-yue and Ying Ko had not as yet retired to rest, she removed her makeup, and with gentle step walked in.<br />
"How is it, miss，" she inquired smiling, "that you have not turned in as yet？"<br />
Tai-yue at once put on a smile. "Sit down, sister, " she rejoined, pressing her to take a seat. --[[User:Li Ruiyang|Li Ruiyang]] ([[User talk:Li Ruiyang|talk]]) 01:37, 21 December 2021 (UTC)<br />
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At night, after Master Bao and nurse Li had fallen asleep, seeing that in the inner chambers, Tai-yue and Ying Ko had not as yet retired to rest, she removed her makeup, and with gentle step walked in."How is it, miss，" she inquired smilingly, "that you have not turned in as yet？" Tai-yue at once put on a smile. "Sit down, my dear sister, " she rejoined, leading her to take a seat.--[[User:Li Shan|Li Shan]] ([[User talk:Li Shan|talk]]) 14:41, 21 December 2021 (UTC)<br />
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==李姗 Lǐ Shān 英语语言文学（英美文学） 女 202120081498==<br />
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袭人在床沿上坐了。鹦哥笑道：“林姑娘在这里伤心，自己淌眼抹泪的说：‘今儿才来了，就惹出你们哥儿的病来。倘或摔坏了那玉，岂不是因我之过？’<br />
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Hsi-jen then sat by the bedside. Ying Ko ridiculed, "Miss Lin was feeling sad with her tears dropping down today, saying that 'It is the first day that I come here, while I have triggered the relapse of your young master. If his precious jade was truly broken apart, then I am sure to blame."--[[User:Li Shan|Li Shan]] ([[User talk:Li Shan|talk]]) 14:29, 21 December 2021 (UTC)<br />
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Aroma then sat by the bedside. Brother Parrot ridiculed, "Miss Lin feels sad with her tears dropping down today, saying that 'It is the first day that I come here, while I have triggered the relapse of your young master. If his precious jade is truly broken apart, then I'll be sure to blame."--[[User:Li Shuang|Li Shuang]] ([[User talk:Li Shuang|talk]]) 02:45, 22 December 2021 (UTC)<br />
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==李双 Lǐ Shuāng 翻译学 女 202120081499==<br />
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所以伤心。我好容易劝好了。”袭人道：“姑娘快别这么着。将来只怕比这更奇怪的笑话儿还有呢。<br />
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“She is therefore so sad, and I had a hard time persuading her to stop crying.” Aroma said: “Please don’t look so sad, young lady. There will be more stranger jokes in the future.”--[[User:Li Shuang|Li Shuang]] ([[User talk:Li Shuang|talk]]) 02:40, 22 December 2021 (UTC)<br />
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“She is therefore so sad, and I had a hard time persuading her to stop crying.” Aroma said: “Please don’t look so sad, my mistress. There will be more stranger jokes in the future.”--[[User:Li Wenxuan|Li Wenxuan]] ([[User talk:Li Wenxuan|talk]]) 10:51, 22 December 2021 (UTC)<br />
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==李文璇 Lǐ Wénxuán 英语语言文学（英美文学） 女 202120081500==<br />
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若为他这种行状，你多心伤感，只怕你还伤感不了呢。快别多心。”黛玉道：“姐姐们说的，我记着就是了。”<br />
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“If you feel sad for his behavior, I’m afraid that you can’t be so. Don’t think too much.” Daiyu said: “I will remember what our sisters has said.” --[[User:Li Wenxuan|Li Wenxuan]] ([[User talk:Li Wenxuan|talk]]) 00:23, 20 December 2021 (UTC)<br />
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==李雯 Lǐ Wén 英语语言文学（英美文学） 女 202120081501==<br />
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又叙了一会，方才安歇。次早起来，省过贾母，因往王夫人处来。正值王夫人与熙凤在一处拆金陵来的书信，又有王夫人的兄嫂处遣来的两个媳妇儿来说话。<br />
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==李新星 Lǐ Xīnxīng 亚非语言文学 女 202120081503==<br />
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黛玉虽不知原委，探春等却晓得是议论金陵城中居住的薛家姨母之子、表兄薛蟠倚财仗势，打死人命，现在应天府案下审理。如今舅舅王子腾得了信，遣人来告诉这边，意欲唤取进京之意。<br />
Although Daiyu did not know the exact cause, Tanchun and others knew that it was xue Pan, son and cousin of aunt Xue who lived in Jinling city, who killed a man by taking advantage of his wealth and power, and was now being tried by the Tianfu court. Now uncle Prince teng got the letter, send people to tell here, intended to call the meaning of Beijing.--[[User:Li Xinxing|Li Xinxing]] ([[User talk:Li Xinxing|talk]]) 12:24, 19 December 2021 (UTC)<br />
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Although Dai Yu did not know the original commission, Tan Chun and others knew that it was a discussion of Xue Pan, the son of the Xue family's aunt and cousin Xue Pan, who lived in Jinling City, who relied on wealth and power to kill people, and now it should be tried under the Tianfu case. Now that his uncle Prince Teng had received the letter, he sent someone to tell this side, intending to summon the intention of entering the capital.--[[User:Li Yi|Li Yi]] ([[User talk:Li Yi|talk]]) 12:27, 19 December 2021 (UTC)<br />
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==李怡 Lǐ Yí 法语语言文学 女 202120081504==<br />
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毕竟怎的，下回分解。<br />
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起复——即重新起用被停职或撤职的官员，包括因父母丧停职回家守孝及因被弹劾而遭撤职的官员。<br />
If you want to know what happened, the answer is next time<br />
Reinstatement – Reinstate officials who have been suspended or removed from their posts, including those who have been suspended from their posts for the death of their parents and who have been removed from office for impeachment.--[[User:Li Yi|Li Yi]] ([[User talk:Li Yi|talk]]) 12:14, 19 December 2021 (UTC)<br />
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After all, I'll break it down next time.<br />
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Reinstatement - reinstatement of officials who have been suspended or removed from office, including those who have been removed from office due to the death of their parents and those who have been removed from office due to impeachment.--[[User:Liu Peiting|Liu Peiting]] ([[User talk:Liu Peiting|talk]]) 12:24, 19 December 2021 (UTC)<br />
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==刘沛婷 Liú Pèitíng 英语语言文学（英美文学） 女 202120081505==<br />
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邸(dǐ底)报——亦称“邸抄”、“抄报”、“宫门抄”，清代或称“京报”。中国古代官方报纸的通称。<br />
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Di Pao -- also known as "Di Copy", "copy newspaper" or "Palace Gate Copy" -- is also known as "Beijing Newspaper" during the Qing Dynasty. The general name of the official newspaper in ancient China.--[[User:Liu Peiting|Liu Peiting]] ([[User talk:Liu Peiting|talk]]) 12:23, 19 December 2021 (UTC)<br />
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Di Bao -- also known as "Di Copy", "copy newspaper" or "Palace Gate Copy" -- is also known as "Beijing Newspaper" during the Qing Dynasty. The general name of the official newspaper in ancient China.--[[User:Liu Shengnan|Liu Shengnan]] ([[User talk:Liu Shengnan|talk]]) 12:10, 22 December 2021 (UTC)<br />
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==刘胜楠 Liú Shèngnán 翻译学 女 202120081506==<br />
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承办者或为地方官府驻京办事机构，或为朝廷。邸报专门抄发诏令、奏章及朝政新闻，以供地方官及时了解。 邸：原指战国时各诸侯在都城的客馆，后泛指地方官府驻京办事处。<br />
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The undertaker is either the local government office in Beijing or the imperial court. The residence newspaper specially copied and issued imperial edicts, memorials and government news for local officials to understand in time. Di: it used to refer to the guest houses of various princes in the capital during the Warring States period, and later it generally refers to the offices of local officials in Beijing. --[[User:Liu Shengnan|Liu Shengnan]] ([[User talk:Liu Shengnan|talk]]) 12:09, 22 December 2021 (UTC)<br />
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The undertaker is either the local government office in Beijing or the imperial court. The "Di" newspaper(a official gazette) specially copied and issued imperial edicts, memorials and government news for local officials to understand in time."Di": Originally referred to the guest hall of the princes in the capital during the Warring States Period, and later generally referred to the Beijing office of the local government. --[[User:Liu Wei|Liu Wei]] ([[User talk:Liu Wei|talk]]) 14:40, 22 December 2021 (UTC)Liu Wei<br />
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==刘薇 Liú Wēi 国别 女 202120081507==<br />
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贱荆——亦称“拙荆”、“山荆”等。谦词。对人称自己的妻子。 荆：“荆钗布裙”的省称。<br />
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"Jian Jing" ——also known as "Zhuo Jing", "Shan Jing" etc. It's a modest word when a man mention his wife in front of others. "Jing"is a short name for "JingChaiBuQun"(the female have only a thorn for a hairpin and plain cloth for a skirt). --[[User:Liu Wei|Liu Wei]] ([[User talk:Liu Wei|talk]]) 12:36, 19 December 2021 (UTC)Liu Wei<br />
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Jian Jing, also known as "Zhuo Jing", "Shan Jing",etc, is a humle term for quoting one's own wife. Jing is an abbreviation for "Jingchaibuqun", that is, a thorn for a hairpin and palin cloth for a skirt.--[[User:Liu Xiao|Liu Xiao]] ([[User talk:Liu Xiao|talk]]) 06:59, 20 December 2021 (UTC)<br />
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==刘晓 Liú Xiǎo 英语语言文学（英美文学） 女 202120081508==<br />
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形容妇人极为简朴的服饰。语出汉·刘向《列女传》(见《太平御览》卷七一八引)：“梁鸿妻孟光，荆钗布裙。” 荆钗：即以木棍为钗。<br />
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Jing, used to describe women's plain, simple and unadorned clothes, is originated from a sentence in the ''Biographies of Exemplary Women'' written by Liu Xiang in the Han Dynasty (see ''Imperial Review under the Reign of Taizong in the Song Dynasty'', Vol.718): "Meng Guang, wife of Liang Hong has only a thorn for a hairpin and plain cloth for a skirt." Jingchai means a thron for a hairpin.--[[User:Liu Xiao|Liu Xiao]] ([[User talk:Liu Xiao|talk]]) 06:53, 20 December 2021 (UTC)<br />
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Jing, used to describe the extremely simple dress of a woman. In Han, Liu Xiang's "Biography of the Female" (see ”Taiping Yu Lan“（Imperial Review under the Reign of Taizong in the Song Dynasty）, vol. 718): "Liang Hong's wife, Meng Guang, was dressed in a woven hairpin and cloth skirt." Jingchai (荆钗): a wooden stick used as a hairpin.--[[User:Liu Yue|Liu Yue]] ([[User talk:Liu Yue|talk]]) 05:02, 22 December 2021 (UTC)<br />
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==刘越 Liú Yuè 亚非语言文学 女 202120081509==<br />
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内顾之忧──语出北朝魏·袁翻《安置蠕蠕表》：“且蠕蠕尚存，则高车犹有内顾之忧，未暇窥窬上国；<br />
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The Worries of Internal Concern - From Yuan Fan's " Settlement Zoran Policy ", And if Zoran still existed, then Gao Che (a generic term used by the Northern Dynasties for a part of the nomadic tribes in the north of the desert) would still have internal concerns and would not have had the strength to covet the vassal states.--[[User:Liu Yue|Liu Yue]] ([[User talk:Liu Yue|talk]]) 04:55, 22 December 2021 (UTC)<br />
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Internal Concerns – derived from Yuan Fan's "Policy on the Settlement of Ruru". “And if Ruru survived, Gaoche would have internal concerns and would have no time to covet the territory of the Emperor.--[[User:Liu Yunxin|Liu Yunxin]] ([[User talk:Liu Yunxin|talk]]) 13:30, 25 December 2021 (UTC)<br />
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==刘运心 Liú Yùnxīn 英语语言文学（英美文学） 女 202120081510==<br />
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若蠕蠕全灭，则高车跋扈之计，岂易可知？”(蠕蠕：“柔然”的别称，亦称“芮芮”、“茹茹”。我国古代北方少数民族名。<br />
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“If Ruru was annihilated, wasn’t it easy to know the conceited plan of Gaoche?” (“Ruru”, an alternative name for Rouran Khaganate, can also be called “Ruirui” or “Ruru”. The name of an ancient northern ethnic minorities.)--[[User:Liu Yunxin|Liu Yunxin]] ([[User talk:Liu Yunxin|talk]]) 12:53, 25 December 2021 (UTC)<br />
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If Ruru was annihilated, would it be easy to know the conceited plan of Gaoche?" (“Ruru”,: an alias for Rouran Khaganate, also known as "Ruirui" and "Ruru". The name of an ancient northern minority group in the region of China.--[[User:Luo Anyi|Luo Anyi]] ([[User talk:Luo Anyi|talk]]) 11:44, 26 December 2021 (UTC)<br />
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==罗安怡 Luó Ānyí 英语语言文学（英美文学） 女 202120081511==<br />
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高车：亦称“狄历”、“敕勒”、“铁勒”、“丁零”。 我国古代北方少数民族名。)意谓因对家事或国事的顾念而担忧。<br />
Gao Che: also known as Di Li, Cile, Tie Le, Ding Zero. The name of a minority group in the north of China in ancient times). It means to worry about family or national affairs.--[[User:Luo Anyi|Luo Anyi]] ([[User talk:Luo Anyi|talk]]) 11:39, 26 December 2021 (UTC)<br />
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==罗曦 Luó Xī 英语语言文学（英美文学） 女 202120081512==<br />
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这里指家庭需要照顾的人或事。<br />
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垂花门──旧时较为讲究的四合院二门。门顶如屋顶式样，其四角和前后多有下垂的雕花，故称。<br />
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This refers to the person or thing that family members need to take care of. <br />
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Floral-Pendant Gates: It was the second gate of a courtyard house with exquisite decoration in the old days. The top of the door was like a roof, with drooping carvings on the back and front of four corners, so it is called "Floral-Pendant Gates".--[[User:Ma Xin|Ma Xin]] ([[User talk:Ma Xin|talk]]) 11:25, 22 December 2021 (UTC)<br />
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==马新 Mǎ Xīn 外国语言学及应用语言学 女 202120081513==<br />
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超手游廊──亦作“超手回廊”、“抄手游廊”。房廊像两手笼入袖筒，两袖成环形状，故称。<br />
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Verandah Chao Shou —— also known as "Corridor Chao Shou" and "Cross Hand Verandah". Its gallery looked like a two-handed cage into the sleeves, and the two sleeves formed a ring shape, so it was called "Verandah Chao Shou".--[[User:Ma Xin|Ma Xin]] ([[User talk:Ma Xin|talk]]) 07:37, 20 December 2021 (UTC)<br />
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Verandah Chao Shou: It was also known as “Corridor Chao Shou” or “Verandah Cross Hand”. Its gallery was similar to a two-handed cage into the two sleeves which formed a ring shape, so it was called “Verandah Chao Shou”. --[[User:Mao Yawen|Mao Yawen]] ([[User talk:Mao Yawen|talk]]) 06:26, 22 December 2021 (UTC)<br />
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==毛雅文 Máo Yǎwén 英语语言文学（英美文学） 女 202120081514==<br />
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穿山游廊──指与厅房两边山墙门通连的回廊。以其可由山墙门穿行，故称。 山：即房屋两侧的山墙。<br />
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Chuan Shan You Lang: A veranda or corridor connected with the gable doors on both sides of the hall. People can pass through the corridor after entering into the gable doors, so this kind of corridor is called such a name. Shan: The gable doors on both sides of a house.--[[User:Mao Yawen|Mao Yawen]] ([[User talk:Mao Yawen|talk]]) 13:22, 19 December 2021 (UTC)<br />
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Chuan Shan You Lang: It refers to the corridor connected to the door of the wall on either side of the room. People can pass through the corridor after entering into the gable doors, so this kind of corridor is called such a name. Shan: The gable doors on both sides of a house.--[[User:Mao You|Mao You]] ([[User talk:Mao You|talk]]) 13:33, 19 December 2021 (UTC)<br />
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==毛优 Máo Yōu 俄语语言文学 女 202120081515==<br />
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“第一个”六句──这是对迎春形象的描写。 微丰：稍胖。 腮凝新荔：形容腮帮子像荔枝般的红润。<br />
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The first six lines - It is a description of Yingchun's image. Wei Feng: Slightly fat. Sai Ning Xin Li：The cheeks are as red as lychees.--[[User:Mao You|Mao You]] ([[User talk:Mao You|talk]]) 13:29, 19 December 2021 (UTC)<br />
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The first six lines - It is a description of Yingchun's image. Wei Feng: Slightly fat. Sai Ning Xin Li：The cheeks are as red and shiny as lychees.--[[User:Mou Yixin|Mou Yixin]] ([[User talk:Mou Yixin|talk]]) 07:21, 20 December 2021 (UTC)<br />
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==牟一心 Móu Yīxīn 英语语言文学（英美文学） 女 202120081516==<br />
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鼻腻鹅脂：形容鼻端像鹅脂般光润。<br />
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“第二个”七句──这是对探春形象的描写。 削肩：俗称溜肩。<br />
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Bi Ni E Zhi: an idiom to describe someone’s tip of nose is as shiny and smooth as goose grease.<br />
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“The second” seven lines —— this is a depiction of the look of Tanchun. Rounded shoulders: commonly known as sloping shoulders --[[User:Mou Yixin|Mou Yixin]] ([[User talk:Mou Yixin|talk]]) 07:18, 20 December 2021 (UTC)<br />
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Bi Ni E Zhi: a Chiniese idiom to describe someone’s tip of nose is as shiny and smooth as goose grease.<br />
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“The second” seven lines —— this is a depiction of Tanchun'image. Cuted shoulders: commonly known as sloping shoulders--[[User:Peng Ruixue|Peng Ruixue]] ([[User talk:Peng Ruixue|talk]]) 07:00, 25 December 2021 (UTC)<br />
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==彭瑞雪 Péng Ruìxuě 法语语言文学 女 202120081517==<br />
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倾斜的双肩。古人以为美人肩。<br />
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长挑身材：瘦高的身材。 鸭蛋脸儿：犹如鸭蛋似的长圆形脸盘。<br />
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Sloping shoulders. The ancients considered these to be the shoulders of beauty.<br />
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Long, tall figure: a tall, thin figure. Duck egg face: an oblong face like a duck egg.--[[User:Peng Ruixue|Peng Ruixue]] ([[User talk:Peng Ruixue|talk]]) 06:32, 20 December 2021 (UTC)<br />
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Sloping shoulders. The ancient people considered such shoulders as the shoulders of beauty.<br />
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Long, tall figure: a tall, thin figure. Duck egg face: an oblong face like a duck egg--[[User:Qing Jianan|Qing Jianan]] ([[User talk:Qing Jianan|talk]]) 11:59, 26 December 2021 (UTC)<br />
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==秦建安 Qín Jiànān 外国语言学及应用语言学 女 202120081518==<br />
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俊眼修眉：秀美的眼睛，长长的秀眉。 顾盼神飞：左顾右盼，目光炯炯，神采飞扬。 文彩精华：光彩照人，精神十足。<br />
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Jun Yan Xiu Mei: charming eyes with long and delicate eyebrows. Gu Pan Shen Fei: looking left and right, with shining eyes and soaring spirit. Wen Cai Jing Hua: being radiant and full of energy.--[[User:Qing Jianan|Qing Jianan]] ([[User talk:Qing Jianan|talk]]) 11:58, 26 December 2021 (UTC)<br />
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Jun Yan Xiu Mei: beautiful eyes with long and delicate eyebrows. Gu Pan Shen Fei: looking left and right, with shining eyes and soaring spirit. Wen Cai Jing Hua: being radiant and full of energy.--[[User:Qiu Tingting|Qiu Tingting]] ([[User talk:Qiu Tingting|talk]]) 01:21, 22 December 2021 (UTC)<br />
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==邱婷婷 Qiū Tíngtíng 英语语言文学（语言学）女 202120081519==<br />
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见之忘俗：意谓别人见了就会忘了俗气，变得高雅起来。形容探春一身高雅之气。<br />
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“第三个”两句──这是对惜春形象的描写。<br />
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To see is to forget vulgarity: It means that when others see something or someone will forget the secular atmosphere and become more elegant. In this sentence, it describes Tanchun has a great elegant temperament.<br />
"The third" two sentences ─ thses are the description of the image of Xi Chun.--[[User:Qiu Tingting|Qiu Tingting]] ([[User talk:Qiu Tingting|talk]]) 02:50, 20 December 2021 (UTC)<br />
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Jian Zhi Wang Su: It means that others will forget the vulgarity and become elegant when they see it. It is used to describe Tanchun's elegance. <br />
The two sentences containing “ the third” — are the image depiction of Sichun.--[[User:Rao Jinying|Rao Jinying]] ([[User talk:Rao Jinying|talk]]) 12:27, 19 December 2021 (UTC)<br />
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==饶金盈 Ráo Jīnyíng 英语语言文学（语言学） 女 202120081520==<br />
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形容惜春年纪尚小，身材和容貌都还没有发育成熟。<br />
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人参养荣丸──以人参、当归、黄芪、陈皮、白芍、熟地、桂心等配制而成的丸药，主治脾胃气血亏虚等症。<br />
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It is used to describe the Xichun, who is still young and body and appearance are not developed.<br />
Ginseng Yangrong Pill- A pill made of ginseng, angelica, astragalus, Chen Pi, Bai Shao, Shu Di, Gui Xin, etc., mainly used for treating deficiency of qi and blood in the spleen and stomach.--[[User:Rao Jinying|Rao Jinying]] ([[User talk:Rao Jinying|talk]]) 12:22, 19 December 2021 (UTC)<br />
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It is used to depict Xichun, who is still in her young age and underdeveloped stature as well as appearance.<br />
Ginseng tonic bolus- a sort of pill composed of ginseng, Angelica sinensis, astragalus, tangerine peel, white paleontology root, rehmannia glutinousa, laurel heart, etc. is mainly used to treat diseases such as deficiency in spleen, stomach, qi as well as blood.--[[User:Shi Liqing|Shi Liqing]] ([[User talk:Shi Liqing|talk]]) 13:00, 19 December 2021 (UTC)<br />
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==石丽青 Shí Lìqīng 英语语言文学（英美文学） 女 202120081521==<br />
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荣：中医指血脉。 养荣丸：似有双关之意：除了保养血脉之意外，还有保养荣誉之意，与薛宝钗的“冷香丸”相对，以寓二人的不同性格。<br />
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“Rong” refers to blood vessel in the field of traditional Chinese medicine. Tonic bolus embraces double meaning. Apart from the maintenance of blood, it also boasts the function of maintaining the honor, which is opposite to “Cold Fragrant Pellet” of Xue Baochai. This is the revelation of different personalities between these two people.--[[User:Shi Liqing|Shi Liqing]] ([[User talk:Shi Liqing|talk]]) 12:45, 19 December 2021 (UTC)<br />
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“Rong” refers to blood vessel in the field of traditional Chinese medicine. Tonic bolus embraces double meanings. Apart from the maintenance of blood vessel, it also boasts the function of maintaining the honor, which is opposite to “Cold Fragrant Pellet” of Xue Baochai. This is the revelation of different personalities between these two people.--[[User:Sun Yashi|Sun Yashi]] ([[User talk:Sun Yashi|talk]]) 02:27, 20 December 2021 (UTC)<br />
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==孙雅诗 Sūn Yǎshī 外国语言学及应用语言学 女 202120081522==<br />
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窄褃(kèn掯)袄──即紧身妖。 窄：瘦小。 褃：是上衣前后幅两侧接缝部分的名称。<br />
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Narrow ken coat ── is a tight quilted jacket.Narrow: thin.Ken: It is the name of the seams on the front and rear sides of the jacket.--[[User:Sun Yashi|Sun Yashi]] ([[User talk:Sun Yashi|talk]]) 02:18, 20 December 2021 (UTC)<br />
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Narrow Ken coat ── namely tight quilted jacket. Narrow: thin. Ken: the name of the seams on the front and back of the jacket. --[[User:Wang Lifei|Wang Lifei]] ([[User talk:Wang Lifei|talk]]) 08:50, 21 December 2021 (UTC)<br />
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==王李菲 Wáng Lǐfēi 英语语言文学（英美文学） 女 202120081523==<br />
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仪门──原指官署大门里的第二道正门。之所以称“仪门”，是因为官员至此门必须整齐仪表。<br />
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Etiquette Gate ── originally refers to the second main gate in the main gate of the government office. The reason why it is called "Etiquette Gate" is because the officers must be well-groomed when he arrives. --[[User:Wang Lifei|Wang Lifei]] ([[User talk:Wang Lifei|talk]]) 08:38, 21 December 2021 (UTC)<br />
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Etiquette Gate--Originally, it refers to the second main door in the gate of the official office. The reason why it is called "Etiquette Gate" is that officials must be neat and tidy when they arrive at this gate.--[[User:Wang Yifan21|Wang Yifan21]] ([[User talk:Wang Yifan21|talk]]) 06:30, 23 December 2021 (UTC)<br />
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==王逸凡 Wáng Yìfán 亚非语言文学 女 202120081524==<br />
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《明会典·礼部十七·官员礼》：“新官到任之日……先至神庙祭祀毕，引至仪门前下马，具官服，从中道入。”<br />
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The Ming Canon - Rituals XVII - Official Rites: "On the day the new official arrives, he first goes to the temple to offer sacrifice, after which he is led to dismount in front of the ceremonial gate, with his official uniform, and enters from the middle road."--[[User:Wang Yifan21|Wang Yifan21]] ([[User talk:Wang Yifan21|talk]]) 06:27, 23 December 2021 (UTC)<br />
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==王镇隆 Wáng Zhènlóng 英语语言文学（英美文学） 男 202120081525==<br />
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又《江宁府志·建制·官署》：“其制大门之内为仪门，仪门内为莅事堂。”后加以引申，大家府第的第二道正门也称仪门。<br />
"Jiangning official records，organizational system，official ": "inside the system gate is the instrument gate, and inside the instrument gate is the visiting hall." Later extended, the second main gate of everyone's house is also called Yimen. --[[User:Wang Zhenlong|Wang Zhenlong]] ([[User talk:Wang Zhenlong|talk]]) 11:12, 22 December 2021 (UTC)<br />
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"Jiangning official records，organizational system，government ": "inside the system gate is the etiquette gate, and inside the etiquette gate is the visiting hall." Later extended, the second main gate of mansion is also called etiquette gate.--[[User:Wei Yiwen|Wei Yiwen]] ([[User talk:Wei Yiwen|talk]]) 09:52, 26 December 2021 (UTC)<br />
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==卫怡雯 Wèi Yíwén 英语语言文学（英美文学） 女 202120081526==<br />
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鹿顶耳房钻山──这里是指在正房两侧与东西厢房北侧之间建有两座平顶耳房，并在耳房山墙上开门。如此则使正房、东西耳房、东西厢房皆可相通，便于穿行，所以下句说“四通八达”。<br />
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Luding Erfang Zuanshan—— it refers to two flat top ear rooms which are situated on the two sides of principal room and northern side of east and west wings and open up the door on the gable. Then it makes a connection between principal room, east and west ear room as well as east and west wings so that it is more convenient for people to pass. It is called “extend in all directions” as described below.--[[User:Wei Yiwen|Wei Yiwen]] ([[User talk:Wei Yiwen|talk]]) 02:14, 22 December 2021 (UTC)<br />
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Luding Erfang Zuanshan——it refers to two flat-top ear rooms which are situated on the two sides of principal room and northern side of east and west wings and open up the door on the gable of ear rooms. Then it makes a connection between principal room, east and west ear rooms as well as east and west wings so that it is very convenient for people to pass. Therefore, it is called “accessible in all directions” as described below.--[[User:Wei Chuxuan|Wei Chuxuan]] ([[User talk:Wei Chuxuan|talk]]) 07:44, 22 December 2021 (UTC)<br />
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==魏楚璇 Wèi Chǔxuán 英语语言文学（英美文学） 女 202120081527==<br />
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鹿顶：亦作“盝顶”。即平屋顶。 耳房：紧靠正房或厢房两侧并利用其山墙建造的房屋。<br />
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Luding: flat roof. Ear room: a room built by using a gable on either side of a principle room or wing-room.--[[User:Wei Chuxuan|Wei Chuxuan]] ([[User talk:Wei Chuxuan|talk]]) 02:00, 22 December 2021 (UTC)<br />
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Lu Ding: this means flat roof. Er Fang: a room built by using the gable on both sides of a principle room or wing-room.--[[User:Wei Zhaoyan|Wei Zhaoyan]] ([[User talk:Wei Zhaoyan|talk]]) 12:13, 22 December 2021 (UTC)<br />
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==魏兆妍 Wèi Zhàoyán 英语语言文学（英美文学） 女 202120081528==<br />
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因其位于正房两侧，犹如人的两只耳朵，故称。 钻山：指打通房屋两侧的山墙，以与相邻的房屋或回廊相通。<br />
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As they are located on both sides of the main house just like people’s ears, they are called “wings”. Zuan Shan: this means breaking through the gables on both sides of the house to connect to adjacent houses and cloisters.--[[User:Wei Zhaoyan|Wei Zhaoyan]] ([[User talk:Wei Zhaoyan|talk]]) 07:45, 20 December 2021 (UTC)<br />
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Because they are located on both sides of the main house just like people’s ears, they are called “wings”. Zuan Shan: this means breaking through the gables on both sides of the house to connect to adjacent houses and cloisters. --[[User:Wu Jingyue|Wu Jingyue]] ([[User talk:Wu Jingyue|talk]]) 05:48, 21 December 2021 (UTC)<br />
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==吴婧悦 Wú Jìngyuè 俄语语言文学 女 202120081529==<br />
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赤金九龙青地大匾──以赤金涂饰的九条雕龙为边框的黑底大匾。 九龙：古代传说龙生九子，性格各异。但说法各异。<br />
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The horizontal board, which is decorated with pink gold, night dragon and tuff - the board is black and is made of motifs of dragon and phoenix. The nine dragons: it is said that, in the ancient time, the dragon had nine sons, whose character were totally different. But there were different ideas about it.--[[User:Wu Jingyue|Wu Jingyue]] ([[User talk:Wu Jingyue|talk]]) 14:02, 19 December 2021 (UTC)<br />
A large plaque with nine dragons painted in red gold on a black background. Nine Dragons: Ancient legend has it that dragons are born with nine sons, each with a different character. However, there are different sayings.--[[User:Wu Yinghong|Wu Yinghong]] ([[User talk:Wu Yinghong|talk]]) 05:36, 22 December 2021 (UTC)<br />
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==吴映红 Wú Yìnghóng 日语语言文学 女 202120081530==<br />
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明·杨慎《升庵外集·动物一·龙生九子》说：“龙生九子不成龙，各有所好：囚牛，平生好音乐，今胡琴头上刻兽是其遗像；<br />
Ming-Yang Shen's "Sheng'an Waiji - Animals I - The Nine Sons of the Dragon" says: "The nine sons of the dragon were born without becoming dragons, but each had his own interests: the prisoner bull, who was good at music in his life, and the beast carved on the head of the huqin today is his posthumous image.<br />
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Yang Shen of Ming Dynasty said in "Sheng an Outside collection · Animal · Long Sheng Jiu Zi" : "Long sheng Jiu Zi is not a dragon, each has his own good points: prisoner ox, good music in his life, the beast carved on the head of Huqin is his portrait; --[[User:Xiao Yiyao|Xiao Yiyao]] ([[User talk:Xiao Yiyao|talk]]) 10:32, 26 December 2021 (UTC)<br />
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==肖毅瑶 Xiāo Yìyáo 英语语言文学（英美文学） 女 202120081531==<br />
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睚毗，平生好杀，金刀柄上龙吞口是其遗像；嘲风，平生好险，今殿角走兽是其遗像；蒲牢，平生好鸣，今钟上兽纽是其遗像；<br />
Yapi, who was easy to kill, has a portrait of a dragon swallowing mouth on the gold hilt. Scene wind, life good risk, this temple corner beast is its portrait; Pu Lao, life good Ming, this bell on the beast new is its portrait; --[[User:Xiao Yiyao|Xiao Yiyao]] ([[User talk:Xiao Yiyao|talk]]) 10:28, 26 December 2021 (UTC)<br />
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Yapi, who likes killing, so the image of a dragon swallowing mouth on the gold hilt is its portrait. Chaofeng, who likes being at risk, so the image of the temple corner beast is its portrait; Pulao, who likes chirping, so the image of the bell on the beast new is its portrait.--[[User:Xie Jiafen|Xie Jiafen]] ([[User talk:Xie Jiafen|talk]]) 11:34, 26 December 2021 (UTC)<br />
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==谢佳芬 Xiè Jiāfēn 英语语言文学（英美文学） 女 202120081532==<br />
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狻猊，平生好坐，今佛座狮子是其遗像；霸下，平生好负重，今碑座兽是其遗像；陛犴，平生好讼，今狱门上狮子头是其遗像；<br />
Suan ni likes sitting all its life , it looks alike a lion, which usually appears in the pedestal of Buddha ; Ba xia likes bearing a heavy burden all its life, so its image usually appears under the stone monuments as a stele monster; Bi'an likes lawsuit all its life, so its image usually appears in the cell doors.--[[User:Xie Jiafen|Xie Jiafen]] ([[User talk:Xie Jiafen|talk]]) 07:02, 20 December 2021 (UTC)<br />
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Suan ni likes sitting all its life , it looks alike a lion, which usually appears in the pedestal of Buddha ; Ba xia likes bearing a heavy burden all its life, so its image usually appears under the stone monuments as a stele monster; Bi'an likes lawsuit all its life, so its image usually appears in the cell doors.--[[User:Xie Qinglin|Xie Qinglin]] ([[User talk:Xie Qinglin|talk]]) 07:25, 20 December 2021 (UTC)<br />
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==谢庆琳 Xiè Qìnglín 俄语语言文学 女 202120081533==<br />
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屓屭，平生好文，今碑两旁龙是其遗像；蚩吻，平生好吞，今殿脊兽头是其遗像。”明·焦竑《玉堂丛语·卷一·文学》则说：<br />
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The clamshell, life is good literature, today the two sides of the monument dragon is its image; Chi kiss, life is good swallow, today the temple ridge beast head is its image." Ming - Jiao Hong "Yu Tang Congye - Volume 1 - Literature" said.--[[User:Xie Qinglin|Xie Qinglin]] ([[User talk:Xie Qinglin|talk]]) 07:24, 20 December 2021 (UTC)<br />
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==熊敏 Xióng Mǐn 英语语言文学（英美文学） 女 202120081534==<br />
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“俗传龙生九子不成龙，各有所好……一曰赑屭，形似龟，好负重，今石碑下龟趺是也；二曰螭吻，形似兽，性好望，今屋上兽头是也；<br />
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“ It is said that the nine sons of dragons are not born into dragons, and each has its own features...One is Bixi shaped like a tortoise, and it is so heavy. It is also a tortoise under the stone tablet; the second is Liwen shaped like a beast, and it is well-known.<br />
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"It is said that the nine sons of dragons are not born into dragons, and each has its own features...One is Bi'xi, whose shapes like a tortoise, and it likes carrying heavy things. It is also a tortoise under the stone tablet; The second is Li'wen, whose shapes like a beast, and it is well-known, It often apperas in roof.--[[User:Xu Minyun|Xu Minyun]] ([[User talk:Xu Minyun|talk]]) 11:39, 26 December 2021 (UTC)<br />
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==徐敏赟 Xú Mǐnyūn 语言智能与跨文化传播研究 男 202120081535==<br />
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三曰蒲牢，形似龙而小，性好叫吼，今钟上纽是也；四曰狴犴，形似虎，有威力，故立于狱门；五曰饕餮，好饮食，故立于鼎盖；<br />
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The third one is Pu'lao, who looks like a dragon but is small and easy to roar, and it often appeared in chime. The fourth was Bi'an, who looks like a tiger and is so powerful that he stand in the door of the prison. The fifth is Tao'tie, like eating food, so stand in tripod cover;--[[User:Xu Minyun|Xu Minyun]] ([[User talk:Xu Minyun|talk]]) 11:32, 26 December 2021 (UTC)<br />
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The third one is Pu'lao, who looks like a dragon but is small and easy to roar, and it often appeared in chime. The fourth was Bi'an, who looks like a tiger and is so powerful that he stands in the door of the prison. The fifth is Tao'tie, like eating food, so stands in tripod cover;--[[User:Yan Jing|Yan Jing]] ([[User talk:Yan Jing|talk]]) 11:47, 26 December 2021 (UTC)<br />
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==颜静 Yán Jìng 语言智能与跨文化传播研究 女 202120081536==<br />
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六曰，性好水，故立于桥柱；七曰睚毗，性好杀，故立于刀环；八曰金猊，形似狮，性好烟火，故立于香炉；<br />
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The sixth is good at water, so it stands on the bridge column; The seventh is called Ya Zi, good at killing, so it stands in the knife ring; The eighth is Jin Ni, like a lion, has a good nature of fireworks, so it stands in the incense burner;--[[User:Yan Jing|Yan Jing]] ([[User talk:Yan Jing|talk]]) 11:04, 26 December 2021 (UTC)<br />
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==颜莉莉 Yán Lìlì 国别 女 202120081537==<br />
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九曰椒图，形似螺蚌，性好闭，故立于门铺首。”明·沈德符《万历野获编·卷七·内阁·龙子》又说：“长沙李文正公在阁，孝宗忽下御札，问龙生九子之详。<br />
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The ninth is called Jiao Tu, which is shaped like a screw and likes to close its mouth, so it is used as decoration on the door. Shen Defu of the Ming Dynasty, in his book ''Wanli Ye Huo, Vol.7, Cabinet, Longzi,'' also said, "When Duke Li Wenzheng of Changsha was in the cabinet, Emperor Xiaozong suddenly got down to ask the details of the birth of nine longzi.<br />
--[[User:Yan Lili|Yan Lili]] ([[User talk:Yan Lili|talk]]) 13:30, 21 December 2021 (UTC)<br />
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When Duke Li Wenzheng of Changsha was in the pavilion, Emperor Xiaozong suddenly sent a royal letter and asked about the details of Long Sheng's nine sons.--[[User:Yan Zihan|Yan Zihan]] ([[User talk:Yan Zihan|talk]]) 07:32, 26 December 2021 (UTC)<br />
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==颜子涵 Yán Zǐhán 国别 女 202120081538==<br />
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文正对云：‘其子蒲牢好鸣，今为钟上钮鼻；囚牛好音，今为胡琴头刻兽；睚眦好杀，今为刀剑上吞口；<br />
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Wen Zheng said , "his son Pu Lao likes roaring，the dragon shaped animal button on the Hong Zhong is its relic; The Qiu Niu loves music all his life. He often squats on the head of the piano to enjoy the music of plucking strings, so his portrait is engraved on the head of the HuQin; Ya Ci is the second child. He is aggressive and likes killing all his life，and swallow a sword with his mouth.--[[User:Yan Zihan|Yan Zihan]] ([[User talk:Yan Zihan|talk]]) 07:29, 26 December 2021 (UTC)<br />
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Wen Zheng said , "his son Pu Lao likes roaring，and often appears on the bell; Qiu Niu loves music all his life,accordingly, he becomes a decoration for music instrument, such as two-stringed bowed violin (huqin); Ya Ci likes fighting and killing，people see him as the patron saint of weapons, so he often appears on the weapons.--[[User:Yang Jiaying|Yang Jiaying]] ([[User talk:Yang Jiaying|talk]]) 08:08, 26 December 2021 (UTC)<br />
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==阳佳颖 Yáng Jiāyǐng 国别 女 202120081540==<br />
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嘲风好险，今为殿阁走兽；狻猊好坐，今为佛座骑象；霸下好负重，今为碑碣石趺；<br />
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Chao Feng loves adventure,he now often appears on the corner on the housetop; Suan Ni loves sitting quietly, his image is often found in temples as the mount of Buddha; Bi xi has the power of strength. He loves to carry heavy stuff to show off his magic energy, so under the stele can people see his appearing.--[[User:Yang Jiaying|Yang Jiaying]] ([[User talk:Yang Jiaying|talk]]) 08:12, 26 December 2021 (UTC)<br />
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Chao Feng loves adventure. It's the small decorative animal on the eaves of the housetop; Suan Ni loves sitting quietly. Its image is often found in temples as the mount of Buddha; Bi xi has the power of strength. He loves to carry heavy stuff to show off his magic energy, so under the stele can people see his appearing.--[[User:Yang Aijiang|Yang Aijiang]] ([[User talk:Yang Aijiang|talk]]) 11:30, 26 December 2021 (UTC)<br />
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==杨爱江 Yáng Àijiāng 英语语言文学（ 语言学） 女 202120081541==<br />
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狴犴好讼，今为狱户首镇压；屓屭好文，今为碑两旁蜿蜒；蚩吻好吞，今为殿脊兽头。’”<br />
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Bi An is eager for justice and righteousness and can distinguish right from wrong. Now they generally stand on both sides of the lobby of the government office to deter those who violate the law and discipline. Bi Xi, which is fond of reading and writing articles, is gentle. And now it’s the animal winding around on both sides of the stele. Chi Wen which has the ability of swallowing fire becomes the beast heads at both ends of the palace roof. --[[User:Yang Aijiang|Yang Aijiang]] ([[User talk:Yang Aijiang|talk]]) 07:24, 26 December 2021 (UTC)<br />
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Bi An is eager for justice and righteousness and can distinguish right from wrong. Now it generally stands on both sides of the lobby of the government office to deter those who violate the law and discipline. Bi Xi, which is fond of reading and writing articles, is gentle. And now it’s the animal winding around on both sides of the stele. Chi Wen, who has the ability of swallowing fire, becomes the beast heads at both ends of the palace roof. --[[User:Yang Kun|Yang Kun]] ([[User talk:Yang Kun|talk]]) 11:14, 26 December 2021 (UTC)<br />
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==杨堃 Yáng Kūn 法语语言文学 女 202120081542==<br />
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此外，明·陈仁锡《潜确类书》、明·胡侍《真珠船·龙生九子》、清·褚人获《坚瓠十集·龙九子》、清·高士奇《天禄识馀·龙种》，对九龙的名称、性格、用途的说法也各不相同，可见出于民间传说。世人多用作装饰，以示祥瑞。<br />
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In addition, the names, characters and uses of the Dragon's nine sons, which can be seen from folklore, are also different in ''Reference Book of Qian Que'' by Chen Renxi of Ming Dynasty, ''Zhenzhu Boat·The Nine Sons of the Dragon'' by Hu Shi of Ming Dynasty,''Jian Hu's Collection-Vol.10·The Nine Sons of the Dragon'' by Chu Renhuo of Qing Dynasty and ''Tian Lu Shi Yu· Dragon Species'' by Gao Shiqi of Qing Dynasty. People often use the images of the Dragon's nine sons as decoration, to show good luck. --[[User:Yang Kun|Yang Kun]] ([[User talk:Yang Kun|talk]]) 03:53, 23 December 2021 (UTC)<br />
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In addition, the names, characters and usages of the Dragon's nine sons, which can be seen from folklore, also differ in ''Reference Book of Qian Que'' by Chen Renxi of Ming Dynasty, ''Zhenzhu Boat·The Nine Sons of the Dragon'' by Hu Shi of Ming Dynasty,''Jian Hu's Collection-Vol.10·The Nine Sons of the Dragon'' by Chu Renhuo of Qing Dynasty and ''Tian Lu Shi Yu· Dragon Species'' by Gao Shiqi of Qing Dynasty. People often use the images of the Dragon's nine sons as decoration show auspiciousness.--[[User:Yang Liuqing|Yang Liuqing]] ([[User talk:Yang Liuqing|talk]]) 11:18, 26 December 2021 (UTC)<br />
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==杨柳青 Yáng Liǔqīng 英语语言文学（英美文学） 女 202120081543==<br />
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万幾宸(chén辰)翰之宝──此为皇帝印章所刻的文字。 万幾：国家纷繁复杂的政务。典出《尚书·虞书·皋陶谟》：“兢兢业业，一日二日万幾。”<br />
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The treature of the emporer's notes of handling affairs──This was the inscription on the emperor's seal. Wanji: Country's compliccated government affairs. It was recorded in ''ShangShu·YuShu·The Srategy of Gao Tao'':"Be cautious and practical. There were thousands of complicated government affairs needed to be handled."<br />
[ ''ShangShu·YuShu·The Srategy of Gao Tao'' were important documents that recorded the plans and deliberations of emperors and ministers.]<br />
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The treasure of the Emperor's seal and ink imprint──This refers to the inscription on the emperor's seal. Wanji: Country's complicated executive affairs. It was what's recorded in ''Gao Tao Mo, Book Yu, The Book of History'', which is "Be cautious and practical. There were thousands of complicated government affairs needed to be handled."--[[User:Ye Weijie|Ye Weijie]] ([[User talk:Ye Weijie|talk]]) 14:31, 26 December 2021 (UTC)<br />
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==叶维杰 Yè Wéijié 国别 男 202120081544==<br />
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孔颖达传云：“幾，微也，言当戒惧万事之微。”意谓尽管政务繁重，也不能忽略任何小事。亦称“万机”。<br />
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Kong Yingda said: "Ji, which refers to the slighest thing, meaning that should be aware of the very small things." It means that despite the arduous government affairs, no small things can be ignored. Also known as "Wan Ji".<br />
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Kong Yingda's biography said: "The word Ji means that before saying something one should fear the smallest of all things." This means that despite the heavy workload of the government, one should not neglect any small matters. It is also referred to as "ten thousand business".--[[User:Yi Yangfan|Yi Yangfan]] ([[User talk:Yi Yangfan|talk]]) 10:00, 26 December 2021 (UTC)Yi Yangfan<br />
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==易扬帆 Yì Yángfān 英语语言文学（英美文学） 女 202120081545==<br />
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典出《汉书·百官公卿表上》：“相国、丞相皆秦官，金印紫绶，掌丞天子，助理万机。”这里是形容皇帝日理万机，政务繁忙。<br />
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The allusion is from ''Han Shu: The List of Hundred Officials（Previous）'': "The Minister of State and the Prime Minister were both Qin officials, with gold seals and purple ribbons, and were in charge of the Emperor and assisted in all affairs." This is a description of the emperor's busy schedule of affairs.--[[User:Yi Yangfan|Yi Yangfan]] ([[User talk:Yi Yangfan|talk]]) 09:24, 25 December 2021 (UTC)Yi Yangfan<br />
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The allusion is from Han Shu: The List of Hundred Officials（Previous）: "The Ministers of State and the Prime Ministers were Qin officials, with golden seals and purple ribbons, and they were in charge of the Emperor and assisted in all affairs." Here is the description of the emperor's busy schedule of affairs.--[[User:Yin Huizhen|Yin Huizhen]] ([[User talk:Yin Huizhen|talk]]) 12:37, 25 December 2021 (UTC)<br />
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==殷慧珍 Yīn Huìzhēn 英语语言文学（英美文学） 女 202120081546==<br />
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宸：“北宸”的省称。即北极星。因皇帝上朝坐北朝南，遂为皇帝的代称。翰：本义是羽毛，因古代以羽毛为笔，引申为墨迹(书写的字)。<br />
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Chen:is short for "Beichen", that is, the Polaris, which was the alternative name of the Emperors because they sat in the North and faced the South in the imperial court. Han：the original meaning is feather, because in ancient times, feather was used as a pen，and it extended the meanig to ink（written words）.--[[User:Yin Huizhen|Yin Huizhen]] ([[User talk:Yin Huizhen|talk]]) 12:29, 22 December 2021 (UTC)<br />
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Chen: short for "Beichen", is the Polaris, which was the alternative name of the Emperors because they sat facing the South in the imperial court. Han：the original meaning was feather, because in ancient times, feather was used as a pen，which extended the meaning to ink（written words）.--[[User:Yin Meida|Yin Meida]] ([[User talk:Yin Meida|talk]]) 08:45, 24 December 2021 (UTC)<br />
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==殷美达 Yīn Měidá 英语语言文学（语言学） 女 202120081547==<br />
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宝：这里指皇帝的印章。上古天子、诸侯均以圭璧制印，故称“宝”。唐以后只有帝、后之印可称“宝”。<br />
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Bao refers to the emperors' seals. In ancient time, emperors and dukes all had their seals made of Gui and Bi(precious jade),from which it derived the name "Bao". After Tang Dynasty, "Bao" was used exclusively by the emperors and empresses.--[[User:Yin Meida|Yin Meida]] ([[User talk:Yin Meida|talk]]) 09:02, 24 December 2021 (UTC)<br />
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Bao refers to the emperors' seals in the article. In ancient time, emperors and dukes all had their seals made of Gui and Bi(precious jade),from which it derived the name "Bao". Since Tang Dynasty, "Bao" was used exclusively to describe the seals of the emperors and empresses.--[[User:Yin Yuan|Yin Yuan]] ([[User talk:Yin Yuan|talk]]) 09:32, 25 December 2021 (UTC)<br />
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==尹媛 Yǐn Yuán 英语语言文学（英美文学） 女 202120081548==<br />
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“座上”对联──珠玑：本义为珠宝，引申为名贵装饰。 昭日月：形容装饰光亮如日月。 昭：明亮。<br />
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Couplet "above the seat"─ Gem's original meaning is jewelry, which extended for precious decorations. Zhao Riyue means the decorations as bright as the sun and the moon. Zhao means brightness.--[[User:Yin Yuan|Yin Yuan]] ([[User talk:Yin Yuan|talk]]) 12:21, 22 December 2021 (UTC)<br />
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Couplet "above the seat"─ Gem's original meaning is jewelry, which extended for precious decorations. Zhao Riyue means the decorations as bright as the sun and the moon. Zhao means brightness.--[[User:Zhan Ruoxuan|Zhan Ruoxuan]] ([[User talk:Zhan Ruoxuan|talk]]) 12:44, 26 December 2021 (UTC)<br />
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==詹若萱 Zhān Ruòxuān 英语语言文学（英美文学） 女 202120081549==<br />
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黼黻(fǔ fú府服)：泛指绣有华美花纹的礼服。《晏子春秋·谏下十五》：“公衣黼黻之衣，素绣之裳，一衣而王采具焉。” 黼：黑白相间的斧形花纹。<br />
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Fu Fu (f incarnation fu fu clothing) : refers to embroidered with colorful decorative pattern of the dress. "Yan Zi Spring And Autumn · Jian Next 15" : "I: gongclothes: both clothes, plain embroidered clothes, one dress and Wang CAI yan." I: black and white axe shape.--[[User:Zhan Ruoxuan|Zhan Ruoxuan]] ([[User talk:Zhan Ruoxuan|talk]]) 12:43, 26 December 2021 (UTC)<br />
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Fu Fu (f incarnation fu fu clothing) : refers to embroidered with colorful decorative pattern of the dress. "Yan Zi Spring And Autumn · Jian Next 15" : "I: gongclothes: both clothes, plain embroidered clothes, one dress and Wang CAI yan." I: black and white axe shape.--[[User:Zhang Qiuyi|Zhang Qiuyi]] ([[User talk:Zhang Qiuyi|talk]]) 11:18, 26 December 2021 (UTC)<br />
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==张秋怡 Zhāng Qiūyí 亚非语言文学 女 202120081550==<br />
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黻：黑与青相间的亚形花纹。 焕烟霞：形容绣服放射出如烟如霞的光彩，绚丽多姿。 焕：放射光彩。此联形容主宾皆珠光宝气，服饰华丽。<br />
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Fu: black and blue sub-pattern. Huan Yanxia: to describe the embroidered clothing radiates the radiance of smoke and clouds. Huan: radiate brilliance. This couplet describes the guests of honor are glittering jewels, gorgeous clothes.--[[User:Zhang Qiuyi|Zhang Qiuyi]] ([[User talk:Zhang Qiuyi|talk]]) 11:17, 26 December 2021 (UTC)<br />
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Fu: black and green sub shaped pattern. Huanyanxia: to describe that the embroidered clothes radiate smoke like glow, gorgeous and colorful. Huan: radiate brilliance. This couplet describes the guests of honor are glittering jewels, gorgeous clothes.--[[User:Zhang Yang|Zhang Yang]] ([[User talk:Zhang Yang|talk]]) 11:30, 26 December 2021 (UTC)<br />
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==张扬 Zhāng Yáng 国别 男 202120081551==<br />
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汝窑美人觚(gū孤)──出自著名汝窑的一种盛酒器。 汝窑：即北宋汝州瓷窑。因其青瓷器皿质量特佳，多为贡品，故名闻天下，后世成为收藏珍品。<br />
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Ruyao Beauty Gu-- A wine container from the famous Ruyao. Ruyao: Ruzhou porcelain kiln in the Northern Song Dynasty. Because its celadon ware is of excellent quality and mostly tribute, it is famous all over the world and has become a collection treasure in future generations.--[[User:Zhang Yang|Zhang Yang]] ([[User talk:Zhang Yang|talk]]) 09:59, 26 December 2021 (UTC)<br />
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Beauty porcelain（mei ren gu）-- A wine container from the famous Ruyao. Ru kiln: Ruzhou porcelain kiln in the Northern Song Dynasty. Because its celadon ware is of excellent quality and mostly tribute, it is famous all over the world and has become a collection treasure in future generations.--[[User:Zhang Yiran|Zhang Yiran]] ([[User talk:Zhang Yiran|talk]]) 14:34, 26 December 2021 (UTC)<br />
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==张怡然 Zhāng Yírán 俄语语言文学 女 202120081552==<br />
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美人觚：因其体长腰细，形似美人，故名。<br />
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椅搭──又称“椅披”。是一种长方形织物的椅用装饰品。因搭或披在椅背和椅坐上，故名。<br />
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Beauty porcelain（mei ren gu）: so named because of its long waist and resemblance to a beauty. <br />
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Chairs（Yi da） - also known as 'chairs'（Yi pi）. This is a rectangular fabric chair upholstery. The name is derived from the fact that it is placed on the back of the chair or on the seat of the chair.<br />
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Beauty porcelain ：so named because of its long body and slender waist and resemblance to a beauty. <br />
Chairs（Yi da） - also known as 'chairs'（Yi pi）. This is a rectangular fabric chair upholstery. The name is derived from the fact that it is placed on the back of the chair or on the seat of the chair.--[[User:Zhong Yifei|Zhong Yifei]] ([[User talk:Zhong Yifei|talk]]) 10:12, 26 December 2021 (UTC)<br />
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==钟义菲 Zhōng Yìfēi 英语语言文学（英美文学） 女 202120081553==<br />
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掐牙——是一种装饰性衣服花边。即以锦缎等折叠成细条，镶嵌在衣边上，以为美观。 掐：嵌入之意。<br />
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Qia Ya— a kind of decorative lace. That is to fold brocade into thin strips and inlay them on the edge of the clothes to look beautiful. Qia: embedded.--[[User:Zhong Yifei|Zhong Yifei]] ([[User talk:Zhong Yifei|talk]]) 12:30, 19 December 2021 (UTC)<br />
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Qia Ya— a kind of decorative lace. That is to fold brocade into thin strips and inlay them on the edge of the clothes to look beautiful. Qia: it means “embedded”.--[[User:Zhong Yulu|Zhong Yulu]] ([[User talk:Zhong Yulu|talk]]) 08:04, 20 December 2021 (UTC)<br />
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==钟雨露 Zhōng Yǔlù 英语语言文学（英美文学） 女 202120081554==<br />
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牙：即“牙子”。器物突出的边沿。<br />
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《四书》──即《论语》、《孟子》、《大学》、《中庸》(后两种原为《礼记》中的两篇)。<br />
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“Ya”: also called "Ya Zi" in Chinese. It means the protruding edge of an object. <br />
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''The Four Books'' includes— ''The Confucian Analects'', ''The Works of Mencius'', ''The Great Learning'', and ''The Doctrine of the Mean'' (the latter two were originally two books from ''The Book of Rites'').--[[User:Zhong Yulu|Zhong Yulu]] ([[User talk:Zhong Yulu|talk]]) 12:22, 19 December 2021 (UTC)<br />
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''The Four Books'' includes— ''The Confucian Analects'', ''The Works of Mencius'', ''The Great Learning'', and ''The Doctrine of the Mean'' (the latter two were originally two books chosen from ''The Book of Rites'').--[[User:Zhou Jiu|Zhou Jiu]] ([[User talk:Zhou Jiu|talk]]) 01:18, 22 December 2021 (UTC)<br />
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==周玖 Zhōu Jiǔ 英语语言文学（英美文学） 女 202120081555==<br />
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宋代朱熹选定并定名《四书》，遂成为元、明、清三代科举考试的必读之书。<br />
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抹额：原指束在额上的头巾。其起源似乎很早。<br />
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During the Song dynasty, Zhu xi chose and named ''Four Books'' which became the required readings in Imperial Competitive Examinations of Yuan dynasty, Ming dynasty, and Qing dynasty.<br />
Mo E: It originally refers to a kerchief tied around the forehead. Its origin seems to be very early.<br />
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During the Song dynasty, Zhu xi chose and named ''Four Books'' which became the required readings in Imperial Competitive Examinations of Yuan dynasty, Ming dynasty, and Qing dynasty.<br />
Headband: It originally refers to a kerchief tied around the forehead. Its origin seems to be very early.--[[User:Zhou Junhui|Zhou Junhui]] ([[User talk:Zhou Junhui|talk]]) 01:25, 22 December 2021 (UTC)<br />
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==周俊辉 Zhōu Jùnhuī 法语语言文学 女 202120081556==<br />
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宋·高承《事物纪原·戎容兵械·抹额》引《二仪实录》曰：“禹娶涂山之夕，大风雷电，中有甲卒千人，其不披甲者，以红绡帕抹其头额，云海神来朝。<br />
Song Gaocheng quoted the ''Record of Eryi'' in his book ''Things Documentary-Armed Soldiers-Headband'': “When Yu married the Tushan lady, there was a strong wind, thunder and rain. There were a thousand soldiers in gear, and those who were not wore equipment bound a thin red handkerchiefs on their foreheads in anticipation of the arrival of the god of clouds."--[[User:Zhou Junhui|Zhou Junhui]] ([[User talk:Zhou Junhui|talk]]) 01:21, 22 December 2021 (UTC)<br />
Song Gaocheng quoted the ''Record of Eryi'' in his book ''Things Documentary-Armed Soldiers-Headband'': “When Yu married Tu Shan,under the cicumstance of a strong wind, together with thunder and lighting, there were a thousand soldiers who were not equipped with armor but decorated their foreheads with a thin red handkerchiefs in anticipation of the arrival of the god of clouds.--[[User:Zhou Qiao1|Zhou Qiao1]] ([[User talk:Zhou Qiao1|talk]]) 12:42, 22 December 2021 (UTC)<br />
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==周巧 Zhōu Qiǎo 英语语言文学（语言学） 女 202120081557==<br />
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禹问之，对曰：‘此武士之首服也。’秦始皇至海上，有神朝，皆抹额、绯衫、大口袴。侍卫自此抹额，遂为军容之服。<br />
Yu asked and replied, "this is the surrender of a warrior." When the first emperor of Qin went to the sea, there was the divine Dynasty where people wore red upper garment and loose trousers and decorated with smear. Since then, bodyguards decorated their forehead with smear, which has become a kind of military costume.--[[User:Zhou Qiao1|Zhou Qiao1]] ([[User talk:Zhou Qiao1|talk]]) 13:17, 19 December 2021 (UTC)<br />
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Yu asked, and said, ‘this is the first choice for samurai. When Qin Shihuang arrived at the sea, there was a dynasty, and all the people here wiped their foreheads, crimson shirts, and hakama. Since then, the guards wiped their foreheads and became the uniforms of the military.--[[User:Zhou Qing|Zhou Qing]] ([[User talk:Zhou Qing|talk]]) 11:04, 22 December 2021 (UTC)<br />
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==周清 Zhōu Qīng 法语语言文学 女 202120081558==<br />
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可知原为军人的标志。后普及到一般男子，平民以布巾束发，富人用金箍束发，兼为头饰。<br />
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箭袖──亦称“箭衣”。<br />
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It can be seen that it was originally a symbol of a soldier. Later, it was promoted to be used by ordinary men. Common people used cloth towels to tie their hair, and the rich used gold hoops to tie their hair, which also served as headwear. <br />
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Arrow sleeve ─ ─ also known as "arrow suit".<br />
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It was a sign of soldiers. Later, it was popularized to ordinary men. Common people tied their hair with cloth towels, and the rich tied their hair with gold hoops, which was also used as headwear. <br />
Arrow Sleeves - also known as "Arrow Suit".--[[User:Zhou Xiaoxue|Zhou Xiaoxue]] ([[User talk:Zhou Xiaoxue|talk]]) 05:13, 24 December 2021 (UTC)<br />
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==周小雪 Zhōu Xiǎoxuě 日语语言文学 女 202120081559==<br />
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是一种窄袖长袍。其袖口呈斜切状，朝手背的袖口长，朝手心的袖口短，便于射箭，故名。其斜袖口又形似马蹄，故又称马蹄袖。<br />
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It's a kind of robe with narrow sleeves. Its cuffs were in a diagonal cut shape. The cuffs facing the back of the hand are long and the cuffs facing the palm are short, which is convenient for archery, so it is named Arrow Sleeves. Its oblique cuff is also shaped like a horseshoe, so it is also called horseshoe sleeve.--[[User:Zhou Xiaoxue|Zhou Xiaoxue]] ([[User talk:Zhou Xiaoxue|talk]]) 05:55, 20 December 2021 (UTC)<br />
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It's a kind of robe with narrow sleeves. Its cuffs were in diagonal cut shape. The cuffs facing the back of the hand are long and the cuffs facing the palm are short, which is convenient for archery, so it is named Arrow Sleeves. Its oblique cuff is also shaped like horseshoe, so it is also called horseshoe sleeve.--[[User:Zhu Suzhen|Zhu Suzhen]] ([[User talk:Zhu Suzhen|talk]]) 14:08, 26 December 2021 (UTC)<br />
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==朱素珍 Zhū Sùzhēn 英语语言文学（语言学） 女 202120081561==<br />
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后成为一种服式，不射箭的男子也穿。<br />
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“倒像”两句──似有双关之意：一者暗指贾宝玉的化身神瑛侍者在太虚幻境用甘露浇灌林黛玉的化身绛珠仙草；<br />
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Later, it became a sort of clothing style, which can also worn by men who did not shoot arrows. The two sentences of "inverted image" seem to have a double meaning: one alluded to Jia Baoyu’s incarnation Shenying waiter watering Lin Daiyu’s incarnation—— the celestial grass with nectar in the Tai Xu fantasy.<br />
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Later, it became a kind of clothing style, which was also worn by men who did not shoot arrows. <br />
Two sentences of "inverted image" -- there seems to be a pun: one implies that Jia Baoyu's incarnation Shenying waiter watered Lin Daiyu's incarnation Jiangzhu fairy grass with nectar in Taixu fantasy;--[[User:Zou Yueli|Zou Yueli]] ([[User talk:Zou Yueli|talk]]) 11:03, 20 December 2021 (UTC)<br />
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==邹岳丽 Zōu Yuèlí 日语语言文学 女 202120081562==<br />
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再者隐寓二人心有灵犀一点通，一见锺情。下文贾宝玉说“这个妹妹我曾见过的”、“心里倒像是远别重逢的一般”，其用意同此。<br />
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In addition, it implies that the two people share the same heartand fall in love at first sight. Below, Jia Baoyu said that "I have seen this sister" and "I feel like I am far from meeting again". His intention is the same. <br />
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==Nadia 202011080004==<br />
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请安──这里指的是清代一种见面问好的特殊礼仪：<br />
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==Mahzad Heydarian 玛莎 202021080004==<br />
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男子须在口称“请某某安”的同时，右膝弯曲或跪地(俗称打千)；<br />
A man must bend his right knee or kneel on the ground while showing respect.<br />
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==Mariam Toure 2020GBJ002301==<br />
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女子则在口称“请某某安”的同时，双手扶左膝，右腿微屈，身体半蹲。<br />
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==Rouabah Soumaya 202121080001==<br />
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寄名锁──旧时父母为保佑幼儿长命百岁，让幼儿作僧、道的“寄名”弟子，并在幼儿项下悬挂锁形饰物，谓之“寄名锁”。<br />
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==Muhammad Numan 202121080002==<br />
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面如傅粉──语本南朝宋·刘义庆《世说新语·容止》：<br />
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==Atta Ur Rahman 202121080003==<br />
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“何平叔(晏)美姿仪，面至白。<br />
"Uncle He Ping's face is white and beautiful.<br />
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==Muhammad Saqib Mehran 202121080004==<br />
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魏明帝疑其傅粉，正夏月，与热汤饼。<br />
Weiming Siuyuy Full, Full Day, with hot soup.<br />
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==Zohaib Chand 202121080005==<br />
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既啖，大汗出，以朱衣自拭，色转皎然。”<br />
Both, but sweat, to Zhu Jiazi, the color turned. "<br />
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==Jawad Ahmad 202121080006==<br />
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(皎然：洁白貌。)原指何晏的脸上好像抹了香粉般洁白。<br />
English: (Jiao Ran: pure and white appearance.) The original means, He Yan's face it's like white as powdered.<br />
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==Nizam Uddin 202121080007==<br />
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引申以泛喻男子姿容洁白秀美。<br />
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==Öncü 202121080008==<br />
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《西江月》二词──即按照《西江月》词牌填写的两首(也称“阕”)词。<br />
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Two words in "Westlake Moon" According，Fill in two poems (also known as "Que") in the poem of "Westlake Moon".--[[User:AkiraJantarat|AkiraJantarat]] ([[User talk:AkiraJantarat|talk]]) 04:45, 21 December 2021 (UTC)<br />
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==Akira Jantarat 202121080009==<br />
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词：原本指歌曲中的文词，后来文词与曲调分离，遂变成文体之一。<br />
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Words: Originally refers to the words in the song. Later, the words and the tune were separated and became one of the styles.--[[User:AkiraJantarat|AkiraJantarat]] ([[User talk:AkiraJantarat|talk]]) 04:33, 21 December 2021 (UTC)<br />
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Word: It originally referred to the words in a song. In time, the words and the tune separated and became one of the styles. --[[User:Benjamin Wellsand|Benjamin Wellsand]] ([[User talk:Benjamin Wellsand|talk]]) 13:14, 19 December 2021 (UTC)<br />
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==Benjamin Wellsand 202111080118==<br />
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但仍须按曲填词，于是发展出许多词牌，每个词牌都有字数、句数、韵脚等规定，还有双调、长调、小令之别。<br />
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However, it is still necessary to fill in the lyrics according to the tune. So many poems have been developed. Each poem has a word count, sentence count, rhymes and other provisions, as well as the difference between two-tone, long tune, and short meter.--[[User:Benjamin Wellsand|Benjamin Wellsand]] ([[User talk:Benjamin Wellsand|talk]]) 13:10, 19 December 2021 (UTC)<br />
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However, it is still necessary to fill in lyrics according to the tune, so many lyric cards have been developed. Each lyric card has regulations on the number of words, sentences, and rhymes, as well as the differences between double tune, long tune, and short meter. --[[User:Asep Budiman|Asep Budiman]] ([[User talk:Asep Budiman|talk]]) 07:58, 21 December 2021 (UTC)<br />
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==Asep Budiman 202111080020==<br />
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故作词谓之“填词”，就是按照词牌的规范填写文字，不可越雷池一步。<br />
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The preceding phrase "filling in words" means to fill in the words in accordance with the specifications of the words and phrases, and do not go beyond those criteria. --[[User:Asep Budiman|Asep Budiman]] ([[User talk:Asep Budiman|talk]]) 07:56, 21 December 2021 (UTC)<br />
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The preceding phrase "filling in words" means to fill in the words in accordance with the specifications of the words and phrases, and do not overstep the prescribed limit. --[[User:EIMONKYAW|EIMONKYAW]] ([[User talk:EIMONKYAW|talk]]) 09:23, 22 December 2021 (UTC)Ei Mon Kyaw<br />
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==Ei Mon Kyaw 202111080021==<br />
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《西江月》就是词牌之一。本书用了不少词牌，以下不再一一注释。<br />
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"Westlake Moon" is one of the poems. This book uses a lot of words, so I won’t annotate them one by one below. --[[User:EIMONKYAW|EIMONKYAW]] ([[User talk:EIMONKYAW|talk]]) 08:59, 22 December 2021 (UTC)Ei Mon Kyaw<br />
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"Westlake Moon" is one of the tune names of poem. There are a lot of tune names in the book, which will not annotated one by one below.--[[User:Chen Jing|Chen Jing]] ([[User talk:Chen Jing|talk]]) 11:49, 26 December 2021 (UTC)</div>Chen Huinihttps://bou.de/u/index.php?title=20211222_homework&diff=13438120211222 homework2021-12-27T06:10:29Z<p>Chen Huini: /* 陈惠妮 Chén Huìnī 英语语言文学（英美文学） 女 202120081479 */</p>
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<div>Quicklinks: [[Introduction_to_Translation_Studies_2021|Back to course homepage]] [https://bou.de/u/wiki/uvu:Community_Portal#Frequently_asked_questions_FAQ FAQ] [https://bou.de/u/wiki/uvu:Community_Portal Manual] [[20210926_homework|Back to all homework webpages overview]] [[20220112_final_exam|final exam page]]<br />
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PLEASE READ [[Joint_translation_terms|Joint translation terms]] <br />
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PLEASE ALSO READ THE PREVIOUS PARTS, AT LEAST THE SENTENCES BEFORE YOUR OWN PART IN CHAPTER 19 [[20210303_culture|1, Mar 3 Chapters 1-4]], [[20210310_culture|2, Mar 10 Chapters 6-7]], [[20210317_culture|3, Mar 17 Chapters 11-13]], [[20210324_culture|4, Mar 24 Chapters 15-17]], [[20210331_culture|5, Mar 31 Chapters 4-7]], [[20210407_culture|6, Apr 7 Chapters 8-10]], [[20210414_culture|7, Apr 14 Chapters 13-15]] , [[20210519_culture|12, May 19 Chapters 17-19]], [[20210929_homework#Hongloumeng|for Sep 29 - rest of HLM Chapter 19]] [[20211013_homework|for Oct 13 - HLM Chapters 20-21]] [[20211020_homework|for Oct 20 - HLM Chapters 21-22]] etc.<br />
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==陈静 Chén Jìng 国别 女 202020080595==<br />
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闲静似娇花照水，行动如弱柳扶风。心较比干多一窍，病如西子胜三分。宝玉看罢，笑道：“这个妹妹我曾见过的。”<br />
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She, who is demure as delicate flowers and act like weak willow, is more clever than Bigan(the most clever man in the Legend of Deification) and more beautiful than Xishi(the most beautiful woman in acient China). Precious Jade simled, “I have seen her.”--[[User:Chen Jing|Chen Jing]] ([[User talk:Chen Jing|talk]]) 11:26, 26 December 2021 (UTC)<br />
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Leisure is like a delicate flower shining on the water, and action is like a weak willow supporting the wind. The heart has more than one orifices than the stem, and like a disease wins Xizi. Baoyu said with a smile, "I've seen this sister."<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 11:18, 26 December 2021 (UTC)<br />
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==蔡珠凤 Cài Zhūfèng 日语语言文学 女 202120081477==<br />
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贾母笑道：“又胡说了，你何曾见过？”宝玉笑道：“虽没见过，却看着面善，心里倒像是远别重逢的一般。”贾母笑道：“好，好！这么更相和睦了。”<br />
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Grandma Merchant smiled and said, "nonsense again. Have you ever seen her?" Baoyu said with a smile, "although I haven't seen her, I look good and feel like I'm far from meeting again." Grandma Merchant said with a smile, "OK, OK! It's more harmonious."<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 11:22, 26 December 2021 (UTC)<br />
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Dowager Jia smiled and said, "nonsense again. Have you ever seen it?" Baoyu said with a smile, "although I haven't seen it, I look good and feel like I'm far from meeting again." Dowager Jia said with a smile, "OK, OK! It's more harmonious."--[[User:Zeng Junlin|Zeng Junlin]] ([[User talk:Zeng Junlin|talk]]) 13:18, 21 December 2021 (UTC)<br />
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Grandma Merchant smiled and said, "Nonsense again. Have you ever seen it?" Precious Jade said with a smile, "Although I haven't seen it, I look good and feel like I'm far from meeting again." Grandma Merchant said with a smile, "OK, OK! It's more harmonious."--[[User:Zeng Junlin|Zeng Junlin]] ([[User talk:Zeng Junlin|talk]]) 13:18, 21 December 2021 (UTC)<br />
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==曾俊霖 Zēng Jùnlín 国别 男 202120081478==<br />
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宝玉便走向黛玉身边坐下，又细细打量一番，因问：“妹妹可曾读书？”黛玉道：“不曾读书，只上了一年学，些须认得几个字。”<br />
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Baoyu went to Daiyu and sat down. She looked at her carefully, because she asked, "has your sister ever read?" Daiyu said, "I didn't study. I only studied for a year. I have to recognize a few words."--[[User:Zeng Junlin|Zeng Junlin]] ([[User talk:Zeng Junlin|talk]]) 13:12, 21 December 2021 (UTC)<br />
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==陈惠妮 Chén Huìnī 英语语言文学（英美文学） 女 202120081479==<br />
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宝玉又道：“妹妹尊名？”黛玉便说了名。宝玉又道：“表字？”黛玉道：“无字。”<br />
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Baoyuu asked, "What is your name?" Daiyu told her name to him. Baoyu said, "Watch characters?" "No words," Said Daiyu.--[[User:Chen Huini|Chen Huini]] ([[User talk:Chen Huini|talk]]) 06:10, 27 December 2021 (UTC)Chen Huini<br />
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==陈湘琼 Chén Xiāngqióng 外国语言学及应用语言学 女 202120081480==<br />
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宝玉笑道：“我送妹妹一字：莫若‘颦颦’二字极妙。”探春便道：“何处出典？”宝玉道：“《古今人物通考》上说：<br />
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Baoyu smiled and said:" I want to describe you with two words—Ping Ping, and no words are better than them." Tanchun then asked:"In which book did you find them?" Baoyu said:" On ''General Study of Ancient and Modern Characters''"--[[User:Chen Xiangqiong|Chen Xiangqiong]] ([[User talk:Chen Xiangqiong|talk]]) 01:04, 20 December 2021 (UTC)<br />
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Precious Jade smiled and said:" I want to give away two words to you—Pingping, and no words are better than them." Tanchun then asked:"In which book did you find them?" Jade said:"''On General Study of Ancient and Modern Characters''."--[[User:Chen Xinyi|Chen Xinyi]] ([[User talk:Chen Xinyi|talk]]) 06:37, 22 December 2021 (UTC)<br />
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==陈心怡 Chén Xīnyí 翻译学 女 202120081481==<br />
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‘西方有石名黛，可代画眉之墨。’况这妹妹眉尖若蹙，取这个字，岂不甚美？”探春笑道：“只怕又是杜撰。”<br />
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'There is a stone in the west named Dai, it can replace the ink of painting eyebrows.' The sister's eyebrows are frown, if take this word for name, isn’t it very beautiful?" Tanchun laughed: "I'm afraid it's a fabrication again."--[[User:Chen Xinyi|Chen Xinyi]] ([[User talk:Chen Xinyi|talk]]) 06:29, 22 December 2021 (UTC)<br />
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‘There is a stone in the west named Dai, it can replace the ink of drawing eyebrows.' Besides, her eyebrows are frown, if take this word for name, isn’t it very beautiful?" Tanchun laughed: "I'm afraid it's a fabrication again."--[[User:Cheng Yang|Cheng Yang]] ([[User talk:Cheng Yang|talk]]) 11:33, 26 December 2021 (UTC)<br />
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==程杨 Chéng Yáng 英语语言文学（英美文学） 女 202120081482==<br />
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宝玉笑道：“除了《四书》，杜撰的也太多呢。”因又问黛玉：“可有玉没有？”众人都不解。<br />
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Baoyu laughed and said, "Besides the Four Books, there are too many things made up." Again he asked Daiyu, "Do you have any jade?" Everyone was puzzled.--[[User:Cheng Yang|Cheng Yang]] ([[User talk:Cheng Yang|talk]]) 11:35, 26 December 2021 (UTC)<br />
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==丁旋 Dīng Xuán 英语语言文学（英美文学） 女 202120081483==<br />
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黛玉便忖度着：“因他有玉，所以才问我的。”便答道：“我没有玉。你那玉也是件稀罕物儿，岂能人人皆有？”<br />
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Mascara Jade Forest contemplated, “He asked me because he has a jade.” So she answered, “I have no jade. Your jade is a rarity. How could everyone have it?”--[[User:Ding Xuan|Ding Xuan]] ([[User talk:Ding Xuan|talk]]) 13:45, 22 December 2021 (UTC)<br />
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Mascara Jade Forest presumed, “He asked me because he has a jade.” So she answered, “I have no jade. Your jade is a rarity. How could everyone have it?”--[[User:Du Lina|Du Lina]] ([[User talk:Du Lina|talk]]) 08:25, 23 December 2021 (UTC)<br />
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==杜莉娜 Dù Lìnuó 英语语言文学（语言学） 女 202120081484==<br />
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宝玉听了，登时发作起狂病来，摘下那玉就狠命摔去，骂道：“什么罕物！人的高下不识，还说灵不灵呢！我也不要这劳什子！”<br />
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After hearing that,Precious Jade Merchant suddenly went mad. And he took off and dropped the jade with cursing that “What the hell is a rare thing! You all say that it is divine, but it can't tell lowliness or nobleness.I won't have the waste now!” --[[User:Du Lina|Du Lina]] ([[User talk:Du Lina|talk]]) 12:29, 19 December 2021 (UTC)<br />
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After hearing that,Precious Jade Merchant suddenly went mad. And he took off and dropped the jade with cursing that “What the hell is a rare thing! You all say that it is divine, but nobody could tell lowliness or nobleness.I won't have the waste now!” --[[User:Fu Hongyan|Fu Hongyan]] ([[User talk:Fu Hongyan|talk]]) 11:18, 26 December 2021 (UTC)<br />
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--[[User:Fu Hongyan|Fu Hongyan]] ([[User talk:Fu Hongyan|talk]]) 11:19, 26 December 2021 (UTC)==付红岩 Fù Hóngyán 英语语言文学（英美文学） 女 202120081485==<br />
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吓的地下众人一拥争去拾玉。贾母急的搂了宝玉道：“孽障，你生气，要打骂人容易，何苦摔那命根子？”宝玉满面泪痕，哭道：“家里姐姐妹妹都没有，单我有，我说没趣儿；<br />
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The aside servants were scared and then rushed to pick up the jade.Jia's mother anxiously hugged Baoyu and said:" poor kid, if you are angry, why do you bother to fall the jade rahtere to beat and curse."Covered with tears, Baoyu cried:" my beloved elder and litter sisters have no one. I'm ashamed of owning one."--[[User:Fu Hongyan|Fu Hongyan]] ([[User talk:Fu Hongyan|talk]]) 11:19, 26 December 2021 (UTC)<br />
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All those, who stood below, were startled; and in a body they pressed forward, vying with each other as to who should pick up the gem.<br />
Grandma Merchant was so distressed that she clasped Precious Jade in her embrace. "You child of wrath," she exclaimed. "When you get into a passion, it's easy enough for you to beat and abuse people; but what makes you fling away that stem of life?"<br />
Precious Jade’s face was covered with the traces of tears. "All my cousins here, senior as well as junior," he rejoined, as he sobbed, "have no gem, and if it's only I to have one, there's no fun in it, I maintain! “.--[[User:Fu Shiyu|Fu Shiyu]] ([[User talk:Fu Shiyu|talk]]) 06:27, 22 December 2021 (UTC)<br />
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==付诗雨 Fù Shīyǔ 日语语言文学 女 202120081486==<br />
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如今来了这个神仙似的妹妹也没有：可知这不是个好东西。”贾母忙哄他道：“你这妹妹原有玉来着，因你姑妈去世时，舍不得你妹妹，无法可处，遂将他的玉带了去：<br />
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And now comes this angelic sort of cousin, and she too has none, so that it's clear enough that it is no profitable thing." Grandma Merchant hastened to coax him. "This cousin of yours," she explained, "would, under former circumstances, have come here with a jade; and it's because your aunt felt unable, as she lay on her death-bed, to reconcile herself to the separation from your cousin, that in the absence of any remedy, she forthwith took the gem belonging to her (daughter), along with her (in the grave); --[[User:Fu Shiyu|Fu Shiyu]] ([[User talk:Fu Shiyu|talk]]) 12:24, 19 December 2021 (UTC)<br />
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Now the newly arrived cousin who is as lovely as a fairy hasn't got one either, so it can't be any good." "Your cousin did have one once," said Dowager lady Chia to soothe him, "but when your aunt was dying she was unwilling to leave your cousin, the best she could do was to take the jade with her instead. --[[User:Gao Mi|Gao Mi]] ([[User talk:Gao Mi|talk]]) 04:49, 20 December 2021 (UTC)<br />
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==高蜜 Gāo Mì 翻译学 女 202120081487==<br />
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一则全殉葬之礼，尽你妹妹的孝心；二则你姑妈的阴灵儿也可权作见了你妹妹了。因此他说没有，也是不便自己夸张的意思啊。<br />
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In that way, your cousin showed her filial piety by letting the jade be buried with her; in the meantime, your aunt’s spirit could see your cousin through the jade. Therefore, when your cousin said she hadn’t got one, it was because she didn’t want to boast about it. --[[User:Gao Mi|Gao Mi]] ([[User talk:Gao Mi|talk]]) 04:50, 20 December 2021 (UTC)<br />
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In that way, your cousin showed her filial piety by letting the jade be buried with her; in the meantime, your aunt’s spirit could see your cousin through the jade. Therefore, when your cousin said she hadn’t got one, it was because she didn't want to publicise it.--[[User:Gong Boya|Gong Boya]] ([[User talk:Gong Boya|talk]]) 07:00, 22 December 2021 (UTC)<br />
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==宫博雅 Gōng Bóyǎ 俄语语言文学 女 202120081488==<br />
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你还不好生带上，仔细你娘知道。”说着，便向丫鬟手中接来，亲与他带上。宝玉听如此说，想了一想，也就不生别论。<br />
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"If you don't take it with you, be careful your mother knows" As she spoke, she took the jade from the servant girl and adorned him herself. When Precious Jade Merchant heard her say this, he thought for a while and said nothing else.--[[User:Gong Boya|Gong Boya]] ([[User talk:Gong Boya|talk]]) 06:56, 22 December 2021 (UTC)<br />
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"You‘d better keep it well in case your mother notices.” As she spoke, she took the jade from the maid and adorned him herself. When Precious Jade Merchant heard her saying this, he thought for a while and said nothing else. --[[User:He Qin|He Qin]] ([[User talk:He Qin|talk]]) 12:18, 22 December 2021 (UTC)<br />
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==何芩 Hé Qín 翻译学 女 202120081489==<br />
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当下奶娘来问黛玉房舍，贾母便说：“将宝玉挪出来，同我在套间暖阁里，把你林姑娘暂且安置在碧纱厨里。等过了残冬，春天再给他们收拾房屋，另作一番安置罢。”<br />
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When the nanny came to ask where Mascara Jade should stay, Lady Dowager answered, “ Place Precious Jade in the warm house in my suit and settle your Miss Forest in the Green Voile House temporarily until the winter ends. In next spring, you’ll rearrange the room for them.”--[[User:He Qin|He Qin]] ([[User talk:He Qin|talk]]) 12:34, 22 December 2021 (UTC)<br />
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Hardly when the nanny came to ask the room of Mascara Jade, Lady Dowager said, “ Place Precious Jade in the warm house with me and settle your Miss Lin inside the room of the partition door temporarily until the winter ends. In next spring, you’ll rearrange the room for them.”--[[User:Hu Shuqing|Hu Shuqing]] ([[User talk:Hu Shuqing|talk]]) 11:31, 26 December 2021 (UTC)<br />
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==胡舒情 Hú Shūqíng 英语语言文学（语言学） 女 202120081490==<br />
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宝玉道：“好祖宗，我就在碧纱厨外的床上很妥当，又何必出来，闹的老祖宗不得安静呢？”贾母想一想说：“也罢了。<br />
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Baoyu said:” Dear grandma, I would rather stay at the bed outside the partition door, than at your room to bother you.” The Lady Dowager said thoughtfully:”That’s Ok.”--[[User:Hu Shuqing|Hu Shuqing]] ([[User talk:Hu Shuqing|talk]]) 05:38, 21 December 2021 (UTC)<br />
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Presious Jade said:"Dear mother, I would rather stay on the bed outside the partition door rather than at grandma's to bother her." The Lady Dowager said thoughtfully:"That's OK."--[[User:Huang Jinyun|Huang Jinyun]] ([[User talk:Huang Jinyun|talk]]) 09:06, 25 December 2021 (UTC)<br />
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==黄锦云 Huáng Jǐnyún 英语语言文学（语言学） 女 202120081491==<br />
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每人一个奶娘并一个丫头照管，馀者在外间上夜听唤。”一面早有熙凤命人送了一顶藕合色花帐并锦被、缎褥之类。<br />
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But let each one of you have a nurse, as well as a waiting-maid to attend on you; the other servants can remain in the outside rooms and keep night watch and be ready to answer any call." At an early hour, besides, Hsi-feng had sent a servant round with a grey flowered curtain, embroidered coverlets and satin quilts and other such articles.--[[User:Huang Jinyun|Huang Jinyun]] ([[User talk:Huang Jinyun|talk]]) 14:25, 19 December 2021 (UTC)<br />
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But let each one of you have a nurse and a waiting-maid; the other servants can remain in the outside rooms and keep night watch and be ready to answer any call." At an early hour, besides, Hsi-feng had sent a servant round with a grey flowered curtain, embroidered coverlets and satin quilts and other such articles.--[[User:Huang Yiyan1|Huang Yiyan1]] ([[User talk:Huang Yiyan1|talk]]) 14:22, 22 December 2021 (UTC)<br />
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==黄逸妍 Huáng Yìyán 外国语言学及应用语言学 女 202120081492==<br />
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黛玉只带了两个人来：一个是自己的奶娘王嬷嬷；一个是十岁的小丫头，名唤雪雁。贾母见雪雁甚小，一团孩气；<br />
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Daiyu had brought her old Wet-nurse Nanny Wang and ten-year-old Xueyan,who had also attended her since she was a child. The Lady Dowager considered Xueyan too young;--[[User:Huang Yiyan1|Huang Yiyan1]] ([[User talk:Huang Yiyan1|talk]]) 14:17, 22 December 2021 (UTC)<br />
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Daiyu had brought her old Wet-nurse Nanny Wang and ten-year-old Xueyan,who had also attended her since she was a child. The Lady Dowager considered Xueyan too young;--[[User:Huang Zhuliang|Huang Zhuliang]] ([[User talk:Huang Zhuliang|talk]]) 01:41, 26 December 2021 (UTC)Huang Zhuliang<br />
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==黄柱梁 Huáng Zhùliáng 国别 男 202120081493==<br />
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王嬷嬷又极老：料黛玉皆不遂心，将自己身边一个二等小丫头，名唤鹦哥的与了黛玉。亦如迎春等一般：每人除自幼乳母外，另有四个教引嬷嬷；Mammy(Here mammy not means the lady who gives birth to a baby, but a lady who looks after some noble children) Wang is very old: she is not expectd to look after Daiyu well. So,Daiyu's grandmother gave Daiyu to a second-class little page girl named Yingge. Daiyu's arrangement is also like Jia Yingchun who not only has the nursing mother, but also four teaching mothers.--[[User:Huang Zhuliang|Huang Zhuliang]] ([[User talk:Huang Zhuliang|talk]]) 14:02, 19 December 2021 (UTC)Huang Zhuliang<br />
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Mammy(Here mammy not means the lady who gives birth to a baby, but a lady who looks after some noble children) Wang is very old: she is not expectd to look after Daiyu well. So,Daiyu's grandmother gave Daiyu to a second-class little girl named Polly. Daiyu's arrangement is also like Jia Yingchun who not only has the nursing mother, but also four teaching mummys.--[[User:Jin Xiaotong|Jin Xiaotong]] ([[User talk:Jin Xiaotong|talk]]) 14:40, 20 December 2021 (UTC)<br />
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==金晓童 Jīn Xiǎotóng 202120081494==<br />
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除贴身掌管钗钏盥沐两个丫头外，另有四五个洒扫房屋、来往使役的小丫头。当下王嬷嬷与鹦哥陪侍黛玉在碧纱厨内，宝玉乳母李嬷嬷并大丫头名唤袭人的陪侍在外面大床上。<br />
In addition to the two servants who are in charge of jewelry and toiletries, there are four or five little maids who sweep the house and do chores. At the moment King mammy and polly accompany daiyu in green gauze room, Baoyu’s mammy li and big maid Xiren accompany on the big bed outside.--[[User:Jin Xiaotong|Jin Xiaotong]] ([[User talk:Jin Xiaotong|talk]]) 14:37, 20 December 2021 (UTC)<br />
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==邝艳丽 Kuàng Yànl 英语语言文学（语言学） 女 202120081495==<br />
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原来这袭人亦是贾母之婢，本名蕊珠，贾母因溺爱宝玉，恐宝玉之婢不中使，素喜蕊珠心地纯良，遂与宝玉。宝玉因知他本姓花，又曾见旧人诗句有“花气袭人”之句，遂回明贾母，即把蕊珠更名袭人。<br />
This maid Xi Ren, whose real name is Rui Zhu, also belongs to Lady Dowager. Lady Dowager enjoyed Rui Zhu’s purity and kindness then assigned her to Baoyu, for Lady Dowager coddled him and worried that the maids of Baoyu not work well. Baoyu knew her last name was Hua, and saw once poetic sentence “the fragrance of flowers assails noses”, then he talked it with Lady Dowager, and then Rui Zhu was named Xi Ren.--[[User:Kuang Yanli|Kuang Yanli]] ([[User talk:Kuang Yanli|talk]]) 07:12, 20 December 2021 (UTC)<br />
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This maid Xi Ren, whose real name is Rui Zhu, also belongs to Lady Dowager. Lady Dowager enjoyed Rui Zhu’s purity and kindness, then assigned her to serve Jade, for Lady Dowager coddled him and worried that the maids of Jade not professional. Jade knew her last name was Flower, and saw once a poetic sentence “the fragrance of flowers assails noses”, then he talked it with Lady Dowager. And then Rui Zhu was named Xi Ren.--[[User:Li Aixuan|Li Aixuan]] ([[User talk:Li Aixuan|talk]]) 12:33, 20 December 2021 (UTC)<br />
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==李爱璇 Lǐ Àixuán 英语语言文学（语言学） 女 202120081496==<br />
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却说袭人倒有些痴处：伏侍贾母时，心中只有贾母；如今跟了宝玉，心中又只有宝玉了。只因宝玉性情乖僻，每每规谏，见宝玉不听，心中着实忧郁。<br />
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However, it is said that Hsi-jen is crazy: when seving Jia's mother, only Jia's mother is in her heart; now serving Jade, there is only Jade in her heart. Because of Jade's perverse temperament, when Jade doesn't listen to her advise, Hsi-jen is really depressed.--[[User:Li Aixuan|Li Aixuan]] ([[User talk:Li Aixuan|talk]]) 07:11, 25 December 2021 (UTC)<br />
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However, Hsi Jen had several simple traits. While in attendance upon dowager lady Chia, in her heart and her eyes there was no one but her venerable ladyship, and her alone; and now in her attendance upon Pao-yue, her heart and her eyes were again full of Pao-yue, and him alone. But as Pao-yue was of a perverse temperament and did not heed her repeated injunctions, she felt at heart exceedingly grieved.--[[User:Li Ruiyang|Li Ruiyang]] ([[User talk:Li Ruiyang|talk]]) 00:42, 21 December 2021 (UTC)<br />
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==李瑞洋 Lǐ Ruìyáng 英语语言文学（英美文学） 女 202120081497==<br />
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是晚，宝玉、李嬷嬷已睡了，他见里面黛玉、鹦哥犹未安歇，他自卸了妆，悄悄的进来，笑问：“姑娘怎么还不安歇？”黛玉忙笑让：“姐姐请坐。”<br />
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At night, after nurse Li had fallen asleep, seeing that in the inner chambers, Tai-yue and Ying Ko had not as yet retired to rest, she removed her makeup, and with gentle step walked in.<br />
"How is it, miss，" she inquired smiling, "that you have not turned in as yet？"<br />
Tai-yue at once put on a smile. "Sit down, sister, " she rejoined, pressing her to take a seat. --[[User:Li Ruiyang|Li Ruiyang]] ([[User talk:Li Ruiyang|talk]]) 01:37, 21 December 2021 (UTC)<br />
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At night, after Master Bao and nurse Li had fallen asleep, seeing that in the inner chambers, Tai-yue and Ying Ko had not as yet retired to rest, she removed her makeup, and with gentle step walked in."How is it, miss，" she inquired smilingly, "that you have not turned in as yet？" Tai-yue at once put on a smile. "Sit down, my dear sister, " she rejoined, leading her to take a seat.--[[User:Li Shan|Li Shan]] ([[User talk:Li Shan|talk]]) 14:41, 21 December 2021 (UTC)<br />
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==李姗 Lǐ Shān 英语语言文学（英美文学） 女 202120081498==<br />
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袭人在床沿上坐了。鹦哥笑道：“林姑娘在这里伤心，自己淌眼抹泪的说：‘今儿才来了，就惹出你们哥儿的病来。倘或摔坏了那玉，岂不是因我之过？’<br />
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Hsi-jen then sat by the bedside. Ying Ko ridiculed, "Miss Lin was feeling sad with her tears dropping down today, saying that 'It is the first day that I come here, while I have triggered the relapse of your young master. If his precious jade was truly broken apart, then I am sure to blame."--[[User:Li Shan|Li Shan]] ([[User talk:Li Shan|talk]]) 14:29, 21 December 2021 (UTC)<br />
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Aroma then sat by the bedside. Brother Parrot ridiculed, "Miss Lin feels sad with her tears dropping down today, saying that 'It is the first day that I come here, while I have triggered the relapse of your young master. If his precious jade is truly broken apart, then I'll be sure to blame."--[[User:Li Shuang|Li Shuang]] ([[User talk:Li Shuang|talk]]) 02:45, 22 December 2021 (UTC)<br />
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==李双 Lǐ Shuāng 翻译学 女 202120081499==<br />
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所以伤心。我好容易劝好了。”袭人道：“姑娘快别这么着。将来只怕比这更奇怪的笑话儿还有呢。<br />
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“She is therefore so sad, and I had a hard time persuading her to stop crying.” Aroma said: “Please don’t look so sad, young lady. There will be more stranger jokes in the future.”--[[User:Li Shuang|Li Shuang]] ([[User talk:Li Shuang|talk]]) 02:40, 22 December 2021 (UTC)<br />
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“She is therefore so sad, and I had a hard time persuading her to stop crying.” Aroma said: “Please don’t look so sad, my mistress. There will be more stranger jokes in the future.”--[[User:Li Wenxuan|Li Wenxuan]] ([[User talk:Li Wenxuan|talk]]) 10:51, 22 December 2021 (UTC)<br />
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==李文璇 Lǐ Wénxuán 英语语言文学（英美文学） 女 202120081500==<br />
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若为他这种行状，你多心伤感，只怕你还伤感不了呢。快别多心。”黛玉道：“姐姐们说的，我记着就是了。”<br />
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“If you feel sad for his behavior, I’m afraid that you can’t be so. Don’t think too much.” Daiyu said: “I will remember what our sisters has said.” --[[User:Li Wenxuan|Li Wenxuan]] ([[User talk:Li Wenxuan|talk]]) 00:23, 20 December 2021 (UTC)<br />
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==李雯 Lǐ Wén 英语语言文学（英美文学） 女 202120081501==<br />
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又叙了一会，方才安歇。次早起来，省过贾母，因往王夫人处来。正值王夫人与熙凤在一处拆金陵来的书信，又有王夫人的兄嫂处遣来的两个媳妇儿来说话。<br />
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==李新星 Lǐ Xīnxīng 亚非语言文学 女 202120081503==<br />
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黛玉虽不知原委，探春等却晓得是议论金陵城中居住的薛家姨母之子、表兄薛蟠倚财仗势，打死人命，现在应天府案下审理。如今舅舅王子腾得了信，遣人来告诉这边，意欲唤取进京之意。<br />
Although Daiyu did not know the exact cause, Tanchun and others knew that it was xue Pan, son and cousin of aunt Xue who lived in Jinling city, who killed a man by taking advantage of his wealth and power, and was now being tried by the Tianfu court. Now uncle Prince teng got the letter, send people to tell here, intended to call the meaning of Beijing.--[[User:Li Xinxing|Li Xinxing]] ([[User talk:Li Xinxing|talk]]) 12:24, 19 December 2021 (UTC)<br />
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Although Dai Yu did not know the original commission, Tan Chun and others knew that it was a discussion of Xue Pan, the son of the Xue family's aunt and cousin Xue Pan, who lived in Jinling City, who relied on wealth and power to kill people, and now it should be tried under the Tianfu case. Now that his uncle Prince Teng had received the letter, he sent someone to tell this side, intending to summon the intention of entering the capital.--[[User:Li Yi|Li Yi]] ([[User talk:Li Yi|talk]]) 12:27, 19 December 2021 (UTC)<br />
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==李怡 Lǐ Yí 法语语言文学 女 202120081504==<br />
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毕竟怎的，下回分解。<br />
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起复——即重新起用被停职或撤职的官员，包括因父母丧停职回家守孝及因被弹劾而遭撤职的官员。<br />
If you want to know what happened, the answer is next time<br />
Reinstatement – Reinstate officials who have been suspended or removed from their posts, including those who have been suspended from their posts for the death of their parents and who have been removed from office for impeachment.--[[User:Li Yi|Li Yi]] ([[User talk:Li Yi|talk]]) 12:14, 19 December 2021 (UTC)<br />
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After all, I'll break it down next time.<br />
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Reinstatement - reinstatement of officials who have been suspended or removed from office, including those who have been removed from office due to the death of their parents and those who have been removed from office due to impeachment.--[[User:Liu Peiting|Liu Peiting]] ([[User talk:Liu Peiting|talk]]) 12:24, 19 December 2021 (UTC)<br />
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==刘沛婷 Liú Pèitíng 英语语言文学（英美文学） 女 202120081505==<br />
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邸(dǐ底)报——亦称“邸抄”、“抄报”、“宫门抄”，清代或称“京报”。中国古代官方报纸的通称。<br />
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Di Pao -- also known as "Di Copy", "copy newspaper" or "Palace Gate Copy" -- is also known as "Beijing Newspaper" during the Qing Dynasty. The general name of the official newspaper in ancient China.--[[User:Liu Peiting|Liu Peiting]] ([[User talk:Liu Peiting|talk]]) 12:23, 19 December 2021 (UTC)<br />
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Di Bao -- also known as "Di Copy", "copy newspaper" or "Palace Gate Copy" -- is also known as "Beijing Newspaper" during the Qing Dynasty. The general name of the official newspaper in ancient China.--[[User:Liu Shengnan|Liu Shengnan]] ([[User talk:Liu Shengnan|talk]]) 12:10, 22 December 2021 (UTC)<br />
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==刘胜楠 Liú Shèngnán 翻译学 女 202120081506==<br />
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承办者或为地方官府驻京办事机构，或为朝廷。邸报专门抄发诏令、奏章及朝政新闻，以供地方官及时了解。 邸：原指战国时各诸侯在都城的客馆，后泛指地方官府驻京办事处。<br />
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The undertaker is either the local government office in Beijing or the imperial court. The residence newspaper specially copied and issued imperial edicts, memorials and government news for local officials to understand in time. Di: it used to refer to the guest houses of various princes in the capital during the Warring States period, and later it generally refers to the offices of local officials in Beijing. --[[User:Liu Shengnan|Liu Shengnan]] ([[User talk:Liu Shengnan|talk]]) 12:09, 22 December 2021 (UTC)<br />
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The undertaker is either the local government office in Beijing or the imperial court. The "Di" newspaper(a official gazette) specially copied and issued imperial edicts, memorials and government news for local officials to understand in time."Di": Originally referred to the guest hall of the princes in the capital during the Warring States Period, and later generally referred to the Beijing office of the local government. --[[User:Liu Wei|Liu Wei]] ([[User talk:Liu Wei|talk]]) 14:40, 22 December 2021 (UTC)Liu Wei<br />
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==刘薇 Liú Wēi 国别 女 202120081507==<br />
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贱荆——亦称“拙荆”、“山荆”等。谦词。对人称自己的妻子。 荆：“荆钗布裙”的省称。<br />
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"Jian Jing" ——also known as "Zhuo Jing", "Shan Jing" etc. It's a modest word when a man mention his wife in front of others. "Jing"is a short name for "JingChaiBuQun"(the female have only a thorn for a hairpin and plain cloth for a skirt). --[[User:Liu Wei|Liu Wei]] ([[User talk:Liu Wei|talk]]) 12:36, 19 December 2021 (UTC)Liu Wei<br />
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Jian Jing, also known as "Zhuo Jing", "Shan Jing",etc, is a humle term for quoting one's own wife. Jing is an abbreviation for "Jingchaibuqun", that is, a thorn for a hairpin and palin cloth for a skirt.--[[User:Liu Xiao|Liu Xiao]] ([[User talk:Liu Xiao|talk]]) 06:59, 20 December 2021 (UTC)<br />
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==刘晓 Liú Xiǎo 英语语言文学（英美文学） 女 202120081508==<br />
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形容妇人极为简朴的服饰。语出汉·刘向《列女传》(见《太平御览》卷七一八引)：“梁鸿妻孟光，荆钗布裙。” 荆钗：即以木棍为钗。<br />
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Jing, used to describe women's plain, simple and unadorned clothes, is originated from a sentence in the ''Biographies of Exemplary Women'' written by Liu Xiang in the Han Dynasty (see ''Imperial Review under the Reign of Taizong in the Song Dynasty'', Vol.718): "Meng Guang, wife of Liang Hong has only a thorn for a hairpin and plain cloth for a skirt." Jingchai means a thron for a hairpin.--[[User:Liu Xiao|Liu Xiao]] ([[User talk:Liu Xiao|talk]]) 06:53, 20 December 2021 (UTC)<br />
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Jing, used to describe the extremely simple dress of a woman. In Han, Liu Xiang's "Biography of the Female" (see ”Taiping Yu Lan“（Imperial Review under the Reign of Taizong in the Song Dynasty）, vol. 718): "Liang Hong's wife, Meng Guang, was dressed in a woven hairpin and cloth skirt." Jingchai (荆钗): a wooden stick used as a hairpin.--[[User:Liu Yue|Liu Yue]] ([[User talk:Liu Yue|talk]]) 05:02, 22 December 2021 (UTC)<br />
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==刘越 Liú Yuè 亚非语言文学 女 202120081509==<br />
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内顾之忧──语出北朝魏·袁翻《安置蠕蠕表》：“且蠕蠕尚存，则高车犹有内顾之忧，未暇窥窬上国；<br />
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The Worries of Internal Concern - From Yuan Fan's " Settlement Zoran Policy ", And if Zoran still existed, then Gao Che (a generic term used by the Northern Dynasties for a part of the nomadic tribes in the north of the desert) would still have internal concerns and would not have had the strength to covet the vassal states.--[[User:Liu Yue|Liu Yue]] ([[User talk:Liu Yue|talk]]) 04:55, 22 December 2021 (UTC)<br />
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Internal Concerns – derived from Yuan Fan's "Policy on the Settlement of Ruru". “And if Ruru survived, Gaoche would have internal concerns and would have no time to covet the territory of the Emperor.--[[User:Liu Yunxin|Liu Yunxin]] ([[User talk:Liu Yunxin|talk]]) 13:30, 25 December 2021 (UTC)<br />
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==刘运心 Liú Yùnxīn 英语语言文学（英美文学） 女 202120081510==<br />
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若蠕蠕全灭，则高车跋扈之计，岂易可知？”(蠕蠕：“柔然”的别称，亦称“芮芮”、“茹茹”。我国古代北方少数民族名。<br />
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“If Ruru was annihilated, wasn’t it easy to know the conceited plan of Gaoche?” (“Ruru”, an alternative name for Rouran Khaganate, can also be called “Ruirui” or “Ruru”. The name of an ancient northern ethnic minorities.)--[[User:Liu Yunxin|Liu Yunxin]] ([[User talk:Liu Yunxin|talk]]) 12:53, 25 December 2021 (UTC)<br />
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If Ruru was annihilated, would it be easy to know the conceited plan of Gaoche?" (“Ruru”,: an alias for Rouran Khaganate, also known as "Ruirui" and "Ruru". The name of an ancient northern minority group in the region of China.--[[User:Luo Anyi|Luo Anyi]] ([[User talk:Luo Anyi|talk]]) 11:44, 26 December 2021 (UTC)<br />
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==罗安怡 Luó Ānyí 英语语言文学（英美文学） 女 202120081511==<br />
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高车：亦称“狄历”、“敕勒”、“铁勒”、“丁零”。 我国古代北方少数民族名。)意谓因对家事或国事的顾念而担忧。<br />
Gao Che: also known as Di Li, Cile, Tie Le, Ding Zero. The name of a minority group in the north of China in ancient times). It means to worry about family or national affairs.--[[User:Luo Anyi|Luo Anyi]] ([[User talk:Luo Anyi|talk]]) 11:39, 26 December 2021 (UTC)<br />
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==罗曦 Luó Xī 英语语言文学（英美文学） 女 202120081512==<br />
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这里指家庭需要照顾的人或事。<br />
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垂花门──旧时较为讲究的四合院二门。门顶如屋顶式样，其四角和前后多有下垂的雕花，故称。<br />
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This refers to the person or thing that family members need to take care of. <br />
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Floral-Pendant Gates: It was the second gate of a courtyard house with exquisite decoration in the old days. The top of the door was like a roof, with drooping carvings on the back and front of four corners, so it is called "Floral-Pendant Gates".--[[User:Ma Xin|Ma Xin]] ([[User talk:Ma Xin|talk]]) 11:25, 22 December 2021 (UTC)<br />
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==马新 Mǎ Xīn 外国语言学及应用语言学 女 202120081513==<br />
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超手游廊──亦作“超手回廊”、“抄手游廊”。房廊像两手笼入袖筒，两袖成环形状，故称。<br />
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Verandah Chao Shou —— also known as "Corridor Chao Shou" and "Cross Hand Verandah". Its gallery looked like a two-handed cage into the sleeves, and the two sleeves formed a ring shape, so it was called "Verandah Chao Shou".--[[User:Ma Xin|Ma Xin]] ([[User talk:Ma Xin|talk]]) 07:37, 20 December 2021 (UTC)<br />
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Verandah Chao Shou: It was also known as “Corridor Chao Shou” or “Verandah Cross Hand”. Its gallery was similar to a two-handed cage into the two sleeves which formed a ring shape, so it was called “Verandah Chao Shou”. --[[User:Mao Yawen|Mao Yawen]] ([[User talk:Mao Yawen|talk]]) 06:26, 22 December 2021 (UTC)<br />
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==毛雅文 Máo Yǎwén 英语语言文学（英美文学） 女 202120081514==<br />
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穿山游廊──指与厅房两边山墙门通连的回廊。以其可由山墙门穿行，故称。 山：即房屋两侧的山墙。<br />
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Chuan Shan You Lang: A veranda or corridor connected with the gable doors on both sides of the hall. People can pass through the corridor after entering into the gable doors, so this kind of corridor is called such a name. Shan: The gable doors on both sides of a house.--[[User:Mao Yawen|Mao Yawen]] ([[User talk:Mao Yawen|talk]]) 13:22, 19 December 2021 (UTC)<br />
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Chuan Shan You Lang: It refers to the corridor connected to the door of the wall on either side of the room. People can pass through the corridor after entering into the gable doors, so this kind of corridor is called such a name. Shan: The gable doors on both sides of a house.--[[User:Mao You|Mao You]] ([[User talk:Mao You|talk]]) 13:33, 19 December 2021 (UTC)<br />
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==毛优 Máo Yōu 俄语语言文学 女 202120081515==<br />
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“第一个”六句──这是对迎春形象的描写。 微丰：稍胖。 腮凝新荔：形容腮帮子像荔枝般的红润。<br />
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The first six lines - It is a description of Yingchun's image. Wei Feng: Slightly fat. Sai Ning Xin Li：The cheeks are as red as lychees.--[[User:Mao You|Mao You]] ([[User talk:Mao You|talk]]) 13:29, 19 December 2021 (UTC)<br />
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The first six lines - It is a description of Yingchun's image. Wei Feng: Slightly fat. Sai Ning Xin Li：The cheeks are as red and shiny as lychees.--[[User:Mou Yixin|Mou Yixin]] ([[User talk:Mou Yixin|talk]]) 07:21, 20 December 2021 (UTC)<br />
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==牟一心 Móu Yīxīn 英语语言文学（英美文学） 女 202120081516==<br />
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鼻腻鹅脂：形容鼻端像鹅脂般光润。<br />
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“第二个”七句──这是对探春形象的描写。 削肩：俗称溜肩。<br />
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Bi Ni E Zhi: an idiom to describe someone’s tip of nose is as shiny and smooth as goose grease.<br />
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“The second” seven lines —— this is a depiction of the look of Tanchun. Rounded shoulders: commonly known as sloping shoulders --[[User:Mou Yixin|Mou Yixin]] ([[User talk:Mou Yixin|talk]]) 07:18, 20 December 2021 (UTC)<br />
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Bi Ni E Zhi: a Chiniese idiom to describe someone’s tip of nose is as shiny and smooth as goose grease.<br />
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“The second” seven lines —— this is a depiction of Tanchun'image. Cuted shoulders: commonly known as sloping shoulders--[[User:Peng Ruixue|Peng Ruixue]] ([[User talk:Peng Ruixue|talk]]) 07:00, 25 December 2021 (UTC)<br />
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==彭瑞雪 Péng Ruìxuě 法语语言文学 女 202120081517==<br />
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倾斜的双肩。古人以为美人肩。<br />
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长挑身材：瘦高的身材。 鸭蛋脸儿：犹如鸭蛋似的长圆形脸盘。<br />
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Sloping shoulders. The ancients considered these to be the shoulders of beauty.<br />
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Long, tall figure: a tall, thin figure. Duck egg face: an oblong face like a duck egg.--[[User:Peng Ruixue|Peng Ruixue]] ([[User talk:Peng Ruixue|talk]]) 06:32, 20 December 2021 (UTC)<br />
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Sloping shoulders. The ancient people considered such shoulders as the shoulders of beauty.<br />
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Long, tall figure: a tall, thin figure. Duck egg face: an oblong face like a duck egg--[[User:Qing Jianan|Qing Jianan]] ([[User talk:Qing Jianan|talk]]) 11:59, 26 December 2021 (UTC)<br />
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==秦建安 Qín Jiànān 外国语言学及应用语言学 女 202120081518==<br />
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俊眼修眉：秀美的眼睛，长长的秀眉。 顾盼神飞：左顾右盼，目光炯炯，神采飞扬。 文彩精华：光彩照人，精神十足。<br />
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Jun Yan Xiu Mei: charming eyes with long and delicate eyebrows. Gu Pan Shen Fei: looking left and right, with shining eyes and soaring spirit. Wen Cai Jing Hua: being radiant and full of energy.--[[User:Qing Jianan|Qing Jianan]] ([[User talk:Qing Jianan|talk]]) 11:58, 26 December 2021 (UTC)<br />
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Jun Yan Xiu Mei: beautiful eyes with long and delicate eyebrows. Gu Pan Shen Fei: looking left and right, with shining eyes and soaring spirit. Wen Cai Jing Hua: being radiant and full of energy.--[[User:Qiu Tingting|Qiu Tingting]] ([[User talk:Qiu Tingting|talk]]) 01:21, 22 December 2021 (UTC)<br />
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==邱婷婷 Qiū Tíngtíng 英语语言文学（语言学）女 202120081519==<br />
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见之忘俗：意谓别人见了就会忘了俗气，变得高雅起来。形容探春一身高雅之气。<br />
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“第三个”两句──这是对惜春形象的描写。<br />
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To see is to forget vulgarity: It means that when others see something or someone will forget the secular atmosphere and become more elegant. In this sentence, it describes Tanchun has a great elegant temperament.<br />
"The third" two sentences ─ thses are the description of the image of Xi Chun.--[[User:Qiu Tingting|Qiu Tingting]] ([[User talk:Qiu Tingting|talk]]) 02:50, 20 December 2021 (UTC)<br />
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Jian Zhi Wang Su: It means that others will forget the vulgarity and become elegant when they see it. It is used to describe Tanchun's elegance. <br />
The two sentences containing “ the third” — are the image depiction of Sichun.--[[User:Rao Jinying|Rao Jinying]] ([[User talk:Rao Jinying|talk]]) 12:27, 19 December 2021 (UTC)<br />
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==饶金盈 Ráo Jīnyíng 英语语言文学（语言学） 女 202120081520==<br />
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形容惜春年纪尚小，身材和容貌都还没有发育成熟。<br />
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人参养荣丸──以人参、当归、黄芪、陈皮、白芍、熟地、桂心等配制而成的丸药，主治脾胃气血亏虚等症。<br />
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It is used to describe the Xichun, who is still young and body and appearance are not developed.<br />
Ginseng Yangrong Pill- A pill made of ginseng, angelica, astragalus, Chen Pi, Bai Shao, Shu Di, Gui Xin, etc., mainly used for treating deficiency of qi and blood in the spleen and stomach.--[[User:Rao Jinying|Rao Jinying]] ([[User talk:Rao Jinying|talk]]) 12:22, 19 December 2021 (UTC)<br />
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It is used to depict Xichun, who is still in her young age and underdeveloped stature as well as appearance.<br />
Ginseng tonic bolus- a sort of pill composed of ginseng, Angelica sinensis, astragalus, tangerine peel, white paleontology root, rehmannia glutinousa, laurel heart, etc. is mainly used to treat diseases such as deficiency in spleen, stomach, qi as well as blood.--[[User:Shi Liqing|Shi Liqing]] ([[User talk:Shi Liqing|talk]]) 13:00, 19 December 2021 (UTC)<br />
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==石丽青 Shí Lìqīng 英语语言文学（英美文学） 女 202120081521==<br />
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荣：中医指血脉。 养荣丸：似有双关之意：除了保养血脉之意外，还有保养荣誉之意，与薛宝钗的“冷香丸”相对，以寓二人的不同性格。<br />
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“Rong” refers to blood vessel in the field of traditional Chinese medicine. Tonic bolus embraces double meaning. Apart from the maintenance of blood, it also boasts the function of maintaining the honor, which is opposite to “Cold Fragrant Pellet” of Xue Baochai. This is the revelation of different personalities between these two people.--[[User:Shi Liqing|Shi Liqing]] ([[User talk:Shi Liqing|talk]]) 12:45, 19 December 2021 (UTC)<br />
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“Rong” refers to blood vessel in the field of traditional Chinese medicine. Tonic bolus embraces double meanings. Apart from the maintenance of blood vessel, it also boasts the function of maintaining the honor, which is opposite to “Cold Fragrant Pellet” of Xue Baochai. This is the revelation of different personalities between these two people.--[[User:Sun Yashi|Sun Yashi]] ([[User talk:Sun Yashi|talk]]) 02:27, 20 December 2021 (UTC)<br />
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==孙雅诗 Sūn Yǎshī 外国语言学及应用语言学 女 202120081522==<br />
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窄褃(kèn掯)袄──即紧身妖。 窄：瘦小。 褃：是上衣前后幅两侧接缝部分的名称。<br />
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Narrow ken coat ── is a tight quilted jacket.Narrow: thin.Ken: It is the name of the seams on the front and rear sides of the jacket.--[[User:Sun Yashi|Sun Yashi]] ([[User talk:Sun Yashi|talk]]) 02:18, 20 December 2021 (UTC)<br />
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Narrow Ken coat ── namely tight quilted jacket. Narrow: thin. Ken: the name of the seams on the front and back of the jacket. --[[User:Wang Lifei|Wang Lifei]] ([[User talk:Wang Lifei|talk]]) 08:50, 21 December 2021 (UTC)<br />
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==王李菲 Wáng Lǐfēi 英语语言文学（英美文学） 女 202120081523==<br />
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仪门──原指官署大门里的第二道正门。之所以称“仪门”，是因为官员至此门必须整齐仪表。<br />
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Etiquette Gate ── originally refers to the second main gate in the main gate of the government office. The reason why it is called "Etiquette Gate" is because the officers must be well-groomed when he arrives. --[[User:Wang Lifei|Wang Lifei]] ([[User talk:Wang Lifei|talk]]) 08:38, 21 December 2021 (UTC)<br />
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Etiquette Gate--Originally, it refers to the second main door in the gate of the official office. The reason why it is called "Etiquette Gate" is that officials must be neat and tidy when they arrive at this gate.--[[User:Wang Yifan21|Wang Yifan21]] ([[User talk:Wang Yifan21|talk]]) 06:30, 23 December 2021 (UTC)<br />
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==王逸凡 Wáng Yìfán 亚非语言文学 女 202120081524==<br />
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《明会典·礼部十七·官员礼》：“新官到任之日……先至神庙祭祀毕，引至仪门前下马，具官服，从中道入。”<br />
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The Ming Canon - Rituals XVII - Official Rites: "On the day the new official arrives, he first goes to the temple to offer sacrifice, after which he is led to dismount in front of the ceremonial gate, with his official uniform, and enters from the middle road."--[[User:Wang Yifan21|Wang Yifan21]] ([[User talk:Wang Yifan21|talk]]) 06:27, 23 December 2021 (UTC)<br />
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==王镇隆 Wáng Zhènlóng 英语语言文学（英美文学） 男 202120081525==<br />
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又《江宁府志·建制·官署》：“其制大门之内为仪门，仪门内为莅事堂。”后加以引申，大家府第的第二道正门也称仪门。<br />
"Jiangning official records，organizational system，official ": "inside the system gate is the instrument gate, and inside the instrument gate is the visiting hall." Later extended, the second main gate of everyone's house is also called Yimen. --[[User:Wang Zhenlong|Wang Zhenlong]] ([[User talk:Wang Zhenlong|talk]]) 11:12, 22 December 2021 (UTC)<br />
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"Jiangning official records，organizational system，government ": "inside the system gate is the etiquette gate, and inside the etiquette gate is the visiting hall." Later extended, the second main gate of mansion is also called etiquette gate.--[[User:Wei Yiwen|Wei Yiwen]] ([[User talk:Wei Yiwen|talk]]) 09:52, 26 December 2021 (UTC)<br />
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==卫怡雯 Wèi Yíwén 英语语言文学（英美文学） 女 202120081526==<br />
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鹿顶耳房钻山──这里是指在正房两侧与东西厢房北侧之间建有两座平顶耳房，并在耳房山墙上开门。如此则使正房、东西耳房、东西厢房皆可相通，便于穿行，所以下句说“四通八达”。<br />
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Luding Erfang Zuanshan—— it refers to two flat top ear rooms which are situated on the two sides of principal room and northern side of east and west wings and open up the door on the gable. Then it makes a connection between principal room, east and west ear room as well as east and west wings so that it is more convenient for people to pass. It is called “extend in all directions” as described below.--[[User:Wei Yiwen|Wei Yiwen]] ([[User talk:Wei Yiwen|talk]]) 02:14, 22 December 2021 (UTC)<br />
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Luding Erfang Zuanshan——it refers to two flat-top ear rooms which are situated on the two sides of principal room and northern side of east and west wings and open up the door on the gable of ear rooms. Then it makes a connection between principal room, east and west ear rooms as well as east and west wings so that it is very convenient for people to pass. Therefore, it is called “accessible in all directions” as described below.--[[User:Wei Chuxuan|Wei Chuxuan]] ([[User talk:Wei Chuxuan|talk]]) 07:44, 22 December 2021 (UTC)<br />
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==魏楚璇 Wèi Chǔxuán 英语语言文学（英美文学） 女 202120081527==<br />
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鹿顶：亦作“盝顶”。即平屋顶。 耳房：紧靠正房或厢房两侧并利用其山墙建造的房屋。<br />
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Luding: flat roof. Ear room: a room built by using a gable on either side of a principle room or wing-room.--[[User:Wei Chuxuan|Wei Chuxuan]] ([[User talk:Wei Chuxuan|talk]]) 02:00, 22 December 2021 (UTC)<br />
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Lu Ding: this means flat roof. Er Fang: a room built by using the gable on both sides of a principle room or wing-room.--[[User:Wei Zhaoyan|Wei Zhaoyan]] ([[User talk:Wei Zhaoyan|talk]]) 12:13, 22 December 2021 (UTC)<br />
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==魏兆妍 Wèi Zhàoyán 英语语言文学（英美文学） 女 202120081528==<br />
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因其位于正房两侧，犹如人的两只耳朵，故称。 钻山：指打通房屋两侧的山墙，以与相邻的房屋或回廊相通。<br />
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As they are located on both sides of the main house just like people’s ears, they are called “wings”. Zuan Shan: this means breaking through the gables on both sides of the house to connect to adjacent houses and cloisters.--[[User:Wei Zhaoyan|Wei Zhaoyan]] ([[User talk:Wei Zhaoyan|talk]]) 07:45, 20 December 2021 (UTC)<br />
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Because they are located on both sides of the main house just like people’s ears, they are called “wings”. Zuan Shan: this means breaking through the gables on both sides of the house to connect to adjacent houses and cloisters. --[[User:Wu Jingyue|Wu Jingyue]] ([[User talk:Wu Jingyue|talk]]) 05:48, 21 December 2021 (UTC)<br />
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==吴婧悦 Wú Jìngyuè 俄语语言文学 女 202120081529==<br />
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赤金九龙青地大匾──以赤金涂饰的九条雕龙为边框的黑底大匾。 九龙：古代传说龙生九子，性格各异。但说法各异。<br />
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The horizontal board, which is decorated with pink gold, night dragon and tuff - the board is black and is made of motifs of dragon and phoenix. The nine dragons: it is said that, in the ancient time, the dragon had nine sons, whose character were totally different. But there were different ideas about it.--[[User:Wu Jingyue|Wu Jingyue]] ([[User talk:Wu Jingyue|talk]]) 14:02, 19 December 2021 (UTC)<br />
A large plaque with nine dragons painted in red gold on a black background. Nine Dragons: Ancient legend has it that dragons are born with nine sons, each with a different character. However, there are different sayings.--[[User:Wu Yinghong|Wu Yinghong]] ([[User talk:Wu Yinghong|talk]]) 05:36, 22 December 2021 (UTC)<br />
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==吴映红 Wú Yìnghóng 日语语言文学 女 202120081530==<br />
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明·杨慎《升庵外集·动物一·龙生九子》说：“龙生九子不成龙，各有所好：囚牛，平生好音乐，今胡琴头上刻兽是其遗像；<br />
Ming-Yang Shen's "Sheng'an Waiji - Animals I - The Nine Sons of the Dragon" says: "The nine sons of the dragon were born without becoming dragons, but each had his own interests: the prisoner bull, who was good at music in his life, and the beast carved on the head of the huqin today is his posthumous image.<br />
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Yang Shen of Ming Dynasty said in "Sheng an Outside collection · Animal · Long Sheng Jiu Zi" : "Long sheng Jiu Zi is not a dragon, each has his own good points: prisoner ox, good music in his life, the beast carved on the head of Huqin is his portrait; --[[User:Xiao Yiyao|Xiao Yiyao]] ([[User talk:Xiao Yiyao|talk]]) 10:32, 26 December 2021 (UTC)<br />
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==肖毅瑶 Xiāo Yìyáo 英语语言文学（英美文学） 女 202120081531==<br />
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睚毗，平生好杀，金刀柄上龙吞口是其遗像；嘲风，平生好险，今殿角走兽是其遗像；蒲牢，平生好鸣，今钟上兽纽是其遗像；<br />
Yapi, who was easy to kill, has a portrait of a dragon swallowing mouth on the gold hilt. Scene wind, life good risk, this temple corner beast is its portrait; Pu Lao, life good Ming, this bell on the beast new is its portrait; --[[User:Xiao Yiyao|Xiao Yiyao]] ([[User talk:Xiao Yiyao|talk]]) 10:28, 26 December 2021 (UTC)<br />
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Yapi, who likes killing, so the image of a dragon swallowing mouth on the gold hilt is its portrait. Chaofeng, who likes being at risk, so the image of the temple corner beast is its portrait; Pulao, who likes chirping, so the image of the bell on the beast new is its portrait.--[[User:Xie Jiafen|Xie Jiafen]] ([[User talk:Xie Jiafen|talk]]) 11:34, 26 December 2021 (UTC)<br />
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==谢佳芬 Xiè Jiāfēn 英语语言文学（英美文学） 女 202120081532==<br />
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狻猊，平生好坐，今佛座狮子是其遗像；霸下，平生好负重，今碑座兽是其遗像；陛犴，平生好讼，今狱门上狮子头是其遗像；<br />
Suan ni likes sitting all its life , it looks alike a lion, which usually appears in the pedestal of Buddha ; Ba xia likes bearing a heavy burden all its life, so its image usually appears under the stone monuments as a stele monster; Bi'an likes lawsuit all its life, so its image usually appears in the cell doors.--[[User:Xie Jiafen|Xie Jiafen]] ([[User talk:Xie Jiafen|talk]]) 07:02, 20 December 2021 (UTC)<br />
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Suan ni likes sitting all its life , it looks alike a lion, which usually appears in the pedestal of Buddha ; Ba xia likes bearing a heavy burden all its life, so its image usually appears under the stone monuments as a stele monster; Bi'an likes lawsuit all its life, so its image usually appears in the cell doors.--[[User:Xie Qinglin|Xie Qinglin]] ([[User talk:Xie Qinglin|talk]]) 07:25, 20 December 2021 (UTC)<br />
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==谢庆琳 Xiè Qìnglín 俄语语言文学 女 202120081533==<br />
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屓屭，平生好文，今碑两旁龙是其遗像；蚩吻，平生好吞，今殿脊兽头是其遗像。”明·焦竑《玉堂丛语·卷一·文学》则说：<br />
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The clamshell, life is good literature, today the two sides of the monument dragon is its image; Chi kiss, life is good swallow, today the temple ridge beast head is its image." Ming - Jiao Hong "Yu Tang Congye - Volume 1 - Literature" said.--[[User:Xie Qinglin|Xie Qinglin]] ([[User talk:Xie Qinglin|talk]]) 07:24, 20 December 2021 (UTC)<br />
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==熊敏 Xióng Mǐn 英语语言文学（英美文学） 女 202120081534==<br />
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“俗传龙生九子不成龙，各有所好……一曰赑屭，形似龟，好负重，今石碑下龟趺是也；二曰螭吻，形似兽，性好望，今屋上兽头是也；<br />
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“ It is said that the nine sons of dragons are not born into dragons, and each has its own features...One is Bixi shaped like a tortoise, and it is so heavy. It is also a tortoise under the stone tablet; the second is Liwen shaped like a beast, and it is well-known.<br />
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"It is said that the nine sons of dragons are not born into dragons, and each has its own features...One is Bi'xi, whose shapes like a tortoise, and it likes carrying heavy things. It is also a tortoise under the stone tablet; The second is Li'wen, whose shapes like a beast, and it is well-known, It often apperas in roof.--[[User:Xu Minyun|Xu Minyun]] ([[User talk:Xu Minyun|talk]]) 11:39, 26 December 2021 (UTC)<br />
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==徐敏赟 Xú Mǐnyūn 语言智能与跨文化传播研究 男 202120081535==<br />
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三曰蒲牢，形似龙而小，性好叫吼，今钟上纽是也；四曰狴犴，形似虎，有威力，故立于狱门；五曰饕餮，好饮食，故立于鼎盖；<br />
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The third one is Pu'lao, who looks like a dragon but is small and easy to roar, and it often appeared in chime. The fourth was Bi'an, who looks like a tiger and is so powerful that he stand in the door of the prison. The fifth is Tao'tie, like eating food, so stand in tripod cover;--[[User:Xu Minyun|Xu Minyun]] ([[User talk:Xu Minyun|talk]]) 11:32, 26 December 2021 (UTC)<br />
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The third one is Pu'lao, who looks like a dragon but is small and easy to roar, and it often appeared in chime. The fourth was Bi'an, who looks like a tiger and is so powerful that he stands in the door of the prison. The fifth is Tao'tie, like eating food, so stands in tripod cover;--[[User:Yan Jing|Yan Jing]] ([[User talk:Yan Jing|talk]]) 11:47, 26 December 2021 (UTC)<br />
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==颜静 Yán Jìng 语言智能与跨文化传播研究 女 202120081536==<br />
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六曰，性好水，故立于桥柱；七曰睚毗，性好杀，故立于刀环；八曰金猊，形似狮，性好烟火，故立于香炉；<br />
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The sixth is good at water, so it stands on the bridge column; The seventh is called Ya Zi, good at killing, so it stands in the knife ring; The eighth is Jin Ni, like a lion, has a good nature of fireworks, so it stands in the incense burner;--[[User:Yan Jing|Yan Jing]] ([[User talk:Yan Jing|talk]]) 11:04, 26 December 2021 (UTC)<br />
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==颜莉莉 Yán Lìlì 国别 女 202120081537==<br />
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九曰椒图，形似螺蚌，性好闭，故立于门铺首。”明·沈德符《万历野获编·卷七·内阁·龙子》又说：“长沙李文正公在阁，孝宗忽下御札，问龙生九子之详。<br />
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The ninth is called Jiao Tu, which is shaped like a screw and likes to close its mouth, so it is used as decoration on the door. Shen Defu of the Ming Dynasty, in his book ''Wanli Ye Huo, Vol.7, Cabinet, Longzi,'' also said, "When Duke Li Wenzheng of Changsha was in the cabinet, Emperor Xiaozong suddenly got down to ask the details of the birth of nine longzi.<br />
--[[User:Yan Lili|Yan Lili]] ([[User talk:Yan Lili|talk]]) 13:30, 21 December 2021 (UTC)<br />
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When Duke Li Wenzheng of Changsha was in the pavilion, Emperor Xiaozong suddenly sent a royal letter and asked about the details of Long Sheng's nine sons.--[[User:Yan Zihan|Yan Zihan]] ([[User talk:Yan Zihan|talk]]) 07:32, 26 December 2021 (UTC)<br />
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==颜子涵 Yán Zǐhán 国别 女 202120081538==<br />
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文正对云：‘其子蒲牢好鸣，今为钟上钮鼻；囚牛好音，今为胡琴头刻兽；睚眦好杀，今为刀剑上吞口；<br />
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Wen Zheng said , "his son Pu Lao likes roaring，the dragon shaped animal button on the Hong Zhong is its relic; The Qiu Niu loves music all his life. He often squats on the head of the piano to enjoy the music of plucking strings, so his portrait is engraved on the head of the HuQin; Ya Ci is the second child. He is aggressive and likes killing all his life，and swallow a sword with his mouth.--[[User:Yan Zihan|Yan Zihan]] ([[User talk:Yan Zihan|talk]]) 07:29, 26 December 2021 (UTC)<br />
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Wen Zheng said , "his son Pu Lao likes roaring，and often appears on the bell; Qiu Niu loves music all his life,accordingly, he becomes a decoration for music instrument, such as two-stringed bowed violin (huqin); Ya Ci likes fighting and killing，people see him as the patron saint of weapons, so he often appears on the weapons.--[[User:Yang Jiaying|Yang Jiaying]] ([[User talk:Yang Jiaying|talk]]) 08:08, 26 December 2021 (UTC)<br />
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==阳佳颖 Yáng Jiāyǐng 国别 女 202120081540==<br />
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嘲风好险，今为殿阁走兽；狻猊好坐，今为佛座骑象；霸下好负重，今为碑碣石趺；<br />
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Chao Feng loves adventure,he now often appears on the corner on the housetop; Suan Ni loves sitting quietly, his image is often found in temples as the mount of Buddha; Bi xi has the power of strength. He loves to carry heavy stuff to show off his magic energy, so under the stele can people see his appearing.--[[User:Yang Jiaying|Yang Jiaying]] ([[User talk:Yang Jiaying|talk]]) 08:12, 26 December 2021 (UTC)<br />
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Chao Feng loves adventure. It's the small decorative animal on the eaves of the housetop; Suan Ni loves sitting quietly. Its image is often found in temples as the mount of Buddha; Bi xi has the power of strength. He loves to carry heavy stuff to show off his magic energy, so under the stele can people see his appearing.--[[User:Yang Aijiang|Yang Aijiang]] ([[User talk:Yang Aijiang|talk]]) 11:30, 26 December 2021 (UTC)<br />
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==杨爱江 Yáng Àijiāng 英语语言文学（ 语言学） 女 202120081541==<br />
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狴犴好讼，今为狱户首镇压；屓屭好文，今为碑两旁蜿蜒；蚩吻好吞，今为殿脊兽头。’”<br />
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Bi An is eager for justice and righteousness and can distinguish right from wrong. Now they generally stand on both sides of the lobby of the government office to deter those who violate the law and discipline. Bi Xi, which is fond of reading and writing articles, is gentle. And now it’s the animal winding around on both sides of the stele. Chi Wen which has the ability of swallowing fire becomes the beast heads at both ends of the palace roof. --[[User:Yang Aijiang|Yang Aijiang]] ([[User talk:Yang Aijiang|talk]]) 07:24, 26 December 2021 (UTC)<br />
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Bi An is eager for justice and righteousness and can distinguish right from wrong. Now it generally stands on both sides of the lobby of the government office to deter those who violate the law and discipline. Bi Xi, which is fond of reading and writing articles, is gentle. And now it’s the animal winding around on both sides of the stele. Chi Wen, who has the ability of swallowing fire, becomes the beast heads at both ends of the palace roof. --[[User:Yang Kun|Yang Kun]] ([[User talk:Yang Kun|talk]]) 11:14, 26 December 2021 (UTC)<br />
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==杨堃 Yáng Kūn 法语语言文学 女 202120081542==<br />
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此外，明·陈仁锡《潜确类书》、明·胡侍《真珠船·龙生九子》、清·褚人获《坚瓠十集·龙九子》、清·高士奇《天禄识馀·龙种》，对九龙的名称、性格、用途的说法也各不相同，可见出于民间传说。世人多用作装饰，以示祥瑞。<br />
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In addition, the names, characters and uses of the Dragon's nine sons, which can be seen from folklore, are also different in ''Reference Book of Qian Que'' by Chen Renxi of Ming Dynasty, ''Zhenzhu Boat·The Nine Sons of the Dragon'' by Hu Shi of Ming Dynasty,''Jian Hu's Collection-Vol.10·The Nine Sons of the Dragon'' by Chu Renhuo of Qing Dynasty and ''Tian Lu Shi Yu· Dragon Species'' by Gao Shiqi of Qing Dynasty. People often use the images of the Dragon's nine sons as decoration, to show good luck. --[[User:Yang Kun|Yang Kun]] ([[User talk:Yang Kun|talk]]) 03:53, 23 December 2021 (UTC)<br />
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In addition, the names, characters and usages of the Dragon's nine sons, which can be seen from folklore, also differ in ''Reference Book of Qian Que'' by Chen Renxi of Ming Dynasty, ''Zhenzhu Boat·The Nine Sons of the Dragon'' by Hu Shi of Ming Dynasty,''Jian Hu's Collection-Vol.10·The Nine Sons of the Dragon'' by Chu Renhuo of Qing Dynasty and ''Tian Lu Shi Yu· Dragon Species'' by Gao Shiqi of Qing Dynasty. People often use the images of the Dragon's nine sons as decoration show auspiciousness.--[[User:Yang Liuqing|Yang Liuqing]] ([[User talk:Yang Liuqing|talk]]) 11:18, 26 December 2021 (UTC)<br />
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==杨柳青 Yáng Liǔqīng 英语语言文学（英美文学） 女 202120081543==<br />
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万幾宸(chén辰)翰之宝──此为皇帝印章所刻的文字。 万幾：国家纷繁复杂的政务。典出《尚书·虞书·皋陶谟》：“兢兢业业，一日二日万幾。”<br />
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The treature of the emporer's notes of handling affairs──This was the inscription on the emperor's seal. Wanji: Country's compliccated government affairs. It was recorded in ''ShangShu·YuShu·The Srategy of Gao Tao'':"Be cautious and practical. There were thousands of complicated government affairs needed to be handled."<br />
[ ''ShangShu·YuShu·The Srategy of Gao Tao'' were important documents that recorded the plans and deliberations of emperors and ministers.]<br />
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The treasure of the Emperor's seal and ink imprint──This refers to the inscription on the emperor's seal. Wanji: Country's complicated executive affairs. It was what's recorded in ''Gao Tao Mo, Book Yu, The Book of History'', which is "Be cautious and practical. There were thousands of complicated government affairs needed to be handled."--[[User:Ye Weijie|Ye Weijie]] ([[User talk:Ye Weijie|talk]]) 14:31, 26 December 2021 (UTC)<br />
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==叶维杰 Yè Wéijié 国别 男 202120081544==<br />
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孔颖达传云：“幾，微也，言当戒惧万事之微。”意谓尽管政务繁重，也不能忽略任何小事。亦称“万机”。<br />
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Kong Yingda said: "Ji, which refers to the slighest thing, meaning that should be aware of the very small things." It means that despite the arduous government affairs, no small things can be ignored. Also known as "Wan Ji".<br />
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Kong Yingda's biography said: "The word Ji means that before saying something one should fear the smallest of all things." This means that despite the heavy workload of the government, one should not neglect any small matters. It is also referred to as "ten thousand business".--[[User:Yi Yangfan|Yi Yangfan]] ([[User talk:Yi Yangfan|talk]]) 10:00, 26 December 2021 (UTC)Yi Yangfan<br />
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==易扬帆 Yì Yángfān 英语语言文学（英美文学） 女 202120081545==<br />
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典出《汉书·百官公卿表上》：“相国、丞相皆秦官，金印紫绶，掌丞天子，助理万机。”这里是形容皇帝日理万机，政务繁忙。<br />
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The allusion is from ''Han Shu: The List of Hundred Officials（Previous）'': "The Minister of State and the Prime Minister were both Qin officials, with gold seals and purple ribbons, and were in charge of the Emperor and assisted in all affairs." This is a description of the emperor's busy schedule of affairs.--[[User:Yi Yangfan|Yi Yangfan]] ([[User talk:Yi Yangfan|talk]]) 09:24, 25 December 2021 (UTC)Yi Yangfan<br />
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The allusion is from Han Shu: The List of Hundred Officials（Previous）: "The Ministers of State and the Prime Ministers were Qin officials, with golden seals and purple ribbons, and they were in charge of the Emperor and assisted in all affairs." Here is the description of the emperor's busy schedule of affairs.--[[User:Yin Huizhen|Yin Huizhen]] ([[User talk:Yin Huizhen|talk]]) 12:37, 25 December 2021 (UTC)<br />
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==殷慧珍 Yīn Huìzhēn 英语语言文学（英美文学） 女 202120081546==<br />
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宸：“北宸”的省称。即北极星。因皇帝上朝坐北朝南，遂为皇帝的代称。翰：本义是羽毛，因古代以羽毛为笔，引申为墨迹(书写的字)。<br />
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Chen:is short for "Beichen", that is, the Polaris, which was the alternative name of the Emperors because they sat in the North and faced the South in the imperial court. Han：the original meaning is feather, because in ancient times, feather was used as a pen，and it extended the meanig to ink（written words）.--[[User:Yin Huizhen|Yin Huizhen]] ([[User talk:Yin Huizhen|talk]]) 12:29, 22 December 2021 (UTC)<br />
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Chen: short for "Beichen", is the Polaris, which was the alternative name of the Emperors because they sat facing the South in the imperial court. Han：the original meaning was feather, because in ancient times, feather was used as a pen，which extended the meaning to ink（written words）.--[[User:Yin Meida|Yin Meida]] ([[User talk:Yin Meida|talk]]) 08:45, 24 December 2021 (UTC)<br />
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==殷美达 Yīn Měidá 英语语言文学（语言学） 女 202120081547==<br />
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宝：这里指皇帝的印章。上古天子、诸侯均以圭璧制印，故称“宝”。唐以后只有帝、后之印可称“宝”。<br />
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Bao refers to the emperors' seals. In ancient time, emperors and dukes all had their seals made of Gui and Bi(precious jade),from which it derived the name "Bao". After Tang Dynasty, "Bao" was used exclusively by the emperors and empresses.--[[User:Yin Meida|Yin Meida]] ([[User talk:Yin Meida|talk]]) 09:02, 24 December 2021 (UTC)<br />
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Bao refers to the emperors' seals in the article. In ancient time, emperors and dukes all had their seals made of Gui and Bi(precious jade),from which it derived the name "Bao". Since Tang Dynasty, "Bao" was used exclusively to describe the seals of the emperors and empresses.--[[User:Yin Yuan|Yin Yuan]] ([[User talk:Yin Yuan|talk]]) 09:32, 25 December 2021 (UTC)<br />
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==尹媛 Yǐn Yuán 英语语言文学（英美文学） 女 202120081548==<br />
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“座上”对联──珠玑：本义为珠宝，引申为名贵装饰。 昭日月：形容装饰光亮如日月。 昭：明亮。<br />
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Couplet "above the seat"─ Gem's original meaning is jewelry, which extended for precious decorations. Zhao Riyue means the decorations as bright as the sun and the moon. Zhao means brightness.--[[User:Yin Yuan|Yin Yuan]] ([[User talk:Yin Yuan|talk]]) 12:21, 22 December 2021 (UTC)<br />
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Couplet "above the seat"─ Gem's original meaning is jewelry, which extended for precious decorations. Zhao Riyue means the decorations as bright as the sun and the moon. Zhao means brightness.--[[User:Zhan Ruoxuan|Zhan Ruoxuan]] ([[User talk:Zhan Ruoxuan|talk]]) 12:44, 26 December 2021 (UTC)<br />
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==詹若萱 Zhān Ruòxuān 英语语言文学（英美文学） 女 202120081549==<br />
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黼黻(fǔ fú府服)：泛指绣有华美花纹的礼服。《晏子春秋·谏下十五》：“公衣黼黻之衣，素绣之裳，一衣而王采具焉。” 黼：黑白相间的斧形花纹。<br />
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Fu Fu (f incarnation fu fu clothing) : refers to embroidered with colorful decorative pattern of the dress. "Yan Zi Spring And Autumn · Jian Next 15" : "I: gongclothes: both clothes, plain embroidered clothes, one dress and Wang CAI yan." I: black and white axe shape.--[[User:Zhan Ruoxuan|Zhan Ruoxuan]] ([[User talk:Zhan Ruoxuan|talk]]) 12:43, 26 December 2021 (UTC)<br />
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Fu Fu (f incarnation fu fu clothing) : refers to embroidered with colorful decorative pattern of the dress. "Yan Zi Spring And Autumn · Jian Next 15" : "I: gongclothes: both clothes, plain embroidered clothes, one dress and Wang CAI yan." I: black and white axe shape.--[[User:Zhang Qiuyi|Zhang Qiuyi]] ([[User talk:Zhang Qiuyi|talk]]) 11:18, 26 December 2021 (UTC)<br />
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==张秋怡 Zhāng Qiūyí 亚非语言文学 女 202120081550==<br />
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黻：黑与青相间的亚形花纹。 焕烟霞：形容绣服放射出如烟如霞的光彩，绚丽多姿。 焕：放射光彩。此联形容主宾皆珠光宝气，服饰华丽。<br />
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Fu: black and blue sub-pattern. Huan Yanxia: to describe the embroidered clothing radiates the radiance of smoke and clouds. Huan: radiate brilliance. This couplet describes the guests of honor are glittering jewels, gorgeous clothes.--[[User:Zhang Qiuyi|Zhang Qiuyi]] ([[User talk:Zhang Qiuyi|talk]]) 11:17, 26 December 2021 (UTC)<br />
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Fu: black and green sub shaped pattern. Huanyanxia: to describe that the embroidered clothes radiate smoke like glow, gorgeous and colorful. Huan: radiate brilliance. This couplet describes the guests of honor are glittering jewels, gorgeous clothes.--[[User:Zhang Yang|Zhang Yang]] ([[User talk:Zhang Yang|talk]]) 11:30, 26 December 2021 (UTC)<br />
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==张扬 Zhāng Yáng 国别 男 202120081551==<br />
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汝窑美人觚(gū孤)──出自著名汝窑的一种盛酒器。 汝窑：即北宋汝州瓷窑。因其青瓷器皿质量特佳，多为贡品，故名闻天下，后世成为收藏珍品。<br />
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Ruyao Beauty Gu-- A wine container from the famous Ruyao. Ruyao: Ruzhou porcelain kiln in the Northern Song Dynasty. Because its celadon ware is of excellent quality and mostly tribute, it is famous all over the world and has become a collection treasure in future generations.--[[User:Zhang Yang|Zhang Yang]] ([[User talk:Zhang Yang|talk]]) 09:59, 26 December 2021 (UTC)<br />
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Beauty porcelain（mei ren gu）-- A wine container from the famous Ruyao. Ru kiln: Ruzhou porcelain kiln in the Northern Song Dynasty. Because its celadon ware is of excellent quality and mostly tribute, it is famous all over the world and has become a collection treasure in future generations.--[[User:Zhang Yiran|Zhang Yiran]] ([[User talk:Zhang Yiran|talk]]) 14:34, 26 December 2021 (UTC)<br />
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==张怡然 Zhāng Yírán 俄语语言文学 女 202120081552==<br />
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美人觚：因其体长腰细，形似美人，故名。<br />
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椅搭──又称“椅披”。是一种长方形织物的椅用装饰品。因搭或披在椅背和椅坐上，故名。<br />
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Beauty porcelain（mei ren gu）: so named because of its long waist and resemblance to a beauty. <br />
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Chairs（Yi da） - also known as 'chairs'（Yi pi）. This is a rectangular fabric chair upholstery. The name is derived from the fact that it is placed on the back of the chair or on the seat of the chair.<br />
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Beauty porcelain ：so named because of its long body and slender waist and resemblance to a beauty. <br />
Chairs（Yi da） - also known as 'chairs'（Yi pi）. This is a rectangular fabric chair upholstery. The name is derived from the fact that it is placed on the back of the chair or on the seat of the chair.--[[User:Zhong Yifei|Zhong Yifei]] ([[User talk:Zhong Yifei|talk]]) 10:12, 26 December 2021 (UTC)<br />
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==钟义菲 Zhōng Yìfēi 英语语言文学（英美文学） 女 202120081553==<br />
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掐牙——是一种装饰性衣服花边。即以锦缎等折叠成细条，镶嵌在衣边上，以为美观。 掐：嵌入之意。<br />
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Qia Ya— a kind of decorative lace. That is to fold brocade into thin strips and inlay them on the edge of the clothes to look beautiful. Qia: embedded.--[[User:Zhong Yifei|Zhong Yifei]] ([[User talk:Zhong Yifei|talk]]) 12:30, 19 December 2021 (UTC)<br />
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Qia Ya— a kind of decorative lace. That is to fold brocade into thin strips and inlay them on the edge of the clothes to look beautiful. Qia: it means “embedded”.--[[User:Zhong Yulu|Zhong Yulu]] ([[User talk:Zhong Yulu|talk]]) 08:04, 20 December 2021 (UTC)<br />
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==钟雨露 Zhōng Yǔlù 英语语言文学（英美文学） 女 202120081554==<br />
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牙：即“牙子”。器物突出的边沿。<br />
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《四书》──即《论语》、《孟子》、《大学》、《中庸》(后两种原为《礼记》中的两篇)。<br />
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“Ya”: also called "Ya Zi" in Chinese. It means the protruding edge of an object. <br />
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''The Four Books'' includes— ''The Confucian Analects'', ''The Works of Mencius'', ''The Great Learning'', and ''The Doctrine of the Mean'' (the latter two were originally two books from ''The Book of Rites'').--[[User:Zhong Yulu|Zhong Yulu]] ([[User talk:Zhong Yulu|talk]]) 12:22, 19 December 2021 (UTC)<br />
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''The Four Books'' includes— ''The Confucian Analects'', ''The Works of Mencius'', ''The Great Learning'', and ''The Doctrine of the Mean'' (the latter two were originally two books chosen from ''The Book of Rites'').--[[User:Zhou Jiu|Zhou Jiu]] ([[User talk:Zhou Jiu|talk]]) 01:18, 22 December 2021 (UTC)<br />
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==周玖 Zhōu Jiǔ 英语语言文学（英美文学） 女 202120081555==<br />
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宋代朱熹选定并定名《四书》，遂成为元、明、清三代科举考试的必读之书。<br />
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抹额：原指束在额上的头巾。其起源似乎很早。<br />
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During the Song dynasty, Zhu xi chose and named ''Four Books'' which became the required readings in Imperial Competitive Examinations of Yuan dynasty, Ming dynasty, and Qing dynasty.<br />
Mo E: It originally refers to a kerchief tied around the forehead. Its origin seems to be very early.<br />
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During the Song dynasty, Zhu xi chose and named ''Four Books'' which became the required readings in Imperial Competitive Examinations of Yuan dynasty, Ming dynasty, and Qing dynasty.<br />
Headband: It originally refers to a kerchief tied around the forehead. Its origin seems to be very early.--[[User:Zhou Junhui|Zhou Junhui]] ([[User talk:Zhou Junhui|talk]]) 01:25, 22 December 2021 (UTC)<br />
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==周俊辉 Zhōu Jùnhuī 法语语言文学 女 202120081556==<br />
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宋·高承《事物纪原·戎容兵械·抹额》引《二仪实录》曰：“禹娶涂山之夕，大风雷电，中有甲卒千人，其不披甲者，以红绡帕抹其头额，云海神来朝。<br />
Song Gaocheng quoted the ''Record of Eryi'' in his book ''Things Documentary-Armed Soldiers-Headband'': “When Yu married the Tushan lady, there was a strong wind, thunder and rain. There were a thousand soldiers in gear, and those who were not wore equipment bound a thin red handkerchiefs on their foreheads in anticipation of the arrival of the god of clouds."--[[User:Zhou Junhui|Zhou Junhui]] ([[User talk:Zhou Junhui|talk]]) 01:21, 22 December 2021 (UTC)<br />
Song Gaocheng quoted the ''Record of Eryi'' in his book ''Things Documentary-Armed Soldiers-Headband'': “When Yu married Tu Shan,under the cicumstance of a strong wind, together with thunder and lighting, there were a thousand soldiers who were not equipped with armor but decorated their foreheads with a thin red handkerchiefs in anticipation of the arrival of the god of clouds.--[[User:Zhou Qiao1|Zhou Qiao1]] ([[User talk:Zhou Qiao1|talk]]) 12:42, 22 December 2021 (UTC)<br />
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==周巧 Zhōu Qiǎo 英语语言文学（语言学） 女 202120081557==<br />
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禹问之，对曰：‘此武士之首服也。’秦始皇至海上，有神朝，皆抹额、绯衫、大口袴。侍卫自此抹额，遂为军容之服。<br />
Yu asked and replied, "this is the surrender of a warrior." When the first emperor of Qin went to the sea, there was the divine Dynasty where people wore red upper garment and loose trousers and decorated with smear. Since then, bodyguards decorated their forehead with smear, which has become a kind of military costume.--[[User:Zhou Qiao1|Zhou Qiao1]] ([[User talk:Zhou Qiao1|talk]]) 13:17, 19 December 2021 (UTC)<br />
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Yu asked, and said, ‘this is the first choice for samurai. When Qin Shihuang arrived at the sea, there was a dynasty, and all the people here wiped their foreheads, crimson shirts, and hakama. Since then, the guards wiped their foreheads and became the uniforms of the military.--[[User:Zhou Qing|Zhou Qing]] ([[User talk:Zhou Qing|talk]]) 11:04, 22 December 2021 (UTC)<br />
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==周清 Zhōu Qīng 法语语言文学 女 202120081558==<br />
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可知原为军人的标志。后普及到一般男子，平民以布巾束发，富人用金箍束发，兼为头饰。<br />
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箭袖──亦称“箭衣”。<br />
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It can be seen that it was originally a symbol of a soldier. Later, it was promoted to be used by ordinary men. Common people used cloth towels to tie their hair, and the rich used gold hoops to tie their hair, which also served as headwear. <br />
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Arrow sleeve ─ ─ also known as "arrow suit".<br />
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It was a sign of soldiers. Later, it was popularized to ordinary men. Common people tied their hair with cloth towels, and the rich tied their hair with gold hoops, which was also used as headwear. <br />
Arrow Sleeves - also known as "Arrow Suit".--[[User:Zhou Xiaoxue|Zhou Xiaoxue]] ([[User talk:Zhou Xiaoxue|talk]]) 05:13, 24 December 2021 (UTC)<br />
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==周小雪 Zhōu Xiǎoxuě 日语语言文学 女 202120081559==<br />
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是一种窄袖长袍。其袖口呈斜切状，朝手背的袖口长，朝手心的袖口短，便于射箭，故名。其斜袖口又形似马蹄，故又称马蹄袖。<br />
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It's a kind of robe with narrow sleeves. Its cuffs were in a diagonal cut shape. The cuffs facing the back of the hand are long and the cuffs facing the palm are short, which is convenient for archery, so it is named Arrow Sleeves. Its oblique cuff is also shaped like a horseshoe, so it is also called horseshoe sleeve.--[[User:Zhou Xiaoxue|Zhou Xiaoxue]] ([[User talk:Zhou Xiaoxue|talk]]) 05:55, 20 December 2021 (UTC)<br />
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It's a kind of robe with narrow sleeves. Its cuffs were in diagonal cut shape. The cuffs facing the back of the hand are long and the cuffs facing the palm are short, which is convenient for archery, so it is named Arrow Sleeves. Its oblique cuff is also shaped like horseshoe, so it is also called horseshoe sleeve.--[[User:Zhu Suzhen|Zhu Suzhen]] ([[User talk:Zhu Suzhen|talk]]) 14:08, 26 December 2021 (UTC)<br />
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==朱素珍 Zhū Sùzhēn 英语语言文学（语言学） 女 202120081561==<br />
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后成为一种服式，不射箭的男子也穿。<br />
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“倒像”两句──似有双关之意：一者暗指贾宝玉的化身神瑛侍者在太虚幻境用甘露浇灌林黛玉的化身绛珠仙草；<br />
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Later, it became a sort of clothing style, which can also worn by men who did not shoot arrows. The two sentences of "inverted image" seem to have a double meaning: one alluded to Jia Baoyu’s incarnation Shenying waiter watering Lin Daiyu’s incarnation—— the celestial grass with nectar in the Tai Xu fantasy.<br />
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Later, it became a kind of clothing style, which was also worn by men who did not shoot arrows. <br />
Two sentences of "inverted image" -- there seems to be a pun: one implies that Jia Baoyu's incarnation Shenying waiter watered Lin Daiyu's incarnation Jiangzhu fairy grass with nectar in Taixu fantasy;--[[User:Zou Yueli|Zou Yueli]] ([[User talk:Zou Yueli|talk]]) 11:03, 20 December 2021 (UTC)<br />
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==邹岳丽 Zōu Yuèlí 日语语言文学 女 202120081562==<br />
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再者隐寓二人心有灵犀一点通，一见锺情。下文贾宝玉说“这个妹妹我曾见过的”、“心里倒像是远别重逢的一般”，其用意同此。<br />
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In addition, it implies that the two people share the same heartand fall in love at first sight. Below, Jia Baoyu said that "I have seen this sister" and "I feel like I am far from meeting again". His intention is the same. <br />
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==Nadia 202011080004==<br />
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请安──这里指的是清代一种见面问好的特殊礼仪：<br />
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==Mahzad Heydarian 玛莎 202021080004==<br />
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男子须在口称“请某某安”的同时，右膝弯曲或跪地(俗称打千)；<br />
A man must bend his right knee or kneel on the ground while showing respect.<br />
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==Mariam Toure 2020GBJ002301==<br />
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女子则在口称“请某某安”的同时，双手扶左膝，右腿微屈，身体半蹲。<br />
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==Rouabah Soumaya 202121080001==<br />
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寄名锁──旧时父母为保佑幼儿长命百岁，让幼儿作僧、道的“寄名”弟子，并在幼儿项下悬挂锁形饰物，谓之“寄名锁”。<br />
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==Muhammad Numan 202121080002==<br />
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面如傅粉──语本南朝宋·刘义庆《世说新语·容止》：<br />
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==Atta Ur Rahman 202121080003==<br />
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“何平叔(晏)美姿仪，面至白。<br />
"Uncle He Ping's face is white and beautiful.<br />
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==Muhammad Saqib Mehran 202121080004==<br />
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魏明帝疑其傅粉，正夏月，与热汤饼。<br />
Weiming Siuyuy Full, Full Day, with hot soup.<br />
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==Zohaib Chand 202121080005==<br />
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既啖，大汗出，以朱衣自拭，色转皎然。”<br />
Both, but sweat, to Zhu Jiazi, the color turned. "<br />
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==Jawad Ahmad 202121080006==<br />
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(皎然：洁白貌。)原指何晏的脸上好像抹了香粉般洁白。<br />
English: (Jiao Ran: pure and white appearance.) The original means, He Yan's face it's like white as powdered.<br />
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==Nizam Uddin 202121080007==<br />
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引申以泛喻男子姿容洁白秀美。<br />
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==Öncü 202121080008==<br />
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《西江月》二词──即按照《西江月》词牌填写的两首(也称“阕”)词。<br />
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Two words in "Westlake Moon" According，Fill in two poems (also known as "Que") in the poem of "Westlake Moon".--[[User:AkiraJantarat|AkiraJantarat]] ([[User talk:AkiraJantarat|talk]]) 04:45, 21 December 2021 (UTC)<br />
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==Akira Jantarat 202121080009==<br />
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词：原本指歌曲中的文词，后来文词与曲调分离，遂变成文体之一。<br />
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Words: Originally refers to the words in the song. Later, the words and the tune were separated and became one of the styles.--[[User:AkiraJantarat|AkiraJantarat]] ([[User talk:AkiraJantarat|talk]]) 04:33, 21 December 2021 (UTC)<br />
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Word: It originally referred to the words in a song. In time, the words and the tune separated and became one of the styles. --[[User:Benjamin Wellsand|Benjamin Wellsand]] ([[User talk:Benjamin Wellsand|talk]]) 13:14, 19 December 2021 (UTC)<br />
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==Benjamin Wellsand 202111080118==<br />
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但仍须按曲填词，于是发展出许多词牌，每个词牌都有字数、句数、韵脚等规定，还有双调、长调、小令之别。<br />
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However, it is still necessary to fill in the lyrics according to the tune. So many poems have been developed. Each poem has a word count, sentence count, rhymes and other provisions, as well as the difference between two-tone, long tune, and short meter.--[[User:Benjamin Wellsand|Benjamin Wellsand]] ([[User talk:Benjamin Wellsand|talk]]) 13:10, 19 December 2021 (UTC)<br />
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However, it is still necessary to fill in lyrics according to the tune, so many lyric cards have been developed. Each lyric card has regulations on the number of words, sentences, and rhymes, as well as the differences between double tune, long tune, and short meter. --[[User:Asep Budiman|Asep Budiman]] ([[User talk:Asep Budiman|talk]]) 07:58, 21 December 2021 (UTC)<br />
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==Asep Budiman 202111080020==<br />
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故作词谓之“填词”，就是按照词牌的规范填写文字，不可越雷池一步。<br />
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The preceding phrase "filling in words" means to fill in the words in accordance with the specifications of the words and phrases, and do not go beyond those criteria. --[[User:Asep Budiman|Asep Budiman]] ([[User talk:Asep Budiman|talk]]) 07:56, 21 December 2021 (UTC)<br />
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The preceding phrase "filling in words" means to fill in the words in accordance with the specifications of the words and phrases, and do not overstep the prescribed limit. --[[User:EIMONKYAW|EIMONKYAW]] ([[User talk:EIMONKYAW|talk]]) 09:23, 22 December 2021 (UTC)Ei Mon Kyaw<br />
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==Ei Mon Kyaw 202111080021==<br />
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《西江月》就是词牌之一。本书用了不少词牌，以下不再一一注释。<br />
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"Westlake Moon" is one of the poems. This book uses a lot of words, so I won’t annotate them one by one below. --[[User:EIMONKYAW|EIMONKYAW]] ([[User talk:EIMONKYAW|talk]]) 08:59, 22 December 2021 (UTC)Ei Mon Kyaw<br />
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"Westlake Moon" is one of the tune names of poem. There are a lot of tune names in the book, which will not annotated one by one below.--[[User:Chen Jing|Chen Jing]] ([[User talk:Chen Jing|talk]]) 11:49, 26 December 2021 (UTC)</div>Chen Huinihttps://bou.de/u/index.php?title=20211229_homework&diff=13438020211229 homework2021-12-27T06:05:26Z<p>Chen Huini: /* 陈惠妮 Chén Huìnī 英语语言文学（英美文学） 女 202120081479 */</p>
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<div>Quicklinks: [[Introduction_to_Translation_Studies_2021|Back to course homepage]] [https://bou.de/u/wiki/uvu:Community_Portal#Frequently_asked_questions_FAQ FAQ] [https://bou.de/u/wiki/uvu:Community_Portal Manual] [[20210926_homework|Back to all homework webpages overview]] [[20220112_final_exam|final exam page]]<br />
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PLEASE READ [[Joint_translation_terms|Joint translation terms]] <br />
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PLEASE ALSO READ THE PREVIOUS PARTS, AT LEAST THE SENTENCES BEFORE YOUR OWN PART IN CHAPTER 19 [[20210303_culture|1, Mar 3 Chapters 1-4]], [[20210310_culture|2, Mar 10 Chapters 6-7]], [[20210317_culture|3, Mar 17 Chapters 11-13]], [[20210324_culture|4, Mar 24 Chapters 15-17]], [[20210331_culture|5, Mar 31 Chapters 4-7]], [[20210407_culture|6, Apr 7 Chapters 8-10]], [[20210414_culture|7, Apr 14 Chapters 13-15]] , [[20210519_culture|12, May 19 Chapters 17-19]], [[20210929_homework#Hongloumeng|for Sep 29 - rest of HLM Chapter 19]] [[20211013_homework|for Oct 13 - HLM Chapters 20-21]] [[20211020_homework|for Oct 20 - HLM Chapters 21-22]] [[20211027_homework|for Oct 27 - HLM Chapters 23-24]] etc.<br />
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==陈静 Chén Jìng 国别 女 202020080595==<br />
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心较比干多一窍──比干：暴君商(殷)纣王之叔，被誉为圣人。据《史记·殷本纪》载：纣王厌恶比干谏诤不已，怒曰：“吾闻圣人心有七窍。”于是“剖比干，观其心”。<br />
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The heart is one more hole than Bigan. Bigan, the uncle of tyrant Shang King Zhou, is known as a saint. According to Historical Records: Yin Dynasty, King Zhou dislikes the advisement of Bigan, so said with anger,"I heard that a saint has seven hole in his heart." Thus, Bigan was anatomized to observe his heart.--[[User:Chen Jing|Chen Jing]] ([[User talk:Chen Jing|talk]]) 11:44, 26 December 2021 (UTC)<br />
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==蔡珠凤 Cài Zhūfèng 法语语言文学 女 202120081477==<br />
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古人以为心窍越多越聪明，故以“心较比干多一窍” 形容黛玉绝顶聪明。<br />
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病如西子胜三分──西子：即西施。《庄子·天运》说：“西施病心而颦(皱眉)”，益增娇艳。<br />
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The ancients thought that the more the mind, the smarter it was, so they described Daiyu as extremely clever. <br />
Illness like Xi Zi wins three points - Xi Zi: Xi Shi. Zhuangzi Tianyun said: "Xi Shi frowns (frowns) when she is ill", which increases her beauty.--[[User:Zeng Junlin|Zeng Junlin]] ([[User talk:Zeng Junlin|talk]]) 12:01, 26 December 2021 (UTC)<br />
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==曾俊霖 Zēng Jùnlín 国别 男 202120081478==<br />
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故以“病如西子胜三分”形容黛玉病弱而娇美。 胜：胜过，超过。 下面贾宝玉替林黛玉起表字为“颦颦”，亦用西施颦眉之典，但又不敢明说，故编了一套谎活，杜撰了《古今人物通考》书名。<br />
Therefore, Dai Yu is described as weak and beautiful by "sick as Xizi wins three points". Next, Jia Baoyu wrote "Pingping" for Lin Daiyu. He also used the code of Xi shi’s frown, but he didn't dare to say it clearly, so he made up a set of lies and invented the title of the general examination of ancient and modern characters. --[[User:Zeng Junlin|Zeng Junlin]] ([[User talk:Zeng Junlin|talk]]) 12:00, 26 December 2021 (UTC)<br />
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==陈惠妮 Chén Huìnī 英语语言文学（英美文学） 女 202120081479==<br />
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教引嬷嬷──清代专司教导年幼皇子的女子，称“谙达”。后来世家大族也仿效而行。<br />
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“花气袭人”之句：是宋·陆游《村居书喜》中的半句，原诗为七言律诗：<br />
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Jiao Yin Mammy -- a woman who was in charge of teaching the young emperor's son in the Qing Dynasty, known as "Jiuda". Later, the big families followed the suit. <br />
The sentence "flower spirit attacks people" is half of a sentence in "Village Residence Book Xi" by Song · Lu You. The original poem is a seven-word poem: --[[User:Chen Huini|Chen Huini]] ([[User talk:Chen Huini|talk]]) 06:05, 27 December 2021 (UTC)Chen Huini<br />
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==陈湘琼 Chén Xiāngqióng 外国语言学及应用语言学 女 202120081480==<br />
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“红桥梅市晓山横，白塔樊江春水生。花气袭人知骤暖，鹊声穿树喜新晴。坊场酒贱贫犹醉，原野泥深老亦耕。<br />
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==陈心怡 Chén Xīnyí 翻译学 女 202120081481==<br />
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最喜先期官赋足，经年无吏叩柴荆。”意谓因闻到花香，才知天气已经骤然暖和了。第二十三回和二十八回均引作“花气袭人知昼暖”，将“骤”误为“昼”，可能是曹雪芹误记。<br />
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==程杨 Chéng Yáng 英语语言文学（英美文学） 女 202120081482==<br />
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省(xǐ ng醒)——典出《礼记·曲礼上》：“凡为人子之礼，冬温而夏凊，昏定而晨省。”[凊( jìng净)：凉。]<br />
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Xing (pronounced xǐng) – canonical originated from ''The Book of Rites • Qu Li'': "The etiquette of being sons is: make his parents feel warm in winter, cool in the summer, serve them to bed at night, and greet them in the morning. [Jing (pronounced jìng)]--[[User:Cheng Yang|Cheng Yang]] ([[User talk:Cheng Yang|talk]]) 11:27, 26 December 2021 (UTC)<br />
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==丁旋 Dīng Xuán 英语语言文学（英美文学） 女 202120081483==<br />
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意谓子女冬天要为父母焐暖被褥，夏天要为父母扇凉床席，每天早上要向父母请安问好，晚上要服侍父母安寝。泛指子女对父母的孝敬无微不至。故“省”即“晨省”的略称。<br />
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==杜莉娜 Dù Lìnuó 英语语言文学（语言学） 女 202120081484==<br />
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指子女早晨向父母请安问候的礼节。<br />
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第四回 薄命女偏逢薄命郎，葫芦僧判断葫芦案<br />
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==付红岩 Fù Hóngyán 英语语言文学（英美文学） 女 202120081485==<br />
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却说黛玉同姐妹们至王夫人处，见王夫人正和兄嫂处的来使计议家务，又说姨母家遭人命官司等语。因见王夫人事情冗杂，姐妹们遂出来 ,至寡嫂李氏房中来了。原来这李氏即贾珠之妻。<br />
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==付诗雨 Fù Shīyǔ 日语语言文学 女 202120081486==<br />
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珠虽夭亡，幸存一子，取名贾兰，今方五岁，已入学攻书。这李氏亦系金陵名宦之女。父名李守中，曾为国子祭酒；族中男女无不读诗书者。<br />
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Although Bead Merchant had died at an early age, he had the good fortune of leaving behind him a son, to whom the name of Cymbidium Merchant was given. He was, at this period, just in his fifth year, and had already entered school, and applied himself to books. This Silk Plum was also the daughter of an official of note in Gold Mausoleum. Her father's name was Midfielder Plum, who had, at one time, been Imperial Libationer. Among his kindred, men as well as women had all devoted themselves to poetry and letters. --[[User:Fu Shiyu|Fu Shiyu]] ([[User talk:Fu Shiyu|talk]]) 07:24, 25 December 2021 (UTC)<br />
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==高蜜 Gāo Mì 翻译学 女 202120081487==<br />
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至李守中继续以来，便谓“女子无才便是德”，故生了此女，不曾叫他十分认真读书，只不过将些 《女四书》、 《烈女传》读读，认得几个字，记得前朝这几个贤女便了；<br />
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==宫博雅 Gōng Bóyǎ 俄语语言文学 女 202120081488==<br />
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却以纺绩女红为要，因取名为李纨，字宫裁。所以这李纨虽青春丧偶，且居处于膏粱锦绣之中，竟如槁木死灰一般，一概不问不闻，惟知侍亲养子，闲时陪侍小姑等针黹、诵读而已。<br />
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==何芩 Hé Qín 翻译学 女 202120081489==<br />
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今黛玉虽客居于此，已有这几个姑嫂相伴，除老父之外，馀者也就无用虑了。如今且说贾雨村授了应天府，一到任，就有件人命官司详至案下，却是两家争买一婢，各不相让，以致殴伤人命。彼时雨村即拘原告来审。<br />
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==胡舒情 Hú Shūqíng 英语语言文学（语言学） 女 202120081490==<br />
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那原告道：“被打死的乃是小人的主人。因那日买了个丫头，不想系拐子拐来卖的。这拐子先已得了我家的银子，我家小主人原说第二日方是好日，再接入门；<br />
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==黄锦云 Huáng Jǐnyún 英语语言文学（语言学） 女 202120081491==<br />
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这拐子又悄悄的卖与了薛家，被我们知道了，去找拿卖主，夺取丫头。无奈薛家原系金陵一霸，倚财仗势，众豪奴将我小主人竟打死了。凶身主仆已皆逃走，无有踪迹，只剩了几个局外的人。<br />
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But this kidnapper stealthily sold her over again to the Hsueeh family. When we came to know of this, we went in search of the seller to lay hold of him, and bring back the girl by force. But the Hsueeh party has been all along the bully of Chin Ling, full of confidence in his wealth and prestige; and his arrogant menials in a body seized our master and beat him to death.The murderous master and his crew have all long ago made good their escape, leaving no trace behind them, while there only remain several parties not concerned in the affair. --[[User:Huang Jinyun|Huang Jinyun]] ([[User talk:Huang Jinyun|talk]]) 13:37, 25 December 2021 (UTC)<br />
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==黄逸妍 Huáng Yìyán 外国语言学及应用语言学 女 202120081492==<br />
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小人告了一年的状，竟无人作主。求太老爷拘拿凶犯，以扶善良，存殁感激天恩不尽！”雨村听了，大怒道：“那有这等事：打死人竟白白的走了，拿不来的？”<br />
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==黄柱梁 Huáng Zhùliáng 国别 男 202120081493==<br />
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便发签差公人，立刻将凶犯家属拿来拷问。只见案旁站着一个门子，使眼色不叫他发签。雨村心下狐疑，只得停了手。He sent a signature to send the official and immediately tortured the family members of the murderer. Seeing a boy page of the court standing by the case, who didn't ask Yucun to sign. Yucun was suspicious and had to stop.--[[User:Huang Zhuliang|Huang Zhuliang]] ([[User talk:Huang Zhuliang|talk]]) 01:45, 26 December 2021 (UTC)Huang Zhuliang<br />
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He sent a signature to send the official and immediately tortured the family members of the murderer. Seeing a boy page of the court standing by the case, who didn't ask Yucun to sign. Yucun was suspicious and had to stop to do it.--[[User:Jin Xiaotong|Jin Xiaotong]] ([[User talk:Jin Xiaotong|talk]]) 11:17, 26 December 2021 (UTC)<br />
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==金晓童 Jīn Xiǎotóng 202120081494==<br />
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退堂至密室，令从人退去，只留这门子一人伏侍。门子忙上前请安，笑问：“老爷一向加官进禄，八九年来，就忘了我了？”<br />
He retreated to the secret room and ordered everyone to leave the door man alone. The door man is busy forward to ask for his respect, smile to ask: "the master has been adding officials into the salary, eight or nine years, forget me?"--[[User:Jin Xiaotong|Jin Xiaotong]] ([[User talk:Jin Xiaotong|talk]]) 11:20, 26 December 2021 (UTC)<br />
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He retreated to the secret room and ordered everyone to leave except for the door man Menzi. Menzi is busy forward to ask for his respect, smile to ask: "the master has been adding officials into the salary, eight or nine years, forget me?"--[[User:Kuang Yanli|Kuang Yanli]] ([[User talk:Kuang Yanli|talk]]) 01:27, 27 December 2021 (UTC)<br />
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==邝艳丽 Kuàng Yànl 英语语言文学（语言学） 女 202120081495==<br />
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雨村道：“我看你十分眼熟，但一时总想不起来。”门子笑道：“老爷怎么把出身之地竟忘了？老爷不记得当年葫芦庙里的事么？”雨村大惊，方想起往事。<br />
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Yucun said, “You look so familiar, but I can’t remember you at once.” Menzi laughed, “How could you forget your birthplace, my Master? Do you forget what happened in the Gourd Temple?” After listening, Yucun felt surprised, and the remembered the past.--[[User:Kuang Yanli|Kuang Yanli]] ([[User talk:Kuang Yanli|talk]]) 01:22, 27 December 2021 (UTC)<br />
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==李爱璇 Lǐ Àixuán 英语语言文学（语言学） 女 202120081496==<br />
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原来这门子本是葫芦庙里一个小沙弥，因被火之后无处安身，想这件生意倒还轻省，耐不得寺院凄凉，遂趁年纪轻，蓄了发，充当门子。雨村那里想得是他。<br />
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It turned out that the gatekeeper was originally a little monk in Bottle-gourd Temple. Because he had no place to settle down after the temple being burned by the fire, he thought this business was easy and could not bear the desolation of the temple. So he saved his hair and acted as a gatekeeper while he was young. Yue-ts'un didn't think it was him.--[[User:Li Aixuan|Li Aixuan]] ([[User talk:Li Aixuan|talk]]) 07:10, 25 December 2021 (UTC)<br />
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The fact is that this Retainer had been a young monk in the Hu Lu temple, but because of its destruction by fire, he had no place to rest his frame, he remembered how light and easy was, after all, this kind of occupation, and being unable to reconcile himself to the solitude and quiet of a temple, he accordingly availed himself of his years, which were as yet few, to let his hair grow, and become a retainer. Yue-ts'un had had no idea that it was him. --[[User:Li Ruiyang|Li Ruiyang]] ([[User talk:Li Ruiyang|talk]]) 11:03, 26 December 2021 (UTC)<br />
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==李瑞洋 Lǐ Ruìyáng 英语语言文学（英美文学） 女 202120081497==<br />
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便忙携手笑道：“原来还是故人。”因赏他坐了说话。这门子不敢坐。雨村笑道：“你也算贫贱之交了。此系私室，但坐不妨。”门子才斜签着坐下。<br />
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Hastily taking his hand, he smilingly said, "You are, indeed, an old acquaintance!" and then pressed him to take a seat, so as to have a chat with more ease, but the Retainer would not presume to sit down. "Friendships," Yue-ts'un remarked, putting on a smiling expression, "contracted in poor circumstances should not be forgotten! This is a private room, so that if you sat down, what would it matter?" The Retainer thereupon craved permission to take a seat and sat down gingerly.--[[User:Li Ruiyang|Li Ruiyang]] ([[User talk:Li Ruiyang|talk]]) 11:04, 26 December 2021 (UTC)<br />
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==李姗 Lǐ Shān 英语语言文学（英美文学） 女 202120081498==<br />
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雨村道：“方才何故不令发签？”门子道：“老爷荣任到此，难道就没抄一张本省的‘护官符’来不成？”雨村忙问：“何为‘护官符’？”<br />
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Chia Yu-tsun asked, "Why did you not grant me the passport just now?" The doorman answered that "Your Excellency, when you are to assume office here, haven't you hold some relations to a guard officer? " Yu-tsun was confused and thus continued, "guard officer?".--[[User:Li Shan|Li Shan]] ([[User talk:Li Shan|talk]]) 13:30, 25 December 2021 (UTC)<br />
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==李双 Lǐ Shuāng 翻译学 女 202120081499==<br />
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门子道：“如今凡作地方官的，都有一个私单，上面写的是本省最有权势极富贵的大乡绅名姓，各省皆然。倘若不知，一时触犯了这样的人家，不但官爵，只怕连性命也难保呢！<br />
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==李文璇 Lǐ Wénxuán 英语语言文学（英美文学） 女 202120081500==<br />
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所以叫做‘护官符’。方才所说的这薛家，老爷如何惹得他！他这件官司并无难断之处，从前的官府都因碍着情分脸面，所以如此。”<br />
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“So it was called “the amulet of protection from the feudal official. The family Xue we talked just now, we can’t offend them, my lord. His lawsuit had no difficulty, however, the former official had trouble in the relationship, thus causing the situation then.”. --[[User:Li Wenxuan|Li Wenxuan]] ([[User talk:Li Wenxuan|talk]]) 09:46, 25 December 2021 (UTC)<br />
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==李雯 Lǐ Wén 英语语言文学（英美文学） 女 202120081501==<br />
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一面说，一面从顺袋中取出一张抄的“护官符”来，递与雨村看时，上面皆是本地大族名宦之家的俗谚口碑，云：贾不假，白玉为堂金作马。<br />
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==李新星 Lǐ Xīnxīng 亚非语言文学 女 202120081503==<br />
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阿房宫，三百里，住不下金陵一个史。东海缺少白玉床，龙王来请金陵王。丰年好大雪，珍珠如土金如铁。<br />
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==李怡 Lǐ Yí 法语语言文学 女 202120081504==<br />
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雨村尚未看完，忽闻传点，报：“王老爷来拜。”雨村忙具衣冠接迎，有顿饭工夫，方回来问这门子。门子道：“四家皆连络有亲，一损俱损，一荣俱荣。<br />
Yucun has not finished reading, suddenly smell spread point, report: "Wang master came to visit." Yucun hurriedly arranged his clothes to meet him and had a meal before he came back to ask about it. Siemens way: "the four are connected to have relatives, a failure other destroyed, a glory other glory.--[[User:Li Yi|Li Yi]] ([[User talk:Li Yi|talk]]) 06:45, 25 December 2021 (UTC)<br />
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Yucun has not finished reading, but suddenly heard from the messenger saying : "Wang master come to visit." Yucun hurriedly arranged his clothes to welcome him. Only after a meal did he come back to ask Menzi, who said: "the four families are closely connected, so do their honor and failure.--[[User:Liu Peiting|Liu Peiting]] ([[User talk:Liu Peiting|talk]]) 07:12, 25 December 2021 (UTC)<br />
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==刘沛婷 Liú Pèitíng 英语语言文学（英美文学） 女 202120081505==<br />
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今告打死人之薛，就是‘丰年大雪’之薛。不单靠这三家，他的世交亲友在都在外的本也不少，老爷如今拿谁去？”雨村听说，便笑问门子道：“这样说来，却怎么了结此案？<br />
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The xue of killing people is the xue of 'heavy snow in the year of plenty'. He has not only these three families, but also many family friends and relatives who are away from home. Who are you going to take now?" Rain village heard, then smiled and asked Siemens way: "So say, but how to settle the case?--[[User:Liu Peiting|Liu Peiting]] ([[User talk:Liu Peiting|talk]]) 07:05, 25 December 2021 (UTC)<br />
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==刘胜楠 Liú Shèngnán 翻译学 女 202120081506==<br />
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你大约也深知这凶犯躲的方向了？”门子笑道：“不瞒老爷说，不但这凶犯躲的方向，并这拐的人我也知道，死鬼买主也深知道，待我细说与老爷听：<br />
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==刘薇 Liú Wēi 国别 女 202120081507==<br />
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这个被打死的是一个小乡宦之子，名唤冯渊，父母俱亡，又无兄弟，守着些薄产度日。年纪十八九岁，酷爱男风，不好女色。这也是前生冤孽，可巧遇见这丫头，他便一眼看上了，立意买来作妾，设誓不近男色，也不再娶第二个了。<br />
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The man who was killed was the son of a small township official, named Feng Yuan. His parents died and had no brothers. He lived on a low income. He is eighteen or nine years old. He loves men and is not good at women. This is also an injustice in his previous life. But when he happened to meet this girl, he took a fancy to it and decided to buy it as a concubine. He swore that he would not be close to a man and would not marry a second one. --[[User:Liu Wei|Liu Wei]] ([[User talk:Liu Wei|talk]]) 05:38, 27 December 2021 (UTC)Liu Wei<br />
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==刘晓 Liú Xiǎo 英语语言文学（英美文学） 女 202120081508==<br />
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所以郑重其事，必得三日后方进门。谁知这拐子又偷卖与薛家，他意欲卷了两家的银子逃去；谁知又走不脱，两家拿住，打了个半死，都不肯收银，各要领人。<br />
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==刘越 Liú Yuè 亚非语言文学 女 202120081509==<br />
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那薛公子便喝令下人动手，将冯公子打了个稀烂，抬回去三日竟死了。这薛公子原择下日子要上京的，既打了人，夺了丫头，他便没事人一般，只管带了家眷走他的路，并非为此而逃；<br />
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He then rudely ordered his subordinates to do something about it, and beat Feng up so badly that he was carried home and died within three days. The Duke of Xue had intended to go to the capital in a few days, and since he had beaten and robbed the maid, he acted as if nothing had happened, and simply took his family away, not because of this escape;--[[User:Liu Yue|Liu Yue]] ([[User talk:Liu Yue|talk]]) 06:59, 25 December 2021 (UTC)<br />
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==刘运心 Liú Yùnxīn 英语语言文学（英美文学） 女 202120081510==<br />
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这人命些些小事，自有他弟兄、奴仆在此料理。这且别说，老爷可知这被卖的丫头是谁？”雨村道：“我如何晓得？”门子冷笑道：“这人还是老爷的大恩人呢！<br />
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==罗安怡 Luó Ānyí 英语语言文学（英美文学） 女 202120081511==<br />
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他就是葫芦庙旁住的甄老爷的女儿，小名英莲的。”雨村骇然道：“原来是他！听见他自五岁被人拐去，怎么如今才卖呢？”门子道：“这种拐子单拐幼女，养至十二三岁，带至他乡转卖。<br />
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==罗曦 Luó Xī 英语语言文学（英美文学） 女 202120081512==<br />
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当日这英莲，我们天天哄他玩耍，极相熟的，所以隔了七八年，虽模样儿出脱的齐整，然大段未改，所以认得；且他眉心中原有米粒大的一点胭脂记，从胎里带来的。<br />
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When Yinglian was a little girl, we played with her every day and were very familiar with each other. Her appearance didn’t change a lot after seven or eight years though she has grown prettier than before, so we still remembered her; besides, her eyebrows came to a little carmine point (the size of a grain of rice) in the middle, which was the birthmark.--[[User:Ma Xin|Ma Xin]] ([[User talk:Ma Xin|talk]]) 05:53, 27 December 2021 (UTC)<br />
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==马新 Mǎ Xīn 外国语言学及应用语言学 女 202120081513==<br />
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偏这拐子又租了我的房子居住，那日拐子不在家，我也曾问他。他说是打怕了的，万不敢说，只说拐子是他的亲爹，因无钱还债才卖的。再四哄他，他又哭了，只说：‘我原不记得小时的事。’<br />
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The trafficker had rented my house to live in by coincidence. I had ever asked her one day when the trafficker was not at home. She said that she dared not to say anything after being attacked for a long time, and only answered that he was her father who sold her to pay off the debts. By coaxing her for several times, she cried again and said that "I don’t remember what happened when I was a child".--[[User:Ma Xin|Ma Xin]] ([[User talk:Ma Xin|talk]]) 07:14, 25 December 2021 (UTC)<br />
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The kidnapper just happened to rent the houses from me. One day, when he was not at home, I asked her about such a thing. She told me that she was afraid to say anything after being beaten so much; she only insisted that he was her father who sold her to pay off his debts. When I tried repeatedly to coax it out of her, she burst into tears and said that 'I do not remember what happened in my childhood.'--[[User:Mao Yawen|Mao Yawen]] ([[User talk:Mao Yawen|talk]]) 08:29, 25 December 2021 (UTC)<br />
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==毛雅文 Máo Yǎwén 英语语言文学（英美文学） 女 202120081514==<br />
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这无可疑了。那日冯公子相见了，兑了银子，因拐子醉了，英莲自叹说：‘我今日罪孽可满了！’后又听见三日后才过门，他又转有忧愁之态。<br />
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There is not doubt that the girl who was carried off by the kidnapper is Yinglian all right. The day when Feng Yuan met her and paid down his silver, the kidnapper had got drunk. And then, Yinglian sighed, 'I am overwhelmed by my sins today!' However, her gloom started deepening again, when she heard that Feng Yuan would not be coming and picking her up for three days.--[[User:Mao Yawen|Mao Yawen]] ([[User talk:Mao Yawen|talk]]) 08:14, 25 December 2021 (UTC)<br />
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==毛优 Máo Yōu 俄语语言文学 女 202120081515==<br />
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我又不忍，等拐子出去，又叫内人去解劝他：‘这冯公子必待好日期来接，可知必不以丫鬟相看。况他是个绝风流人品，家里颇过得，素性又最厌恶堂客，今竟破价买你，后事不言可知。<br />
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==牟一心 Móu Yīxīn 英语语言文学（英美文学） 女 202120081516==<br />
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只耐得三两日，何必忧闷？’他听如此说，方略解些，自谓从此得所。谁料天下竟有不如意事，第二日，他偏又卖与了薛家。<br />
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Only for three or two days, why bother to be depressed? Hearing this, he relieved a little bit, saying that he would get a place to settle since then. Unexpectedly, everything is never perfect. On the next day, he was sold to the Xue.--[[User:Mou Yixin|Mou Yixin]] ([[User talk:Mou Yixin|talk]]) 07:13, 25 December 2021 (UTC)<br />
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==彭瑞雪 Péng Ruìxuě 法语语言文学 女 202120081517==<br />
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若卖与第二家还好，这薛公子的混名，人称他‘呆霸王’，最是天下第一个弄性尚气的人，而且使钱如土。只打了个落花流水，生拖死拽，把个英莲拖去，如今也不知死活。<br />
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==秦建安 Qín Jiànān 外国语言学及应用语言学 女 202120081518==<br />
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这冯公子空喜一场，一念未遂，反花了钱，送了命，岂不可叹！”雨村听了，也叹道：“这也是他们的孽障遭遇，亦非偶然，不然这冯渊如何偏只看上了这英莲？<br />
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==邱婷婷 Qiū Tíngtíng 英语语言文学（语言学）女 202120081519==<br />
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这英莲受了拐子这几年折磨，才得了个路头，且又是个多情的，若果聚合了，倒是件美事，偏又生出这段事来。这薛家纵比冯家富贵，想其为人，自然姬妾众多，淫佚无度，未必及冯渊定情于一人。<br />
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==饶金盈 Ráo Jīnyíng 英语语言文学（语言学） 女 202120081520==<br />
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这正是梦幻情缘，恰遇见一对薄命儿女。且不要议论他人，只目今这官司如何剖断才好？”门子笑道：“老爷当年何其明决，今日何反成个没主意的人了？<br />
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It should be the love of dream, only to be an ill-fated couple. Don’t talk about others for the moment. It’s crucial that this case be judged properly.” The servant said with a smile, “ how decisive you were in those days. Why are you so irresolute at the present ?”--[[User:Shi Liqing|Shi Liqing]] ([[User talk:Shi Liqing|talk]]) 07:31, 25 December 2021 (UTC)<br />
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==石丽青 Shí Lìqīng 英语语言文学（英美文学） 女 202120081521==<br />
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小的听见老爷补升此任，系贾府、王府之力。此薛蟠即贾府之亲，老爷何不顺水行舟，做个人情，将此案了结，日后也好去见贾、王二公。”<br />
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I heard that you respected master assumed office with the help of Jia Mansion and Wang Mansion. Xue Pan is a relative of Jia Mansion. Why don’t you do him a special favor, making use of the opportunity to settle the case, so that you can make a smooth explanation to master Jia and Wang in days to come.”--[[User:Shi Liqing|Shi Liqing]] ([[User talk:Shi Liqing|talk]]) 07:03, 25 December 2021 (UTC)<br />
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==孙雅诗 Sūn Yǎshī 外国语言学及应用语言学 女 202120081522==<br />
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雨村道：“你说的何尝不是，但事关人命，蒙皇上隆恩，起复委用，正竭力图报之时，岂可因私枉法？是实不忍为的。”门子听了，冷笑道：<br />
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==王李菲 Wáng Lǐfēi 英语语言文学（英美文学） 女 202120081523==<br />
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“老爷说的自是正理，但如今世上是行不去的。岂不闻古人说的：‘大丈夫相时而动。’又说：‘趋吉避凶者为君子。’依老爷这话，不但不能报效朝廷，亦且自身不保，还要三思为妥。”<br />
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“What lord said is reasonable, but it is unfeasible in the current world. Have you not heard what the ancients said:’ A real man can take action according to the specific situation’, and ‘The one who can avoid calamity and bring on good fortune is a gentleman.’ According to lord’s words, you not only can’t serve the court, but also can’t protect yourself. You’d better think it over. ‘ --[[User:Wang Lifei|Wang Lifei]] ([[User talk:Wang Lifei|talk]]) 15:40, 25 December 2021 (UTC)<br />
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==王逸凡 Wáng Yìfán 亚非语言文学 女 202120081524==<br />
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雨村低了头，半日方说道：“依你怎么着？”门子道：“小人已想了个很好的主意在此：老爷明日坐堂，只管虚张声势，动文书，发签拿人。<br />
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==王镇隆 Wáng Zhènlóng 英语语言文学（英美文学） 男 202120081525==<br />
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凶犯自然是拿不来的，原告固是不依，只用将薛家族人及奴仆人等拿几个来拷问；小的在暗中调停，令他们报个‘暴病身亡’，合族中及地方上共递一张保呈。<br />
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==卫怡雯 Wèi Yíwén 英语语言文学（英美文学） 女 202120081526==<br />
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老爷只说善能扶鸾请仙，堂上设了乩坛，令军民人等只管来看。老爷便说：‘乩仙批了，死者冯渊与薛蟠原系夙孽，今犯狭路相遇，原应了结：<br />
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The lord requested to set out the altar in order to invite immortals to come, and let the military and people to come to see. The lord then said that after coscinomancy finished, the dead Feng Yuan and Xue Pan should have come to an end because they used to be long-standing and are bound to meet head-on on a narrow road.--[[User:Wei Yiwen|Wei Yiwen]] ([[User talk:Wei Yiwen|talk]]) 14:46, 26 December 2021 (UTC)<br />
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==魏楚璇 Wèi Chǔxuán 英语语言文学（英美文学） 女 202120081527==<br />
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今薛蟠已得了无名之病，被冯渊的魂魄追索而死。其祸皆由拐子而起，除将拐子按法处治外，馀不累及’等语。小人暗中嘱咐拐子，令其实招。<br />
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==魏兆妍 Wèi Zhàoyán 英语语言文学（英美文学） 女 202120081528==<br />
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众人见乩仙批语与拐子相符，自然不疑了。薛家有的是钱，老爷断一千也可，五百也可，与冯家作烧埋之费。那冯家也无甚要紧的人，不过为的是钱，有了银子，也就无话了。<br />
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==吴婧悦 Wú Jìngyuè 俄语语言文学 女 202120081529==<br />
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老爷细想，此计如何？”雨村笑道：“不妥，不妥。等我再斟酌斟酌，压服得口声才好。”二人计议已定。至次日坐堂，勾取一干有名人犯，雨村详加审问。<br />
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The lord thought carefully, and asked how about this plan? Yucun laughed and said: “ It’s not the right way, it’s not the right way. Let me think the matter over, the plan should be convinced by all the others.” Then they confirmed the plan. At tomorrow’s court session, convening all criminals, whose name was known, Yucun questioned them seriously. --[[User:Wu Jingyue|Wu Jingyue]] ([[User talk:Wu Jingyue|talk]]) 14:31, 25 December 2021 (UTC)<br />
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==吴映红 Wú Yìnghóng 日语语言文学 女 202120081530==<br />
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果见冯家人口稀少，不过赖此欲得些烧埋之银；薛家仗势倚情，偏不相让：故致颠倒未决。雨村便徇情枉法，胡乱判断了此案。<br />
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==肖毅瑶 Xiāo Yìyáo 英语语言文学（英美文学） 女 202120081531==<br />
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冯家得了许多烧埋银子，也就无甚话说了。雨村便疾忙修书二封与贾政并京营节度使王子腾，不过说“令甥之事已完，不必过虑”之言寄去。<br />
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The Feng family got a lot of buried silver and had nothing to say. Rain village will quickly repair two letters and Jia Zheng and Jingying jie make Prince Teng, but said "nephew has finished, do not have to worry about" words to send.--[[User:Xiao Yiyao|Xiao Yiyao]] ([[User talk:Xiao Yiyao|talk]]) 10:25, 26 December 2021 (UTC)<br />
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==谢佳芬 Xiè Jiāfēn 英语语言文学（英美文学） 女 202120081532==<br />
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此事皆由葫芦庙内沙弥新门子所为，雨村又恐他对人说出当日贫贱时事来，因此心中大不乐意。后来到底寻了他一个不是，远远的充发了才罢。当下言不着雨村。<br />
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It was all done by a novice monk Xinmenzi in Gourd Temple. Yucun was afraid that he would tell people about the awful current affairs of that day, so he was very unsatisfied. Later, Yucan pick holes in him , and banished him far away. Now, there was no one talking about bad things about Yucun.--[[User:Xie Jiafen|Xie Jiafen]] ([[User talk:Xie Jiafen|talk]]) 14:05, 25 December 2021 (UTC)<br />
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==谢庆琳 Xiè Qìnglín 俄语语言文学 女 202120081533==<br />
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且说那买了英莲、打死冯渊的薛公子，亦系金陵人氏，本是书香继世之家。只是如今这薛公子幼年丧父，寡母又怜他是个独根孤种，未免溺爱纵容些，遂致老大无成；<br />
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==熊敏 Xióng Mǐn 英语语言文学（英美文学） 女 202120081534==<br />
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且家中有百万之富，现领着内帑钱粮，采办杂料。这薛公子学名薛蟠，表字文起，性情奢侈，言语傲慢；虽也上过学，不过略识几个字，终日惟有斗鸡走马，游山玩景而已。<br />
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In addition, there are countless money in the family, and now people are taking the domestic money and food to purchase stuffs. The Mr. Xue so-called Xue Pan, is entitled as Wenqi with extravagant temperament and arrogant speech. Although he has also gone to school, but he knows a few words, he only like fighting cock walking around the mountains and enjoying the scenery all day long.<br />
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In addition, there are countless money in the family, and now people are taking the domestic money and food to purchase stuffs. Mr.Xue, whose name is Xue Pan, is entitled as Wenqi with extravagant temperament and arrogant speech. Although he has also gone to school, he knows a few words, he only like fighting cock walking around the mountains and enjoying the scenery all day long.--[[User:Xu Minyun|Xu Minyun]] ([[User talk:Xu Minyun|talk]]) 11:21, 26 December 2021 (UTC)<br />
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==徐敏赟 Xú Mǐnyūn 语言智能与跨文化传播研究 男 202120081535==<br />
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虽是皇商，一应经纪世事全然不知，不过赖祖、父旧日的情分，户部挂个虚名，支领钱粮；其馀事体，自有伙计、老家人等措办。<br />
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Although he was a royal merchant, he knew nothing about economics. However, due to the old affection of his grandfathers and fathers, he was given a virtual position in Board of Revenue to received money and grain, and the rest of affairs were handled by his clerks and old family members.--[[User:Xu Minyun|Xu Minyun]] ([[User talk:Xu Minyun|talk]]) 09:43, 26 December 2021 (UTC)<br />
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Although he was a royal merchant, he knew nothing about economics. However, due to the old affection of his ancestors and his father, he was given a virtual position in Board of Revenue to received money and grain, and the rest of affairs were handled by his clerks and old family members.--[[User:Yan Jing|Yan Jing]] ([[User talk:Yan Jing|talk]]) 11:08, 26 December 2021 (UTC)<br />
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==颜静 Yán Jìng 语言智能与跨文化传播研究 女 202120081536==<br />
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寡母王氏，乃现任京营节度使王子腾之妹，与荣国府贾政的夫人王氏是一母所生的姊妹，今年方五十上下，只有薛蟠一子。还有一女，比薛蟠小两岁，乳名宝钗，生得肌骨莹润，举止娴雅。<br />
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Wang, the widowed mother, is the sister of Wang Ziteng, the current governor of Jingying Festival and the sister of Wang, the wife of Jia Zheng in the Rongguo mansion. This year, she is about 50, and has only a son Xue Pan. Besides, she has a daughter, whose milk name is Bao Chai, two years younger than Xue Pan. Bao Chai has beautiful body and behave elegantly .<br />
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==颜莉莉 Yán Lìlì 国别 女 202120081537==<br />
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当时他父亲在日极爱此女，令其读书识字，较之乃兄竟高十倍。自父亲死后，见哥哥不能安慰母心，他便不以书字为念，只留心针黹、家计等事，好为母亲分忧代劳。<br />
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His father had been so fond of her that he had sent her to read ten times better than her brother. Seeing that her brother could not pacify her mother after her father's death, she stopped thinking about reading and only cared about needle-work and family livelihood in order to share her mother's cares and duties.<br />
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==颜子涵 Yán Zǐhán 国别 女 202120081538==<br />
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近因今上崇尚诗礼，征采才能，降不世之隆恩，除聘选妃嫔外，凡世宦名家之女，皆得亲名达部，以备选择为公主、郡主入学陪侍，充为才人、赞善之职。<br />
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==阳佳颖 Yáng Jiāyǐng 国别 女 202120081540==<br />
<br />
自薛蟠父亲死后，各省中所有的买卖承局、总管、伙计人等，见薛蟠年轻不谙世事，便趁时拐骗起来，京都几处生意，渐亦销耗。<br />
<br />
Ever since the death of Xue Pan's father， all the assistants， managers and partners， and other employees in the respective provinces， perceiving how youthful and inexperienced Xue Pan was in years， readily availed themselves of the time to begin swindling and defrauding. As a result, The business， carried on in various different places in the capital，gradually also began to fall off and to show a deficit.--[[User:Yang Jiaying|Yang Jiaying]] ([[User talk:Yang Jiaying|talk]]) 08:33, 26 December 2021 (UTC)<br />
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==杨爱江 Yáng Àijiāng 英语语言文学（语言学） 女 202120081541==<br />
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薛蟠素闻得都中乃第一繁华之地，正思一游，便趁此机会：一来送妹待选；二来望亲；三来亲自入部销算旧账，再计新支；其实只为游览上国风光之意。<br />
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==杨堃 Yáng Kūn 法语语言文学 女 202120081542==<br />
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因此早已检点下行装细软，以及馈送亲友各色土物人情等类，正择日起身，不想偏遇着那拐子卖英莲。薛蟠见英莲生的不俗，立意买了作妾，又遇冯家来夺，因恃强喝令豪奴将冯渊打死。<br />
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==杨柳青 Yáng Liǔqīng 英语语言文学（英美文学） 女 202120081543==<br />
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便将家中事务，一一嘱托了族中人并几个老家人；自己同着母亲、妹子，竟自起身长行去了。人命官司，他却视为儿戏，自谓花上几个钱，没有不了的。<br />
<br />
Dragon Marshgrass entrusted the household affairs to the clan middleman and old family members. Then he just went away with his mother and sister. He should deem the affair of murder as a trifling matter and believed it could be easily solved through money.--[[User:Yang Liuqing|Yang Liuqing]] ([[User talk:Yang Liuqing|talk]]) 12:31, 26 December 2021 (UTC)<br />
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==叶维杰 Yè Wéijié 国别 男 202120081544==<br />
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在路不计其日。那日已将入都，又听见母舅王子腾升了九省统制，奉旨出都查边。薛蟠心中暗喜道：“我正愁进京去有舅舅管辖，不能任意挥霍；如今升出去，可知天从人愿。”<br />
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==易扬帆 Yì Yángfān 英语语言文学（英美文学） 女 202120081545==<br />
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因和母亲商议道：“咱们京中虽有几处房舍，只是这十来年没人居住，那看守的人未免偷着租赁给人住，须得先着人去打扫收拾才好。”他母亲道：“何必如此招摇？”<br />
<br />
So he discussed with his mother, "Although we have a few premises in the capital, no one has lived there for ten years, the guards may sneakily rent to people to live, we must first ask someone to clean and tidy up." His mother said, "Why do you have to be so flashy? "--[[User:Yi Yangfan|Yi Yangfan]] ([[User talk:Yi Yangfan|talk]]) 09:23, 25 December 2021 (UTC)Yi Yangfan<br />
<br />
So he discussed with his mother, "Although we have a few houses in the capital, no one has lived there for ten years. The guards may sneakily rent the house to other people, so we must first send someone to tidy up the house. His mother said, "Why do you have to be so flashy? "--[[User:Yin Huizhen|Yin Huizhen]] ([[User talk:Yin Huizhen|talk]]) 13:50, 25 December 2021 (UTC)<br />
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==殷慧珍 Yīn Huìzhēn 英语语言文学（英美文学） 女 202120081546==<br />
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咱们这进京去，原是先拜望亲友，或是在你舅舅处，或是你姨父家，他两家的房舍极是宽敞的，咱们且住下，再慢慢儿的着人去收拾，岂不消停些？”<br />
<br />
Now we go to the capital Beijing, and we should visit our relatives first. Your uncle‘s or your aunt‘s husband’s house are good choices, and their houses are very spacious. Let's stay there for a while and then send someone to clean up the house，and it will be more inconspicuous.--[[User:Yin Huizhen|Yin Huizhen]] ([[User talk:Yin Huizhen|talk]]) 13:38, 25 December 2021 (UTC)<br />
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==殷美达 Yīn Měidá 英语语言文学（语言学） 女 202120081547==<br />
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薛蟠道：“如今舅舅正升了外省去，家里自然忙乱起身，咱们这会子反一窝一拖的奔了去，岂不没眼色呢？”他母亲道：“你舅舅虽升了去，还有你姨父家。<br />
<br />
==尹媛 Yǐn Yuán 英语语言文学（英美文学） 女 202120081548==<br />
<br />
况这几年来，你舅舅、姨娘两处，每每带信捎书接咱们来；如今既来了，你舅舅虽忙着起身，你贾家的姨娘未必不苦留我们，咱们且忙忙的收拾房子，岂不使人见怪？你的意思，我早知道了：<br />
<br />
==詹若萱 Zhān Ruòxuān 英语语言文学（英美文学） 女 202120081549==<br />
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守着舅舅、姨母住着，未免拘紧了；不如各自住着，好任意施为。你既如此，你自去挑所宅子去住；我和你姨娘，姊妹们别了这几年，却要住几日，我带了你妹子去投你姨娘家去。你道好不好？”<br />
<br />
==张秋怡 Zhāng Qiūyí 亚非语言文学 女 202120081550==<br />
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薛蟠见母亲如此说，情知扭不过，只得吩咐人夫，一路奔荣国府而来。那时王夫人已知薛蟠官司一事，亏贾雨村就中维持了，才放了心。<br />
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==张扬 Zhāng Yáng 国别 男 202120081551==<br />
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又见哥哥升了边缺，正愁少了娘家的亲戚来往，略觉寂寞。过了几日，忽家人报：“姨太太带了哥儿、姐儿，合家进京，在门外下车了。”<br />
<br />
Seeing that her brother was promoted, she was worried about the lack of relatives in her mother's family, and felt a little lonely. A few days later, suddenly her family reported: "concubine brought her brothers and sisters to Beijing and got off outside the door."--[[User:Zhang Yang|Zhang Yang]] ([[User talk:Zhang Yang|talk]]) 10:02, 26 December 2021 (UTC)<br />
<br />
Seeing that her brother was promoted, Dragon Marshgrass was worried about the lack of relatives in her mother's family, and felt a little lonely. A few days later, suddenly her family reported: "concubine brought her brothers and sisters to Beijing and got off outside the door."--[[User:Zhang Yiran|Zhang Yiran]] ([[User talk:Zhang Yiran|talk]]) 14:38, 26 December 2021 (UTC)<br />
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==张怡然 Zhāng Yírán 俄语语言文学 女 202120081552==<br />
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喜的王夫人忙带了人，接到大厅上，将薛姨妈等接进去了。姊妹们一朝相见，悲喜交集，自不必说。叙了一番契阔，又引着拜见贾母，将人情土物各种酬献了，合家俱厮见过，又治席接风。<br />
<br />
Lady King was so happy that she brought someone to the hall and took Aunt Marshgrass in. The sisters were joy tempered with sorrow to see each other that it goes without saying. Told a story of great deeds, and led to visit Grandma Merchant, all kinds of reward will be offered, together with the furniture saw, and treat the seat to receive wind.--[[User:Zhang Yiran|Zhang Yiran]] ([[User talk:Zhang Yiran|talk]]) 14:35, 26 December 2021 (UTC)<br />
<br />
Mr. Wang was so happy that she brought someone to the hall and took Aunt Xue in. The sisters were in joy tempered with sorrow to see each other that it goes without saying. Told a story of great deeds, and led to visit Grandma Merchant, all kinds of reward will be offered, together with the furniture saw, and treat the seat to receive wind.--[[User:Zhong Yifei|Zhong Yifei]] ([[User talk:Zhong Yifei|talk]]) 10:07, 26 December 2021 (UTC)<br />
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==钟义菲 Zhōng Yìfēi 英语语言文学（英美文学） 女 202120081553==<br />
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薛蟠拜见过贾政、贾琏，又引着见了贾赦、贾珍等。贾政便使人进来对王夫人说：“姨太太已有了年纪，外甥年轻，不知庶务，在外住着，恐又要生事。<br />
<br />
Xue Pan met Jia Zheng and Jia Lian and introduced Jia She and Jia Zhen. Jia Zheng sent someone in and said to Mrs. Wang, "my aunt is old, and my nephew is young. He doesn't know about general affairs. If he is living outside, I am afraid that something will happen again.--[[User:Zhong Yifei|Zhong Yifei]] ([[User talk:Zhong Yifei|talk]]) 10:10, 25 December 2021 (UTC)<br />
<br />
Xue Pan met Jia Zheng and Jia Lian and introduced Jia She and Jia Zhen. Jia Zheng sent someone in and said to Mrs. Wang, "my aunt is old, and my nephew is young. He doesn't know about general affairs. If he lives outside, I am afraid that he will make some trouble.--[[User:Zhong Yulu|Zhong Yulu]] ([[User talk:Zhong Yulu|talk]]) 13:01, 25 December 2021 (UTC)<br />
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==钟雨露 Zhōng Yǔlù 英语语言文学（英美文学） 女 202120081554==<br />
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咱们东南角上梨香院那一所房十来间白空闲着，叫人请了姨太太和姐儿、哥儿住了甚好。”王夫人原要留住。贾母也遣人来说：“请姨太太就在这里住下，大家亲密些。”<br />
<br />
“We have a room in the southeast corner of the Li Xiang courtyard that is vacant, and ask someone to invite the aunt and sister and brother to live here.” Mrs. Wang originally wanted to stay. Mrs. Jia also sent someone to say: “Please invite the aunt to stay here, the relationship between us will be closer.”--[[User:Zhong Yulu|Zhong Yulu]] ([[User talk:Zhong Yulu|talk]]) 12:58, 25 December 2021 (UTC)<br />
<br />
“We have dozens of room in the southeast corner of the Li Xiang courtyard that is vacant, and ask someone to invite the aunt and sister and brother to live here.” Mrs. Wang originally wanted to stay. Mrs. Jia also sent someone to say: “Please stay here, the relationship between us will be closer.”--[[User:Zhou Jiu|Zhou Jiu]] ([[User talk:Zhou Jiu|talk]]) 02:51, 26 December 2021 (UTC)<br />
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==周玖 Zhōu Jiǔ 英语语言文学（英美文学） 女 202120081555==<br />
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薛姨妈正欲同居一处，方可拘紧些儿子；若另住在外边，又恐他纵性惹祸：遂忙应允。又私与王夫人说明：“一应日费供给，一概都免，方是处常之法。”<br />
<br />
Aunt Xue wanted to live here so that she could supervise her son. If she lived elsewhere, she feared that her son would get into trouble again. So he agreed. She said to Mrs. Wang privately, "The Xue family will pay for all the supplies in the Jia mansion by themselves. This is the only way to get along with them for a long time."<br />
<br />
==周俊辉 Zhōu Jùnhuī 法语语言文学 女 202120081556==<br />
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王夫人知他家不难于此，遂亦从其自便。从此后，薛家母女就在梨香院住了。原来这梨香院乃当日荣公暮年养静之所，小小巧巧，约有十馀间房舍，前厅后舍俱全。<br />
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==周巧 Zhōu Qiǎo 英语语言文学（语言学） 女 202120081557==<br />
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另有一门通街，薛蟠的家人就走此门出入。西南上又有一个角门，通着夹道子，出了夹道，便是王夫人正房的东院了。每日或饭后或晚间，薛姨妈便过来，或与贾母闲谈，或与王夫人相叙；<br />
<br />
==周清 Zhōu Qīng 法语语言文学 女 202120081558==<br />
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宝钗日与黛玉、迎春姊妹等一处，或看书下棋，或做针黹：倒也十分相安。只是薛蟠起初原不欲在贾府中居住，生恐姨父管束，不得自在。无奈母亲执意在此，且贾宅中又十分殷勤苦留，只得暂且住下；<br />
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==周小雪 Zhōu Xiǎoxuě 日语语言文学 女 202120081559==<br />
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一面使人打扫出自家的房屋，再移居过去。谁知自此间住了不上一月，贾宅族中凡有的子侄，俱已认熟了一半，都是那些纨袴气习，莫不喜与他来往。<br />
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==朱素珍 Zhū Sùzhēn 英语语言文学（语言学） 女 202120081561==<br />
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今日会酒，明日观花，甚至聚赌嫖娼，无所不至，引诱的薛蟠比当日更坏了十倍。虽说贾政训子有方，治家有法，一则族大人多，照管不到；二则现在房长乃是贾珍，彼乃宁府长孙，又现袭职，凡族中事，都是他掌管；<br />
Staying together and drinking wine today, appreciating flowers tomorrow, and even gambling and prostitution, everything will be done. Xue Pan, who is seduced, is ten times worse than that day. Although Jia Zhengxun is good at governing family, on the one hand,there are so many people in the family that he can not look after everyone; On the other hand, the house chief is Jia Zhen, and he is the eldest grandson of the Ning Mansion, now everything is in charge of him.<br />
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==邹岳丽 Zōu Yuèlí 日语语言文学 女 202120081562==<br />
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三则公私冗杂，且素性潇洒，不以俗事为要，每公暇之时，不过看书、着棋而已；况这梨香院相隔两层房舍，又有街门别开，任意可以出入：<br />
<br />
==Nadia 202011080004==<br />
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这些子弟们所以只管放意畅怀的，因此薛蟠遂将移居之念渐渐打灭了。<br />
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==Mahzad Heydarian 玛莎 202021080004==<br />
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日后如何，下回分解。葫芦僧判断葫芦案──<br />
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==Mariam Toure 2020GBJ002301==<br />
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“葫芦”的谐音为糊涂，故其意谓糊涂僧糊涂判案。<br />
<br />
==Rouabah Soumaya 202121080001==<br />
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指知县贾雨村按照现为衙门门子而原为葫芦庙小沙弥的主意糊里糊涂判结了薛蟠强买甄英莲并打死人命一案。<br />
<br />
Zhizhi County Jia Yucun was confused and convicted the case of Xue Panqiang buying Zhen Yinglian and killing people based on the idea that he is now Yamenzi but was originally a young novice monk in the Gourd Temple.<br />
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==Muhammad Numan 202121080002==<br />
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女子无才便是德──语出明·张岱《公祭祁夫人文》：<br />
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==Atta Ur Rahman 202121080003==<br />
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“(陈)眉公曰：‘丈夫有德便是才，女子无才便是德。’此语殊为未确。”<br />
<br />
==Muhammad Saqib Mehran 202121080004==<br />
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(又见清·石成金《家训钞》引)<br />
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==Zohaib Chand 202121080005==<br />
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意谓女子如果读书识字，便可能受到小说、戏曲的不良影响，做出伤风败俗的事，倒不如不识字而能保持妇德。<br />
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==Jawad Ahmad 202121080006==<br />
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《女四书》、《列女传》──都是记述历代贤德女子的事迹，以宣扬封建妇德的书。<br />
English:The Four Books on Women and the Biography of Lienu ─ ─ both describe the deeds of <br />
<br />
virtuous women in past dynasties to publicize the feudal virtues of women.<br />
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==Nizam Uddin 202121080007==<br />
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《女四书》：明·王相模仿南宋·朱熹所编《四书》而辑成，包括东汉·班昭的《女诫》、唐·宋若莘和宋若昭的《女论语》、明·永乐皇后徐氏的《内训》、王相之母刘氏的《女范捷录》四种专讲女德的书，故称。<br />
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==Öncü 202121080008==<br />
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《列女传》：西汉·刘向编撰。全书七卷，每卷为一类，分别为母仪、贤明、仁智、贞顺、节义、辩通、嬖孽，共收妇女故事一百零四则。<br />
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==Akira Jantarat 202121080009==<br />
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纺绩女红(gōng工)──泛指女子应做的家务活计。<br />
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Fangji Female Red (''gong'')──refers to the household chores of women.--[[User:Benjamin Wellsand|Benjamin Wellsand]] ([[User talk:Benjamin Wellsand|talk]]) 19:13, 25 December 2021 (UTC)<br />
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==Benjamin Wellsand 202111080118==<br />
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纺绩：“纺”是把丝纺成纱，“绩”是把麻绩成线。<br />
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''Fangji'': "Fang" means to spin silk into yarn, "Ji" means to turn the hemp into thread.--[[User:Benjamin Wellsand|Benjamin Wellsand]] ([[User talk:Benjamin Wellsand|talk]]) 19:06, 25 December 2021 (UTC)<br />
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==Asep Budiman 202111080020==<br />
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女红：又作“女工”或“女功”。<br />
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==Ei Mon Kyaw 202111080021==<br />
<br />
是指纺织、缝纫、刺绣等。</div>Chen Huinihttps://bou.de/u/index.php?title=20211215_homework&diff=13408220211215 homework2021-12-20T12:24:43Z<p>Chen Huini: /* 陈惠妮 Chén Huìnī 英语语言文学（英美文学） 女 202120081479 */</p>
<hr />
<div>Quicklinks: [[Introduction_to_Translation_Studies_2021|Back to course homepage]] [https://bou.de/u/wiki/uvu:Community_Portal#Frequently_asked_questions_FAQ FAQ] [https://bou.de/u/wiki/uvu:Community_Portal Manual] [[20210926_homework|Back to all homework webpages overview]] [[20220112_final_exam|final exam page]]<br />
<br />
==陈静 Chén Jìng 国别 女 202020080595==<br />
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鼎：古代食器。胡羼(chàn忏) ──胡闹。 羼：本义为群羊杂居。引申为杂乱不纯，乱七八糟。<br />
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Tripod (Ding in Chinese): ancient food utensil. Hu Chan in Chinese means nonsense. Chan in Chinese originally means that the sheep live together, whose extensive meaning is mess.<br />
----<br />
<br />
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Tripod: ancient food utensil. Hi Chan - nonsense. The original meaning is that sheep live together. It is extended meaning to be messy, impure and messy.<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 01:25, 12 December 2021 (UTC)<br />
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==蔡珠凤 Cài Zhūfèng 法语语言文学 女 202120081477==<br />
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抓──即“抓周”，亦称“试儿”、“试周”。旧俗于婴儿满周岁时，父母摆列各种小件器物，任其抓取，以测试其秉性、智愚、志趣。此俗始于江南，后亦传到北方。<br />
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Grasping -- namely "grasping the week", also known as "trying the child" and "trying the week". The old custom is that when a baby reaches the age of one year, his parents arrange all kinds of small objects and let him grab them to test his temperament, intelligence and interest. This custom began in the south of the Yangtze River and later spread to the north.<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 01:23, 12 December 2021 (UTC)<br />
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Catch ─ ─ means "catch the week", also known as "test" and "test week". The old custom is when the baby reaches one year old, the parents arrange all kinds of small utensils and let them grab them to test their disposition, wisdom and ambition. This custom began in the south of the Yangtze River and then spread to the north.--[[User:Zeng Junlin|Zeng Junlin]] ([[User talk:Zeng Junlin|talk]]) 07:41, 11 December 2021 (UTC)<br />
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==曾俊霖 Zēng Jùnlín 国别 男 202120081478==<br />
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事见北朝周·颜之推《颜氏家训·风操》：“江南风俗，儿生一期(年)，为制新衣，盥浴装饰，男则用弓矢纸笔，女则刀尺针缕(线)，并加饮食之物及珍宝服玩，置之儿前，观其发意所取，以验贪亷智愚，名之为试儿。”<br />
<br />
It is said in Yan's family instructions and customs by Yan Zhitui of the Northern Dynasty that "the custom in the south of the Yangtze River was born in the first year. It was to make new clothes and decorate bathrooms. Men used bows and arrows, paper and pens, women used knives, rulers, needles and threads (lines), and played with food and precious clothes. They were placed in front of their children and looked at what they wanted to take to test their greed, wisdom and stupidity. They were called test children."--[[User:Zeng Junlin|Zeng Junlin]] ([[User talk:Zeng Junlin|talk]]) 07:37, 11 December 2021 (UTC)<br />
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It is said in Yan's family instructions and customs by Yan Zhitui of ''the Northern Dynasty'' that the custom in the south of the Yangtze River was born in the first year. It was to make new clothes and decorate bathrooms. Men used bows and arrows, paper and pens, women used knives, rulers, needles and threads (lines), and played with food and precious clothes. They were placed in front of their children and looked at what they wanted to take to test their greed, wisdom and stupidity. They were called test children."--[[User:Chen Huini|Chen Huini]] ([[User talk:Chen Huini|talk]]) 12:20, 20 December 2021 (UTC)Chen Huini<br />
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==陈惠妮 Chén Huìnī 英语语言文学（英美文学） 女 202120081479==<br />
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(宋·赵彦卫《云麓漫钞》卷二也有相同记载)又宋·叶真《爱日斋丛钞》卷一：“《玉壶野史》记曹武惠王(曹彬)始生周晬日，父母以百玩之具罗于席，观其所取。<br />
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(Song · Zhao Yanwei's ''Yunlu Manchao'', Volume 2 has the same record) and Song · Ye Zhen, Volume 1, ''Ai Ri Zhai Cong Chao'': "''In the History of Jade Pot'', when King Cao Wu hui (Cao Bin) was on the year ahead in the first week of his birth, his parents viewed the year he took with a hundred toys on the table.--[[User:Chen Huini|Chen Huini]] ([[User talk:Chen Huini|talk]]) 12:24, 20 December 2021 (UTC)Chen Huini<br />
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== Headline text ==<br />
==陈湘琼 Chén Xiāngqióng 外国语言学及应用语言学 女 202120081480==<br />
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武惠王左手提干戈，右手提俎豆，斯须取一印，馀无所视。曹，真定人。江南遗俗乃在此(指真定)，今俗谓试周是也。”<br />
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Lord Wuhui holds weapons with his left hand and dinnerware in his right hand.Then he looks at a seal and graps it without seeing anything else.Lord Wuhui, whose first name is Cao, comes from Zhen Ding county. The place is called Shi Zhou now, on whiche Jiang Nan's old cities lay.--[[User:Chen Xiangqiong|Chen Xiangqiong]] ([[User talk:Chen Xiangqiong|talk]]) 00:23, 14 December 2021 (UTC)<br />
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Lord Wuhui holds weapons with his left hand and dinnerware in his right hand.Then he looks at a seal and graps it without seeing anything else.Lord Wuhui, whose first name is Cao, comes from Zhen Ding county. The place is so-called Shizhou now, on which ancient Jiangnan lay.--[[User:Chen Xinyi|Chen Xinyi]] ([[User talk:Chen Xinyi|talk]]) 05:07, 14 December 2021 (UTC)<br />
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==陈心怡 Chén Xīnyí 翻译学 女 202120081481==<br />
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致知格物──语出《礼记·大学》：“致知在格物，格物而后知至。”意谓要想获得知识，必须探究事物的道理。 致：获得，取得。<br />
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Zhi Zhi Ge Wu- ''From The Book of Rites·Daxue'': "Zhizhi lies in Gewu, and after Gewu, knowledge arrives." It means that in order to gain knowledge, one must inquire into the truth of things. Zhi: To acquire, to obtain.--[[User:Chen Xinyi|Chen Xinyi]] ([[User talk:Chen Xinyi|talk]]) 05:18, 12 December 2021 (UTC)<br />
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==程杨 Chéng Yáng 英语语言文学（英美文学） 女 202120081482==<br />
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格：推究，探究，探讨。尧……张──尧、舜、禹、汤、文、武，即唐尧、虞舜、夏禹、成汤、周文王、周武王，是从上古至西周的明君；<br />
Ge: means deduction, exploration and discussion. Yao...Zhang──Yao, Shun, Yu, Tang, Wen, Wu, namely Tang Yao, Yu Shun, Xia Yu, Cheng Tang, Emperor Wen of Zhou Dynasty, Emperor Wu of Zhou Dynasty, they are all wise emperors from ancient times to the Zhou Dynasty;--[[User:Cheng Yang|Cheng Yang]] ([[User talk:Cheng Yang|talk]]) 13:11, 11 December 2021 (UTC)<br />
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Ge: means deduction, exploration and discussion. Yao...Zhang──Yao, Shun, Yu, Tang, Wen, Wu, namely Tang Yao, Yu Shun, Xia Yu, Cheng Tang, Emperor Wen of Zhou Dynasty, Emperor Wu of Zhou Dynasty, they are all wise emperors from ancient times to Zhou Dynasty;--[[User:Ding Xuan|Ding Xuan]] ([[User talk:Ding Xuan|talk]]) 12:13, 19 December 2021 (UTC)<br />
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==丁旋 Dīng Xuán 英语语言文学（英美文学） 女 202120081483==<br />
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周、召，即周公旦、召公奭，都是西周的贤相；孔、孟，即孔丘(通称孔子)、孟轲(通称孟子)，都是儒学的创始人；董、韩、周、程、朱、张，即汉代董仲舒、唐代韩愈、北宋周敦颐、北宋程颢和程颐兄弟、南宋朱熹、北宋张载，都是儒学理论家。<br />
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Zhou, called the Duke of Zhou, and Zhao, called Duke of Shi, are both talented prime ministers (in feudal China); Kong (generally called Confucius) and Meng (generally called Mencius) are both founders of Confucianism; Dong (Dong Zhongshu in Han Dynasty), Han (Han Yu in Tang Dynasty), Zhou (Zhou Dunyi in the Northern Song Dynasty), Cheng (Cheng Jing and Cheng Yi brothers in the Northern Song Dynasty), Zhu (Zhu Xi in the Southern Song Dynasty), Zhang (Zhang Zai in the Northern Song Dynasty) are all Confucian theorists. --[[User:Ding Xuan|Ding Xuan]] ([[User talk:Ding Xuan|talk]]) 07:19, 12 December 2021 (UTC)<br />
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Zhou and Zhao are respectively the Duke of Zhou and the Duke of Shi and both are talented prime ministers of the Western Zhou Dynasty; both Kong (generally called Confucius) and Meng (generally called Mencius) are founders of Confucianism; Dong (Dong Zhongshu in Han Dynasty), Han (Han Yu in Tang Dynasty), Zhou (Zhou Dunyi in the Northern Song Dynasty), Cheng (Cheng Jing and Cheng Yi brothers in the Northern Song Dynasty), Zhu (Zhu Xi in the Southern Song Dynasty), Zhang (Zhang Zai in the Northern Song Dynasty) are all Confucian theorists. --[[User:Du Lina|Du Lina]] ([[User talk:Du Lina|talk]]) 08:09, 12 December 2021 (UTC)<br />
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==杜莉娜 Dù Lìnuó 英语语言文学（语言学） 女 202120081484==<br />
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这些人皆是儒家竭力推崇的人物。蚩尤……秦桧──蚩尤、共工，都是传说中上古最凶恶的部族首领；桀、纣、始皇，即夏桀、商纣王、秦始皇，都是登峰造极的暴君；<br />
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All these people are strong recommended by confucianists such as Chi You(a mythological warrior engaged in fighting with the Yellow Emperor), Qin Hui (a traitor in the Song dynasty in Chinese history)and so on. Among them both Chi You and Gong Gong (the water god in ancient Chinses history and the devil of floods) are the most ferocious tribal chief in the Chinese legend;and all Xia Jie, Shang Zhou and Qin Shi Huang, being respectively the emperor Jie of Xia Dynasty，the emperor Zhou of Shang Dynasty and the first emperor of Qin Dynasty, are extremely tyrannical.<br />
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All these people are strongly recommended by Confucianists such as Chi You(a mythological warrior engaged in fighting with the Yellow Emperor), Qin Hui (a traitor in the Song dynasty in Chinese history)and so on. Among them both Chi You and Gong Gong (the water god in ancient Chinses history and the devil of floods) are the most ferocious tribal chief in the Chinese legend;and all Xia Jie, Shang Zhou and Qin Shi Huang, being respectively the emperor Jie of Xia Dynasty，the emperor Zhou of Shang Dynasty and the first emperor of Qin Dynasty, are extremely tyrannical.--[[User:Fu Hongyan|Fu Hongyan]] ([[User talk:Fu Hongyan|talk]]) 14:13, 19 December 2021 (UTC)<br />
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==付红岩 Fù Hóngyán 英语语言文学（英美文学） 女 202120081485==<br />
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王莽、曹操、桓温、安禄山、秦桧，他们分别是汉代、三国、东晋、唐代、南宋人，都是大奸臣乃至叛逆之贼。许由……朝云──许由，传说他是上古时为了逃避帝位而终生隐居的贤人；陶潜(即陶渊明)、阮籍、嵇康、刘伶，都是魏晋时期著名文学家及不与流俗同低昂的独行之士；<br />
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Wang Mang, Cao Cao, Huan Wen, An Lushan and Qin Kuei, who were in the Han Dynasty, the Three Kingdoms, the Eastern Jin Dynasty, the Tang Dynasty and the Southern Song Dynasty respectively, were all great treacherous court officials and even traitors. It were Xu You... Zhao Yun -- Xu You who were said a sage who lived in seclusion all his life in order to escape the throne in ancient times. Tao Qian (Tao Yuanming), Ruan Ji, Ji Kang and Liu Ling were all prestigious persons in the Wei and Jin dynasties and mavericks who did not flow along with the turbid currents of the mainstream thought.--[[User:Fu Hongyan|Fu Hongyan]] ([[User talk:Fu Hongyan|talk]]) 14:10, 19 December 2021 (UTC)<br />
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Wang Mang, Cao Cao, Huan Wen, An Lushan and Qin Kuei, who were in the Han Dynasty, the Three Kingdoms, the Eastern Jin Dynasty, the Tang Dynasty and the Southern Song Dynasty respectively, were all great treacherous court officials and even traitors. Xu You... Zhao Yun -- Xu You. It is said that he was a sage who lived in seclusion all his life in order to escape the throne in ancient times. Tao Qian (Tao Yuanming), Ruan Ji, Ji Kang and Liu Ling were all famous writers in the Wei and Jin dynasties and mavericks who did not flow along with the turbid currents of the world.--[[User:Fu Shiyu|Fu Shiyu]] ([[User talk:Fu Shiyu|talk]]) 11:57, 19 December 2021 (UTC)<br />
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==付诗雨 Fù Shīyǔ 日语语言文学 女 202120081486==<br />
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王谢二族，指东晋王导和谢安，都是显贵；顾虎头，即顾恺之，字虎头，是东晋名画家；陈后主、唐明皇、宋徽宗，都是有才气的风流皇帝；<br />
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The Two families of Wang and Xie, namely Wang Dao and Xie An, were both nobility in the Eastern Jin Dynasty. Gu Hutou, also known as Gu Kaizhi, was a famous painter in the Eastern Jin Dynasty. Emperor Chen Shubao of Chen, Emperor Ming of Tang and Emperor Huizong of Song were all talented and romantic emperors.--[[User:Fu Shiyu|Fu Shiyu]] ([[User talk:Fu Shiyu|talk]]) 10:07, 12 December 2021 (UTC)<br />
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The Two families of Wang and Xie, namely Wang Dao and Xie An, were both of the nobility in the Eastern Jin Dynasty. Gu Hutou, also known as Gu Kaizhi, was a famous painter in the Eastern Jin Dynasty. Emperor Chen Shubao of Chen, Emperor Ming of Tang and Emperor Huizong of Song were all talented and romantic emperors. --[[User:Gao Mi|Gao Mi]] ([[User talk:Gao Mi|talk]]) 01:02, 13 December 2021 (UTC)<br />
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==高蜜 Gāo Mì 翻译学 女 202120081487==<br />
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刘庭芝即刘希夷(字庭芝)、温飞卿即温庭筠(字飞卿)，都是唐代名诗人；米南宫即米芾(南宫为世称)，是北宋名画家；石曼卿即石延年(字曼卿)、柳蓍卿即柳永(字蓍卿)、秦少游即秦观(字少游)，都是北宋著名文学家；<br />
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Liu Tingzhi, or Liu Xiyi (courtesy name Tingzhi), Wen Feiqin refers orWen Tingyun (courtesy name Feiqing) are both famous poets of the Tang Dynasty. Mi Nangong, or Mi Fu (nickname Nangong), was a famous painter of the Northern Song Dynasty; Shi Manqing, or Shi Yannian (courtesy name Manqing), Liu Yaoqing, or Liu Yong (courtesy name Yaoqing), and Qin Shaoyou, or Qin Guan (courtesy name Shaoyou), were famous literary scholars of the Northern Song Dynasty.--[[User:Gao Mi|Gao Mi]] ([[User talk:Gao Mi|talk]]) 01:03, 13 December 2021 (UTC)<br />
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Liu Tingzhi, or Liu Xiyi (styled Tingzhi), Wen Feiqin refers orWen Tingyun (styled Feiqing) are both famous poets of the Tang Dynasty. Mi Nangong, or Mi Fu (nickname Nangong), was a famous painter of the Northern Song Dynasty; Shi Manqing, or Shi Yannian (styled Manqing), Liu Yaoqing, or Liu Yong (styled Yaoqing), and Qin Shaoyou, or Qin Guan (styled Shaoyou), were famous literary scholars of the Northern Song Dynasty.--[[User:Gong Boya|Gong Boya]] ([[User talk:Gong Boya|talk]]) 12:27, 15 December 2021 (UTC)<br />
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==宫博雅 Gōng Bóyǎ 俄语语言文学 女 202120081488==<br />
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倪云林即倪瓒，字云林，是元代名画家；唐伯虎即唐寅(字伯虎)、祝枝山即祝允明(字枝山)，都是明代名画家、文学家；李龟年(唐代人)、黄幡绰(唐代人)、敬新磨(五代后唐人)，都是名艺人；<br />
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Ni Yunlin, i.e Ni Zan, was a famous painter in the Yuan Dynasty. Tang Bohu i.e Tang Yin (styled Bohu), Zhu Zhishan i.e Zhu Yunming (styled Zhishan), are famous Ming dynasty painters, litterateurs; Li Gunian (Tang Dynasty), Huang Fan Chuo (Tang Dynasty), Jing Xinmo (five dynasties later Tang dynasty), are famous artists;--[[User:Gong Boya|Gong Boya]] ([[User talk:Gong Boya|talk]]) 12:23, 15 December 2021 (UTC)<br />
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Ni Yunlin, or Ni Zan, or Ni Yunlin, was a famous painter in the Yuan dynasty; Tang Bohu (or Tang Yin) and Zhu Zhishan (or Zhu Yunming) were famous painters and literary figures in the Ming dynasty; Li Guinian (of the Tang dynasty), Huang Fanchuo (of the Tang dynasty), and Jing Xinmo (of the post-Tang dynasty of the Five Dynasties) were all famous artists.--[[User:He Qin|He Qin]] ([[User talk:He Qin|talk]]) 06:39, 15 December 2021 (UTC)<br />
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==何芩 Hé Qín 翻译学 女 202120081489==<br />
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卓文君(已见第一回注)、红拂(先为隋相杨素的侍女，后私奔李靖，也是前蜀·杜光庭《虬髯客传》中的女主人公)、薛涛(唐代才妓)、崔莺(即唐·元稹《会真记》、元·王实甫《西厢记》中的崔莺莺)、朝云(宋代名妓)，他们都是以才貌流芳的名女。<br />
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Zhuo Wenjun (see the first chapter note), Hong Fu (she used to be as the servant of the Sui minister Yang Su, then eloping to Li Jing; she was also the heroine of ''The Legend of the Gnarled Man'' by Du Guangting), Xue Tao (a talented prostitute of the Tang Dynasty), Cui Ying (i.e. Cui Yingying of Yuan Zhen's ''The Book of Hui Zhen'' and Wang Shifu's "The Western Chamber"), Zhaoyun (a famous prostitute of the Song Dynasty), they are all famous for their talent and beauty. --[[User:He Qin|He Qin]] ([[User talk:He Qin|talk]]) 06:32, 15 December 2021 (UTC)<br />
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Zhuo Wenjun ( the first chapter noted), Hong Fu (she used to be as the servant of the minister of Sui Dynasty Yang Su, then eloping to Li Jing, also the heroine of ''The Legend of the Gnarled Man'' by Du Guangting), Xue Tao (a talented prostitute of the Tang Dynasty), Cui Ying (namely Cui Yingying of Yuan Zhen's ''The Book of Hui Zhen'' and Wang Shifu's "The Western Chamber"), Zhaoyun (a famous prostitute of the Song Dynasty), they are all famous for their talent and beauty.--[[User:Hu Shuqing|Hu Shuqing]] ([[User talk:Hu Shuqing|talk]]) 12:25, 19 December 2021 (UTC)<br />
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==胡舒情 Hú Shūqíng 英语语言文学（语言学） 女 202120081490==<br />
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成则公侯败则贼──意谓成功的人便能获得公爵、侯爵之类的高官显爵，失败的人便被看作贼寇。表示世上并无公理，世人不讲是非，只论成功与失败，即只以成败论英雄。这里化用了“败则盗贼，成则帝王”。<br />
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Success makes the Duke while failure makes the theif ——which means that, If one is successful, he will be worshipped as the Duke. While one is unsuccessful, he will be despised as the thief. It expresses that there is no generally acknowledged truth in the world and people neglect justice and only pay attention to success and failure, that is, the sole measure. It coins a phrase here, “Failure makes a thief， success a king.”--[[User:Hu Shuqing|Hu Shuqing]] ([[User talk:Hu Shuqing|talk]]) 12:25, 19 December 2021 (UTC)<br />
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The winner is Duke, the looser is theif means that the person who succeeded would be entitled like Duke and who failed would be dispised as a theif. It presents that there is no generally acknowledged truth in the world and people neglect justice and only pay attention to success and failure, that is, the sole measure. It coins a phrase here, “Failure makes a thief， success a king.”--[[User:Huang Jinyun|Huang Jinyun]] ([[User talk:Huang Jinyun|talk]]) 14:58, 12 December 2021 (UTC)<br />
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==黄锦云 Huáng Jǐnyún 英语语言文学（语言学） 女 202120081491==<br />
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出自宋·邓牧《君道》：“嘻！天下何常之有？败则盗贼，成则帝王。”东床──指女婿。典出《晋书·王羲之传》、南朝宋·刘义庆《世说新语·雅量》：晋朝太尉郗鉴派人至丞相王导家相婿，王丞相令其到东厢房随意挑选。<br />
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It is cited from Deng Mu's How to Be Emperor, a work in Song dynasty, saying "how can the world be immutable! The loser is the thief, and the winner is the emperor." Dongchuang refers to the son-in-law, used in Books of Jin: Wang Xizhi's Biography" and Liu Yiqing's "Shi Shuo Xin Yu · Elegance" (Southern Song dynasty): In Jin dynasty Tai Wei (supreme government official in charge of military affairs) Xijian sent an underlying to the prime minister Wang Dao's house for taking in a son-in-law, and Prime Minister Wang invite him to choose at will in the east wing.--[[User:Huang Jinyun|Huang Jinyun]] ([[User talk:Huang Jinyun|talk]]) 14:43, 12 December 2021 (UTC)<br />
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It is cited from Deng Mu's How to Be Emperor, a work in Song dynasty, saying "how can the world be immutable! The loser is the thief, and the winner is the emperor." Dongchuang refers to the son-in-law, used in Books of Jin: Wang Xizhi's Biography" and Liu Yiqing's "Shi Shuo Xin Yu · Elegance" (Southern Song dynasty): In Jin dynasty Tai Wei (supreme government official in charge of military affairs) Xijian sent an official to the prime minister Wang Dao's house choosing a son-in-law, and Prime Minister Wang invited him to choose at will in the east wing room.--[[User:Huang Yiyan1|Huang Yiyan1]] ([[User talk:Huang Yiyan1|talk]]) 14:51, 12 December 2021 (UTC)<br />
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==黄逸妍 Huáng Yìyán 外国语言学及应用语言学 女 202120081492==<br />
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此人过去一看，见王家诸郎皆很矜持，唯独王羲之坦腹躺在东床之上，毫不在乎。此人回报，郗鉴即选中王羲之为婿。后世即以“东床”、“东床坦腹”、“东床客”、“东床娇客”等代指女婿。<br />
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The man looked over and saw that all the lords of Wang family were very reserved, except Wang Xizhi, who was lying on the east bed and didn't care, showing his belly. In return, Xi Jian chose Wang Xizhi as his son-in-law. Later generations referred to the son-in-law with "East Bed", "East Bed Man Showing Belly", "East Bed Guest", "East Bed Distinguished Guest" and so on.--[[User:Huang Yiyan1|Huang Yiyan1]] ([[User talk:Huang Yiyan1|talk]]) 04:59, 12 December 2021 (UTC)<br />
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The man looked over and saw that all the lords of Wang family were very reserved, except Wang Xizhi, who was lying on the east bed and didn't care, showing his belly. In return, Xi Jian chose Wang Xizhi as his son-in-law. Later generations referred to the son-in-law with "East Bed", "East Bed Man Showing Belly", "East Bed Guest", "East Bed Distinguished Guest" and so on.--[[User:Huang Zhuliang|Huang Zhuliang]] ([[User talk:Huang Zhuliang|talk]]) 13:54, 19 December 2021 (UTC)Huang Zhuliang<br />
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==黄柱梁 Huáng Zhùliáng 国别 男 202120081493==<br />
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退了一舍之地──意谓退避三十里。形容退居其后，不敢与争。 一舍：三十里。 这里化用了“退避三舍”之典。He retreat thirty miles. It describes retreating behind and not daring to compete with. Yishe: Thirty Li. The code of "retreat and give up" is used here.--[[User:Huang Zhuliang|Huang Zhuliang]] ([[User talk:Huang Zhuliang|talk]]) 13:53, 19 December 2021 (UTC)Huang Zhuliang<br />
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--[[User:Jin Xiaotong|Jin Xiaotong]] ([[User talk:Jin Xiaotong|talk]]) 12:02, 19 December 2021 (UTC)<br />
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==金晓童 Jīn Xiǎotóng 202120081494==<br />
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典出《左传·僖公二十三年》：春秋时，晋国公子重耳出奔至楚，楚成王礼遇之，因问道：“公子若反(返)晋国，则何以报不谷？”重耳对曰：“若以君之灵，得反晋国，晋、楚治兵，遇于中原，其辟(避)君三舍。”<br />
This story comes from ''Zuo Zhuan · Xi public twenty three years'': During the Spring and Autumn Period (777-476 BC), Childe Chong Er of the state of Jin went to the state of Chu. King Cheng of Chu gave a banquet for Chong er and asked, "If childe returns to the state of Jin, how will you repay me? Chong Er answered, "If I can return to the state of Jin, if the troops of the state of Jin and the state of Chu meet each other in the Central Plains, I will ask the troops of the state of Jin to retreat 90 li.--[[User:Jin Xiaotong|Jin Xiaotong]] ([[User talk:Jin Xiaotong|talk]]) 06:56, 12 December 2021 (UTC)<br />
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==邝艳丽 Kuàng Yànl 英语语言文学（语言学） 女 202120081495==<br />
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后重耳返国为君，晋、楚城濮(在今山东省鄄城县西南)之战，重耳遵守诺言，晋军果“退三舍以辟之”。<br />
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第三回<br />
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托内兄如海荐西宾 接外孙贾母惜孤女<br />
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Then Childe Chong Er came back to his country as Emperor. In the battle of Jin and Chu in Chengpu (in southwest of Juancheng county in Shandong province), he keeps his promise, then the army of Jin actually retreated to avoid the war. <br />
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Chapter 3<br />
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Entrust cousin Ru Hai with recommendations of distinguished gusts; Accept granddaughter lady Dowager takes pity on orphan girl<br />
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Then Childe Chong Er came back to his country as Emperor. In the battle of Jin and Chu in Chengpu (in southwest of Juancheng county in Shandong province), he keeps his promise, then the army of Jin actually retreated to avoid the war. <br />
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Chapter 3<br />
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Entrust cousin Ru Hai with recommendations of distinguished gusts; Welcoming her granddaughter, lady Dowager takes pity on the orphan girl.--[[User:Li Aixuan|Li Aixuan]] ([[User talk:Li Aixuan|talk]]) 11:13, 20 December 2021 (UTC)<br />
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==李爱璇 Lǐ Àixuán 英语语言文学（语言学） 女 202120081496==<br />
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却说雨村忙回头看时，不是别人，乃是当日同僚一案参革的张如圭。他系此地人，革后家居，今打听得都中奏准起复旧员之信，他便四下里寻情找门路，忽遇见雨村，故忙道喜。二人见了礼，张如圭便将此信告知雨村。<br />
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Yue-ts'un, turning round in a hurry, perceived that the speaker was no other than a certain Chang Ju-kuei, an old colleague of his, who had been denounced and deprived of office, on account of some case or other; a native of that district, who had, since his degradation, resided in his home.Having come to hear the news that a memorial, presented in the capital, that the former officers (who had been cashiered) should be reinstated, had received the imperial consent, he had promptly done all he could, in every nook and corner, to obtain influence, and to find the means (of righting his position,) when he, unexpectedly, came across Yue-ts'un, to whom he therefore lost no time in offering his congratulations. The two friends exchanged the conventional salutations, and Chang Ju-kuei communicated the tidings to Yue-ts'un.<br />
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Yue-ts'un, speedily looking back on, perceived that the speaker was no other than a certain Chang Ju-kuei, an old colleague of his, who had participated in the former case but been denounced and deprived of office, on account of some case or other. He was a native of that district, who had resided at home since his degradation. Having lately come to hear the news that a memorial, presented in the capital, that the former officers (who had been cashiered) should be reinstated, had received the imperial consent, he had promptly done all he could to obtain influence, and to find the means of righting his position. When he, unexpectedly, came across Yue-ts'un, he offered offering his congratulations to him soon. The two friends greeted to each other, exchanging the conventional salutations, and Chang Ju-kuei forthwith passed on the information to Yue-ts'un.--[[User:Li Ruiyang|Li Ruiyang]] ([[User talk:Li Ruiyang|talk]]) 04:55, 15 December 2021 (UTC)<br />
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==李瑞洋 Lǐ Ruìyáng 英语语言文学（英美文学） 女 202120081497==<br />
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雨村欢喜，忙忙叙了两句，各自别去回家。冷子兴听得此言，便忙献计，令雨村央求林如海，转向都中去央烦贾政。雨村领其意而别，回至馆中，忙寻邸报看真确了。次日，面谋之如海。如海道：“天缘凑巧。<br />
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Yue-ts'un was delighted, but after he had made a few remarks in a hurry, each took his leave and sped on his own way homewards. After hearing this conversation, Leng Tzu-hsing hastened at once to propose a plan, advising Yue-ts'un to request Lin Ju-hai, then, in his turn, to appeal to Chia Cheng in the capital for support. Yue-ts'un accepted the suggestion, and took leave of him. Upon returning to the quarter, he made all haste to lay his hand on the Metropolitan Gazette, to ascertain whether the news was authentic or not. On the next day, he had a personal consultation with Ju-hai. "Providence and good fortune are both alike propitious!" said by Ju-hai.--[[User:Li Ruiyang|Li Ruiyang]] ([[User talk:Li Ruiyang|talk]]) 04:57, 15 December 2021 (UTC)<br />
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Yue-ts'un was delighted, but after he they made a short conversation, each of them stepped on their own way homewards. After hearing the words of Yue-ts'un, Leng Tzu-hsing hastened at once to propose a plan, advising Yue-ts'un to request Lin Ju-hai, then, in his turn, to appeal to Chia Cheng in the capital for support. Yue-ts'un accepted the suggestion, and took leave of him. Upon returning to the quarter, he made all haste to read the Metropolitan Gazette, to ascertain the authenticity of that news. On the next day, he made a personal consultation with Ju-hai. Thus, Ju-hai said, "Providence and good fortune are both alike propitious!" --[[User:Li Shan|Li Shan]] ([[User talk:Li Shan|talk]]) 13:06, 15 December 2021 (UTC)<br />
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==李姗 Lǐ Shān 英语语言文学（英美文学） 女 202120081498==<br />
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因贱荆去世，都中家岳母念及小女无人依傍，前已遣了男女、船只来接，因小女未曾大痊，故尚未行。此刻正思送女进京。因向蒙教训之恩，未经酬报，遇此机会，岂有不尽心图报之理？弟已预筹之，修下荐书一封，托内兄务为周全，方可稍尽弟之鄙诚；<br />
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Since my wife has passed away, my mother-in-law has long before dispatched servants and transporting boats here to fetch my lonely daughter. But she has not set off yet due to the fact that she had not fully recovered at that time. As she is in good condition now, I am considering sending her to her grandma's. Once you have taught my daughter but desired no handsome payment; while now you need help, how can I sit on the fence? I have already well prepared for that in advance --- a recommendation letter has been written to my brother-in-law, to ensure your success in career. Only in this way can I show my gratitude towards you.--[[User:Li Shan|Li Shan]] ([[User talk:Li Shan|talk]]) 08:15, 11 December 2021 (UTC)<br />
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Since my wife has passed away, my mother-in-law who lives in the capital worried that my daughter has no one to rely on. So she has long before dispatched servants and transporting boats here to fetch my lonely daughter. But she has not set off yet due to the fact that she had not fully recovered at that time. As she is in good condition now, I am considering sending her to her grandma's. Once you have taught my daughter but desired no handsome payment; while now you need help, how can I sit on the fence? I have already well prepared for that in advance --- a recommendation letter has been written to my brother-in-law, to ensure your success in career. Only in this way can I show my gratitude towards you.--[[User:Li Shuang|Li Shuang]] ([[User talk:Li Shuang|talk]]) 07:43, 12 December 2021 (UTC)<br />
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==李双 Lǐ Shuāng 翻译学 女 202120081499==<br />
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即有所费，弟于内家信中写明，不劳吾兄多虑。”雨村一面打恭，谢不释口；一面又问：“不知令亲大人现居何职？只怕晚生草率，不敢进谒。”如海笑道：“若论舍亲，与尊兄犹系一家，乃荣公之孙：大内兄现袭一等将军之职，名赦，字恩侯；<br />
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“As for the possible costs, I will explain in the letter. You don’t need to worry about it.” Yu Cun bent down and expressed his gratitude, asking: “What does your brother do now? I’m worried that I would take the liberty to pay a visit, it’s too hasty.” Ru Hai laughed and said: “My brother and your brother belong to the same family. They are both descendants of Origin Merchant. My eldest brother is now a first-class general, his name is Pardon Merchant, whose alternative given name is Enhou.”--[[User:Li Shuang|Li Shuang]] ([[User talk:Li Shuang|talk]]) 07:38, 12 December 2021 (UTC)<br />
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“As for the possible costs, I will explain in the letter. You needn’t to worry about it.” Yu Cun bent down and expressed his gratitude, asking: “What does your brother do now? I’m worried that I would take the liberty to pay a visit, it’s too hasty.” Ru Hai laughed and said: “My brother and your brother belong to the same family. They are both descendants of Ronggong. The eldest brother of my wife is now a first-class general, his name is Pardon Merchant, whose alternative given name is Enhou.” --[[User:Li Wenxuan|Li Wenxuan]] ([[User talk:Li Wenxuan|talk]]) 23:46, 12 December 2021 (UTC)<br />
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==李文璇 Lǐ Wénxuán 英语语言文学（英美文学） 女 202120081500==<br />
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二内兄名政，字存周，现任工部员外郎，其为人谦恭厚道，大有祖父遗风，非膏粱轻薄之流，故弟致书烦托，否则不但有污尊兄清操，即弟亦不屑为矣。”雨村听了，心下方信了昨日子兴之言，于是又谢了林如海。<br />
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“The second brother of my wife named Zheng, his style name is Cunzhou. He is the Yuanwai official of the Ministry of Works in feudal China. He is moderate and kind, has the dignity of his grandfather, and is not the flimsy type. Therefore, my brother sent a letter to me. Otherwise, I will not only pollute my brother's operation, but also despise my brother.” After hearing this, Yuchun had believed the words of Zixing yesterday, therefore, he thanked Lin Ruhai again. --[[User:Li Wenxuan|Li Wenxuan]] ([[User talk:Li Wenxuan|talk]]) 01:27, 12 December 2021 (UTC)<br />
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The second brother-in-law is named Zheng, and the word is kept in Zhou. He is currently a member of the Ministry of Engineering. He is courteous and kind. He has a grandfather's legacy. He is not anointing and frivolous. Disdainful. "Yucun listened, and believed in Xing's words from yesterday, so he thanked Lin Ruhai again.--[[User:Li Wen|Li Wen]] ([[User talk:Li Wen|talk]]) 14:12, 12 December 2021 (UTC)<br />
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==李雯 Lǐ Wén 英语语言文学（英美文学） 女 202120081501==<br />
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如海又说：“择了出月初二日小女入都，吾兄即同路而往，岂不两便？”雨村唯唯听命，心中十分得意。如海遂打点礼物并饯行之事，雨村一一领了。那女学生原不忍离亲而去，无奈他外祖母必欲其往，且兼如海说：“汝父年已半百，再无续室之意；<br />
Ruhai also said: "I chose the girl to enter the capital on the second day of the lunar month, and my brother will go the same way. Isn't it both convenient?" Yucun obeyed,and RuHai was very satisfied . Ruhai then took some gifts and walked away, and Yucun took them one by one. The girl student couldn't bear to leave her relatives, but his grandmother wanted to go there. She also said like the sea: "Your father is half a hundred years old, and there is no intention to remarry.--[[User:Li Wen|Li Wen]] ([[User talk:Li Wen|talk]]) 14:11, 12 December 2021 (UTC)<br />
Ruhai said, "I chose the second day of the month to enter the capital, and my brother went the same way. Rain village obedient, the heart is very proud. Such as Haisui make gifts and farewell dinner, Rain village one by one. The girl could not bear to leave her, but her grandmother wanted her to go, saying, "Your father is fifty years old, and has no intention of staying in the house;--[[User:Li Xinxing|Li Xinxing]] ([[User talk:Li Xinxing|talk]]) 12:27, 19 December 2021 (UTC)<br />
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==李新星 Lǐ Xīnxīng 亚非语言文学 女 202120081503==<br />
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且汝多病，年又极小，上无亲母教养，下无姊妹扶持。今去依傍外祖母及舅氏姊妹，正好减我内顾之忧，如何不去？”黛玉听了，方洒泪拜别，随了奶娘及荣府中几个老妇登舟而去。雨村另有船只，带了两个小童，依附黛玉而行。<br />
You are sick, you are young, you have no mother to nurse you, and no sisters to nurse you. Now I am going to my grandmother and my uncle and sisters, which will relieve my worries. Why not?" When Daiyu heard this, she said goodbye with tears and followed the wet nurse and some old women in the Rong House to the boat. Yucun had another boat with two children attached to Daiyu.--[[User:Li Xinxing|Li Xinxing]] ([[User talk:Li Xinxing|talk]]) 12:25, 19 December 2021 (UTC)<br />
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And you are sick, very young, no mother to raise, no sister support. Today I go to rely on my grandmother and uncle's sisters, just to reduce my internal worries, how not to go? Dai Yu listened, and Fang shed tears to say goodbye, and followed the grandmother and several old women in the Rong Mansion to board the boat. There was another boat in the rain village, with two children, who were attached to Daiyu.--[[User:Li Yi|Li Yi]] ([[User talk:Li Yi|talk]]) 12:29, 19 December 2021 (UTC)<br />
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==李怡 Lǐ Yí 法语语言文学 女 202120081504==<br />
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一日到了京都，雨村先整了衣冠，带着童仆，拿了宗侄的名帖，至荣府门上投了。彼时贾政已看了妹丈之书，即忙请入相会。见雨村相貌魁伟，言谈不俗；且这贾政最喜的是读书人，礼贤下士，拯溺救危，大有祖风；况又系妹丈致意：因此优待雨村，更又不同。<br />
One day, when YuCun arrived in Jingdou, he dressed himself, and went to Rongfu with his nephew's name card. At this time Jia Zheng had seen his brother-in-law's letter, immediately invited him to come in to meet. Yucun looked tall and handsome and talked well. And Jia Zheng most like scholar, courtesy, saving, great predecessors style; Therefore, Jia Zheng is very good to Yucun. He is different from others.--[[User:Li Yi|Li Yi]] ([[User talk:Li Yi|talk]]) 08:18, 11 December 2021 (UTC)<br />
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One day, when YuCun arrived in Jingdou, he dressed himself, and went to Rongfu with his nephew's name card. At the very moment that Jia Zheng had received his brother-in-law's letter, he immediately invited him to come in to meet. Yucun looked tall and handsome and talked well. And Jia Zheng most like scholar, courtesy, saving, great predecessors style; Therefore, Jia Zheng is very good to Yucun. He is different from others--[[User:Liu Peiting|Liu Peiting]] ([[User talk:Liu Peiting|talk]]) 12:39, 19 December 2021 (UTC)<br />
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==刘沛婷 Liú Pèitíng 英语语言文学（英美文学） 女 202120081505==<br />
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便极力帮助，题奏之日，谋了一个复职。不上两月，便选了金陵应天府，辞了贾政，择日到任去了，不在话下。且说黛玉自那日弃舟登岸时，便有荣府打发轿子并拉行李车辆伺候。这黛玉尝听得母亲说，他外祖母家与别人家不同<br />
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They tried to help, the day of the title, sought a reinstatement. Within two months, he was elected to Jinling Yingtianfu, resigned from Jia Zheng, and left for his post on a certain day. Now, when Daiyu abandoned her boat and landed on the shore that day, she was served by a sedan chair sent by the Rongfu and a luggage cart. Daiyu heard from her mother that her grandmother's family was different from others.--[[User:Liu Peiting|Liu Peiting]] ([[User talk:Liu Peiting|talk]]) 12:40, 19 December 2021 (UTC)<br />
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They sought to a position the day that he presented a petition to the throne. Within two months, he was elected to Jinling Mansion, resigned from Jia Zheng, and left for his post on a certain day. Now, when Daiyu disembarked that day, she was served by a sedan chair sent by the Rong Mansion and a luggage cart. Daiyu heard from her mother that her grandmother's family was different from others.--[[User:Liu Shengnan|Liu Shengnan]] ([[User talk:Liu Shengnan|talk]]) 12:44, 19 December 2021 (UTC)<br />
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==刘胜楠 Liú Shèngnán 翻译学 女 202120081506==<br />
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他近日所见的这几个三等的仆妇，吃穿用度，已是不凡；何况今至其家，都要步步留心，时时在意，不要多说一句话，不可多行一步路，恐被人耻笑了去。自上了轿，进了城，从纱窗中瞧了一瞧，其街市之繁华，人烟之阜盛，自非别处可比。<br />
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In the past few days, she has been deeply impressed by the food, clothing and behavior of the low- ranking attendants who accompanied her. She decided that in their new home, she must always be vigilant and carefully weigh every word so as not to be ridiculed for any stupid mistake. When she carried into the city, she peeped out through the gauze window on her chair at the bustling and crowded streets, which she had never seen before.--[[User:Liu Shengnan|Liu Shengnan]] ([[User talk:Liu Shengnan|talk]]) 08:32, 11 December 2021 (UTC)<br />
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The three-class servants she had seen recently have an extraordinary cost of food and clothing. What's more, since got on the sedan chair and entered the city, the prosperous market and the populousness of the city through the screen window were not comparable to other places. when coming to grandmother's mansion must pay attention to every step and be cautious with words so as not to be ridiculed by others. Daiyu thought. --[[User:Liu Wei|Liu Wei]] ([[User talk:Liu Wei|talk]]) 15:56, 12 December 2021 (UTC)Liu Wei<br />
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==刘薇 Liú Wēi 国别 女 202120081507==<br />
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又行了半日，忽见街北蹲着两个大石狮子，三间兽头大门，门前列坐着十来个华冠丽服之人。正门不开，只东、西两角门有人出入。正门之上有一匾，匾上大书“敕造宁国府”五个大字。黛玉想道：“这是外祖的长房了。”<br />
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After half day, there are two large stone lions squatting on the north side of the street, three gates decorated with beast head, and a dozen people in gorgeous crowns and clothes are sitting in front of the gate. The main gate is closing, only the east and west corners enterences are accessible. There is a plaque above the main gate with five big characters "Ningguo Mansion". "That must be grandfather's the first son's mansion."Daiyu thought. --[[User:Liu Wei|Liu Wei]] ([[User talk:Liu Wei|talk]]) 15:39, 12 December 2021 (UTC)Liu Wei<br />
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After another half-day walk, they came to a street with two huge stone lions crouching on the north side and three gates decorated with beast head, in front of which ten or more people in gorgeous crowns and clothes were sitting. The main gate was shut, with only people passing in and out of the other two smaller gates. On a board above the main gate was written in big characters "Ningguo Mansion Built at Imperial Command". Daiyu realized that this must be where the elder branch of her grandmather's family lived.--[[User:Liu Xiao|Liu Xiao]] ([[User talk:Liu Xiao|talk]]) 05:40, 13 December 2021 (UTC)<br />
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==刘晓 Liú Xiǎo 英语语言文学（英美文学） 女 202120081508==<br />
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又往西不远，照样也是三间大门，方是荣国府，却不进正门，只由西角门而进。轿子抬着走了一箭之远，将转弯时便歇了轿，后面的婆子也都下来了。另换了四个眉目秀洁的十七八岁的小厮上来抬着轿子，众婆子步下跟随。<br />
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A little further to the west they came to another three gates. This was the Rong Mansion. Instead of going through the main gate, they entered the one on the west. The bearers carried the chair a bow-shot further, and then set it down at the turning and withdrew, the maidservants now going down the chair. Another four seventeen or eighteen smartly dressed lads picked up the chair, followed by the maids.--[[User:Liu Xiao|Liu Xiao]] ([[User talk:Liu Xiao|talk]]) 05:09, 12 December 2021 (UTC)<br />
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Not far to the west is the same three-room gate, which is the Rongguo Mansion. Instead of going through the main gate, they entered the one on the west. The bearers carried the chair a bow-shot further, and then set it down at the turning and withdrew, the maidservants now going down the chair. Another four seventeen or eighteen smartly dressed lads picked up the chair, followed by the maids.--[[User:Liu Yue|Liu Yue]] ([[User talk:Liu Yue|talk]]) 07:26, 12 December 2021 (UTC)<br />
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==刘越 Liú Yuè 亚非语言文学 女 202120081509==<br />
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至一垂花门前落下，那小厮俱肃然退出。众婆子上前打起轿帘，扶黛玉下了轿。黛玉扶着婆子的手，进了垂花门，两边是超手游廊，正中是穿堂，当地放着一个紫檀架子大理石屏风。转过屏风，小小三间厅房。<br />
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When the palanquin was dropped in front of a pendant door, the attendants all retired in silence. The ladies came forward and raised the curtain of the palanquin and helped Daiyu out of the palanquin. The two sides of the door are overhand corridors, and the centre is a hall with a marble screen on a rosewood frame. Turning past the screen, there is a small three-room hall.--[[User:Liu Yue|Liu Yue]] ([[User talk:Liu Yue|talk]]) 07:22, 12 December 2021 (UTC)<br />
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After the palanquin was dropped in front of a floral-pendant gates, the attendants all retreated in silence. The maids came forward and drew the curtain of the palanquin to help Black Jade out of the palanquin. Holding the hands of those maids, Black Jade enter the floral-pendant gates. On the two sides of the door were Chaoshou veranda, and in the center was a vestibule with a marble screen in a rosewood frame. Past the screen, there were three small rooms. --[[User:Liu Yunxin|Liu Yunxin]] ([[User talk:Liu Yunxin|talk]]) 13:01, 19 December 2021 (UTC)<br />
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==刘运心 Liú Yùnxīn 英语语言文学（英美文学） 女 202120081510==<br />
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厅后便是正房大院：正面五间上房，皆是雕梁画栋；两边穿山游廊、厢房，挂着各色鹦鹉、画眉等雀鸟。台阶上坐着几个穿红着绿的丫头，一见他们来了，都笑迎上来道：“刚才老太太还念诵呢，可巧就来了。”于是三四人争着打帘子。一面听得人说：“林姑娘来了。”<br />
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Behind the banqueting hall was the courtyard of the principal rooms: the five principal rooms on the front all had carved beams and painted rafters; from the roof of the verandah on both sides, the cages of parrots, thrushes and various birds hung there. A few girls in red and green sat on the steps. Saw they coming, the girls all smiled and came up: “Just now grandma was taking about you. You arrived just in time.” Then they rushed to pull the curtain. From the other side, a man said: “Miss Lin is coming.”--[[User:Liu Yunxin|Liu Yunxin]] ([[User talk:Liu Yunxin|talk]]) 12:28, 19 December 2021 (UTC)<br />
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Behind the hall was the courtyard of the central house: five principal rooms in the front with carved beams, while along two sides corridors and chambers with colorfully painted birds as parrots and thrush. There were several maids in red and green sitting on the steps. Seeing visitors coming, they greeted them with smiles, saying, "The old lady just talked about you again and again in anticipation, and here you are." then the four maids scrambled to open the curtain. Meanwhile a man said, "Miss Lin is coming."--[[User:Luo Anyi|Luo Anyi]] ([[User talk:Luo Anyi|talk]]) 12:47, 19 December 2021 (UTC)<br />
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==罗安怡 Luó Ānyí 英语语言文学（英美文学） 女 202120081511==<br />
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黛玉方进房，只见两个人扶着一位鬓发如银的老母迎上来。黛玉知是外祖母了，正欲下拜，早被外祖母抱住，搂入怀中，“心肝儿肉”叫着大哭起来。当下侍立之人无不下泪，黛玉也哭个不休。众人慢慢解劝，那黛玉方拜见了外祖母。<br />
An silver-haired old lady supported by two maids came and welcomed her while Black Jade entered the room. Realizing that this was her grandmother, Black Jade was about to bow down to show her respects. Suddenly she was tightly hugged by grandmoa who crying out harrowingly "my sweet heart!" Servants and maids were all in tears, and Black Jade also sobbed unceasingly. People persuaded them softly, then Black Jade was able to pay her respects to her grandmother. --[[User:Luo Anyi|Luo Anyi]] ([[User talk:Luo Anyi|talk]]) 12:14, 19 December 2021 (U<br />
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As Dai Yu just came to the home, there came a silver-haired old woman.Dai Yu knew she was grandma, and as she was going to bow down to show her respect, she has been already hugged by her grandma, who cried:"my sweety!". The surrounding maids all cried, as well as Dai Yu. The crowds slowy persuade her, and Dai Yu then showed her respect to her grandma.--[[User:Luo Xi|Luo Xi]] ([[User talk:Luo Xi|talk]]) 14:01, 19 December 2021 (UTC)<br />
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==罗曦 Luó Xī 英语语言文学（英美文学） 女 202120081512==<br />
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贾母方一一指与黛玉道：“这是你大舅母。这是二舅母。这是你先前珠大哥的媳妇珠大嫂子。”黛玉一一拜见。贾母又说：“请姑娘们。今日远客来了，可以不必上学去。”众人答应了一声，便去了两个。<br />
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Baoyu's grandmother than introduce them respectively:"This is your eldest aunt, and this is your second aunt.This is your Zhu brother's wife. Dai Yu greet them one by one.Baoyu's grandmother than said that:"Invite the gilrs. Today here come the dear guest, so they don't need to go to school." Surrounding people said yes, and two of them left.<br />
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The Lady Dowager then introduced them respectively to Daiyu: "This is your eldest aunt, and this is your second uncle's wife. This is your deceased elder brother Zhu's wife." Daiyu greeted them one by one. Her grandmother then said: "Tell all the girls to come here. We have visitor who came from afar, so they don't need to go to school." Surrounding people answered yes, and two of them left to call them.--[[User:Ma Xin|Ma Xin]] ([[User talk:Ma Xin|talk]]) 07:22, 20 December 2021 (UTC)<br />
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==马新 Mǎ Xīn 外国语言学及应用语言学 女 202120081513==<br />
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不一时，只见三个奶妈并五六个丫鬟，拥着三位姑娘来了：第一个肌肤微丰，身材合中，腮凝新荔，鼻腻鹅脂，温柔沉默，观之可亲；第二个削肩细腰，长挑身材，鸭蛋脸儿，俊眼修眉，顾盼神飞，文彩精华，见之忘俗；<br />
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In a little while, three grannies and five or six servant girls turned up, clustering with three ladies. The first was somewhere plump in figure and of average height; her cheek was in beautiful shape, like a fresh lichee; her nose was glossy like the goose grease; she was gentle and quiet in nature, who looks very friendly. The second was thin and tall with an oval face, sparking eyes and long eyebrows; her elegance and quick-witted mind tickle people’s fancy, letting them forget everything vulgar.--[[User:Ma Xin|Ma Xin]] ([[User talk:Ma Xin|talk]]) 08:11, 11 December 2021 (UTC)<br />
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After a while, the three young ladies showed up, escorted by three wet nurses and five or six maids. The first was slightly plump and of medium height; her cheeks were as smooth and soft as the newly ripened lichees, and her nose was as glossy as goose fat. She was tender and reticent, and looked very affable. The second had drooping shoulders and a slender waist; she was tall and slim, with an oval face, bright and piercing eyes as well as delicate eyebrows. She seemed elegant, quick-witted and in high spirits, with a display of distinctive charm. People who looked at her were to forget everything vulgar and tawdry.--[[User:Mao Yawen|Mao Yawen]] ([[User talk:Mao Yawen|talk]]) 23:42, 11 December 2021 (UTC)<br />
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==毛雅文 Máo Yǎwén 英语语言文学（英美文学） 女 202120081514==<br />
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第三个身量未足，形容尚小：其钗环裙袄，三人皆是一样的妆束。黛玉忙起身，迎上来见礼，互相厮认，归了坐位。丫鬟送上茶来。不过叙些黛玉之母如何得病，如何请医服药，如何送死发丧。不免贾母又伤感起来，因说：“我这些女孩儿，所疼的独有你母亲。<br />
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The third one was not yet fully grown, and she still had the face of a child. All the three young ladies were dressed in similar garments, that is, the tunics and the skirts with the same bracelets and head ornaments. Daiyu hastily rose to greet politely these cousins, and then they introduced to and acquainted with each other, after which they took seats while the maids served the tea. All their talk now was about Daiyu's mother: the culprit for her illness, the medicine that the doctors prescribed for treating her disease, and the conduction of her funeral and mourning ceremonies. Inevitably, the Lady Dowager couldn't help being affected painfully. "Of all my chilren I loved your mother best," she told Daiyu.--[[User:Mao Yawen|Mao Yawen]] ([[User talk:Mao Yawen|talk]]) 07:49, 11 December 2021 (UTC)<br />
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==毛优 Máo Yōu 俄语语言文学 女 202120081515==<br />
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今一旦先我而亡，不得见面，怎不伤心！”说着，携了黛玉的手，又哭起来。众人都忙相劝慰，方略略止住。众人见黛玉年纪虽小，其举止言谈不俗；身体面貌虽弱不胜衣，却有一段风流态度，便知他有不足之症。<br />
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Once she died before me, I could not see her again. She said, taking Daiyu's hand, and cried again. Everyone was busy trying to console her, and soon she slightly stopped. They saw that although Daiyu was young, her manner and speech were not ordinary; although her health was weak, she had graceful and elegant manner, so they knew that she had a disease of deficiency.--[[User:Mao You|Mao You]] ([[User talk:Mao You|talk]]) 08:43, 11 December 2021 (UTC)<br />
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"Once she died before me, it is so sad that I could not see her again." she said, taking Daiyu's hand, and cried again. Everyone was trying to console her, and then she slightly stopped. They saw that although Daiyu was young, her manner and speech were not ordinary; although she was weak, she had graceful and elegant gestures, so they learned that she had a disease of deficiency.--[[User:Mou Yixin|Mou Yixin]] ([[User talk:Mou Yixin|talk]]) 06:35, 12 December 2021 (UTC)<br />
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==牟一心 Móu Yīxīn 英语语言文学（英美文学） 女 202120081516==<br />
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因问：“常服何药？为何不治好了？”黛玉道：“我自来如此，从会吃饭时便吃药到如今了，经过多少名医，总未见效。那一年我才三岁，记得来了一个癞头和尚，说要化我去出家，我父母自是不从。<br />
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So they asked:" What medicine do you usually take? Why doesn't it work?" Daiyu replied:" I am used to getting along with my disease. I have been taking medicine since I could eat. A lot of famous daocters cannot contribute to my illness.When I was three years old, a monk with favus on the head came to persuade me to become a nun,but my parents declined him.--[[User:Mou Yixin|Mou Yixin]] ([[User talk:Mou Yixin|talk]]) 08:07, 11 December 2021 (UTC)<br />
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They then asked, "What medicines do you take regularly? Why can't you cure your illness?" Daiyu said, "I am used to getting along with my disease. I have been taking medicine since I could eat. A lot of famous docters cannot contribute to my illness. I was only three years old when I remember a mangy monk came and said he wanted to convert me to a monk, but my parents refused.--[[User:Peng Ruixue|Peng Ruixue]] ([[User talk:Peng Ruixue|talk]]) 06:29, 13 December 2021 (UTC)<br />
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==彭瑞雪 Péng Ruìxuě 法语语言文学 女 202120081517==<br />
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他又说：‘既舍不得他，但只怕他的病，一生也不能好的；若要好时，除非从此以后，总不许见哭声，除父母之外，凡有外亲，一概不见，方可平安了此一生。’这和尚疯疯癫癫，说了这些不经之谈，也没人理他。<br />
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The monk said, "if you can't bear to part with her she'll probably nerver get well. The only remedy is to keep her from hearing weeping and from seeing any relatives apart from her father and mother. That's her only hope of having a quiet life." No one paid any attention, of course, to such crazy talk.--[[User:Peng Ruixue|Peng Ruixue]] ([[User talk:Peng Ruixue|talk]]) 06:24, 13 December 2021 (UTC)<br />
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The monk said, "if you can't bear to separate with her, she'll probably nerver get well. The only remedy is to keep her from hearing weeping and from seeing any relatives apart from her father and mother. That's her only hope of having a quiet life." No one paid any attention, of course, to such nonsense talk.--[[User:Qing Jianan|Qing Jianan]] ([[User talk:Qing Jianan|talk]]) 12:12, 19 December 2021 (UTC)<br />
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==秦建安 Qín Jiànān 外国语言学及应用语言学 女 202120081518==<br />
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如今还是吃人参养荣丸。”贾母道：“这正好，我这里正配丸药呢，叫他们多配一料就是了。”一语未完，只听后院中有笑语声，说：“我来迟了，没得迎接远客。”黛玉思忖道：“这些人个个皆敛声屏气如此，这来者是谁，这样放诞无礼？”<br />
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Now Lin Daiyu is still taking ginseng pills.And Grandma Jia said:" What a coincidence! The pills are making now, I just tell them to add one." The words have not been finished, but there is a laugh in the back yard, which said:" I come late and fail to welcome our distinguished guest." Daiyu thought: all people here are holding their breath, who is this person that is so arrogant and rude?--[[User:Qing Jianan|Qing Jianan]] ([[User talk:Qing Jianan|talk]]) 08:19, 11 December 2021 (UTC)<br />
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Now Lin Daiyu is still taking ginseng pills. And Grandmother Jia said:" It just so happens that I have been asking them to dispense the pills, just asking them to do one more portion." The words are not finished, but there is a laugh in the back yard, which said:" I am late and fail to welcome our distinguished guest." Daiyu thought: “all people here are holding their breath, who is this person that is so arrogant and rude?”--[[User:Qiu Tingting|Qiu Tingting]] ([[User talk:Qiu Tingting|talk]]) 09:33, 12 December 2021 (UTC)<br />
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==邱婷婷 Qiū Tíngtíng 英语语言文学（语言学）女 202120081519==<br />
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心下想时，只见一群媳妇、丫鬟拥着一个丽人，从后房进来。这个人打扮与姑娘们不同，彩绣辉煌，恍若神妃仙子：头上戴着金丝八宝攒珠髻，绾着朝阳五凤挂珠钗；项上戴着赤金盘螭缨络圈；身上穿着缕金百蝶穿花大红云缎窄褃袄，外罩五彩刻丝石青银鼠褂；<br />
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While Lin Daiyu is still thinking about it, a group of daughters-in-law and maids cluster around a beauty coming in from the back room. She dresses up differently from other girls, with colorful embroidery splendor, and looks like a divine concubine or a fairy: wearing a gold silk beads bun decorated with eight treasures and the five phoenix hairpin hanging with beads on the head; a red gold coiled chi dragon tassel ring around the neck; the bright red made of cloud satin material narrow lining cotton jacket with decorations of wisps of gold hundred butterflies and flowers, and the outer coat with decorations of the multicolored engraved silk stone green silver mouse. --[[User:Qiu Tingting|Qiu Tingting]] ([[User talk:Qiu Tingting|talk]]) 09:34, 12 December 2021 (UTC)<br />
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Lin Daiyu was still thinking about it when she saw a group of daughters-in-law and maids embracing a beautiful woman who came in from the back room. This woman dresses differently from the girls, with colorful embroidery splendor, and looks like a divine concubine fairy: wearing a gold silk eight treasure save beads bun and the sunrise five phoenix hanging beads hairpin on the head; a red gold coiled chi dragon tassel ring around the neck; wearing the bright red made of cloud satin material narrow lining cotton jacket with decorations of wisps of gold hundred butterflies and flowers, and the outer coat with decorations of the multicolored engraved silk stone green silver mouse.--[[User:Rao Jinying|Rao Jinying]] ([[User talk:Rao Jinying|talk]]) 07:47, 11 December 2021 (UTC)<br />
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==饶金盈 Ráo Jīnyíng 英语语言文学（语言学） 女 202120081520==<br />
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下着翡翠撒花洋绉裙。一双丹凤三角眼，两弯柳叶吊梢眉。身量苗条，体格风骚。粉面含春威不露，丹唇未启笑先闻。黛玉连忙起身接见。贾母笑道：“你不认得他。他是我们这里有名的一个泼辣货，南京所谓‘辣子’，你只叫他‘凤辣子’就是了。”<br />
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Wang Xifeng wore a jadeite flowered dress underneath, with a pair of phoenix triangle eyes and two curved willow hanging eyebrows. Her figure is slim and her physique is flirtatious. She can be described with “ the face is delicate and beautiful, spirited character of her is not revealed in the appearance, red lips beautiful, not yet open mouth first heard her laugh”. Lin Daiyu hastily got up to curtsy to her. Lady Dowager said with a smile, "You do not recognize her. She is famous for her boldness and vigorousness here, she is truly the 'chilli woman' in Nanjing dialect, you can just call her ' chilli Feng'."--[[User:Rao Jinying|Rao Jinying]] ([[User talk:Rao Jinying|talk]]) 07:35, 11 December 2021 (UTC)<br />
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Wang Xifeng, characterized by a pair of phoenix triangle eyes and two curved willow hanging eyebrows, wore an emerald flowered crepe skirt. She was slender and coquettish, with a delicate face and a smiling lip. Daiyu promptly rose quickly to greet her. Lady Dowager said with a smile: “ you don’t know him. He is famous for her fierceness and toughness, namely the so-called Nanjing chilli. So you can just call him ‘Chilli Feng’.”--[[User:Shi Liqing|Shi Liqing]] ([[User talk:Shi Liqing|talk]]) 12:48, 11 December 2021 (UTC)<br />
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==石丽青 Shí Lìqīng 英语语言文学（英美文学） 女 202120081521==<br />
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黛玉正不知以何称呼，众姊妹都忙告诉黛玉道：“这是琏二嫂子。”黛玉虽不曾识面，听见他母亲说过：大舅贾赦之子贾琏，娶的就是二舅母王氏的内侄女，自幼假充男儿教养，叫做王熙凤学名。黛玉忙陪笑见礼，以“嫂”呼之。<br />
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Daiyu was insensible of what to call her. Then her sisters told her promptly: “ this is your sister-in-law Lian Er.” Although Daiyu had never met her, she heard of her from his mother: Jia Lian, the son of her Uncle Jia She, had married the niece of Aunt Wang, named scientifically Wang Xifeng, was brought up as a male offspring since childhood. Daiyu was engaged in smiling and saluting at her, calling her “sister-in-law”.--[[User:Shi Liqing|Shi Liqing]] ([[User talk:Shi Liqing|talk]]) 12:32, 11 December 2021 (UTC)<br />
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Daiyu didn't know what to call her. Then her sisters told her promptly: “This is your sister-in-law Lian Er.” Although Daiyu had never met her, she heard of her from his mother: Jia Lian, the son of her Uncle Jia She, had married the niece of Aunt Wang.She was brought up as a male offspring since childhood and her academic name is Wang Xifeng. Daiyu was engaged in smiling and saluting at her, calling her “sister-in-law”.--[[User:Sun Yashi|Sun Yashi]] ([[User talk:Sun Yashi|talk]]) 01:55, 13 December 2021 (UTC)<br />
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==孙雅诗 Sūn Yǎshī 外国语言学及应用语言学 女 202120081522==<br />
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这熙凤携着黛玉的手，上下细细打量了一回，便仍送至贾母身边坐下，因笑道：“天下真有这样标致人儿！我今日才算看见了。况且这通身的气派，竟不像老祖宗的外孙女儿，竟是嫡亲的孙女儿似的，怨不得老祖宗天天嘴里心里放不下。<br />
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Taking Daiyu's hand, Xifeng looked up and down her carefully, then she sent her to Mother Jia's side to sit down.She laughed and said:"There is really such a beautiful person in the world!I didn't see her until today.Moreover,the style of her makes her be more like your son's daughter than your daughter's daughter.It's no wonder that you are concerned about her so much.--[[User:Sun Yashi|Sun Yashi]] ([[User talk:Sun Yashi|talk]]) 01:49, 13 December 2021 (UTC)<br />
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Taking Daiyu's hand, Xifeng looked her up and down carefully, then sent her to Mother Jia's side to sit down. She laughed and said:"There is really such a beautiful person in the world! I haven’t seen her until today. Moreover, her extraordinary temperament makes her be more like your son's daughter rather than your daughter's daughter. It's no wonder that you are concerned about her so much. --[[User:Wang Lifei|Wang Lifei]] ([[User talk:Wang Lifei|talk]]) 06:48, 13 December 2021 (UTC)<br />
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==王李菲 Wáng Lǐfēi 英语语言文学（英美文学） 女 202120081523==<br />
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只可怜我这妹妹这么命苦，怎么姑妈偏就去世了呢？”说着便用帕拭泪。贾母笑道：“我才好了，你又来招我；你妹妹远路才来，身子又弱，也才劝住了：快别再提了。”<br />
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I pity my sister for being so miserable, how could my aunt died so early?" She said, wiping her tears with her handkerchief. Grandma Jia laughed and said, "I've just recovered. You come to provoke me again. Your sister has just arrived from a long journey and is weak, so she has just been persuaded: Don't mention it again."--[[User:Wang Lifei|Wang Lifei]] ([[User talk:Wang Lifei|talk]]) 02:37, 12 December 2021 (UTC)<br />
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I pity my sister who is so miserable, how could my aunt have died?" She said, wiping her tears with her handkerchief. Your sister has only just arrived from a long journey and is weak, so she has only just been persuaded to stop talking about it."--[[User:Wang Yifan21|Wang Yifan21]] ([[User talk:Wang Yifan21|talk]]) 08:22, 11 December 2021 (UTC)<br />
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==王逸凡 Wáng Yìfán 亚非语言文学 女 202120081524==<br />
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熙凤听了，忙转悲为喜道：“正是呢，我一见了妹妹，一心都在他身上，又是喜欢，又是伤心，竟忘了老祖宗了。该打，该打！”又忙拉着黛玉的手问道：“妹妹几岁了？可也上过学？现吃什么药？在这里别想家。<br />
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The first time I saw my sister, I was all over him, and I liked him, and I was sad, and I forgot about my ancestors. You should be beaten, you should be beaten!" He also took Daiyu's hand and asked, "How old is my sister? How old is she? What kind of medicine do you take now? Don't be homesick here.--[[User:Wang Yifan21|Wang Yifan21]] ([[User talk:Wang Yifan21|talk]]) 08:21, 11 December 2021 (UTC)<br />
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==王镇隆 Wáng Zhènlóng 英语语言文学（英美文学） 男 202120081525==<br />
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要什么吃的，什么玩的，只管告诉我；丫头、老婆们不好，也只管告诉我。”黛玉一一答应。一面熙凤又问人：“林姑娘的东西可搬进来了？带了几个人来？你们赶早打扫两间屋子，叫他们歇歇儿去。”<br />
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Just tell me what you want to eat and play; Girls and old servants are not good, just tell me. " Daiyu nodded one by one. On one side, Xifeng asked, "have you moved in Miss Lin's things? How many people have you brought? Clean the two rooms early and tell them to have a rest."--[[User:Wang Zhenlong|Wang Zhenlong]] ([[User talk:Wang Zhenlong|talk]]) 06:54, 12 December 2021 (UTC)<br />
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Tell me what you want to eat and play; And if the maids or old nurses aren't good to you, just let me know. " Daiyu nodded one by one. At the same time, Xifeng asked, "Have Miss Lin's things been moved in? And how many people does she bring? Clean the two rooms as soon as possible and tell them to have a rest there."--[[User:Wei Yiwen|Wei Yiwen]] ([[User talk:Wei Yiwen|talk]]) 08:24, 12 December 2021 (UTC)<br />
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==卫怡雯 Wèi Yíwén 英语语言文学（英美文学） 女 202120081526==<br />
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说话时已摆了果茶上来，熙凤亲自布让。又见二舅母问他：“月钱放完了没有？”熙凤道：“放完了。刚才带了人到后楼上找缎子，找了半日，也没见昨儿太太说的那个。想必太太记错了。”王夫人道：“有没有，什么要紧！”<br />
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Fruits and tea had been prepared when Xifeng was talking, and she arranged them by herself. The second aunt asked her whether the monthly payment has been given out, she answered yes. “I looked for the satin in the back stairs with some people for hours just now, but didn’t find which madam mentioned yesterday. Madam must be wrong.” Wang Xifeng said, and Mrs. Wang answered, “ It doesn’t matter if there is or not.”--[[User:Wei Yiwen|Wei Yiwen]] ([[User talk:Wei Yiwen|talk]]) 07:45, 12 December 2021 (UTC)<br />
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Fruits and tea had been prepared when Xifeng was talking, and she arranged them by herself. The second aunt asked her, "Have the monthly payment been given out?" Xifeng answered, "Yes. And I looked for the satin in the back stairs with some people for hours just now, but didn’t find what madam mentioned yesterday. Madam mabey remember something wrong.” Mrs. Wang replied, “ It doesn’t matter if there is or not.”--[[User:Wei Chuxuan|Wei Chuxuan]] ([[User talk:Wei Chuxuan|talk]]) 09:03, 12 December 2021 (UTC)<br />
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==魏楚璇 Wèi Chǔxuán 英语语言文学（英美文学） 女 202120081527==<br />
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因又说道：“该随手拿出两个来，给你这妹妹裁衣裳啊。等晚上想着，再叫人去拿罢。”熙凤道：“我倒先料着了，知道妹妹这两日必到，我已经预备下了。等太太回去过了目，好送来。”<br />
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Mrs. Wang said, "You should take out a couple of satin pieces to cut your sister's dress. When you think of this matter in the evening, send someone for the satin ." Xifeng said, "I expected it. I knew my sister would arrive in these two days, and I had already made preparations. I will send someone for the satin as soon as you have returned and examined it."--[[User:Wei Chuxuan|Wei Chuxuan]] ([[User talk:Wei Chuxuan|talk]]) 08:52, 12 December 2021 (UTC)<br />
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Mrs. Wang added, "You should take out a couple of satin pieces to cut your sister's dress. When you think of this in the evening, send someone for the satin ." Xifeng said, "I have expected it. I know my sister will arrive in these two days, and I have already made preparations. I will send someone for the satin as soon as you have examined it."--[[User:Wei Zhaoyan|Wei Zhaoyan]] ([[User talk:Wei Zhaoyan|talk]]) 13:58, 12 December 2021 (UTC)<br />
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==魏兆妍 Wèi Zhàoyán 英语语言文学（英美文学） 女 202120081528==<br />
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王夫人一笑，点头不语。当下茶果已撤，贾母命两个老嬷嬷带黛玉去见两个舅舅去。维时贾赦之妻邢氏忙起身笑回道：“我带了外甥女儿过去，到底便宜些。”<br />
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Her Ladyship smiled, nodded and said nothing. Now the refreshments were cleared away and the Lady Dowager ordered two nurses to take Daiyu to see her two uncles. At this time, Mrs. She also immediately stood up, replied with smile, "it's also very convenient for me to take my niece."--[[User:Wei Zhaoyan|Wei Zhaoyan]] ([[User talk:Wei Zhaoyan|talk]]) 13:51, 11 December 2021 (UTC)<br />
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Her Ladyship smiled, nodded but said nothing. Now the refreshments were cleared away and the Lady Dowager ordered two mothers to take Daiyu to see her two uncles. At this time, Mrs. She immediately stood up, replied with a smile, "it's also very convenient for me to take my niece."--[[User:Wu Jingyue|Wu Jingyue]] ([[User talk:Wu Jingyue|talk]]) 08:37, 12 December 2021 (UTC)<br />
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==吴婧悦 Wú Jìngyuè 俄语语言文学 女 202120081529==<br />
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贾母笑道：“正是呢，你也去罢，不必过来了。”那邢夫人答应了，遂带着黛玉，和王夫人作辞，大家送至穿堂。垂花门前早有众小厮拉过一辆翠幄青油车来，邢夫人携了黛玉坐上，众老婆们放下车帘，方命小厮们抬起。<br />
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Jiamu laughed and said: “ Yeah, you can also leave, and don’t have to come here.” Ms. Xing promised, and said goodbye to Ms Wang with Daiyu, all of them went through the hallway. The ingenious green carriage, which drove by a group of manservants stood in front of the floral-pendant gates, Ms. Xing set in the car with Daiyu, several old mothers put down the car shade, instructing boys uplift the carriage. --[[User:Wu Jingyue|Wu Jingyue]] ([[User talk:Wu Jingyue|talk]]) 08:35, 12 December 2021 (UTC)<br />
The mother laughed and said, "Exactly, you also go, no need to come." That Mrs. Xing agreed, so took Daiyu, and Mrs. Wang to say goodbye, we sent to the wear hall. The tent green oil carriage in front of the flower gate, Mrs. Xing took Daiyu to sit on it, the wives put down the curtain, and ordered the boys to lift it.--[[User:Wu Yinghong|Wu Yinghong]] ([[User talk:Wu Yinghong|talk]]) 01:13, 13 December 2021 (UTC)<br />
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==吴映红 Wú Yìnghóng 日语语言文学 女 202120081530==<br />
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拉至宽处，驾上驯骡，出了西角门往东，过荣府正门，入一黑油漆大门内，至仪门前方下了车。邢夫人挽着黛玉的手进入院中。黛玉度其处必是荣府中之花园隔断过来的。<br />
Pulled to a wide place, driving on the tame mule, out of the west corner gate to the east, past the main gate of Rongfu, into a black-painted gate, to the front of the ceremony door down the car. Mrs. Xing took Daiyu's hand and entered the courtyard. The first thing you need to do is to get to the garden.<br />
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Then he took the mule，went out the west Corner gate to the east, passed the main gate of Rongfu, entered a black painted gate, and got off in front of Yi gate. Lady Xing took Daiyu's hand and entered the courtyard. Daiyu spent its place must be the garden in the rong mansion partition come over.--[[User:Xiao Yiyao|Xiao Yiyao]] ([[User talk:Xiao Yiyao|talk]]) 01:38, 15 December 2021 (UTC)<br />
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==肖毅瑶 Xiāo Yìyáo 英语语言文学（英美文学） 女 202120081531==<br />
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进入三层仪门，果见正房、厢房、游廊悉皆小巧别致，不似那边的轩峻壮丽，且院中随处之树木山石皆好。及进入正室，早有许多艳妆丽服之姬妾、丫鬟迎着。邢夫人让黛玉坐了；一面令人到外书房中请贾赦。<br />
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When we entered the three-story ceremonial gate, the main room, wing room and verandah were all small and unique, unlike those of the other side. Besides, the trees, mountains and stones in the courtyard were all good. When they entered the main room, there were many concubines and servant girls dressed in colourful makeup and beautiful clothes waiting for them. Madam Xing asked Daiyu to sit down and then let others invite Jia She in the outer study.--[[User:Xiao Yiyao|Xiao Yiyao]] ([[User talk:Xiao Yiyao|talk]]) 01:02, 15 December 2021 (UTC)<br />
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When entering the three- layers ceremonial gate, Daiyu found that the main room, wing room and verandah were all small and unique, unlike those of the other side. Besides, the trees, mountains and stones in the courtyard were all good. When they entered the main room, there were many concubines and servant girls dressed in heavy makeup and beautiful clothes waiting for them. Madam Xing asked Daiyu to sit down and then let others invite Jia She in the outer study.--[[User:Xie Jiafen|Xie Jiafen]] ([[User talk:Xie Jiafen|talk]]) 12:10, 19 December 2021 (UTC)<br />
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==谢佳芬 Xiè Jiāfēn 英语语言文学（英美文学） 女 202120081532==<br />
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一时回来说：“老爷说了：‘连日身上不好，见了姑娘，彼此伤心，暂且不忍相见。劝姑娘不必伤怀想家，跟着老太太和舅母，是和家里一样的。姐妹们虽拙，大家一处作伴，也可以解些烦闷。或有委屈之处，只管说，别外道了才是。’”<br />
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Mrs.Xing came back and said, "the master said, 'I've been felt not so good for days. I am afraid that I will be emotional if I see you, so I can't bear to see you for the time being. I advise you not to be homesick. It's the same as home to follow the old lady and aunt. Although the sisters are clumsy, you can relieve some boredom if you keep company together. If you have grievances, just tell us and make yourself at home.''--[[User:Xie Jiafen|Xie Jiafen]] ([[User talk:Xie Jiafen|talk]]) 12:07, 19 December 2021 (UTC)<br />
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Mrs.Xing came back and said, "the master said, 'I've been felt not so good for days. I am afraid that I will be emotional if I see you, so I can't bear to see you for the time being. I advise you not to be homesick. It's the same as home to follow the old lady and aunt. Although the sisters are clumsy, you can relieve some boredom if you keep company together. If you have grievances, just tell us and make yourself at home.''--[[User:Xie Qinglin|Xie Qinglin]] ([[User talk:Xie Qinglin|talk]]) 07:45, 20 December 2021 (UTC)<br />
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==谢庆琳 Xiè Qìnglín 俄语语言文学 女 202120081533==<br />
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黛玉忙站起身来，一一答应了。再坐一刻便告辞，邢夫人苦留吃过饭去。黛玉笑回道：“舅母爱惜赐饭，原不应辞；只是还要过去拜见二舅舅，恐去迟了不恭，异日再领。望舅母容谅。”<br />
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Daiyu stood up and agreed one by one. Sitting for a moment and then said goodbye, Mrs. Xing painstakingly stay to eat a meal. Daiyu smile back: "aunt love to give rice, should not resign; just have to go over to see second uncle, afraid to go late disrespectful, another day to receive. I hope aunt forgive me."--[[User:Xie Qinglin|Xie Qinglin]] ([[User talk:Xie Qinglin|talk]]) 07:44, 20 December 2021 (UTC)<br />
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==熊敏 Xióng Mǐn 英语语言文学（英美文学） 女 202120081534==<br />
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邢夫人道：“这也罢了。”遂命两个嬷嬷用方才坐来的车送过去。于是黛玉告辞。邢夫人送至仪门前，又嘱咐了众人几句，眼看着车去了方回来。一时黛玉进入荣府，下了车，只见一条大甬路直接出大门来。<br />
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Mrs. Xing said: “That’s fine.” So she ordered two Sisters to send Daiyu back by Carriage used before. So Daiyu farewell others. Mrs. Xing saw her off and said some words to others, seeing the carriage come back and forth. Once Daiyu entered The House of Rong and got off the carriage, she saw a long and wide road.<br />
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Mrs. Xing said: “That’s fine.” So she ordered two Sisters to send Daiyu back by Carriage used before. So Daiyu farewell others. Mrs. Xing saw her off to the gate of etiquetteand said some words to others, seeing the carriage come back and forth. Once Daiyu entered The House of Rong and got off the carriage, she saw a long and wide road.--[[User:Xu Minyun|Xu Minyun]] ([[User talk:Xu Minyun|talk]]) 11:26, 20 December 2021 (UTC)<br />
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==徐敏赟 Xú Mǐnyūn 语言智能与跨文化传播研究 男 202120081535==<br />
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众嬷嬷引着，便往东转弯，走过一座东西穿堂，向南大厅之后，仪门内大院落：上面五间大正房，两边厢房，鹿顶耳房钻山，四通八达，轩昂壮丽，比各处不同。黛玉便知这方是正内室。<br />
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Led by the mammy, she turned east, passed through an east-west hall, and came to the south hall, where she found a large courtyard inside the Gate of Yi: five main rooms on the top, flanks on both sides, and deer's roof and ears, extending in all directions, magnificent and different from other places. Daiyu knew this was the inner room.--[[User:Xu Minyun|Xu Minyun]] ([[User talk:Xu Minyun|talk]]) 09:10, 18 December 2021 (UTC)<br />
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Led by the mammies, they turned eastward and passed through an east-west hallway and the southward hall, she found a large courtyard inside the secondary gate: five main rooms on the top, flanks on both sides, and a small flat topped house next to the main house, extending in all directions, magnificent and different from other places. Daiyu knew this was the inner room.--[[User:Yan Jing|Yan Jing]] ([[User talk:Yan Jing|talk]]) 10:08, 18 December 2021 (UTC)<br />
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==颜静 Yán Jìng 语言智能与跨文化传播研究 女 202120081536==<br />
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进入堂屋，抬头迎面先见一个赤金九龙青地大匾，匾上写着斗大三个字，是“荣禧堂”；后有一行小字：“某年月日书赐荣国公贾源”，又有“万幾宸翰”之宝。大紫檀雕螭案上，设着三尺多高青绿古铜鼎，悬着待漏随朝墨龙大画，一边是錾金彝，一边是玻璃盆。<br />
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Entering the main room, I looked up and saw a great blue board framed in gilded dragons. On the plaque, there were two big words "Rongxi hall"; Then there is a line of small characters: "on a certain date, this was given to Jia Yuan, the Duke of Honor", and there was the treasure of Emperor's handwriting. On the large red sandalwood table that carved with dragon, there was a green bronze tripod more than three feet high, hanging a large ink dragon painting that seemed to attend the imperial court session in the early morning, with gilded wine vessels on one side and a glass basin on the other.--[[User:Yan Jing|Yan Jing]] ([[User talk:Yan Jing|talk]]) 08:11, 18 December 2021 (UTC)<br />
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there was a green bronze tripod more than three feet high, hanging a large ink dragon painting that seemed to attend the imperial court session in the early morning, with gilded wine vessels on one side and a glass basin on the other.--[[User:Yan Lili|Yan Lili]] ([[User talk:Yan Lili|talk]]) 12:05, 19 December 2021 (UTC)<br />
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==颜莉莉 Yán Lìlì 国别 女 202120081537==<br />
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地下两溜十六张楠木圈椅。又有一副对联，乃是乌木联牌镶着錾金字迹，道是：座上珠玑昭日月，堂前黼黻焕烟霞。下面一行小字是“世教弟勋袭东安郡王穆莳拜手书”。原来王夫人时常居坐宴息也不在这正室中，只在东边的三间耳房内。<br />
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On the ground two rows of 16 nanmu armchairs. There is also a pair of couplets, ebony couplet inset with gold handwriting, it said:The pearl and jade in the seat can shine with the sun and the moon; The people in front of the lobby wearing official clothes, its colors like clouds like clouds. The next line is written by mu Shis, the hereditary king of Dongpyeong County, who is a brother who has been taught by your family for generations.For Lady Wang often sat and reposed not in this main room, but in the three eastern rooms.--[[User:Yan Lili|Yan Lili]] ([[User talk:Yan Lili|talk]]) 03:36, 12 December 2021 (UTC)<br />
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Since Lady Wang seldom sat in this main hall but used three rooms on the east side for relaxation.--[[User:Yan Zihan|Yan Zihan]] ([[User talk:Yan Zihan|talk]]) 10:04, 14 December 2021 (UTC)<br />
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==颜子涵 Yán Zǐhán 国别 女 202120081538==<br />
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于是嬷嬷们引黛玉进东房门来。临窗大炕上铺着猩红洋毯，正面设着大红金钱蟒引枕，秋香色金钱蟒大条褥；两边设一对梅花式洋漆小几：左边几上摆着文王鼎，鼎旁匙箸、香盒；右边几上摆着汝窑美人觚，里面插着时鲜花草。<br />
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So that the nurses led Daiyu through the door of the eastern wing. The large kang by the window was covered with a scarlet foreign rug. In the middle were red back-rests and turquoise bolsters, both with dragon-design medallions, and a long greenish yellow mattress also with dragon medallions. On the two sides， stood one of a pair of small teapoys of foreign lacquer of plum-blossom pattern. On the left-hand table were a tripod, spoons, chopsticks and an incense container; On the right-hand table were a slender-waisted porcelain vase from the Ruzhou Kiln in which were placed seasonable flowers.--[[User:Yan Zihan|Yan Zihan]] ([[User talk:Yan Zihan|talk]]) 09:48, 14 December 2021 (UTC)<br />
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Thereupon,the nurses led Daiyu through the door of the eastern wing. The large kang by the window was covered with a scarlet foreign rug. In the middle were red back-rests and turquoise bolsters, both with dragon-design medallions, and a long greenish yellow mattress also with dragon medallions. On the two sides stood on a pair of small teapoys of foreign lacquer of plum-blossom pattern. On the left-hand table were a tripod, spoons, chopsticks and an incense container; On the right-hand table were a slender-waisted porcelain vase from the Ruzhou Kiln in which were placed seasonable flowers.--[[User:Yang Jiaying|Yang Jiaying]] ([[User talk:Yang Jiaying|talk]]) 13:47, 14 December 2021 (UTC)<br />
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==阳佳颖 Yáng Jiāyǐng 国别 女 202120081540==<br />
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地下面，西一溜四张大椅，都搭着银红撒花椅搭，底下四副脚踏；两边又有一对高几，几上茗碗、瓶花俱备。其馀陈设，不必细说。老嬷嬷让黛玉上炕坐。炕沿上却也有两个锦褥对设。<br />
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On the floor on the west side of the room, were four chairs in a row, all of which were covered with antimacassars, embroidered with silverish-red flowers.Beneath them stood four footstools. On either side, was also a pair of high teapoys which were covered with teacups and flower vases.The rest of the room need not be described in detail.<br />
The nurses urged Daiyu to sit on the kang, on the edge of which were two brocade cushions. --[[User:Yang Jiaying|Yang Jiaying]] ([[User talk:Yang Jiaying|talk]]) 13:42, 14 December 2021 (UTC)<br />
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On the floor facing on the west wall were four chairs in a row, all of which were covered with ornamented cloth embroidered with silverish-red flowers.Beneath them stood four footstools. On either side were a pair of high table with teacups and flower vases.Other decorations in the rest of the room need not be described in detail.The nurses urged Daiyu to sit on the kang, on the edge of which were two brocade cushions.--[[User:Yang Aijiang|Yang Aijiang]] ([[User talk:Yang Aijiang|talk]]) 09:50, 18 December 2021 (UTC)<br />
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==杨爱江 Yáng Àijiāng 英语语言文学（语言学） 女 202120081541==<br />
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黛玉度其位次，便不上炕，只就东边椅上坐了。本房的丫鬟忙捧上茶来。黛玉一面吃了，打量这些丫鬟们妆饰衣裙，举止行动，果与别家不同。<br />
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Considering her status in the family, Daiyu sat on one of the chairs on the east side instead of sitting on the ''kang''. The maids in attendance served tea immediately. When she was sipping the tea, she observed the maids’ make-up,clothes and deportment, which, her thought, were indeed quite different from those in other families.--[[User:Yang Aijiang|Yang Aijiang]] ([[User talk:Yang Aijiang|talk]]) 09:38, 18 December 2021 (UTC)<br />
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Considering her status in the family, Daiyu sat on one of the chairs on the east side instead of sitting on the ''kang''. The maids in attendance served tea immediately.Sipping the tea, she observed the maids’ make-up,clothes and deportment, which, her thought, were indeed quite different from those in other families.--[[User:Yang Kun|Yang Kun]] ([[User talk:Yang Kun|talk]]) 14:18, 18 December 2021 (UTC)<br />
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==杨堃 Yáng Kūn 法语语言文学 女 202120081542==<br />
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茶未吃了，只见一个穿红绫袄、青绸掐牙背心的一个丫鬟走来笑道：“太太说，请林姑娘到那边坐罢。”老嬷嬷听了，于是又引黛玉出来，到了东廊三间小正房内。<br />
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Before the tea was drunk, a servant girl wearing a red silk jacket and a green satin vest came up and smiled, "Mrs. Wang invited Miss Lin to come and sit over there." When the old Mammy heard this, she led Daiyu out again and went to the third small main room on the east porch.--[[User:Yang Kun|Yang Kun]] ([[User talk:Yang Kun|talk]]) 03:33, 12 December 2021 (UTC)<br />
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Before they drank tea over, a servant girl in a red silk jacket and a green satin vest came up and smiled, "Mrs. Wang invited Miss Lin to come and sit over there." When the old Mammy heard this, she led Daiyu out again to the third small main room on the east porch.--[[User:Yang Liuqing|Yang Liuqing]] ([[User talk:Yang Liuqing|talk]]) 11:10, 12 December 2021 (UTC)<br />
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==杨柳青 Yáng Liǔqīng 英语语言文学（英美文学） 女 202120081543==<br />
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正面炕上横设一张炕桌，上面堆着书籍、茶具；靠东壁面西设着半旧的青缎靠背、引枕。王夫人却坐在西边下首，亦是半旧青缎靠背、坐褥。见黛玉来了，便往东让。<br />
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On the kang there was a kang table on which books and tea sets piled up. Half new backrests and pillows made of blue satins were set on the east side of the wall. However, Mrs. Wang set at the foot of the west wall where half new backrests and mattresses made of blue satins were displayed. Mrs. Wang moved to the east side when she saw Lin Daiyu come in.--[[User:Yang Liuqing|Yang Liuqing]] ([[User talk:Yang Liuqing|talk]]) 11:11, 12 December 2021 (UTC)<br />
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On the kang lies a kang table,on which books and tea sets are piled up. Half new backrests and pillows made of blue satins were put on the east side of the wall. However, Mrs. Wang sat at the foot of the west wall where half new backrests and mattresses made of blue satins are displayed. Mrs. Wang moved to the east side when she saw Daiyu coming in.--[[User:Ye Weijie|Ye Weijie]] ([[User talk:Ye Weijie|talk]]) 12:11, 19 December 2021 (UTC)<br />
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==叶维杰 Yè Wéijié 国别 男 202120081544==<br />
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黛玉心中料定这是贾政之位。因见挨炕一溜三张椅子上也搭着半旧的弹花椅袱，黛玉便向椅上坐了。王夫人再三让他上炕，他方挨王夫人坐下。王夫人因说：“你舅舅今日斋戒去了，再见罢。<br />
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Daiyu thought it must be Jia Zhen's seat. Seeing that there were half-old bouncing chair blankets on the three chairs slid by the kang, Daiyu sat on the chair. Mrs. Wang repeatedly asked him to go to the kang, then she sat down next to Mrs. Wang. Mrs. Wang said: "Your uncle has gone fast today, goodbye.<br />
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Daiyu thought that this was Jia Zheng's seat, because she saw that there were three chairs next to the bed with a half-used chair, so Daiyu sat down on the chair. She sat down next to Madam Wang after she had asked her to go to the bed again and again. Mrs. Wang said, "Your uncle went to fast today, see you later.”--[[User:Yi Yangfan|Yi Yangfan]] ([[User talk:Yi Yangfan|talk]]) 12:29, 19 December 2021 (UTC)Yi Yangfan<br />
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==易扬帆 Yì Yángfān 英语语言文学（英美文学） 女 202120081545==<br />
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只是有句话嘱咐你：你三个姐妹倒都极好，以后一处念书认字，学针线，或偶一玩笑，却都有个尽让的。我就只一件不放心：我有一个孽根祸胎，是家里的混世魔王，今日因往庙里还愿去，尚未回来，晚上你看见就知道了。<br />
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I just have one thing to tell you: your three sisters are all very good, and in the future they will study and learn to read and write together, and learn to sew, or occasionally play jokes, but all of them will do their best. There is only one thing I am not sure about: I have a sinful child who is the evil one in my family, and he has not returned yet because he has gone to the temple to pay his respects.--[[User:Yi Yangfan|Yi Yangfan]] ([[User talk:Yi Yangfan|talk]]) 02:19, 13 December 2021 (UTC)Yi Yangfan<br />
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I just want to remind you: your sisters are very kind , and in the future you will study together, and learn to read and write and learn to sew. Sometimes you will play jokes at each other, but you will be very tolerant to each other. There is only one thing I am worried about: there is a naughty boy in our family, and he has not returned yet because he has gone to the temple to redeem his wishes, you will see him in the evening.--[[User:Yin Huizhen|Yin Huizhen]] ([[User talk:Yin Huizhen|talk]]) 07:45, 13 December 2021 (UTC)<br />
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==殷慧珍 Yīn Huìzhēn 英语语言文学（英美文学） 女 202120081546==<br />
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你以后总不用理会他，你这些姐姐妹妹都不敢沾惹他的。”黛玉素闻母亲说过：“有个内侄，乃衔玉而生，顽劣异常，不喜读书，最喜在内帏厮混。外祖母又溺爱，无人敢管。”<br />
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“You can ignore him later and none of your sisters dare to bother him. ” Daiyu heard from her mother: “I have a nephew, who was born with jade in his mouth. He is very naughty and don’t like to read, but prefer to play with girls. His grandma has always spoiled him so that everyone let him be. ”--[[User:Yin Huizhen|Yin Huizhen]] ([[User talk:Yin Huizhen|talk]]) 07:27, 13 December 2021 (UTC)<br />
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"You can ignore him in future because none of your sisters dare to mess up with him". Mascara Jade has long heard from her mother about him: "I have a nephew, born with a jade in his mouth, who is very naughty and doesn’t like to read, but prefers to hang around with girls. His grandma has always spoiled him so that everyone let him be". --[[User:Yin Meida|Yin Meida]] ([[User talk:Yin Meida|talk]]) 12:31, 19 December 2021 (UTC)<br />
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==殷美达 Yīn Měidá 英语语言文学（语言学） 女 202120081547==<br />
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今见王夫人所说，便知是这位表兄。一面陪笑道：“舅母所说，可是衔玉而生的？在家时，记得母亲常说：这位哥哥比我大一岁，小名就叫宝玉，性虽憨顽，说待姊妹们却是极好的。<br />
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What Lady King described today is the cousin for sure. Mascara Jade said while smiling:" Is the person you just mentioned my cousin born with a jade? I remember when I was at home my mother often said that the cousin nicknamed Precious Jade is one year older than me. Although he is a little mischievous, he is very friendly with his sisters".--[[User:Yin Meida|Yin Meida]] ([[User talk:Yin Meida|talk]]) 14:27, 12 December 2021 (UTC)<br />
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Who Lady King described today is the cousin for sure. Mascara Jade said while smiling:" Is this my cousin you just mentioned born with a jade? I remember when I was at home my mother often said that the cousin nicknamed Precious Jade is one year older than me. Although he is a little mischievous, he is very friendly with his sisters".--[[User:Yin Yuan|Yin Yuan]] ([[User talk:Yin Yuan|talk]]) 12:06, 19 December 2021 (UTC)<br />
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==尹媛 Yǐn Yuán 英语语言文学（英美文学） 女 202120081548==<br />
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况我来了，自然和姊妹们一处，弟兄们是另院别房，岂有沾惹之理？”王夫人笑道：“你不知道原故。他和别人不同，自幼因老太太疼爱，原系和姐妹们一处娇养惯了的。<br />
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Now I come here,undoubtfully I live with my sisters. The brothers are in some different houses. Is there any reason to mess with them?" Lady King smiled and said, "You don't know. Unlike the others, he had been coddled by his sisters since he was young for the love of Grandma Merchant.--[[User:Yin Yuan|Yin Yuan]] ([[User talk:Yin Yuan|talk]]) 15:30, 13 December 2021 (UTC)<br />
<br />
Now I come here,undoubtfully I live with my sisters. The brothers are in some different houses. Is there any reason to mess with them?" Lady King smiled and said, "You don't know the reason. Unlike others, he had been coddled by his sisters since he was young for the love of Grandma Merchant.--[[User:Zhan Ruoxuan|Zhan Ruoxuan]] ([[User talk:Zhan Ruoxuan|talk]]) 03:20, 15 December 2021 (UTC)<br />
<br />
==詹若萱 Zhān Ruòxuān 英语语言文学（英美文学） 女 202120081549==<br />
<br />
若姐妹们不理他，他倒还安静些；若一日姐妹们和他多说了一句话，他心上一喜，便生出许多事来：所以嘱咐你别理会他。他嘴里一时甜言蜜语，一时有天没日，疯疯傻傻，只休信他。”黛玉一一的都答应着。<br />
<br />
“If the sisters ignore him, he is a bit quieter: if one day the sisters talk to him more, he is so happy that he will stir up many troubles: so I tell you to ignore him. He may talk sweetly for a while, and he may be crazy and silly for a while, just don't believe him.” Daiyu replied one by one.--[[User:Zhan Ruoxuan|Zhan Ruoxuan]] ([[User talk:Zhan Ruoxuan|talk]]) 08:45, 13 December 2021 (UTC)<br />
If the sisters ignore him, he will be quiet; if one day they talk to him more, he will be happy, and many things will happen: so I tell you to ignore him. His mouth a sweet talk, a moment there is no day, crazy and silly, just do not believe him. Daiyu agreed one by one.--[[User:Zhang Qiuyi|Zhang Qiuyi]] ([[User talk:Zhang Qiuyi|talk]]) 12:06, 19 December 2021 (UTC)<br />
<br />
==张秋怡 Zhāng Qiūyí 亚非语言文学 女 202120081550==<br />
<br />
忽见一个丫鬟来说：“老太太那里传晚饭了。”王夫人忙携了黛玉，出后房门，由后廊往西，出了角门，是一条南北甬路，南边是倒座三间小小抱厦厅，北边立着一个粉油大影壁，后有一个半大门，小小一所房屋。<br />
Suddenly see a servant girl to say: "old lady there spread supper." Lady Wang and Daiyu went out of the back door, leading from the back corridor to the west and out of the corner gate. There was a north-south corridor, with three small rooms in the south, a big screen wall of powder and oil in the north, and a small house with a half gate behind.--[[User:Zhang Qiuyi|Zhang Qiuyi]] ([[User talk:Zhang Qiuyi|talk]]) 13:53, 12 December 2021 (UTC)<br />
<br />
Suddenly a servant girl said, "the old lady has passed on dinner." Lady King hurriedly took Mascara Jade Pearl out of the back door, from the back porch to the west, out of the corner door. It is a North-South corridor. In the south is the inverted three small balcony halls. In the north is a oil-powdered large shadow wall, followed by a half gate and a small house.--[[User:Zhang Yang|Zhang Yang]] ([[User talk:Zhang Yang|talk]]) 12:28, 13 December 2021 (UTC)<br />
<br />
==张扬 Zhāng Yáng 国别 男 202120081551==<br />
<br />
王夫人笑指向黛玉道：“这是你凤姐姐的屋子。回来你好往这里找他去，少什么东西，只管和他说就是了。”这院门上也有几个才总角的小厮，都垂手侍立。王夫人遂携黛玉穿过一个东西穿堂，便是贾母的后院了。<br />
<br />
Lady King smiled at Mascara Jade Pearl and said: "This is your sister Phoenix's house. If you come back, you can find her here. And if there's anything missing, just tell her." On the gate of the courtyard, there were also several young boys who were only in their childhood, all standing with their hands down. Lady King then took Mascara Jade Pearl through an east-west hall, which was Grandma Merchant's backyard.--[[User:Zhang Yang|Zhang Yang]] ([[User talk:Zhang Yang|talk]]) 07:30, 11 December 2021 (UTC)<br />
<br />
Lady King smiled at Mascara Jade Pearl and said: "This is your sister Phoenix's house. If you come back, you can find her here. And if there's anything missing, just tell her." On the gate of the courtyard,there were also a few young boys on the door of this courtyard, all standing with their hands down.. Lady King then took Mascara Jade Pearl through an east-west hall, which was Grandma Merchant's backyard.<br />
<br />
==张怡然 Zhāng Yírán 俄语语言文学 女 202120081552==<br />
<br />
于是进入后房门，已有许多人在此伺候，见王夫人来，方安设桌椅；贾珠之妻李氏捧杯，熙凤安箸，王夫人进羹。贾母正面榻上独坐，两旁四张空椅。熙凤忙拉黛玉在左边第一张椅子上坐下，黛玉十分推让。<br />
<br />
So they entered the back room, where many people were already waiting, and when they saw Lady King coming, they placed the table and chairs; Li, wife of Treasure Merchant, held the cup, Lady King placed the chopsticks, and Splendid Phoenix King drank the soup. Grandma Merchant was sitting alone on a couch, flanked by four empty chairs. Splendid Phoenix King was busy pulling Mascara Jade Forest to sit in the first chair on the left, but Mascara Jade Forest was too embarrassed to sit.--[[User:Zhang Yiran|Zhang Yiran]] ([[User talk:Zhang Yiran|talk]]) 00:57, 13 December 2021 (UTC)<br />
<br />
So they entered the back room, where many people were already waiting, and when they saw Lady King coming, they placed the table and chairs; Li, wife of Treasure Merchant, held the cup, Lady King placed the chopsticks, and Splendid Phoenix King drank the soup. Grandma Merchant was sitting alone on a couch, flanked by four empty chairs. Splendid Phoenix King was busy pulling Mascara Jade Forest to sit in the first chair on the left, but Mascara Jade Forest was too embarrassed to sit.--[[User:Zhong Yifei|Zhong Yifei]] ([[User talk:Zhong Yifei|talk]]) 03:42, 13 December 2021 (UTC)<br />
<br />
==钟义菲 Zhōng Yìfēi 英语语言文学（英美文学） 女 202120081553==<br />
<br />
贾母笑道：“你舅母和嫂子们是不在这里吃饭的。你是客，原该这么坐。”黛玉方告了坐，就坐了。贾母命王夫人也坐了。迎春姊妹三个告了坐，方上来：迎春坐右手第一，探春左第二，惜春右第二。<br />
<br />
Mrs. Jia said with a smile, "your aunt and sister-in-law don't eat here. You are a guest. You should have sat here." Daiyu then sat down. Jia Mu ordered Mrs. Wang to sit down. The three sisters of Yingchun sat down：Yingchun sat first on the right hand, Tanchun second on the left, and Xi Chun second on the right.--[[User:Zhong Yifei|Zhong Yifei]] ([[User talk:Zhong Yifei|talk]]) 10:36, 11 December 2021 (UTC)<br />
<br />
Mrs. Jia said with a smile, "your aunts and sisters-in-law don't eat here. You are a guest. You should have sat here." Daiyu then sat down. Mrs. Jia ordered Mrs. Wang to sit down. The three sisters of Yingchun were asked to sit down: Yingchun sat first on the right hand, Tanchun second on the left, and Xi Chun second on the right.--[[User:Zhong Yulu|Zhong Yulu]] ([[User talk:Zhong Yulu|talk]]) 02:02, 12 December 2021 (UTC)<br />
<br />
==钟雨露 Zhōng Yǔlù 英语语言文学（英美文学） 女 202120081554==<br />
<br />
旁边丫鬟执着拂尘、漱盂、巾帕，李纨、凤姐立于案边布让；外间伺候的媳妇、丫鬟虽多，却连一声咳嗽不闻。饭毕，各各有丫鬟用小茶盘捧上茶来。当日林家教女以惜福养身，每饭后必过片时方吃茶，不伤脾胃；<br />
<br />
Standing at the table, the servant girls held the horsetail whisks, vessels for mouthwash and handkerchiefs. Li Wan and Wang Xifeng sent dishes, refreshments to guests and invited them to eat. Though there were many servant girls in the outer room, they could not be heard to utter a sound. When the meal was over, each servant girl brought tea with a small tray. The daughter of Lin Ruhai, Lin Daiyu took tea after each meal to keep health and not hurt her spleen and stomach.--[[User:Zhong Yulu|Zhong Yulu]] ([[User talk:Zhong Yulu|talk]]) 01:55, 12 December 2021 (UTC)<br />
<br />
The servant girls are standing at the table with the horsetail whisks, vessels for mouthwash and handkerchiefs. Li Wan and Wang Xifeng sent dishes, refreshments to guests and invited them to eat. Though there were many servant girls in the outer room, they could not be heard to utter a sound. When the meal was over, each servant girl brought tea with a small tray. The daughter of Lin Ruhai, Lin Daiyu took tea after each meal to keep health and not hurt her spleen and stomach.--[[User:Zhou Jiu|Zhou Jiu]] ([[User talk:Zhou Jiu|talk]]) 09:23, 13 December 2021 (UTC)<br />
<br />
==周玖 Zhōu Jiǔ 英语语言文学（英美文学） 女 202120081555==<br />
<br />
今黛玉见了这里许多规矩不似家中，也只得随和些。接了茶，又有人捧过漱盂来，黛玉也漱了口，又盥手毕。然后又捧上茶来，这方是吃的茶。贾母便说：“你们去罢，让我们自在说说话儿。”<br />
Now Daiyu saw many rules here are not like the rules of her home. She was also easy-going. After receiving the tea, someone else took a gargle bowl for her. Daiyu also rinsed her mouth and finished washing her hands again. Then tea which was for drinking was brought in. Then Mother Jia said to servants , "You all go and let's have a talk in our own comfort."<br />
<br />
Now Daiyu saw many rules here are not like the rules of her home. She can only be easygoing. She caught the teacup. Some domestics came over with a mouthwash basin. Daiyu gargled and washed her hands. Then the servant brought back tea, and this was tea for drinking.Then Grandma Merchant said to servant, "You all go and let's have a talk in our own comfort."--[[User:Zhou Junhui|Zhou Junhui]] ([[User talk:Zhou Junhui|talk]]) 10:47, 13 December 2021 (UTC)<br />
<br />
==周俊辉 Zhōu Jùnhuī 法语语言文学 女 202120081556==<br />
<br />
王夫人遂起身，又说了两句闲话儿，方引李、凤二人去了。贾母因问黛玉念何书，黛玉道：“刚念了《四书》。”黛玉又问姊妹读何书，贾母道：“读什么书，不过认几个字罢了。”<br />
<br />
Lady Wang stood up and said something idle, then led Lady Li and Splendid Phoenix King to leave. When Grandma Merchant asked Daiyu what books she had read, Daiyu replied, "I just have read the ''Four Books''." When Daiyu asked her sisters what books they read, Grandma Merchant said, "They don't read anything. They only know a few words."--[[User:Zhou Junhui|Zhou Junhui]] ([[User talk:Zhou Junhui|talk]]) 09:25, 13 December 2021 (UTC)<br />
Madame Wang rose as soon as she heard these words, and having made a few irrelevant remarks, she led the way and left the room along with the two ladies, Mrs. Li and lady Feng.Dowager lady Chia, having inquired of Tai-yue what books she was reading, "I have just begun reading the Four Books," Tai-yue replied. "What books are my cousins reading?" Tai-yue went on to ask. "Books, you say!" exclaimed dowager lady Chia; "why all they know are a few characters, that's all."--[[User:Zhou Qiao1|Zhou Qiao1]] ([[User talk:Zhou Qiao1|talk]]) 13:15, 15 December 2021 (UTC)<br />
<br />
==周巧 Zhōu Qiǎo 英语语言文学（语言学） 女 202120081557==<br />
<br />
一语未了，只听外面一阵脚步响，丫鬟进来报道：“宝玉来了。”黛玉心想：“这个宝玉，不知是怎样个惫懒人呢。”及至进来一看，却是位青年公子：头上戴着束发嵌宝紫金冠，齐眉勒着二龙戏珠金抹额；<br />
<br />
The sentence was barely out of her lips, when a continuous sounding of footsteps<br />
was heard outside, and a waiting maid entered and announced that Pao-yue was<br />
coming. Tai-yue was speculating in her mind how it was that this Pao-yue had<br />
turned out such a good-for-nothing fellow, when he happened to walk in.<br />
He was, in fact, a young man of tender years, wearing on his head, to hold his<br />
hair together, a cap of gold of purplish tinge, inlaid with precious gems.<br />
Parallel with his eyebrows was attached a circlet, embroidered with gold, and<br />
representing two dragons snatching a pearl.<br />
--[[User:Zhou Qiao1|Zhou Qiao1]] ([[User talk:Zhou Qiao1|talk]]) 08:36, 13 December 2021 (UTC)<br />
<br />
<br />
After a word, only a sound of footsteps outside, the maid came in and reported: "Baoyu is here." Daiyu thought to herself: "This Baoyu, I don't know what a tired lazy person." When she came in, she was a young man. He wears a purple and gold crown with hair inlaid on his head, and his forehead are tied with gold frontlet（The shape is two dragons playing with pearled）.--[[User:Zhou Qing|Zhou Qing]] ([[User talk:Zhou Qing|talk]]) 15:21, 11 December 2021 (UTC)<br />
<br />
==周清 Zhōu Qīng 法语语言文学 女 202120081558==<br />
<br />
一件二色金百蝶穿花大红箭袖，束着五彩丝攒花结长穗宫绦，外罩石青起花八团倭缎排穗褂；登着青缎粉底小朝靴。面若中秋之月，色如春晓之花；鬓若刀裁，眉如墨画，鼻如悬胆，睛若秋波。<br />
<br />
A big red arrow sleeve decorated with two-color golden butterfly flowers, is tied with multicolored silk and knotted with long spikes, and is covered with azurite and satin rowed gowns; it wears small green satin and powder-soled boots. The face is as round and beautiful as the moon of Mid-Autumn Festival, the complexion is like a flower of spring dawn; the temples are like a knife cut, the eyebrows are like ink painting, the nose is like a hanging gall, and the eyes are like autumn waves.<br />
<br />
A big red arrow sleeve decorated with two-color golden butterfly flowers, is tied with multicolored silk and knotted with long spikes, and is covered with azurite and satin rowed gowns; he wore small green satin and powder-soled boots. The face is as round and beautiful as the moon at mid-autumn, the complexion is like a flower of in spring; the temples as if chiselled with a knife, the eyebrows are like ink painting, the nose is like a a well-cut and shapely nose, and the eyes are like vernal waves.--[[User:Zhou Xiaoxue|Zhou Xiaoxue]] ([[User talk:Zhou Xiaoxue|talk]]) 06:19, 13 December 2021 (UTC)<br />
<br />
==周小雪 Zhōu Xiǎoxuě 日语语言文学 女 202120081559==<br />
<br />
虽怒时而似笑，即嗔视而有情。项上金螭缨络，又有一根五色丝绦，系着一块美玉。黛玉一见，便吃一大惊，心中想道：“好生奇怪：倒像在那里见过的，何等眼熟！”只见这宝玉向贾母请了安，贾母便命：“去见你娘来。”<br />
<br />
His angry look even resembled a smile; his glance was full of sentiment.Round his neck he had a gold dragon necklet with a fringe; also a cord of variegated silk, to which was attached a piece of beautiful jade. When Daiyu saw this, she was shocked."How very strange." she was reflecting in her mind; "it would seem as if I had seen him somewhere or other, for his face appears extremely familiar to my eyes;" Baoyu greeted Lady Dowager "Go and see your mother and then come back," remarked her venerable ladyship.--[[User:Zhou Xiaoxue|Zhou Xiaoxue]] ([[User talk:Zhou Xiaoxue|talk]]) 06:08, 13 December 2021 (UTC)<br />
<br />
<br />
His angry look somentimes even resembled a smile; his glance was full of sentiment.Round his neck he had a gold dragon necklet with a fringe; also a cord of silk of five colours, to which was attached a piece of beautiful jade. When Daiyu saw this, she was shocked and thought to herself: "How strange it is." she was reflecting in her mind; "as if I had seen him somewhere or other, for his face appears extremely familiar to my eyes;" Baoyu greeted Lady Dowager "Go and see your mother and then come back."--[[User:Zhu Suzhen|Zhu Suzhen]] ([[User talk:Zhu Suzhen|talk]]) 11:46, 16 December 2021 (UTC)<br />
<br />
==朱素珍 Zhū Sùzhēn 英语语言文学（语言学） 女 202120081561==<br />
<br />
即转身去了。一会再来时已换了冠带：头上周围一转的短发都结成小辫，红丝结束，共攒至顶中胎发，总编一根大辫，黑亮如漆，从顶至梢，一串四颗大珠，用金八宝坠脚；身上穿着银红撒花半旧大袄；仍旧带着项圈、宝玉、寄名锁、护身符等物；<br />
<br />
Then turned around and went. He has changed the crown band when coming back for a while: the short hair around the head is braided, the red silk ends, and the hair is gathered up to the top of the fetus. The chief editor is a big braid, black and shiny, from top to tip , A string of four large beads, with gold eight treasures falling to the feet; wearing a silver-red half-old coat with flowers; still wearing collars, gems, locks, amulets, etc.;<br />
<br />
He turned away. When he came back later, he had changed his crown belt: the short hair around his head was braided, and the red silk ended. He saved up to the top and middle fetal hair. The chief editor had a big braid, black and bright as paint, a string of four big beads from the top to the tip, and dropped his feet with gold eight treasures; He was wearing a silver red flower sprinkled semi-old coat; Still wearing collars, precious jade, name sending locks, amulets, etc--[[User:Zou Yueli|Zou Yueli]] ([[User talk:Zou Yueli|talk]]) 12:56, 16 December 2021 (UTC)<br />
<br />
==邹岳丽 Zōu Yuèlí 日语语言文学 女 202120081562==<br />
<br />
下面半露松绿撒花绫裤，锦边弹墨袜，厚底大红鞋。越显得面如傅粉，唇若施脂；转盼多情，语言若笑。天然一段风韵，全在眉梢；平生万种情思，悉堆眼角。看其外貌，最是极好，却难知其底细。<br />
His lower body showed loose green flower pants, cotton socks and a pair of thick soled red shoes. This makes him look more beautiful. The lips seem to have been powdered with rouge; When he speaks, he often has a smile on his face, and his eyebrows can convey affection. A person's style and temperament, including what he thinks, can be conveyed through his eyes. His appearance is very good-looking, but I don't know whether he has real connotation.<br />
<br />
==Nadia 202011080004==<br />
<br />
后人有《西江月》二词批的极确，词曰：<br />
<br />
==Mahzad Heydarian 玛莎 202021080004==<br />
<br />
无故寻愁觅恨，有时似傻如狂。<br />
Seeking sorrow and hate for no reason, sometimes seems stupid and crazy.<br />
<br />
==Mariam toure 2020GBJ002301==<br />
<br />
纵然生得好皮囊，腹内原来草莽。<br />
<br />
==Rouabah Soumaya 202121080001==<br />
<br />
潦倒不通庶务，愚顽怕读文章。<br />
<br />
I'm not able to get through general affairs, and I'm afraid of reading articles.<br />
<br />
==Muhammad Numan 202121080002==<br />
<br />
行为偏僻性乖张，那管世人诽谤。<br />
He who behaves in a perverse way has no control over the slander of course.--[[User:Atta Ur Rahman|Atta Ur Rahman]] ([[User talk:Atta Ur Rahman|talk]]) 03:29, 14 December 2021 (UTC)<br />
<br />
==Atta Ur Rahman 202121080003==<br />
<br />
又曰：富贵不知乐业，贫穷难耐凄凉。<br />
It is also known that wealth does not know pleasure and happiness, and poverty cannot endure loneliness.<br />
<br />
==Muhammad Saqib Mehran 202121080004==<br />
<br />
可怜辜负好时光，于国于家无望。<br />
The poor lived up to the good times, and the country was hopeless at home.<br />
<br />
==Zohaib Chand 202121080005==<br />
<br />
天下无能第一，古今不肖无双。<br />
<br />
==Jawad Ahmad 202121080006==<br />
<br />
寄言纨袴与膏粱，莫效此儿形状。<br />
<br />
English; the author is to give some suggestion to playboys of high official that they do not follow the example of Jia Baoyu.<br />
<br />
==Nizam Uddin 202121080007==<br />
<br />
却说贾母见他进来，笑道：“外客没见就脱了衣裳了，还不去见你妹妹呢。”<br />
<br />
But she said that Mother Jia saw him come in and smiled: "The foreigner took off her clothes without seeing him, so she won't go to see your sister."<br />
<br />
==Öncü 202121080008==<br />
<br />
宝玉早已看见了一个袅袅婷婷的女儿，便料定是林姑妈之女，忙来见礼。<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
Baoyu had already seen a daughter with a gentle posture, thought might be the daughter of Aunt Lin, then hurried to meet her.--[[User:AkiraJantarat|AkiraJantarat]] ([[User talk:AkiraJantarat|talk]]) 04:17, 13 December 2021 (UTC)<br />
<br />
==Akira Jantarat 202121080009==<br />
<br />
归了坐细看时，真是与众各别。<br />
<br />
<br />
When I look at you carefully, I think you are different.--[[User:AkiraJantarat|AkiraJantarat]] ([[User talk:AkiraJantarat|talk]]) 09:59, 13 December 2021 (UTC)<br />
<br />
<br />
When I returned to take a closer look, it was really different. --[[User:Benjamin Wellsand|Benjamin Wellsand]] ([[User talk:Benjamin Wellsand|talk]]) 07:41, 12 December 2021 (UTC)<br />
<br />
==Benjamin Wellsand 202111080118==<br />
<br />
只见：两弯似蹙非蹙笼烟眉，一双似喜非喜含情目。<br />
<br />
I saw two bends like the frowning of smoked eyebrows, at first they seemed happy but not really happy, yet affectionate eyebrows. --[[User:Benjamin Wellsand|Benjamin Wellsand]] ([[User talk:Benjamin Wellsand|talk]]) 07:41, 12 December 2021 (UTC)<br />
<br />
I saw: two curved eyebrows that looked like a frown, a pair of eyebrows that seemed to be happy or not. --[[User:Asep Budiman|Asep Budiman]] ([[User talk:Asep Budiman|talk]]) 23:18, 12 December 2021 (UTC)<br />
<br />
==Asep Budiman 202111080020==<br />
<br />
态生两靥之愁，娇袭一身之病。<br />
<br />
The sorrow of the two distresses, the disease of the whole body. --[[User:Asep Budiman|Asep Budiman]] ([[User talk:Asep Budiman|talk]]) 23:17, 12 December 2021 (UTC)<br />
<br />
The sorrow of two distresses, spoiled - the disease of the whole body.------Ei Mon Kyaw [[User:EIMONKYAW|EIMONKYAW]] ([[User talk:EIMONKYAW|talk]]) 09:15, 15 December 2021 (UTC)--[[User:EIMONKYAW|EIMONKYAW]] ([[User talk:EIMONKYAW|talk]]) 09:15, 15 December 2021 (UTC)Ei Mon Kyaw<br />
<br />
==Ei Mon Kyaw 202111080021==<br />
<br />
泪光点点，娇喘微微。<br />
<br />
Tears shone a little, and she breathed slightly.--[[User:EIMONKYAW|EIMONKYAW]] ([[User talk:EIMONKYAW|talk]]) 09:47, 15 December 2021 (UTC)Ei Mon Kyaw -----Ei Mon Kyaw[[User:EIMONKYAW|EIMONKYAW]] ([[User talk:EIMONKYAW|talk]]) 09:47, 15 December 2021 (UTC)<br />
The tears droped, and she breathed slowly. --[[User:Mahzad Heydarian|Mahzad Heydarian]] ([[User talk:Mahzad Heydarian|talk]]) 11:58, 15 December 2021 (UTC)<br />
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The eyes twinkled with tears and she breathed slightly.--[[User:Chen Jing|Chen Jing]] ([[User talk:Chen Jing|talk]]) 12:18, 19 December 2021 (UTC)</div>Chen Huinihttps://bou.de/u/index.php?title=20211215_homework&diff=13408120211215 homework2021-12-20T12:20:13Z<p>Chen Huini: /* 曾俊霖 Zēng Jùnlín 国别 男 202120081478 */</p>
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<div>Quicklinks: [[Introduction_to_Translation_Studies_2021|Back to course homepage]] [https://bou.de/u/wiki/uvu:Community_Portal#Frequently_asked_questions_FAQ FAQ] [https://bou.de/u/wiki/uvu:Community_Portal Manual] [[20210926_homework|Back to all homework webpages overview]] [[20220112_final_exam|final exam page]]<br />
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==陈静 Chén Jìng 国别 女 202020080595==<br />
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鼎：古代食器。胡羼(chàn忏) ──胡闹。 羼：本义为群羊杂居。引申为杂乱不纯，乱七八糟。<br />
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Tripod (Ding in Chinese): ancient food utensil. Hu Chan in Chinese means nonsense. Chan in Chinese originally means that the sheep live together, whose extensive meaning is mess.<br />
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Tripod: ancient food utensil. Hi Chan - nonsense. The original meaning is that sheep live together. It is extended meaning to be messy, impure and messy.<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 01:25, 12 December 2021 (UTC)<br />
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==蔡珠凤 Cài Zhūfèng 法语语言文学 女 202120081477==<br />
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抓──即“抓周”，亦称“试儿”、“试周”。旧俗于婴儿满周岁时，父母摆列各种小件器物，任其抓取，以测试其秉性、智愚、志趣。此俗始于江南，后亦传到北方。<br />
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Grasping -- namely "grasping the week", also known as "trying the child" and "trying the week". The old custom is that when a baby reaches the age of one year, his parents arrange all kinds of small objects and let him grab them to test his temperament, intelligence and interest. This custom began in the south of the Yangtze River and later spread to the north.<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 01:23, 12 December 2021 (UTC)<br />
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Catch ─ ─ means "catch the week", also known as "test" and "test week". The old custom is when the baby reaches one year old, the parents arrange all kinds of small utensils and let them grab them to test their disposition, wisdom and ambition. This custom began in the south of the Yangtze River and then spread to the north.--[[User:Zeng Junlin|Zeng Junlin]] ([[User talk:Zeng Junlin|talk]]) 07:41, 11 December 2021 (UTC)<br />
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==曾俊霖 Zēng Jùnlín 国别 男 202120081478==<br />
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事见北朝周·颜之推《颜氏家训·风操》：“江南风俗，儿生一期(年)，为制新衣，盥浴装饰，男则用弓矢纸笔，女则刀尺针缕(线)，并加饮食之物及珍宝服玩，置之儿前，观其发意所取，以验贪亷智愚，名之为试儿。”<br />
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It is said in Yan's family instructions and customs by Yan Zhitui of the Northern Dynasty that "the custom in the south of the Yangtze River was born in the first year. It was to make new clothes and decorate bathrooms. Men used bows and arrows, paper and pens, women used knives, rulers, needles and threads (lines), and played with food and precious clothes. They were placed in front of their children and looked at what they wanted to take to test their greed, wisdom and stupidity. They were called test children."--[[User:Zeng Junlin|Zeng Junlin]] ([[User talk:Zeng Junlin|talk]]) 07:37, 11 December 2021 (UTC)<br />
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It is said in Yan's family instructions and customs by Yan Zhitui of ''the Northern Dynasty'' that the custom in the south of the Yangtze River was born in the first year. It was to make new clothes and decorate bathrooms. Men used bows and arrows, paper and pens, women used knives, rulers, needles and threads (lines), and played with food and precious clothes. They were placed in front of their children and looked at what they wanted to take to test their greed, wisdom and stupidity. They were called test children."--[[User:Chen Huini|Chen Huini]] ([[User talk:Chen Huini|talk]]) 12:20, 20 December 2021 (UTC)Chen Huini<br />
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==陈惠妮 Chén Huìnī 英语语言文学（英美文学） 女 202120081479==<br />
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(宋·赵彦卫《云麓漫钞》卷二也有相同记载)又宋·叶真《爱日斋丛钞》卷一：“《玉壶野史》记曹武惠王(曹彬)始生周晬日，父母以百玩之具罗于席，观其所取。<br />
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== Headline text ==<br />
==陈湘琼 Chén Xiāngqióng 外国语言学及应用语言学 女 202120081480==<br />
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武惠王左手提干戈，右手提俎豆，斯须取一印，馀无所视。曹，真定人。江南遗俗乃在此(指真定)，今俗谓试周是也。”<br />
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Lord Wuhui holds weapons with his left hand and dinnerware in his right hand.Then he looks at a seal and graps it without seeing anything else.Lord Wuhui, whose first name is Cao, comes from Zhen Ding county. The place is called Shi Zhou now, on whiche Jiang Nan's old cities lay.--[[User:Chen Xiangqiong|Chen Xiangqiong]] ([[User talk:Chen Xiangqiong|talk]]) 00:23, 14 December 2021 (UTC)<br />
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Lord Wuhui holds weapons with his left hand and dinnerware in his right hand.Then he looks at a seal and graps it without seeing anything else.Lord Wuhui, whose first name is Cao, comes from Zhen Ding county. The place is so-called Shizhou now, on which ancient Jiangnan lay.--[[User:Chen Xinyi|Chen Xinyi]] ([[User talk:Chen Xinyi|talk]]) 05:07, 14 December 2021 (UTC)<br />
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==陈心怡 Chén Xīnyí 翻译学 女 202120081481==<br />
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致知格物──语出《礼记·大学》：“致知在格物，格物而后知至。”意谓要想获得知识，必须探究事物的道理。 致：获得，取得。<br />
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Zhi Zhi Ge Wu- ''From The Book of Rites·Daxue'': "Zhizhi lies in Gewu, and after Gewu, knowledge arrives." It means that in order to gain knowledge, one must inquire into the truth of things. Zhi: To acquire, to obtain.--[[User:Chen Xinyi|Chen Xinyi]] ([[User talk:Chen Xinyi|talk]]) 05:18, 12 December 2021 (UTC)<br />
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==程杨 Chéng Yáng 英语语言文学（英美文学） 女 202120081482==<br />
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格：推究，探究，探讨。尧……张──尧、舜、禹、汤、文、武，即唐尧、虞舜、夏禹、成汤、周文王、周武王，是从上古至西周的明君；<br />
Ge: means deduction, exploration and discussion. Yao...Zhang──Yao, Shun, Yu, Tang, Wen, Wu, namely Tang Yao, Yu Shun, Xia Yu, Cheng Tang, Emperor Wen of Zhou Dynasty, Emperor Wu of Zhou Dynasty, they are all wise emperors from ancient times to the Zhou Dynasty;--[[User:Cheng Yang|Cheng Yang]] ([[User talk:Cheng Yang|talk]]) 13:11, 11 December 2021 (UTC)<br />
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Ge: means deduction, exploration and discussion. Yao...Zhang──Yao, Shun, Yu, Tang, Wen, Wu, namely Tang Yao, Yu Shun, Xia Yu, Cheng Tang, Emperor Wen of Zhou Dynasty, Emperor Wu of Zhou Dynasty, they are all wise emperors from ancient times to Zhou Dynasty;--[[User:Ding Xuan|Ding Xuan]] ([[User talk:Ding Xuan|talk]]) 12:13, 19 December 2021 (UTC)<br />
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==丁旋 Dīng Xuán 英语语言文学（英美文学） 女 202120081483==<br />
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周、召，即周公旦、召公奭，都是西周的贤相；孔、孟，即孔丘(通称孔子)、孟轲(通称孟子)，都是儒学的创始人；董、韩、周、程、朱、张，即汉代董仲舒、唐代韩愈、北宋周敦颐、北宋程颢和程颐兄弟、南宋朱熹、北宋张载，都是儒学理论家。<br />
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Zhou, called the Duke of Zhou, and Zhao, called Duke of Shi, are both talented prime ministers (in feudal China); Kong (generally called Confucius) and Meng (generally called Mencius) are both founders of Confucianism; Dong (Dong Zhongshu in Han Dynasty), Han (Han Yu in Tang Dynasty), Zhou (Zhou Dunyi in the Northern Song Dynasty), Cheng (Cheng Jing and Cheng Yi brothers in the Northern Song Dynasty), Zhu (Zhu Xi in the Southern Song Dynasty), Zhang (Zhang Zai in the Northern Song Dynasty) are all Confucian theorists. --[[User:Ding Xuan|Ding Xuan]] ([[User talk:Ding Xuan|talk]]) 07:19, 12 December 2021 (UTC)<br />
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Zhou and Zhao are respectively the Duke of Zhou and the Duke of Shi and both are talented prime ministers of the Western Zhou Dynasty; both Kong (generally called Confucius) and Meng (generally called Mencius) are founders of Confucianism; Dong (Dong Zhongshu in Han Dynasty), Han (Han Yu in Tang Dynasty), Zhou (Zhou Dunyi in the Northern Song Dynasty), Cheng (Cheng Jing and Cheng Yi brothers in the Northern Song Dynasty), Zhu (Zhu Xi in the Southern Song Dynasty), Zhang (Zhang Zai in the Northern Song Dynasty) are all Confucian theorists. --[[User:Du Lina|Du Lina]] ([[User talk:Du Lina|talk]]) 08:09, 12 December 2021 (UTC)<br />
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==杜莉娜 Dù Lìnuó 英语语言文学（语言学） 女 202120081484==<br />
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这些人皆是儒家竭力推崇的人物。蚩尤……秦桧──蚩尤、共工，都是传说中上古最凶恶的部族首领；桀、纣、始皇，即夏桀、商纣王、秦始皇，都是登峰造极的暴君；<br />
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All these people are strong recommended by confucianists such as Chi You(a mythological warrior engaged in fighting with the Yellow Emperor), Qin Hui (a traitor in the Song dynasty in Chinese history)and so on. Among them both Chi You and Gong Gong (the water god in ancient Chinses history and the devil of floods) are the most ferocious tribal chief in the Chinese legend;and all Xia Jie, Shang Zhou and Qin Shi Huang, being respectively the emperor Jie of Xia Dynasty，the emperor Zhou of Shang Dynasty and the first emperor of Qin Dynasty, are extremely tyrannical.<br />
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All these people are strongly recommended by Confucianists such as Chi You(a mythological warrior engaged in fighting with the Yellow Emperor), Qin Hui (a traitor in the Song dynasty in Chinese history)and so on. Among them both Chi You and Gong Gong (the water god in ancient Chinses history and the devil of floods) are the most ferocious tribal chief in the Chinese legend;and all Xia Jie, Shang Zhou and Qin Shi Huang, being respectively the emperor Jie of Xia Dynasty，the emperor Zhou of Shang Dynasty and the first emperor of Qin Dynasty, are extremely tyrannical.--[[User:Fu Hongyan|Fu Hongyan]] ([[User talk:Fu Hongyan|talk]]) 14:13, 19 December 2021 (UTC)<br />
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==付红岩 Fù Hóngyán 英语语言文学（英美文学） 女 202120081485==<br />
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王莽、曹操、桓温、安禄山、秦桧，他们分别是汉代、三国、东晋、唐代、南宋人，都是大奸臣乃至叛逆之贼。许由……朝云──许由，传说他是上古时为了逃避帝位而终生隐居的贤人；陶潜(即陶渊明)、阮籍、嵇康、刘伶，都是魏晋时期著名文学家及不与流俗同低昂的独行之士；<br />
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Wang Mang, Cao Cao, Huan Wen, An Lushan and Qin Kuei, who were in the Han Dynasty, the Three Kingdoms, the Eastern Jin Dynasty, the Tang Dynasty and the Southern Song Dynasty respectively, were all great treacherous court officials and even traitors. It were Xu You... Zhao Yun -- Xu You who were said a sage who lived in seclusion all his life in order to escape the throne in ancient times. Tao Qian (Tao Yuanming), Ruan Ji, Ji Kang and Liu Ling were all prestigious persons in the Wei and Jin dynasties and mavericks who did not flow along with the turbid currents of the mainstream thought.--[[User:Fu Hongyan|Fu Hongyan]] ([[User talk:Fu Hongyan|talk]]) 14:10, 19 December 2021 (UTC)<br />
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Wang Mang, Cao Cao, Huan Wen, An Lushan and Qin Kuei, who were in the Han Dynasty, the Three Kingdoms, the Eastern Jin Dynasty, the Tang Dynasty and the Southern Song Dynasty respectively, were all great treacherous court officials and even traitors. Xu You... Zhao Yun -- Xu You. It is said that he was a sage who lived in seclusion all his life in order to escape the throne in ancient times. Tao Qian (Tao Yuanming), Ruan Ji, Ji Kang and Liu Ling were all famous writers in the Wei and Jin dynasties and mavericks who did not flow along with the turbid currents of the world.--[[User:Fu Shiyu|Fu Shiyu]] ([[User talk:Fu Shiyu|talk]]) 11:57, 19 December 2021 (UTC)<br />
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==付诗雨 Fù Shīyǔ 日语语言文学 女 202120081486==<br />
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王谢二族，指东晋王导和谢安，都是显贵；顾虎头，即顾恺之，字虎头，是东晋名画家；陈后主、唐明皇、宋徽宗，都是有才气的风流皇帝；<br />
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The Two families of Wang and Xie, namely Wang Dao and Xie An, were both nobility in the Eastern Jin Dynasty. Gu Hutou, also known as Gu Kaizhi, was a famous painter in the Eastern Jin Dynasty. Emperor Chen Shubao of Chen, Emperor Ming of Tang and Emperor Huizong of Song were all talented and romantic emperors.--[[User:Fu Shiyu|Fu Shiyu]] ([[User talk:Fu Shiyu|talk]]) 10:07, 12 December 2021 (UTC)<br />
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The Two families of Wang and Xie, namely Wang Dao and Xie An, were both of the nobility in the Eastern Jin Dynasty. Gu Hutou, also known as Gu Kaizhi, was a famous painter in the Eastern Jin Dynasty. Emperor Chen Shubao of Chen, Emperor Ming of Tang and Emperor Huizong of Song were all talented and romantic emperors. --[[User:Gao Mi|Gao Mi]] ([[User talk:Gao Mi|talk]]) 01:02, 13 December 2021 (UTC)<br />
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==高蜜 Gāo Mì 翻译学 女 202120081487==<br />
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刘庭芝即刘希夷(字庭芝)、温飞卿即温庭筠(字飞卿)，都是唐代名诗人；米南宫即米芾(南宫为世称)，是北宋名画家；石曼卿即石延年(字曼卿)、柳蓍卿即柳永(字蓍卿)、秦少游即秦观(字少游)，都是北宋著名文学家；<br />
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Liu Tingzhi, or Liu Xiyi (courtesy name Tingzhi), Wen Feiqin refers orWen Tingyun (courtesy name Feiqing) are both famous poets of the Tang Dynasty. Mi Nangong, or Mi Fu (nickname Nangong), was a famous painter of the Northern Song Dynasty; Shi Manqing, or Shi Yannian (courtesy name Manqing), Liu Yaoqing, or Liu Yong (courtesy name Yaoqing), and Qin Shaoyou, or Qin Guan (courtesy name Shaoyou), were famous literary scholars of the Northern Song Dynasty.--[[User:Gao Mi|Gao Mi]] ([[User talk:Gao Mi|talk]]) 01:03, 13 December 2021 (UTC)<br />
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Liu Tingzhi, or Liu Xiyi (styled Tingzhi), Wen Feiqin refers orWen Tingyun (styled Feiqing) are both famous poets of the Tang Dynasty. Mi Nangong, or Mi Fu (nickname Nangong), was a famous painter of the Northern Song Dynasty; Shi Manqing, or Shi Yannian (styled Manqing), Liu Yaoqing, or Liu Yong (styled Yaoqing), and Qin Shaoyou, or Qin Guan (styled Shaoyou), were famous literary scholars of the Northern Song Dynasty.--[[User:Gong Boya|Gong Boya]] ([[User talk:Gong Boya|talk]]) 12:27, 15 December 2021 (UTC)<br />
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==宫博雅 Gōng Bóyǎ 俄语语言文学 女 202120081488==<br />
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倪云林即倪瓒，字云林，是元代名画家；唐伯虎即唐寅(字伯虎)、祝枝山即祝允明(字枝山)，都是明代名画家、文学家；李龟年(唐代人)、黄幡绰(唐代人)、敬新磨(五代后唐人)，都是名艺人；<br />
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Ni Yunlin, i.e Ni Zan, was a famous painter in the Yuan Dynasty. Tang Bohu i.e Tang Yin (styled Bohu), Zhu Zhishan i.e Zhu Yunming (styled Zhishan), are famous Ming dynasty painters, litterateurs; Li Gunian (Tang Dynasty), Huang Fan Chuo (Tang Dynasty), Jing Xinmo (five dynasties later Tang dynasty), are famous artists;--[[User:Gong Boya|Gong Boya]] ([[User talk:Gong Boya|talk]]) 12:23, 15 December 2021 (UTC)<br />
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Ni Yunlin, or Ni Zan, or Ni Yunlin, was a famous painter in the Yuan dynasty; Tang Bohu (or Tang Yin) and Zhu Zhishan (or Zhu Yunming) were famous painters and literary figures in the Ming dynasty; Li Guinian (of the Tang dynasty), Huang Fanchuo (of the Tang dynasty), and Jing Xinmo (of the post-Tang dynasty of the Five Dynasties) were all famous artists.--[[User:He Qin|He Qin]] ([[User talk:He Qin|talk]]) 06:39, 15 December 2021 (UTC)<br />
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==何芩 Hé Qín 翻译学 女 202120081489==<br />
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卓文君(已见第一回注)、红拂(先为隋相杨素的侍女，后私奔李靖，也是前蜀·杜光庭《虬髯客传》中的女主人公)、薛涛(唐代才妓)、崔莺(即唐·元稹《会真记》、元·王实甫《西厢记》中的崔莺莺)、朝云(宋代名妓)，他们都是以才貌流芳的名女。<br />
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Zhuo Wenjun (see the first chapter note), Hong Fu (she used to be as the servant of the Sui minister Yang Su, then eloping to Li Jing; she was also the heroine of ''The Legend of the Gnarled Man'' by Du Guangting), Xue Tao (a talented prostitute of the Tang Dynasty), Cui Ying (i.e. Cui Yingying of Yuan Zhen's ''The Book of Hui Zhen'' and Wang Shifu's "The Western Chamber"), Zhaoyun (a famous prostitute of the Song Dynasty), they are all famous for their talent and beauty. --[[User:He Qin|He Qin]] ([[User talk:He Qin|talk]]) 06:32, 15 December 2021 (UTC)<br />
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Zhuo Wenjun ( the first chapter noted), Hong Fu (she used to be as the servant of the minister of Sui Dynasty Yang Su, then eloping to Li Jing, also the heroine of ''The Legend of the Gnarled Man'' by Du Guangting), Xue Tao (a talented prostitute of the Tang Dynasty), Cui Ying (namely Cui Yingying of Yuan Zhen's ''The Book of Hui Zhen'' and Wang Shifu's "The Western Chamber"), Zhaoyun (a famous prostitute of the Song Dynasty), they are all famous for their talent and beauty.--[[User:Hu Shuqing|Hu Shuqing]] ([[User talk:Hu Shuqing|talk]]) 12:25, 19 December 2021 (UTC)<br />
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==胡舒情 Hú Shūqíng 英语语言文学（语言学） 女 202120081490==<br />
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成则公侯败则贼──意谓成功的人便能获得公爵、侯爵之类的高官显爵，失败的人便被看作贼寇。表示世上并无公理，世人不讲是非，只论成功与失败，即只以成败论英雄。这里化用了“败则盗贼，成则帝王”。<br />
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Success makes the Duke while failure makes the theif ——which means that, If one is successful, he will be worshipped as the Duke. While one is unsuccessful, he will be despised as the thief. It expresses that there is no generally acknowledged truth in the world and people neglect justice and only pay attention to success and failure, that is, the sole measure. It coins a phrase here, “Failure makes a thief， success a king.”--[[User:Hu Shuqing|Hu Shuqing]] ([[User talk:Hu Shuqing|talk]]) 12:25, 19 December 2021 (UTC)<br />
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The winner is Duke, the looser is theif means that the person who succeeded would be entitled like Duke and who failed would be dispised as a theif. It presents that there is no generally acknowledged truth in the world and people neglect justice and only pay attention to success and failure, that is, the sole measure. It coins a phrase here, “Failure makes a thief， success a king.”--[[User:Huang Jinyun|Huang Jinyun]] ([[User talk:Huang Jinyun|talk]]) 14:58, 12 December 2021 (UTC)<br />
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==黄锦云 Huáng Jǐnyún 英语语言文学（语言学） 女 202120081491==<br />
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出自宋·邓牧《君道》：“嘻！天下何常之有？败则盗贼，成则帝王。”东床──指女婿。典出《晋书·王羲之传》、南朝宋·刘义庆《世说新语·雅量》：晋朝太尉郗鉴派人至丞相王导家相婿，王丞相令其到东厢房随意挑选。<br />
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It is cited from Deng Mu's How to Be Emperor, a work in Song dynasty, saying "how can the world be immutable! The loser is the thief, and the winner is the emperor." Dongchuang refers to the son-in-law, used in Books of Jin: Wang Xizhi's Biography" and Liu Yiqing's "Shi Shuo Xin Yu · Elegance" (Southern Song dynasty): In Jin dynasty Tai Wei (supreme government official in charge of military affairs) Xijian sent an underlying to the prime minister Wang Dao's house for taking in a son-in-law, and Prime Minister Wang invite him to choose at will in the east wing.--[[User:Huang Jinyun|Huang Jinyun]] ([[User talk:Huang Jinyun|talk]]) 14:43, 12 December 2021 (UTC)<br />
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It is cited from Deng Mu's How to Be Emperor, a work in Song dynasty, saying "how can the world be immutable! The loser is the thief, and the winner is the emperor." Dongchuang refers to the son-in-law, used in Books of Jin: Wang Xizhi's Biography" and Liu Yiqing's "Shi Shuo Xin Yu · Elegance" (Southern Song dynasty): In Jin dynasty Tai Wei (supreme government official in charge of military affairs) Xijian sent an official to the prime minister Wang Dao's house choosing a son-in-law, and Prime Minister Wang invited him to choose at will in the east wing room.--[[User:Huang Yiyan1|Huang Yiyan1]] ([[User talk:Huang Yiyan1|talk]]) 14:51, 12 December 2021 (UTC)<br />
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==黄逸妍 Huáng Yìyán 外国语言学及应用语言学 女 202120081492==<br />
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此人过去一看，见王家诸郎皆很矜持，唯独王羲之坦腹躺在东床之上，毫不在乎。此人回报，郗鉴即选中王羲之为婿。后世即以“东床”、“东床坦腹”、“东床客”、“东床娇客”等代指女婿。<br />
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The man looked over and saw that all the lords of Wang family were very reserved, except Wang Xizhi, who was lying on the east bed and didn't care, showing his belly. In return, Xi Jian chose Wang Xizhi as his son-in-law. Later generations referred to the son-in-law with "East Bed", "East Bed Man Showing Belly", "East Bed Guest", "East Bed Distinguished Guest" and so on.--[[User:Huang Yiyan1|Huang Yiyan1]] ([[User talk:Huang Yiyan1|talk]]) 04:59, 12 December 2021 (UTC)<br />
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The man looked over and saw that all the lords of Wang family were very reserved, except Wang Xizhi, who was lying on the east bed and didn't care, showing his belly. In return, Xi Jian chose Wang Xizhi as his son-in-law. Later generations referred to the son-in-law with "East Bed", "East Bed Man Showing Belly", "East Bed Guest", "East Bed Distinguished Guest" and so on.--[[User:Huang Zhuliang|Huang Zhuliang]] ([[User talk:Huang Zhuliang|talk]]) 13:54, 19 December 2021 (UTC)Huang Zhuliang<br />
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==黄柱梁 Huáng Zhùliáng 国别 男 202120081493==<br />
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退了一舍之地──意谓退避三十里。形容退居其后，不敢与争。 一舍：三十里。 这里化用了“退避三舍”之典。He retreat thirty miles. It describes retreating behind and not daring to compete with. Yishe: Thirty Li. The code of "retreat and give up" is used here.--[[User:Huang Zhuliang|Huang Zhuliang]] ([[User talk:Huang Zhuliang|talk]]) 13:53, 19 December 2021 (UTC)Huang Zhuliang<br />
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--[[User:Jin Xiaotong|Jin Xiaotong]] ([[User talk:Jin Xiaotong|talk]]) 12:02, 19 December 2021 (UTC)<br />
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==金晓童 Jīn Xiǎotóng 202120081494==<br />
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典出《左传·僖公二十三年》：春秋时，晋国公子重耳出奔至楚，楚成王礼遇之，因问道：“公子若反(返)晋国，则何以报不谷？”重耳对曰：“若以君之灵，得反晋国，晋、楚治兵，遇于中原，其辟(避)君三舍。”<br />
This story comes from ''Zuo Zhuan · Xi public twenty three years'': During the Spring and Autumn Period (777-476 BC), Childe Chong Er of the state of Jin went to the state of Chu. King Cheng of Chu gave a banquet for Chong er and asked, "If childe returns to the state of Jin, how will you repay me? Chong Er answered, "If I can return to the state of Jin, if the troops of the state of Jin and the state of Chu meet each other in the Central Plains, I will ask the troops of the state of Jin to retreat 90 li.--[[User:Jin Xiaotong|Jin Xiaotong]] ([[User talk:Jin Xiaotong|talk]]) 06:56, 12 December 2021 (UTC)<br />
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==邝艳丽 Kuàng Yànl 英语语言文学（语言学） 女 202120081495==<br />
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后重耳返国为君，晋、楚城濮(在今山东省鄄城县西南)之战，重耳遵守诺言，晋军果“退三舍以辟之”。<br />
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第三回<br />
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托内兄如海荐西宾 接外孙贾母惜孤女<br />
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Then Childe Chong Er came back to his country as Emperor. In the battle of Jin and Chu in Chengpu (in southwest of Juancheng county in Shandong province), he keeps his promise, then the army of Jin actually retreated to avoid the war. <br />
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Chapter 3<br />
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Entrust cousin Ru Hai with recommendations of distinguished gusts; Accept granddaughter lady Dowager takes pity on orphan girl<br />
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Then Childe Chong Er came back to his country as Emperor. In the battle of Jin and Chu in Chengpu (in southwest of Juancheng county in Shandong province), he keeps his promise, then the army of Jin actually retreated to avoid the war. <br />
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Chapter 3<br />
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Entrust cousin Ru Hai with recommendations of distinguished gusts; Welcoming her granddaughter, lady Dowager takes pity on the orphan girl.--[[User:Li Aixuan|Li Aixuan]] ([[User talk:Li Aixuan|talk]]) 11:13, 20 December 2021 (UTC)<br />
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==李爱璇 Lǐ Àixuán 英语语言文学（语言学） 女 202120081496==<br />
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却说雨村忙回头看时，不是别人，乃是当日同僚一案参革的张如圭。他系此地人，革后家居，今打听得都中奏准起复旧员之信，他便四下里寻情找门路，忽遇见雨村，故忙道喜。二人见了礼，张如圭便将此信告知雨村。<br />
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Yue-ts'un, turning round in a hurry, perceived that the speaker was no other than a certain Chang Ju-kuei, an old colleague of his, who had been denounced and deprived of office, on account of some case or other; a native of that district, who had, since his degradation, resided in his home.Having come to hear the news that a memorial, presented in the capital, that the former officers (who had been cashiered) should be reinstated, had received the imperial consent, he had promptly done all he could, in every nook and corner, to obtain influence, and to find the means (of righting his position,) when he, unexpectedly, came across Yue-ts'un, to whom he therefore lost no time in offering his congratulations. The two friends exchanged the conventional salutations, and Chang Ju-kuei communicated the tidings to Yue-ts'un.<br />
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Yue-ts'un, speedily looking back on, perceived that the speaker was no other than a certain Chang Ju-kuei, an old colleague of his, who had participated in the former case but been denounced and deprived of office, on account of some case or other. He was a native of that district, who had resided at home since his degradation. Having lately come to hear the news that a memorial, presented in the capital, that the former officers (who had been cashiered) should be reinstated, had received the imperial consent, he had promptly done all he could to obtain influence, and to find the means of righting his position. When he, unexpectedly, came across Yue-ts'un, he offered offering his congratulations to him soon. The two friends greeted to each other, exchanging the conventional salutations, and Chang Ju-kuei forthwith passed on the information to Yue-ts'un.--[[User:Li Ruiyang|Li Ruiyang]] ([[User talk:Li Ruiyang|talk]]) 04:55, 15 December 2021 (UTC)<br />
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==李瑞洋 Lǐ Ruìyáng 英语语言文学（英美文学） 女 202120081497==<br />
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雨村欢喜，忙忙叙了两句，各自别去回家。冷子兴听得此言，便忙献计，令雨村央求林如海，转向都中去央烦贾政。雨村领其意而别，回至馆中，忙寻邸报看真确了。次日，面谋之如海。如海道：“天缘凑巧。<br />
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Yue-ts'un was delighted, but after he had made a few remarks in a hurry, each took his leave and sped on his own way homewards. After hearing this conversation, Leng Tzu-hsing hastened at once to propose a plan, advising Yue-ts'un to request Lin Ju-hai, then, in his turn, to appeal to Chia Cheng in the capital for support. Yue-ts'un accepted the suggestion, and took leave of him. Upon returning to the quarter, he made all haste to lay his hand on the Metropolitan Gazette, to ascertain whether the news was authentic or not. On the next day, he had a personal consultation with Ju-hai. "Providence and good fortune are both alike propitious!" said by Ju-hai.--[[User:Li Ruiyang|Li Ruiyang]] ([[User talk:Li Ruiyang|talk]]) 04:57, 15 December 2021 (UTC)<br />
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Yue-ts'un was delighted, but after he they made a short conversation, each of them stepped on their own way homewards. After hearing the words of Yue-ts'un, Leng Tzu-hsing hastened at once to propose a plan, advising Yue-ts'un to request Lin Ju-hai, then, in his turn, to appeal to Chia Cheng in the capital for support. Yue-ts'un accepted the suggestion, and took leave of him. Upon returning to the quarter, he made all haste to read the Metropolitan Gazette, to ascertain the authenticity of that news. On the next day, he made a personal consultation with Ju-hai. Thus, Ju-hai said, "Providence and good fortune are both alike propitious!" --[[User:Li Shan|Li Shan]] ([[User talk:Li Shan|talk]]) 13:06, 15 December 2021 (UTC)<br />
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==李姗 Lǐ Shān 英语语言文学（英美文学） 女 202120081498==<br />
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因贱荆去世，都中家岳母念及小女无人依傍，前已遣了男女、船只来接，因小女未曾大痊，故尚未行。此刻正思送女进京。因向蒙教训之恩，未经酬报，遇此机会，岂有不尽心图报之理？弟已预筹之，修下荐书一封，托内兄务为周全，方可稍尽弟之鄙诚；<br />
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Since my wife has passed away, my mother-in-law has long before dispatched servants and transporting boats here to fetch my lonely daughter. But she has not set off yet due to the fact that she had not fully recovered at that time. As she is in good condition now, I am considering sending her to her grandma's. Once you have taught my daughter but desired no handsome payment; while now you need help, how can I sit on the fence? I have already well prepared for that in advance --- a recommendation letter has been written to my brother-in-law, to ensure your success in career. Only in this way can I show my gratitude towards you.--[[User:Li Shan|Li Shan]] ([[User talk:Li Shan|talk]]) 08:15, 11 December 2021 (UTC)<br />
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Since my wife has passed away, my mother-in-law who lives in the capital worried that my daughter has no one to rely on. So she has long before dispatched servants and transporting boats here to fetch my lonely daughter. But she has not set off yet due to the fact that she had not fully recovered at that time. As she is in good condition now, I am considering sending her to her grandma's. Once you have taught my daughter but desired no handsome payment; while now you need help, how can I sit on the fence? I have already well prepared for that in advance --- a recommendation letter has been written to my brother-in-law, to ensure your success in career. Only in this way can I show my gratitude towards you.--[[User:Li Shuang|Li Shuang]] ([[User talk:Li Shuang|talk]]) 07:43, 12 December 2021 (UTC)<br />
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==李双 Lǐ Shuāng 翻译学 女 202120081499==<br />
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即有所费，弟于内家信中写明，不劳吾兄多虑。”雨村一面打恭，谢不释口；一面又问：“不知令亲大人现居何职？只怕晚生草率，不敢进谒。”如海笑道：“若论舍亲，与尊兄犹系一家，乃荣公之孙：大内兄现袭一等将军之职，名赦，字恩侯；<br />
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“As for the possible costs, I will explain in the letter. You don’t need to worry about it.” Yu Cun bent down and expressed his gratitude, asking: “What does your brother do now? I’m worried that I would take the liberty to pay a visit, it’s too hasty.” Ru Hai laughed and said: “My brother and your brother belong to the same family. They are both descendants of Origin Merchant. My eldest brother is now a first-class general, his name is Pardon Merchant, whose alternative given name is Enhou.”--[[User:Li Shuang|Li Shuang]] ([[User talk:Li Shuang|talk]]) 07:38, 12 December 2021 (UTC)<br />
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“As for the possible costs, I will explain in the letter. You needn’t to worry about it.” Yu Cun bent down and expressed his gratitude, asking: “What does your brother do now? I’m worried that I would take the liberty to pay a visit, it’s too hasty.” Ru Hai laughed and said: “My brother and your brother belong to the same family. They are both descendants of Ronggong. The eldest brother of my wife is now a first-class general, his name is Pardon Merchant, whose alternative given name is Enhou.” --[[User:Li Wenxuan|Li Wenxuan]] ([[User talk:Li Wenxuan|talk]]) 23:46, 12 December 2021 (UTC)<br />
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==李文璇 Lǐ Wénxuán 英语语言文学（英美文学） 女 202120081500==<br />
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二内兄名政，字存周，现任工部员外郎，其为人谦恭厚道，大有祖父遗风，非膏粱轻薄之流，故弟致书烦托，否则不但有污尊兄清操，即弟亦不屑为矣。”雨村听了，心下方信了昨日子兴之言，于是又谢了林如海。<br />
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“The second brother of my wife named Zheng, his style name is Cunzhou. He is the Yuanwai official of the Ministry of Works in feudal China. He is moderate and kind, has the dignity of his grandfather, and is not the flimsy type. Therefore, my brother sent a letter to me. Otherwise, I will not only pollute my brother's operation, but also despise my brother.” After hearing this, Yuchun had believed the words of Zixing yesterday, therefore, he thanked Lin Ruhai again. --[[User:Li Wenxuan|Li Wenxuan]] ([[User talk:Li Wenxuan|talk]]) 01:27, 12 December 2021 (UTC)<br />
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The second brother-in-law is named Zheng, and the word is kept in Zhou. He is currently a member of the Ministry of Engineering. He is courteous and kind. He has a grandfather's legacy. He is not anointing and frivolous. Disdainful. "Yucun listened, and believed in Xing's words from yesterday, so he thanked Lin Ruhai again.--[[User:Li Wen|Li Wen]] ([[User talk:Li Wen|talk]]) 14:12, 12 December 2021 (UTC)<br />
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==李雯 Lǐ Wén 英语语言文学（英美文学） 女 202120081501==<br />
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如海又说：“择了出月初二日小女入都，吾兄即同路而往，岂不两便？”雨村唯唯听命，心中十分得意。如海遂打点礼物并饯行之事，雨村一一领了。那女学生原不忍离亲而去，无奈他外祖母必欲其往，且兼如海说：“汝父年已半百，再无续室之意；<br />
Ruhai also said: "I chose the girl to enter the capital on the second day of the lunar month, and my brother will go the same way. Isn't it both convenient?" Yucun obeyed,and RuHai was very satisfied . Ruhai then took some gifts and walked away, and Yucun took them one by one. The girl student couldn't bear to leave her relatives, but his grandmother wanted to go there. She also said like the sea: "Your father is half a hundred years old, and there is no intention to remarry.--[[User:Li Wen|Li Wen]] ([[User talk:Li Wen|talk]]) 14:11, 12 December 2021 (UTC)<br />
Ruhai said, "I chose the second day of the month to enter the capital, and my brother went the same way. Rain village obedient, the heart is very proud. Such as Haisui make gifts and farewell dinner, Rain village one by one. The girl could not bear to leave her, but her grandmother wanted her to go, saying, "Your father is fifty years old, and has no intention of staying in the house;--[[User:Li Xinxing|Li Xinxing]] ([[User talk:Li Xinxing|talk]]) 12:27, 19 December 2021 (UTC)<br />
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==李新星 Lǐ Xīnxīng 亚非语言文学 女 202120081503==<br />
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且汝多病，年又极小，上无亲母教养，下无姊妹扶持。今去依傍外祖母及舅氏姊妹，正好减我内顾之忧，如何不去？”黛玉听了，方洒泪拜别，随了奶娘及荣府中几个老妇登舟而去。雨村另有船只，带了两个小童，依附黛玉而行。<br />
You are sick, you are young, you have no mother to nurse you, and no sisters to nurse you. Now I am going to my grandmother and my uncle and sisters, which will relieve my worries. Why not?" When Daiyu heard this, she said goodbye with tears and followed the wet nurse and some old women in the Rong House to the boat. Yucun had another boat with two children attached to Daiyu.--[[User:Li Xinxing|Li Xinxing]] ([[User talk:Li Xinxing|talk]]) 12:25, 19 December 2021 (UTC)<br />
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And you are sick, very young, no mother to raise, no sister support. Today I go to rely on my grandmother and uncle's sisters, just to reduce my internal worries, how not to go? Dai Yu listened, and Fang shed tears to say goodbye, and followed the grandmother and several old women in the Rong Mansion to board the boat. There was another boat in the rain village, with two children, who were attached to Daiyu.--[[User:Li Yi|Li Yi]] ([[User talk:Li Yi|talk]]) 12:29, 19 December 2021 (UTC)<br />
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==李怡 Lǐ Yí 法语语言文学 女 202120081504==<br />
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一日到了京都，雨村先整了衣冠，带着童仆，拿了宗侄的名帖，至荣府门上投了。彼时贾政已看了妹丈之书，即忙请入相会。见雨村相貌魁伟，言谈不俗；且这贾政最喜的是读书人，礼贤下士，拯溺救危，大有祖风；况又系妹丈致意：因此优待雨村，更又不同。<br />
One day, when YuCun arrived in Jingdou, he dressed himself, and went to Rongfu with his nephew's name card. At this time Jia Zheng had seen his brother-in-law's letter, immediately invited him to come in to meet. Yucun looked tall and handsome and talked well. And Jia Zheng most like scholar, courtesy, saving, great predecessors style; Therefore, Jia Zheng is very good to Yucun. He is different from others.--[[User:Li Yi|Li Yi]] ([[User talk:Li Yi|talk]]) 08:18, 11 December 2021 (UTC)<br />
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One day, when YuCun arrived in Jingdou, he dressed himself, and went to Rongfu with his nephew's name card. At the very moment that Jia Zheng had received his brother-in-law's letter, he immediately invited him to come in to meet. Yucun looked tall and handsome and talked well. And Jia Zheng most like scholar, courtesy, saving, great predecessors style; Therefore, Jia Zheng is very good to Yucun. He is different from others--[[User:Liu Peiting|Liu Peiting]] ([[User talk:Liu Peiting|talk]]) 12:39, 19 December 2021 (UTC)<br />
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==刘沛婷 Liú Pèitíng 英语语言文学（英美文学） 女 202120081505==<br />
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便极力帮助，题奏之日，谋了一个复职。不上两月，便选了金陵应天府，辞了贾政，择日到任去了，不在话下。且说黛玉自那日弃舟登岸时，便有荣府打发轿子并拉行李车辆伺候。这黛玉尝听得母亲说，他外祖母家与别人家不同<br />
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They tried to help, the day of the title, sought a reinstatement. Within two months, he was elected to Jinling Yingtianfu, resigned from Jia Zheng, and left for his post on a certain day. Now, when Daiyu abandoned her boat and landed on the shore that day, she was served by a sedan chair sent by the Rongfu and a luggage cart. Daiyu heard from her mother that her grandmother's family was different from others.--[[User:Liu Peiting|Liu Peiting]] ([[User talk:Liu Peiting|talk]]) 12:40, 19 December 2021 (UTC)<br />
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They sought to a position the day that he presented a petition to the throne. Within two months, he was elected to Jinling Mansion, resigned from Jia Zheng, and left for his post on a certain day. Now, when Daiyu disembarked that day, she was served by a sedan chair sent by the Rong Mansion and a luggage cart. Daiyu heard from her mother that her grandmother's family was different from others.--[[User:Liu Shengnan|Liu Shengnan]] ([[User talk:Liu Shengnan|talk]]) 12:44, 19 December 2021 (UTC)<br />
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==刘胜楠 Liú Shèngnán 翻译学 女 202120081506==<br />
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他近日所见的这几个三等的仆妇，吃穿用度，已是不凡；何况今至其家，都要步步留心，时时在意，不要多说一句话，不可多行一步路，恐被人耻笑了去。自上了轿，进了城，从纱窗中瞧了一瞧，其街市之繁华，人烟之阜盛，自非别处可比。<br />
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In the past few days, she has been deeply impressed by the food, clothing and behavior of the low- ranking attendants who accompanied her. She decided that in their new home, she must always be vigilant and carefully weigh every word so as not to be ridiculed for any stupid mistake. When she carried into the city, she peeped out through the gauze window on her chair at the bustling and crowded streets, which she had never seen before.--[[User:Liu Shengnan|Liu Shengnan]] ([[User talk:Liu Shengnan|talk]]) 08:32, 11 December 2021 (UTC)<br />
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The three-class servants she had seen recently have an extraordinary cost of food and clothing. What's more, since got on the sedan chair and entered the city, the prosperous market and the populousness of the city through the screen window were not comparable to other places. when coming to grandmother's mansion must pay attention to every step and be cautious with words so as not to be ridiculed by others. Daiyu thought. --[[User:Liu Wei|Liu Wei]] ([[User talk:Liu Wei|talk]]) 15:56, 12 December 2021 (UTC)Liu Wei<br />
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==刘薇 Liú Wēi 国别 女 202120081507==<br />
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又行了半日，忽见街北蹲着两个大石狮子，三间兽头大门，门前列坐着十来个华冠丽服之人。正门不开，只东、西两角门有人出入。正门之上有一匾，匾上大书“敕造宁国府”五个大字。黛玉想道：“这是外祖的长房了。”<br />
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After half day, there are two large stone lions squatting on the north side of the street, three gates decorated with beast head, and a dozen people in gorgeous crowns and clothes are sitting in front of the gate. The main gate is closing, only the east and west corners enterences are accessible. There is a plaque above the main gate with five big characters "Ningguo Mansion". "That must be grandfather's the first son's mansion."Daiyu thought. --[[User:Liu Wei|Liu Wei]] ([[User talk:Liu Wei|talk]]) 15:39, 12 December 2021 (UTC)Liu Wei<br />
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After another half-day walk, they came to a street with two huge stone lions crouching on the north side and three gates decorated with beast head, in front of which ten or more people in gorgeous crowns and clothes were sitting. The main gate was shut, with only people passing in and out of the other two smaller gates. On a board above the main gate was written in big characters "Ningguo Mansion Built at Imperial Command". Daiyu realized that this must be where the elder branch of her grandmather's family lived.--[[User:Liu Xiao|Liu Xiao]] ([[User talk:Liu Xiao|talk]]) 05:40, 13 December 2021 (UTC)<br />
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==刘晓 Liú Xiǎo 英语语言文学（英美文学） 女 202120081508==<br />
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又往西不远，照样也是三间大门，方是荣国府，却不进正门，只由西角门而进。轿子抬着走了一箭之远，将转弯时便歇了轿，后面的婆子也都下来了。另换了四个眉目秀洁的十七八岁的小厮上来抬着轿子，众婆子步下跟随。<br />
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A little further to the west they came to another three gates. This was the Rong Mansion. Instead of going through the main gate, they entered the one on the west. The bearers carried the chair a bow-shot further, and then set it down at the turning and withdrew, the maidservants now going down the chair. Another four seventeen or eighteen smartly dressed lads picked up the chair, followed by the maids.--[[User:Liu Xiao|Liu Xiao]] ([[User talk:Liu Xiao|talk]]) 05:09, 12 December 2021 (UTC)<br />
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Not far to the west is the same three-room gate, which is the Rongguo Mansion. Instead of going through the main gate, they entered the one on the west. The bearers carried the chair a bow-shot further, and then set it down at the turning and withdrew, the maidservants now going down the chair. Another four seventeen or eighteen smartly dressed lads picked up the chair, followed by the maids.--[[User:Liu Yue|Liu Yue]] ([[User talk:Liu Yue|talk]]) 07:26, 12 December 2021 (UTC)<br />
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==刘越 Liú Yuè 亚非语言文学 女 202120081509==<br />
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至一垂花门前落下，那小厮俱肃然退出。众婆子上前打起轿帘，扶黛玉下了轿。黛玉扶着婆子的手，进了垂花门，两边是超手游廊，正中是穿堂，当地放着一个紫檀架子大理石屏风。转过屏风，小小三间厅房。<br />
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When the palanquin was dropped in front of a pendant door, the attendants all retired in silence. The ladies came forward and raised the curtain of the palanquin and helped Daiyu out of the palanquin. The two sides of the door are overhand corridors, and the centre is a hall with a marble screen on a rosewood frame. Turning past the screen, there is a small three-room hall.--[[User:Liu Yue|Liu Yue]] ([[User talk:Liu Yue|talk]]) 07:22, 12 December 2021 (UTC)<br />
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After the palanquin was dropped in front of a floral-pendant gates, the attendants all retreated in silence. The maids came forward and drew the curtain of the palanquin to help Black Jade out of the palanquin. Holding the hands of those maids, Black Jade enter the floral-pendant gates. On the two sides of the door were Chaoshou veranda, and in the center was a vestibule with a marble screen in a rosewood frame. Past the screen, there were three small rooms. --[[User:Liu Yunxin|Liu Yunxin]] ([[User talk:Liu Yunxin|talk]]) 13:01, 19 December 2021 (UTC)<br />
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==刘运心 Liú Yùnxīn 英语语言文学（英美文学） 女 202120081510==<br />
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厅后便是正房大院：正面五间上房，皆是雕梁画栋；两边穿山游廊、厢房，挂着各色鹦鹉、画眉等雀鸟。台阶上坐着几个穿红着绿的丫头，一见他们来了，都笑迎上来道：“刚才老太太还念诵呢，可巧就来了。”于是三四人争着打帘子。一面听得人说：“林姑娘来了。”<br />
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Behind the banqueting hall was the courtyard of the principal rooms: the five principal rooms on the front all had carved beams and painted rafters; from the roof of the verandah on both sides, the cages of parrots, thrushes and various birds hung there. A few girls in red and green sat on the steps. Saw they coming, the girls all smiled and came up: “Just now grandma was taking about you. You arrived just in time.” Then they rushed to pull the curtain. From the other side, a man said: “Miss Lin is coming.”--[[User:Liu Yunxin|Liu Yunxin]] ([[User talk:Liu Yunxin|talk]]) 12:28, 19 December 2021 (UTC)<br />
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Behind the hall was the courtyard of the central house: five principal rooms in the front with carved beams, while along two sides corridors and chambers with colorfully painted birds as parrots and thrush. There were several maids in red and green sitting on the steps. Seeing visitors coming, they greeted them with smiles, saying, "The old lady just talked about you again and again in anticipation, and here you are." then the four maids scrambled to open the curtain. Meanwhile a man said, "Miss Lin is coming."--[[User:Luo Anyi|Luo Anyi]] ([[User talk:Luo Anyi|talk]]) 12:47, 19 December 2021 (UTC)<br />
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==罗安怡 Luó Ānyí 英语语言文学（英美文学） 女 202120081511==<br />
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黛玉方进房，只见两个人扶着一位鬓发如银的老母迎上来。黛玉知是外祖母了，正欲下拜，早被外祖母抱住，搂入怀中，“心肝儿肉”叫着大哭起来。当下侍立之人无不下泪，黛玉也哭个不休。众人慢慢解劝，那黛玉方拜见了外祖母。<br />
An silver-haired old lady supported by two maids came and welcomed her while Black Jade entered the room. Realizing that this was her grandmother, Black Jade was about to bow down to show her respects. Suddenly she was tightly hugged by grandmoa who crying out harrowingly "my sweet heart!" Servants and maids were all in tears, and Black Jade also sobbed unceasingly. People persuaded them softly, then Black Jade was able to pay her respects to her grandmother. --[[User:Luo Anyi|Luo Anyi]] ([[User talk:Luo Anyi|talk]]) 12:14, 19 December 2021 (U<br />
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As Dai Yu just came to the home, there came a silver-haired old woman.Dai Yu knew she was grandma, and as she was going to bow down to show her respect, she has been already hugged by her grandma, who cried:"my sweety!". The surrounding maids all cried, as well as Dai Yu. The crowds slowy persuade her, and Dai Yu then showed her respect to her grandma.--[[User:Luo Xi|Luo Xi]] ([[User talk:Luo Xi|talk]]) 14:01, 19 December 2021 (UTC)<br />
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==罗曦 Luó Xī 英语语言文学（英美文学） 女 202120081512==<br />
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贾母方一一指与黛玉道：“这是你大舅母。这是二舅母。这是你先前珠大哥的媳妇珠大嫂子。”黛玉一一拜见。贾母又说：“请姑娘们。今日远客来了，可以不必上学去。”众人答应了一声，便去了两个。<br />
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Baoyu's grandmother than introduce them respectively:"This is your eldest aunt, and this is your second aunt.This is your Zhu brother's wife. Dai Yu greet them one by one.Baoyu's grandmother than said that:"Invite the gilrs. Today here come the dear guest, so they don't need to go to school." Surrounding people said yes, and two of them left.<br />
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The Lady Dowager then introduced them respectively to Daiyu: "This is your eldest aunt, and this is your second uncle's wife. This is your deceased elder brother Zhu's wife." Daiyu greeted them one by one. Her grandmother then said: "Tell all the girls to come here. We have visitor who came from afar, so they don't need to go to school." Surrounding people answered yes, and two of them left to call them.--[[User:Ma Xin|Ma Xin]] ([[User talk:Ma Xin|talk]]) 07:22, 20 December 2021 (UTC)<br />
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==马新 Mǎ Xīn 外国语言学及应用语言学 女 202120081513==<br />
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不一时，只见三个奶妈并五六个丫鬟，拥着三位姑娘来了：第一个肌肤微丰，身材合中，腮凝新荔，鼻腻鹅脂，温柔沉默，观之可亲；第二个削肩细腰，长挑身材，鸭蛋脸儿，俊眼修眉，顾盼神飞，文彩精华，见之忘俗；<br />
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In a little while, three grannies and five or six servant girls turned up, clustering with three ladies. The first was somewhere plump in figure and of average height; her cheek was in beautiful shape, like a fresh lichee; her nose was glossy like the goose grease; she was gentle and quiet in nature, who looks very friendly. The second was thin and tall with an oval face, sparking eyes and long eyebrows; her elegance and quick-witted mind tickle people’s fancy, letting them forget everything vulgar.--[[User:Ma Xin|Ma Xin]] ([[User talk:Ma Xin|talk]]) 08:11, 11 December 2021 (UTC)<br />
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After a while, the three young ladies showed up, escorted by three wet nurses and five or six maids. The first was slightly plump and of medium height; her cheeks were as smooth and soft as the newly ripened lichees, and her nose was as glossy as goose fat. She was tender and reticent, and looked very affable. The second had drooping shoulders and a slender waist; she was tall and slim, with an oval face, bright and piercing eyes as well as delicate eyebrows. She seemed elegant, quick-witted and in high spirits, with a display of distinctive charm. People who looked at her were to forget everything vulgar and tawdry.--[[User:Mao Yawen|Mao Yawen]] ([[User talk:Mao Yawen|talk]]) 23:42, 11 December 2021 (UTC)<br />
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==毛雅文 Máo Yǎwén 英语语言文学（英美文学） 女 202120081514==<br />
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第三个身量未足，形容尚小：其钗环裙袄，三人皆是一样的妆束。黛玉忙起身，迎上来见礼，互相厮认，归了坐位。丫鬟送上茶来。不过叙些黛玉之母如何得病，如何请医服药，如何送死发丧。不免贾母又伤感起来，因说：“我这些女孩儿，所疼的独有你母亲。<br />
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The third one was not yet fully grown, and she still had the face of a child. All the three young ladies were dressed in similar garments, that is, the tunics and the skirts with the same bracelets and head ornaments. Daiyu hastily rose to greet politely these cousins, and then they introduced to and acquainted with each other, after which they took seats while the maids served the tea. All their talk now was about Daiyu's mother: the culprit for her illness, the medicine that the doctors prescribed for treating her disease, and the conduction of her funeral and mourning ceremonies. Inevitably, the Lady Dowager couldn't help being affected painfully. "Of all my chilren I loved your mother best," she told Daiyu.--[[User:Mao Yawen|Mao Yawen]] ([[User talk:Mao Yawen|talk]]) 07:49, 11 December 2021 (UTC)<br />
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==毛优 Máo Yōu 俄语语言文学 女 202120081515==<br />
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今一旦先我而亡，不得见面，怎不伤心！”说着，携了黛玉的手，又哭起来。众人都忙相劝慰，方略略止住。众人见黛玉年纪虽小，其举止言谈不俗；身体面貌虽弱不胜衣，却有一段风流态度，便知他有不足之症。<br />
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Once she died before me, I could not see her again. She said, taking Daiyu's hand, and cried again. Everyone was busy trying to console her, and soon she slightly stopped. They saw that although Daiyu was young, her manner and speech were not ordinary; although her health was weak, she had graceful and elegant manner, so they knew that she had a disease of deficiency.--[[User:Mao You|Mao You]] ([[User talk:Mao You|talk]]) 08:43, 11 December 2021 (UTC)<br />
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"Once she died before me, it is so sad that I could not see her again." she said, taking Daiyu's hand, and cried again. Everyone was trying to console her, and then she slightly stopped. They saw that although Daiyu was young, her manner and speech were not ordinary; although she was weak, she had graceful and elegant gestures, so they learned that she had a disease of deficiency.--[[User:Mou Yixin|Mou Yixin]] ([[User talk:Mou Yixin|talk]]) 06:35, 12 December 2021 (UTC)<br />
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==牟一心 Móu Yīxīn 英语语言文学（英美文学） 女 202120081516==<br />
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因问：“常服何药？为何不治好了？”黛玉道：“我自来如此，从会吃饭时便吃药到如今了，经过多少名医，总未见效。那一年我才三岁，记得来了一个癞头和尚，说要化我去出家，我父母自是不从。<br />
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So they asked:" What medicine do you usually take? Why doesn't it work?" Daiyu replied:" I am used to getting along with my disease. I have been taking medicine since I could eat. A lot of famous daocters cannot contribute to my illness.When I was three years old, a monk with favus on the head came to persuade me to become a nun,but my parents declined him.--[[User:Mou Yixin|Mou Yixin]] ([[User talk:Mou Yixin|talk]]) 08:07, 11 December 2021 (UTC)<br />
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They then asked, "What medicines do you take regularly? Why can't you cure your illness?" Daiyu said, "I am used to getting along with my disease. I have been taking medicine since I could eat. A lot of famous docters cannot contribute to my illness. I was only three years old when I remember a mangy monk came and said he wanted to convert me to a monk, but my parents refused.--[[User:Peng Ruixue|Peng Ruixue]] ([[User talk:Peng Ruixue|talk]]) 06:29, 13 December 2021 (UTC)<br />
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==彭瑞雪 Péng Ruìxuě 法语语言文学 女 202120081517==<br />
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他又说：‘既舍不得他，但只怕他的病，一生也不能好的；若要好时，除非从此以后，总不许见哭声，除父母之外，凡有外亲，一概不见，方可平安了此一生。’这和尚疯疯癫癫，说了这些不经之谈，也没人理他。<br />
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The monk said, "if you can't bear to part with her she'll probably nerver get well. The only remedy is to keep her from hearing weeping and from seeing any relatives apart from her father and mother. That's her only hope of having a quiet life." No one paid any attention, of course, to such crazy talk.--[[User:Peng Ruixue|Peng Ruixue]] ([[User talk:Peng Ruixue|talk]]) 06:24, 13 December 2021 (UTC)<br />
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The monk said, "if you can't bear to separate with her, she'll probably nerver get well. The only remedy is to keep her from hearing weeping and from seeing any relatives apart from her father and mother. That's her only hope of having a quiet life." No one paid any attention, of course, to such nonsense talk.--[[User:Qing Jianan|Qing Jianan]] ([[User talk:Qing Jianan|talk]]) 12:12, 19 December 2021 (UTC)<br />
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==秦建安 Qín Jiànān 外国语言学及应用语言学 女 202120081518==<br />
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如今还是吃人参养荣丸。”贾母道：“这正好，我这里正配丸药呢，叫他们多配一料就是了。”一语未完，只听后院中有笑语声，说：“我来迟了，没得迎接远客。”黛玉思忖道：“这些人个个皆敛声屏气如此，这来者是谁，这样放诞无礼？”<br />
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Now Lin Daiyu is still taking ginseng pills.And Grandma Jia said:" What a coincidence! The pills are making now, I just tell them to add one." The words have not been finished, but there is a laugh in the back yard, which said:" I come late and fail to welcome our distinguished guest." Daiyu thought: all people here are holding their breath, who is this person that is so arrogant and rude?--[[User:Qing Jianan|Qing Jianan]] ([[User talk:Qing Jianan|talk]]) 08:19, 11 December 2021 (UTC)<br />
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Now Lin Daiyu is still taking ginseng pills. And Grandmother Jia said:" It just so happens that I have been asking them to dispense the pills, just asking them to do one more portion." The words are not finished, but there is a laugh in the back yard, which said:" I am late and fail to welcome our distinguished guest." Daiyu thought: “all people here are holding their breath, who is this person that is so arrogant and rude?”--[[User:Qiu Tingting|Qiu Tingting]] ([[User talk:Qiu Tingting|talk]]) 09:33, 12 December 2021 (UTC)<br />
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==邱婷婷 Qiū Tíngtíng 英语语言文学（语言学）女 202120081519==<br />
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心下想时，只见一群媳妇、丫鬟拥着一个丽人，从后房进来。这个人打扮与姑娘们不同，彩绣辉煌，恍若神妃仙子：头上戴着金丝八宝攒珠髻，绾着朝阳五凤挂珠钗；项上戴着赤金盘螭缨络圈；身上穿着缕金百蝶穿花大红云缎窄褃袄，外罩五彩刻丝石青银鼠褂；<br />
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While Lin Daiyu is still thinking about it, a group of daughters-in-law and maids cluster around a beauty coming in from the back room. She dresses up differently from other girls, with colorful embroidery splendor, and looks like a divine concubine or a fairy: wearing a gold silk beads bun decorated with eight treasures and the five phoenix hairpin hanging with beads on the head; a red gold coiled chi dragon tassel ring around the neck; the bright red made of cloud satin material narrow lining cotton jacket with decorations of wisps of gold hundred butterflies and flowers, and the outer coat with decorations of the multicolored engraved silk stone green silver mouse. --[[User:Qiu Tingting|Qiu Tingting]] ([[User talk:Qiu Tingting|talk]]) 09:34, 12 December 2021 (UTC)<br />
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Lin Daiyu was still thinking about it when she saw a group of daughters-in-law and maids embracing a beautiful woman who came in from the back room. This woman dresses differently from the girls, with colorful embroidery splendor, and looks like a divine concubine fairy: wearing a gold silk eight treasure save beads bun and the sunrise five phoenix hanging beads hairpin on the head; a red gold coiled chi dragon tassel ring around the neck; wearing the bright red made of cloud satin material narrow lining cotton jacket with decorations of wisps of gold hundred butterflies and flowers, and the outer coat with decorations of the multicolored engraved silk stone green silver mouse.--[[User:Rao Jinying|Rao Jinying]] ([[User talk:Rao Jinying|talk]]) 07:47, 11 December 2021 (UTC)<br />
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==饶金盈 Ráo Jīnyíng 英语语言文学（语言学） 女 202120081520==<br />
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下着翡翠撒花洋绉裙。一双丹凤三角眼，两弯柳叶吊梢眉。身量苗条，体格风骚。粉面含春威不露，丹唇未启笑先闻。黛玉连忙起身接见。贾母笑道：“你不认得他。他是我们这里有名的一个泼辣货，南京所谓‘辣子’，你只叫他‘凤辣子’就是了。”<br />
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Wang Xifeng wore a jadeite flowered dress underneath, with a pair of phoenix triangle eyes and two curved willow hanging eyebrows. Her figure is slim and her physique is flirtatious. She can be described with “ the face is delicate and beautiful, spirited character of her is not revealed in the appearance, red lips beautiful, not yet open mouth first heard her laugh”. Lin Daiyu hastily got up to curtsy to her. Lady Dowager said with a smile, "You do not recognize her. She is famous for her boldness and vigorousness here, she is truly the 'chilli woman' in Nanjing dialect, you can just call her ' chilli Feng'."--[[User:Rao Jinying|Rao Jinying]] ([[User talk:Rao Jinying|talk]]) 07:35, 11 December 2021 (UTC)<br />
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Wang Xifeng, characterized by a pair of phoenix triangle eyes and two curved willow hanging eyebrows, wore an emerald flowered crepe skirt. She was slender and coquettish, with a delicate face and a smiling lip. Daiyu promptly rose quickly to greet her. Lady Dowager said with a smile: “ you don’t know him. He is famous for her fierceness and toughness, namely the so-called Nanjing chilli. So you can just call him ‘Chilli Feng’.”--[[User:Shi Liqing|Shi Liqing]] ([[User talk:Shi Liqing|talk]]) 12:48, 11 December 2021 (UTC)<br />
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==石丽青 Shí Lìqīng 英语语言文学（英美文学） 女 202120081521==<br />
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黛玉正不知以何称呼，众姊妹都忙告诉黛玉道：“这是琏二嫂子。”黛玉虽不曾识面，听见他母亲说过：大舅贾赦之子贾琏，娶的就是二舅母王氏的内侄女，自幼假充男儿教养，叫做王熙凤学名。黛玉忙陪笑见礼，以“嫂”呼之。<br />
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Daiyu was insensible of what to call her. Then her sisters told her promptly: “ this is your sister-in-law Lian Er.” Although Daiyu had never met her, she heard of her from his mother: Jia Lian, the son of her Uncle Jia She, had married the niece of Aunt Wang, named scientifically Wang Xifeng, was brought up as a male offspring since childhood. Daiyu was engaged in smiling and saluting at her, calling her “sister-in-law”.--[[User:Shi Liqing|Shi Liqing]] ([[User talk:Shi Liqing|talk]]) 12:32, 11 December 2021 (UTC)<br />
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Daiyu didn't know what to call her. Then her sisters told her promptly: “This is your sister-in-law Lian Er.” Although Daiyu had never met her, she heard of her from his mother: Jia Lian, the son of her Uncle Jia She, had married the niece of Aunt Wang.She was brought up as a male offspring since childhood and her academic name is Wang Xifeng. Daiyu was engaged in smiling and saluting at her, calling her “sister-in-law”.--[[User:Sun Yashi|Sun Yashi]] ([[User talk:Sun Yashi|talk]]) 01:55, 13 December 2021 (UTC)<br />
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==孙雅诗 Sūn Yǎshī 外国语言学及应用语言学 女 202120081522==<br />
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这熙凤携着黛玉的手，上下细细打量了一回，便仍送至贾母身边坐下，因笑道：“天下真有这样标致人儿！我今日才算看见了。况且这通身的气派，竟不像老祖宗的外孙女儿，竟是嫡亲的孙女儿似的，怨不得老祖宗天天嘴里心里放不下。<br />
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Taking Daiyu's hand, Xifeng looked up and down her carefully, then she sent her to Mother Jia's side to sit down.She laughed and said:"There is really such a beautiful person in the world!I didn't see her until today.Moreover,the style of her makes her be more like your son's daughter than your daughter's daughter.It's no wonder that you are concerned about her so much.--[[User:Sun Yashi|Sun Yashi]] ([[User talk:Sun Yashi|talk]]) 01:49, 13 December 2021 (UTC)<br />
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Taking Daiyu's hand, Xifeng looked her up and down carefully, then sent her to Mother Jia's side to sit down. She laughed and said:"There is really such a beautiful person in the world! I haven’t seen her until today. Moreover, her extraordinary temperament makes her be more like your son's daughter rather than your daughter's daughter. It's no wonder that you are concerned about her so much. --[[User:Wang Lifei|Wang Lifei]] ([[User talk:Wang Lifei|talk]]) 06:48, 13 December 2021 (UTC)<br />
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==王李菲 Wáng Lǐfēi 英语语言文学（英美文学） 女 202120081523==<br />
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只可怜我这妹妹这么命苦，怎么姑妈偏就去世了呢？”说着便用帕拭泪。贾母笑道：“我才好了，你又来招我；你妹妹远路才来，身子又弱，也才劝住了：快别再提了。”<br />
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I pity my sister for being so miserable, how could my aunt died so early?" She said, wiping her tears with her handkerchief. Grandma Jia laughed and said, "I've just recovered. You come to provoke me again. Your sister has just arrived from a long journey and is weak, so she has just been persuaded: Don't mention it again."--[[User:Wang Lifei|Wang Lifei]] ([[User talk:Wang Lifei|talk]]) 02:37, 12 December 2021 (UTC)<br />
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I pity my sister who is so miserable, how could my aunt have died?" She said, wiping her tears with her handkerchief. Your sister has only just arrived from a long journey and is weak, so she has only just been persuaded to stop talking about it."--[[User:Wang Yifan21|Wang Yifan21]] ([[User talk:Wang Yifan21|talk]]) 08:22, 11 December 2021 (UTC)<br />
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==王逸凡 Wáng Yìfán 亚非语言文学 女 202120081524==<br />
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熙凤听了，忙转悲为喜道：“正是呢，我一见了妹妹，一心都在他身上，又是喜欢，又是伤心，竟忘了老祖宗了。该打，该打！”又忙拉着黛玉的手问道：“妹妹几岁了？可也上过学？现吃什么药？在这里别想家。<br />
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The first time I saw my sister, I was all over him, and I liked him, and I was sad, and I forgot about my ancestors. You should be beaten, you should be beaten!" He also took Daiyu's hand and asked, "How old is my sister? How old is she? What kind of medicine do you take now? Don't be homesick here.--[[User:Wang Yifan21|Wang Yifan21]] ([[User talk:Wang Yifan21|talk]]) 08:21, 11 December 2021 (UTC)<br />
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==王镇隆 Wáng Zhènlóng 英语语言文学（英美文学） 男 202120081525==<br />
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要什么吃的，什么玩的，只管告诉我；丫头、老婆们不好，也只管告诉我。”黛玉一一答应。一面熙凤又问人：“林姑娘的东西可搬进来了？带了几个人来？你们赶早打扫两间屋子，叫他们歇歇儿去。”<br />
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Just tell me what you want to eat and play; Girls and old servants are not good, just tell me. " Daiyu nodded one by one. On one side, Xifeng asked, "have you moved in Miss Lin's things? How many people have you brought? Clean the two rooms early and tell them to have a rest."--[[User:Wang Zhenlong|Wang Zhenlong]] ([[User talk:Wang Zhenlong|talk]]) 06:54, 12 December 2021 (UTC)<br />
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Tell me what you want to eat and play; And if the maids or old nurses aren't good to you, just let me know. " Daiyu nodded one by one. At the same time, Xifeng asked, "Have Miss Lin's things been moved in? And how many people does she bring? Clean the two rooms as soon as possible and tell them to have a rest there."--[[User:Wei Yiwen|Wei Yiwen]] ([[User talk:Wei Yiwen|talk]]) 08:24, 12 December 2021 (UTC)<br />
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==卫怡雯 Wèi Yíwén 英语语言文学（英美文学） 女 202120081526==<br />
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说话时已摆了果茶上来，熙凤亲自布让。又见二舅母问他：“月钱放完了没有？”熙凤道：“放完了。刚才带了人到后楼上找缎子，找了半日，也没见昨儿太太说的那个。想必太太记错了。”王夫人道：“有没有，什么要紧！”<br />
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Fruits and tea had been prepared when Xifeng was talking, and she arranged them by herself. The second aunt asked her whether the monthly payment has been given out, she answered yes. “I looked for the satin in the back stairs with some people for hours just now, but didn’t find which madam mentioned yesterday. Madam must be wrong.” Wang Xifeng said, and Mrs. Wang answered, “ It doesn’t matter if there is or not.”--[[User:Wei Yiwen|Wei Yiwen]] ([[User talk:Wei Yiwen|talk]]) 07:45, 12 December 2021 (UTC)<br />
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Fruits and tea had been prepared when Xifeng was talking, and she arranged them by herself. The second aunt asked her, "Have the monthly payment been given out?" Xifeng answered, "Yes. And I looked for the satin in the back stairs with some people for hours just now, but didn’t find what madam mentioned yesterday. Madam mabey remember something wrong.” Mrs. Wang replied, “ It doesn’t matter if there is or not.”--[[User:Wei Chuxuan|Wei Chuxuan]] ([[User talk:Wei Chuxuan|talk]]) 09:03, 12 December 2021 (UTC)<br />
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==魏楚璇 Wèi Chǔxuán 英语语言文学（英美文学） 女 202120081527==<br />
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因又说道：“该随手拿出两个来，给你这妹妹裁衣裳啊。等晚上想着，再叫人去拿罢。”熙凤道：“我倒先料着了，知道妹妹这两日必到，我已经预备下了。等太太回去过了目，好送来。”<br />
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Mrs. Wang said, "You should take out a couple of satin pieces to cut your sister's dress. When you think of this matter in the evening, send someone for the satin ." Xifeng said, "I expected it. I knew my sister would arrive in these two days, and I had already made preparations. I will send someone for the satin as soon as you have returned and examined it."--[[User:Wei Chuxuan|Wei Chuxuan]] ([[User talk:Wei Chuxuan|talk]]) 08:52, 12 December 2021 (UTC)<br />
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Mrs. Wang added, "You should take out a couple of satin pieces to cut your sister's dress. When you think of this in the evening, send someone for the satin ." Xifeng said, "I have expected it. I know my sister will arrive in these two days, and I have already made preparations. I will send someone for the satin as soon as you have examined it."--[[User:Wei Zhaoyan|Wei Zhaoyan]] ([[User talk:Wei Zhaoyan|talk]]) 13:58, 12 December 2021 (UTC)<br />
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==魏兆妍 Wèi Zhàoyán 英语语言文学（英美文学） 女 202120081528==<br />
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王夫人一笑，点头不语。当下茶果已撤，贾母命两个老嬷嬷带黛玉去见两个舅舅去。维时贾赦之妻邢氏忙起身笑回道：“我带了外甥女儿过去，到底便宜些。”<br />
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Her Ladyship smiled, nodded and said nothing. Now the refreshments were cleared away and the Lady Dowager ordered two nurses to take Daiyu to see her two uncles. At this time, Mrs. She also immediately stood up, replied with smile, "it's also very convenient for me to take my niece."--[[User:Wei Zhaoyan|Wei Zhaoyan]] ([[User talk:Wei Zhaoyan|talk]]) 13:51, 11 December 2021 (UTC)<br />
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Her Ladyship smiled, nodded but said nothing. Now the refreshments were cleared away and the Lady Dowager ordered two mothers to take Daiyu to see her two uncles. At this time, Mrs. She immediately stood up, replied with a smile, "it's also very convenient for me to take my niece."--[[User:Wu Jingyue|Wu Jingyue]] ([[User talk:Wu Jingyue|talk]]) 08:37, 12 December 2021 (UTC)<br />
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==吴婧悦 Wú Jìngyuè 俄语语言文学 女 202120081529==<br />
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贾母笑道：“正是呢，你也去罢，不必过来了。”那邢夫人答应了，遂带着黛玉，和王夫人作辞，大家送至穿堂。垂花门前早有众小厮拉过一辆翠幄青油车来，邢夫人携了黛玉坐上，众老婆们放下车帘，方命小厮们抬起。<br />
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Jiamu laughed and said: “ Yeah, you can also leave, and don’t have to come here.” Ms. Xing promised, and said goodbye to Ms Wang with Daiyu, all of them went through the hallway. The ingenious green carriage, which drove by a group of manservants stood in front of the floral-pendant gates, Ms. Xing set in the car with Daiyu, several old mothers put down the car shade, instructing boys uplift the carriage. --[[User:Wu Jingyue|Wu Jingyue]] ([[User talk:Wu Jingyue|talk]]) 08:35, 12 December 2021 (UTC)<br />
The mother laughed and said, "Exactly, you also go, no need to come." That Mrs. Xing agreed, so took Daiyu, and Mrs. Wang to say goodbye, we sent to the wear hall. The tent green oil carriage in front of the flower gate, Mrs. Xing took Daiyu to sit on it, the wives put down the curtain, and ordered the boys to lift it.--[[User:Wu Yinghong|Wu Yinghong]] ([[User talk:Wu Yinghong|talk]]) 01:13, 13 December 2021 (UTC)<br />
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==吴映红 Wú Yìnghóng 日语语言文学 女 202120081530==<br />
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拉至宽处，驾上驯骡，出了西角门往东，过荣府正门，入一黑油漆大门内，至仪门前方下了车。邢夫人挽着黛玉的手进入院中。黛玉度其处必是荣府中之花园隔断过来的。<br />
Pulled to a wide place, driving on the tame mule, out of the west corner gate to the east, past the main gate of Rongfu, into a black-painted gate, to the front of the ceremony door down the car. Mrs. Xing took Daiyu's hand and entered the courtyard. The first thing you need to do is to get to the garden.<br />
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Then he took the mule，went out the west Corner gate to the east, passed the main gate of Rongfu, entered a black painted gate, and got off in front of Yi gate. Lady Xing took Daiyu's hand and entered the courtyard. Daiyu spent its place must be the garden in the rong mansion partition come over.--[[User:Xiao Yiyao|Xiao Yiyao]] ([[User talk:Xiao Yiyao|talk]]) 01:38, 15 December 2021 (UTC)<br />
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==肖毅瑶 Xiāo Yìyáo 英语语言文学（英美文学） 女 202120081531==<br />
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进入三层仪门，果见正房、厢房、游廊悉皆小巧别致，不似那边的轩峻壮丽，且院中随处之树木山石皆好。及进入正室，早有许多艳妆丽服之姬妾、丫鬟迎着。邢夫人让黛玉坐了；一面令人到外书房中请贾赦。<br />
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When we entered the three-story ceremonial gate, the main room, wing room and verandah were all small and unique, unlike those of the other side. Besides, the trees, mountains and stones in the courtyard were all good. When they entered the main room, there were many concubines and servant girls dressed in colourful makeup and beautiful clothes waiting for them. Madam Xing asked Daiyu to sit down and then let others invite Jia She in the outer study.--[[User:Xiao Yiyao|Xiao Yiyao]] ([[User talk:Xiao Yiyao|talk]]) 01:02, 15 December 2021 (UTC)<br />
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When entering the three- layers ceremonial gate, Daiyu found that the main room, wing room and verandah were all small and unique, unlike those of the other side. Besides, the trees, mountains and stones in the courtyard were all good. When they entered the main room, there were many concubines and servant girls dressed in heavy makeup and beautiful clothes waiting for them. Madam Xing asked Daiyu to sit down and then let others invite Jia She in the outer study.--[[User:Xie Jiafen|Xie Jiafen]] ([[User talk:Xie Jiafen|talk]]) 12:10, 19 December 2021 (UTC)<br />
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==谢佳芬 Xiè Jiāfēn 英语语言文学（英美文学） 女 202120081532==<br />
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一时回来说：“老爷说了：‘连日身上不好，见了姑娘，彼此伤心，暂且不忍相见。劝姑娘不必伤怀想家，跟着老太太和舅母，是和家里一样的。姐妹们虽拙，大家一处作伴，也可以解些烦闷。或有委屈之处，只管说，别外道了才是。’”<br />
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Mrs.Xing came back and said, "the master said, 'I've been felt not so good for days. I am afraid that I will be emotional if I see you, so I can't bear to see you for the time being. I advise you not to be homesick. It's the same as home to follow the old lady and aunt. Although the sisters are clumsy, you can relieve some boredom if you keep company together. If you have grievances, just tell us and make yourself at home.''--[[User:Xie Jiafen|Xie Jiafen]] ([[User talk:Xie Jiafen|talk]]) 12:07, 19 December 2021 (UTC)<br />
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Mrs.Xing came back and said, "the master said, 'I've been felt not so good for days. I am afraid that I will be emotional if I see you, so I can't bear to see you for the time being. I advise you not to be homesick. It's the same as home to follow the old lady and aunt. Although the sisters are clumsy, you can relieve some boredom if you keep company together. If you have grievances, just tell us and make yourself at home.''--[[User:Xie Qinglin|Xie Qinglin]] ([[User talk:Xie Qinglin|talk]]) 07:45, 20 December 2021 (UTC)<br />
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==谢庆琳 Xiè Qìnglín 俄语语言文学 女 202120081533==<br />
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黛玉忙站起身来，一一答应了。再坐一刻便告辞，邢夫人苦留吃过饭去。黛玉笑回道：“舅母爱惜赐饭，原不应辞；只是还要过去拜见二舅舅，恐去迟了不恭，异日再领。望舅母容谅。”<br />
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Daiyu stood up and agreed one by one. Sitting for a moment and then said goodbye, Mrs. Xing painstakingly stay to eat a meal. Daiyu smile back: "aunt love to give rice, should not resign; just have to go over to see second uncle, afraid to go late disrespectful, another day to receive. I hope aunt forgive me."--[[User:Xie Qinglin|Xie Qinglin]] ([[User talk:Xie Qinglin|talk]]) 07:44, 20 December 2021 (UTC)<br />
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==熊敏 Xióng Mǐn 英语语言文学（英美文学） 女 202120081534==<br />
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邢夫人道：“这也罢了。”遂命两个嬷嬷用方才坐来的车送过去。于是黛玉告辞。邢夫人送至仪门前，又嘱咐了众人几句，眼看着车去了方回来。一时黛玉进入荣府，下了车，只见一条大甬路直接出大门来。<br />
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Mrs. Xing said: “That’s fine.” So she ordered two Sisters to send Daiyu back by Carriage used before. So Daiyu farewell others. Mrs. Xing saw her off and said some words to others, seeing the carriage come back and forth. Once Daiyu entered The House of Rong and got off the carriage, she saw a long and wide road.<br />
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Mrs. Xing said: “That’s fine.” So she ordered two Sisters to send Daiyu back by Carriage used before. So Daiyu farewell others. Mrs. Xing saw her off to the gate of etiquetteand said some words to others, seeing the carriage come back and forth. Once Daiyu entered The House of Rong and got off the carriage, she saw a long and wide road.--[[User:Xu Minyun|Xu Minyun]] ([[User talk:Xu Minyun|talk]]) 11:26, 20 December 2021 (UTC)<br />
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==徐敏赟 Xú Mǐnyūn 语言智能与跨文化传播研究 男 202120081535==<br />
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众嬷嬷引着，便往东转弯，走过一座东西穿堂，向南大厅之后，仪门内大院落：上面五间大正房，两边厢房，鹿顶耳房钻山，四通八达，轩昂壮丽，比各处不同。黛玉便知这方是正内室。<br />
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Led by the mammy, she turned east, passed through an east-west hall, and came to the south hall, where she found a large courtyard inside the Gate of Yi: five main rooms on the top, flanks on both sides, and deer's roof and ears, extending in all directions, magnificent and different from other places. Daiyu knew this was the inner room.--[[User:Xu Minyun|Xu Minyun]] ([[User talk:Xu Minyun|talk]]) 09:10, 18 December 2021 (UTC)<br />
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Led by the mammies, they turned eastward and passed through an east-west hallway and the southward hall, she found a large courtyard inside the secondary gate: five main rooms on the top, flanks on both sides, and a small flat topped house next to the main house, extending in all directions, magnificent and different from other places. Daiyu knew this was the inner room.--[[User:Yan Jing|Yan Jing]] ([[User talk:Yan Jing|talk]]) 10:08, 18 December 2021 (UTC)<br />
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==颜静 Yán Jìng 语言智能与跨文化传播研究 女 202120081536==<br />
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进入堂屋，抬头迎面先见一个赤金九龙青地大匾，匾上写着斗大三个字，是“荣禧堂”；后有一行小字：“某年月日书赐荣国公贾源”，又有“万幾宸翰”之宝。大紫檀雕螭案上，设着三尺多高青绿古铜鼎，悬着待漏随朝墨龙大画，一边是錾金彝，一边是玻璃盆。<br />
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Entering the main room, I looked up and saw a great blue board framed in gilded dragons. On the plaque, there were two big words "Rongxi hall"; Then there is a line of small characters: "on a certain date, this was given to Jia Yuan, the Duke of Honor", and there was the treasure of Emperor's handwriting. On the large red sandalwood table that carved with dragon, there was a green bronze tripod more than three feet high, hanging a large ink dragon painting that seemed to attend the imperial court session in the early morning, with gilded wine vessels on one side and a glass basin on the other.--[[User:Yan Jing|Yan Jing]] ([[User talk:Yan Jing|talk]]) 08:11, 18 December 2021 (UTC)<br />
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there was a green bronze tripod more than three feet high, hanging a large ink dragon painting that seemed to attend the imperial court session in the early morning, with gilded wine vessels on one side and a glass basin on the other.--[[User:Yan Lili|Yan Lili]] ([[User talk:Yan Lili|talk]]) 12:05, 19 December 2021 (UTC)<br />
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==颜莉莉 Yán Lìlì 国别 女 202120081537==<br />
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地下两溜十六张楠木圈椅。又有一副对联，乃是乌木联牌镶着錾金字迹，道是：座上珠玑昭日月，堂前黼黻焕烟霞。下面一行小字是“世教弟勋袭东安郡王穆莳拜手书”。原来王夫人时常居坐宴息也不在这正室中，只在东边的三间耳房内。<br />
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On the ground two rows of 16 nanmu armchairs. There is also a pair of couplets, ebony couplet inset with gold handwriting, it said:The pearl and jade in the seat can shine with the sun and the moon; The people in front of the lobby wearing official clothes, its colors like clouds like clouds. The next line is written by mu Shis, the hereditary king of Dongpyeong County, who is a brother who has been taught by your family for generations.For Lady Wang often sat and reposed not in this main room, but in the three eastern rooms.--[[User:Yan Lili|Yan Lili]] ([[User talk:Yan Lili|talk]]) 03:36, 12 December 2021 (UTC)<br />
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Since Lady Wang seldom sat in this main hall but used three rooms on the east side for relaxation.--[[User:Yan Zihan|Yan Zihan]] ([[User talk:Yan Zihan|talk]]) 10:04, 14 December 2021 (UTC)<br />
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==颜子涵 Yán Zǐhán 国别 女 202120081538==<br />
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于是嬷嬷们引黛玉进东房门来。临窗大炕上铺着猩红洋毯，正面设着大红金钱蟒引枕，秋香色金钱蟒大条褥；两边设一对梅花式洋漆小几：左边几上摆着文王鼎，鼎旁匙箸、香盒；右边几上摆着汝窑美人觚，里面插着时鲜花草。<br />
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So that the nurses led Daiyu through the door of the eastern wing. The large kang by the window was covered with a scarlet foreign rug. In the middle were red back-rests and turquoise bolsters, both with dragon-design medallions, and a long greenish yellow mattress also with dragon medallions. On the two sides， stood one of a pair of small teapoys of foreign lacquer of plum-blossom pattern. On the left-hand table were a tripod, spoons, chopsticks and an incense container; On the right-hand table were a slender-waisted porcelain vase from the Ruzhou Kiln in which were placed seasonable flowers.--[[User:Yan Zihan|Yan Zihan]] ([[User talk:Yan Zihan|talk]]) 09:48, 14 December 2021 (UTC)<br />
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Thereupon,the nurses led Daiyu through the door of the eastern wing. The large kang by the window was covered with a scarlet foreign rug. In the middle were red back-rests and turquoise bolsters, both with dragon-design medallions, and a long greenish yellow mattress also with dragon medallions. On the two sides stood on a pair of small teapoys of foreign lacquer of plum-blossom pattern. On the left-hand table were a tripod, spoons, chopsticks and an incense container; On the right-hand table were a slender-waisted porcelain vase from the Ruzhou Kiln in which were placed seasonable flowers.--[[User:Yang Jiaying|Yang Jiaying]] ([[User talk:Yang Jiaying|talk]]) 13:47, 14 December 2021 (UTC)<br />
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==阳佳颖 Yáng Jiāyǐng 国别 女 202120081540==<br />
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地下面，西一溜四张大椅，都搭着银红撒花椅搭，底下四副脚踏；两边又有一对高几，几上茗碗、瓶花俱备。其馀陈设，不必细说。老嬷嬷让黛玉上炕坐。炕沿上却也有两个锦褥对设。<br />
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On the floor on the west side of the room, were four chairs in a row, all of which were covered with antimacassars, embroidered with silverish-red flowers.Beneath them stood four footstools. On either side, was also a pair of high teapoys which were covered with teacups and flower vases.The rest of the room need not be described in detail.<br />
The nurses urged Daiyu to sit on the kang, on the edge of which were two brocade cushions. --[[User:Yang Jiaying|Yang Jiaying]] ([[User talk:Yang Jiaying|talk]]) 13:42, 14 December 2021 (UTC)<br />
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On the floor facing on the west wall were four chairs in a row, all of which were covered with ornamented cloth embroidered with silverish-red flowers.Beneath them stood four footstools. On either side were a pair of high table with teacups and flower vases.Other decorations in the rest of the room need not be described in detail.The nurses urged Daiyu to sit on the kang, on the edge of which were two brocade cushions.--[[User:Yang Aijiang|Yang Aijiang]] ([[User talk:Yang Aijiang|talk]]) 09:50, 18 December 2021 (UTC)<br />
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==杨爱江 Yáng Àijiāng 英语语言文学（语言学） 女 202120081541==<br />
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黛玉度其位次，便不上炕，只就东边椅上坐了。本房的丫鬟忙捧上茶来。黛玉一面吃了，打量这些丫鬟们妆饰衣裙，举止行动，果与别家不同。<br />
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Considering her status in the family, Daiyu sat on one of the chairs on the east side instead of sitting on the ''kang''. The maids in attendance served tea immediately. When she was sipping the tea, she observed the maids’ make-up,clothes and deportment, which, her thought, were indeed quite different from those in other families.--[[User:Yang Aijiang|Yang Aijiang]] ([[User talk:Yang Aijiang|talk]]) 09:38, 18 December 2021 (UTC)<br />
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Considering her status in the family, Daiyu sat on one of the chairs on the east side instead of sitting on the ''kang''. The maids in attendance served tea immediately.Sipping the tea, she observed the maids’ make-up,clothes and deportment, which, her thought, were indeed quite different from those in other families.--[[User:Yang Kun|Yang Kun]] ([[User talk:Yang Kun|talk]]) 14:18, 18 December 2021 (UTC)<br />
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==杨堃 Yáng Kūn 法语语言文学 女 202120081542==<br />
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茶未吃了，只见一个穿红绫袄、青绸掐牙背心的一个丫鬟走来笑道：“太太说，请林姑娘到那边坐罢。”老嬷嬷听了，于是又引黛玉出来，到了东廊三间小正房内。<br />
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Before the tea was drunk, a servant girl wearing a red silk jacket and a green satin vest came up and smiled, "Mrs. Wang invited Miss Lin to come and sit over there." When the old Mammy heard this, she led Daiyu out again and went to the third small main room on the east porch.--[[User:Yang Kun|Yang Kun]] ([[User talk:Yang Kun|talk]]) 03:33, 12 December 2021 (UTC)<br />
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Before they drank tea over, a servant girl in a red silk jacket and a green satin vest came up and smiled, "Mrs. Wang invited Miss Lin to come and sit over there." When the old Mammy heard this, she led Daiyu out again to the third small main room on the east porch.--[[User:Yang Liuqing|Yang Liuqing]] ([[User talk:Yang Liuqing|talk]]) 11:10, 12 December 2021 (UTC)<br />
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==杨柳青 Yáng Liǔqīng 英语语言文学（英美文学） 女 202120081543==<br />
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正面炕上横设一张炕桌，上面堆着书籍、茶具；靠东壁面西设着半旧的青缎靠背、引枕。王夫人却坐在西边下首，亦是半旧青缎靠背、坐褥。见黛玉来了，便往东让。<br />
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On the kang there was a kang table on which books and tea sets piled up. Half new backrests and pillows made of blue satins were set on the east side of the wall. However, Mrs. Wang set at the foot of the west wall where half new backrests and mattresses made of blue satins were displayed. Mrs. Wang moved to the east side when she saw Lin Daiyu come in.--[[User:Yang Liuqing|Yang Liuqing]] ([[User talk:Yang Liuqing|talk]]) 11:11, 12 December 2021 (UTC)<br />
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On the kang lies a kang table,on which books and tea sets are piled up. Half new backrests and pillows made of blue satins were put on the east side of the wall. However, Mrs. Wang sat at the foot of the west wall where half new backrests and mattresses made of blue satins are displayed. Mrs. Wang moved to the east side when she saw Daiyu coming in.--[[User:Ye Weijie|Ye Weijie]] ([[User talk:Ye Weijie|talk]]) 12:11, 19 December 2021 (UTC)<br />
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==叶维杰 Yè Wéijié 国别 男 202120081544==<br />
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黛玉心中料定这是贾政之位。因见挨炕一溜三张椅子上也搭着半旧的弹花椅袱，黛玉便向椅上坐了。王夫人再三让他上炕，他方挨王夫人坐下。王夫人因说：“你舅舅今日斋戒去了，再见罢。<br />
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Daiyu thought it must be Jia Zhen's seat. Seeing that there were half-old bouncing chair blankets on the three chairs slid by the kang, Daiyu sat on the chair. Mrs. Wang repeatedly asked him to go to the kang, then she sat down next to Mrs. Wang. Mrs. Wang said: "Your uncle has gone fast today, goodbye.<br />
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Daiyu thought that this was Jia Zheng's seat, because she saw that there were three chairs next to the bed with a half-used chair, so Daiyu sat down on the chair. She sat down next to Madam Wang after she had asked her to go to the bed again and again. Mrs. Wang said, "Your uncle went to fast today, see you later.”--[[User:Yi Yangfan|Yi Yangfan]] ([[User talk:Yi Yangfan|talk]]) 12:29, 19 December 2021 (UTC)Yi Yangfan<br />
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==易扬帆 Yì Yángfān 英语语言文学（英美文学） 女 202120081545==<br />
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只是有句话嘱咐你：你三个姐妹倒都极好，以后一处念书认字，学针线，或偶一玩笑，却都有个尽让的。我就只一件不放心：我有一个孽根祸胎，是家里的混世魔王，今日因往庙里还愿去，尚未回来，晚上你看见就知道了。<br />
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I just have one thing to tell you: your three sisters are all very good, and in the future they will study and learn to read and write together, and learn to sew, or occasionally play jokes, but all of them will do their best. There is only one thing I am not sure about: I have a sinful child who is the evil one in my family, and he has not returned yet because he has gone to the temple to pay his respects.--[[User:Yi Yangfan|Yi Yangfan]] ([[User talk:Yi Yangfan|talk]]) 02:19, 13 December 2021 (UTC)Yi Yangfan<br />
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I just want to remind you: your sisters are very kind , and in the future you will study together, and learn to read and write and learn to sew. Sometimes you will play jokes at each other, but you will be very tolerant to each other. There is only one thing I am worried about: there is a naughty boy in our family, and he has not returned yet because he has gone to the temple to redeem his wishes, you will see him in the evening.--[[User:Yin Huizhen|Yin Huizhen]] ([[User talk:Yin Huizhen|talk]]) 07:45, 13 December 2021 (UTC)<br />
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==殷慧珍 Yīn Huìzhēn 英语语言文学（英美文学） 女 202120081546==<br />
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你以后总不用理会他，你这些姐姐妹妹都不敢沾惹他的。”黛玉素闻母亲说过：“有个内侄，乃衔玉而生，顽劣异常，不喜读书，最喜在内帏厮混。外祖母又溺爱，无人敢管。”<br />
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“You can ignore him later and none of your sisters dare to bother him. ” Daiyu heard from her mother: “I have a nephew, who was born with jade in his mouth. He is very naughty and don’t like to read, but prefer to play with girls. His grandma has always spoiled him so that everyone let him be. ”--[[User:Yin Huizhen|Yin Huizhen]] ([[User talk:Yin Huizhen|talk]]) 07:27, 13 December 2021 (UTC)<br />
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"You can ignore him in future because none of your sisters dare to mess up with him". Mascara Jade has long heard from her mother about him: "I have a nephew, born with a jade in his mouth, who is very naughty and doesn’t like to read, but prefers to hang around with girls. His grandma has always spoiled him so that everyone let him be". --[[User:Yin Meida|Yin Meida]] ([[User talk:Yin Meida|talk]]) 12:31, 19 December 2021 (UTC)<br />
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==殷美达 Yīn Měidá 英语语言文学（语言学） 女 202120081547==<br />
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今见王夫人所说，便知是这位表兄。一面陪笑道：“舅母所说，可是衔玉而生的？在家时，记得母亲常说：这位哥哥比我大一岁，小名就叫宝玉，性虽憨顽，说待姊妹们却是极好的。<br />
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What Lady King described today is the cousin for sure. Mascara Jade said while smiling:" Is the person you just mentioned my cousin born with a jade? I remember when I was at home my mother often said that the cousin nicknamed Precious Jade is one year older than me. Although he is a little mischievous, he is very friendly with his sisters".--[[User:Yin Meida|Yin Meida]] ([[User talk:Yin Meida|talk]]) 14:27, 12 December 2021 (UTC)<br />
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Who Lady King described today is the cousin for sure. Mascara Jade said while smiling:" Is this my cousin you just mentioned born with a jade? I remember when I was at home my mother often said that the cousin nicknamed Precious Jade is one year older than me. Although he is a little mischievous, he is very friendly with his sisters".--[[User:Yin Yuan|Yin Yuan]] ([[User talk:Yin Yuan|talk]]) 12:06, 19 December 2021 (UTC)<br />
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==尹媛 Yǐn Yuán 英语语言文学（英美文学） 女 202120081548==<br />
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况我来了，自然和姊妹们一处，弟兄们是另院别房，岂有沾惹之理？”王夫人笑道：“你不知道原故。他和别人不同，自幼因老太太疼爱，原系和姐妹们一处娇养惯了的。<br />
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Now I come here,undoubtfully I live with my sisters. The brothers are in some different houses. Is there any reason to mess with them?" Lady King smiled and said, "You don't know. Unlike the others, he had been coddled by his sisters since he was young for the love of Grandma Merchant.--[[User:Yin Yuan|Yin Yuan]] ([[User talk:Yin Yuan|talk]]) 15:30, 13 December 2021 (UTC)<br />
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Now I come here,undoubtfully I live with my sisters. The brothers are in some different houses. Is there any reason to mess with them?" Lady King smiled and said, "You don't know the reason. Unlike others, he had been coddled by his sisters since he was young for the love of Grandma Merchant.--[[User:Zhan Ruoxuan|Zhan Ruoxuan]] ([[User talk:Zhan Ruoxuan|talk]]) 03:20, 15 December 2021 (UTC)<br />
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==詹若萱 Zhān Ruòxuān 英语语言文学（英美文学） 女 202120081549==<br />
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若姐妹们不理他，他倒还安静些；若一日姐妹们和他多说了一句话，他心上一喜，便生出许多事来：所以嘱咐你别理会他。他嘴里一时甜言蜜语，一时有天没日，疯疯傻傻，只休信他。”黛玉一一的都答应着。<br />
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“If the sisters ignore him, he is a bit quieter: if one day the sisters talk to him more, he is so happy that he will stir up many troubles: so I tell you to ignore him. He may talk sweetly for a while, and he may be crazy and silly for a while, just don't believe him.” Daiyu replied one by one.--[[User:Zhan Ruoxuan|Zhan Ruoxuan]] ([[User talk:Zhan Ruoxuan|talk]]) 08:45, 13 December 2021 (UTC)<br />
If the sisters ignore him, he will be quiet; if one day they talk to him more, he will be happy, and many things will happen: so I tell you to ignore him. His mouth a sweet talk, a moment there is no day, crazy and silly, just do not believe him. Daiyu agreed one by one.--[[User:Zhang Qiuyi|Zhang Qiuyi]] ([[User talk:Zhang Qiuyi|talk]]) 12:06, 19 December 2021 (UTC)<br />
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==张秋怡 Zhāng Qiūyí 亚非语言文学 女 202120081550==<br />
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忽见一个丫鬟来说：“老太太那里传晚饭了。”王夫人忙携了黛玉，出后房门，由后廊往西，出了角门，是一条南北甬路，南边是倒座三间小小抱厦厅，北边立着一个粉油大影壁，后有一个半大门，小小一所房屋。<br />
Suddenly see a servant girl to say: "old lady there spread supper." Lady Wang and Daiyu went out of the back door, leading from the back corridor to the west and out of the corner gate. There was a north-south corridor, with three small rooms in the south, a big screen wall of powder and oil in the north, and a small house with a half gate behind.--[[User:Zhang Qiuyi|Zhang Qiuyi]] ([[User talk:Zhang Qiuyi|talk]]) 13:53, 12 December 2021 (UTC)<br />
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Suddenly a servant girl said, "the old lady has passed on dinner." Lady King hurriedly took Mascara Jade Pearl out of the back door, from the back porch to the west, out of the corner door. It is a North-South corridor. In the south is the inverted three small balcony halls. In the north is a oil-powdered large shadow wall, followed by a half gate and a small house.--[[User:Zhang Yang|Zhang Yang]] ([[User talk:Zhang Yang|talk]]) 12:28, 13 December 2021 (UTC)<br />
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==张扬 Zhāng Yáng 国别 男 202120081551==<br />
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王夫人笑指向黛玉道：“这是你凤姐姐的屋子。回来你好往这里找他去，少什么东西，只管和他说就是了。”这院门上也有几个才总角的小厮，都垂手侍立。王夫人遂携黛玉穿过一个东西穿堂，便是贾母的后院了。<br />
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Lady King smiled at Mascara Jade Pearl and said: "This is your sister Phoenix's house. If you come back, you can find her here. And if there's anything missing, just tell her." On the gate of the courtyard, there were also several young boys who were only in their childhood, all standing with their hands down. Lady King then took Mascara Jade Pearl through an east-west hall, which was Grandma Merchant's backyard.--[[User:Zhang Yang|Zhang Yang]] ([[User talk:Zhang Yang|talk]]) 07:30, 11 December 2021 (UTC)<br />
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Lady King smiled at Mascara Jade Pearl and said: "This is your sister Phoenix's house. If you come back, you can find her here. And if there's anything missing, just tell her." On the gate of the courtyard,there were also a few young boys on the door of this courtyard, all standing with their hands down.. Lady King then took Mascara Jade Pearl through an east-west hall, which was Grandma Merchant's backyard.<br />
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==张怡然 Zhāng Yírán 俄语语言文学 女 202120081552==<br />
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于是进入后房门，已有许多人在此伺候，见王夫人来，方安设桌椅；贾珠之妻李氏捧杯，熙凤安箸，王夫人进羹。贾母正面榻上独坐，两旁四张空椅。熙凤忙拉黛玉在左边第一张椅子上坐下，黛玉十分推让。<br />
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So they entered the back room, where many people were already waiting, and when they saw Lady King coming, they placed the table and chairs; Li, wife of Treasure Merchant, held the cup, Lady King placed the chopsticks, and Splendid Phoenix King drank the soup. Grandma Merchant was sitting alone on a couch, flanked by four empty chairs. Splendid Phoenix King was busy pulling Mascara Jade Forest to sit in the first chair on the left, but Mascara Jade Forest was too embarrassed to sit.--[[User:Zhang Yiran|Zhang Yiran]] ([[User talk:Zhang Yiran|talk]]) 00:57, 13 December 2021 (UTC)<br />
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So they entered the back room, where many people were already waiting, and when they saw Lady King coming, they placed the table and chairs; Li, wife of Treasure Merchant, held the cup, Lady King placed the chopsticks, and Splendid Phoenix King drank the soup. Grandma Merchant was sitting alone on a couch, flanked by four empty chairs. Splendid Phoenix King was busy pulling Mascara Jade Forest to sit in the first chair on the left, but Mascara Jade Forest was too embarrassed to sit.--[[User:Zhong Yifei|Zhong Yifei]] ([[User talk:Zhong Yifei|talk]]) 03:42, 13 December 2021 (UTC)<br />
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==钟义菲 Zhōng Yìfēi 英语语言文学（英美文学） 女 202120081553==<br />
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贾母笑道：“你舅母和嫂子们是不在这里吃饭的。你是客，原该这么坐。”黛玉方告了坐，就坐了。贾母命王夫人也坐了。迎春姊妹三个告了坐，方上来：迎春坐右手第一，探春左第二，惜春右第二。<br />
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Mrs. Jia said with a smile, "your aunt and sister-in-law don't eat here. You are a guest. You should have sat here." Daiyu then sat down. Jia Mu ordered Mrs. Wang to sit down. The three sisters of Yingchun sat down：Yingchun sat first on the right hand, Tanchun second on the left, and Xi Chun second on the right.--[[User:Zhong Yifei|Zhong Yifei]] ([[User talk:Zhong Yifei|talk]]) 10:36, 11 December 2021 (UTC)<br />
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Mrs. Jia said with a smile, "your aunts and sisters-in-law don't eat here. You are a guest. You should have sat here." Daiyu then sat down. Mrs. Jia ordered Mrs. Wang to sit down. The three sisters of Yingchun were asked to sit down: Yingchun sat first on the right hand, Tanchun second on the left, and Xi Chun second on the right.--[[User:Zhong Yulu|Zhong Yulu]] ([[User talk:Zhong Yulu|talk]]) 02:02, 12 December 2021 (UTC)<br />
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==钟雨露 Zhōng Yǔlù 英语语言文学（英美文学） 女 202120081554==<br />
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旁边丫鬟执着拂尘、漱盂、巾帕，李纨、凤姐立于案边布让；外间伺候的媳妇、丫鬟虽多，却连一声咳嗽不闻。饭毕，各各有丫鬟用小茶盘捧上茶来。当日林家教女以惜福养身，每饭后必过片时方吃茶，不伤脾胃；<br />
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Standing at the table, the servant girls held the horsetail whisks, vessels for mouthwash and handkerchiefs. Li Wan and Wang Xifeng sent dishes, refreshments to guests and invited them to eat. Though there were many servant girls in the outer room, they could not be heard to utter a sound. When the meal was over, each servant girl brought tea with a small tray. The daughter of Lin Ruhai, Lin Daiyu took tea after each meal to keep health and not hurt her spleen and stomach.--[[User:Zhong Yulu|Zhong Yulu]] ([[User talk:Zhong Yulu|talk]]) 01:55, 12 December 2021 (UTC)<br />
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The servant girls are standing at the table with the horsetail whisks, vessels for mouthwash and handkerchiefs. Li Wan and Wang Xifeng sent dishes, refreshments to guests and invited them to eat. Though there were many servant girls in the outer room, they could not be heard to utter a sound. When the meal was over, each servant girl brought tea with a small tray. The daughter of Lin Ruhai, Lin Daiyu took tea after each meal to keep health and not hurt her spleen and stomach.--[[User:Zhou Jiu|Zhou Jiu]] ([[User talk:Zhou Jiu|talk]]) 09:23, 13 December 2021 (UTC)<br />
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==周玖 Zhōu Jiǔ 英语语言文学（英美文学） 女 202120081555==<br />
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今黛玉见了这里许多规矩不似家中，也只得随和些。接了茶，又有人捧过漱盂来，黛玉也漱了口，又盥手毕。然后又捧上茶来，这方是吃的茶。贾母便说：“你们去罢，让我们自在说说话儿。”<br />
Now Daiyu saw many rules here are not like the rules of her home. She was also easy-going. After receiving the tea, someone else took a gargle bowl for her. Daiyu also rinsed her mouth and finished washing her hands again. Then tea which was for drinking was brought in. Then Mother Jia said to servants , "You all go and let's have a talk in our own comfort."<br />
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Now Daiyu saw many rules here are not like the rules of her home. She can only be easygoing. She caught the teacup. Some domestics came over with a mouthwash basin. Daiyu gargled and washed her hands. Then the servant brought back tea, and this was tea for drinking.Then Grandma Merchant said to servant, "You all go and let's have a talk in our own comfort."--[[User:Zhou Junhui|Zhou Junhui]] ([[User talk:Zhou Junhui|talk]]) 10:47, 13 December 2021 (UTC)<br />
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==周俊辉 Zhōu Jùnhuī 法语语言文学 女 202120081556==<br />
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王夫人遂起身，又说了两句闲话儿，方引李、凤二人去了。贾母因问黛玉念何书，黛玉道：“刚念了《四书》。”黛玉又问姊妹读何书，贾母道：“读什么书，不过认几个字罢了。”<br />
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Lady Wang stood up and said something idle, then led Lady Li and Splendid Phoenix King to leave. When Grandma Merchant asked Daiyu what books she had read, Daiyu replied, "I just have read the ''Four Books''." When Daiyu asked her sisters what books they read, Grandma Merchant said, "They don't read anything. They only know a few words."--[[User:Zhou Junhui|Zhou Junhui]] ([[User talk:Zhou Junhui|talk]]) 09:25, 13 December 2021 (UTC)<br />
Madame Wang rose as soon as she heard these words, and having made a few irrelevant remarks, she led the way and left the room along with the two ladies, Mrs. Li and lady Feng.Dowager lady Chia, having inquired of Tai-yue what books she was reading, "I have just begun reading the Four Books," Tai-yue replied. "What books are my cousins reading?" Tai-yue went on to ask. "Books, you say!" exclaimed dowager lady Chia; "why all they know are a few characters, that's all."--[[User:Zhou Qiao1|Zhou Qiao1]] ([[User talk:Zhou Qiao1|talk]]) 13:15, 15 December 2021 (UTC)<br />
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==周巧 Zhōu Qiǎo 英语语言文学（语言学） 女 202120081557==<br />
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一语未了，只听外面一阵脚步响，丫鬟进来报道：“宝玉来了。”黛玉心想：“这个宝玉，不知是怎样个惫懒人呢。”及至进来一看，却是位青年公子：头上戴着束发嵌宝紫金冠，齐眉勒着二龙戏珠金抹额；<br />
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The sentence was barely out of her lips, when a continuous sounding of footsteps<br />
was heard outside, and a waiting maid entered and announced that Pao-yue was<br />
coming. Tai-yue was speculating in her mind how it was that this Pao-yue had<br />
turned out such a good-for-nothing fellow, when he happened to walk in.<br />
He was, in fact, a young man of tender years, wearing on his head, to hold his<br />
hair together, a cap of gold of purplish tinge, inlaid with precious gems.<br />
Parallel with his eyebrows was attached a circlet, embroidered with gold, and<br />
representing two dragons snatching a pearl.<br />
--[[User:Zhou Qiao1|Zhou Qiao1]] ([[User talk:Zhou Qiao1|talk]]) 08:36, 13 December 2021 (UTC)<br />
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After a word, only a sound of footsteps outside, the maid came in and reported: "Baoyu is here." Daiyu thought to herself: "This Baoyu, I don't know what a tired lazy person." When she came in, she was a young man. He wears a purple and gold crown with hair inlaid on his head, and his forehead are tied with gold frontlet（The shape is two dragons playing with pearled）.--[[User:Zhou Qing|Zhou Qing]] ([[User talk:Zhou Qing|talk]]) 15:21, 11 December 2021 (UTC)<br />
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==周清 Zhōu Qīng 法语语言文学 女 202120081558==<br />
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一件二色金百蝶穿花大红箭袖，束着五彩丝攒花结长穗宫绦，外罩石青起花八团倭缎排穗褂；登着青缎粉底小朝靴。面若中秋之月，色如春晓之花；鬓若刀裁，眉如墨画，鼻如悬胆，睛若秋波。<br />
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A big red arrow sleeve decorated with two-color golden butterfly flowers, is tied with multicolored silk and knotted with long spikes, and is covered with azurite and satin rowed gowns; it wears small green satin and powder-soled boots. The face is as round and beautiful as the moon of Mid-Autumn Festival, the complexion is like a flower of spring dawn; the temples are like a knife cut, the eyebrows are like ink painting, the nose is like a hanging gall, and the eyes are like autumn waves.<br />
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A big red arrow sleeve decorated with two-color golden butterfly flowers, is tied with multicolored silk and knotted with long spikes, and is covered with azurite and satin rowed gowns; he wore small green satin and powder-soled boots. The face is as round and beautiful as the moon at mid-autumn, the complexion is like a flower of in spring; the temples as if chiselled with a knife, the eyebrows are like ink painting, the nose is like a a well-cut and shapely nose, and the eyes are like vernal waves.--[[User:Zhou Xiaoxue|Zhou Xiaoxue]] ([[User talk:Zhou Xiaoxue|talk]]) 06:19, 13 December 2021 (UTC)<br />
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==周小雪 Zhōu Xiǎoxuě 日语语言文学 女 202120081559==<br />
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虽怒时而似笑，即嗔视而有情。项上金螭缨络，又有一根五色丝绦，系着一块美玉。黛玉一见，便吃一大惊，心中想道：“好生奇怪：倒像在那里见过的，何等眼熟！”只见这宝玉向贾母请了安，贾母便命：“去见你娘来。”<br />
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His angry look even resembled a smile; his glance was full of sentiment.Round his neck he had a gold dragon necklet with a fringe; also a cord of variegated silk, to which was attached a piece of beautiful jade. When Daiyu saw this, she was shocked."How very strange." she was reflecting in her mind; "it would seem as if I had seen him somewhere or other, for his face appears extremely familiar to my eyes;" Baoyu greeted Lady Dowager "Go and see your mother and then come back," remarked her venerable ladyship.--[[User:Zhou Xiaoxue|Zhou Xiaoxue]] ([[User talk:Zhou Xiaoxue|talk]]) 06:08, 13 December 2021 (UTC)<br />
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His angry look somentimes even resembled a smile; his glance was full of sentiment.Round his neck he had a gold dragon necklet with a fringe; also a cord of silk of five colours, to which was attached a piece of beautiful jade. When Daiyu saw this, she was shocked and thought to herself: "How strange it is." she was reflecting in her mind; "as if I had seen him somewhere or other, for his face appears extremely familiar to my eyes;" Baoyu greeted Lady Dowager "Go and see your mother and then come back."--[[User:Zhu Suzhen|Zhu Suzhen]] ([[User talk:Zhu Suzhen|talk]]) 11:46, 16 December 2021 (UTC)<br />
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==朱素珍 Zhū Sùzhēn 英语语言文学（语言学） 女 202120081561==<br />
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即转身去了。一会再来时已换了冠带：头上周围一转的短发都结成小辫，红丝结束，共攒至顶中胎发，总编一根大辫，黑亮如漆，从顶至梢，一串四颗大珠，用金八宝坠脚；身上穿着银红撒花半旧大袄；仍旧带着项圈、宝玉、寄名锁、护身符等物；<br />
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Then turned around and went. He has changed the crown band when coming back for a while: the short hair around the head is braided, the red silk ends, and the hair is gathered up to the top of the fetus. The chief editor is a big braid, black and shiny, from top to tip , A string of four large beads, with gold eight treasures falling to the feet; wearing a silver-red half-old coat with flowers; still wearing collars, gems, locks, amulets, etc.;<br />
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He turned away. When he came back later, he had changed his crown belt: the short hair around his head was braided, and the red silk ended. He saved up to the top and middle fetal hair. The chief editor had a big braid, black and bright as paint, a string of four big beads from the top to the tip, and dropped his feet with gold eight treasures; He was wearing a silver red flower sprinkled semi-old coat; Still wearing collars, precious jade, name sending locks, amulets, etc--[[User:Zou Yueli|Zou Yueli]] ([[User talk:Zou Yueli|talk]]) 12:56, 16 December 2021 (UTC)<br />
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==邹岳丽 Zōu Yuèlí 日语语言文学 女 202120081562==<br />
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下面半露松绿撒花绫裤，锦边弹墨袜，厚底大红鞋。越显得面如傅粉，唇若施脂；转盼多情，语言若笑。天然一段风韵，全在眉梢；平生万种情思，悉堆眼角。看其外貌，最是极好，却难知其底细。<br />
His lower body showed loose green flower pants, cotton socks and a pair of thick soled red shoes. This makes him look more beautiful. The lips seem to have been powdered with rouge; When he speaks, he often has a smile on his face, and his eyebrows can convey affection. A person's style and temperament, including what he thinks, can be conveyed through his eyes. His appearance is very good-looking, but I don't know whether he has real connotation.<br />
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==Nadia 202011080004==<br />
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后人有《西江月》二词批的极确，词曰：<br />
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==Mahzad Heydarian 玛莎 202021080004==<br />
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无故寻愁觅恨，有时似傻如狂。<br />
Seeking sorrow and hate for no reason, sometimes seems stupid and crazy.<br />
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==Mariam toure 2020GBJ002301==<br />
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纵然生得好皮囊，腹内原来草莽。<br />
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==Rouabah Soumaya 202121080001==<br />
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潦倒不通庶务，愚顽怕读文章。<br />
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I'm not able to get through general affairs, and I'm afraid of reading articles.<br />
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==Muhammad Numan 202121080002==<br />
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行为偏僻性乖张，那管世人诽谤。<br />
He who behaves in a perverse way has no control over the slander of course.--[[User:Atta Ur Rahman|Atta Ur Rahman]] ([[User talk:Atta Ur Rahman|talk]]) 03:29, 14 December 2021 (UTC)<br />
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==Atta Ur Rahman 202121080003==<br />
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又曰：富贵不知乐业，贫穷难耐凄凉。<br />
It is also known that wealth does not know pleasure and happiness, and poverty cannot endure loneliness.<br />
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==Muhammad Saqib Mehran 202121080004==<br />
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可怜辜负好时光，于国于家无望。<br />
The poor lived up to the good times, and the country was hopeless at home.<br />
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==Zohaib Chand 202121080005==<br />
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天下无能第一，古今不肖无双。<br />
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==Jawad Ahmad 202121080006==<br />
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寄言纨袴与膏粱，莫效此儿形状。<br />
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English; the author is to give some suggestion to playboys of high official that they do not follow the example of Jia Baoyu.<br />
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==Nizam Uddin 202121080007==<br />
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却说贾母见他进来，笑道：“外客没见就脱了衣裳了，还不去见你妹妹呢。”<br />
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But she said that Mother Jia saw him come in and smiled: "The foreigner took off her clothes without seeing him, so she won't go to see your sister."<br />
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==Öncü 202121080008==<br />
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宝玉早已看见了一个袅袅婷婷的女儿，便料定是林姑妈之女，忙来见礼。<br />
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Baoyu had already seen a daughter with a gentle posture, thought might be the daughter of Aunt Lin, then hurried to meet her.--[[User:AkiraJantarat|AkiraJantarat]] ([[User talk:AkiraJantarat|talk]]) 04:17, 13 December 2021 (UTC)<br />
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==Akira Jantarat 202121080009==<br />
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归了坐细看时，真是与众各别。<br />
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When I look at you carefully, I think you are different.--[[User:AkiraJantarat|AkiraJantarat]] ([[User talk:AkiraJantarat|talk]]) 09:59, 13 December 2021 (UTC)<br />
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When I returned to take a closer look, it was really different. --[[User:Benjamin Wellsand|Benjamin Wellsand]] ([[User talk:Benjamin Wellsand|talk]]) 07:41, 12 December 2021 (UTC)<br />
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==Benjamin Wellsand 202111080118==<br />
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只见：两弯似蹙非蹙笼烟眉，一双似喜非喜含情目。<br />
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I saw two bends like the frowning of smoked eyebrows, at first they seemed happy but not really happy, yet affectionate eyebrows. --[[User:Benjamin Wellsand|Benjamin Wellsand]] ([[User talk:Benjamin Wellsand|talk]]) 07:41, 12 December 2021 (UTC)<br />
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I saw: two curved eyebrows that looked like a frown, a pair of eyebrows that seemed to be happy or not. --[[User:Asep Budiman|Asep Budiman]] ([[User talk:Asep Budiman|talk]]) 23:18, 12 December 2021 (UTC)<br />
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==Asep Budiman 202111080020==<br />
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态生两靥之愁，娇袭一身之病。<br />
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The sorrow of the two distresses, the disease of the whole body. --[[User:Asep Budiman|Asep Budiman]] ([[User talk:Asep Budiman|talk]]) 23:17, 12 December 2021 (UTC)<br />
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The sorrow of two distresses, spoiled - the disease of the whole body.------Ei Mon Kyaw [[User:EIMONKYAW|EIMONKYAW]] ([[User talk:EIMONKYAW|talk]]) 09:15, 15 December 2021 (UTC)--[[User:EIMONKYAW|EIMONKYAW]] ([[User talk:EIMONKYAW|talk]]) 09:15, 15 December 2021 (UTC)Ei Mon Kyaw<br />
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==Ei Mon Kyaw 202111080021==<br />
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泪光点点，娇喘微微。<br />
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Tears shone a little, and she breathed slightly.--[[User:EIMONKYAW|EIMONKYAW]] ([[User talk:EIMONKYAW|talk]]) 09:47, 15 December 2021 (UTC)Ei Mon Kyaw -----Ei Mon Kyaw[[User:EIMONKYAW|EIMONKYAW]] ([[User talk:EIMONKYAW|talk]]) 09:47, 15 December 2021 (UTC)<br />
The tears droped, and she breathed slowly. --[[User:Mahzad Heydarian|Mahzad Heydarian]] ([[User talk:Mahzad Heydarian|talk]]) 11:58, 15 December 2021 (UTC)<br />
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The eyes twinkled with tears and she breathed slightly.--[[User:Chen Jing|Chen Jing]] ([[User talk:Chen Jing|talk]]) 12:18, 19 December 2021 (UTC)</div>Chen Huinihttps://bou.de/u/index.php?title=Machine_Trans_EN_11&diff=133904Machine Trans EN 112021-12-16T11:24:26Z<p>Chen Huini: /* Conclusion */</p>
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<div>'''Machine Translation - A challenge or a chance for human translators?'''<br />
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===Abstract===<br />
At present, globalization is accelerating and the market demand for language services is rapidly increasing . Machine translation, as an important translation method, can greatly improve translation efficiency due to its low cost and high speed. However, because of the limitations of machine translation and the differences between Chinese and English language, machine translation is not accurate enough. In order to balance translation efficiency and translation quality, a great number of manual revisions in translation are required for the machine translating texts. Medical papers are specialized, special and purposeful, so it requires accurate,qualified and professional translation. However, the quality of translations by machine is inefficient to meet the high-quality requirements of medical papers translation. Therefore, the introduction of pre-editing can greatly improve the efficiency and quality of machine translation.<br />
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===Key words===<br />
Pre-editing, Machine translation, Medical texts<br />
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===题目===<br />
Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts<br />
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===摘要===<br />
在全球化加速发展的今天，市场对语言服务的需求迅速增加。机器翻译作为一种重要的翻译途径，由于其成本低、速度快，可以大大提高翻译效率。然而，由于机器翻译的局限性以及中英文语言的差异，机器翻译的准确性不高。为了平衡翻译效率和翻译质量，机器翻译文本需要大量的手工修改。<br />
医学论文具有专业性、特殊性和目的性，要求其译文准确、合格、专业。然而，机器翻译的质量较低，无法满足医学论文对翻译的高质量要求。因此，译前编辑的引入可以大大提高机器翻译的效率和质量。<br />
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===关键词===<br />
译前编辑；机器翻译；医学文本<br />
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===1. Introduction===<br />
===1.1 Definition of Machine Translation===<br />
As Cronin(2013) revealed: "Translation is undergoing a revolutionary upheaval. The influence of digital technology and the Internet on translation is continuous, extensive and profound. From the popularity of automatic online translation applications, translation revolution is everywhere (Cronin,2013) However,the concept of machine translation was firstly proposed in the 1930s. Since 1940s, the machine translation technology has been evolving from rule-based machine translation (RBMT) to statistical machine translation (SMT), and to neural machine translation (NMT). Machine translation refers to the automatic translation of source language into target language by using a computer system. That is, machine translation refers to the automatic translation of text from one language into another natural language by computer software or other online translation webs. Machine translation is also defined as the process of “using a computer system to automatically translate text or speech from one natural language to another” according to the definition by ISO (Cui 2014:68-73).O 'Brien (2002) defines it as "the behavior of modifying errors in machine translation to ensure that the target translation meets certain quality requirements". On the basic level, machine translation performs mechanical substitution of words in one language for words in another language, but that rarely produces a good translation, therefore, recognition of the whole phrases and their closest counterparts in the target language is needed. Not all words in one language have equivalents in another language, and many words have more than one meaning. A huge demand for translation is greatly needed in today’s global world, which creates new opportunities for the development of machine translation, attracts more and more attention and becomes one of the current research focuses.<br />
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===1.2 Definition of Pre-editing===<br />
Pre-editing means to adjust and modify the source language to make it fit more with the characteristics of the machine translation software before putting the source language before the into machine translation, so as to improve the quality of the translation machine translation (Wei Changhong 2008:93-94). Pre-editing is to modify the original text before putting it into machine translation software, in order to improve the recognition rate of machine translation, optimize the output quality of translated text and reduce the workload of post-editing. Because pre-translation editing only needs to be modified in one language, the operation is simpler than post-translation editing, which can realize the double improvement of quality and efficiency. A good pre-editing translation can help machine translation more smoothly, thus improving the machine readability and quality of the output translation. Pre-editing is mostly applied in the following situations: one is when the original text is of poor quality and the machine is difficult to recognize the meaning of the sentence, such as user generated content with poor readability and translatability (Gerlach et al 2003:45-53); Documents that need to be published in multiple languages; Next is when the original text contains a lot of jargon; The last is the original text has a corresponding translation memory bank. If the original text is edited, it can better match the content of the translation memory bank. Pre-editing is a process of identifying problems. It requires to pre-edit the source texts before putting it into machine translation according to the requirements, listing the expressions or sentences that may have trouble in machine translation and then pre-edit it by human. The purpose is to enable the computer to translate better, improve the translatability of machine translation. (Slype G V & Guinet J F & Seitz F 1984:115)<br />
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===1.3 Machine Translation Mode===<br />
According to the different knowledge acquisition methods, machine translation modes can be classified as follows: one is rule-based machine translation, which is based on bilingual dictionaries and a library of language rules for each language. The quality of translation depends on whether the source language conforms to the existing rules, but the inexhaustible rules are the hinders of this model. The second mode is machine translation based on statistics. This translation model relies on the principles of mathematics and statistics to find various existing translations corresponding to the translation tasks through the employment of corpus, analyzing the frequency of their occurrence and selecting the translation with the highest frequency for output. The disadvantage of this translation model is that it ignores the flexibility of language and the importance of context. The last one is neural network language model. This model is different from previous translation models in that it uses end-to-end neural network to realize automatic translation between natural languages. At present, the quality of its translation is much higher than that of the previous translation models.<br />
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===1.4 Source of Study Abstracts===<br />
The core medical journals from domestic are selected in order to make the paper more representative and authoritative, such as National Medical Journal of China, Journal of Peking University (Health Science), and Journal of Third Military Medical University. All of them include basic medical science, biomedical technology, laboratory medical science and other fields. In this way, the pre-editing approaches included are applicable enough to machine translation of medical abstracts.<br />
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===1.5 Selection of Translation Software===<br />
Generally, machine translation software includes Google Translation, Youdao Translator and Niu Translation, which has their own special use in translation. Take Google as an example, it is more like neurons in human brain, enabling to learn and collect information to establish connections with its neural machine translator’s neurons. However, it also causes many errors because of the lack of enough information. In this paper, contrastive analysis will be carried on by using Google translation. On the one hand, being a pioneer in the translation of NET, it is inevitably to sue Google as the translation software to translate medical texts in this paper. Comparing the program called “Google brain”, other NMT of translation software are relatively disadvantages. On the other hand, the Google translation enjoys the largest users in the world, with its downloads of more than one billion. The output quality of Google translation is more correct and complete than other machine translation software. With years of development and improvement, Google Translation has been greatly promoted. In this paper, Chinese medical abstract will be automatically translated by Google translation, and then the translation output will be compared with the translation by human and the publication of English abstracts. The main purpose is to prove that the improvement and promotion of quality and accuracy in the medical abstracts will be obtained through the pre-editing approaches.<br />
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===2.Language Characteristics and Error Division===<br />
Since Chinese and English are two different languages, it is quite neccessary to identify their own characteristics so as to better analyze and understand the two languages. Tytler (1978: 118-119) argued in his Essay on the Principles of Translation that there are three principles of translation that in all the translation should give readers the same feelings as the source text, except for complete transcript of ideas. There actually also exit some mistakes of these two languages. So the following will make some clarrifications of these two languages to ensure more accurate translation.<br />
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===2.1 Language Characteristics of Medical Abstracts===<br />
Chinese and English belong to different language systems, so there are differences in their language structure and the way users think of the languages. When using machine translation from Chinese to English, due to the unequal language levels, there will be many mistakes in the translation process, especially for ESP texts, such as medical papers.Here, these differences mainly refer to the linguistic characteristics of medical abstracts. In medical abstracts, it usually includes structured and unstructured abstracts. Although in different forms, they both describe the purpose, methods and conclusions of the research.<br />
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In the method section of medial abstract, several Chinese sentences can be connected with commas, but each sentence may convey different information. In contrast, English sentences contain a great deal of information, but in order to ensure clarity, some modifiers need to be isolated and then reconstructed. However, in Chinese-English machine translation, a lot of information is put into the sentence because the machine segments the sentence on the basis of the full comma.<br />
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In addition, subjectless sentence is used in the objective and method parts of Chinese medical abstracts. The subjectless sentence means the sentence without or free from subject and is usually employed in two contexts. The first is "needless to say". In Chinese sentences, it is common to omit the subject of the sentence. Chinese sentences can convey meanings by using incomplete sentence structures, so Chinese speakers can understand the meanings of the sentences even though the sentence subject is omitted. The second is "emphasis of action". In this context, subjectless sentences are used to describe behaviors, especially in the study of traditional Chinese medicine abstracts. Comparatively speaking, it should be avoided in English medical abstracts. Abstract sentences are more subjective when describing the learning process, while the essential requirement of English medical abstract is objectivity. From this point, sentences without subject should be avoided in English medical abstracts. Another feature is voice. Few words with passive meanings appear in Chinese abstracts. English sentences are more favoured in using passive voice. When translating from Chinese to English, passive voice should be used to make the contents objective, which is also the basic requirement of medical papers.<br />
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These are the linguistic features of Chinese medical abstracts. There are great differences in sentence structure and expression between Chinese and English medical abstracts. These differences may reduce the accuracy of machine translation, so it is necessary to introduce pre-editing to edit the source text to ensure that the source text can be accurately recognized by the machine and fully translated into English.<br />
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===2.2 Error Division of Machine Translation in Translating Abstracts of Medical Papers from Chinese to English===<br />
In this paper, errors in machine translation are listed out after analyzing. Some are due to the principles, such as statistical-based MT and NMT, employed by machine translation. Based on the original errors, they can be studied from two levels. One is from the macro-level, referring to mistakes caused by objective factors. These are not human factors, but the disadvantage of machine translation and the limitations of language. The limitations of machine translation are derived from the principles and models adopted and manifests itself as a reliance on reference sources. In this case, semantic ambiguity and textual incoherence may occur in the absence of reference sources.<br />
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However, for ESP texts such as medical papers, the requirements for terms and sentence patterns are far beyond the existing corpora. For unfamiliar texts (that is, the corresponding texts cannot be found in the corpora). The quality of output translation will be relatively low, which is determined by the principles of machine translation. As mentioned above, machine translation has evolved over the past few decades from rules-based to corp-based statistics to NMT. However, there still exist some limitations in machine translation.<br />
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Mistakes on micro-level are mainly caused by the variations and differences of linguistic structure between Chinese and English. Chinese is an implicit language, while English is an explicit one (Lian, 2010) To put it in another way, Chinese expression does not depend on language structures, while English does the opposite. This may result in mismatches between the original and machine translated sentences. This kind of error is mainly divided into two types: component fragment and component missing. For a medical paper, such errors are very serious. As a kind of ESP discourse, medical paper has the characteristics of fixed, objective and accurate language structure. In order to reproduce the characteristics of medical abstracts in translation, it is necessary to avoid errors at the level of words and sentences and pay attention to logic and consistency.<br />
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Lexical errors here refer to the inconsistency of fixed expressions in the translation of terms. Although these terms are machine translation based on the corpus, the corpus may not be perfect for ESP texts such as medical papers. Therefore, fixed expressions are not used in term translation. This is also the most common mistake in machine translation (the term here includes but is not limited to nouns). For ESP texts, medical text in particular, the fixed expressions and the accuracy of terms are of great importance.<br />
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===3.Approaches Proposed for Pre-editing ===<br />
Generally speaking, there is a close relationship between pre-editing and post-editing, both of which aim to convey information and ensure high or publishable translation quality. Proper pre-editing can improve the quality of machine translation in terms of adequacy and consistency. <br />
Complexity of natural language and people use language arbitrarily, bringing many difficulties to Chinese-English machine translation, Hu Qingping (2005:24) has proposed "the research of translation software and the development of the controlled language are the two directions to improve the quality of machine translation : the former aims at the difficulty in the natural language processing, the latter overcomes the arbitrariness of natural language". Feng Quangong and Gao Lin (2017: 63-68) put forward: "The writing principles of controlled language can be applied to pre-editing of machine translation. Pre-editing based on controlled language can effectively reduce the complexity and ambiguity of source text, improve the identifiability of machine translation (the translatability of source text itself), and thus reduce (fully) the workload of post-editing".<br />
The central task of pre-editing is to transform human-friendly content into machine-friendly content, so words and sentences need to be repositioned or even changed. Based on the analysis of the language characteristics of Chinese and English, the Chinese ideographic group should be split before the Chinese source text is input into the machine software to translate so that the sentence structure is complete which can be easily recognized by the machine translation software.<br />
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===3.1 Extraction and Replacement of Terms in the Source Text===<br />
As a branch of applied English, medical English is the product of the combination of English language knowledge and medical knowledge. The terms in medical papers have the characteristics of fixed and complex language structure. Although Google Translation is based on a corpus, the language structure is not fixed due to the limited size of the corpus. Therefore, the following two steps should be followed before machine translation. The first is to extract terms and create a glossary, and then replace the Chinese terms in the source text with the corresponding English expressions in the glossary. Of course, the terms of extraction should be searched and verified according to the actual situation. All of these words are verified before they can be replaced. This method can not only ensure the accuracy of term translation, but also maintain the consistency of expression, especially when dealing with long texts.<br />
Example1<br />
Source abstract: 口腔白斑病癌变相关缺氧应答基因和微小RNA的芯片及表达验证。<br />
Google translation: Microarray detection/Chip detection and expression verification of hypoxia response genes and microRNAs related to oral leukoplakia canceration.<br />
Published translation:Transcriptome array screening and verification of oral leukoplakia carcinogenesis-related hypoxia-responsive gene and microRNA. <br />
Analysis: The expression “ Affymetrix GeneChip” empolyed in this thesis is a human transcription array for transcriptome array. In the source abstract, “芯片检测“ can be meant “screening” but not detection, so the proper and appropriate translation of these expression should be “transcription array screening”, but the Google translates it into “microarry detection of chip detection”. Therefore, term extraction is essential and inevitable before putting the source abstracts into the machine translation software.<br />
After pre-editing source abstracts: 对 oral leukoplakia carcinogenesis 相关 hypoxia-responsive gene 和微小RNA进行 transcriptome array screening 及表达验证。<br />
After pre-editing translation: Transcriptome array screening and expression- verification of hypoxia-responsive genes and microRNAs related to oral leukoplakia carcinogenesis.<br />
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===3.2 Explication of Subordination===<br />
As mentioned above, the subordination of Chinese sentences is judged by certain specific words in machine translation . Chinese is a paratactic language, and sentences are often connected by internal logical relationships; while English depends on sentence structure, so sentences are often closely linked by various language forms. In order to improve the accuracy of machine translation, we can adjust the sentence structure and adjust the position of adjectives and their modifiers. <br />
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Example 2<br />
Source abstracts:回顾性分析南京医科大学附属儿童医院中重度HIE患儿49例及同期就诊的无神经系统症状体征的足月新生儿为对照组31例的头颅磁共振成像（MRI）资料。<br />
Google translation: A retrospective analysis that the brain magnetic resonance imaging (MRI) data of 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University and full-term neonates with no neurological symptoms and signs during the same period were included in the control group.<br />
Published translation: A total of 49 children with moderate to severe HIE admitted to the children’s Hospital Affiliated to Nanjing Medical University were retrospectively analyzed. Cranial magnetic resonance imaging (MRI) date of 31 full-term neonates without neurological symptoms and signs who visited the hospital during the same period were recruited as the control group. <br />
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Analysis: As the above example shows that “中重度HIE患儿” and “足月新生儿” in the source abstracts are from the Children’s Hospital Affiliated Nanjing Medical University. The Google translation shows that 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University. There is an error due to the misuse of subordination. There are some advice in order to solve the problem. That is, first to segment the sentence and then reconstruct the sentence. For instance, the Chinese expression “南京医科大学附属儿童医院” carries many modifiers, which requires to cut the modifiers and reconstruct the sentence. <br />
Therefore, the Chinese expression “南京医科大学附属儿童医院’can be divided into abstractions, leaving two sentences instead of one long sentence with modifiers..<br />
After pre-editing source abstracts: 对49例中重度HIE患儿以及31例无神经系统症状体征的足月新生儿的MRI资料进行回顾性分析。这些患者收治于南京医科大学儿童附属医院。<br />
After pre-editing translation: The MRI data of 49 children with moderate to severe HIE and 31 full-term newborns without neurological symptoms and signs were retrospectively analyzed. These patients were admitted to the Children’s Hospital of Nanjing Medical University.<br />
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===3.3 Explication of Subject through Voice Changing===<br />
The extensive use of subjectless sentences are quite common in the Chinese abstracts, while English prefers to employ passive voice in the sentences so as to make them object and accurate. In order to take the characteristics of English sentences into great and careful consideration, the sentences written in passive voice in particular, it will be helpful for machine translation to find out the subject and reconstruct a sentence in passive voice (Wang Yan: 2008). The first is to discover the “doee” in a sentence and then is to reconstruct the sentence structure and put the “doee” before the verb. The last is to explicate the passive relation between the noun and the verb.<br />
Example 3<br />
Source abstract: 回顾性分析2018年1月至2020年12月解放军总医院第七医学中心收治的500例老年髋部骨折患者的资料。<br />
Google translation: A retrospective analysis of the data of 500 elderly patients with hip fractures admitted to the Seventh Medical Center of the PLA General Hospital from January 2018 to December 2020.<br />
Published translation: From January 2018 to December 2020, the data of 500 elderly patients with hip fracture treated in the Seventh Medical Center of PLA General Hospital were analyzed retrospectively.<br />
Analysis: The above example indicates that the “回顾性分析”in the Google Translate is a noun phrase, but the source abstracts actually describes an action or a behavior. The reason for such a mistake is that the machine can’t recognize the Chinese subjetless sentences. To find the subject of the sentence, the source abstracts can be revised and adjusted. Finding the “doee” is the first step, which refers to “500例老年髋部骨折患者的资料”in the source abstracts. And next is to put it in front of the verb, that is to place it in the beginning of the sentence. Last but not least, the passive voice should be explicated in the sentence. <br />
After pre-editing source abstract: 500例老年髋部骨折患者的资料被回顾性分析，他们在2018年1月之2020年12月期间收治于解放军总医院第七医学中心。<br />
After pre-editing translation: The data of 500 elderly hip fracture patients were retrospectively analyzed. They were admitted to the Seventh Medical Center of the PLA General Hospital between January 2018 and December 2020.<br />
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===3.4 Relocation of Modifiers===<br />
Modifiers, including noun, adverbial and attributive are constantly employed in the Chinese medical papers in order to add information to subject and object in the sentence. By doing this, complicated sentences can be structured, thus causing obstacles to machine translation for recognizing this complex sentence structures when translating Chinese-English sentences. To make the machine translation successfully recognize the sentence structure, simplifying the sentence structure is quite necessary. The key is to moving the location of the modifiers and thus making the modifiers an independent sentence. In other words, the modifiers ought to be put before or behind the main part of the sentence to satisfy the common use of English. <br />
Usually, the modifiers in Chinese sentence can only be placed in front of the core word, while in English, modifiers are very flexible. It is all right to place the modifiers in front of or behind the major part of the sentences with adjective or a connective noun.<br />
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Example 4<br />
Source abstracts: 利用DNA重组技术以pET-28a表达系统在E.coli BL21(DE3) 中重组表达Hepl。<br />
Google Translation: Hepl is recombined in E.coli BL21 (DE3) using DNA recombination technology with pET-28a expression system.<br />
Published translation: The recombinant Hcpl protein was expressed by using DNA recombination technology through pET-28a expression system in E. coli BL21 (De3).<br />
Analysis: The Chinese medical text includes two modifiers, “利用DNA”and “以pET-28a)表达系统”. These two modifiers will be translated by machine, which catches more attention to the two constituents. From the Google translation above, one failure is obvious that it misplaces the location of the two modifiers and presents it in not accurate form. To make it translate correctly by machine translation, dividing the sentences is very important so that the source abstracts can be correctly recognized by machine translation.<br />
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After pre-editing source abstract: 利用DNA重组技术，Hcp1重组表达在E.coli BL21 (DE3)中， 通过pET-28a表达系统在E. coli BL21 (DE3)中。<br />
Google translation: Using DNA recombination technology, Hcp1 recombination is expressed in E.coli BL21 (DE3), via pET-28a expression system in E. coli BL21 (DE3).<br />
The second is the independence of modifiers, that is, modifiers can also be reconstructed into clauses to modify core words. Compared with English, There is no subordinate clause in Chinese, so redundant Chinese modifiers need to be reconstructed into subordinate clauses in Chinese-English translation to meet the characteristics of English. English, especially sentences with multiple modifiers. Otherwise, sentence structure may be confused, such as scattered modifiers of core words. To avoid this mistake, it is necessary to separate the modifier from the main part of the sentence. These modifiers should be reconstituted into clauses. Also, keep your sentences simple and easy to understand.<br />
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===3.5 Proper Omission and Deletion of Category Words===<br />
Category words are commonly used in Chinese. Category words complement the meaning of words, including problems, positions, situations and jobs. Adding this supplementary word is more in line with Chinese custom. In many cases, it has no real meaning, so it can be omitted in translation. In Chinese, category words are frequently used. However, it is rarely used in English, which is one of the differences between Chinese and English. There are many kinds of category words. Considering objectivity and the fixed structure of language, redundant category words should be deleted in Chinese-English translation. In the general, the most commonly used category of words in the Chinese medical abstracts are "process", "behavior", and "situation". These categories of words hinder the language conversion of Chinese and English, resulting in redundancy. <br />
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Example 5<br />
Source abstract: 探讨口腔白斑病癌变进程中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia response genes and associated microRNAs in the process of oral leukoplakia cancer was discussed.<br />
Published translation: To study the hypoxia response gene and microRNA (miRNA)expression profiles in the pathogenesis and progression of oral leukoplakia (OLK).<br />
Analysis: As the above example shows, the Chinese words “进程” belongs to a category word. However, the Chinese expression “癌变”contains the process, so the “进程” expression can be deleted before placing it into the machine translation, because the meaning of it has been overlapping between the expression “癌变”. Therefore, its place can be replaced with preposition “during”.<br />
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After pre-editing source abstract: 探讨口腔白斑病癌变中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia responsive genes and miRNA in oral leukoplakia cancer was investigated.<br />
<br />
===Conclusion===<br />
After years of development, machine translation has made great progress. The accuracy of machine translation has been greatly improved in both text recognition and sentence pattern conversion. However, machine translation has its own limitations. In other words, it needs to rely on the parallel corpus as a reference source for improving its accuracy. ESP text, in particular, is harder to get the high quality by machine translation. <br />
<br />
As one of the research papers, the characteristics of medical abstracts are fixed language structures, objectivity and accuracy (Qin Yi 2004:421-423). Therefore, medical translation must be accurate, object and understandable to follow the specific demands of the medical paper. Being an important field in the human society, medical paper translation is on a great demand, which means that it needs a huge demand for human labor. However, with the machine translation promoting, it will be more efficient to translate medical papers combining the effort by human and machine. The improvement and development of machine translation requires the joint efforts of computer science, information science, statistics, linguistics and other academic circles to achieve more mature human-computer mutual assistance translation (Li Yafei, Zhang Ruihua 2019:38-45).<br />
<br />
However, errors can occur during the process of machine translation of Chinese- English, because of the differences of the Chinese and English and the processing of the machine. Errors from the perspective of linguistic or grammar can affect the machine translation a lot. After division and recognition of errors, some pre-editing approaches are put forward to help the machine translation more accurate and readable, that are, extraction and replacement of terms in the source text, relocation of modifiers, explication of subordination, proper omission, deletion of category words and explication of subject through voice changing. <br />
<br />
<br />
The paper mainly focuses on the pre-editing machine translation by using medical papers as a case study. The errors of machine translation occurring in the translation of medical abstracts and pre-editing approaches for machine translation. The quality of machine translation of medical papers is greatly improved after employing the pre-editing methods. However, machine translation is not as flexible and accurate as human brain, so it is of importance to combine pre-editing and post-editing approaches with machine translation in order to produce more accurate, more object machine translation of medical papers.<br />
<br />
===References===<br />
<br />
Cui Qiliang崔启亮(2014).论机器翻译的译后编辑[J] ''On Post-Editing of Machine Translatio''. 中国翻译 Chinese Translators Journal, 035(006):68-73<br />
<br />
Feng Quangong, Gao Lin冯全功,高琳 (2017). 基于受控语言的译前编辑对机器翻译的影响[J] ''Influence of Pre-editing Based on Controlled Language on Machine Translation''. 当代外语研究Contemporary Foreign Language Research,(2): 63-68+87+110.<br />
<br />
GERLACH J, et al ( 2013). ''Combining Pre-editing and Post-editing to Improve SMT of User-generated Content''[M]// Proceedings of MT Summit XIV Workshop on Post-editing Technology and Practice. 45-53<br />
<br />
Hu Qingping胡清平(2005). 机器翻译中的受控语言[J] ''Controlled Language in Machine Translation''. 中国科技翻译 Chinese Science and Technology Translation, (03): 24-27. <br />
<br />
Lian Shuneng连淑能 (2010). 英汉对比研究增订本[M]''An Updated Version of English-Chinese Contrastive Studies'' . 北京:高等教育出版社Beijing: Higher Education Publishing House. 35-36.<br />
<br />
Li Yafei, Zhang Ruihua黎亚飞,张瑞华 (2019). 机器翻译发展与现状[J]''The Development and Current Situation of Machine Translation''. 中国轻工教育 China Light Industry Education, (5):38-45. <br />
<br />
Qin Yi秦毅(2004),从翻译基本标准议医学英语的翻译[J] ''On the Translation of Medical English from the Basic Standard of Translation''. 遵义医学院学报 Journal of Zunyi Medical College,27 (4): 421-423. <br />
<br />
Slype G V & Guinet J F & Seitz F (1984). ''Better Translation for Better Communication'' [M] . Oxford: Pergamon Press Ltd (U.K.). 90-93<br />
<br />
O'Brien S (2002). Teaching Post-editing: A Proposal for Course Con-tent [EB/OL]. http://mt-archive. Info/EAMT-2002-0brien. Pdf.<br />
<br />
Tytler, A. F. (1978). ''Essay On The Principles of Translation''[M]. Amsterdam: JohnBenjamins Publishing. 118-119<br />
<br />
Wang Yan王燕 (2008). 医学英语翻译与写作教程[M] ''Medical English Translation and Writing Course''. 重庆:重庆大学出版社 Chongqing: Chongqing University Press. 60-61<br />
<br />
Written by --[[User:Chen Huini|Chen Huini]] ([[User talk:Chen Huini|talk]]) 10:50, 15 December 2021 (UTC)Chen Huini</div>Chen Huinihttps://bou.de/u/index.php?title=Machine_translation&diff=133366Machine translation2021-12-15T10:59:51Z<p>Chen Huini: /* Chapter 12 蔡珠凤 The Mistranslation of C-J Machine Translation of Political Statements */</p>
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[[Machine_Trans_EN_1]] [[Machine_Trans_EN_2]] [[Machine_Trans_EN_3]] [[Machine_Trans_EN_4]] [[Machine_Trans_EN_5]] [[Machine_Trans_EN_6]] [[Machine_Trans_EN_7]] [[Machine_Trans_EN_8]] [[Machine_Trans_EN_9]] [[Machine_Trans_EN_10]] [[Machine_Trans_EN_11]] [[Machine_Trans_EN_12]] [[Machine_Trans_EN_13]] [[Machine_Trans_EN_14]] [[Machine_Trans_EN_15]] [[Machine_Trans_EN_16]] [[Machine_Trans_EN_17]] [[Machine_Trans_EN_18]] [[Machine_Trans_EN_19]] [[Machine_Trans_EN_20]] [[Machine_Trans_EN_21]] [[Machine_Trans_EN_22]] [[Machine_Trans_EN_23]] [[Machine_Trans_EN_24]] [[Machine_Trans_EN_25]] [[Machine_Trans_EN_26]] [[Machine_Trans_EN_27]] [[Machine_Trans_EN_28]] [[Machine_Trans_EN_29]] [[Machine_Trans_EN_30]] ...<br />
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<br />
=Chapter 1:A Comparison Between the Quality of Machine Translation and Human Translation——A Case Study of the Application of artificial intelligence in Sports Events=<br />
'''论机器翻译与人工翻译的质量对比——以人工智能在体育赛事领域的应用为例'''<br />
<br />
卫怡雯 Wei Yiwen, Hunan Normal University, China<br />
<br />
[[Machine_Trans_EN_1]]<br />
<br />
=Chapter 2：On the Realm Advantages And Mutual Development of Machine Translation And Huamn Translation)=<br />
"'论机器翻译与人工翻译的领域优势及共同发展'"<br />
<br />
肖毅瑶 Xiao Yiyao, Hunan Normal University, China<br />
<br />
[[Machine_Trans_EN_2]]<br />
<br />
=Chapter 4 : A Comparison Between Machine Translation of Netease and Traditional Human Translation—A Case Study of The Economist Articles)= <br />
'''网易有道机器翻译与人工翻译的译文对比——以经济学人语料为例'''<br />
<br />
王李菲 Wang Lifei, Hunan Normal University<br />
<br />
[[Machine_Trans_EN_4]]<br />
<br />
=Chapter 5: Muhammad Saqib Mehran - Problems in translation studies=<br />
[[Machine_Trans_EN_5]]<br />
<br />
=6 徐敏赟(Machine Translation Based on Neural Network --Challenge or Chance?)=<br />
[[Machine_Trans_EN_6]]<br />
<br />
=Chapter 7: The Cultivation of artificial intelligence and translation talents in the Belt and Road Initiative=<br />
[[Machine_Trans_EN_7]]<br />
<br />
一带一路背景下人工智能与翻译人才的培养<br />
<br />
颜莉莉 Yan Lili, Hunan Normal University, China<br />
<br />
=Chapter 8: On Machine Translation Under Lanuguage Intelligence——An Option and Opportunity for Human Translators=<br />
'''论语言智能下的机器翻译——译者的选择与机遇'''<br />
<br />
颜静 Yan Jing, Hunan Normal University, China<br />
<br />
[[Machine_Trans_EN_8]]<br />
<br />
=9 谢佳芬（ Machine Translation and Artificial Translation in the Era of Artificial Intelligence）=<br />
[[Machine_Trans_EN_9]]<br />
<br />
=Chapter 10 熊敏 Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts=<br />
机器翻译对各类型文本的英汉翻译能力探究<br />
<br />
熊敏, Xiong Min, Hunan Normal University<br />
<br />
[[Machine_Trans_EN_10]]<br />
<br />
=Chapter 11 Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts=<br />
<br />
机器翻译的译前编辑研究——以医学类文摘为例<br />
<br />
陈惠妮 Chen Huini, Hunan Normal University<br />
<br />
[[Machine_Trans_EN_11]]<br />
<br />
<br />
<br />
Written by --[[User:Chen Huini|Chen Huini]] ([[User talk:Chen Huini|talk]]) 04:58, 15 December 2021 (UTC)Chen Huini<br />
<br />
=Chapter 12 The Mistranslation of C-J Machine Translation of Political Statements=<br />
<br />
机器翻译中政治发言中译日的误译<br />
<br />
蔡珠凤 Cai Zhufeng, Hunan Normal University<br />
<br />
[[Machine_Trans_EN_12]]</div>Chen Huinihttps://bou.de/u/index.php?title=Machine_translation&diff=133363Machine translation2021-12-15T10:58:49Z<p>Chen Huini: /* Chapter 11 陈惠妮 Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts */</p>
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[[Machine_Trans_EN_1]] [[Machine_Trans_EN_2]] [[Machine_Trans_EN_3]] [[Machine_Trans_EN_4]] [[Machine_Trans_EN_5]] [[Machine_Trans_EN_6]] [[Machine_Trans_EN_7]] [[Machine_Trans_EN_8]] [[Machine_Trans_EN_9]] [[Machine_Trans_EN_10]] [[Machine_Trans_EN_11]] [[Machine_Trans_EN_12]] [[Machine_Trans_EN_13]] [[Machine_Trans_EN_14]] [[Machine_Trans_EN_15]] [[Machine_Trans_EN_16]] [[Machine_Trans_EN_17]] [[Machine_Trans_EN_18]] [[Machine_Trans_EN_19]] [[Machine_Trans_EN_20]] [[Machine_Trans_EN_21]] [[Machine_Trans_EN_22]] [[Machine_Trans_EN_23]] [[Machine_Trans_EN_24]] [[Machine_Trans_EN_25]] [[Machine_Trans_EN_26]] [[Machine_Trans_EN_27]] [[Machine_Trans_EN_28]] [[Machine_Trans_EN_29]] [[Machine_Trans_EN_30]] ...<br />
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<br />
=Chapter 1:A Comparison Between the Quality of Machine Translation and Human Translation——A Case Study of the Application of artificial intelligence in Sports Events=<br />
'''论机器翻译与人工翻译的质量对比——以人工智能在体育赛事领域的应用为例'''<br />
<br />
卫怡雯 Wei Yiwen, Hunan Normal University, China<br />
<br />
[[Machine_Trans_EN_1]]<br />
<br />
=Chapter 2：On the Realm Advantages And Mutual Development of Machine Translation And Huamn Translation)=<br />
"'论机器翻译与人工翻译的领域优势及共同发展'"<br />
<br />
肖毅瑶 Xiao Yiyao, Hunan Normal University, China<br />
<br />
[[Machine_Trans_EN_2]]<br />
<br />
=Chapter 4 : A Comparison Between Machine Translation of Netease and Traditional Human Translation—A Case Study of The Economist Articles)= <br />
'''网易有道机器翻译与人工翻译的译文对比——以经济学人语料为例'''<br />
<br />
王李菲 Wang Lifei, Hunan Normal University<br />
<br />
[[Machine_Trans_EN_4]]<br />
<br />
=Chapter 5: Muhammad Saqib Mehran - Problems in translation studies=<br />
[[Machine_Trans_EN_5]]<br />
<br />
=6 徐敏赟(Machine Translation Based on Neural Network --Challenge or Chance?)=<br />
[[Machine_Trans_EN_6]]<br />
<br />
=Chapter 7: The Cultivation of artificial intelligence and translation talents in the Belt and Road Initiative=<br />
[[Machine_Trans_EN_7]]<br />
<br />
一带一路背景下人工智能与翻译人才的培养<br />
<br />
颜莉莉 Yan Lili, Hunan Normal University, China<br />
<br />
=Chapter 8: On Machine Translation Under Lanuguage Intelligence——An Option and Opportunity for Human Translators=<br />
'''论语言智能下的机器翻译——译者的选择与机遇'''<br />
<br />
颜静 Yan Jing, Hunan Normal University, China<br />
<br />
[[Machine_Trans_EN_8]]<br />
<br />
=9 谢佳芬（ Machine Translation and Artificial Translation in the Era of Artificial Intelligence）=<br />
[[Machine_Trans_EN_9]]<br />
<br />
=Chapter 10 熊敏 Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts=<br />
机器翻译对各类型文本的英汉翻译能力探究<br />
<br />
熊敏, Xiong Min, Hunan Normal University<br />
<br />
[[Machine_Trans_EN_10]]<br />
<br />
=Chapter 11 Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts=<br />
<br />
机器翻译的译前编辑研究——以医学类文摘为例<br />
<br />
陈惠妮 Chen Huini, Hunan Normal University<br />
<br />
[[Machine_Trans_EN_11]]<br />
<br />
<br />
<br />
Written by --[[User:Chen Huini|Chen Huini]] ([[User talk:Chen Huini|talk]]) 04:58, 15 December 2021 (UTC)Chen Huini<br />
<br />
=Chapter 12 蔡珠凤 The Mistranslation of C-J Machine Translation of Political Statements=<br />
<br />
机器翻译中政治发言中译日的误译<br />
<br />
蔡珠凤 Cai Zhufeng, Hunan Normal University<br />
<br />
[[Machine_Trans_EN_12]]</div>Chen Huinihttps://bou.de/u/index.php?title=Machine_Trans_EN_12&diff=133361Machine Trans EN 122021-12-15T10:57:10Z<p>Chen Huini: </p>
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===Abstract===<br />
Language is the main way of communication between people. With the continuous development of globalization, the scale of cross-border exchanges is also expanding. However, due to cultural differences and diversity, the languages of different countries and regions are very different, which seriously hinders people's communication. The demand for efficient and convenient translation tools is increasing. At the same time, with the development of network technology and artificial intelligence, recognition technology based on deep learning is more and more widely used in English, Japanese and other fields.<br />
<br />
===Key words===<br />
machine translation; political statements; mistranslation of C-J machine translation<br />
===题目===<br />
The Mistranslation of C-J Machine Translation of Political Statements<br />
===摘要===<br />
语言是人与人之间交流的主要方式。随着全球化的不断发展，跨境交流的规模也在不断扩大。然而，由于文化的差异和多样性，不同国家和地区的语言差异很大，这严重阻碍了人们的交流。对高效便捷的翻译工具的需求正在增加。同时，随着网络技术和人工智能的发展，基于深度学习的识别技术在英语、日语等领域的应用越来越广泛。<br />
<br />
===关键词===<br />
机器翻译；政治发言；政治发言中译日的误译<br />
===1. Introduction===<br />
===Introduction to machine translation===<br />
Machine translation, also known as automatic translation, is a process of using computers to convert one natural language (source language) into another natural language (target language). It is a branch of computational linguistics, one of the ultimate goals of artificial intelligence, and has important scientific research value.At the same time, machine translation has important practical value. With the rapid development of economic globalization and the Internet, machine translation technology plays a more and more important role in promoting political, economic and cultural exchanges.The development of machine translation technology has been closely accompanied by the development of computer technology, information theory, linguistics and other disciplines. From the early dictionary matching, to the rule translation of dictionaries combined with linguistic expert knowledge, and then to the statistical machine translation based on corpus, with the improvement of computer computing power and the explosive growth of multilingual information, machine translation technology gradually stepped out of the ivory tower and began to provide real-time and convenient translation services for general users.（Zhang 2019:5-6)<br />
<br />
=== C-J machine translation software===<br />
Today's online machine translation software includes Baidu translation, Tencent translation, Google translation, Youdao translation, Bing translation and so on. Google was the first company to launch the machine translation system, and Baidu was the first company to import the machine translation system in China. In addition, Tencent and Youdao have attracted much attention.Machine translation is the process of using computers to convert one natural language into another. It usually refers to sentence and full-text translation between natural languages. In order to continuously improve the translation quality, R & D personnel have added artificial intelligence technologies such as speech recognition, image processing and deep neural network to machine translation on the basis of traditional machine translation based on rules, statistics and examples.With the increase of using machine translation, the joint cooperation between manual translation and machine translation will also increase significantly in the future. What criteria should be used to evaluate the quality of machine translation? In the evolving field of machine translation, there is an urgent need to clarify the unsolvable questions and solved problems.(Lv 1996:3)<br />
<br />
===The history of machine translation===<br />
The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. alchuni put forward the idea of using machines for translation. In 1933, Soviet inventor П.П. Trojansky designed a machine to translate one language into another, and registered his invention on September 5 of the same year; However, due to the low technical level in the 1930s, his translation machine was not made. In 1946, the first modern electronic computer ENIAC was born. Shortly after that, W. weaver, an American scientist and A. D. booth, a British engineer, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947 when discussing the application scope of electronic computer. In 1949, W. Weaver published the translation memorandum, which formally put forward the idea of machine translation. After 60 years of ups and downs, machine translation has experienced a tortuous and long development path. The academic community generally divides it into the following four stages:<br />
<br />
Pioneering period（1947-1964）<br />
<br />
In 1954, with the cooperation of IBM, Georgetown University completed the English Russian machine translation experiment with ibm-701 computer for the first time, showing the feasibility of machine translation to the public and the scientific community, thus opening the prelude to the study of machine translation.<br />
It is not too late for China to start this research. As early as 1956, the state included this research in the national scientific work development plan. The topic name is "machine translation, the construction of natural language translation rules and the mathematical theory of natural language". In 1957, the Institute of language and the Institute of computing technology of the Chinese Academy of Sciences cooperated in the Russian Chinese machine translation experiment, translating 9 different types of more complex sentences.From the 1950s to the first half of the 1960s, machine translation research has been on the rise. The United States and the former Soviet Union, two superpowers, have provided a lot of financial support for machine translation projects for military, political and economic purposes, while European countries have also paid considerable attention to machine translation research due to geopolitical and economic needs, and machine translation has become an upsurge for a time. In this period, although machine translation is just in the pioneering stage, it has entered an optimistic period of prosperity.<br />
<br />
Frustrated period（1964-1975）<br />
<br />
In 1964, in order to evaluate the research progress of machine translation, the American Academy of Sciences established the automatic language processing Advisory Committee (Alpac Committee) and began a two-year comprehensive investigation, analysis and test.In November 1966, the committee published a report entitled "language and machine" (Alpac report for short), which comprehensively denied the feasibility of machine translation and suggested stopping the financial support for machine translation projects. The publication of this report has dealt a blow to the booming machine translation, and the research of machine translation has fallen into a standstill. Coincidentally, during this period, China broke out the "ten-year Cultural Revolution", and basically these studies also stagnated. Machine translation has entered a depression.<br />
<br />
convalescence（1975-1989）<br />
<br />
Since the 1970s, with the development of science and technology and the increasingly frequent exchange of scientific and technological information among countries, the language barriers between countries have become more serious. The traditional manual operation mode has been far from meeting the needs, and there is an urgent need for computers to engage in translation. At the same time, the development of computer science and linguistics, especially the substantial improvement of computer hardware technology and the application of artificial intelligence in natural language processing, have promoted the recovery of machine translation research from the technical level. Machine translation projects have begun to develop again, and various practical and experimental systems have been launched successively, such as weinder system Eurpotra multilingual translation system, taum-meteo system, etc.However, after the end of the "ten-year holocaust", China has perked up again, and machine translation research has been put on the agenda again. The "784" project has paid enough attention to machine translation research. After the mid-1980s, the development of machine translation research in China has further accelerated. Firstly, two English Chinese machine translation systems, ky-1 and MT / ec863, have been successfully developed, indicating that China has made great progress in machine translation technology.(Chen 2016:5)<br />
<br />
New period(1990 present)<br />
<br />
With the universal application of the Internet, the acceleration of the process of world economic integration and the increasingly frequent exchanges in the international community, the traditional way of manual operation is far from meeting the rapidly growing needs of translation. People's demand for machine translation has increased unprecedentedly, and machine translation has ushered in a new development opportunity. International conferences on machine translation research have been held frequently, and China has made unprecedented achievements. A series of machine translation software have been launched, such as "Yixing", "Yaxin", "Tongyi", "Huajian", etc. Driven by the market demand, the commercial machine translation system has entered the practical stage, entered the market and came to the users.Since the new century, with the emergence and popularization of the Internet, the amount of data has increased sharply, and statistical methods have been fully applied. Internet companies have set up machine translation research groups and developed machine translation systems based on Internet big data, so as to make machine translation really practical, such as "Baidu translation", "Google translation", etc. In recent years, with the progress of in-depth learning, machine translation technology has further developed, which has promoted the rapid improvement of translation quality, and the translation in oral and other fields is more authentic and fluent.(Liu 2014:6)<br />
<br />
===The problem of machine translation at present===<br />
Error is inevitable<br />
Many people have misunderstandings about machine translation. They think that machine translation has great deviation and can't help people solve any problems. In fact, the error is inevitable. The reason is that machine translation uses linguistic principles. The machine automatically recognizes grammar, calls the stored thesaurus and automatically performs corresponding translation. However, errors are inevitable due to changes or irregularities in grammar, morphology and syntax, such as sentences with adverbials after "give me a reason to kill you first" in Dahua journey to the West. After all, a machine is a machine. No one has special feelings for language. How can it feel the lasting charm of "the tenderness of lowering its head, like the shame of a water lotus? After all, the meaning of Chinese is very different due to the changes of morphology, grammar and syntax and the change of context. Even many Chinese people are zhanger monks - they can't touch their heads, let alone machines.(Liu 2014：3）<br />
<br />
Bottleneck<br />
In fact, no matter which method, the biggest factor affecting the development of machine translation lies in the quality of translation. Judging from the achievements, the quality of machine translation is still far from the ultimate goal.<br />
Chinese mathematician and linguist Zhou Haizhong once pointed out in his paper "fifty years of machine translation": to improve the quality of machine translation, the first thing to solve is the problem of language itself rather than programming; It is certainly impossible to improve the quality of machine translation by relying on several programs alone. At the same time, he also pointed out that it is impossible for machine translation to achieve the degree of "faithfulness, expressiveness and elegance" when human beings have not yet understood how the brain performs fuzzy recognition and logical judgment of language. This view may reveal the bottleneck restricting the quality of translation. <br />
It is worth mentioning that American inventor and futurist ray cozwell predicted in an interview with Huffington Post that the quality of machine translation will reach the level of human translation by 2029. There are still many disputes about this thesis in the academic circles.（Cui 2019：4）<br />
<br />
===2.Mistranslation of Chinese Japanese machine translation===<br />
===2.1Vocabulary mistranslation in Chinese Japanese machine translation===<br />
===2.1.1Mistranslation of proper nouns===<br />
In political materials, there are often political dignitaries' names, place names or a large number of proper nouns in the political field. The morphemes of such words are definite and inseparable. Mistranslation will make the source language lose its specific meaning. <br />
<br />
Japanese translation into Chinese Chinese translation into Japanese<br />
<br />
original text translation by Youdao reference translation original text translation by Youdao reference translation<br />
<br />
朱鎔基 朱基 朱镕基 栗战书 栗戰史書 栗戰書<br />
<br />
労安 劳安 劳安 李克强 李克強 李克強 <br />
<br />
筑紫哲也 筑紫哲也 筑紫哲也 习近平 習近平 習近平<br />
<br />
山口百惠 山口百惠 山口百惠 韩正 韓中 韓正<br />
<br />
田中角栄 田中角荣 田中角荣 王沪宁 王上海氏 王滬寧<br />
<br />
東条英機 东条英社 东条英机 汪洋 汪洋 汪洋<br />
<br />
毛沢东 毛泽东 毛泽东 赵乐际 趙樂南 趙樂際<br />
<br />
トウ・ショウヘイ 大酱 邓小平 江泽民 江沢民 江沢民<br />
<br />
周恩来 周恩来 周恩来<br />
<br />
クリントン 克林顿 克林顿<br />
<br />
The above table counts 18 special names in the two texts, and 7 machine translation errors. In terms of mistranslation, there are not only "战书" but also "戰史" and "史書" in Chinese. "沪" is the abbreviation of Shanghai. In other words, since "战书" and "沪" are originally common nouns, this disrupts the choice of target language in machine translation. Except Katakana interference (except for トウシゃウヘイ, most of the mistranslations appear in the new Standing Committee. It can be seen that the machine translation system does not update the thesaurus in time. Different from other words, people's names have particularity. Especially as members of the Standing Committee of the Political Bureau of the CPC Central Committee, the translation of their names is officially unified. In this regard, public figures such as political dignitaries, stars, famous hosts and important. In addition, the machine also translates Japanese surnames such as "タ二モト" (谷本), "アンドウ" (安藤) into "塔尼莫特" and "龙胆". It can be found that the language feature that Japanese will be written together with Chinese characters and Hiragana also directly affects the translation quality of Japanese Chinese machine translation. Japanese people often use Katakana pronunciation. For example, when Japanese people talk with Premier Zhu, they often use "二ーハウ" (Hello). However, the machine only recognizes the two pseudonyms "二" and "ハ" and transliterates them into "尼哈" , ignoring the long sound after "二" and "ハ".(Guan 2018:10-12)<br />
<br />
original text Translation by Youdao reference translation<br />
<br />
日美安全体制 日米の安全体制 日米安保体制<br />
<br />
中国共产党第十九次全国代表大会 中国共産党第19回全国代表大会 中国共産党第19回全国代表大会（第19回党大会）<br />
<br />
十八大 十八大 第18回党大会中国特色社会主义<br />
<br />
中国特色社会主義 中国の特色ある社会主義 第18回党大会<br />
<br />
中国共产党中央委员会 中国共産党中央委員会 中国共産党中央委員会<br />
<br />
中国共産党中央委員会十八届中共中央政治局常委 第18代中国共產党中央政治局常務委員 第18期中共中央政治局常務委員<br />
<br />
十八届中共中央政治局委员 18期の中国共產党中央政治局委員 第18期中共中央政治局委員<br />
<br />
十九届中共中央政治局常委 十九回中国共產党中央政治局常務委員 第19期中央政治局常務委員<br />
<br />
中共十九届一中全会 中国共產党第十九回一中央委員会 第19期中央委員会第1回全体会議<br />
<br />
The above table is a comparison of the original and translated versions of some proper nouns. As shown in the table, the mistranslation problems are mainly reflected in the mismatch of numerals + quantifiers, the wrong addition of case auxiliary word "の", the lack of connectors, the Mistranslation of abbreviations, dead translation, and the writing errors of Chinese characters. The following is a specific analysis one by one.<br />
<br />
"十八届中共中央政治局常委", "十八届中共中央政治局委员", "十九届中共中央政治局常委" and "中共十九届一中全会" all have quantifiers "届", which are translated into "代", "期" and "回" respectively. The meaning is vague and should be uniformly translated into "期"; Among them, the translation of the last three proper nouns lacks the Chinese conjunction "第". The case auxiliary word "の" was added by mistake in the translation of "十八届中共中央政治局委员" and "日美安全体制". The "中国共产党中央委员会" did not write in the form of Japanese Ming Dynasty characters, but directly used simplified Chinese characters.<br />
<br />
The full name of "中共十九届一中全会" is "中国共产党第十九届中央委员会第一次全体会议", in which "一" stands for "第一次", which is an ordinal word rather than a cardinal word. Machine translation does not produce a correct translation. The fundamental reason is that there are no "规则" for translating such words in machine translation. If we can formulate corresponding rules for such words (for example, 中共m'1届m2 中全会→第m1期中央委员会第m2回全体会议), the translation system must be able to translate well no matter how many plenary sessions of the CPC Central Committee.(Guan 2018:6-7)<br />
<br />
===2.1.2Mistranslation of Polysemy===<br />
original text Translation by Youdao reference translation<br />
<br />
スタジオ 摄影棚/工作室 直播现场/演播厅<br />
<br />
日中関係の話 中日关系的故事 就中日关系(话题)<br />
<br />
溝 水沟 鸿沟<br />
<br />
それでは日中の問題について質問のある方。 那么对白天的问题有提问的人。 关于中日问题的话题，举手提问。<br />
<br />
私たちのクラスは20人ちょっとですが、 我们班有20人左右， 我们班二十多人的意见统一很难，<br />
<br />
いろいろな意見が出て、まとめるのは大変です。 但是又各种各样的意见，总结起来很困难。 <br />
<br />
一体どうやって、13億人もの人をまとめているんですか。 到底是怎么处理13亿的人的呢？ 中国是怎样把13亿人凝聚在一起的？<br />
<br />
In the original text, the word "スタジオ" appeared four times and was translated into "摄影棚" or "工作室" respectively, but the context is on-site interview, not the production site of photography or film. "話" has many semantics, such as "说话", "事情", "道理", etc. In accordance with the practice of the interview program, Premier Zhu Rongji was invited to answer five questions on the topic of China Japan relations. Obviously, the meaning of the word "故事" is very abrupt. The inherent Japanese word "日中" means "晌午、白天". At the same time, it is also the abbreviation of the names of the two countries. The machine failed to deal with it correctly according to the context. "溝" refers to the gap between China and Japan, not a "水沟". The former "まとめる" appears in the original text with the word "意见", which is intended to describe that it is difficult to unify everyone's opinions. The latter "まとめている" also refers to unifying the thoughts of 1.3 billion people, not "处理". Therefore, it is more appropriate to use "统一" and "处理" in human translation, rather than "总结意见" or "处理意见".(Zhang 2019:5)<br />
<br />
Mistranslation of polysemy has always been a difficult problem in machine translation research. The translation of each word is correct, but it is often very different from the original expression in the context. Zhang Zhengzeng said that ambiguity is a common phenomenon in natural language. Its essence is that the same language form may have different meanings, which is also one of the differences between natural language and artificial language. Therefore, one of the difficulties faced by machine translation is language disambiguation (Zhang Zheng, 2005:60). In this regard, we can mark all the meanings of polysemous words and judge which meaning to choose by common collocation with other words. At the same time, strengthen the text recognition ability of the machine to avoid the translation inconsistent with the current context. In this way, we can avoid the mistake of "大酱" in the place where famous figures such as Deng Xiaoping should have appeared in the previous political articles.(Wang 2020:7-9)<br />
<br />
===2.1.3Mistranslation of compound words===<br />
Multi class words refer to a word with two or more parts of speech, also known as the same word and different classes.<br />
<br />
original text Translation by Youdao reference translation<br />
<br />
1998年江泽民主席曾经访问日本, 1998年の江沢民国家主席の日本訪問し、 1998年、江沢民総書記が日本を訪問し、かつ<br />
<br />
同已故小渊首相签署了联合宣言。 かつて同じ故小渕首相が署名した共同宣言 亡くなられた小渕総理と宣言に調印されました<br />
<br />
Chinese "同" has two parts of speech: prepositions and conjunctions: "故" has many parts of speech, such as nouns, verbs, adjectives and conjunctions. This directly affects the judgment of the machine at the source language level. The word "同" in the above table is used as a conjunction to indicate the other party of a common act. "故" is used as a verb with the semantic meaning of "死亡", which is a modifier of "小渊首相". The machine regards "同" as an adjective and "故" as a noun "原因", which leads to confusion in the structure and unclear semantics of the translation. Different from the polysemy problem, multi category words have at least two parts of speech, and there is often not only one meaning under each part of speech. In this regard, software R & D personnel should fully consider the existence of multi category words, so that the translation machine can distinguish the meaning of words on the basis of marking the part of speech, so as to select the translation through the context and the components of the word in the sentence. Of course, the realization of this function is difficult, and we need to give full play to the wisdom of R & D personnel.(Guan 2018:9-12)<br />
<br />
===2.2 Syntactic mistranslation in Chinese Japanese machine translation===<br />
===2.2.1Mistranslation of tenses===<br />
original text：History is written by the people, and all achievements are attributed to the people. <br />
<br />
Translation by Youdao：歴史は人民が書いたものであり、すべての成果は人民のためである。<br />
<br />
reference translation：歴史は人民が綴っていくものであり、すべての成果は人民に帰することとなります。<br />
<br />
The original sentence meaning is "历史是人民书写的历史" or "历史是人民书写的东西". When translated into Japanese, due to the influence of Japanese language habits, the formal noun "もの" should be supplemented accordingly. In this sentence, both the machine and the interpreter have translated correctly. However, the machine's recognition of "的" is biased, resulting in tense translation errors. The past, present and future will become "history", and this is a continuous action. The form translated as "~ ていく" not only reflects that the action is a continuous action, but also conforms to the tense and semantic information of the original text. In addition, the machine's treatment of the preposition "于" is also inappropriate. In the original text, "人民" is the recipient of action, not the target language. As the most obvious feature of isolated language, function words in Chinese play an important role in semantic expression, and their translation should be regarded as a focus of machine translation research.(Zuo 2021:8)<br />
<br />
original text：李克强同志是十六届中共中央政治局常委,其他五位同志都是十六届中共中央政治局委员。<br />
<br />
Translation by Youdao：李克強総理は第16代中国共産党中央政治局常務委員であり、他の５人の同志はいずれも16期の中国共産党中央政治局委員である。<br />
<br />
reference translation：李克強同志は第16期中国共産党中央政治局常務委員を務め、他の５人は第16期中共中央政治局委員を務めました。<br />
<br />
Judging the verb "是" in the original text plays its most basic positive role, but due to the complexity of Chinese language, "是" often can not be completely transformed into the form of "だ / である" in the process of translation. This sentence is a judgment of what has happened. Compared with the 19th session, the 18th session has become history. This is an implicit temporal information. It is very difficult for machines without human brain to recognize this implicit information. "中共中央政治局常委" is the abbreviation of "中共中央政治局常务委员会委员" and it is a kind of position. In Japanese, often use it with verbs such as "務める","担当する",etc. The translator adopts the past tense of "務める", which conforms to the expression habit of Japanese and deals with the problem of tense at the same time. However, machine translation only mechanically translates this sentence into judgment sentence, and fails to correctly deal with the past information implied by the word "十八届".(Guan 2018:4)<br />
<br />
===2.2.2Mistranslation of honorifics===<br />
Honorific language is a language means to show respect to the listener. Different from Japanese, there is no grammatical category of honorifics in Chinese. There is no specific fixed grammatical form to express honorifics, self modesty and politeness. Instead, specific words such as "您", "请" and "劳驾" are used to express all kinds of respect or self modesty. (Yang 2020:5-9)<br />
<br />
Original text translation by Youdao reference translation<br />
<br />
女士们，先生们，同志们，朋友们 さんたち、先生たち、同志たち、お友达さん ご列席の皆さん<br />
<br />
谢谢大家！ ありがとうございます！ ご清聴ありがとうございました。<br />
<br />
これはどうされますか。 这是怎么回事呢? 您将如何解决这一问题？<br />
<br />
こうした問題をどうお考えでしょうか。 我们会如何考虑这些问题呢？ 您如何看待这一问题？<br />
<br />
For example, for the processing of "女士们，先生们，同志们，朋友们", the machine makes the translation correspond to the original one by one based on the principle of unchanged format, but there are errors in semantic communication and pragmatic habits. When speaking on formal occasions, Chinese expression tends to be comprehensive and detailed, as well as the address of the audience. In contrast, Japanese usually uses general terms such as "皆様", "ご臨席の皆さん" or "代表団の方々" as the opening remarks. In terms of Japanese Chinese translation, the machine also failed to recognize the usage of Japanese honorifics such as "さ れ る" and "お考え". It can be seen that Chinese Japanese machine translation has a low ability to deal with honorific expressions. The occasion is more formal. A good translation should not only be fluent in meaning, but also conform to the expression habits of the target language and match the current translation environment.(Che 2021:3-7)<br />
<br />
===Conclusion===<br />
In the process of discussing the Mistranslation of machine translation, this paper mainly cites the Mistranslation of Youdao translator. When I studied the problem of machine translation mistranslation, I also used a large number of translation software such as Google and Baidu for parallel comparison, and found that these translation software also had similar problems with Youdao translator. For example, the "新世界新未来" in Chinese ABAC structural phrases is translated by Baidu as "新し世界の新しい未来", which, like Youdao, is not translated in the form of parallel phrases; Google online translation translates the word "“全心全意" into a completely wrong "全面的に"; The original sentence "我吃了很多亏" in the Mistranslation of the unique expression in the language is translated by Google online as "私はたくさんの損失を食べました". Because the "吃" and "亏" in the original text are not closely adjacent, the machine can not recognize this echo and mistakenly treats "亏" as a kind of food, so the machine translates the predicate as "食べました"; Japanese "こうした問題をどう考えでしょうか", Google's online translation is "你如何看待这些问题?", "你" tone can not reflect the tone of Japanese honorific, while Baidu translates as "你怎么想这样的问题呢?" Although the meaning can be understood, it is an irregular spoken language. Google and Baidu also made the same mistakes as Youdao in the translation of proper nouns. For example, they translated "十七届(中共中央政治局常委)”" into "第17回..." .In short, similar mistranslations of Youdao are also common in Baidu and Google. Due to space constraints, they will not be listed one by one here.(Cui 2019:7)<br />
<br />
Mr. Liu Yongquan, Institute of language, Chinese Academy of Social Sciences (1997) It has been pointed out that machine translation is a linguistic problem in the final analysis. Although corpus based machine translation does not require a lot of linguistic knowledge, language expression is ever-changing. Machine translation based solely on statistical ideas can not avoid the translation quality problems caused by the lack of language rules. The following is based on the analysis of lexical, syntactic and other mistranslations From the perspective of the characteristics of the source language, this paper summarizes some difficulties of Chinese and Japanese in machine translation.(Liu 2014:8)<br />
<br />
(1) The difficulties of Chinese in machine translation <br />
<br />
Chinese is a typical isolated language. The relationship between words needs to be reflected by word order and function words. We should pay attention to the transformation of function words such as "的", "在", "向" and "了". Some special verbs, such as "是", "做" and "作", are widely used. How to translate on the basis of conforming to Japanese pragmatic habits and expressions is a difficulty in machine translation research. In terms of word order, Chinese is basically "subject→predicate→object". At the same time, Chinese pays attention to parataxis, and there is no need to be clear in expression with meaningful cohesion, which increases the difficulty of Chinese Japanese machine translation. When there are multiple verbs or modifier components in complex long sentences, machine translation usually can not accurately divide the components of the sentence, resulting in the result that the translation is completely unreadable.(Guan 2018:6-12) <br />
<br />
(2) Difficulties of Japanese in machine translation <br />
<br />
Both Chinese and Japanese languages use Chinese characters, and most machines produce the target language through the corresponding translation of Chinese characters. However, pseudonyms in Japanese play an important role in judging the part of speech and meaning, and can not only recognize Chinese characters and judge the structure and semantics of sentences. Japanese vocabulary is composed of Chinese vocabulary, inherent vocabulary and foreign vocabulary. Among them, the first two have a great impact on Chinese Japanese machine translation. For example "提出" is translated as "提出する" or "打ち出す"; and "重视" is translated as "重視する" or "大切にする". Japanese is an adhesive language, which contains a lot of "けど", "が" and "けれども" Some of them are transitional and progressive structures, but there are also some sequential expressions that do not need translation, and these translation software often can not accurately grasp them.(Che 2021:10)<br />
<br />
===References===<br />
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[8]Xu Xueyuan.(2021).Machine learning-based prediction of urban soil environment and corpus translation teaching[J].Arabian Journal of Geosciences,14(11). <br />
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[9]Chen Bingchang 陈丙昌(2016).機械翻訳の誤訳分析【D】.Error analysis of mechanical translation.贵州大学.2016(05) <br />
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[10]Lv Yinqiu 呂寅秋(1996).機械翻訳の言語規則と伝統文法との相違点.【D】The language rules of mechanical translation, the traditional grammar, and the points of contradiction.日本学研究.Japanese Studies.1996(00):21-22 <br />
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[11]Liu Jun 刘君(2014).基于语料库的中日同形词词义用法对比及其日中机器翻译研究【D】.A Corpus-based Comparison of the Meanings of Chinese and Japanese Homographs and Research on Japanese-Chinese Machine Translation.广西大学.(03) <br />
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[12]Cun Qianqian 崔倩倩(2019).机器翻译错误与译后编辑策略研究【D】.Research on Machine Translation Errors and Post-Editing Strategies.北京外国语大学.(09) <br />
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[13]Zhang Yi 张义(2019).机器翻译的译文分析【D】.Translation analysis of machine translation.西安外国语大学.(10) <br />
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[14]Zhang Linqian 张琳婧(2019).在线机器翻译中日翻译错误原因及对策【D】.Causes and countermeasures of online machine translation errors in Chinese-Japanese translation.山西大学.(02)<br />
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[15]Wang Dan 王丹(2020).基于机器翻译的专利文本译后编辑对策研究【D】.Research on countermeasures for post-translational editing of patent texts based on machine translation.大连理工大学.(06)<br />
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[16]Yang Xiaokun 杨晓琨(2020).日中机器翻译中的前编辑规则与效果验证【D】.Pre-editing rules and effect verification in Japanese-Chinese machine translation.大连理工大学.(06)<br />
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[17]Zuo Jia 左嘉(2021). 机器翻译日译汉误译研究【D】. Research on Mistranslation of Machine Translation from Japanese to Chinese.北京第二外国语学院.<br />
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[18]Guan Biying 关碧莹(2018).关于政治类发言的汉日机器翻译误译分析【D】.Analysis of Chinese-Japanese Machine Translation Mistranslations of Political Speeches.哈尔滨理工大学.<br />
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[19]Che Tong 车彤(2021).汉译日机器翻译质量评估及译后编辑策略研究【D】.Research on Quality Evaluation of Chinese-Japanese Machine Translation and Post-translation Editing Strategies.北京外国语大学.(09)<br />
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Networking Linking<br />
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http://www.elecfans.com/rengongzhineng/692245.html<br />
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https://baike.baidu.com/item/%E6%9C%BA%E5%99%A8%E7%BF%BB%E8%AF%91/411793<br />
<br />
=13 陈湘琼Chen Xiangqiong（Study on Post-editing from the Perspective of Functional Equivalence Theory ）=<br />
[[Machine_Trans_EN_13]]<br />
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=Chapter 14 Bi bi Nadia（Machine Translation a Challenge for Human Translators)=<br />
[[Machine_Trans_EN_14]]<br />
<br />
Bi bi Nadia, Hunan Normal University, China</div>Chen Huinihttps://bou.de/u/index.php?title=Machine_translation&diff=133359Machine translation2021-12-15T10:56:27Z<p>Chen Huini: </p>
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<div>'''Machine Translation - A challenge or a chance for human translators?'''<br />
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[[Machine_translation|Overview Page of Machine Translation]]<br />
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30 Chapters（0/30)<br />
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[[Machine_Trans_EN_1]] [[Machine_Trans_EN_2]] [[Machine_Trans_EN_3]] [[Machine_Trans_EN_4]] [[Machine_Trans_EN_5]] [[Machine_Trans_EN_6]] [[Machine_Trans_EN_7]] [[Machine_Trans_EN_8]] [[Machine_Trans_EN_9]] [[Machine_Trans_EN_10]] [[Machine_Trans_EN_11]] [[Machine_Trans_EN_12]] [[Machine_Trans_EN_13]] [[Machine_Trans_EN_14]] [[Machine_Trans_EN_15]] [[Machine_Trans_EN_16]] [[Machine_Trans_EN_17]] [[Machine_Trans_EN_18]] [[Machine_Trans_EN_19]] [[Machine_Trans_EN_20]] [[Machine_Trans_EN_21]] [[Machine_Trans_EN_22]] [[Machine_Trans_EN_23]] [[Machine_Trans_EN_24]] [[Machine_Trans_EN_25]] [[Machine_Trans_EN_26]] [[Machine_Trans_EN_27]] [[Machine_Trans_EN_28]] [[Machine_Trans_EN_29]] [[Machine_Trans_EN_30]] ...<br />
<br />
[[Book_projects|Back to translation project overview]]<br />
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[[DCG-To-Do|To the To Do list]]<br />
<br />
=Chapter 1:A Comparison Between the Quality of Machine Translation and Human Translation——A Case Study of the Application of artificial intelligence in Sports Events=<br />
'''论机器翻译与人工翻译的质量对比——以人工智能在体育赛事领域的应用为例'''<br />
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卫怡雯 Wei Yiwen, Hunan Normal University, China<br />
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[[Machine_Trans_EN_1]]<br />
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=Chapter 2：On the Realm Advantages And Mutual Development of Machine Translation And Huamn Translation)=<br />
"'论机器翻译与人工翻译的领域优势及共同发展'"<br />
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肖毅瑶 Xiao Yiyao, Hunan Normal University, China<br />
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[[Machine_Trans_EN_2]]<br />
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=Chapter 4 : A Comparison Between Machine Translation of Netease and Traditional Human Translation—A Case Study of The Economist Articles)= <br />
'''网易有道机器翻译与人工翻译的译文对比——以经济学人语料为例'''<br />
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王李菲 Wang Lifei, Hunan Normal University<br />
<br />
[[Machine_Trans_EN_4]]<br />
<br />
=Chapter 5: Muhammad Saqib Mehran - Problems in translation studies=<br />
[[Machine_Trans_EN_5]]<br />
<br />
=6 徐敏赟(Machine Translation Based on Neural Network --Challenge or Chance?)=<br />
[[Machine_Trans_EN_6]]<br />
<br />
=Chapter 7: The Cultivation of artificial intelligence and translation talents in the Belt and Road Initiative=<br />
[[Machine_Trans_EN_7]]<br />
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一带一路背景下人工智能与翻译人才的培养<br />
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颜莉莉 Yan Lili, Hunan Normal University, China<br />
<br />
=Chapter 8: On Machine Translation Under Lanuguage Intelligence——An Option and Opportunity for Human Translators=<br />
'''论语言智能下的机器翻译——译者的选择与机遇'''<br />
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颜静 Yan Jing, Hunan Normal University, China<br />
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[[Machine_Trans_EN_8]]<br />
<br />
=9 谢佳芬（ Machine Translation and Artificial Translation in the Era of Artificial Intelligence）=<br />
[[Machine_Trans_EN_9]]<br />
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=Chapter 10 熊敏 Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts=<br />
机器翻译对各类型文本的英汉翻译能力探究<br />
<br />
熊敏, Xiong Min, Hunan Normal University<br />
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[[Machine_Trans_EN_10]]<br />
<br />
=Chapter 11 陈惠妮 Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts=<br />
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机器翻译的译前编辑研究——以医学类文摘为例<br />
<br />
陈惠妮 Chen Huini, Hunan Normal University<br />
<br />
[[Machine_Trans_EN_11]]<br />
<br />
<br />
<br />
Written by --[[User:Chen Huini|Chen Huini]] ([[User talk:Chen Huini|talk]]) 04:58, 15 December 2021 (UTC)Chen Huini<br />
<br />
=Chapter 12 蔡珠凤 The Mistranslation of C-J Machine Translation of Political Statements=<br />
<br />
机器翻译中政治发言中译日的误译<br />
<br />
蔡珠凤 Cai Zhufeng, Hunan Normal University<br />
<br />
[[Machine_Trans_EN_12]]</div>Chen Huinihttps://bou.de/u/index.php?title=Machine_Trans_EN_11&diff=133354Machine Trans EN 112021-12-15T10:53:03Z<p>Chen Huini: /* Conclusion */</p>
<hr />
<div>'''Machine Translation - A challenge or a chance for human translators?'''<br />
<br />
[[Machine_translation|Overview Page of Machine Translation]]<br />
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===Abstract===<br />
At present, globalization is accelerating and the market demand for language services is rapidly increasing . Machine translation, as an important translation method, can greatly improve translation efficiency due to its low cost and high speed. However, because of the limitations of machine translation and the differences between Chinese and English language, machine translation is not accurate enough. In order to balance translation efficiency and translation quality, a great number of manual revisions in translation are required for the machine translating texts. Medical papers are specialized, special and purposeful, so it requires accurate,qualified and professional translation. However, the quality of translations by machine is inefficient to meet the high-quality requirements of medical papers translation. Therefore, the introduction of pre-editing can greatly improve the efficiency and quality of machine translation.<br />
<br />
===Key words===<br />
Pre-editing, Machine translation, Medical texts<br />
<br />
===题目===<br />
Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts<br />
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===摘要===<br />
在全球化加速发展的今天，市场对语言服务的需求迅速增加。机器翻译作为一种重要的翻译途径，由于其成本低、速度快，可以大大提高翻译效率。然而，由于机器翻译的局限性以及中英文语言的差异，机器翻译的准确性不高。为了平衡翻译效率和翻译质量，机器翻译文本需要大量的手工修改。<br />
医学论文具有专业性、特殊性和目的性，要求其译文准确、合格、专业。然而，机器翻译的质量较低，无法满足医学论文对翻译的高质量要求。因此，译前编辑的引入可以大大提高机器翻译的效率和质量。<br />
<br />
===关键词===<br />
译前编辑；机器翻译；医学文本<br />
<br />
===1. Introduction===<br />
===1.1 Definition of Machine Translation===<br />
As Cronin(2013) revealed: "Translation is undergoing a revolutionary upheaval. The influence of digital technology and the Internet on translation is continuous, extensive and profound. From the popularity of automatic online translation applications, translation revolution is everywhere (Cronin,2013) However,the concept of machine translation was firstly proposed in the 1930s. Since 1940s, the machine translation technology has been evolving from rule-based machine translation (RBMT) to statistical machine translation (SMT), and to neural machine translation (NMT). Machine translation refers to the automatic translation of source language into target language by using a computer system. That is, machine translation refers to the automatic translation of text from one language into another natural language by computer software or other online translation webs. Machine translation is also defined as the process of “using a computer system to automatically translate text or speech from one natural language to another” according to the definition by ISO (Cui 2014:68-73).O 'Brien (2002) defines it as "the behavior of modifying errors in machine translation to ensure that the target translation meets certain quality requirements". On the basic level, machine translation performs mechanical substitution of words in one language for words in another language, but that rarely produces a good translation, therefore, recognition of the whole phrases and their closest counterparts in the target language is needed. Not all words in one language have equivalents in another language, and many words have more than one meaning. A huge demand for translation is greatly needed in today’s global world, which creates new opportunities for the development of machine translation, attracts more and more attention and becomes one of the current research focuses.<br />
<br />
===1.2 Definition of Pre-editing===<br />
Pre-editing means to adjust and modify the source language to make it fit more with the characteristics of the machine translation software before putting the source language before the into machine translation, so as to improve the quality of the translation machine translation (Wei Changhong 2008:93-94). Pre-editing is to modify the original text before putting it into machine translation software, in order to improve the recognition rate of machine translation, optimize the output quality of translated text and reduce the workload of post-editing. Because pre-translation editing only needs to be modified in one language, the operation is simpler than post-translation editing, which can realize the double improvement of quality and efficiency. A good pre-editing translation can help machine translation more smoothly, thus improving the machine readability and quality of the output translation. Pre-editing is mostly applied in the following situations: one is when the original text is of poor quality and the machine is difficult to recognize the meaning of the sentence, such as user generated content with poor readability and translatability (Gerlach et al 2003:45-53); Documents that need to be published in multiple languages; Next is when the original text contains a lot of jargon; The last is the original text has a corresponding translation memory bank. If the original text is edited, it can better match the content of the translation memory bank. Pre-editing is a process of identifying problems. It requires to pre-edit the source texts before putting it into machine translation according to the requirements, listing the expressions or sentences that may have trouble in machine translation and then pre-edit it by human. The purpose is to enable the computer to translate better, improve the translatability of machine translation. (Slype G V & Guinet J F & Seitz F 1984:115)<br />
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===1.3 Machine Translation Mode===<br />
According to the different knowledge acquisition methods, machine translation modes can be classified as follows: one is rule-based machine translation, which is based on bilingual dictionaries and a library of language rules for each language. The quality of translation depends on whether the source language conforms to the existing rules, but the inexhaustible rules are the hinders of this model. The second mode is machine translation based on statistics. This translation model relies on the principles of mathematics and statistics to find various existing translations corresponding to the translation tasks through the employment of corpus, analyzing the frequency of their occurrence and selecting the translation with the highest frequency for output. The disadvantage of this translation model is that it ignores the flexibility of language and the importance of context. The last one is neural network language model. This model is different from previous translation models in that it uses end-to-end neural network to realize automatic translation between natural languages. At present, the quality of its translation is much higher than that of the previous translation models.<br />
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===1.4 Source of Study Abstracts===<br />
The core medical journals from domestic are selected in order to make the paper more representative and authoritative, such as National Medical Journal of China, Journal of Peking University (Health Science), and Journal of Third Military Medical University. All of them include basic medical science, biomedical technology, laboratory medical science and other fields. In this way, the pre-editing approaches included are applicable enough to machine translation of medical abstracts.<br />
<br />
===1.5 Selection of Translation Software===<br />
Generally, machine translation software includes Google Translation, Youdao Translator and Niu Translation, which has their own special use in translation. Take Google as an example, it is more like neurons in human brain, enabling to learn and collect information to establish connections with its neural machine translator’s neurons. However, it also causes many errors because of the lack of enough information. In this paper, contrastive analysis will be carried on by using Google translation. On the one hand, being a pioneer in the translation of NET, it is inevitably to sue Google as the translation software to translate medical texts in this paper. Comparing the program called “Google brain”, other NMT of translation software are relatively disadvantages. On the other hand, the Google translation enjoys the largest users in the world, with its downloads of more than one billion. The output quality of Google translation is more correct and complete than other machine translation software. With years of development and improvement, Google Translation has been greatly promoted. In this paper, Chinese medical abstract will be automatically translated by Google translation, and then the translation output will be compared with the translation by human and the publication of English abstracts. The main purpose is to prove that the improvement and promotion of quality and accuracy in the medical abstracts will be obtained through the pre-editing approaches.<br />
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===2.Language Characteristics and Error Division===<br />
Since Chinese and English are two different languages, it is quite neccessary to identify their own characteristics so as to better analyze and understand the two languages. Tytler (1978: 118-119) argued in his Essay on the Principles of Translation that there are three principles of translation that in all the translation should give readers the same feelings as the source text, except for complete transcript of ideas. There actually also exit some mistakes of these two languages. So the following will make some clarrifications of these two languages to ensure more accurate translation.<br />
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===2.1 Language Characteristics of Medical Abstracts===<br />
Chinese and English belong to different language systems, so there are differences in their language structure and the way users think of the languages. When using machine translation from Chinese to English, due to the unequal language levels, there will be many mistakes in the translation process, especially for ESP texts, such as medical papers.Here, these differences mainly refer to the linguistic characteristics of medical abstracts. In medical abstracts, it usually includes structured and unstructured abstracts. Although in different forms, they both describe the purpose, methods and conclusions of the research.<br />
<br />
In the method section of medial abstract, several Chinese sentences can be connected with commas, but each sentence may convey different information. In contrast, English sentences contain a great deal of information, but in order to ensure clarity, some modifiers need to be isolated and then reconstructed. However, in Chinese-English machine translation, a lot of information is put into the sentence because the machine segments the sentence on the basis of the full comma.<br />
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In addition, subjectless sentence is used in the objective and method parts of Chinese medical abstracts. The subjectless sentence means the sentence without or free from subject and is usually employed in two contexts. The first is "needless to say". In Chinese sentences, it is common to omit the subject of the sentence. Chinese sentences can convey meanings by using incomplete sentence structures, so Chinese speakers can understand the meanings of the sentences even though the sentence subject is omitted. The second is "emphasis of action". In this context, subjectless sentences are used to describe behaviors, especially in the study of traditional Chinese medicine abstracts. Comparatively speaking, it should be avoided in English medical abstracts. Abstract sentences are more subjective when describing the learning process, while the essential requirement of English medical abstract is objectivity. From this point, sentences without subject should be avoided in English medical abstracts. Another feature is voice. Few words with passive meanings appear in Chinese abstracts. English sentences are more favoured in using passive voice. When translating from Chinese to English, passive voice should be used to make the contents objective, which is also the basic requirement of medical papers.<br />
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These are the linguistic features of Chinese medical abstracts. There are great differences in sentence structure and expression between Chinese and English medical abstracts. These differences may reduce the accuracy of machine translation, so it is necessary to introduce pre-editing to edit the source text to ensure that the source text can be accurately recognized by the machine and fully translated into English.<br />
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===2.2 Error Division of Machine Translation in Translating Abstracts of Medical Papers from Chinese to English===<br />
In this paper, errors in machine translation are listed out after analyzing. Some are due to the principles, such as statistical-based MT and NMT, employed by machine translation. Based on the original errors, they can be studied from two levels. One is from the macro-level, referring to mistakes caused by objective factors. These are not human factors, but the disadvantage of machine translation and the limitations of language. The limitations of machine translation are derived from the principles and models adopted and manifests itself as a reliance on reference sources. In this case, semantic ambiguity and textual incoherence may occur in the absence of reference sources.<br />
<br />
However, for ESP texts such as medical papers, the requirements for terms and sentence patterns are far beyond the existing corpora. For unfamiliar texts (that is, the corresponding texts cannot be found in the corpora). The quality of output translation will be relatively low, which is determined by the principles of machine translation. As mentioned above, machine translation has evolved over the past few decades from rules-based to corp-based statistics to NMT. However, there still exist some limitations in machine translation.<br />
<br />
Mistakes on micro-level are mainly caused by the variations and differences of linguistic structure between Chinese and English. Chinese is an implicit language, while English is an explicit one (Lian, 2010) To put it in another way, Chinese expression does not depend on language structures, while English does the opposite. This may result in mismatches between the original and machine translated sentences. This kind of error is mainly divided into two types: component fragment and component missing. For a medical paper, such errors are very serious. As a kind of ESP discourse, medical paper has the characteristics of fixed, objective and accurate language structure. In order to reproduce the characteristics of medical abstracts in translation, it is necessary to avoid errors at the level of words and sentences and pay attention to logic and consistency.<br />
<br />
Lexical errors here refer to the inconsistency of fixed expressions in the translation of terms. Although these terms are machine translation based on the corpus, the corpus may not be perfect for ESP texts such as medical papers. Therefore, fixed expressions are not used in term translation. This is also the most common mistake in machine translation (the term here includes but is not limited to nouns). For ESP texts, medical text in particular, the fixed expressions and the accuracy of terms are of great importance.<br />
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===3.Approaches Proposed for Pre-editing ===<br />
Generally speaking, there is a close relationship between pre-editing and post-editing, both of which aim to convey information and ensure high or publishable translation quality. Proper pre-editing can improve the quality of machine translation in terms of adequacy and consistency. <br />
Complexity of natural language and people use language arbitrarily, bringing many difficulties to Chinese-English machine translation, Hu Qingping (2005:24) has proposed "the research of translation software and the development of the controlled language are the two directions to improve the quality of machine translation : the former aims at the difficulty in the natural language processing, the latter overcomes the arbitrariness of natural language". Feng Quangong and Gao Lin (2017: 63-68) put forward: "The writing principles of controlled language can be applied to pre-editing of machine translation. Pre-editing based on controlled language can effectively reduce the complexity and ambiguity of source text, improve the identifiability of machine translation (the translatability of source text itself), and thus reduce (fully) the workload of post-editing".<br />
The central task of pre-editing is to transform human-friendly content into machine-friendly content, so words and sentences need to be repositioned or even changed. Based on the analysis of the language characteristics of Chinese and English, the Chinese ideographic group should be split before the Chinese source text is input into the machine software to translate so that the sentence structure is complete which can be easily recognized by the machine translation software.<br />
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===3.1 Extraction and Replacement of Terms in the Source Text===<br />
As a branch of applied English, medical English is the product of the combination of English language knowledge and medical knowledge. The terms in medical papers have the characteristics of fixed and complex language structure. Although Google Translation is based on a corpus, the language structure is not fixed due to the limited size of the corpus. Therefore, the following two steps should be followed before machine translation. The first is to extract terms and create a glossary, and then replace the Chinese terms in the source text with the corresponding English expressions in the glossary. Of course, the terms of extraction should be searched and verified according to the actual situation. All of these words are verified before they can be replaced. This method can not only ensure the accuracy of term translation, but also maintain the consistency of expression, especially when dealing with long texts.<br />
Example1<br />
Source abstract: 口腔白斑病癌变相关缺氧应答基因和微小RNA的芯片及表达验证。<br />
Google translation: Microarray detection/Chip detection and expression verification of hypoxia response genes and microRNAs related to oral leukoplakia canceration.<br />
Published translation:Transcriptome array screening and verification of oral leukoplakia carcinogenesis-related hypoxia-responsive gene and microRNA. <br />
Analysis: The expression “ Affymetrix GeneChip” empolyed in this thesis is a human transcription array for transcriptome array. In the source abstract, “芯片检测“ can be meant “screening” but not detection, so the proper and appropriate translation of these expression should be “transcription array screening”, but the Google translates it into “microarry detection of chip detection”. Therefore, term extraction is essential and inevitable before putting the source abstracts into the machine translation software.<br />
After pre-editing source abstracts: 对 oral leukoplakia carcinogenesis 相关 hypoxia-responsive gene 和微小RNA进行 transcriptome array screening 及表达验证。<br />
After pre-editing translation: Transcriptome array screening and expression- verification of hypoxia-responsive genes and microRNAs related to oral leukoplakia carcinogenesis.<br />
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===3.2 Explication of Subordination===<br />
As mentioned above, the subordination of Chinese sentences is judged by certain specific words in machine translation . Chinese is a paratactic language, and sentences are often connected by internal logical relationships; while English depends on sentence structure, so sentences are often closely linked by various language forms. In order to improve the accuracy of machine translation, we can adjust the sentence structure and adjust the position of adjectives and their modifiers. <br />
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Example 2<br />
Source abstracts:回顾性分析南京医科大学附属儿童医院中重度HIE患儿49例及同期就诊的无神经系统症状体征的足月新生儿为对照组31例的头颅磁共振成像（MRI）资料。<br />
Google translation: A retrospective analysis that the brain magnetic resonance imaging (MRI) data of 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University and full-term neonates with no neurological symptoms and signs during the same period were included in the control group.<br />
Published translation: A total of 49 children with moderate to severe HIE admitted to the children’s Hospital Affiliated to Nanjing Medical University were retrospectively analyzed. Cranial magnetic resonance imaging (MRI) date of 31 full-term neonates without neurological symptoms and signs who visited the hospital during the same period were recruited as the control group. <br />
<br />
Analysis: As the above example shows that “中重度HIE患儿” and “足月新生儿” in the source abstracts are from the Children’s Hospital Affiliated Nanjing Medical University. The Google translation shows that 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University. There is an error due to the misuse of subordination. There are some advice in order to solve the problem. That is, first to segment the sentence and then reconstruct the sentence. For instance, the Chinese expression “南京医科大学附属儿童医院” carries many modifiers, which requires to cut the modifiers and reconstruct the sentence. <br />
Therefore, the Chinese expression “南京医科大学附属儿童医院’can be divided into abstractions, leaving two sentences instead of one long sentence with modifiers..<br />
After pre-editing source abstracts: 对49例中重度HIE患儿以及31例无神经系统症状体征的足月新生儿的MRI资料进行回顾性分析。这些患者收治于南京医科大学儿童附属医院。<br />
After pre-editing translation: The MRI data of 49 children with moderate to severe HIE and 31 full-term newborns without neurological symptoms and signs were retrospectively analyzed. These patients were admitted to the Children’s Hospital of Nanjing Medical University.<br />
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===3.3 Explication of Subject through Voice Changing===<br />
The extensive use of subjectless sentences are quite common in the Chinese abstracts, while English prefers to employ passive voice in the sentences so as to make them object and accurate. In order to take the characteristics of English sentences into great and careful consideration, the sentences written in passive voice in particular, it will be helpful for machine translation to find out the subject and reconstruct a sentence in passive voice (Wang Yan: 2008). The first is to discover the “doee” in a sentence and then is to reconstruct the sentence structure and put the “doee” before the verb. The last is to explicate the passive relation between the noun and the verb.<br />
Example 3<br />
Source abstract: 回顾性分析2018年1月至2020年12月解放军总医院第七医学中心收治的500例老年髋部骨折患者的资料。<br />
Google translation: A retrospective analysis of the data of 500 elderly patients with hip fractures admitted to the Seventh Medical Center of the PLA General Hospital from January 2018 to December 2020.<br />
Published translation: From January 2018 to December 2020, the data of 500 elderly patients with hip fracture treated in the Seventh Medical Center of PLA General Hospital were analyzed retrospectively.<br />
Analysis: The above example indicates that the “回顾性分析”in the Google Translate is a noun phrase, but the source abstracts actually describes an action or a behavior. The reason for such a mistake is that the machine can’t recognize the Chinese subjetless sentences. To find the subject of the sentence, the source abstracts can be revised and adjusted. Finding the “doee” is the first step, which refers to “500例老年髋部骨折患者的资料”in the source abstracts. And next is to put it in front of the verb, that is to place it in the beginning of the sentence. Last but not least, the passive voice should be explicated in the sentence. <br />
After pre-editing source abstract: 500例老年髋部骨折患者的资料被回顾性分析，他们在2018年1月之2020年12月期间收治于解放军总医院第七医学中心。<br />
After pre-editing translation: The data of 500 elderly hip fracture patients were retrospectively analyzed. They were admitted to the Seventh Medical Center of the PLA General Hospital between January 2018 and December 2020.<br />
<br />
===3.4 Relocation of Modifiers===<br />
Modifiers, including noun, adverbial and attributive are constantly employed in the Chinese medical papers in order to add information to subject and object in the sentence. By doing this, complicated sentences can be structured, thus causing obstacles to machine translation for recognizing this complex sentence structures when translating Chinese-English sentences. To make the machine translation successfully recognize the sentence structure, simplifying the sentence structure is quite necessary. The key is to moving the location of the modifiers and thus making the modifiers an independent sentence. In other words, the modifiers ought to be put before or behind the main part of the sentence to satisfy the common use of English. <br />
Usually, the modifiers in Chinese sentence can only be placed in front of the core word, while in English, modifiers are very flexible. It is all right to place the modifiers in front of or behind the major part of the sentences with adjective or a connective noun.<br />
<br />
Example 4<br />
Source abstracts: 利用DNA重组技术以pET-28a表达系统在E.coli BL21(DE3) 中重组表达Hepl。<br />
Google Translation: Hepl is recombined in E.coli BL21 (DE3) using DNA recombination technology with pET-28a expression system.<br />
Published translation: The recombinant Hcpl protein was expressed by using DNA recombination technology through pET-28a expression system in E. coli BL21 (De3).<br />
Analysis: The Chinese medical text includes two modifiers, “利用DNA”and “以pET-28a)表达系统”. These two modifiers will be translated by machine, which catches more attention to the two constituents. From the Google translation above, one failure is obvious that it misplaces the location of the two modifiers and presents it in not accurate form. To make it translate correctly by machine translation, dividing the sentences is very important so that the source abstracts can be correctly recognized by machine translation.<br />
<br />
After pre-editing source abstract: 利用DNA重组技术，Hcp1重组表达在E.coli BL21 (DE3)中， 通过pET-28a表达系统在E. coli BL21 (DE3)中。<br />
Google translation: Using DNA recombination technology, Hcp1 recombination is expressed in E.coli BL21 (DE3), via pET-28a expression system in E. coli BL21 (DE3).<br />
The second is the independence of modifiers, that is, modifiers can also be reconstructed into clauses to modify core words. Compared with English, There is no subordinate clause in Chinese, so redundant Chinese modifiers need to be reconstructed into subordinate clauses in Chinese-English translation to meet the characteristics of English. English, especially sentences with multiple modifiers. Otherwise, sentence structure may be confused, such as scattered modifiers of core words. To avoid this mistake, it is necessary to separate the modifier from the main part of the sentence. These modifiers should be reconstituted into clauses. Also, keep your sentences simple and easy to understand.<br />
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===3.5 Proper Omission and Deletion of Category Words===<br />
Category words are commonly used in Chinese. Category words complement the meaning of words, including problems, positions, situations and jobs. Adding this supplementary word is more in line with Chinese custom. In many cases, it has no real meaning, so it can be omitted in translation. In Chinese, category words are frequently used. However, it is rarely used in English, which is one of the differences between Chinese and English. There are many kinds of category words. Considering objectivity and the fixed structure of language, redundant category words should be deleted in Chinese-English translation. In the general, the most commonly used category of words in the Chinese medical abstracts are "process", "behavior", and "situation". These categories of words hinder the language conversion of Chinese and English, resulting in redundancy. <br />
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Example 5<br />
Source abstract: 探讨口腔白斑病癌变进程中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia response genes and associated microRNAs in the process of oral leukoplakia cancer was discussed.<br />
Published translation: To study the hypoxia response gene and microRNA (miRNA)expression profiles in the pathogenesis and progression of oral leukoplakia (OLK).<br />
Analysis: As the above example shows, the Chinese words “进程” belongs to a category word. However, the Chinese expression “癌变”contains the process, so the “进程” expression can be deleted before placing it into the machine translation, because the meaning of it has been overlapping between the expression “癌变”. Therefore, its place can be replaced with preposition “during”.<br />
<br />
After pre-editing source abstract: 探讨口腔白斑病癌变中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia responsive genes and miRNA in oral leukoplakia cancer was investigated.<br />
<br />
===Conclusion===<br />
After years of development, machine translation has made great progress. The accuracy of machine translation has been greatly improved in both text recognition and sentence pattern conversion. However, machine translation has its own limitations. In other words, it needs to rely on the parallel corpus as a reference source for improving its accuracy. ESP text, in particular, is harder to get the high quality by machine translation. <br />
<br />
As one of the research papers, the characteristics of medical abstracts are fixed language structures, objectivity and accuracy (Qin Yi 2004:421-423). Therefore, medical translation must be accurate, object and understandable to follow the specific demands of the medical paper. Being an important field in the human society, medical paper translation is on a great demand, which means that it needs a huge demand for human labor. However, with the machine translation promoting, it will be more efficient to translate medical papers combining the effort by human and machine. The improvement and development of machine translation requires the joint efforts of computer science, information science, statistics, linguistics and other academic circles to achieve more mature human-computer mutual assistance translation (Li Yafei, Zhang Ruihua 2019:38-45).<br />
<br />
However, errors can occur during the process of machine translation of Chinese- English, because of the differences of the Chinese and English and the processing of the machine. Errors from the perspective of linguistic or grammar can affect the machine translation a lot. After division and recognition of errors, some pre-editing approaches are put forward to help the machine translation more accurate and readable, that are, extraction and replacement of terms in the source text, relocation of modifiers, explication of subordination, proper omission, deletion of category words and explication of subject through voice changing. <br />
<br />
<br />
The paper mainly focuses on the pre-editing machine translation by using medical papers as a case study. The errors of machine translation occurring in the translation of medical abstracts and pre-editing approaches for machine translation. The quality of machine translation of medical papers is greatly improved after employing the pre-editing methods. However, machine translation is not as flexible and accurate as human brain, so it is of importance to combine pre-editing and post-editing approaches with machine translation in order to produce more accurate, more object machine translation of medical papers.<br />
<br />
===References===<br />
<br />
Cui Qiliang崔启亮(2014).论机器翻译的译后编辑[J] ''On Post-Editing of Machine Translatio''. 中国翻译 Chinese Translators Journal, 035(006):68-73<br />
<br />
Feng Quangong, Gao Lin冯全功,高琳 (2017). 基于受控语言的译前编辑对机器翻译的影响[J] ''Influence of Pre-editing Based on Controlled Language on Machine Translation''. 当代外语研究Contemporary Foreign Language Research,(2): 63-68+87+110.<br />
<br />
GERLACH J, et al ( 2013). ''Combining Pre-editing and Post-editing to Improve SMT of User-generated Content''[M]// Proceedings of MT Summit XIV Workshop on Post-editing Technology and Practice. 45-53<br />
<br />
Hu Qingping胡清平(2005). 机器翻译中的受控语言[J] ''Controlled Language in Machine Translation''. 中国科技翻译 Chinese Science and Technology Translation, (03): 24-27. <br />
<br />
Lian Shuneng连淑能 (2010). 英汉对比研究增订本[M]''An Updated Version of English-Chinese Contrastive Studies'' . 北京:高等教育出版社Beijing: Higher Education Publishing House. 35-36.<br />
<br />
Li Yafei, Zhang Ruihua黎亚飞,张瑞华 (2019). 机器翻译发展与现状[J]''The Development and Current Situation of Machine Translation''. 中国轻工教育 China Light Industry Education, (5):38-45. <br />
<br />
Qin Yi秦毅(2004),从翻译基本标准议医学英语的翻译[J] ''On the Translation of Medical English from the Basic Standard of Translation''. 遵义医学院学报 Journal of Zunyi Medical College,27 (4): 421-423. <br />
<br />
Slype G V & Guinet J F & Seitz F (1984). ''Better Translation for Better Communication'' [M] . Oxford: Pergamon Press Ltd (U.K.). 90-93<br />
<br />
O'Brien S (2002). Teaching Post-editing: A Proposal for Course Con-tent [EB/OL]. http://mt-archive. Info/EAMT-2002-0brien. Pdf.<br />
<br />
Tytler, A. F. (1978). ''Essay On The Principles of Translation''[M]. Amsterdam: JohnBenjamins Publishing. 118-119<br />
<br />
Wang Yan王燕 (2008). 医学英语翻译与写作教程[M] ''Medical English Translation and Writing Course''. 重庆:重庆大学出版社 Chongqing: Chongqing University Press. 60-61<br />
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Written by --[[User:Chen Huini|Chen Huini]] ([[User talk:Chen Huini|talk]]) 10:50, 15 December 2021 (UTC)Chen Huini</div>Chen Huinihttps://bou.de/u/index.php?title=Machine_Trans_EN_11&diff=133353Machine Trans EN 112021-12-15T10:50:04Z<p>Chen Huini: </p>
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<div>'''Machine Translation - A challenge or a chance for human translators?'''<br />
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===Abstract===<br />
At present, globalization is accelerating and the market demand for language services is rapidly increasing . Machine translation, as an important translation method, can greatly improve translation efficiency due to its low cost and high speed. However, because of the limitations of machine translation and the differences between Chinese and English language, machine translation is not accurate enough. In order to balance translation efficiency and translation quality, a great number of manual revisions in translation are required for the machine translating texts. Medical papers are specialized, special and purposeful, so it requires accurate,qualified and professional translation. However, the quality of translations by machine is inefficient to meet the high-quality requirements of medical papers translation. Therefore, the introduction of pre-editing can greatly improve the efficiency and quality of machine translation.<br />
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===Key words===<br />
Pre-editing, Machine translation, Medical texts<br />
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===题目===<br />
Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts<br />
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===摘要===<br />
在全球化加速发展的今天，市场对语言服务的需求迅速增加。机器翻译作为一种重要的翻译途径，由于其成本低、速度快，可以大大提高翻译效率。然而，由于机器翻译的局限性以及中英文语言的差异，机器翻译的准确性不高。为了平衡翻译效率和翻译质量，机器翻译文本需要大量的手工修改。<br />
医学论文具有专业性、特殊性和目的性，要求其译文准确、合格、专业。然而，机器翻译的质量较低，无法满足医学论文对翻译的高质量要求。因此，译前编辑的引入可以大大提高机器翻译的效率和质量。<br />
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===关键词===<br />
译前编辑；机器翻译；医学文本<br />
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===1. Introduction===<br />
===1.1 Definition of Machine Translation===<br />
As Cronin(2013) revealed: "Translation is undergoing a revolutionary upheaval. The influence of digital technology and the Internet on translation is continuous, extensive and profound. From the popularity of automatic online translation applications, translation revolution is everywhere (Cronin,2013) However,the concept of machine translation was firstly proposed in the 1930s. Since 1940s, the machine translation technology has been evolving from rule-based machine translation (RBMT) to statistical machine translation (SMT), and to neural machine translation (NMT). Machine translation refers to the automatic translation of source language into target language by using a computer system. That is, machine translation refers to the automatic translation of text from one language into another natural language by computer software or other online translation webs. Machine translation is also defined as the process of “using a computer system to automatically translate text or speech from one natural language to another” according to the definition by ISO (Cui 2014:68-73).O 'Brien (2002) defines it as "the behavior of modifying errors in machine translation to ensure that the target translation meets certain quality requirements". On the basic level, machine translation performs mechanical substitution of words in one language for words in another language, but that rarely produces a good translation, therefore, recognition of the whole phrases and their closest counterparts in the target language is needed. Not all words in one language have equivalents in another language, and many words have more than one meaning. A huge demand for translation is greatly needed in today’s global world, which creates new opportunities for the development of machine translation, attracts more and more attention and becomes one of the current research focuses.<br />
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===1.2 Definition of Pre-editing===<br />
Pre-editing means to adjust and modify the source language to make it fit more with the characteristics of the machine translation software before putting the source language before the into machine translation, so as to improve the quality of the translation machine translation (Wei Changhong 2008:93-94). Pre-editing is to modify the original text before putting it into machine translation software, in order to improve the recognition rate of machine translation, optimize the output quality of translated text and reduce the workload of post-editing. Because pre-translation editing only needs to be modified in one language, the operation is simpler than post-translation editing, which can realize the double improvement of quality and efficiency. A good pre-editing translation can help machine translation more smoothly, thus improving the machine readability and quality of the output translation. Pre-editing is mostly applied in the following situations: one is when the original text is of poor quality and the machine is difficult to recognize the meaning of the sentence, such as user generated content with poor readability and translatability (Gerlach et al 2003:45-53); Documents that need to be published in multiple languages; Next is when the original text contains a lot of jargon; The last is the original text has a corresponding translation memory bank. If the original text is edited, it can better match the content of the translation memory bank. Pre-editing is a process of identifying problems. It requires to pre-edit the source texts before putting it into machine translation according to the requirements, listing the expressions or sentences that may have trouble in machine translation and then pre-edit it by human. The purpose is to enable the computer to translate better, improve the translatability of machine translation. (Slype G V & Guinet J F & Seitz F 1984:115)<br />
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===1.3 Machine Translation Mode===<br />
According to the different knowledge acquisition methods, machine translation modes can be classified as follows: one is rule-based machine translation, which is based on bilingual dictionaries and a library of language rules for each language. The quality of translation depends on whether the source language conforms to the existing rules, but the inexhaustible rules are the hinders of this model. The second mode is machine translation based on statistics. This translation model relies on the principles of mathematics and statistics to find various existing translations corresponding to the translation tasks through the employment of corpus, analyzing the frequency of their occurrence and selecting the translation with the highest frequency for output. The disadvantage of this translation model is that it ignores the flexibility of language and the importance of context. The last one is neural network language model. This model is different from previous translation models in that it uses end-to-end neural network to realize automatic translation between natural languages. At present, the quality of its translation is much higher than that of the previous translation models.<br />
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===1.4 Source of Study Abstracts===<br />
The core medical journals from domestic are selected in order to make the paper more representative and authoritative, such as National Medical Journal of China, Journal of Peking University (Health Science), and Journal of Third Military Medical University. All of them include basic medical science, biomedical technology, laboratory medical science and other fields. In this way, the pre-editing approaches included are applicable enough to machine translation of medical abstracts.<br />
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===1.5 Selection of Translation Software===<br />
Generally, machine translation software includes Google Translation, Youdao Translator and Niu Translation, which has their own special use in translation. Take Google as an example, it is more like neurons in human brain, enabling to learn and collect information to establish connections with its neural machine translator’s neurons. However, it also causes many errors because of the lack of enough information. In this paper, contrastive analysis will be carried on by using Google translation. On the one hand, being a pioneer in the translation of NET, it is inevitably to sue Google as the translation software to translate medical texts in this paper. Comparing the program called “Google brain”, other NMT of translation software are relatively disadvantages. On the other hand, the Google translation enjoys the largest users in the world, with its downloads of more than one billion. The output quality of Google translation is more correct and complete than other machine translation software. With years of development and improvement, Google Translation has been greatly promoted. In this paper, Chinese medical abstract will be automatically translated by Google translation, and then the translation output will be compared with the translation by human and the publication of English abstracts. The main purpose is to prove that the improvement and promotion of quality and accuracy in the medical abstracts will be obtained through the pre-editing approaches.<br />
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===2.Language Characteristics and Error Division===<br />
Since Chinese and English are two different languages, it is quite neccessary to identify their own characteristics so as to better analyze and understand the two languages. Tytler (1978: 118-119) argued in his Essay on the Principles of Translation that there are three principles of translation that in all the translation should give readers the same feelings as the source text, except for complete transcript of ideas. There actually also exit some mistakes of these two languages. So the following will make some clarrifications of these two languages to ensure more accurate translation.<br />
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===2.1 Language Characteristics of Medical Abstracts===<br />
Chinese and English belong to different language systems, so there are differences in their language structure and the way users think of the languages. When using machine translation from Chinese to English, due to the unequal language levels, there will be many mistakes in the translation process, especially for ESP texts, such as medical papers.Here, these differences mainly refer to the linguistic characteristics of medical abstracts. In medical abstracts, it usually includes structured and unstructured abstracts. Although in different forms, they both describe the purpose, methods and conclusions of the research.<br />
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In the method section of medial abstract, several Chinese sentences can be connected with commas, but each sentence may convey different information. In contrast, English sentences contain a great deal of information, but in order to ensure clarity, some modifiers need to be isolated and then reconstructed. However, in Chinese-English machine translation, a lot of information is put into the sentence because the machine segments the sentence on the basis of the full comma.<br />
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In addition, subjectless sentence is used in the objective and method parts of Chinese medical abstracts. The subjectless sentence means the sentence without or free from subject and is usually employed in two contexts. The first is "needless to say". In Chinese sentences, it is common to omit the subject of the sentence. Chinese sentences can convey meanings by using incomplete sentence structures, so Chinese speakers can understand the meanings of the sentences even though the sentence subject is omitted. The second is "emphasis of action". In this context, subjectless sentences are used to describe behaviors, especially in the study of traditional Chinese medicine abstracts. Comparatively speaking, it should be avoided in English medical abstracts. Abstract sentences are more subjective when describing the learning process, while the essential requirement of English medical abstract is objectivity. From this point, sentences without subject should be avoided in English medical abstracts. Another feature is voice. Few words with passive meanings appear in Chinese abstracts. English sentences are more favoured in using passive voice. When translating from Chinese to English, passive voice should be used to make the contents objective, which is also the basic requirement of medical papers.<br />
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These are the linguistic features of Chinese medical abstracts. There are great differences in sentence structure and expression between Chinese and English medical abstracts. These differences may reduce the accuracy of machine translation, so it is necessary to introduce pre-editing to edit the source text to ensure that the source text can be accurately recognized by the machine and fully translated into English.<br />
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===2.2 Error Division of Machine Translation in Translating Abstracts of Medical Papers from Chinese to English===<br />
In this paper, errors in machine translation are listed out after analyzing. Some are due to the principles, such as statistical-based MT and NMT, employed by machine translation. Based on the original errors, they can be studied from two levels. One is from the macro-level, referring to mistakes caused by objective factors. These are not human factors, but the disadvantage of machine translation and the limitations of language. The limitations of machine translation are derived from the principles and models adopted and manifests itself as a reliance on reference sources. In this case, semantic ambiguity and textual incoherence may occur in the absence of reference sources.<br />
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However, for ESP texts such as medical papers, the requirements for terms and sentence patterns are far beyond the existing corpora. For unfamiliar texts (that is, the corresponding texts cannot be found in the corpora). The quality of output translation will be relatively low, which is determined by the principles of machine translation. As mentioned above, machine translation has evolved over the past few decades from rules-based to corp-based statistics to NMT. However, there still exist some limitations in machine translation.<br />
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Mistakes on micro-level are mainly caused by the variations and differences of linguistic structure between Chinese and English. Chinese is an implicit language, while English is an explicit one (Lian, 2010) To put it in another way, Chinese expression does not depend on language structures, while English does the opposite. This may result in mismatches between the original and machine translated sentences. This kind of error is mainly divided into two types: component fragment and component missing. For a medical paper, such errors are very serious. As a kind of ESP discourse, medical paper has the characteristics of fixed, objective and accurate language structure. In order to reproduce the characteristics of medical abstracts in translation, it is necessary to avoid errors at the level of words and sentences and pay attention to logic and consistency.<br />
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Lexical errors here refer to the inconsistency of fixed expressions in the translation of terms. Although these terms are machine translation based on the corpus, the corpus may not be perfect for ESP texts such as medical papers. Therefore, fixed expressions are not used in term translation. This is also the most common mistake in machine translation (the term here includes but is not limited to nouns). For ESP texts, medical text in particular, the fixed expressions and the accuracy of terms are of great importance.<br />
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===3.Approaches Proposed for Pre-editing ===<br />
Generally speaking, there is a close relationship between pre-editing and post-editing, both of which aim to convey information and ensure high or publishable translation quality. Proper pre-editing can improve the quality of machine translation in terms of adequacy and consistency. <br />
Complexity of natural language and people use language arbitrarily, bringing many difficulties to Chinese-English machine translation, Hu Qingping (2005:24) has proposed "the research of translation software and the development of the controlled language are the two directions to improve the quality of machine translation : the former aims at the difficulty in the natural language processing, the latter overcomes the arbitrariness of natural language". Feng Quangong and Gao Lin (2017: 63-68) put forward: "The writing principles of controlled language can be applied to pre-editing of machine translation. Pre-editing based on controlled language can effectively reduce the complexity and ambiguity of source text, improve the identifiability of machine translation (the translatability of source text itself), and thus reduce (fully) the workload of post-editing".<br />
The central task of pre-editing is to transform human-friendly content into machine-friendly content, so words and sentences need to be repositioned or even changed. Based on the analysis of the language characteristics of Chinese and English, the Chinese ideographic group should be split before the Chinese source text is input into the machine software to translate so that the sentence structure is complete which can be easily recognized by the machine translation software.<br />
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===3.1 Extraction and Replacement of Terms in the Source Text===<br />
As a branch of applied English, medical English is the product of the combination of English language knowledge and medical knowledge. The terms in medical papers have the characteristics of fixed and complex language structure. Although Google Translation is based on a corpus, the language structure is not fixed due to the limited size of the corpus. Therefore, the following two steps should be followed before machine translation. The first is to extract terms and create a glossary, and then replace the Chinese terms in the source text with the corresponding English expressions in the glossary. Of course, the terms of extraction should be searched and verified according to the actual situation. All of these words are verified before they can be replaced. This method can not only ensure the accuracy of term translation, but also maintain the consistency of expression, especially when dealing with long texts.<br />
Example1<br />
Source abstract: 口腔白斑病癌变相关缺氧应答基因和微小RNA的芯片及表达验证。<br />
Google translation: Microarray detection/Chip detection and expression verification of hypoxia response genes and microRNAs related to oral leukoplakia canceration.<br />
Published translation:Transcriptome array screening and verification of oral leukoplakia carcinogenesis-related hypoxia-responsive gene and microRNA. <br />
Analysis: The expression “ Affymetrix GeneChip” empolyed in this thesis is a human transcription array for transcriptome array. In the source abstract, “芯片检测“ can be meant “screening” but not detection, so the proper and appropriate translation of these expression should be “transcription array screening”, but the Google translates it into “microarry detection of chip detection”. Therefore, term extraction is essential and inevitable before putting the source abstracts into the machine translation software.<br />
After pre-editing source abstracts: 对 oral leukoplakia carcinogenesis 相关 hypoxia-responsive gene 和微小RNA进行 transcriptome array screening 及表达验证。<br />
After pre-editing translation: Transcriptome array screening and expression- verification of hypoxia-responsive genes and microRNAs related to oral leukoplakia carcinogenesis.<br />
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===3.2 Explication of Subordination===<br />
As mentioned above, the subordination of Chinese sentences is judged by certain specific words in machine translation . Chinese is a paratactic language, and sentences are often connected by internal logical relationships; while English depends on sentence structure, so sentences are often closely linked by various language forms. In order to improve the accuracy of machine translation, we can adjust the sentence structure and adjust the position of adjectives and their modifiers. <br />
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Example 2<br />
Source abstracts:回顾性分析南京医科大学附属儿童医院中重度HIE患儿49例及同期就诊的无神经系统症状体征的足月新生儿为对照组31例的头颅磁共振成像（MRI）资料。<br />
Google translation: A retrospective analysis that the brain magnetic resonance imaging (MRI) data of 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University and full-term neonates with no neurological symptoms and signs during the same period were included in the control group.<br />
Published translation: A total of 49 children with moderate to severe HIE admitted to the children’s Hospital Affiliated to Nanjing Medical University were retrospectively analyzed. Cranial magnetic resonance imaging (MRI) date of 31 full-term neonates without neurological symptoms and signs who visited the hospital during the same period were recruited as the control group. <br />
<br />
Analysis: As the above example shows that “中重度HIE患儿” and “足月新生儿” in the source abstracts are from the Children’s Hospital Affiliated Nanjing Medical University. The Google translation shows that 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University. There is an error due to the misuse of subordination. There are some advice in order to solve the problem. That is, first to segment the sentence and then reconstruct the sentence. For instance, the Chinese expression “南京医科大学附属儿童医院” carries many modifiers, which requires to cut the modifiers and reconstruct the sentence. <br />
Therefore, the Chinese expression “南京医科大学附属儿童医院’can be divided into abstractions, leaving two sentences instead of one long sentence with modifiers..<br />
After pre-editing source abstracts: 对49例中重度HIE患儿以及31例无神经系统症状体征的足月新生儿的MRI资料进行回顾性分析。这些患者收治于南京医科大学儿童附属医院。<br />
After pre-editing translation: The MRI data of 49 children with moderate to severe HIE and 31 full-term newborns without neurological symptoms and signs were retrospectively analyzed. These patients were admitted to the Children’s Hospital of Nanjing Medical University.<br />
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===3.3 Explication of Subject through Voice Changing===<br />
The extensive use of subjectless sentences are quite common in the Chinese abstracts, while English prefers to employ passive voice in the sentences so as to make them object and accurate. In order to take the characteristics of English sentences into great and careful consideration, the sentences written in passive voice in particular, it will be helpful for machine translation to find out the subject and reconstruct a sentence in passive voice (Wang Yan: 2008). The first is to discover the “doee” in a sentence and then is to reconstruct the sentence structure and put the “doee” before the verb. The last is to explicate the passive relation between the noun and the verb.<br />
Example 3<br />
Source abstract: 回顾性分析2018年1月至2020年12月解放军总医院第七医学中心收治的500例老年髋部骨折患者的资料。<br />
Google translation: A retrospective analysis of the data of 500 elderly patients with hip fractures admitted to the Seventh Medical Center of the PLA General Hospital from January 2018 to December 2020.<br />
Published translation: From January 2018 to December 2020, the data of 500 elderly patients with hip fracture treated in the Seventh Medical Center of PLA General Hospital were analyzed retrospectively.<br />
Analysis: The above example indicates that the “回顾性分析”in the Google Translate is a noun phrase, but the source abstracts actually describes an action or a behavior. The reason for such a mistake is that the machine can’t recognize the Chinese subjetless sentences. To find the subject of the sentence, the source abstracts can be revised and adjusted. Finding the “doee” is the first step, which refers to “500例老年髋部骨折患者的资料”in the source abstracts. And next is to put it in front of the verb, that is to place it in the beginning of the sentence. Last but not least, the passive voice should be explicated in the sentence. <br />
After pre-editing source abstract: 500例老年髋部骨折患者的资料被回顾性分析，他们在2018年1月之2020年12月期间收治于解放军总医院第七医学中心。<br />
After pre-editing translation: The data of 500 elderly hip fracture patients were retrospectively analyzed. They were admitted to the Seventh Medical Center of the PLA General Hospital between January 2018 and December 2020.<br />
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===3.4 Relocation of Modifiers===<br />
Modifiers, including noun, adverbial and attributive are constantly employed in the Chinese medical papers in order to add information to subject and object in the sentence. By doing this, complicated sentences can be structured, thus causing obstacles to machine translation for recognizing this complex sentence structures when translating Chinese-English sentences. To make the machine translation successfully recognize the sentence structure, simplifying the sentence structure is quite necessary. The key is to moving the location of the modifiers and thus making the modifiers an independent sentence. In other words, the modifiers ought to be put before or behind the main part of the sentence to satisfy the common use of English. <br />
Usually, the modifiers in Chinese sentence can only be placed in front of the core word, while in English, modifiers are very flexible. It is all right to place the modifiers in front of or behind the major part of the sentences with adjective or a connective noun.<br />
<br />
Example 4<br />
Source abstracts: 利用DNA重组技术以pET-28a表达系统在E.coli BL21(DE3) 中重组表达Hepl。<br />
Google Translation: Hepl is recombined in E.coli BL21 (DE3) using DNA recombination technology with pET-28a expression system.<br />
Published translation: The recombinant Hcpl protein was expressed by using DNA recombination technology through pET-28a expression system in E. coli BL21 (De3).<br />
Analysis: The Chinese medical text includes two modifiers, “利用DNA”and “以pET-28a)表达系统”. These two modifiers will be translated by machine, which catches more attention to the two constituents. From the Google translation above, one failure is obvious that it misplaces the location of the two modifiers and presents it in not accurate form. To make it translate correctly by machine translation, dividing the sentences is very important so that the source abstracts can be correctly recognized by machine translation.<br />
<br />
After pre-editing source abstract: 利用DNA重组技术，Hcp1重组表达在E.coli BL21 (DE3)中， 通过pET-28a表达系统在E. coli BL21 (DE3)中。<br />
Google translation: Using DNA recombination technology, Hcp1 recombination is expressed in E.coli BL21 (DE3), via pET-28a expression system in E. coli BL21 (DE3).<br />
The second is the independence of modifiers, that is, modifiers can also be reconstructed into clauses to modify core words. Compared with English, There is no subordinate clause in Chinese, so redundant Chinese modifiers need to be reconstructed into subordinate clauses in Chinese-English translation to meet the characteristics of English. English, especially sentences with multiple modifiers. Otherwise, sentence structure may be confused, such as scattered modifiers of core words. To avoid this mistake, it is necessary to separate the modifier from the main part of the sentence. These modifiers should be reconstituted into clauses. Also, keep your sentences simple and easy to understand.<br />
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===3.5 Proper Omission and Deletion of Category Words===<br />
Category words are commonly used in Chinese. Category words complement the meaning of words, including problems, positions, situations and jobs. Adding this supplementary word is more in line with Chinese custom. In many cases, it has no real meaning, so it can be omitted in translation. In Chinese, category words are frequently used. However, it is rarely used in English, which is one of the differences between Chinese and English. There are many kinds of category words. Considering objectivity and the fixed structure of language, redundant category words should be deleted in Chinese-English translation. In the general, the most commonly used category of words in the Chinese medical abstracts are "process", "behavior", and "situation". These categories of words hinder the language conversion of Chinese and English, resulting in redundancy. <br />
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Example 5<br />
Source abstract: 探讨口腔白斑病癌变进程中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia response genes and associated microRNAs in the process of oral leukoplakia cancer was discussed.<br />
Published translation: To study the hypoxia response gene and microRNA (miRNA)expression profiles in the pathogenesis and progression of oral leukoplakia (OLK).<br />
Analysis: As the above example shows, the Chinese words “进程” belongs to a category word. However, the Chinese expression “癌变”contains the process, so the “进程” expression can be deleted before placing it into the machine translation, because the meaning of it has been overlapping between the expression “癌变”. Therefore, its place can be replaced with preposition “during”.<br />
<br />
After pre-editing source abstract: 探讨口腔白斑病癌变中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia responsive genes and miRNA in oral leukoplakia cancer was investigated.<br />
<br />
===Conclusion===<br />
After years of development, machine translation has made great progress. The accuracy of machine translation has been greatly improved in both text recognition and sentence pattern conversion. However, machine translation has its own limitations. In other words, it needs to rely on the parallel corpus as a reference source for improving its accuracy. ESP text, in particular, is harder to get the high quality by machine translation. <br />
<br />
As one of the research papers, the characteristics of medical abstracts are fixed language structures, objectivity and accuracy (Qin Yi 2004:421-423). Therefore, medical translation must be accurate, object and understandable to follow the specific demands of the medical paper. Being an important field in the human society, medical paper translation is on a great demand, which means that it needs a huge demand for human labor. However, with the machine translation promoting, it will be more efficient to translate medical papers combining the effort by human and machine. The improvement and development of machine translation requires the joint efforts of computer science, information science, statistics, linguistics and other academic circles to achieve more mature human-computer mutual assistance translation (Li Yafei, Zhang Ruihua 2019:38-45).<br />
<br />
However, errors can occur during the process of machine translation of Chinese- English, because of the differences of the Chinese and English and the processing of the machine. Errors from the perspective of linguistic or grammar can affect the machine translation a lot. After division and recognition of errors, some pre-editing approaches are put forward to help the machine translation more accurate and readable, that are, extraction and replacement of terms in the source text, relocation of modifiers, explication of subordination, proper omission, deletion of category words and explication of subject through voice changing. <br />
<br />
<br />
The paper mainly focuses on the pre-editing machine translation by using medical papers as a case study. The errors of machine translation occurring in the translation of medical abstracts and pre-editing approaches for machine translation. The quality of machine translation of medical papers is greatly improved after employing the pre-editing methods. However, machine translation is not as flexible and accurate as human brain, so it is of importance to combine pre-editing and post-editing approaches with machine translation in order to produce more accurate, more object machine translation of medical papers.<br />
<br />
As for as I am concerned, it would be better if there are more examples to show the differences caused by machine translation and how effective it is if the pre-editing methods are adopted. Corrected by --[[User:Chen Huini|Chen Huini]] ([[User talk:Chen Huini|talk]]) 10:50, 15 December 2021 (UTC)Cai Zhufeng<br />
<br />
===References===<br />
<br />
Cui Qiliang崔启亮(2014).论机器翻译的译后编辑[J] ''On Post-Editing of Machine Translatio''. 中国翻译 Chinese Translators Journal, 035(006):68-73<br />
<br />
Feng Quangong, Gao Lin冯全功,高琳 (2017). 基于受控语言的译前编辑对机器翻译的影响[J] ''Influence of Pre-editing Based on Controlled Language on Machine Translation''. 当代外语研究Contemporary Foreign Language Research,(2): 63-68+87+110.<br />
<br />
GERLACH J, et al ( 2013). ''Combining Pre-editing and Post-editing to Improve SMT of User-generated Content''[M]// Proceedings of MT Summit XIV Workshop on Post-editing Technology and Practice. 45-53<br />
<br />
Hu Qingping胡清平(2005). 机器翻译中的受控语言[J] ''Controlled Language in Machine Translation''. 中国科技翻译 Chinese Science and Technology Translation, (03): 24-27. <br />
<br />
Lian Shuneng连淑能 (2010). 英汉对比研究增订本[M]''An Updated Version of English-Chinese Contrastive Studies'' . 北京:高等教育出版社Beijing: Higher Education Publishing House. 35-36.<br />
<br />
Li Yafei, Zhang Ruihua黎亚飞,张瑞华 (2019). 机器翻译发展与现状[J]''The Development and Current Situation of Machine Translation''. 中国轻工教育 China Light Industry Education, (5):38-45. <br />
<br />
Qin Yi秦毅(2004),从翻译基本标准议医学英语的翻译[J] ''On the Translation of Medical English from the Basic Standard of Translation''. 遵义医学院学报 Journal of Zunyi Medical College,27 (4): 421-423. <br />
<br />
Slype G V & Guinet J F & Seitz F (1984). ''Better Translation for Better Communication'' [M] . Oxford: Pergamon Press Ltd (U.K.). 90-93<br />
<br />
O'Brien S (2002). Teaching Post-editing: A Proposal for Course Con-tent [EB/OL]. http://mt-archive. Info/EAMT-2002-0brien. Pdf.<br />
<br />
Tytler, A. F. (1978). ''Essay On The Principles of Translation''[M]. Amsterdam: JohnBenjamins Publishing. 118-119<br />
<br />
Wang Yan王燕 (2008). 医学英语翻译与写作教程[M] ''Medical English Translation and Writing Course''. 重庆:重庆大学出版社 Chongqing: Chongqing University Press. 60-61<br />
<br />
Written by --[[User:Chen Huini|Chen Huini]] ([[User talk:Chen Huini|talk]]) 10:50, 15 December 2021 (UTC)Chen Huini</div>Chen Huinihttps://bou.de/u/index.php?title=Machine_translation&diff=133349Machine translation2021-12-15T10:40:08Z<p>Chen Huini: </p>
<hr />
<div>'''Machine Translation - A challenge or a chance for human translators?'''<br />
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[[Machine_translation|Overview Page of Machine Translation]]<br />
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30 Chapters（0/30)<br />
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[[Machine_Trans_EN_1]] [[Machine_Trans_EN_2]] [[Machine_Trans_EN_3]] [[Machine_Trans_EN_4]] [[Machine_Trans_EN_5]] [[Machine_Trans_EN_6]] [[Machine_Trans_EN_7]] [[Machine_Trans_EN_8]] [[Machine_Trans_EN_9]] [[Machine_Trans_EN_10]] [[Machine_Trans_EN_11]] [[Machine_Trans_EN_12]] [[Machine_Trans_EN_13]] [[Machine_Trans_EN_14]] [[Machine_Trans_EN_15]] [[Machine_Trans_EN_16]] [[Machine_Trans_EN_17]] [[Machine_Trans_EN_18]] [[Machine_Trans_EN_19]] [[Machine_Trans_EN_20]] [[Machine_Trans_EN_21]] [[Machine_Trans_EN_22]] [[Machine_Trans_EN_23]] [[Machine_Trans_EN_24]] [[Machine_Trans_EN_25]] [[Machine_Trans_EN_26]] [[Machine_Trans_EN_27]] [[Machine_Trans_EN_28]] [[Machine_Trans_EN_29]] [[Machine_Trans_EN_30]] ...<br />
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[[Book_projects|Back to translation project overview]]<br />
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[[DCG-To-Do|To the To Do list]]<br />
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=Chapter 1:A Comparison Between the Quality of Machine Translation and Human Translation——A Case Study of the Application of artificial intelligence in Sports Events=<br />
'''论机器翻译与人工翻译的质量对比——以人工智能在体育赛事领域的应用为例'''<br />
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卫怡雯 Wei Yiwen, Hunan Normal University, China<br />
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[[Machine_Trans_EN_1]]<br />
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=Chapter 2：On the Realm Advantages And Mutual Development of Machine Translation And Huamn Translation)=<br />
"'论机器翻译与人工翻译的领域优势及共同发展'"<br />
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肖毅瑶 Xiao Yiyao, Hunan Normal University, China<br />
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[[Machine_Trans_EN_2]]<br />
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=Chapter 4 : A Comparison Between Machine Translation of Netease and Traditional Human Translation—A Case Study of The Economist Articles)= <br />
'''网易有道机器翻译与人工翻译的译文对比——以经济学人语料为例'''<br />
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王李菲 Wang Lifei, Hunan Normal University<br />
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[[Machine_Trans_EN_4]]<br />
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=Chapter 5: Muhammad Saqib Mehran - Problems in translation studies=<br />
[[Machine_Trans_EN_5]]<br />
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=6 徐敏赟(Machine Translation Based on Neural Network --Challenge or Chance?)=<br />
[[Machine_Trans_EN_6]]<br />
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=Chapter 7: The Cultivation of artificial intelligence and translation talents in the Belt and Road Initiative=<br />
[[Machine_Trans_EN_7]]<br />
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一带一路背景下人工智能与翻译人才的培养<br />
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颜莉莉 Yan Lili, Hunan Normal University, China<br />
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=Chapter 8: On Machine Translation Under Lanuguage Intelligence——An Option and Opportunity for Human Translators=<br />
'''论语言智能下的机器翻译——译者的选择与机遇'''<br />
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颜静 Yan Jing, Hunan Normal University, China<br />
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[[Machine_Trans_EN_8]]<br />
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=9 谢佳芬（ Machine Translation and Artificial Translation in the Era of Artificial Intelligence）=<br />
[[Machine_Trans_EN_9]]<br />
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=Chapter 10 熊敏 Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts=<br />
机器翻译对各类型文本的英汉翻译能力探究<br />
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熊敏, Xiong Min, Hunan Normal University<br />
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[[Machine_Trans_EN_10]]<br />
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=Chapter 11 陈惠妮 Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts=<br />
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机器翻译的译前编辑研究——以医学类文摘为例<br />
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陈惠妮 Chen Huini, Hunan Normal University<br />
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[[Machine_Trans_EN_11]]<br />
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Written by --[[User:Chen Huini|Chen Huini]] ([[User talk:Chen Huini|talk]]) 04:58, 15 December 2021 (UTC)Chen Huini<br />
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=12 蔡珠凤 The Mistranslation of C-J Machine Translation of Political Statements=<br />
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机器翻译中政治发言中译日的误译<br />
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蔡珠凤 Cai Zhufeng, Hunan Normal University<br />
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[[Machine_Trans_EN_12]]<br />
===Abstract===<br />
Language is the main way of communication between people. With the continuous development of globalization, the scale of cross-border exchanges is also expanding. However, due to cultural differences and diversity, the languages of different countries and regions are very different, which seriously hinders people's communication. The demand for efficient and convenient translation tools is increasing. At the same time, with the development of network technology and artificial intelligence, recognition technology based on deep learning is more and more widely used in English, Japanese and other fields.<br />
<br />
===Key words===<br />
machine translation; political statements; mistranslation of C-J machine translation<br />
===题目===<br />
The Mistranslation of C-J Machine Translation of Political Statements<br />
===摘要===<br />
语言是人与人之间交流的主要方式。随着全球化的不断发展，跨境交流的规模也在不断扩大。然而，由于文化的差异和多样性，不同国家和地区的语言差异很大，这严重阻碍了人们的交流。对高效便捷的翻译工具的需求正在增加。同时，随着网络技术和人工智能的发展，基于深度学习的识别技术在英语、日语等领域的应用越来越广泛。<br />
<br />
===关键词===<br />
机器翻译；政治发言；政治发言中译日的误译<br />
===1. Introduction===<br />
===Introduction to machine translation===<br />
Machine translation, also known as automatic translation, is a process of using computers to convert one natural language (source language) into another natural language (target language). It is a branch of computational linguistics, one of the ultimate goals of artificial intelligence, and has important scientific research value.At the same time, machine translation has important practical value. With the rapid development of economic globalization and the Internet, machine translation technology plays a more and more important role in promoting political, economic and cultural exchanges.The development of machine translation technology has been closely accompanied by the development of computer technology, information theory, linguistics and other disciplines. From the early dictionary matching, to the rule translation of dictionaries combined with linguistic expert knowledge, and then to the statistical machine translation based on corpus, with the improvement of computer computing power and the explosive growth of multilingual information, machine translation technology gradually stepped out of the ivory tower and began to provide real-time and convenient translation services for general users.（Zhang 2019:5-6)<br />
<br />
=== C-J machine translation software===<br />
Today's online machine translation software includes Baidu translation, Tencent translation, Google translation, Youdao translation, Bing translation and so on. Google was the first company to launch the machine translation system, and Baidu was the first company to import the machine translation system in China. In addition, Tencent and Youdao have attracted much attention.Machine translation is the process of using computers to convert one natural language into another. It usually refers to sentence and full-text translation between natural languages. In order to continuously improve the translation quality, R & D personnel have added artificial intelligence technologies such as speech recognition, image processing and deep neural network to machine translation on the basis of traditional machine translation based on rules, statistics and examples.With the increase of using machine translation, the joint cooperation between manual translation and machine translation will also increase significantly in the future. What criteria should be used to evaluate the quality of machine translation? In the evolving field of machine translation, there is an urgent need to clarify the unsolvable questions and solved problems.(Lv 1996:3)<br />
<br />
===The history of machine translation===<br />
The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. alchuni put forward the idea of using machines for translation. In 1933, Soviet inventor П.П. Trojansky designed a machine to translate one language into another, and registered his invention on September 5 of the same year; However, due to the low technical level in the 1930s, his translation machine was not made. In 1946, the first modern electronic computer ENIAC was born. Shortly after that, W. weaver, an American scientist and A. D. booth, a British engineer, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947 when discussing the application scope of electronic computer. In 1949, W. Weaver published the translation memorandum, which formally put forward the idea of machine translation. After 60 years of ups and downs, machine translation has experienced a tortuous and long development path. The academic community generally divides it into the following four stages:<br />
<br />
Pioneering period（1947-1964）<br />
<br />
In 1954, with the cooperation of IBM, Georgetown University completed the English Russian machine translation experiment with ibm-701 computer for the first time, showing the feasibility of machine translation to the public and the scientific community, thus opening the prelude to the study of machine translation.<br />
It is not too late for China to start this research. As early as 1956, the state included this research in the national scientific work development plan. The topic name is "machine translation, the construction of natural language translation rules and the mathematical theory of natural language". In 1957, the Institute of language and the Institute of computing technology of the Chinese Academy of Sciences cooperated in the Russian Chinese machine translation experiment, translating 9 different types of more complex sentences.From the 1950s to the first half of the 1960s, machine translation research has been on the rise. The United States and the former Soviet Union, two superpowers, have provided a lot of financial support for machine translation projects for military, political and economic purposes, while European countries have also paid considerable attention to machine translation research due to geopolitical and economic needs, and machine translation has become an upsurge for a time. In this period, although machine translation is just in the pioneering stage, it has entered an optimistic period of prosperity.<br />
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Frustrated period（1964-1975）<br />
<br />
In 1964, in order to evaluate the research progress of machine translation, the American Academy of Sciences established the automatic language processing Advisory Committee (Alpac Committee) and began a two-year comprehensive investigation, analysis and test.In November 1966, the committee published a report entitled "language and machine" (Alpac report for short), which comprehensively denied the feasibility of machine translation and suggested stopping the financial support for machine translation projects. The publication of this report has dealt a blow to the booming machine translation, and the research of machine translation has fallen into a standstill. Coincidentally, during this period, China broke out the "ten-year Cultural Revolution", and basically these studies also stagnated. Machine translation has entered a depression.<br />
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convalescence（1975-1989）<br />
<br />
Since the 1970s, with the development of science and technology and the increasingly frequent exchange of scientific and technological information among countries, the language barriers between countries have become more serious. The traditional manual operation mode has been far from meeting the needs, and there is an urgent need for computers to engage in translation. At the same time, the development of computer science and linguistics, especially the substantial improvement of computer hardware technology and the application of artificial intelligence in natural language processing, have promoted the recovery of machine translation research from the technical level. Machine translation projects have begun to develop again, and various practical and experimental systems have been launched successively, such as weinder system Eurpotra multilingual translation system, taum-meteo system, etc.However, after the end of the "ten-year holocaust", China has perked up again, and machine translation research has been put on the agenda again. The "784" project has paid enough attention to machine translation research. After the mid-1980s, the development of machine translation research in China has further accelerated. Firstly, two English Chinese machine translation systems, ky-1 and MT / ec863, have been successfully developed, indicating that China has made great progress in machine translation technology.(Chen 2016:5)<br />
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New period(1990 present)<br />
<br />
With the universal application of the Internet, the acceleration of the process of world economic integration and the increasingly frequent exchanges in the international community, the traditional way of manual operation is far from meeting the rapidly growing needs of translation. People's demand for machine translation has increased unprecedentedly, and machine translation has ushered in a new development opportunity. International conferences on machine translation research have been held frequently, and China has made unprecedented achievements. A series of machine translation software have been launched, such as "Yixing", "Yaxin", "Tongyi", "Huajian", etc. Driven by the market demand, the commercial machine translation system has entered the practical stage, entered the market and came to the users.Since the new century, with the emergence and popularization of the Internet, the amount of data has increased sharply, and statistical methods have been fully applied. Internet companies have set up machine translation research groups and developed machine translation systems based on Internet big data, so as to make machine translation really practical, such as "Baidu translation", "Google translation", etc. In recent years, with the progress of in-depth learning, machine translation technology has further developed, which has promoted the rapid improvement of translation quality, and the translation in oral and other fields is more authentic and fluent.(Liu 2014:6)<br />
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===The problem of machine translation at present===<br />
Error is inevitable<br />
Many people have misunderstandings about machine translation. They think that machine translation has great deviation and can't help people solve any problems. In fact, the error is inevitable. The reason is that machine translation uses linguistic principles. The machine automatically recognizes grammar, calls the stored thesaurus and automatically performs corresponding translation. However, errors are inevitable due to changes or irregularities in grammar, morphology and syntax, such as sentences with adverbials after "give me a reason to kill you first" in Dahua journey to the West. After all, a machine is a machine. No one has special feelings for language. How can it feel the lasting charm of "the tenderness of lowering its head, like the shame of a water lotus? After all, the meaning of Chinese is very different due to the changes of morphology, grammar and syntax and the change of context. Even many Chinese people are zhanger monks - they can't touch their heads, let alone machines.(Liu 2014：3）<br />
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Bottleneck<br />
In fact, no matter which method, the biggest factor affecting the development of machine translation lies in the quality of translation. Judging from the achievements, the quality of machine translation is still far from the ultimate goal.<br />
Chinese mathematician and linguist Zhou Haizhong once pointed out in his paper "fifty years of machine translation": to improve the quality of machine translation, the first thing to solve is the problem of language itself rather than programming; It is certainly impossible to improve the quality of machine translation by relying on several programs alone. At the same time, he also pointed out that it is impossible for machine translation to achieve the degree of "faithfulness, expressiveness and elegance" when human beings have not yet understood how the brain performs fuzzy recognition and logical judgment of language. This view may reveal the bottleneck restricting the quality of translation. <br />
It is worth mentioning that American inventor and futurist ray cozwell predicted in an interview with Huffington Post that the quality of machine translation will reach the level of human translation by 2029. There are still many disputes about this thesis in the academic circles.（Cui 2019：4）<br />
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===2.Mistranslation of Chinese Japanese machine translation===<br />
===2.1Vocabulary mistranslation in Chinese Japanese machine translation===<br />
===2.1.1Mistranslation of proper nouns===<br />
In political materials, there are often political dignitaries' names, place names or a large number of proper nouns in the political field. The morphemes of such words are definite and inseparable. Mistranslation will make the source language lose its specific meaning. <br />
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Japanese translation into Chinese Chinese translation into Japanese<br />
<br />
original text translation by Youdao reference translation original text translation by Youdao reference translation<br />
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朱鎔基 朱基 朱镕基 栗战书 栗戰史書 栗戰書<br />
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労安 劳安 劳安 李克强 李克強 李克強 <br />
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筑紫哲也 筑紫哲也 筑紫哲也 习近平 習近平 習近平<br />
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山口百惠 山口百惠 山口百惠 韩正 韓中 韓正<br />
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田中角栄 田中角荣 田中角荣 王沪宁 王上海氏 王滬寧<br />
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東条英機 东条英社 东条英机 汪洋 汪洋 汪洋<br />
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毛沢东 毛泽东 毛泽东 赵乐际 趙樂南 趙樂際<br />
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トウ・ショウヘイ 大酱 邓小平 江泽民 江沢民 江沢民<br />
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周恩来 周恩来 周恩来<br />
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クリントン 克林顿 克林顿<br />
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The above table counts 18 special names in the two texts, and 7 machine translation errors. In terms of mistranslation, there are not only "战书" but also "戰史" and "史書" in Chinese. "沪" is the abbreviation of Shanghai. In other words, since "战书" and "沪" are originally common nouns, this disrupts the choice of target language in machine translation. Except Katakana interference (except for トウシゃウヘイ, most of the mistranslations appear in the new Standing Committee. It can be seen that the machine translation system does not update the thesaurus in time. Different from other words, people's names have particularity. Especially as members of the Standing Committee of the Political Bureau of the CPC Central Committee, the translation of their names is officially unified. In this regard, public figures such as political dignitaries, stars, famous hosts and important. In addition, the machine also translates Japanese surnames such as "タ二モト" (谷本), "アンドウ" (安藤) into "塔尼莫特" and "龙胆". It can be found that the language feature that Japanese will be written together with Chinese characters and Hiragana also directly affects the translation quality of Japanese Chinese machine translation. Japanese people often use Katakana pronunciation. For example, when Japanese people talk with Premier Zhu, they often use "二ーハウ" (Hello). However, the machine only recognizes the two pseudonyms "二" and "ハ" and transliterates them into "尼哈" , ignoring the long sound after "二" and "ハ".(Guan 2018:10-12)<br />
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original text Translation by Youdao reference translation<br />
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日美安全体制 日米の安全体制 日米安保体制<br />
<br />
中国共产党第十九次全国代表大会 中国共産党第19回全国代表大会 中国共産党第19回全国代表大会（第19回党大会）<br />
<br />
十八大 十八大 第18回党大会中国特色社会主义<br />
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中国特色社会主義 中国の特色ある社会主義 第18回党大会<br />
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中国共产党中央委员会 中国共産党中央委員会 中国共産党中央委員会<br />
<br />
中国共産党中央委員会十八届中共中央政治局常委 第18代中国共產党中央政治局常務委員 第18期中共中央政治局常務委員<br />
<br />
十八届中共中央政治局委员 18期の中国共產党中央政治局委員 第18期中共中央政治局委員<br />
<br />
十九届中共中央政治局常委 十九回中国共產党中央政治局常務委員 第19期中央政治局常務委員<br />
<br />
中共十九届一中全会 中国共產党第十九回一中央委員会 第19期中央委員会第1回全体会議<br />
<br />
The above table is a comparison of the original and translated versions of some proper nouns. As shown in the table, the mistranslation problems are mainly reflected in the mismatch of numerals + quantifiers, the wrong addition of case auxiliary word "の", the lack of connectors, the Mistranslation of abbreviations, dead translation, and the writing errors of Chinese characters. The following is a specific analysis one by one.<br />
<br />
"十八届中共中央政治局常委", "十八届中共中央政治局委员", "十九届中共中央政治局常委" and "中共十九届一中全会" all have quantifiers "届", which are translated into "代", "期" and "回" respectively. The meaning is vague and should be uniformly translated into "期"; Among them, the translation of the last three proper nouns lacks the Chinese conjunction "第". The case auxiliary word "の" was added by mistake in the translation of "十八届中共中央政治局委员" and "日美安全体制". The "中国共产党中央委员会" did not write in the form of Japanese Ming Dynasty characters, but directly used simplified Chinese characters.<br />
<br />
The full name of "中共十九届一中全会" is "中国共产党第十九届中央委员会第一次全体会议", in which "一" stands for "第一次", which is an ordinal word rather than a cardinal word. Machine translation does not produce a correct translation. The fundamental reason is that there are no "规则" for translating such words in machine translation. If we can formulate corresponding rules for such words (for example, 中共m'1届m2 中全会→第m1期中央委员会第m2回全体会议), the translation system must be able to translate well no matter how many plenary sessions of the CPC Central Committee.(Guan 2018:6-7)<br />
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===2.1.2Mistranslation of Polysemy===<br />
original text Translation by Youdao reference translation<br />
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スタジオ 摄影棚/工作室 直播现场/演播厅<br />
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日中関係の話 中日关系的故事 就中日关系(话题)<br />
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溝 水沟 鸿沟<br />
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それでは日中の問題について質問のある方。 那么对白天的问题有提问的人。 关于中日问题的话题，举手提问。<br />
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私たちのクラスは20人ちょっとですが、 我们班有20人左右， 我们班二十多人的意见统一很难，<br />
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いろいろな意見が出て、まとめるのは大変です。 但是又各种各样的意见，总结起来很困难。 <br />
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一体どうやって、13億人もの人をまとめているんですか。 到底是怎么处理13亿的人的呢？ 中国是怎样把13亿人凝聚在一起的？<br />
<br />
In the original text, the word "スタジオ" appeared four times and was translated into "摄影棚" or "工作室" respectively, but the context is on-site interview, not the production site of photography or film. "話" has many semantics, such as "说话", "事情", "道理", etc. In accordance with the practice of the interview program, Premier Zhu Rongji was invited to answer five questions on the topic of China Japan relations. Obviously, the meaning of the word "故事" is very abrupt. The inherent Japanese word "日中" means "晌午、白天". At the same time, it is also the abbreviation of the names of the two countries. The machine failed to deal with it correctly according to the context. "溝" refers to the gap between China and Japan, not a "水沟". The former "まとめる" appears in the original text with the word "意见", which is intended to describe that it is difficult to unify everyone's opinions. The latter "まとめている" also refers to unifying the thoughts of 1.3 billion people, not "处理". Therefore, it is more appropriate to use "统一" and "处理" in human translation, rather than "总结意见" or "处理意见".(Zhang 2019:5)<br />
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Mistranslation of polysemy has always been a difficult problem in machine translation research. The translation of each word is correct, but it is often very different from the original expression in the context. Zhang Zhengzeng said that ambiguity is a common phenomenon in natural language. Its essence is that the same language form may have different meanings, which is also one of the differences between natural language and artificial language. Therefore, one of the difficulties faced by machine translation is language disambiguation (Zhang Zheng, 2005:60). In this regard, we can mark all the meanings of polysemous words and judge which meaning to choose by common collocation with other words. At the same time, strengthen the text recognition ability of the machine to avoid the translation inconsistent with the current context. In this way, we can avoid the mistake of "大酱" in the place where famous figures such as Deng Xiaoping should have appeared in the previous political articles.(Wang 2020:7-9)<br />
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===2.1.3Mistranslation of compound words===<br />
Multi class words refer to a word with two or more parts of speech, also known as the same word and different classes.<br />
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original text Translation by Youdao reference translation<br />
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1998年江泽民主席曾经访问日本, 1998年の江沢民国家主席の日本訪問し、 1998年、江沢民総書記が日本を訪問し、かつ<br />
<br />
同已故小渊首相签署了联合宣言。 かつて同じ故小渕首相が署名した共同宣言 亡くなられた小渕総理と宣言に調印されました<br />
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Chinese "同" has two parts of speech: prepositions and conjunctions: "故" has many parts of speech, such as nouns, verbs, adjectives and conjunctions. This directly affects the judgment of the machine at the source language level. The word "同" in the above table is used as a conjunction to indicate the other party of a common act. "故" is used as a verb with the semantic meaning of "死亡", which is a modifier of "小渊首相". The machine regards "同" as an adjective and "故" as a noun "原因", which leads to confusion in the structure and unclear semantics of the translation. Different from the polysemy problem, multi category words have at least two parts of speech, and there is often not only one meaning under each part of speech. In this regard, software R & D personnel should fully consider the existence of multi category words, so that the translation machine can distinguish the meaning of words on the basis of marking the part of speech, so as to select the translation through the context and the components of the word in the sentence. Of course, the realization of this function is difficult, and we need to give full play to the wisdom of R & D personnel.(Guan 2018:9-12)<br />
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===2.2 Syntactic mistranslation in Chinese Japanese machine translation===<br />
===2.2.1Mistranslation of tenses===<br />
original text：History is written by the people, and all achievements are attributed to the people. <br />
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Translation by Youdao：歴史は人民が書いたものであり、すべての成果は人民のためである。<br />
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reference translation：歴史は人民が綴っていくものであり、すべての成果は人民に帰することとなります。<br />
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The original sentence meaning is "历史是人民书写的历史" or "历史是人民书写的东西". When translated into Japanese, due to the influence of Japanese language habits, the formal noun "もの" should be supplemented accordingly. In this sentence, both the machine and the interpreter have translated correctly. However, the machine's recognition of "的" is biased, resulting in tense translation errors. The past, present and future will become "history", and this is a continuous action. The form translated as "~ ていく" not only reflects that the action is a continuous action, but also conforms to the tense and semantic information of the original text. In addition, the machine's treatment of the preposition "于" is also inappropriate. In the original text, "人民" is the recipient of action, not the target language. As the most obvious feature of isolated language, function words in Chinese play an important role in semantic expression, and their translation should be regarded as a focus of machine translation research.(Zuo 2021:8)<br />
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original text：李克强同志是十六届中共中央政治局常委,其他五位同志都是十六届中共中央政治局委员。<br />
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Translation by Youdao：李克強総理は第16代中国共産党中央政治局常務委員であり、他の５人の同志はいずれも16期の中国共産党中央政治局委員である。<br />
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reference translation：李克強同志は第16期中国共産党中央政治局常務委員を務め、他の５人は第16期中共中央政治局委員を務めました。<br />
<br />
Judging the verb "是" in the original text plays its most basic positive role, but due to the complexity of Chinese language, "是" often can not be completely transformed into the form of "だ / である" in the process of translation. This sentence is a judgment of what has happened. Compared with the 19th session, the 18th session has become history. This is an implicit temporal information. It is very difficult for machines without human brain to recognize this implicit information. "中共中央政治局常委" is the abbreviation of "中共中央政治局常务委员会委员" and it is a kind of position. In Japanese, often use it with verbs such as "務める","担当する",etc. The translator adopts the past tense of "務める", which conforms to the expression habit of Japanese and deals with the problem of tense at the same time. However, machine translation only mechanically translates this sentence into judgment sentence, and fails to correctly deal with the past information implied by the word "十八届".(Guan 2018:4)<br />
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===2.2.2Mistranslation of honorifics===<br />
Honorific language is a language means to show respect to the listener. Different from Japanese, there is no grammatical category of honorifics in Chinese. There is no specific fixed grammatical form to express honorifics, self modesty and politeness. Instead, specific words such as "您", "请" and "劳驾" are used to express all kinds of respect or self modesty. (Yang 2020:5-9)<br />
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Original text translation by Youdao reference translation<br />
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女士们，先生们，同志们，朋友们 さんたち、先生たち、同志たち、お友达さん ご列席の皆さん<br />
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谢谢大家！ ありがとうございます！ ご清聴ありがとうございました。<br />
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これはどうされますか。 这是怎么回事呢? 您将如何解决这一问题？<br />
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こうした問題をどうお考えでしょうか。 我们会如何考虑这些问题呢？ 您如何看待这一问题？<br />
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For example, for the processing of "女士们，先生们，同志们，朋友们", the machine makes the translation correspond to the original one by one based on the principle of unchanged format, but there are errors in semantic communication and pragmatic habits. When speaking on formal occasions, Chinese expression tends to be comprehensive and detailed, as well as the address of the audience. In contrast, Japanese usually uses general terms such as "皆様", "ご臨席の皆さん" or "代表団の方々" as the opening remarks. In terms of Japanese Chinese translation, the machine also failed to recognize the usage of Japanese honorifics such as "さ れ る" and "お考え". It can be seen that Chinese Japanese machine translation has a low ability to deal with honorific expressions. The occasion is more formal. A good translation should not only be fluent in meaning, but also conform to the expression habits of the target language and match the current translation environment.(Che 2021:3-7)<br />
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===Conclusion===<br />
In the process of discussing the Mistranslation of machine translation, this paper mainly cites the Mistranslation of Youdao translator. When I studied the problem of machine translation mistranslation, I also used a large number of translation software such as Google and Baidu for parallel comparison, and found that these translation software also had similar problems with Youdao translator. For example, the "新世界新未来" in Chinese ABAC structural phrases is translated by Baidu as "新し世界の新しい未来", which, like Youdao, is not translated in the form of parallel phrases; Google online translation translates the word "“全心全意" into a completely wrong "全面的に"; The original sentence "我吃了很多亏" in the Mistranslation of the unique expression in the language is translated by Google online as "私はたくさんの損失を食べました". Because the "吃" and "亏" in the original text are not closely adjacent, the machine can not recognize this echo and mistakenly treats "亏" as a kind of food, so the machine translates the predicate as "食べました"; Japanese "こうした問題をどう考えでしょうか", Google's online translation is "你如何看待这些问题?", "你" tone can not reflect the tone of Japanese honorific, while Baidu translates as "你怎么想这样的问题呢?" Although the meaning can be understood, it is an irregular spoken language. Google and Baidu also made the same mistakes as Youdao in the translation of proper nouns. For example, they translated "十七届(中共中央政治局常委)”" into "第17回..." .In short, similar mistranslations of Youdao are also common in Baidu and Google. Due to space constraints, they will not be listed one by one here.(Cui 2019:7)<br />
<br />
Mr. Liu Yongquan, Institute of language, Chinese Academy of Social Sciences (1997) It has been pointed out that machine translation is a linguistic problem in the final analysis. Although corpus based machine translation does not require a lot of linguistic knowledge, language expression is ever-changing. Machine translation based solely on statistical ideas can not avoid the translation quality problems caused by the lack of language rules. The following is based on the analysis of lexical, syntactic and other mistranslations From the perspective of the characteristics of the source language, this paper summarizes some difficulties of Chinese and Japanese in machine translation.(Liu 2014:8)<br />
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(1) The difficulties of Chinese in machine translation <br />
<br />
Chinese is a typical isolated language. The relationship between words needs to be reflected by word order and function words. We should pay attention to the transformation of function words such as "的", "在", "向" and "了". Some special verbs, such as "是", "做" and "作", are widely used. How to translate on the basis of conforming to Japanese pragmatic habits and expressions is a difficulty in machine translation research. In terms of word order, Chinese is basically "subject→predicate→object". At the same time, Chinese pays attention to parataxis, and there is no need to be clear in expression with meaningful cohesion, which increases the difficulty of Chinese Japanese machine translation. When there are multiple verbs or modifier components in complex long sentences, machine translation usually can not accurately divide the components of the sentence, resulting in the result that the translation is completely unreadable.(Guan 2018:6-12) <br />
<br />
(2) Difficulties of Japanese in machine translation <br />
<br />
Both Chinese and Japanese languages use Chinese characters, and most machines produce the target language through the corresponding translation of Chinese characters. However, pseudonyms in Japanese play an important role in judging the part of speech and meaning, and can not only recognize Chinese characters and judge the structure and semantics of sentences. Japanese vocabulary is composed of Chinese vocabulary, inherent vocabulary and foreign vocabulary. Among them, the first two have a great impact on Chinese Japanese machine translation. For example "提出" is translated as "提出する" or "打ち出す"; and "重视" is translated as "重視する" or "大切にする". Japanese is an adhesive language, which contains a lot of "けど", "が" and "けれども" Some of them are transitional and progressive structures, but there are also some sequential expressions that do not need translation, and these translation software often can not accurately grasp them.(Che 2021:10)<br />
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===References===<br />
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[6]Shaimaa Marzouk.(2021(prepublish))An in-depth analysis of the individual impact of controlled language rules on machine translation output: a mixed-methods approach[J].Machine Translation,1-37.<br />
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[8]Xu Xueyuan.(2021).Machine learning-based prediction of urban soil environment and corpus translation teaching[J].Arabian Journal of Geosciences,14(11). <br />
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[9]Chen Bingchang 陈丙昌(2016).機械翻訳の誤訳分析【D】.Error analysis of mechanical translation.贵州大学.2016(05) <br />
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[10]Lv Yinqiu 呂寅秋(1996).機械翻訳の言語規則と伝統文法との相違点.【D】The language rules of mechanical translation, the traditional grammar, and the points of contradiction.日本学研究.Japanese Studies.1996(00):21-22 <br />
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[11]Liu Jun 刘君(2014).基于语料库的中日同形词词义用法对比及其日中机器翻译研究【D】.A Corpus-based Comparison of the Meanings of Chinese and Japanese Homographs and Research on Japanese-Chinese Machine Translation.广西大学.(03) <br />
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[12]Cun Qianqian 崔倩倩(2019).机器翻译错误与译后编辑策略研究【D】.Research on Machine Translation Errors and Post-Editing Strategies.北京外国语大学.(09) <br />
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[13]Zhang Yi 张义(2019).机器翻译的译文分析【D】.Translation analysis of machine translation.西安外国语大学.(10) <br />
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[14]Zhang Linqian 张琳婧(2019).在线机器翻译中日翻译错误原因及对策【D】.Causes and countermeasures of online machine translation errors in Chinese-Japanese translation.山西大学.(02)<br />
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[15]Wang Dan 王丹(2020).基于机器翻译的专利文本译后编辑对策研究【D】.Research on countermeasures for post-translational editing of patent texts based on machine translation.大连理工大学.(06)<br />
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[16]Yang Xiaokun 杨晓琨(2020).日中机器翻译中的前编辑规则与效果验证【D】.Pre-editing rules and effect verification in Japanese-Chinese machine translation.大连理工大学.(06)<br />
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[17]Zuo Jia 左嘉(2021). 机器翻译日译汉误译研究【D】. Research on Mistranslation of Machine Translation from Japanese to Chinese.北京第二外国语学院.<br />
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[18]Guan Biying 关碧莹(2018).关于政治类发言的汉日机器翻译误译分析【D】.Analysis of Chinese-Japanese Machine Translation Mistranslations of Political Speeches.哈尔滨理工大学.<br />
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[19]Che Tong 车彤(2021).汉译日机器翻译质量评估及译后编辑策略研究【D】.Research on Quality Evaluation of Chinese-Japanese Machine Translation and Post-translation Editing Strategies.北京外国语大学.(09)<br />
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Networking Linking<br />
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http://www.elecfans.com/rengongzhineng/692245.html<br />
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https://baike.baidu.com/item/%E6%9C%BA%E5%99%A8%E7%BF%BB%E8%AF%91/411793<br />
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=13 陈湘琼Chen Xiangqiong（Study on Post-editing from the Perspective of Functional Equivalence Theory ）=<br />
[[Machine_Trans_EN_13]]<br />
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=Chapter 14 Bi bi Nadia（Machine Translation a Challenge for Human Translators)=<br />
[[Machine_Trans_EN_14]]<br />
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Bi bi Nadia, Hunan Normal University, China</div>Chen Huinihttps://bou.de/u/index.php?title=Machine_translation&diff=133239Machine translation2021-12-15T05:13:30Z<p>Chen Huini: /* 6.Conclusion */</p>
<hr />
<div>'''Machine Translation - A challenge or a chance for human translators?'''<br />
<br />
[[Machine_translation|Overview Page of Machine Translation]]<br />
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30 Chapters（0/30)<br />
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[[Machine_Trans_EN_1]] [[Machine_Trans_EN_2]] [[Machine_Trans_EN_3]] [[Machine_Trans_EN_4]] [[Machine_Trans_EN_5]] [[Machine_Trans_EN_6]] [[Machine_Trans_EN_7]] [[Machine_Trans_EN_8]] [[Machine_Trans_EN_9]] [[Machine_Trans_EN_10]] [[Machine_Trans_EN_11]] [[Machine_Trans_EN_12]] [[Machine_Trans_EN_13]] [[Machine_Trans_EN_14]] [[Machine_Trans_EN_15]] [[Machine_Trans_EN_16]] [[Machine_Trans_EN_17]] [[Machine_Trans_EN_18]] [[Machine_Trans_EN_19]] [[Machine_Trans_EN_20]] [[Machine_Trans_EN_21]] [[Machine_Trans_EN_22]] [[Machine_Trans_EN_23]] [[Machine_Trans_EN_24]] [[Machine_Trans_EN_25]] [[Machine_Trans_EN_26]] [[Machine_Trans_EN_27]] [[Machine_Trans_EN_28]] [[Machine_Trans_EN_29]] [[Machine_Trans_EN_30]] ...<br />
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[[Book_projects|Back to translation project overview]]<br />
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[[DCG-To-Do|To the To Do list]]<br />
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=Chapter 1:A Comparison Between the Quality of Machine Translation and Human Translation——A Case Study of the Application of artificial intelligence in Sports Events=<br />
'''论机器翻译与人工翻译的质量对比——以人工智能在体育赛事领域的应用为例'''<br />
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卫怡雯 Wei Yiwen, Hunan Normal University, China<br />
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[[Machine_Trans_EN_1]]<br />
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=Chapter 3：On the Realm Advantages And Mutual Development of Machine Translation And Huamn Translation)=<br />
"'论机器翻译与人工翻译的领域优势及共同发展'"<br />
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肖毅瑶 Xiao Yiyao, Hunan Normal University, China<br />
[[Machine_Trans_EN_3]]<br />
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=Chapter 4 : A Comparison Between Machine Translation of Netease and Traditional Human Translation—A Case Study of The Economist Articles)= <br />
'''网易有道机器翻译与人工翻译的译文对比——以经济学人语料为例'''<br />
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王李菲 Wang Lifei, Hunan Normal University<br />
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[[Machine_Trans_EN_4]]<br />
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=Chapter 5: Muhammad Saqib Mehran - Problems in translation studies=<br />
[[Machine_Trans_EN_5]]<br />
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=6 徐敏赟(Machine Translation Based on Neural Network --Challenge or Chance?)=<br />
[[Machine_Trans_EN_6]]<br />
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=Chapter 7: The Cultivation of artificial intelligence and translation talents in the Belt and Road Initiative=<br />
[[Machine_Trans_EN_7]]<br />
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一带一路背景下人工智能与翻译人才的培养<br />
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颜莉莉 Yan Lili, Hunan Normal University, China<br />
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=Chapter 8: On Machine Translation Under Lanuguage Intelligence——An Option and Opportunity for Human Translators=<br />
'''论语言智能下的机器翻译——译者的选择与机遇'''<br />
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颜静 Yan Jing, Hunan Normal University, China<br />
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[[Machine_Trans_EN_8]]<br />
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=9 谢佳芬（ Machine Translation and Artificial Translation in the Era of Artificial Intelligence）=<br />
[[Machine_Trans_EN_9]]<br />
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=10 熊敏(Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts)=<br />
机器翻译对各类型文本的英汉翻译能力探究<br />
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熊敏, Xiong Min, Hunan Normal University<br />
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[[Machine_Trans_EN_10]]<br />
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===Abstract===<br />
The history of machine translation can be traced to 1940s, undergoing germination, recession, revival and flourish. Nowadays, with the rapid development of information technology, machine translation technology emerged and is gradually becoming mature. Whether manual translation can be replaced by machine translation becomes a widely-disputed topic. So in order to explore the ability of machine translation, I adopts two versions of translation, which are manual translation and machine translation(this paper uses Youdao translation) for different types of texts(according to Peter Newmark's types of text：informative text, expressive text and vocative text). The results are quite different in terms of quality and accuracy. The results show that machine translation is more suitable for informative text for its brevity, while the expressive texts especially literary works and vocative texts are difficult to be translated, because there are too many loaded words and cultural images in expressive text and dependence on the readers. To draw a conclusion, the relationship between machine translation and manual translation should be complementary rather than competitive.<br />
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===Key words===<br />
machine translation; manual translation; Newmark's type of texts<br />
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===题目===<br />
Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts<br />
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===摘要===<br />
机器翻译的历史可以追溯到上个世纪40年代，经历了萌芽期、萧条期、复兴期和繁荣期四个阶段。如今，随着信息技术的高速发展，机器翻译技术日益成熟，于是机器翻译能不能替代人工翻译这个话题引起了广泛热议。为了探究机器翻译的能力水平是否足以替代人工翻译，本人根据皮特纽马克的文本类型分类理论（信息类文本，表达文本，呼吁文本），选择了相应的译文类型，并且将其机器翻译的版本以及人工翻译的版本进行对比。结果发现，就质量和准确度而言，译文的水平大相径庭。因为其简洁的特性，机器比较适合翻译信息文本。而就表达文本，尤其是文学作品，因其存在文化负载词及相应的文化现象，所以机器翻译这类文本存在难度。而呼唤文本因其目的性以及对读者的依赖性较强，翻译难度较大。由此得知，机器翻译和人工翻译的关系应该是互补的，而不是竞争的。<br />
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===关键词===<br />
机器翻译；人工翻译；纽马克文本类型<br />
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===1. Introduction===<br />
====1.1.Introduction to machine translation====<br />
Machine translation, also known as computer translation is a technique to translating through machine translator, such as Google translation and Youdao translation. Machine translation is one of the branches of computational linguistics, ranging from computer science, statistics, information science and so on. Machine translation plays an important role in all aspects.<br />
Machine translation can be traced back to 1940s, when British engineer Booth and American engineer Weaver proposed using computer to translate and started to study machines used for translation. And the first machine translating system launched in 1954, used in English and Russian translation. And this means machine translation becomes a reality. However, the arrival of new things always accompanies barriers and setbacks. In the 1960s, reports from ALPAC (Automated Language Processing Advisory Committee) showed studies on machine translation had stagnated for a decade. At that time, due to the high cost of machine, choosing human translators to finish translating works was more economical for most companies. What’s worse, machine had difficulty in understanding semantic and pragmatic meanings. Fortunately, in the 1970s, with the advance and popularity of computer, machine translation was gradually back on track. With the development of science and technology, machine translation has better technological guarantee, such as artificial intelligence and computer technology. In the last decades, from the late 90s till now, machine translation is becoming more and more mature. The initial rule-oriented machine translation has been updated and Corpus-aided machine translation appears. And nowadays, technology in voice recognition has been further developed. This technique is frequently applied in speech translation products. Users only need to speak source language to the online translator and instantly it will speak out the target version. But machine can’t fully provide precise and accurate translation without any grammatical or semantic problems. Machine can understand the literal meaning of texts, but not the meaning in context. For example, there is polysemy in English, which refers to words having multiple meanings. So when translating these words, translators should take contexts into consideration. But machine has troubles in this way. In addition, machine has no feeling or intention. Hence, it is also difficult to convey the feelings from the source language.(Wei 2021:5)#<br />
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====1.2.Process of machine translation====<br />
The process of manual translation is different from that of machine translation. Here is the process of the former. (1) Understand source language. (2) Use target language to organize language. (3) Generate translation. Unlike manual translation, machine translation tends to analyze and code source language first, then look for related codes in corpus, and work out the code that represents target language, generating translation. But they share a common feature, which is that Lexicon, grammatical rules and syntactic structure are taken into consideration. This is one of the biggest challenges for machine translation.<br />
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===2.Theorectical framework===<br />
====2.1Newmark’s text typology====<br />
Text typology is a theory from linguistics. It undergoes several phrases. Karl Buhler, a German psychologist and linguist defines communicative functions into expressive, information and vocative. Then Roman Jakobson proposes categorization of texts, which are intralingual translation, interlingual translation and intersemiotic translation. Accordingly, he defines six main functions of language, including the referential function, conative function, emotive function, poetic function, metalingual function and the phatic function. Based on the two theories, Peter Newmark, a translation theorist, summarizes the language function into six parts: the informative function, the vocative function, the expressive function, the phatic function, the aesthetic function, and the metalingual function. Later, he further divided texts into informative type, expressive text and vocative type according to the linguistic functions of various texts. (Newmark 2002:2)#<br />
The core of the informative texts is the truth. It is to convey facts, information, knowledge of certain topics, reality and theories. The language style of the text is objective, logical and standardized. Reports, papers, scientific and technological textbooks are all attributed to informative texts. Translators are required to take format into account for different styles.<br />
The core of the expressive text is the sender’s emotions and attitudes. It is to express sender’s preferences, feelings, views and so on. The language style of it is subjective. Imaginative literature, including fictions, poems and dramas, autobiography and authoritative statements belong to expressive text. It requires translators to be faithful to the source language and maintain the style.<br />
The core of the vocative text is readership. It is to call upon readers to act, think, feel and react in the way intended by the text. So it is reader-oriented. Such texts as advertisement, propaganda and notices are of vocative text. Newmark noted that translators need to consider cultural background of the source language and the semantic and pragmatic effect of target language. If readers can comprehend the meaning at once and do as the text says, then the goal of information communication is achieved. (Liu 2021:3)#<br />
To draw a conclusion, informative texts focus on the information and facts. Expressive texts center on the mind of addressers, including feelings, prejudices and so on. And vocative texts are oriented on readers and call on them to practice as the texts say.<br />
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====2.2Study method====<br />
Manual translations of the three texts are selected from authoritative versions and universally acknowledged. And machine translations of those come from Youdao Translator. And in this thesis I will compare and evaluate the two methods in word diction, sentence structure, word order and redundancy.<br />
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===3.Comparison and analysis of machine translation and manual translation ===<br />
====3.1Informative text ====<br />
（1）English into Chinese<br />
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①Source language:<br />
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Keep the tip of Apple Pencil clean, as dirt and other small particles may cause excessive wear to the tip or damage the screen of i-pad.<br />
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Target language:<br />
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Machine translation: Apple Pencil笔尖应保持清洁，灰尘等小颗粒可能会导致笔尖过度磨损或损坏ipad屏幕。<br />
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Manual translation: 保持Apple Pencil铅笔的笔尖干净，因为灰尘和其他微粒可能会导致笔尖的过度磨损或损坏iPad屏幕。<br />
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Analysis: Here is the instruction of Apple Pencil. And the manual translation is the Chinese version on the instruction.Product instruction tends to be professional, since there are many terms for some concepts. Machine can easily identify these terms and provide related words to translate. The machine version is faithful and expressive to the source language. So it is well-qualified and readable for readers to understand the instruction. So we can use machine to translate informative text.<br />
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②Source language:<br />
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China on Saturday launched a rocket carrying three astronauts-two men and one woman - to the core module of a future space station where they will live and work for six months, the longest orbit for Chinese astronauts.<br />
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Target language:<br />
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Machine translation: 周六，中国发射了一枚运载三名宇航员(两男一女)的火箭，进入未来空间站的核心舱，他们将在那里生活和工作6个月，这是中国宇航员最长的轨道。<br />
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Manual translation: 周六，中国发射了一枚搭载三名宇航员(两男一女)的火箭，进入未来空间站的核心舱，他们将在那里生活和工作6个月，这是中国宇航员最漫长的一次轨道飞行。<br />
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Analysis: This is a news from Reuters, reporting that China has launched a rocket.The meaning of the two translations is almost the same, except for some word diction. But there are some details dealt with different choice. For example, the last sentence of the machine translation is a bit of obscure and direct. There are some ambiguous words and expressions.<br />
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(2)Chinese into English<br />
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Source language:湖南省博物馆是湖南省最大的历史艺术类博物馆，占地面积4.9万平方米，总建筑面积为9.1万平方米，是首批国家一级博物馆，中央地方共建的八个国家级重点博物馆之一、全国文化系统先进集体、文化强省建设有突出贡献先进集体。<br />
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Target language:<br />
Manual translation: As the largest history and art museum in Hunan province, the Hunan Museum covers an area of 49,000㎡, with the building area reaching 91,000㎡. It is one of the first batch of national first-level museums and one of the first eight national museums co-funded by central and local governments.<br />
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Machine translation: Museum in hunan province is one of the largest historical art museum in hunan province, covers an area of 49000 square meters, a total construction area of 91000 square meters, is the first national museum, the central place to build one of the eight national key museum, national cultural system advanced collectives, strong culture began with outstanding contribution of advanced collective.<br />
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Analysis: Machine translation is not faithful enough in content. For instance, “首批国家一级博物馆” is translated into “first national museum”, which is not the meaning of the source language. And there are some obvious grammar mistakes in the machine translation. For example, machine translates it into just one sentence but there are multiple predicates in it. So it is not grammatically permissible. What’s more, the sentence structure of machine translation is confusing and the focus is not specific enough.<br />
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====3.2Expressive text ====<br />
(1)English into Chinese<br />
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Source language:<br />
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An individual human existence should be like a river- small at first, narrowly contained within its banks, and rushing passionately past rocks and over waterfalls. Gradually the river grows wider, the banks recede, the waters flow more quietly, and in the end, without any visible breaks, they become merged in the sea, and painlessly lose their individual being.()<br />
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Target language:<br />
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Machine translation: 一个人的存在应该像一条河流——开始很小，被紧紧地夹在两岸中间，然后热情奔放地冲过岩石，飞下瀑布。渐渐地，河面变宽，两岸后退，水流更加平缓，最后，没有任何明显的停顿，它们汇入大海，毫无痛苦地失去了自己的存在。<br />
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Manual translation:人生在世，如若河流；河口初始狭窄，河岸虬曲，而后狂涛击石，飞泻成瀑。河道渐趋开阔，峡岸退去，水流潺缓，终了，一马平川，汇于大海，消逝无影。<br />
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Analysis: Here is a well-known metaphor in the prose How to Grow Old written by Bertrand Russell. The manual translation is written by Tian Rongchang.This is a philosophical prose with graceful language. Literary translation is a most important and difficult branch of translation. Translator should focus on the literal meaning, culture, writing style and so on. It is a combination of beauty and elegance. Therefore, translators find it in a dilemma of beauty and faithfulness, let alone translating machine. Compared with manual translation, machine translation has difficulty in word choice. It is faithful and expressive, but not elegant enough.<br />
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(2)Chinese into English<br />
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Source language:没有一个人将小草叫做“大力士”，但是它的力量之大，的确是世界无比。这种力，是一般人看不见的生命力，只要生命存在，这种力就要显现，上面的石块，丝毫不足以阻挡。因为它是一种“长期抗战”的力，有弹性，能屈能伸的力，有韧性，不达目的不止的力。(Zhang, 2007:186)#<br />
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Target language:<br />
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Machine translation: No one calls the little grass "hercules", but its power is truly matchless in the world. This force is invisible life force. As long as there is life, this force will show itself. The stone above is not strong enough to stop it. Because it is a "long-term resistance" of the force, elastic, can bend and extend force, tenacity, not to achieve the purpose of the force.<br />
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Manual translation: Though nobody describes the little grass as a “husky”, yet its herculean strength is unrivalled. It is the force of life invisible to naked eye. It will display itself so long as there is life. The rock is utterly helpless before this force- a force that will forever remain militant, a force that is resilient and can take temporary setbacks calmly, a force that is tenacity itself and will never give up until the goal is reached. (by Zhang Peiji)<br />
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Analysis:This is the excerpt of a well-known Chinese prose written by Xia Yan. It is written during the war of Resistance Against Japan. So the prose holds symbolic meaning, eulogizing the invisible tenacious vitality so as to encourage Chinese to have confidence in the anti-aggression war. Compared with manual translation, machine translation is much more abstract and confusing, especially for the word diction. For example, “大力士” is translated into “hercules” which is a man of exceptional strength and size in Greek and Roman Mythology, making it difficult to understand if readers of target language have no idea of the allusion. What’s worse, the machine version doesn’t reveal the symbolic meaning of the text, which is the core of this prose.<br />
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====3.3Vocative text ====<br />
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(1)English into Chinese<br />
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①Source language:<br />
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iPhone went to film school, so you don’t have to. (Advertisement of iPhone13)<br />
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Target language:<br />
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Machine translation: iPhone上的是电影学院，所以你不用去。<br />
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Manual translation:电影专业课，iPhone同学替你上完了。<br />
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Analysis：Here are advertisements of iPhone on Apple official website. There is a personification in the source language. It is used to stress the advancement and proficiency in camera, which is an appealing selling point to potential buyers. Compared with manual translation, machine translation is plain and not eye-catching enough for customers.<br />
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②Source language: <br />
<br />
5G speed OMGGGGG<br />
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Machine language: 5克的速度 OMGGGGG<br />
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Manual translation:<br />
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iPhone的5G 巨巨巨巨巨5G<br />
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Analysis: The “G” in the source language is the unit of speed, standing for generation. However, it is mistaken as a unit of weight, representing gram in the machine translation. So the meaning is not faithful to the source language at all. As for manual translation, it complies with the source in form. Specifically speaking, five “G”s in the former complies with five characters “巨”in the latter. And the pronunciation of the two is similar. There are two layers of meaning for the 5 “G”s. One exclaims the fast speed of 5 generation network and the other new technology. In the manual version, “巨”can be used to show degree, meaning “quite” or “very”. <br />
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③Source language: <br />
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History, faith and reason show the way, the way of unity. We can see each other not as adversaries but as neighbors. We can treat each other with dignity and respect, we can join forces, stop the shouting and lower the temperature. For without unity, there is no peace, only bitterness and fury."<br />
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Target language:<br />
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Machine translation: 历史、信仰和理性指明了团结的道路。我们可以把彼此视为邻居，而不是对手。我们可以尊严地对待彼此，我们可以联合起来，停止大喊大叫，降低温度。因为没有团结，就没有和平，只有痛苦和愤怒。<br />
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Manual translation:历史、信仰和理性为我们指明道路。那是团结之路。我们可以把彼此视为邻居，而不是对手。我们可以有尊严地相互尊重。我们可以联合起来，停止喊叫，减少愤怒。因为没有团结就没有和平，只有痛苦和愤怒<br />
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Analysis: Speech is a way to propagate some activity in public. It is an art to inspire emotion of the audience. The source language is the excerpt of Joe Biden’s inaugural speech. The speech should be inspiring and logic. The machine translation has some misunderstanding. Taking the translation of “lower the temperature” for example, machine only translates its literal meaning, relating to the temperature itself, without considering the context. What’s more, it is less logic than the manual one. Therefore, it adds difficulty to inspire the audience and infect their emotion.<br />
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===4.Common mistakes in machine translation ===<br />
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====4.1 lexical mistakes ====<br />
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Common lexical mistakes include misunderstandings in word category, lexical meaning and emotive and evaluative meaning. Misunderstanding in word category shows in the classification of word in the source language. As for misunderstanding in lexical meaning, machine has difficulty in precisely reflecting the meaning of the original texts, due to different cultural background and different language system. And for misunderstanding in emotive meaning, machine has no intention and emotion like human-beings. Therefore, it’s impossible for it to know writers’ feelings and their writing purposes. So sometimes, it may translate something negative into something positive. (Wang 2008:45)#<br />
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====4.2 grammatical mistakes====<br />
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Grammatical analysis plays an important part in translation. Normally speaking, every language has its own unique grammatical rules. So in the process of translation, if translators don’t know the formation rule well, the sentence meaning will be affected. Even though all the lexical meanings are well-known by translators, the lack of consciousness of grammaticality makes it harder to arrange words according to sequential rule. English tends to be hypotactic, while Chinese tends to be paratactic. English sentences are connected through syntactic devices and lexical devices. While Chinese sentences are semantically connected, which means there are limited logical words and connection words in Chinese. So when translating English sentence, we should first analyze its grammaticality and logical structure and then rearrange its sequence. However, online translating machine has troubles in grammatical analysis, which makes its improvement more difficult.<br />
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====4.3 other mistakes====<br />
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The two mistakes above are the internal ones. Apart from mistakes in linguistic system, there are some mistakes in other aspects, such as cultural background.<br />
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===5.Reasons for its common mistakes ===<br />
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====5.1 Difference in two linguistic system====<br />
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With different history, English and Chinese have different ways of expression. Commonly speaking, English is synthetic language which expresses grammatical meaning through inflection such as tense and Chinese is analytic language which expresses grammatical meaning through word order and function word. In addition, English is more compact with full sentences. Subordinate sentence is one of the most important features in modern English. Chinese, on the other hand, is more diffusive with minor sentences.<br />
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====5.2 Difference in thinking patterns and cultural background====<br />
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According to Sapir-Whorf’s Hypothesis, our language helps mould our way of thinking and consequently, different languages may probably express their unique ways of understanding the world. For two different speech communities, the greater their structural differentiations are, the more diverse their conceptualization of the world will be. For example, western culture is more direct and eastern culture more euphemistic. What’s more, English culture tends to be individualism, focusing on detail, through which it reflects the whole, while Chinese culture tends to be collective. Different thinking patterns will add difficulty for machine to translate texts.<br />
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====5.3 Limitation of computer====<br />
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Recently, there are some breakthroughs and innovation in machine translation. However, due to its own limitation, online translation has limitation in some ways. Firstly, compared with machine, human brain is much more complicated, consisting of ten billions of neuron, each of which has different function to affect human’s daily activities and help humans avoid some errors. However, computer can only function according to preset programming has no intention or consciousness. Until now, countless related scholars have invested much time in machine translation. They upload massive language database, which include almost all linguistic rules. But computers still fail to precisely reflect the meaning of source language for many times due to the complexity and flexibility of language. On the other hand, computers can’t take context into consideration. During translation, it is often the case that machine chooses the most-frequently used meaning of one word. So without the correct and exact meaning, readers are easier to feel confused and even misunderstand the meaning of source language. (Qiu 2021:4)#<br />
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===6.Conclusion===<br />
From the analysis above, we can draw a conclusion that machine deals with informative text best, followed by non-literary translation of expressive text. What’s more, machine can be a useful tool to get to know the gist and main idea of a specific topic, for the simple sentence structure and numerous terms. And it can improve translating efficiency with high speed. But machine has difficulty in translating literary works, especially proses and poems.<br />
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Machine translation has mixed future. From the perspective of commercial, machine translation boasts a bright future. With the process of globalization, the demand for translation is increasing accordingly. On one hand, if we only depend on human translator to deal with translating works, the quality and accuracy of translation can be greatly affected. On the other hand, if machine is used properly to do some basic work, human translators only need to make preparation before translating, progress, polish and other advanced work, contributing to highly-qualified translation and high working efficiency.<br />
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However, compared with manual translation, machine translation has a bleak future. It is still impossible for machine to replace interpreter or translator in a short term. With intelligence and initiative, humans are able to learn new knowledge constantly, which machine will never accomplish. Besides, machine is not used to replace translators but to assist them in work. In other words, translators and machine carry out their own duties and they are not incompatible.(He 2021:5)#<br />
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To draw a conclusion, although there are certain limitations of machine translation, it can serve as a catalyst for translating works. Therefore, with the rapid development of artificial intelligence and related technology, there are still many opportunities for machine translation.<br />
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In my opinion, it would be better if the first letter of the subtitles are being capitalized. Corrected by--[[User:Chen Huini|Chen Huini]] ([[User talk:Chen Huini|talk]]) 05:13, 15 December 2021 (UTC)Chen Huini<br />
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===Reference ===<br />
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Chen Cheng陈诚.机器翻译技术的综述[J][Overview of Machine Translation Technology].Electronic Techonology 电子技术,2021,50(11):290-291.<br />
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Cui Zihan 崔子涵.机器翻译译文质量对比——以谷歌翻译和DeepL为例[J] [Comparison among Machine Translation--Taking Google Translation and Deepl for Example].Overseas English 海外英语,2021(15):182-183.<br />
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He Xinyu何馨宇.机器翻译的发展及其对翻译职业化的影响研究[J] [The Development of Machine Translation and its Effect on Professional Transltors].Overseas English 海外英语,2021(20):48-49.<br />
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He Wen 何雯, Wang Xiufeng 王秀峰.信息型文本的在线机器翻译错误研究[J][Research on Errors in Online Machine Translation of Informative text ].Overseas English海外英语,2021(15):188-189.<br />
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Li Deyi 李德毅. (2018). 人工智能导论 [Introduction to Artificial Intelligence]. Beijing: China Science and Technology Press 中国科学技术出版社.<br />
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Liu Qin刘琴.功能目的论对于不同文本类型的翻译解读[J][Analysis of Translations in Different Types of Text based on Functionalist Approaches].Overseas Engliosh 海外英语,2021(17):8-9.<br />
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Li Hanji 李晗佶. (2021). 人工智能时代翻译技术与译者关系演变与重构 [Evolution and reconstruction of the relationship between translation technology and translators in the era of artificial intelligence]. 西华师范大学学报(哲学社会科学版) Journal of West China Normal University (PHILOSOPHY AND SOCIAL SCIENCES EDITION) (2021-12-04) 1-6.<br />
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(英) Peter Newmark A Textbook of Translation[M] Shanghai Foreign Education Press, 2002<br />
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Qiu Quanju 仇全菊.大数据时代背景下机器翻译及其发展趋势[J][Machine Translation and its Development Trend under the Background of Big Data Era]. English Teachers 英语教师,2021,21(16):60-62.<br />
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Wang Hongqi 王红旗. (2008). 语言学概论 [Introduction to Linguistics]. Beijing: Peking University Press 北京大学出版社.<br />
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Wei Guang魏光. 人工翻译与机器翻译译文编辑比较研究[J][Comparative Study of Translation Editing between Manual Translation and Machine Translation]. Overseas English 海外英语,2021(19):18-19+21.<br />
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Zhuo Jianbin 卓键滨,Liu Wenxian 刘文娴,Peng Zili 彭子莉.机器翻译对各类型文本的德汉翻译能力探究[J][Research on the German Chinese Translation Ability of Machine Translation for Various Types of Texts]. Comparative Study of Cultural innovation 文化创新比较研究,2021,5(28):122-125.<br />
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--[[User:Xiong Min|Xiong Min]] ([[User talk:Xiong Min|talk]]) 01:36, 15 December 2021 (UTC)<br />
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=Chapter 11 陈惠妮 Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts=<br />
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机器翻译的译前编辑研究——以医学类文摘为例<br />
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陈惠妮 Chen Huini, Hunan Normal University<br />
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[[Machine_Trans_EN_11]]<br />
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===Abstract===<br />
At present, globalization is accelerating and the market demand for language services is rapidly increasing . Machine translation, as an important translation method, can greatly improve translation efficiency due to its low cost and high speed. However, because of the limitations of machine translation and the differences between Chinese and English language, machine translation is not accurate enough. In order to balance translation efficiency and translation quality, a great number of manual revisions in translation are required for the machine translating texts. Medical papers are specialized, special and purposeful, so it requires accurate,qualified and professional translation. However, the quality of translations by machine is inefficient to meet the high-quality requirements of medical papers translation. Therefore, the introduction of pre-editing can greatly improve the efficiency and quality of machine translation.<br />
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===Key words===<br />
Pre-editing, Machine translation, Medical texts<br />
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===题目===<br />
Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts<br />
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===摘要===<br />
在全球化加速发展的今天，市场对语言服务的需求迅速增加。机器翻译作为一种重要的翻译途径，由于其成本低、速度快，可以大大提高翻译效率。然而，由于机器翻译的局限性以及中英文语言的差异，机器翻译的准确性不高。为了平衡翻译效率和翻译质量，机器翻译文本需要大量的手工修改。<br />
医学论文具有专业性、特殊性和目的性，要求其译文准确、合格、专业。然而，机器翻译的质量较低，无法满足医学论文对翻译的高质量要求。因此，译前编辑的引入可以大大提高机器翻译的效率和质量。<br />
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===关键词===<br />
译前编辑；机器翻译；医学文本<br />
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===1. Introduction===<br />
===1.1 Definition of Machine Translation===<br />
As Cronin(2013) revealed: "Translation is undergoing a revolutionary upheaval. The influence of digital technology and the Internet on translation is continuous, extensive and profound. From the popularity of automatic online translation applications, translation revolution is everywhere (Cronin,2013) However,the concept of machine translation was firstly proposed in the 1930s. Since 1940s, the machine translation technology has been evolving from rule-based machine translation (RBMT) to statistical machine translation (SMT), and to neural machine translation (NMT). Machine translation refers to the automatic translation of source language into target language by using a computer system. That is, machine translation refers to the automatic translation of text from one language into another natural language by computer software or other online translation webs. Machine translation is also defined as the process of “using a computer system to automatically translate text or speech from one natural language to another” according to the definition by ISO (Cui 2014:68-73).O 'Brien (2002) defines it as "the behavior of modifying errors in machine translation to ensure that the target translation meets certain quality requirements". On the basic level, machine translation performs mechanical substitution of words in one language for words in another language, but that rarely produces a good translation, therefore, recognition of the whole phrases and their closest counterparts in the target language is needed. Not all words in one language have equivalents in another language, and many words have more than one meaning. A huge demand for translation is greatly needed in today’s global world, which creates new opportunities for the development of machine translation, attracts more and more attention and becomes one of the current research focuses.<br />
<br />
As Cronin(2013) revealed: "Translation is undergoing a revolutionary upheaval. The influence of digital technology and the Internet on translation is continuous, extensive and profound. From the popularity of automatic online translation applications, translation revolution is everywhere (Cronin,2013) However,the concept of machine translation was firstly proposed in the 1930s. Since 1940s, the machine translation technology has been evolving from rule-based machine translation (RBMT) to statistical machine translation (SMT), and to neural machine translation (NMT). Machine translation refers to the automatic translation of source language into target language by using a computer system. That is, machine translation refers to the automatic translation of text from one language into another natural language by computer software or other online translation webs. Machine translation is also defined as the process of “using a computer system to automatically translate text or speech from one natural language to another” according to the definition by ISO (Cui 2014:68-73).O 'Brien (2002) defines it as "the behavior of modifying errors in machine translation to ensure that the target translation meets certain quality requirements". On the basic level, machine translation performs mechanical substitution of words in one language for words in another language, but that rarely produces a good translation, therefore, recognition of the whole phrases and their closest counterparts in the target language is needed. Not all words in one language have equivalents in another language, and many words have more than one meaning. A huge demand for translation is greatly needed in today’s global world, which creates new opportunities for the development of machine translation, attracts more and more attention and becomes one of the current research focuses.<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:34, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
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===1.2 Definition of Pre-editing===<br />
Pre-editing means to adjust and modify the source language to make it fit more with the characteristics of the machine translation software before putting the source language before the into machine translation, so as to improve the quality of the translation machine translation (Wei Changhong 2008:93-94). Pre-editing is to modify the original text before putting it into machine translation software, in order to improve the recognition rate of machine translation, optimize the output quality of translated text and reduce the workload of post-editing. Because pre-translation editing only needs to be modified in one language, the operation is simpler than post-translation editing, which can realize the double improvement of quality and efficiency. A good pre-editing translation can help machine translation more smoothly, thus improving the machine readability and quality of the output translation. Pre-editing is mostly applied in the following situations: one is when the original text is of poor quality and the machine is difficult to recognize the meaning of the sentence, such as user generated content with poor readability and translatability (Gerlach et al 2003:45-53); Documents that need to be published in multiple languages; Next is when the original text contains a lot of jargon; The last is the original text has a corresponding translation memory bank. If the original text is edited, it can better match the content of the translation memory bank. Pre-editing is a process of identifying problems. It requires to pre-edit the source texts before putting it into machine translation according to the requirements, listing the expressions or sentences that may have trouble in machine translation and then pre-edit it by human. The purpose is to enable the computer to translate better, improve the translatability of machine translation. (Slype G V & Guinet J F & Seitz F 1984:115)<br />
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Pre-editing means to adjust and modify the source language to make it fit more with the characteristics of the machine translation software before putting the source language before the into machine translation, so as to improve the quality of the translation machine translation (Wei Changhong 2008:93-94). Pre-editing is to modify the original text before putting it into machine translation software, in order to improve the recognition rate of machine translation, optimize the output quality of translated text and reduce the workload of post-editing. Because pre-translation editing only needs to be modified in one language, the operation is simpler than post-translation editing, which can realize the double improvement of quality and efficiency. A good pre-editing translation can help machine translation more smoothly, thus improving the machine readability and quality of the output translation. Pre-editing is mostly applied in the following situations: one is when the original text is of poor quality and the machine is difficult to recognize the meaning of the sentence, such as user generated content with poor readability and translatability (Gerlach et al 2003:45-53); Documents that need to be published in multiple languages; Next is when the original text contains a lot of jargon; The last is the original text has a corresponding translation memory bank. If the original text is edited, it can better match the content of the translation memory bank. Pre-editing is a process of identifying problems. It requires to pre-edit the source texts before putting it into machine translation according to the requirements, listing the expressions or sentences that may have trouble in machine translation and then pre-edit it by human. The purpose is to enable the computer to translate better, improve the translatability of machine translation. (Slype G V & Guinet J F & Seitz F 1984:115)<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:36, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
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===1.3 Machine Translation Mode===<br />
According to the different knowledge acquisition methods, machine translation modes can be classified as follows: one is rule-based machine translation, which is based on bilingual dictionaries and a library of language rules for each language. The quality of translation depends on whether the source language conforms to the existing rules, but the inexhaustible rules are the hinders of this model. The second mode is machine translation based on statistics. This translation model relies on the principles of mathematics and statistics to find various existing translations corresponding to the translation tasks through the employment of corpus, analyzing the frequency of their occurrence and selecting the translation with the highest frequency for output. The disadvantage of this translation model is that it ignores the flexibility of language and the importance of context. The last one is neural network language model. This model is different from previous translation models in that it uses end-to-end neural network to realize automatic translation between natural languages. At present, the quality of its translation is much higher than that of the previous translation models.<br />
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According to the different knowledge acquisition methods, machine translation modes can be classified as follows: one is rule-based machine translation, which is based on bilingual dictionaries and a library of language rules for each language. The quality of translation depends on whether the source language conforms to the existing rules, but the inexhaustible rules are the hinders of this model. The second mode is machine translation based on statistics. This translation model relies on the principles of mathematics and statistics to find various existing translations corresponding to the translation tasks through the employment of corpus, analyzing the frequency of their occurrence and selecting the translation with the highest frequency for output. The disadvantage of this translation model is that it ignores the flexibility of language and the importance of context. The last one is neural network language model. This model is different from previous translation models in that it uses end-to-end neural network to realize automatic translation between natural languages. At present, the quality of its translation is much higher than that of the previous translation models.<br />
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--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
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===1.4 Source of Study Abstracts===<br />
The core medical journals from domestic are selected in order to make the paper more representative and authoritative, such as National Medical Journal of China, Journal of Peking University (Health Science), and Journal of Third Military Medical University. All of them include basic medical science, biomedical technology, laboratory medical science and other fields. In this way, the pre-editing approaches included are applicable enough to machine translation of medical abstracts.<br />
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The core medical journals from domestic are selected in order to make the paper more representative and authoritative, such as National Medical Journal of China, Journal of Peking University (Health Science), and Journal of Third Military Medical University. All of them include basic medical science, biomedical technology, laboratory medical science and other fields. In this way, the pre-editing approaches included are applicable enough to machine translation of medical abstracts.<br />
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--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
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===1.5 Selection of Translation Software===<br />
Generally, machine translation software includes Google Translation, Youdao Translator and Niu Translation, which has their own special use in translation. Take Google as an example, it is more like neurons in human brain, enabling to learn and collect information to establish connections with its neural machine translator’s neurons. However, it also causes many errors because of the lack of enough information. In this paper, contrastive analysis will be carried on by using Google translation. On the one hand, being a pioneer in the translation of NET, it is inevitably to sue Google as the translation software to translate medical texts in this paper. Comparing the program called “Google brain”, other NMT of translation software are relatively disadvantages. On the other hand, the Google translation enjoys the largest users in the world, with its downloads of more than one billion. The output quality of Google translation is more correct and complete than other machine translation software. With years of development and improvement, Google Translation has been greatly promoted. In this paper, Chinese medical abstract will be automatically translated by Google translation, and then the translation output will be compared with the translation by human and the publication of English abstracts. The main purpose is to prove that the improvement and promotion of quality and accuracy in the medical abstracts will be obtained through the pre-editing approaches.<br />
<br />
Generally, machine translation software includes Google Translation, Youdao Translator and Niu Translation, which has their own special use in translation. Take Google as an example, it is more like neurons in human brain, enabling to learn and collect information to establish connections with its neural machine translator’s neurons. However, it also causes many errors because of the lack of enough information. In this paper, contrastive analysis will be carried on by using Google translation. On the one hand, being a pioneer in the translation of NET, it is inevitably to sue Google as the translation software to translate medical texts in this paper. Comparing the program called “Google brain”, other NMT of translation software are relatively disadvantages. On the other hand, the Google translation enjoys the largest users in the world, with its downloads of more than one billion. The output quality of Google translation is more correct and complete than other machine translation software. With years of development and improvement, Google Translation has been greatly promoted. In this paper, Chinese medical abstract will be automatically translated by Google translation, and then the translation output will be compared with the translation by human and the publication of English abstracts. The main purpose is to prove that the improvement and promotion of quality and accuracy in the medical abstracts will be obtained through the pre-editing approaches.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
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===2.Language Characteristics and Error Division===<br />
Since Chinese and English are two different languages, it is quite neccessary to identify their own characteristics so as to better analyze and understand the two languages. Tytler (1978: 118-119) argued in his Essay on the Principles of Translation that there are three principles of translation that in all the translation should give readers the same feelings as the source text, except for complete transcript of ideas. There actually also exit some mistakes of these two languages. So the following will make some clarrifications of these two languages to ensure more accurate translation.<br />
<br />
Since Chinese and English are two different languages, it is quite neccessary to identify their own characteristics so as to better analyze and understand the two languages. Tytler (1978: 118-119) argued in his Essay on the Principles of Translation that there are three principles of translation that in all the translation should give readers the same feelings as the source text, except for complete transcript of ideas. There actually also exit some mistakes of these two languages. So the following will make some clarrifications of these two languages to ensure more accurate translation.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
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===2.1 Language Characteristics of Medical Abstracts===<br />
Chinese and English belong to different language systems, so there are differences in their language structure and the way users think of the languages. When using machine translation from Chinese to English, due to the unequal language levels, there will be many mistakes in the translation process, especially for ESP texts, such as medical papers.Here, these differences mainly refer to the linguistic characteristics of medical abstracts. In medical abstracts, it usually includes structured and unstructured abstracts. Although in different forms, they both describe the purpose, methods and conclusions of the research.<br />
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In the method section of medial abstract, several Chinese sentences can be connected with commas, but each sentence may convey different information. In contrast, English sentences contain a great deal of information, but in order to ensure clarity, some modifiers need to be isolated and then reconstructed. However, in Chinese-English machine translation, a lot of information is put into the sentence because the machine segments the sentence on the basis of the full comma.<br />
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In addition, subjectless sentence is used in the objective and method parts of Chinese medical abstracts. The subjectless sentence means the sentence without or free from subject and is usually employed in two contexts. The first is "needless to say". In Chinese sentences, it is common to omit the subject of the sentence. Chinese sentences can convey meanings by using incomplete sentence structures, so Chinese speakers can understand the meanings of the sentences even though the sentence subject is omitted. The second is "emphasis of action". In this context, subjectless sentences are used to describe behaviors, especially in the study of traditional Chinese medicine abstracts. Comparatively speaking, it should be avoided in English medical abstracts. Abstract sentences are more subjective when describing the learning process, while the essential requirement of English medical abstract is objectivity. From this point, sentences without subject should be avoided in English medical abstracts. Another feature is voice. Few words with passive meanings appear in Chinese abstracts. English sentences are more favoured in using passive voice. When translating from Chinese to English, passive voice should be used to make the contents objective, which is also the basic requirement of medical papers.<br />
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These are the linguistic features of Chinese medical abstracts. There are great differences in sentence structure and expression between Chinese and English medical abstracts. These differences may reduce the accuracy of machine translation, so it is necessary to introduce pre-editing to edit the source text to ensure that the source text can be accurately recognized by the machine and fully translated into English.<br />
<br />
Chinese and English belong to different language systems, so there are differences in their language structure and the way users think of the languages. When using machine translation from Chinese to English, due to the unequal language levels, there will be many mistakes in the translation process, especially for ESP texts, such as medical papers.Here, these differences mainly refer to the linguistic characteristics of medical abstracts. In medical abstracts, it usually includes structured and unstructured abstracts. Although in different forms, they both describe the purpose, methods and conclusions of the research.<br />
<br />
In the method section of medial abstract, several Chinese sentences can be connected with commas, but each sentence may convey different information. In contrast, English sentences contain a great deal of information, but in order to ensure clarity, some modifiers need to be isolated and then reconstructed. However, in Chinese-English machine translation, a lot of information is put into the sentence because the machine segments the sentence on the basis of the full comma.<br />
<br />
In addition, subjectless sentence is used in the objective and method parts of Chinese medical abstracts. The subjectless sentence means the sentence without or free from subject and is usually employed in two contexts. The first is "needless to say". In Chinese sentences, it is common to omit the subject of the sentence. Chinese sentences can convey meanings by using incomplete sentence structures, so Chinese speakers can understand the meanings of the sentences even though the sentence subject is omitted. The second is "emphasis of action". In this context, subjectless sentences are used to describe behaviors, especially in the study of traditional Chinese medicine abstracts. Comparatively speaking, it should be avoided in English medical abstracts. Abstract sentences are more subjective when describing the learning process, while the essential requirement of English medical abstract is objectivity. From this point, sentences without subject should be avoided in English medical abstracts. Another feature is voice. Few words with passive meanings appear in Chinese abstracts. English sentences are more favoured in using passive voice. When translating from Chinese to English, passive voice should be used to make the contents objective, which is also the basic requirement of medical papers.<br />
<br />
These are the linguistic features of Chinese medical abstracts. There are great differences in sentence structure and expression between Chinese and English medical abstracts. These differences may reduce the accuracy of machine translation, so it is necessary to introduce pre-editing to edit the source text to ensure that the source text can be accurately recognized by the machine and fully translated into English.<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
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===2.2 Error Division of Machine Translation in Translating Abstracts of Medical Papers from Chinese to English===<br />
In this paper, errors in machine translation are listed out after analyzing. Some are due to the principles, such as statistical-based MT and NMT, employed by machine translation. Based on the original errors, they can be studied from two levels. One is from the macro-level, referring to mistakes caused by objective factors. These are not human factors, but the disadvantage of machine translation and the limitations of language. The limitations of machine translation are derived from the principles and models adopted and manifests itself as a reliance on reference sources. In this case, semantic ambiguity and textual incoherence may occur in the absence of reference sources.<br />
<br />
However, for ESP texts such as medical papers, the requirements for terms and sentence patterns are far beyond the existing corpora. For unfamiliar texts (that is, the corresponding texts cannot be found in the corpora). The quality of output translation will be relatively low, which is determined by the principles of machine translation. As mentioned above, machine translation has evolved over the past few decades from rules-based to corp-based statistics to NMT. However, there still exist some limitations in machine translation.<br />
<br />
Mistakes on micro-level are mainly caused by the variations and differences of linguistic structure between Chinese and English. Chinese is an implicit language, while English is an explicit one (Lian, 2010) To put it in another way, Chinese expression does not depend on language structures, while English does the opposite. This may result in mismatches between the original and machine translated sentences. This kind of error is mainly divided into two types: component fragment and component missing. For a medical paper, such errors are very serious. As a kind of ESP discourse, medical paper has the characteristics of fixed, objective and accurate language structure. In order to reproduce the characteristics of medical abstracts in translation, it is necessary to avoid errors at the level of words and sentences and pay attention to logic and consistency.<br />
<br />
Lexical errors here refer to the inconsistency of fixed expressions in the translation of terms. Although these terms are machine translation based on the corpus, the corpus may not be perfect for ESP texts such as medical papers. Therefore, fixed expressions are not used in term translation. This is also the most common mistake in machine translation (the term here includes but is not limited to nouns). For ESP texts, medical text in particular, the fixed expressions and the accuracy of terms are of great importance.<br />
<br />
In this paper, errors in machine translation are listed out after analyzing. Some are due to the principles, such as statistical-based MT and NMT, employed by machine translation. Based on the original errors, they can be studied from two levels. One is from the macro-level, referring to mistakes caused by objective factors. These are not human factors, but the disadvantage of machine translation and the limitations of language. The limitations of machine translation are derived from the principles and models adopted and manifests itself as a reliance on reference sources. In this case, semantic ambiguity and textual incoherence may occur in the absence of reference sources.<br />
<br />
However, for ESP texts such as medical papers, the requirements for terms and sentence patterns are far beyond the existing corpora. For unfamiliar texts (that is, the corresponding texts cannot be found in the corpora). The quality of output translation will be relatively low, which is determined by the principles of machine translation. As mentioned above, machine translation has evolved over the past few decades from rules-based to corp-based statistics to NMT. However, there still exist some limitations in machine translation.<br />
<br />
Mistakes on micro-level are mainly caused by the variations and differences of linguistic structure between Chinese and English. Chinese is an implicit language, while English is an explicit one (Lian, 2010) To put it in another way, Chinese expression does not depend on language structures, while English does the opposite. This may result in mismatches between the original and machine translated sentences. This kind of error is mainly divided into two types: component fragment and component missing. For a medical paper, such errors are very serious. As a kind of ESP discourse, medical paper has the characteristics of fixed, objective and accurate language structure. In order to reproduce the characteristics of medical abstracts in translation, it is necessary to avoid errors at the level of words and sentences and pay attention to logic and consistency.<br />
<br />
Lexical errors here refer to the inconsistency of fixed expressions in the translation of terms. Although these terms are machine translation based on the corpus, the corpus may not be perfect for ESP texts such as medical papers. Therefore, fixed expressions are not used in term translation. This is also the most common mistake in machine translation (the term here includes but is not limited to nouns). For ESP texts, medical text in particular, the fixed expressions and the accuracy of terms are of great importance.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
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===3.Approaches Proposed for Pre-editing ===<br />
Generally speaking, there is a close relationship between pre-editing and post-editing, both of which aim to convey information and ensure high or publishable translation quality. Proper pre-editing can improve the quality of machine translation in terms of adequacy and consistency. <br />
Complexity of natural language and people use language arbitrarily, bringing many difficulties to Chinese-English machine translation, Hu Qingping (2005:24) has proposed "the research of translation software and the development of the controlled language are the two directions to improve the quality of machine translation : the former aims at the difficulty in the natural language processing, the latter overcomes the arbitrariness of natural language". Feng Quangong and Gao Lin (2017: 63-68) put forward: "The writing principles of controlled language can be applied to pre-editing of machine translation. Pre-editing based on controlled language can effectively reduce the complexity and ambiguity of source text, improve the identifiability of machine translation (the translatability of source text itself), and thus reduce (fully) the workload of post-editing".<br />
The central task of pre-editing is to transform human-friendly content into machine-friendly content, so words and sentences need to be repositioned or even changed. Based on the analysis of the language characteristics of Chinese and English, the Chinese ideographic group should be split before the Chinese source text is input into the machine software to translate so that the sentence structure is complete which can be easily recognized by the machine translation software.<br />
<br />
Generally speaking, there is a close relationship between pre-editing and post-editing, both of which aim to convey information and ensure high or publishable translation quality. Proper pre-editing can improve the quality of machine translation in terms of adequacy and consistency. <br />
<br />
Complexity of natural language and people use language arbitrarily, bringing many difficulties to Chinese-English machine translation, Hu Qingping (2005:24) has proposed "the research of translation software and the development of the controlled language are the two directions to improve the quality of machine translation : the former aims at the difficulty in the natural language processing, the latter overcomes the arbitrariness of natural language". Feng Quangong and Gao Lin (2017: 63-68) put forward: "The writing principles of controlled language can be applied to pre-editing of machine translation. Pre-editing based on controlled language can effectively reduce the complexity and ambiguity of source text, improve the identifiability of machine translation (the translatability of source text itself), and thus reduce (fully) the workload of post-editing".<br />
<br />
The central task of pre-editing is to transform human-friendly content into machine-friendly content, so words and sentences need to be repositioned or even changed. Based on the analysis of the language characteristics of Chinese and English, the Chinese ideographic group should be split before the Chinese source text is input into the machine software to translate so that the sentence structure is complete which can be easily recognized by the machine translation software.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufefng<br />
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===3.1 Extraction and Replacement of Terms in the Source Text===<br />
As a branch of applied English, medical English is the product of the combination of English language knowledge and medical knowledge. The terms in medical papers have the characteristics of fixed and complex language structure. Although Google Translation is based on a corpus, the language structure is not fixed due to the limited size of the corpus. Therefore, the following two steps should be followed before machine translation. The first is to extract terms and create a glossary, and then replace the Chinese terms in the source text with the corresponding English expressions in the glossary. Of course, the terms of extraction should be searched and verified according to the actual situation. All of these words are verified before they can be replaced. This method can not only ensure the accuracy of term translation, but also maintain the consistency of expression, especially when dealing with long texts.<br />
Example1<br />
Source abstract: 口腔白斑病癌变相关缺氧应答基因和微小RNA的芯片及表达验证。<br />
Google translation: Microarray detection/Chip detection and expression verification of hypoxia response genes and microRNAs related to oral leukoplakia canceration.<br />
Published translation:Transcriptome array screening and verification of oral leukoplakia carcinogenesis-related hypoxia-responsive gene and microRNA. <br />
Analysis: The expression “ Affymetrix GeneChip” empolyed in this thesis is a human transcription array for transcriptome array. In the source abstract, “芯片检测“ can be meant “screening” but not detection, so the proper and appropriate translation of these expression should be “transcription array screening”, but the Google translates it into “microarry detection of chip detection”. Therefore, term extraction is essential and inevitable before putting the source abstracts into the machine translation software.<br />
After pre-editing source abstracts: 对 oral leukoplakia carcinogenesis 相关 hypoxia-responsive gene 和微小RNA进行 transcriptome array screening 及表达验证。<br />
After pre-editing translation: Transcriptome array screening and expression- verification of hypoxia-responsive genes and microRNAs related to oral leukoplakia carcinogenesis.<br />
<br />
As a branch of applied English, medical English is the product of the combination of English language knowledge and medical knowledge. The terms in medical papers have the characteristics of fixed and complex language structure. Although Google Translation is based on a corpus, the language structure is not fixed due to the limited size of the corpus. Therefore, the following two steps should be followed before machine translation. The first is to extract terms and create a glossary, and then replace the Chinese terms in the source text with the corresponding English expressions in the glossary. Of course, the terms of extraction should be searched and verified according to the actual situation. All of these words are verified before they can be replaced. This method can not only ensure the accuracy of term translation, but also maintain the consistency of expression, especially when dealing with long texts.<br />
<br />
Example1<br />
Source abstract: 口腔白斑病癌变相关缺氧应答基因和微小RNA的芯片及表达验证。<br />
Google translation: Microarray detection/Chip detection and expression verification of hypoxia response genes and microRNAs related to oral leukoplakia canceration.<br />
Published translation:Transcriptome array screening and verification of oral leukoplakia carcinogenesis-related hypoxia-responsive gene and microRNA. <br />
Analysis: The expression “ Affymetrix GeneChip” empolyed in this thesis is a human transcription array for transcriptome array. In the source abstract, “芯片检测“ can be meant “screening” but not detection, so the proper and appropriate translation of these expression should be “transcription array screening”, but the Google translates it into “microarry detection of chip detection”. Therefore, term extraction is essential and inevitable before putting the source abstracts into the machine translation software.<br />
After pre-editing source abstracts: 对 oral leukoplakia carcinogenesis 相关 hypoxia-responsive gene 和微小RNA进行 transcriptome array screening 及表达验证。<br />
After pre-editing translation: Transcriptome array screening and expression- verification of hypoxia-responsive genes and microRNAs related to oral leukoplakia carcinogenesis.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
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===3.2 Explication of Subordination===<br />
As mentioned above, the subordination of Chinese sentences is judged by certain specific words in machine translation . Chinese is a paratactic language, and sentences are often connected by internal logical relationships; while English depends on sentence structure, so sentences are often closely linked by various language forms. In order to improve the accuracy of machine translation, we can adjust the sentence structure and adjust the position of adjectives and their modifiers. <br />
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Example 2<br />
Source abstracts:回顾性分析南京医科大学附属儿童医院中重度HIE患儿49例及同期就诊的无神经系统症状体征的足月新生儿为对照组31例的头颅磁共振成像（MRI）资料。<br />
Google translation: A retrospective analysis that the brain magnetic resonance imaging (MRI) data of 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University and full-term neonates with no neurological symptoms and signs during the same period were included in the control group.<br />
Published translation: A total of 49 children with moderate to severe HIE admitted to the children’s Hospital Affiliated to Nanjing Medical University were retrospectively analyzed. Cranial magnetic resonance imaging (MRI) date of 31 full-term neonates without neurological symptoms and signs who visited the hospital during the same period were recruited as the control group. <br />
<br />
Analysis: As the above example shows that “中重度HIE患儿” and “足月新生儿” in the source abstracts are from the Children’s Hospital Affiliated Nanjing Medical University. The Google translation shows that 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University. There is an error due to the misuse of subordination. There are some advice in order to solve the problem. That is, first to segment the sentence and then reconstruct the sentence. For instance, the Chinese expression “南京医科大学附属儿童医院” carries many modifiers, which requires to cut the modifiers and reconstruct the sentence. <br />
Therefore, the Chinese expression “南京医科大学附属儿童医院’can be divided into abstractions, leaving two sentences instead of one long sentence with modifiers..<br />
After pre-editing source abstracts: 对49例中重度HIE患儿以及31例无神经系统症状体征的足月新生儿的MRI资料进行回顾性分析。这些患者收治于南京医科大学儿童附属医院。<br />
After pre-editing translation: The MRI data of 49 children with moderate to severe HIE and 31 full-term newborns without neurological symptoms and signs were retrospectively analyzed. These patients were admitted to the Children’s Hospital of Nanjing Medical University.<br />
<br />
As mentioned above, the subordination of Chinese sentences is judged by certain specific words in machine translation . Chinese is a paratactic language, and sentences are often connected by internal logical relationships; while English depends on sentence structure, so sentences are often closely linked by various language forms. In order to improve the accuracy of machine translation, we can adjust the sentence structure and adjust the position of adjectives and their modifiers. <br />
<br />
Example 2<br />
Source abstracts:回顾性分析南京医科大学附属儿童医院中重度HIE患儿49例及同期就诊的无神经系统症状体征的足月新生儿为对照组31例的头颅磁共振成像（MRI）资料。<br />
Google translation: A retrospective analysis that the brain magnetic resonance imaging (MRI) data of 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University and full-term neonates with no neurological symptoms and signs during the same period were included in the control group.<br />
Published translation: A total of 49 children with moderate to severe HIE admitted to the children’s Hospital Affiliated to Nanjing Medical University were retrospectively analyzed. Cranial magnetic resonance imaging (MRI) date of 31 full-term neonates without neurological symptoms and signs who visited the hospital during the same period were recruited as the control group. <br />
<br />
Analysis: As the above example shows that “中重度HIE患儿” and “足月新生儿” in the source abstracts are from the Children’s Hospital Affiliated Nanjing Medical University. The Google translation shows that 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University. There is an error due to the misuse of subordination. There are some advice in order to solve the problem. That is, first to segment the sentence and then reconstruct the sentence. For instance, the Chinese expression “南京医科大学附属儿童医院” carries many modifiers, which requires to cut the modifiers and reconstruct the sentence. <br />
<br />
Therefore, the Chinese expression “南京医科大学附属儿童医院’can be divided into abstractions, leaving two sentences instead of one long sentence with modifiers..<br />
After pre-editing source abstracts: 对49例中重度HIE患儿以及31例无神经系统症状体征的足月新生儿的MRI资料进行回顾性分析。这些患者收治于南京医科大学儿童附属医院。<br />
After pre-editing translation: The MRI data of 49 children with moderate to severe HIE and 31 full-term newborns without neurological symptoms and signs were retrospectively analyzed. These patients were admitted to the Children’s Hospital of Nanjing Medical University.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===3.3 Explication of Subject through Voice Changing===<br />
The extensive use of subjectless sentences are quite common in the Chinese abstracts, while English prefers to employ passive voice in the sentences so as to make them object and accurate. In order to take the characteristics of English sentences into great and careful consideration, the sentences written in passive voice in particular, it will be helpful for machine translation to find out the subject and reconstruct a sentence in passive voice (Wang Yan: 2008). The first is to discover the “doee” in a sentence and then is to reconstruct the sentence structure and put the “doee” before the verb. The last is to explicate the passive relation between the noun and the verb.<br />
Example 3<br />
Source abstract: 回顾性分析2018年1月至2020年12月解放军总医院第七医学中心收治的500例老年髋部骨折患者的资料。<br />
Google translation: A retrospective analysis of the data of 500 elderly patients with hip fractures admitted to the Seventh Medical Center of the PLA General Hospital from January 2018 to December 2020.<br />
Published translation: From January 2018 to December 2020, the data of 500 elderly patients with hip fracture treated in the Seventh Medical Center of PLA General Hospital were analyzed retrospectively.<br />
Analysis: The above example indicates that the “回顾性分析”in the Google Translate is a noun phrase, but the source abstracts actually describes an action or a behavior. The reason for such a mistake is that the machine can’t recognize the Chinese subjetless sentences. To find the subject of the sentence, the source abstracts can be revised and adjusted. Finding the “doee” is the first step, which refers to “500例老年髋部骨折患者的资料”in the source abstracts. And next is to put it in front of the verb, that is to place it in the beginning of the sentence. Last but not least, the passive voice should be explicated in the sentence. <br />
After pre-editing source abstract: 500例老年髋部骨折患者的资料被回顾性分析，他们在2018年1月之2020年12月期间收治于解放军总医院第七医学中心。<br />
After pre-editing translation: The data of 500 elderly hip fracture patients were retrospectively analyzed. They were admitted to the Seventh Medical Center of the PLA General Hospital between January 2018 and December 2020.<br />
<br />
The extensive use of subjectless sentences are quite common in the Chinese abstracts, while English prefers to employ passive voice in the sentences so as to make them object and accurate. In order to take the characteristics of English sentences into great and careful consideration, the sentences written in passive voice in particular, it will be helpful for machine translation to find out the subject and reconstruct a sentence in passive voice (Wang Yan: 2008). The first is to discover the “doee” in a sentence and then is to reconstruct the sentence structure and put the “doee” before the verb. The last is to explicate the passive relation between the noun and the verb.<br />
<br />
Example 3<br />
Source abstract: 回顾性分析2018年1月至2020年12月解放军总医院第七医学中心收治的500例老年髋部骨折患者的资料。<br />
Google translation: A retrospective analysis of the data of 500 elderly patients with hip fractures admitted to the Seventh Medical Center of the PLA General Hospital from January 2018 to December 2020.<br />
Published translation: From January 2018 to December 2020, the data of 500 elderly patients with hip fracture treated in the Seventh Medical Center of PLA General Hospital were analyzed retrospectively.<br />
<br />
Analysis: The above example indicates that the “回顾性分析”in the Google Translate is a noun phrase, but the source abstracts actually describes an action or a behavior. The reason for such a mistake is that the machine can’t recognize the Chinese subjetless sentences. To find the subject of the sentence, the source abstracts can be revised and adjusted. Finding the “doee” is the first step, which refers to “500例老年髋部骨折患者的资料”in the source abstracts. And next is to put it in front of the verb, that is to place it in the beginning of the sentence. Last but not least, the passive voice should be explicated in the sentence. <br />
After pre-editing source abstract: 500例老年髋部骨折患者的资料被回顾性分析，他们在2018年1月之2020年12月期间收治于解放军总医院第七医学中心。<br />
After pre-editing translation: The data of 500 elderly hip fracture patients were retrospectively analyzed. They were admitted to the Seventh Medical Center of the PLA General Hospital between January 2018 and December 2020.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===3.4 Relocation of Modifiers===<br />
Modifiers, including noun, adverbial and attributive are constantly employed in the Chinese medical papers in order to add information to subject and object in the sentence. By doing this, complicated sentences can be structured, thus causing obstacles to machine translation for recognizing this complex sentence structures when translating Chinese-English sentences. To make the machine translation successfully recognize the sentence structure, simplifying the sentence structure is quite necessary. The key is to moving the location of the modifiers and thus making the modifiers an independent sentence. In other words, the modifiers ought to be put before or behind the main part of the sentence to satisfy the common use of English. <br />
Usually, the modifiers in Chinese sentence can only be placed in front of the core word, while in English, modifiers are very flexible. It is all right to place the modifiers in front of or behind the major part of the sentences with adjective or a connective noun.<br />
<br />
Example 4<br />
Source abstracts: 利用DNA重组技术以pET-28a表达系统在E.coli BL21(DE3) 中重组表达Hepl。<br />
Google Translation: Hepl is recombined in E.coli BL21 (DE3) using DNA recombination technology with pET-28a expression system.<br />
Published translation: The recombinant Hcpl protein was expressed by using DNA recombination technology through pET-28a expression system in E. coli BL21 (De3).<br />
Analysis: The Chinese medical text includes two modifiers, “利用DNA”and “以pET-28a)表达系统”. These two modifiers will be translated by machine, which catches more attention to the two constituents. From the Google translation above, one failure is obvious that it misplaces the location of the two modifiers and presents it in not accurate form. To make it translate correctly by machine translation, dividing the sentences is very important so that the source abstracts can be correctly recognized by machine translation.<br />
<br />
After pre-editing source abstract: 利用DNA重组技术，Hcp1重组表达在E.coli BL21 (DE3)中， 通过pET-28a表达系统在E. coli BL21 (DE3)中。<br />
Google translation: Using DNA recombination technology, Hcp1 recombination is expressed in E.coli BL21 (DE3), via pET-28a expression system in E. coli BL21 (DE3).<br />
The second is the independence of modifiers, that is, modifiers can also be reconstructed into clauses to modify core words. Compared with English, There is no subordinate clause in Chinese, so redundant Chinese modifiers need to be reconstructed into subordinate clauses in Chinese-English translation to meet the characteristics of English. English, especially sentences with multiple modifiers. Otherwise, sentence structure may be confused, such as scattered modifiers of core words. To avoid this mistake, it is necessary to separate the modifier from the main part of the sentence. These modifiers should be reconstituted into clauses. Also, keep your sentences simple and easy to understand.<br />
<br />
Modifiers, including noun, adverbial and attributive are constantly employed in the Chinese medical papers in order to add information to subject and object in the sentence. By doing this, complicated sentences can be structured, thus causing obstacles to machine translation for recognizing this complex sentence structures when translating Chinese-English sentences. To make the machine translation successfully recognize the sentence structure, simplifying the sentence structure is quite necessary. The key is to moving the location of the modifiers and thus making the modifiers an independent sentence. In other words, the modifiers ought to be put before or behind the main part of the sentence to satisfy the common use of English. <br />
Usually, the modifiers in Chinese sentence can only be placed in front of the core word, while in English, modifiers are very flexible. It is all right to place the modifiers in front of or behind the major part of the sentences with adjective or a connective noun.<br />
<br />
Example 4<br />
Source abstracts: 利用DNA重组技术以pET-28a表达系统在E.coli BL21(DE3) 中重组表达Hepl。<br />
Google Translation: Hepl is recombined in E.coli BL21 (DE3) using DNA recombination technology with pET-28a expression system.<br />
Published translation: The recombinant Hcpl protein was expressed by using DNA recombination technology through pET-28a expression system in E. coli BL21 (De3).<br />
Analysis: The Chinese medical text includes two modifiers, “利用DNA”and “以pET-28a)表达系统”. These two modifiers will be translated by machine, which catches more attention to the two constituents. From the Google translation above, one failure is obvious that it misplaces the location of the two modifiers and presents it in not accurate form. To make it translate correctly by machine translation, dividing the sentences is very important so that the source abstracts can be correctly recognized by machine translation.<br />
<br />
After pre-editing source abstract: 利用DNA重组技术，Hcp1重组表达在E.coli BL21 (DE3)中， 通过pET-28a表达系统在E. coli BL21 (DE3)中。<br />
Google translation: Using DNA recombination technology, Hcp1 recombination is expressed in E.coli BL21 (DE3), via pET-28a expression system in E. coli BL21 (DE3).<br />
The second is the independence of modifiers, that is, modifiers can also be reconstructed into clauses to modify core words. Compared with English, There is no subordinate clause in Chinese, so redundant Chinese modifiers need to be reconstructed into subordinate clauses in Chinese-English translation to meet the characteristics of English. English, especially sentences with multiple modifiers. Otherwise, sentence structure may be confused, such as scattered modifiers of core words. To avoid this mistake, it is necessary to separate the modifier from the main part of the sentence. These modifiers should be reconstituted into clauses. Also, keep your sentences simple and easy to understand.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===3.5 Proper Omission and Deletion of Category Words===<br />
Category words are commonly used in Chinese. Category words complement the meaning of words, including problems, positions, situations and jobs. Adding this supplementary word is more in line with Chinese custom. In many cases, it has no real meaning, so it can be omitted in translation. In Chinese, category words are frequently used. However, it is rarely used in English, which is one of the differences between Chinese and English. There are many kinds of category words. Considering objectivity and the fixed structure of language, redundant category words should be deleted in Chinese-English translation. In the general, the most commonly used category of words in the Chinese medical abstracts are "process", "behavior", and "situation". These categories of words hinder the language conversion of Chinese and English, resulting in redundancy. <br />
<br />
Example 5<br />
Source abstract: 探讨口腔白斑病癌变进程中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia response genes and associated microRNAs in the process of oral leukoplakia cancer was discussed.<br />
Published translation: To study the hypoxia response gene and microRNA (miRNA)expression profiles in the pathogenesis and progression of oral leukoplakia (OLK).<br />
Analysis: As the above example shows, the Chinese words “进程” belongs to a category word. However, the Chinese expression “癌变”contains the process, so the “进程” expression can be deleted before placing it into the machine translation, because the meaning of it has been overlapping between the expression “癌变”. Therefore, its place can be replaced with preposition “during”.<br />
<br />
After pre-editing source abstract: 探讨口腔白斑病癌变中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia responsive genes and miRNA in oral leukoplakia cancer was investigated.<br />
<br />
Category words are commonly used in Chinese. Category words complement the meaning of words, including problems, positions, situations and jobs. Adding this supplementary word is more in line with Chinese custom. In many cases, it has no real meaning, so it can be omitted in translation. In Chinese, category words are frequently used. However, it is rarely used in English, which is one of the differences between Chinese and English. There are many kinds of category words. Considering objectivity and the fixed structure of language, redundant category words should be deleted in Chinese-English translation. In the general, the most commonly used category of words in the Chinese medical abstracts are "process", "behavior", and "situation". These categories of words hinder the language conversion of Chinese and English, resulting in redundancy. <br />
<br />
Example 5<br />
Source abstract: 探讨口腔白斑病癌变进程中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia response genes and associated microRNAs in the process of oral leukoplakia cancer was discussed.<br />
Published translation: To study the hypoxia response gene and microRNA (miRNA)expression profiles in the pathogenesis and progression of oral leukoplakia (OLK).<br />
Analysis: As the above example shows, the Chinese words “进程” belongs to a category word. However, the Chinese expression “癌变”contains the process, so the “进程” expression can be deleted before placing it into the machine translation, because the meaning of it has been overlapping between the expression “癌变”. Therefore, its place can be replaced with preposition “during”.<br />
<br />
After pre-editing source abstract: 探讨口腔白斑病癌变中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia responsive genes and miRNA in oral leukoplakia cancer was investigated.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===Conclusion===<br />
After years of development, machine translation has made great progress. The accuracy of machine translation has been greatly improved in both text recognition and sentence pattern conversion. However, machine translation has its own limitations. In other words, it needs to rely on the parallel corpus as a reference source for improving its accuracy. ESP text, in particular, is harder to get the high quality by machine translation. <br />
<br />
As one of the research papers, the characteristics of medical abstracts are fixed language structures, objectivity and accuracy (Qin Yi 2004:421-423). Therefore, medical translation must be accurate, object and understandable to follow the specific demands of the medical paper. Being an important field in the human society, medical paper translation is on a great demand, which means that it needs a huge demand for human labor. However, with the machine translation promoting, it will be more efficient to translate medical papers combining the effort by human and machine. The improvement and development of machine translation requires the joint efforts of computer science, information science, statistics, linguistics and other academic circles to achieve more mature human-computer mutual assistance translation (Li Yafei, Zhang Ruihua 2019:38-45).<br />
<br />
However, errors can occur during the process of machine translation of Chinese- English, because of the differences of the Chinese and English and the processing of the machine. Errors from the perspective of linguistic or grammar can affect the machine translation a lot. After division and recognition of errors, some pre-editing approaches are put forward to help the machine translation more accurate and readable, that are, extraction and replacement of terms in the source text, relocation of modifiers, explication of subordination, proper omission, deletion of category words and explication of subject through voice changing. <br />
<br />
<br />
The paper mainly focuses on the pre-editing machine translation by using medical papers as a case study. The errors of machine translation occurring in the translation of medical abstracts and pre-editing approaches for machine translation. The quality of machine translation of medical papers is greatly improved after employing the pre-editing methods. However, machine translation is not as flexible and accurate as human brain, so it is of importance to combine pre-editing and post-editing approaches with machine translation in order to produce more accurate, more object machine translation of medical papers.<br />
<br />
After years of development, machine translation has made great progress. The accuracy of machine translation has been greatly improved in both text recognition and sentence pattern conversion. However, machine translation has its own limitations. In other words, it needs to rely on the parallel corpus as a reference source for improving its accuracy. ESP text, in particular, is harder to get the high quality by machine translation. <br />
<br />
As one of the research papers, the characteristics of medical abstracts are fixed language structures, objectivity and accuracy (Qin Yi 2004:421-423). Therefore, medical translation must be accurate, object and understandable to follow the specific demands of the medical paper. Being an important field in the human society, medical paper translation is on a great demand, which means that it needs a huge demand for human labor. However, with the machine translation promoting, it will be more efficient to translate medical papers combining the effort by human and machine. The improvement and development of machine translation requires the joint efforts of computer science, information science, statistics, linguistics and other academic circles to achieve more mature human-computer mutual assistance translation (Li Yafei, Zhang Ruihua 2019:38-45).<br />
<br />
However, errors can occur during the process of machine translation of Chinese- English, because of the differences of the Chinese and English and the processing of the machine. Errors from the perspective of linguistic or grammar can affect the machine translation a lot. After division and recognition of errors, some pre-editing approaches are put forward to help the machine translation more accurate and readable, that are, extraction and replacement of terms in the source text, relocation of modifiers, explication of subordination, proper omission, deletion of category words and explication of subject through voice changing. <br />
<br />
The paper mainly focuses on the pre-editing machine translation by using medical papers as a case study. The errors of machine translation occurring in the translation of medical abstracts and pre-editing approaches for machine translation. The quality of machine translation of medical papers is greatly improved after employing the pre-editing methods. However, machine translation is not as flexible and accurate as human brain, so it is of importance to combine pre-editing and post-editing approaches with machine translation in order to produce more accurate, more object machine translation of medical papers.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===References===<br />
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Cui Qiliang崔启亮(2014).论机器翻译的译后编辑[J] ''On Post-Editing of Machine Translatio''. 中国翻译 Chinese Translators Journal, 035(006):68-73<br />
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Feng Quangong, Gao Lin冯全功,高琳 (2017). 基于受控语言的译前编辑对机器翻译的影响[J] ''Influence of Pre-editing Based on Controlled Language on Machine Translation''. 当代外语研究Contemporary Foreign Language Research,(2): 63-68+87+110.<br />
<br />
GERLACH J, et al ( 2013). ''Combining Pre-editing and Post-editing to Improve SMT of User-generated Content''[M]// Proceedings of MT Summit XIV Workshop on Post-editing Technology and Practice. 45-53<br />
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Hu Qingping胡清平(2005). 机器翻译中的受控语言[J] ''Controlled Language in Machine Translation''. 中国科技翻译 Chinese Science and Technology Translation, (03): 24-27. <br />
<br />
Lian Shuneng连淑能 (2010). 英汉对比研究增订本[M]''An Updated Version of English-Chinese Contrastive Studies'' . 北京:高等教育出版社Beijing: Higher Education Publishing House. 35-36.<br />
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Li Yafei, Zhang Ruihua黎亚飞,张瑞华 (2019). 机器翻译发展与现状[J]''The Development and Current Situation of Machine Translation''. 中国轻工教育 China Light Industry Education, (5):38-45. <br />
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Qin Yi秦毅(2004),从翻译基本标准议医学英语的翻译[J] ''On the Translation of Medical English from the Basic Standard of Translation''. 遵义医学院学报 Journal of Zunyi Medical College,27 (4): 421-423. <br />
<br />
Slype G V & Guinet J F & Seitz F (1984). ''Better Translation for Better Communication'' [M] . Oxford: Pergamon Press Ltd (U.K.). 90-93<br />
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O'Brien S (2002). Teaching Post-editing: A Proposal for Course Con-tent [EB/OL]. http://mt-archive. Info/EAMT-2002-0brien. Pdf.<br />
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Tytler, A. F. (1978). ''Essay On The Principles of Translation''[M]. Amsterdam: JohnBenjamins Publishing. 118-119<br />
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Wang Yan王燕 (2008). 医学英语翻译与写作教程[M] ''Medical English Translation and Writing Course''. 重庆:重庆大学出版社 Chongqing: Chongqing University Press. 60-61<br />
<br />
Written by --[[User:Chen Huini|Chen Huini]] ([[User talk:Chen Huini|talk]]) 04:58, 15 December 2021 (UTC)Chen Huini<br />
<br />
=12 蔡珠凤 The Mistranslation of C-J Machine Translation of Political Statements=<br />
<br />
机器翻译中政治发言中译日的误译<br />
<br />
蔡珠凤 Cai Zhufeng, Hunan Normal University<br />
<br />
[[Machine_Trans_EN_12]]<br />
===Abstract===<br />
Language is the main way of communication between people. With the continuous development of globalization, the scale of cross-border exchanges is also expanding. However, due to cultural differences and diversity, the languages of different countries and regions are very different, which seriously hinders people's communication. The demand for efficient and convenient translation tools is increasing. At the same time, with the development of network technology and artificial intelligence, recognition technology based on deep learning is more and more widely used in English, Japanese and other fields.<br />
<br />
===Key words===<br />
machine translation; political statements; mistranslation of C-J machine translation<br />
===题目===<br />
The Mistranslation of C-J Machine Translation of Political Statements<br />
===摘要===<br />
语言是人与人之间交流的主要方式。随着全球化的不断发展，跨境交流的规模也在不断扩大。然而，由于文化的差异和多样性，不同国家和地区的语言差异很大，这严重阻碍了人们的交流。对高效便捷的翻译工具的需求正在增加。同时，随着网络技术和人工智能的发展，基于深度学习的识别技术在英语、日语等领域的应用越来越广泛。<br />
<br />
===关键词===<br />
机器翻译；政治发言；政治发言中译日的误译<br />
===1. Introduction===<br />
===Introduction to machine translation===<br />
Machine translation, also known as automatic translation, is a process of using computers to convert one natural language (source language) into another natural language (target language). It is a branch of computational linguistics, one of the ultimate goals of artificial intelligence, and has important scientific research value.At the same time, machine translation has important practical value. With the rapid development of economic globalization and the Internet, machine translation technology plays a more and more important role in promoting political, economic and cultural exchanges.The development of machine translation technology has been closely accompanied by the development of computer technology, information theory, linguistics and other disciplines. From the early dictionary matching, to the rule translation of dictionaries combined with linguistic expert knowledge, and then to the statistical machine translation based on corpus, with the improvement of computer computing power and the explosive growth of multilingual information, machine translation technology gradually stepped out of the ivory tower and began to provide real-time and convenient translation services for general users.（Zhang 2019:5-6)<br />
<br />
=== C-J machine translation software===<br />
Today's online machine translation software includes Baidu translation, Tencent translation, Google translation, Youdao translation, Bing translation and so on. Google was the first company to launch the machine translation system, and Baidu was the first company to import the machine translation system in China. In addition, Tencent and Youdao have attracted much attention.Machine translation is the process of using computers to convert one natural language into another. It usually refers to sentence and full-text translation between natural languages. In order to continuously improve the translation quality, R & D personnel have added artificial intelligence technologies such as speech recognition, image processing and deep neural network to machine translation on the basis of traditional machine translation based on rules, statistics and examples.With the increase of using machine translation, the joint cooperation between manual translation and machine translation will also increase significantly in the future. What criteria should be used to evaluate the quality of machine translation? In the evolving field of machine translation, there is an urgent need to clarify the unsolvable questions and solved problems.(Lv 1996:3)<br />
<br />
===The history of machine translation===<br />
The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. alchuni put forward the idea of using machines for translation. In 1933, Soviet inventor П.П. Trojansky designed a machine to translate one language into another, and registered his invention on September 5 of the same year; However, due to the low technical level in the 1930s, his translation machine was not made. In 1946, the first modern electronic computer ENIAC was born. Shortly after that, W. weaver, an American scientist and A. D. booth, a British engineer, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947 when discussing the application scope of electronic computer. In 1949, W. Weaver published the translation memorandum, which formally put forward the idea of machine translation. After 60 years of ups and downs, machine translation has experienced a tortuous and long development path. The academic community generally divides it into the following four stages:<br />
<br />
Pioneering period（1947-1964）<br />
<br />
In 1954, with the cooperation of IBM, Georgetown University completed the English Russian machine translation experiment with ibm-701 computer for the first time, showing the feasibility of machine translation to the public and the scientific community, thus opening the prelude to the study of machine translation.<br />
It is not too late for China to start this research. As early as 1956, the state included this research in the national scientific work development plan. The topic name is "machine translation, the construction of natural language translation rules and the mathematical theory of natural language". In 1957, the Institute of language and the Institute of computing technology of the Chinese Academy of Sciences cooperated in the Russian Chinese machine translation experiment, translating 9 different types of more complex sentences.From the 1950s to the first half of the 1960s, machine translation research has been on the rise. The United States and the former Soviet Union, two superpowers, have provided a lot of financial support for machine translation projects for military, political and economic purposes, while European countries have also paid considerable attention to machine translation research due to geopolitical and economic needs, and machine translation has become an upsurge for a time. In this period, although machine translation is just in the pioneering stage, it has entered an optimistic period of prosperity.<br />
<br />
Frustrated period（1964-1975）<br />
<br />
In 1964, in order to evaluate the research progress of machine translation, the American Academy of Sciences established the automatic language processing Advisory Committee (Alpac Committee) and began a two-year comprehensive investigation, analysis and test.In November 1966, the committee published a report entitled "language and machine" (Alpac report for short), which comprehensively denied the feasibility of machine translation and suggested stopping the financial support for machine translation projects. The publication of this report has dealt a blow to the booming machine translation, and the research of machine translation has fallen into a standstill. Coincidentally, during this period, China broke out the "ten-year Cultural Revolution", and basically these studies also stagnated. Machine translation has entered a depression.<br />
<br />
convalescence（1975-1989）<br />
<br />
Since the 1970s, with the development of science and technology and the increasingly frequent exchange of scientific and technological information among countries, the language barriers between countries have become more serious. The traditional manual operation mode has been far from meeting the needs, and there is an urgent need for computers to engage in translation. At the same time, the development of computer science and linguistics, especially the substantial improvement of computer hardware technology and the application of artificial intelligence in natural language processing, have promoted the recovery of machine translation research from the technical level. Machine translation projects have begun to develop again, and various practical and experimental systems have been launched successively, such as weinder system Eurpotra multilingual translation system, taum-meteo system, etc.However, after the end of the "ten-year holocaust", China has perked up again, and machine translation research has been put on the agenda again. The "784" project has paid enough attention to machine translation research. After the mid-1980s, the development of machine translation research in China has further accelerated. Firstly, two English Chinese machine translation systems, ky-1 and MT / ec863, have been successfully developed, indicating that China has made great progress in machine translation technology.(Chen 2016:5)<br />
<br />
New period(1990 present)<br />
<br />
With the universal application of the Internet, the acceleration of the process of world economic integration and the increasingly frequent exchanges in the international community, the traditional way of manual operation is far from meeting the rapidly growing needs of translation. People's demand for machine translation has increased unprecedentedly, and machine translation has ushered in a new development opportunity. International conferences on machine translation research have been held frequently, and China has made unprecedented achievements. A series of machine translation software have been launched, such as "Yixing", "Yaxin", "Tongyi", "Huajian", etc. Driven by the market demand, the commercial machine translation system has entered the practical stage, entered the market and came to the users.Since the new century, with the emergence and popularization of the Internet, the amount of data has increased sharply, and statistical methods have been fully applied. Internet companies have set up machine translation research groups and developed machine translation systems based on Internet big data, so as to make machine translation really practical, such as "Baidu translation", "Google translation", etc. In recent years, with the progress of in-depth learning, machine translation technology has further developed, which has promoted the rapid improvement of translation quality, and the translation in oral and other fields is more authentic and fluent.(Liu 2014:6)<br />
<br />
===The problem of machine translation at present===<br />
Error is inevitable<br />
Many people have misunderstandings about machine translation. They think that machine translation has great deviation and can't help people solve any problems. In fact, the error is inevitable. The reason is that machine translation uses linguistic principles. The machine automatically recognizes grammar, calls the stored thesaurus and automatically performs corresponding translation. However, errors are inevitable due to changes or irregularities in grammar, morphology and syntax, such as sentences with adverbials after "give me a reason to kill you first" in Dahua journey to the West. After all, a machine is a machine. No one has special feelings for language. How can it feel the lasting charm of "the tenderness of lowering its head, like the shame of a water lotus? After all, the meaning of Chinese is very different due to the changes of morphology, grammar and syntax and the change of context. Even many Chinese people are zhanger monks - they can't touch their heads, let alone machines.(Liu 2014：3）<br />
<br />
Bottleneck<br />
In fact, no matter which method, the biggest factor affecting the development of machine translation lies in the quality of translation. Judging from the achievements, the quality of machine translation is still far from the ultimate goal.<br />
Chinese mathematician and linguist Zhou Haizhong once pointed out in his paper "fifty years of machine translation": to improve the quality of machine translation, the first thing to solve is the problem of language itself rather than programming; It is certainly impossible to improve the quality of machine translation by relying on several programs alone. At the same time, he also pointed out that it is impossible for machine translation to achieve the degree of "faithfulness, expressiveness and elegance" when human beings have not yet understood how the brain performs fuzzy recognition and logical judgment of language. This view may reveal the bottleneck restricting the quality of translation. <br />
It is worth mentioning that American inventor and futurist ray cozwell predicted in an interview with Huffington Post that the quality of machine translation will reach the level of human translation by 2029. There are still many disputes about this thesis in the academic circles.（Cui 2019：4）<br />
<br />
===2.Mistranslation of Chinese Japanese machine translation===<br />
===2.1Vocabulary mistranslation in Chinese Japanese machine translation===<br />
===2.1.1Mistranslation of proper nouns===<br />
In political materials, there are often political dignitaries' names, place names or a large number of proper nouns in the political field. The morphemes of such words are definite and inseparable. Mistranslation will make the source language lose its specific meaning. <br />
<br />
Japanese translation into Chinese Chinese translation into Japanese<br />
<br />
original text translation by Youdao reference translation original text translation by Youdao reference translation<br />
<br />
朱鎔基 朱基 朱镕基 栗战书 栗戰史書 栗戰書<br />
<br />
労安 劳安 劳安 李克强 李克強 李克強 <br />
<br />
筑紫哲也 筑紫哲也 筑紫哲也 习近平 習近平 習近平<br />
<br />
山口百惠 山口百惠 山口百惠 韩正 韓中 韓正<br />
<br />
田中角栄 田中角荣 田中角荣 王沪宁 王上海氏 王滬寧<br />
<br />
東条英機 东条英社 东条英机 汪洋 汪洋 汪洋<br />
<br />
毛沢东 毛泽东 毛泽东 赵乐际 趙樂南 趙樂際<br />
<br />
トウ・ショウヘイ 大酱 邓小平 江泽民 江沢民 江沢民<br />
<br />
周恩来 周恩来 周恩来<br />
<br />
クリントン 克林顿 克林顿<br />
<br />
The above table counts 18 special names in the two texts, and 7 machine translation errors. In terms of mistranslation, there are not only "战书" but also "戰史" and "史書" in Chinese. "沪" is the abbreviation of Shanghai. In other words, since "战书" and "沪" are originally common nouns, this disrupts the choice of target language in machine translation. Except Katakana interference (except for トウシゃウヘイ, most of the mistranslations appear in the new Standing Committee. It can be seen that the machine translation system does not update the thesaurus in time. Different from other words, people's names have particularity. Especially as members of the Standing Committee of the Political Bureau of the CPC Central Committee, the translation of their names is officially unified. In this regard, public figures such as political dignitaries, stars, famous hosts and important. In addition, the machine also translates Japanese surnames such as "タ二モト" (谷本), "アンドウ" (安藤) into "塔尼莫特" and "龙胆". It can be found that the language feature that Japanese will be written together with Chinese characters and Hiragana also directly affects the translation quality of Japanese Chinese machine translation. Japanese people often use Katakana pronunciation. For example, when Japanese people talk with Premier Zhu, they often use "二ーハウ" (Hello). However, the machine only recognizes the two pseudonyms "二" and "ハ" and transliterates them into "尼哈" , ignoring the long sound after "二" and "ハ".(Guan 2018:10-12)<br />
<br />
original text Translation by Youdao reference translation<br />
<br />
日美安全体制 日米の安全体制 日米安保体制<br />
<br />
中国共产党第十九次全国代表大会 中国共産党第19回全国代表大会 中国共産党第19回全国代表大会（第19回党大会）<br />
<br />
十八大 十八大 第18回党大会中国特色社会主义<br />
<br />
中国特色社会主義 中国の特色ある社会主義 第18回党大会<br />
<br />
中国共产党中央委员会 中国共産党中央委員会 中国共産党中央委員会<br />
<br />
中国共産党中央委員会十八届中共中央政治局常委 第18代中国共產党中央政治局常務委員 第18期中共中央政治局常務委員<br />
<br />
十八届中共中央政治局委员 18期の中国共產党中央政治局委員 第18期中共中央政治局委員<br />
<br />
十九届中共中央政治局常委 十九回中国共產党中央政治局常務委員 第19期中央政治局常務委員<br />
<br />
中共十九届一中全会 中国共產党第十九回一中央委員会 第19期中央委員会第1回全体会議<br />
<br />
The above table is a comparison of the original and translated versions of some proper nouns. As shown in the table, the mistranslation problems are mainly reflected in the mismatch of numerals + quantifiers, the wrong addition of case auxiliary word "の", the lack of connectors, the Mistranslation of abbreviations, dead translation, and the writing errors of Chinese characters. The following is a specific analysis one by one.<br />
<br />
"十八届中共中央政治局常委", "十八届中共中央政治局委员", "十九届中共中央政治局常委" and "中共十九届一中全会" all have quantifiers "届", which are translated into "代", "期" and "回" respectively. The meaning is vague and should be uniformly translated into "期"; Among them, the translation of the last three proper nouns lacks the Chinese conjunction "第". The case auxiliary word "の" was added by mistake in the translation of "十八届中共中央政治局委员" and "日美安全体制". The "中国共产党中央委员会" did not write in the form of Japanese Ming Dynasty characters, but directly used simplified Chinese characters.<br />
<br />
The full name of "中共十九届一中全会" is "中国共产党第十九届中央委员会第一次全体会议", in which "一" stands for "第一次", which is an ordinal word rather than a cardinal word. Machine translation does not produce a correct translation. The fundamental reason is that there are no "规则" for translating such words in machine translation. If we can formulate corresponding rules for such words (for example, 中共m'1届m2 中全会→第m1期中央委员会第m2回全体会议), the translation system must be able to translate well no matter how many plenary sessions of the CPC Central Committee.(Guan 2018:6-7)<br />
<br />
===2.1.2Mistranslation of Polysemy===<br />
original text Translation by Youdao reference translation<br />
<br />
スタジオ 摄影棚/工作室 直播现场/演播厅<br />
<br />
日中関係の話 中日关系的故事 就中日关系(话题)<br />
<br />
溝 水沟 鸿沟<br />
<br />
それでは日中の問題について質問のある方。 那么对白天的问题有提问的人。 关于中日问题的话题，举手提问。<br />
<br />
私たちのクラスは20人ちょっとですが、 我们班有20人左右， 我们班二十多人的意见统一很难，<br />
<br />
いろいろな意見が出て、まとめるのは大変です。 但是又各种各样的意见，总结起来很困难。 <br />
<br />
一体どうやって、13億人もの人をまとめているんですか。 到底是怎么处理13亿的人的呢？ 中国是怎样把13亿人凝聚在一起的？<br />
<br />
In the original text, the word "スタジオ" appeared four times and was translated into "摄影棚" or "工作室" respectively, but the context is on-site interview, not the production site of photography or film. "話" has many semantics, such as "说话", "事情", "道理", etc. In accordance with the practice of the interview program, Premier Zhu Rongji was invited to answer five questions on the topic of China Japan relations. Obviously, the meaning of the word "故事" is very abrupt. The inherent Japanese word "日中" means "晌午、白天". At the same time, it is also the abbreviation of the names of the two countries. The machine failed to deal with it correctly according to the context. "溝" refers to the gap between China and Japan, not a "水沟". The former "まとめる" appears in the original text with the word "意见", which is intended to describe that it is difficult to unify everyone's opinions. The latter "まとめている" also refers to unifying the thoughts of 1.3 billion people, not "处理". Therefore, it is more appropriate to use "统一" and "处理" in human translation, rather than "总结意见" or "处理意见".(Zhang 2019:5)<br />
<br />
Mistranslation of polysemy has always been a difficult problem in machine translation research. The translation of each word is correct, but it is often very different from the original expression in the context. Zhang Zhengzeng said that ambiguity is a common phenomenon in natural language. Its essence is that the same language form may have different meanings, which is also one of the differences between natural language and artificial language. Therefore, one of the difficulties faced by machine translation is language disambiguation (Zhang Zheng, 2005:60). In this regard, we can mark all the meanings of polysemous words and judge which meaning to choose by common collocation with other words. At the same time, strengthen the text recognition ability of the machine to avoid the translation inconsistent with the current context. In this way, we can avoid the mistake of "大酱" in the place where famous figures such as Deng Xiaoping should have appeared in the previous political articles.(Wang 2020:7-9)<br />
<br />
===2.1.3Mistranslation of compound words===<br />
Multi class words refer to a word with two or more parts of speech, also known as the same word and different classes.<br />
<br />
original text Translation by Youdao reference translation<br />
<br />
1998年江泽民主席曾经访问日本, 1998年の江沢民国家主席の日本訪問し、 1998年、江沢民総書記が日本を訪問し、かつ<br />
<br />
同已故小渊首相签署了联合宣言。 かつて同じ故小渕首相が署名した共同宣言 亡くなられた小渕総理と宣言に調印されました<br />
<br />
Chinese "同" has two parts of speech: prepositions and conjunctions: "故" has many parts of speech, such as nouns, verbs, adjectives and conjunctions. This directly affects the judgment of the machine at the source language level. The word "同" in the above table is used as a conjunction to indicate the other party of a common act. "故" is used as a verb with the semantic meaning of "死亡", which is a modifier of "小渊首相". The machine regards "同" as an adjective and "故" as a noun "原因", which leads to confusion in the structure and unclear semantics of the translation. Different from the polysemy problem, multi category words have at least two parts of speech, and there is often not only one meaning under each part of speech. In this regard, software R & D personnel should fully consider the existence of multi category words, so that the translation machine can distinguish the meaning of words on the basis of marking the part of speech, so as to select the translation through the context and the components of the word in the sentence. Of course, the realization of this function is difficult, and we need to give full play to the wisdom of R & D personnel.(Guan 2018:9-12)<br />
<br />
===2.2 Syntactic mistranslation in Chinese Japanese machine translation===<br />
===2.2.1Mistranslation of tenses===<br />
original text：History is written by the people, and all achievements are attributed to the people. <br />
<br />
Translation by Youdao：歴史は人民が書いたものであり、すべての成果は人民のためである。<br />
<br />
reference translation：歴史は人民が綴っていくものであり、すべての成果は人民に帰することとなります。<br />
<br />
The original sentence meaning is "历史是人民书写的历史" or "历史是人民书写的东西". When translated into Japanese, due to the influence of Japanese language habits, the formal noun "もの" should be supplemented accordingly. In this sentence, both the machine and the interpreter have translated correctly. However, the machine's recognition of "的" is biased, resulting in tense translation errors. The past, present and future will become "history", and this is a continuous action. The form translated as "~ ていく" not only reflects that the action is a continuous action, but also conforms to the tense and semantic information of the original text. In addition, the machine's treatment of the preposition "于" is also inappropriate. In the original text, "人民" is the recipient of action, not the target language. As the most obvious feature of isolated language, function words in Chinese play an important role in semantic expression, and their translation should be regarded as a focus of machine translation research.(Zuo 2021:8)<br />
<br />
original text：李克强同志是十六届中共中央政治局常委,其他五位同志都是十六届中共中央政治局委员。<br />
<br />
Translation by Youdao：李克強総理は第16代中国共産党中央政治局常務委員であり、他の５人の同志はいずれも16期の中国共産党中央政治局委員である。<br />
<br />
reference translation：李克強同志は第16期中国共産党中央政治局常務委員を務め、他の５人は第16期中共中央政治局委員を務めました。<br />
<br />
Judging the verb "是" in the original text plays its most basic positive role, but due to the complexity of Chinese language, "是" often can not be completely transformed into the form of "だ / である" in the process of translation. This sentence is a judgment of what has happened. Compared with the 19th session, the 18th session has become history. This is an implicit temporal information. It is very difficult for machines without human brain to recognize this implicit information. "中共中央政治局常委" is the abbreviation of "中共中央政治局常务委员会委员" and it is a kind of position. In Japanese, often use it with verbs such as "務める","担当する",etc. The translator adopts the past tense of "務める", which conforms to the expression habit of Japanese and deals with the problem of tense at the same time. However, machine translation only mechanically translates this sentence into judgment sentence, and fails to correctly deal with the past information implied by the word "十八届".(Guan 2018:4)<br />
<br />
===2.2.2Mistranslation of honorifics===<br />
Honorific language is a language means to show respect to the listener. Different from Japanese, there is no grammatical category of honorifics in Chinese. There is no specific fixed grammatical form to express honorifics, self modesty and politeness. Instead, specific words such as "您", "请" and "劳驾" are used to express all kinds of respect or self modesty. (Yang 2020:5-9)<br />
<br />
Original text translation by Youdao reference translation<br />
<br />
女士们，先生们，同志们，朋友们 さんたち、先生たち、同志たち、お友达さん ご列席の皆さん<br />
<br />
谢谢大家！ ありがとうございます！ ご清聴ありがとうございました。<br />
<br />
これはどうされますか。 这是怎么回事呢? 您将如何解决这一问题？<br />
<br />
こうした問題をどうお考えでしょうか。 我们会如何考虑这些问题呢？ 您如何看待这一问题？<br />
<br />
For example, for the processing of "女士们，先生们，同志们，朋友们", the machine makes the translation correspond to the original one by one based on the principle of unchanged format, but there are errors in semantic communication and pragmatic habits. When speaking on formal occasions, Chinese expression tends to be comprehensive and detailed, as well as the address of the audience. In contrast, Japanese usually uses general terms such as "皆様", "ご臨席の皆さん" or "代表団の方々" as the opening remarks. In terms of Japanese Chinese translation, the machine also failed to recognize the usage of Japanese honorifics such as "さ れ る" and "お考え". It can be seen that Chinese Japanese machine translation has a low ability to deal with honorific expressions. The occasion is more formal. A good translation should not only be fluent in meaning, but also conform to the expression habits of the target language and match the current translation environment.(Che 2021:3-7)<br />
<br />
===Conclusion===<br />
In the process of discussing the Mistranslation of machine translation, this paper mainly cites the Mistranslation of Youdao translator. When I studied the problem of machine translation mistranslation, I also used a large number of translation software such as Google and Baidu for parallel comparison, and found that these translation software also had similar problems with Youdao translator. For example, the "新世界新未来" in Chinese ABAC structural phrases is translated by Baidu as "新し世界の新しい未来", which, like Youdao, is not translated in the form of parallel phrases; Google online translation translates the word "“全心全意" into a completely wrong "全面的に"; The original sentence "我吃了很多亏" in the Mistranslation of the unique expression in the language is translated by Google online as "私はたくさんの損失を食べました". Because the "吃" and "亏" in the original text are not closely adjacent, the machine can not recognize this echo and mistakenly treats "亏" as a kind of food, so the machine translates the predicate as "食べました"; Japanese "こうした問題をどう考えでしょうか", Google's online translation is "你如何看待这些问题?", "你" tone can not reflect the tone of Japanese honorific, while Baidu translates as "你怎么想这样的问题呢?" Although the meaning can be understood, it is an irregular spoken language. Google and Baidu also made the same mistakes as Youdao in the translation of proper nouns. For example, they translated "十七届(中共中央政治局常委)”" into "第17回..." .In short, similar mistranslations of Youdao are also common in Baidu and Google. Due to space constraints, they will not be listed one by one here.(Cui 2019:7)<br />
<br />
Mr. Liu Yongquan, Institute of language, Chinese Academy of Social Sciences (1997) It has been pointed out that machine translation is a linguistic problem in the final analysis. Although corpus based machine translation does not require a lot of linguistic knowledge, language expression is ever-changing. Machine translation based solely on statistical ideas can not avoid the translation quality problems caused by the lack of language rules. The following is based on the analysis of lexical, syntactic and other mistranslations From the perspective of the characteristics of the source language, this paper summarizes some difficulties of Chinese and Japanese in machine translation.(Liu 2014:8)<br />
<br />
(1) The difficulties of Chinese in machine translation <br />
<br />
Chinese is a typical isolated language. The relationship between words needs to be reflected by word order and function words. We should pay attention to the transformation of function words such as "的", "在", "向" and "了". Some special verbs, such as "是", "做" and "作", are widely used. How to translate on the basis of conforming to Japanese pragmatic habits and expressions is a difficulty in machine translation research. In terms of word order, Chinese is basically "subject→predicate→object". At the same time, Chinese pays attention to parataxis, and there is no need to be clear in expression with meaningful cohesion, which increases the difficulty of Chinese Japanese machine translation. When there are multiple verbs or modifier components in complex long sentences, machine translation usually can not accurately divide the components of the sentence, resulting in the result that the translation is completely unreadable.(Guan 2018:6-12) <br />
<br />
(2) Difficulties of Japanese in machine translation <br />
<br />
Both Chinese and Japanese languages use Chinese characters, and most machines produce the target language through the corresponding translation of Chinese characters. However, pseudonyms in Japanese play an important role in judging the part of speech and meaning, and can not only recognize Chinese characters and judge the structure and semantics of sentences. Japanese vocabulary is composed of Chinese vocabulary, inherent vocabulary and foreign vocabulary. Among them, the first two have a great impact on Chinese Japanese machine translation. For example "提出" is translated as "提出する" or "打ち出す"; and "重视" is translated as "重視する" or "大切にする". Japanese is an adhesive language, which contains a lot of "けど", "が" and "けれども" Some of them are transitional and progressive structures, but there are also some sequential expressions that do not need translation, and these translation software often can not accurately grasp them.(Che 2021:10)<br />
<br />
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<br />
[13]Zhang Yi 张义(2019).机器翻译的译文分析【D】.Translation analysis of machine translation.西安外国语大学.(10) <br />
<br />
[14]Zhang Linqian 张琳婧(2019).在线机器翻译中日翻译错误原因及对策【D】.Causes and countermeasures of online machine translation errors in Chinese-Japanese translation.山西大学.(02)<br />
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[15]Wang Dan 王丹(2020).基于机器翻译的专利文本译后编辑对策研究【D】.Research on countermeasures for post-translational editing of patent texts based on machine translation.大连理工大学.(06)<br />
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[16]Yang Xiaokun 杨晓琨(2020).日中机器翻译中的前编辑规则与效果验证【D】.Pre-editing rules and effect verification in Japanese-Chinese machine translation.大连理工大学.(06)<br />
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[17]Zuo Jia 左嘉(2021). 机器翻译日译汉误译研究【D】. Research on Mistranslation of Machine Translation from Japanese to Chinese.北京第二外国语学院.<br />
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[18]Guan Biying 关碧莹(2018).关于政治类发言的汉日机器翻译误译分析【D】.Analysis of Chinese-Japanese Machine Translation Mistranslations of Political Speeches.哈尔滨理工大学.<br />
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[19]Che Tong 车彤(2021).汉译日机器翻译质量评估及译后编辑策略研究【D】.Research on Quality Evaluation of Chinese-Japanese Machine Translation and Post-translation Editing Strategies.北京外国语大学.(09)<br />
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Networking Linking<br />
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http://www.elecfans.com/rengongzhineng/692245.html<br />
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https://baike.baidu.com/item/%E6%9C%BA%E5%99%A8%E7%BF%BB%E8%AF%91/411793<br />
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=13 陈湘琼Chen Xiangqiong（Study on Post-editing from the Perspective of Functional Equivalence Theory ）=<br />
[[Machine_Trans_EN_13]]<br />
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=14 Bi bi Nadia（Machine Translation a Challenge for Human Translators)=<br />
[[Machine_Trans_EN_14]]<br />
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===Abstract===<br />
Machine translation is a big obstacle in the way of Human translators or interpreters although it is quick and less time consuming.People are trying to get translation of their target language using through source language.For this purpose they are using digital apps like Google translation Google translation does not give accurate and exact interpretation.Google translator is translating word to word but it doesn't clarify it's true and actual meaning.On the other hand human translators can give exact and accurate translation ,they take care of grammatical mistakes , diction and sentence structure.They clarify the purpose of target language through using source language.<br />
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===Keywords===<br />
Descriptive translation, academic, interdiscipline, comparative literature, localization,Translatology,school of thought,translation , studies, linguistics, corresponding.<br />
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===Body of article===<br />
Translation is the process of reworking text from one language into another to maintain the original message and communication. But, like anything else, there are different methods of translation, and they vary in form and function. What is translation? The term has become widely used among knowledge transferring researchers and practitioners, especially in the fields of health and health care. In a landmark review, Jonathan Lomas began to argue that 'The tasks… may be defined as to establish and maintain links between researchers and their audience, via the appropriate translation of research findings' (Lomas 1997, p 4). In 2004, World Health Organization's World Report on Knowledge for Better Health suggested that 'One of the key contributions of research to health systems is the translation of knowledge into actions' (WHO 2004, p 33 and p 100). By 2006, special issues of WHO's Bulletin as well as the journals Evaluation and the Health Professions and the Journal of Continuing Education in the Health Professions were dedicated to translation.But what does 'translation' mean? It may be a new word for an old problem, meaning nothing more than 'transfer'. The rapidly emerging field of 'translational medicine' seems to take translation to mean generally what transfer might have meant, that is the transmission of knowledge and evidence 'from bench to bedside'. As one commentator has observed, "Translational medicine" as a fashionable term is being increasingly used to describe the wish of biomedical researchers to ultimately help patients' (Wehling 2008, abstract). <br />
Semantic uncertainty persists, not least because of the different interests of scientists, clinicians, patients and commercial firms (Littman et al 2007): 'Translational research means different things to different people, but it seems important to almost everyone' (Woolf 2008, p 211).<br />
In related fields, such as public health (Armstrong et al 2006), 'translation' seems to signify dissatisfaction with 'transfer'. It wants to move away from thinking of knowledge transfer as a form of technology transfer or dissemination, rejecting if only by implication its mechanistic assumptions and its model of linear messaging from A to B. But still, what does it signify?<br />
<br />
===Why translation?===<br />
'Translation' indicates a closer attention to the problem of shared meaning and how it might be developed. It seems to represent some new epistemological lubricant, facilitating the dissemination of texts and the application and use of the knowledge and information they in. Simply, translation might be the key to transfer. And yet, when we stop to think, we are more ambivalent. What is translated often seems somehow inferior, not real or original. Note how readily commentators reach for the idea that things might be 'lost in translation'. Knowing at a distance – made in and mediated by translation - makes for incomplete renditions, blurred images, partial truths. So what might 'translation' really mean? The purpose of this paper is to set out, for policy makers and practitioners, the theoretical and conceptual resources translation holds and seems to represent. In doing so, it explores understandings of translation in the fields of literature and linguistics and in the sociology of science and technology. It begins by setting out just why this idea of translation should make immediate, intuitive sense in relation to research, policy and practice.<br />
1translation in research, policy and practice research as translation<br />
Research often entails translation from one language to another: where data is collected from more than one ethnic group, for example, or where the language of the researcher is other than that of the research subject. It may draw on a secondary literature or source documents written in different languages, and may be published and disseminated in languages other than the one in which it is first written up.<br />
2In a different way, to conduct an interview is to ask for an account of experience and its meanings, but it is also to construct and translate that experience in terms defined at least in part by the researcher. In representing what is said, transcripts then select data, usually excluding significant gesture and eye-contact, for example. Often, certain characteristics of speech-acts (such as hesitations) will be edited out. In turn, the format of the transcript shapes the analytic use the researcher may make of it. The basis of research 'findings', then, is an artefact, a transcript or translation, not an original interaction (Ochs 1979, Barnes, Bloor and Henry 1996, Ross 2009).<br />
3In this way, the researcher recasts aspects of his or her problem or topic in new, scientific form: 'All researchers "translate" the experiences of others' (Temple 1997, p 609). Research is invariably conducted in a sort of 'metalanguage' (Hantrais and Ager 1985): the research process can be conceived as one of successive translations (from theoretical formulation to operationalization, transcription, interpretation and dissemination). Theorization is a process of reciprocal back and forth between theory and fact, in which conceptions of each are revised in order that one fit the other (Baldamus 1974).<br />
4 It is a kind of translation: a rereading, re-use, re-application or re-representation of what we know in new terms (Turner 1980). Referencing, too, is an act of translation, a form of appropriation and incorporation of one text by another (Gilbert 1977).policy as translation Each of the fields covered by the paper is diverse and ill-defined, and there is no intention here to provide a comprehensive account of any them. Sources have been chosen for their relevance: main references are cited in the text, and additional sources listed in footnotes.<br />
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2For a brief introduction to the technical issues involved in social research in more than one language, see Birbili (2000). For translation issues in survey research and question design in general, see Ervin and Bower (1952) and Deutscher (1968); on translating survey research instruments (in this instance health-related quality of life measures), Bowden and Fox-Rushby (2003). On the use of translators and interpreters, see Temple (1997) and Jentsch (1998).<br />
3For an interesting discussion of this problem, see Bourdieu (1999), esp pp 621-626, 'The risks of writing'. <br />
===Difference between machine translation and human translators===<br />
Few people disagree on the differences between the two, but many argue over the quality of the translations. How accurate are machine translations? How reliable are human translations? Some say machine translation produces near-perfect translations while others are adamant that translations are incomprehensible and cause more problems than they solve. Results will, of course, vary depending on the source and target languages, the machine translation service used (e.g. Google Translate), and the complexity of the original text.<br />
Machine translation, love it or hate it, is here to stay. In fact, the machine translation market is growing at such a fast pace that it is predicted to reach $980 million by 2022.<br />
===Machine translation: the pros and cons===<br />
The advantages of machine translation generally come down to two factors: it’s faster and cheaper. The downside to this is the standard of translation can be anywhere from inaccurate, to incomprehensible, and potentially dangerous (more on that shortly).<br />
==The advantages of machine translation==<br />
Many free tools are readily available (Google Translate, Skype Translator, etc.)<br />
Quick turnaround time <br />
You can translate between multiple languages using one tool<br />
Translation technology is constantly improving<br />
The disadvantages of machine translation<br />
Level of accuracy can be very low <br />
Accuracy is also very inconsistent across different languages<br />
==Machines can't translate context ==<br />
Mistakes are sometimes costly <br />
Sometimes translation simply doesn’t work<br />
The most important thing to consider with any kind of translation is the cost of potential mistakes. Translating instructions for medical equipment, aviation manuals, legal documents and many other kinds of content require 100% accuracy. In such cases, mistakes can cost lives, huge amounts of money and irreparable damage to your company’s image. So choose carefully!<br />
Human translation: the pros and cons<br />
Human translation essentially switches the table in terms of pros and cons. A higher standard of accuracy comes at the price of longer turnaround times and higher costs. What you have to decide is whether that initial investment outweighs the potential cost of mistakes. Alternatively, whether mistakes simply aren’t an option, like the cases we looked at in the previous section.<br />
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==The advantages of human translation ==<br />
It’s a translator’s job to ensure the highest accuracy<br />
Humans can interpret context and capture the same meaning, rather than simply translating words<br />
Human translators can review their work and provide a quality process<br />
Humans can interpret the creative use of language, e.g. puns, metaphors, slogans, etc.<br />
Professional translators understand the idiomatic differences between their languages<br />
Humans can spot pieces of content where literal translation isn’t possible and find the most suitable alternative<br />
The disadvantages of human translation<br />
Turnaround time is longer <br />
Translators rarely work for free <br />
Unless you use a translation agency, with access to thousands of translators, you’re limited to the languages any one translator can work with<br />
Simply put, human translation is your best option when accuracy is even remotely important. Other considerations to make are the complexity of your source material and the two languages you’re translating between – both of which can render machines pretty useless.<br />
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When to use machine and human translation<br />
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The truth is, the debate over machine vs human translation is an unnecessary distraction. What we should really be talking about is when to use these two different types of translation services, because they both serve a very valid purpose.<br />
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Examples of when to use machine translation<br />
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When you have a large bulk of content to translate and the general meaning is enough<br />
When your translation never reaches the final audience, e.g. you’re translating a resource as research for another piece of content<br />
Translating documents for internal use within a company, provided 100% accuracy isn’t needed<br />
To partially translate large chunks of content for a human translator to improve upon<br />
Examples of when to use human translation<br />
When accuracy is important<br />
Most cases where your translated content is received by a consumer audience<br />
When you have a duty of care to provide accurate translations (e.g. legal documents, product instructions, medical guidelines or health and safety content)<br />
When translating marketing material or other texts for creative language uses.<br />
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types of machine translation.<br />
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What is Machine Translation? Rule Based Machine Translation vs. Statistical Machine Translation. Machine translation (MT) is automated translation. It is the process by which computer software is used to translate a text from one natural language (such as English) to another (such as Spanish).<br />
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To process any translation, human or automated, the meaning of a text in the original (source) language must be fully restored in the target language, i.e. the translation. While on the surface this seems straightforward, it is far more complex. Translation is not a mere word-for-word substitution. A translator must interpret and analyze all of the elements in the text and know how each word may influence another. This requires extensive expertise in grammar, syntax (sentence structure), semantics (meanings), etc., in the source and target languages, as well as familiarity with each local region.<br />
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Human and machine translation each have their share of challenges. For example, no two individual translators can produce identical translations of the same text in the same language pair, and it may take several rounds of revisions to meet customer satisfaction. But the greater challenge lies in how machine translation can produce publishable quality translations.<br />
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Rule-Based Machine Translation Technology<br />
Rule-based machine translation relies on countless built-in linguistic rules and millions of bilingual dictionaries for each language pair.<br />
The software parses text and creates a transitional representation from which the text in the target language is generated. This process requires extensive lexicons with morphological, syntactic, and semantic information, and large sets of rules. The software uses these complex rule sets and then transfers the grammatical structure of the source language into the target language.<br />
Translations are built on gigantic dictionaries and sophisticated linguistic rules. Users can improve the out-of-the-box translation quality by adding their terminology into the translation process. They create user-defined dictionaries which override the system’s default settings.<br />
In most cases, there are two steps: an initial investment that significantly increases the quality at a limited cost, and an ongoing investment to increase quality incrementally. While rule-based MT brings companies to the quality threshold and beyond, the quality improvement process may be long and expensive.<br />
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Statistical Machine Translation Technology<br />
Statistical machine translation utilizes statistical translation models whose parameters stem from the analysis of monolingual and bilingual corpora. Building statistical translation models is a quick process, but the technology relies heavily on existing multilingual corpora. A minimum of 2 million words for a specific domain and even more for general language are required. Theoretically it is possible to reach the quality threshold but most companies do not have such large amounts of existing multilingual corpora to build the necessary translation models. Additionally, statistical machine translation is CPU intensive and requires an extensive hardware configuration to run translation models for average performance levels.<br />
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Rule-Based MT vs. Statistical MT<br />
Rule-based MT provides good out-of-domain quality and is by nature predictable. Dictionary-based customization guarantees improved quality and compliance with corporate terminology. But translation results may lack the fluency readers expect. In terms of investment, the customization cycle needed to reach the quality threshold can be long and costly. The performance is high even on standard hardware.<br />
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Statistical MT provides good quality when large and qualified corpora are available. The translation is fluent, meaning it reads well and therefore meets user expectations. However, the translation is neither predictable nor consistent. Training from good corpora is automated and cheaper. But training on general language corpora, meaning text other than the specified domain, is poor. Furthermore, statistical MT requires significant hardware to build and manage large translation models.<br />
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Rule-Based MT Statistical MT<br />
+ Consistent and predictable quality – Unpredictable translation quality<br />
+ Out-of-domain translation quality – Poor out-of-domain quality<br />
+ Knows grammatical rules – Does not know grammar <br />
+ High performance and robustness – High CPU and disk space requirements<br />
+ Consistency between versions – Inconsistency between versions <br />
– Lack of fluency + Good fluency<br />
– Hard to handle exceptions to rules + Good for catching exceptions to rules <br />
– High development and customization costs + Rapid and cost-effective development costs provided the required corpus exists<br />
Given the overall requirements, there is a clear need for a third approach through which users would reach better translation quality and high performance (similar to rule-based MT), with less investment (similar to statistical MT).<br />
Post-Edited Machine Translation (PEMT)<br />
Often, PEMT is used to bridge the gap between the speed of machine translation and the quality of human translation, as translators review, edit and improve machine-translated texts. PEMT services cost more than plain machine translations but less than 100% human translation, especially since the post-editors don’t have to be fluently bilingual—they just have to be skilled proofreaders with some experience in the language and target region.<br />
Successful translation is about more than just the words, which is why we advocate for not just human translation by skilled linguists, but for translation by people deeply familiar with the cultures they’re writing for. Life experience, study and the knowledge that only comes from living in a geographic region can make the difference between words that are understandable and language that is capable of having real, positive impact. <br />
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PacTranz<br />
The HUGE list of 51 translation types, methods and techniques<br />
Upper section of infographic of 51 common types of translation classified in 4 broad categoriesThere are a bewildering number of different types of translation.<br />
So we’ve identified the 51 types you’re most likely to come across, and explain exactly what each one means.<br />
This includes all the main translation methods, techniques, strategies, procedures and areas of specialisation.<br />
It’s our way of helping you make sense of the many different kinds of translation – and deciding which ones are right for you.<br />
Don’t miss our free summary pdf download later in the article!<br />
The 51 types of translation we’ve identified fall neatly into four distinct categories.<br />
Translation Category A: 15 types of translation based on the technical field or subject area of the text<br />
Icons representing 15 types of translation categorised by the technical field or subject area of the textTranslation companies often define the various kinds of translation they provide according to the subject area of the text.<br />
This is a useful way of classifying translation types because specialist texts normally require translators with specialist knowledge.<br />
Here are the most common types you’re like to come across in this category.<br />
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1. General Translation<br />
What is it?<br />
The translation of non-specialised text. That is, text that we can all understand without needing specialist knowledge in some area.<br />
The text may still contain some technical terms and jargon, but these will either be widely understood, or easily researched.<br />
What this means<br />
The implication is that you don’t need someone with specialist knowledge for this type of translation – any professional translator can handle them.<br />
Translators who only do this kind of translation (don’t have a specialist field) are sometimes referred to as ‘generalist’ or ‘general purpose’ translators.<br />
Examples<br />
Most business correspondence, website content, company and product/service info, non-technical reports.<br />
Most of the rest of the translation types in this Category do require specialist translators.<br />
Check out our video on 13 types of translation requiring special translator expertise:<br />
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2. Technical Translation<br />
What is it?<br />
We use the term “technical translation” in two different ways:<br />
Broad meaning: any translation where the translator needs specialist knowledge in some domain or area.<br />
This definition would include almost all the translation types described in this section.<br />
Narrow meaning: limited to the translation of engineering (in all its forms), IT and industrial texts.<br />
This narrower meaning would exclude legal, financial and medical translations for example, where these would be included in the broader definition.<br />
What this means<br />
Technical translations require knowledge of the specialist field or domain of the text.<br />
That’s because without it translators won’t completely understand the text and its implications. And this is essential if we want a fully accurate and appropriate translation.Good to know Many technical translation projects also have a typesetting/dtp requirement. Be sure your translation provider can handle this component, and that you’ve allowed for it in your project costings and time frames.<br />
Examples<br />
Manuals, specialist reports, product brochures<br />
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3. Scientific Translation<br />
What is it?<br />
The translation of scientific research or documents relating to it.<br />
What this means<br />
These texts invariably contain domain-specific terminology, and often involve cutting edge research.<br />
So it’s imperative the translator has the necessary knowledge of the field to fully understand the text. That’s why scientific translators are typically either experts in the field who have turned to translation, or professionally qualified translators who also have qualifications and/or experience in that domain.<br />
On occasion the translator may have to consult either with the author or other domain experts to fully comprehend the material and so translate it appropriately.<br />
Examples<br />
Research papers, journal articles, experiment/trial results<br />
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4. Medical Translation<br />
What is it?<br />
The translation of healthcare, medical product, pharmaceutical and biotechnology materials.<br />
Medical translation is a very broad term covering a wide variety of specialist areas and materials – everything from patient information to regulatory, marketing and technical documents.<br />
As a result, this translation type has numerous potential sub-categories – ‘medical device translations’ and ‘clinical trial translations’, for example.<br />
What this means<br />
As with any text, the translators need to fully understand the materials they’re translating. That means sound knowledge of medical terminology and they’ll often also need specific subject-matter expertise.<br />
Good to know<br />
Many countries have specific requirements governing the translation of medical device and pharmaceutical documentation. This includes both your client-facing and product-related materials.<br />
Examples<br />
Medical reports, product instructions, labeling, clinical trial documentation<br />
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5. Financial Translation<br />
What is it?<br />
In broad terms, the translation of banking, stock exchange, forex, financing and financial reporting documents.<br />
However, the term is generally used only for the more technical of these documents that require translators with knowledge of the field.<br />
Any competent translator could translate a bank statement, for example, so that wouldn’t typically be considered a financial translation.<br />
What this means<br />
You need translators with domain expertise to correctly understand and translate the financial terminology in these texts.<br />
Examples<br />
Company accounts, annual reports, fund or product prospectuses, audit reports, IPO documentation<br />
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6. Economic Translations<br />
What is it?<br />
1. Sometimes used as a synonym for financial translations.<br />
2. Other times used somewhat loosely to refer to any area of economic activity – so combining business/commercial, financial and some types of technical translations.<br />
3. More narrowly, the translation of documents relating specifically to the economy and the field of economics.<br />
What this means<br />
As always, you need translators with the relevant expertise and knowledge for this type of translation.<br />
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7. Legal Translation<br />
What is it?<br />
The translation of documents relating to the law and legal process.<br />
What this means<br />
Legal texts require translators with a legal background.<br />
That’s because without it, a translator may not:<br />
– fully understand the legal concepts<br />
– write in legal style<br />
– understand the differences between legal systems, and how best to translate concepts that don’t correspond.<br />
And we need all that to produce professional quality legal translations – translations that are accurate, terminologically correct and stylistically appropriate.<br />
Examples<br />
Contracts, legal reports, court judgments, expert opinions, legislation<br />
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8. Juridical Translation<br />
What is it?<br />
1. Generally used as a synonym for legal translations.<br />
2. Alternatively, can refer to translations requiring some form of legal verification, certification or notarization that is common in many jurisdictions.<br />
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9. Judicial Translation<br />
What is it?<br />
1. Most commonly a synonym for legal translations.<br />
2. Rarely, used to refer specifically to the translation of court proceeding documentation – so judgments, minutes, testimonies, etc. <br />
<br />
10. Patent Translation<br />
What is it?<br />
The translation of intellectual property and patent-related documents.<br />
Key features<br />
Patents have a specific structure, established terminology and a requirement for complete consistency throughout – read more on this here. These are key aspects to patent translations that translators need to get right.<br />
In addition, subject matter can be highly technical.<br />
What this means<br />
You need translators who have been trained in the specific requirements for translating patent documents. And with the domain expertise needed to handle any technical content.<br />
Examples<br />
Patent specifications, prior art documents, oppositions, opinions<br />
<br />
11. Literary Translation<br />
What is it?<br />
The translation of literary works – novels, short stories, plays, essays, poems.<br />
Key features<br />
Literary translation is widely regarded as the most difficult form of translation.<br />
That’s because it involves much more than simply conveying all meaning in an appropriate style. The translator’s challenge is to also reproduce the character, subtlety and impact of the original – the essence of what makes that work unique.<br />
This is a monumental task, and why it’s often said that the translation of a literary work should be a literary work in its own right.<br />
What this means<br />
Literary translators must be talented wordsmiths with exceptional creative writing skills.<br />
Because few translators have this skillset, you should only consider dedicated literary translators for this type of translation.<br />
<br />
<br />
12. Commercial Translation<br />
What is it?<br />
The translation of documents relating to the world of business.<br />
This is a very generic, wide-reaching translation type. It includes other more specialised forms of translation – legal, financial and technical, for example. And all types of more general business documentation.<br />
Also, some documents will require familiarity with business jargon and an ability to write in that style.<br />
What this means<br />
Different translators will be required for different document types – specialists should handle materials involving technical and specialist fields, whereas generalist translators can translate non-specialist materials.<br />
Examples<br />
Business correspondence, reports, marketing and promotional materials, sales proposals<br />
<br />
13. Business Translations<br />
What is it?<br />
A synonym for Commercial Translations.<br />
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14. Administrative Translations<br />
What is it?<br />
The translation of business management and administration documents.<br />
So it’s a subset of business / commercial translations.<br />
What this means<br />
The implication is these documents will include business jargon and ‘management speak’, so require a translator familiar with, and practised at, writing in that style.<br />
Examples<br />
Management reports and proposals<br />
<br />
15. Marketing Translations<br />
What is it?<br />
The translation of advertising, marketing and promotional materials.<br />
This is a subset of business or commercial translations.<br />
Key features<br />
Marketing copy is designed to have a specific impact on the audience – to appeal and persuade.<br />
So the translated copy must do this too.<br />
But a direct translation will seldom achieve this – so translators need to adapt their wording to produce the impact the text is seeking.<br />
And sometimes a completely new message might be needed – see transcreation in our next category of translation types.<br />
What this means<br />
Marketing translations require translators who are skilled writers with a flair for producing persuasive, impactful copy.<br />
As relatively few translators have these skills, engaging the right translator is key.<br />
Good to know<br />
This type of translation often comes with a typesetting or dtp requirement – particularly for adverts, posters, brochures, etc.<br />
Its best for your translation provider to handle this component. That’s because multilingual typesetters understand the design and aesthetic conventions in other languages/cultures. And these are essential to ensure your materials have the desired impact and appeal in your target markets.<br />
Examples<br />
Advertising, brochures, some website/social media text.<br />
Translation Category B: 14 types of translation based on the end product or use of the translation<br />
This category is all about how the translation is going to be used or the end product that’s produced.<br />
Most of these types involve either adapting or processing a completed translation in some way, or converting or incorporating it into another program or format.<br />
You’ll see that some are very specialised, and complex.<br />
It’s another way translation providers refer to the range of services they provide.<br />
Check out our video of the most specialised of these types of translation:<br />
<br />
16. Document Translations<br />
What is it?<br />
The translation of documents of all sorts.<br />
Here the translation itself is the end product and needs no further processing beyond standard formatting and layout.<br />
<br />
17. Text Translations<br />
What is it?<br />
A synonym for document translation.<br />
<br />
18. Certified Translations<br />
What is it?<br />
A translation with some form of certification.<br />
Key features<br />
The certification can take many forms. It can be a statement by the translation company, signed and dated, and optionally with their company seal. Or a similar certification by the translator.<br />
The exact format and wording will depend on what clients and authorities require – here’s an example.<br />
<br />
19. Official Translations<br />
What is it?<br />
1. Generally used as a synonym for certified translations.<br />
2. Can also refer to the translation of ‘official’ documents issued by the authorities in a foreign country. These will almost always need to be certified.<br />
<br />
20. Software Localisation<br />
What is it?<br />
Adapting software for another language/culture.<br />
Key features<br />
The goal of software localisation is not just to make the program or product available in other languages. It’s also about ensuring the user experience in those languages is as natural and effective as possible.<br />
Translating the user interface, messaging, documentation, etc is a major part of the process.<br />
Also key is a customisation process to ensure everything matches the conventions, norms and expectations of the target cultures.<br />
Adjusting time, date and currency formats are examples of simple customisations. Others might involve adapting symbols, graphics, colours and even concepts and ideas.<br />
Localisation is often preceded by internationalisation – a review process to ensure the software is optimally designed to handle other languages.<br />
And it’s almost always followed by thorough testing – to ensure all text is in the correct place and fits the space, and that everything makes sense, functions as intended and is culturally appropriate.<br />
Localisation is often abbreviated to L10N, internationalisation to i18n.<br />
What this means<br />
Software localisation is a specialised kind of translation, and you should always engage a company that specialises in it.<br />
They’ll have the systems, tools, personnel and experience needed to achieve top quality outcomes for your product.<br />
<br />
21. Game Localisation<br />
What is it?<br />
Adapting games for other languages and markets.<br />
<br />
It’s a subset of software localisation.<br />
Key features<br />
The goal of game localisation is to provide an engaging and fun gaming experience for speakers of other languages.<br />
<br />
It involves translating all text and recording any required foreign language audio.<br />
<br />
But also adapting anything that would clash with the target culture’s customs, sensibilities and regulations.<br />
<br />
For example, content involving alcohol, violence or gambling may either be censored or inappropriate in the target market.<br />
<br />
And at a more basic level, anything that makes users feel uncomfortable or awkward will detract from their experience and thus the success of the game in that market.<br />
<br />
So portions of the game may have to be removed, added to or re-worked.<br />
<br />
Game localisation involves at least the steps of translation, adaptation, integrating the translations and adaptations into the game, and testing.<br />
<br />
What this means<br />
Game localisation is a very specialised type of translation best left to those with specific expertise and experience in this area.<br />
<br />
22. Multimedia Localisation<br />
What is it?<br />
Adapting multimedia for other languages and cultures.<br />
<br />
Multimedia refers to any material that combines visual, audio and/or interactive elements. So videos and movies, on-line presentations, e-Learning courses, etc.<br />
Key features<br />
Anything a user can see or hear may need localising.<br />
<br />
That means the audio and any text appearing on screen or in images and animations.<br />
<br />
Plus it can mean reviewing and adapting the visuals and/or script if these aren’t suitable for the target culture.<br />
<br />
The localisation process will typical involve:<br />
– Translation<br />
– Modifying the translation for cultural reasons and/or to meet technical requirements<br />
– Producing the other language versions<br />
<br />
Audio output may be voice-overs, dubbing or subtitling.<br />
<br />
And output for visuals can involve re-creating elements, or supplying the translated text for the designers/engineers to incorporate.<br />
<br />
What this means<br />
Multimedia localisation projects vary hugely, and it’s essential your translation providers have the specific expertise needed for your materials.<br />
<br />
<br />
<br />
23. Script Translations<br />
What is it?<br />
Preparing the text of recorded material for recording in other languages.<br />
Key features<br />
There are several issues with script translation.<br />
<br />
One is that translations typically end up longer than the original script. So voicing the translation would take up more space/time on the video than the original language.<br />
<br />
Sometimes that space will be available and this will be OK.<br />
<br />
But generally it won’t be. So the translation has to be edited back until it can be comfortably voiced within the time available on the video.<br />
<br />
Another challenge is the translation may have to synchronise with specific actions, animations or text on screen.<br />
<br />
Also, some scripts also deal with technical subject areas involving specialist technical terminology.<br />
<br />
Finally, some scripts may be very culture-specific – featuring humour, customs or activities that won’t work well in another language. Here the script, and sometimes also the associated visuals, may need to be adjusted before beginning the translation process.<br />
<br />
It goes without saying that a script translation must be done well. If it’s not, there’ll be problems producing a good foreign language audio, which will compromise the effectiveness of the video.<br />
<br />
Translators typically work from a time-coded transcript. This is the original script marked to show the time available for each section of the translation.<br />
<br />
What this means<br />
There are several potential pitfalls in script translations. So it’s vital your translation provider is practiced at this type of translation and able to handle any technical content.<br />
<br />
<br />
<br />
24. Voice-over and Dubbing Projects<br />
What is it?<br />
Translation and recording of scripts in other languages.<br />
<br />
Voice-overs vs dubbing<br />
There is a technical difference.<br />
A voice-over adds a new track to the production, dubbing replaces an existing one.<br />
<br />
<br />
Key features<br />
These projects involve two parts:<br />
– a script translation (as described above), and<br />
– producing the audio<br />
<br />
So they involve the combined efforts of translators and voice artists.<br />
The task for the voice artist is to produce a high quality read. That’s one that matches the style, tone and richness of the original.<br />
<br />
Often each section of the new audio will need to be the same length as the original.<br />
<br />
But sometimes the segments will need to be shorter – for example where the voice-over lags the original by a second or two. This is common in interviews etc, where the original voice is heard initially then drops out.<br />
<br />
The most difficult form of dubbing is lip-syncing – where the new audio needs to synchronise with the original speaker’s lip movements, gestures and actions.<br />
<br />
Lip-syncing requires an exceptionally skilled voice talent and considerable time spent rehearsing and fine tuning the translation.<br />
<br />
What this means<br />
You need to use experienced professionals every step of the way in this type of project.<br />
<br />
That’s to ensure firstly that your foreign-language scripts are first class, then that the voicing is of high professional standard.<br />
<br />
Anything less will mean your foreign language versions will be way less effective and appealing to your target audience.<br />
<br />
<br />
<br />
25. Subtitle Translations<br />
What is it?<br />
Producing foreign language captions for sub or surtitles.<br />
Key features<br />
The goal with subtitling is to produce captions that viewers can comfortably read in the time available and still follow what’s happening on the video.<br />
<br />
To achieve this, languages have “rules” governing the number of characters per line and the minimum time each subtitle should display.<br />
<br />
Sticking to these guidelines is essential if your subtitles are to be effective.<br />
<br />
But this is no easy task – it requires simple language, short words, and a very succinct style. Translators will spend considerable time mulling over and re-working their translation to get it just right.<br />
<br />
Most subtitle translators use specialised software that will output the captions in the format sound engineers need for incorporation into the video.<br />
<br />
What this means<br />
As with other specialised types of translation, you should only use translators with specific expertise and experience in subtitling.<br />
<br />
<br />
<br />
26. Website Localisation<br />
What is it?<br />
The translation and adapting of relevant content on a website to best suit the target language and culture.<br />
<br />
Note: Many providers use the term website translation as a synonym for localisation. Strictly speaking though, translation is just one part of localisation.<br />
Key features<br />
<br />
Not all pages on a website may need to be localised – clients should review their content to identify what’s relevant for the other language versions.<br />
Some content may need specialist translators – legal and technical pages for example.<br />
There may also be videos, linked documents, and text or captions in graphics to translate.<br />
Adaptation can mean changing date, time, currency and number formats, units of measure, etc.<br />
But also images, colours and even the overall site design and style if these won’t have the desired impact in the target culture.<br />
Translated files can be supplied in a wide range of formats – translators usually coordinate output with the site webmasters.<br />
New language versions are normally thoroughly reviewed and tested before going live to confirm everything is displaying correctly, works as intended and is cultural appropriate.<br />
What this means<br />
The first step should be to review your content and identify what needs to be translated. This might lead you to modify some pages for the foreign language versions.<br />
<br />
In choosing your translation providers be sure they can:<br />
– handle any technical or legal content,<br />
– provide your webmaster with the file types they want.<br />
<br />
And you should always get your translators to systematically review the foreign language versions before going live.<br />
<br />
<br />
<br />
27. Transcreation<br />
What is it?<br />
Adapting a message to elicit the same emotional response in another language and culture.<br />
Translation is all about conveying the message or meaning of a text in another language. But sometimes that message or meaning won’t have the desired effect in the target culture.<br />
<br />
This is where transcreation comes in. Transcreation creates a new message that will get the desired emotional response in that culture, while preserving the style and tone of the original.<br />
<br />
So it’s a sort of creative translation – which is where the word comes from, a combination of ‘translation’ and ‘creation’.<br />
<br />
At one level transcreation may be as simple as choosing an appropriate idiom to convey the same intent in the target language – something translators do all the time.<br />
<br />
But mostly the term is used to refer to adapting key advertising and marketing messaging. Which requires copywriting skills, cultural awareness and an excellent knowledge of the target market.<br />
<br />
Who does it?<br />
Some translation companies have suitably skilled personnel and offer transcreation services.<br />
<br />
Often though it’s done in the target country by specialist copywriters or an advertising or marketing agency – particularly for significant campaigns and to establish a brand in the target marketplace.<br />
<br />
What this means<br />
Most general marketing and promotional texts won’t need transcreation – they can be handled by a translator with excellent creative writing skills.<br />
<br />
But slogans, by-lines, advertising copy and branding statements often do.<br />
<br />
Whether you should opt for a translation company or an in-market agency will depend on the nature and importance of the material, and of course your budget.<br />
<br />
<br />
<br />
28. Audio Translations<br />
What is it?<br />
Broad meaning: the translation of any type of recorded material into another language.<br />
<br />
More commonly: the translation of a foreign language video or audio recording into your own language. So this is where you want to know and document what a recording says.<br />
Key features<br />
The first challenge with audio translations is it’s often impossible to pick up every word that’s said. That’s because audio quality, speech clarity and speaking speed can all vary enormously.<br />
<br />
It’s also a mentally challenging task to listen to an audio and translate it directly into another language. It’s easy to miss a word or an aspect of meaning.<br />
<br />
So best practice is to first transcribe the audio (type up exactly what is said in the language it is spoken in), then translate that transcription.<br />
<br />
However, this is time consuming and therefore costly, and there are other options if lesser precision is acceptable.<br />
<br />
What this means<br />
It’s best to discuss your requirements for this kind of translation with your translation provider. They’ll be able to suggest the best translation process for your needs.<br />
<br />
Examples<br />
Interviews, product videos, police recordings, social media videos.<br />
<br />
<br />
<br />
29. Translations with DTP<br />
What is it?<br />
Translation incorporated into graphic design files.multilingual dtp example in the form of a Rubik's Cube with foreign text on each square<br />
Key features<br />
Graphic design programs are used by professional designers and graphic artists to combine text and images to create brochures, books, posters, packaging, etc.<br />
<br />
Translation plus dtp projects involve 3 steps – translation, typesetting, output.<br />
<br />
The typesetting component requires specific expertise and resources – software and fonts, typesetting know-how, an appreciation of foreign language display conventions and aesthetics.<br />
<br />
What this means<br />
Make sure your translation company has the required multilingual typesetting/desktop publishing expertise whenever you’re translating a document created in a graphic design program.<br />
<br />
Translation Category C: 13 types of translation based on the translation method employed<br />
This category has two sub-groups:<br />
– the practical methods translation providers use to produce their translations, and<br />
– the translation strategies/methods identified and discussed within academia.<br />
<br />
The translation methods translation providers use<br />
There are 4 main methods used in the translation industry today. We have an overview of each below, but for more detail, including when to use each one, see our comprehensive blog article.<br />
<br />
Or watch our video.<br />
<br />
Important: If you’re a client you need to understand these 4 methods – choose the wrong one and the translation you end up with may not meet your needs!<br />
<br />
<br />
30. Machine Translation (MT)<br />
What is it?<br />
A translation produced entirely by a software program with no human intervention.<br />
<br />
A widely used, and free, example is Google Translate. And there are also commercial MT engines, generally tailored to specific domains, languages and/or clients.<br />
<br />
Pros and cons<br />
There are two limitations to MT:<br />
– they make mistakes (incorrect translations), and<br />
– quality of wording is patchy (some parts good, others unnatural or even nonsensical)<br />
<br />
On they positive side they are virtually instantaneous and many are free.<br />
<br />
Best suited for:<br />
Getting the general idea of what a text says.<br />
<br />
This method should never be relied on when high accuracy and/or good quality wording is needed.<br />
<br />
<br />
<br />
31. Machine Translation plus Human Editing (PEMT)<br />
What is it?<br />
A machine translation subsequently edited by a human translator or editor (often called Post-editing Machine Translation = PEMT).<br />
<br />
The editing process is designed to rectify some of the deficiencies of a machine translation.<br />
<br />
This process can take different forms, with different desired outcomes. Probably most common is a ‘light editing’ process where the editor ensures the text is understandable, without trying to fix quality of expression.<br />
<br />
Pros and cons<br />
This method won’t necessarily eliminate all translation mistakes. That’s because the program may have chosen a wrong word (meaning) that wasn’t obvious to the editor.<br />
<br />
And wording won’t generally be as good as a professional human translator would produce.<br />
<br />
Its advantage is it’s generally quicker and a little cheaper than a full translation by a professional translator.<br />
<br />
Best suited for:<br />
Translations for information purposes only.<br />
<br />
Again, this method shouldn’t be used when full accuracy and/or consistent, natural wording is needed.<br />
<br />
<br />
<br />
32. Human Translation<br />
What is it?<br />
Translation by a professional human translator.<br />
Pros and cons<br />
Professional translators should produce translations that are fully accurate and well-worded.<br />
<br />
That said, there is always the possibility of ‘human error’, which is why translation companies like us typically offer an additional review process – see next method.<br />
<br />
This method will take a little longer and likely cost more than the PEMT method.<br />
<br />
Best suited for:<br />
Most if not all translation purposes.<br />
<br />
<br />
<br />
33. Human Translation + Revision<br />
What is it?<br />
A human translation with an additional review by a second translator.<br />
<br />
The review is essentially a safety check – designed to pick up any translation errors and refine wording if need be.<br />
<br />
Pros and cons<br />
This produces the highest level of translation quality.<br />
<br />
It’s also the most expensive of the 4 methods, and takes the longest.<br />
<br />
Best suited for:<br />
All translation purposes.<br />
<br />
Gearwheel with 5 practical translation methods written on the teeth <br />
There’s also one other common term used by practitioners and academics alike to describe a type (method) of translation:<br />
<br />
34. Computer-Assisted Translation (CAT)<br />
What is it?<br />
A human translator using computer tools to aid the translation process.<br />
<br />
Key features<br />
Virtually all translators use such tools these days.<br />
<br />
The most prevalent tool is Translation Memory (TM) software. This creates a database of previous translations that can be accessed for future work.<br />
<br />
TM software is particularly useful when dealing with repeated and closely-matching text, and for ensuring consistency of terminology. For certain projects it can speed up the translation process.<br />
<br />
<br />
<br />
The translation methods described by academia<br />
A great deal has been written within academia analysing how human translators go about their craft.<br />
<br />
Seminal has been the work of Newmark, and the following methods of translation attributed to him are widely discussed in the literature.Gearwheel with Newmark's 8 translation methods written on the teeth <br />
These methods are approaches and strategies for translating the text as a whole, not techniques for handling smaller text units, which we discuss in our final translation category.<br />
<br />
<br />
<br />
35. Word-for-word Translation<br />
This method translates each word into the other language using its most common meaning and keeping the word order of the original language.<br />
<br />
So the translator deliberately ignores context and target language grammar and syntax.<br />
<br />
Its main purpose is to help understand the source language structure and word use.<br />
<br />
Often the translation will be placed below the original text to aid comparison.<br />
<br />
<br />
<br />
36. Literal Translation<br />
Words are again translated independently using their most common meanings and out of context, but word order changed to the closest acceptable target language grammatical structure to the original.<br />
<br />
Its main suggested purpose is to help someone read the original text.<br />
<br />
<br />
<br />
37. Faithful Translation<br />
Faithful translation focuses on the intention of the author and seeks to convey the precise meaning of the original text.<br />
<br />
It uses correct target language structures, but structure is less important than meaning.<br />
<br />
<br />
<br />
38. Semantic Translation<br />
Semantic translation is also author-focused and seeks to convey the exact meaning.<br />
<br />
Where it differs from faithful translation is that it places equal emphasis on aesthetics, ie the ‘sounds’ of the text – repetition, word play, assonance, etc.<br />
<br />
In this method form is as important as meaning as it seeks to “recreate the precise flavour and tone of the original” (Newmark).slide showing definition of semantic translation as a translation method<br />
<br />
39. Communicative Translation<br />
Seeks to communicate the message and meaning of the text in a natural and easily understood way.<br />
<br />
It’s described as reader-focused, seeking to produce the same effect on the reader as the original text.<br />
<br />
A good comparison of Communicative and Semantic translation can be found here.<br />
<br />
40. Free Translation<br />
Here conveying the meaning and effect of the original are all important.<br />
<br />
There are no constraints on grammatical form or word choice to achieve this.<br />
<br />
Often the translation will paraphrase, so may be of markedly different length to the original.<br />
<br />
41. Adaptation<br />
Mainly used for poetry and plays, this method involves re-writing the text where the translation would otherwise lack the same resonance and impact on the audience.<br />
<br />
Themes, storylines and characters will generally be retained, but cultural references, acts and situations adapted to relevant target culture ones.<br />
<br />
So this is effectively a re-creation of the work for the target culture.<br />
<br />
42. Idiomatic Translation<br />
Reproduces the meaning or message of the text using idioms and colloquial expressions and language wherever possible.<br />
<br />
The goal is to produce a translation with language that is as natural as possible.<br />
<br />
Translation Category D: 9 types of translation based on the translation technique used<br />
These translation types are specific strategies, techniques and procedures for dealing with short chunks of text – generally words or phrases.<br />
<br />
They’re often thought of as techniques for solving translation problems.<br />
<br />
They differ from the translation methods of the previous category which deal with the text as a whole.<br />
9 translation techniques as titles of books in a bookcase<br />
<br />
43. Borrowing<br />
What is it?<br />
Using a word or phrase from the original text unchanged in the translation.<br />
<br />
Key features<br />
With this procedure we don’t translate the word or phrase at all – we simply ‘borrow’ it from the source language.<br />
<br />
Borrowing is a very common strategy across languages. Initially, borrowed words seem clearly ‘foreign’, but as they become more familiar, they can lose that ‘foreignness’.<br />
<br />
Translators use this technique:<br />
– when it’s the best word to use – either because it has become the standard, or it’s the most precise term, or<br />
– for stylist effect – borrowings can add a prestigious or scholarly flavour.<br />
<br />
Borrowed words or phrases are often italicised in English.<br />
<br />
Examples of borrowings in English<br />
grand prix, kindergarten, tango, perestroika, barista, sampan, karaoke, tofu<br />
<br />
<br />
44. Transliteration<br />
What is it?<br />
Reproducing the approximate sounds of a name or term from a language with a different writing system.<br />
<br />
Key features<br />
In English we use the Roman (Latin) alphabet in common with many other languages including almost all European languages.<br />
<br />
Other writing systems include Arabic, Cyrillic, Chinese, Japanese, Korean, Thai, and the Indian languages.<br />
<br />
Transliteration from such systems into the Roman alphabet is also called romanisation.<br />
<br />
There are accepted systems for how individual letters/sounds should be romanised from most other languages – there are three common systems for Chinese, for example.<br />
<br />
English borrowings from languages using non-Roman writing systems also require transliteration – perestroika, sampan, karaoke, tofu are examples from the above list.<br />
<br />
Translators mostly use transliteration as a procedure for translating proper names.<br />
<br />
Examples<br />
毛泽东 Mao Tse-tung or Mao Zedong<br />
Владимир Путин Vladimir Putin<br />
서울 Seoul<br />
ភ្នំពេញ Phnom Penh<br />
<br />
45. Calque or Loan Translation<br />
What is it?<br />
A literal translation of a foreign word or phrase to create a new term with the same meaning in the target language.<br />
<br />
So a calque is a borrowing with translation if you like. The new term may be changed slightly to reflect target language structures.<br />
<br />
Examples<br />
German ‘Kindergarten’ has been calqued as детский сад in Russian, literally ‘children garden’ in both languages.<br />
<br />
Chinese 洗腦 ‘wash’ + ‘brain’ is the origin of ‘brainwash’ in English.<br />
<br />
English skyscraper is calqued as gratte-ciel in French and rascacielos in Spanish, literally ‘scratches sky’ in both languages.<br />
<br />
46. Word-for-word translation<br />
What is it?<br />
A literal translation that is natural and correct in the target language.<br />
<br />
Alternative names are ‘literal translation’ or ‘metaphrase’.<br />
<br />
Note: this technique is different to the translation method of the same name, which does not produce correct and natural text and has a different purpose.<br />
<br />
Key features<br />
This translation strategy will only work between languages that have very similar grammatical structures.<br />
<br />
And even then, only sometimes.<br />
<br />
For example, standard word order in Turkish is Subject-Object-Verb whereas in English it’s Subject-Verb-Object. So a literal translation between these two will seldom work:<br />
– Yusuf elmayı yedi is literally ‘Joseph the apple ate’.<br />
<br />
When word-for-word translations don’t produce natural and correct text, translators resort to some of the other techniques described below.<br />
Examples<br />
French ‘Quelle heure est-il?’ works into English as ‘What time is it?’.<br />
<br />
Russian ‘Oн хочет что-нибудь поесть’ is ‘He wants something to eat’.<br />
<br />
47. Transposition<br />
What is it?<br />
Translation with a change of grammatical structure.<br />
<br />
This technique gives the translation more natural wording and/or makes it grammatically correct.<br />
<br />
Examples<br />
A change in word order:<br />
Our Turkish example Yusuf elmayı yedi (literally ‘Joseph the apple ate’) –> Joseph ate the apple.<br />
<br />
Spanish La Casa Blanca (literally ‘The House White’) –> The White House<br />
<br />
A change in grammatical category:<br />
German Er hört gerne Musik (literally ‘he listens gladly [to] music’)<br />
= subject pronoun + verb + adverb + noun<br />
becomes Spanish Le gusta escuchar música (literally ‘[to] him [it] pleases to listen [to] music’)<br />
= indirect object pronoun + verb + infinitive + noun<br />
and English He likes listening to music<br />
= subject pronoun + verb + gerund + noun.<br />
<br />
48. Modulation<br />
What is it?<br />
Translation with a change of focus or point of view in the target language.<br />
<br />
This technique makes the translation more idiomatic – how people would normally say it in the language.<br />
<br />
Examples<br />
English talks of the ‘top floor’ of a building, French the dernier étage = last floor. ‘Last floor’ would be unnatural in English, so too ‘top floor’ in French.<br />
<br />
German uses the term Lebensgefahr (literally ‘danger to life’) where in English we’d be more likely to say ‘risk of death’.<br />
In English we’d say ‘I dropped the key’, in Spanish se me cayó la llave, literally ‘the key fell from me’. The English perspective is that I did something (dropped the key), whereas in Spanish something happened to me – I’m the recipient of the action.<br />
<br />
49. Equivalence or Reformulation<br />
What is it?<br />
Translating the underlying concept or meaning using a totally different expression.<br />
<br />
This technique is widely used when translating idioms and proverbs.<br />
<br />
And it’s common in titles and advertising slogans.<br />
<br />
It’s a common strategy where a direct translation either wouldn’t make sense or wouldn’t resonate in the same way.<br />
<br />
Examples<br />
Here are some equivalents of the English saying “Pigs may fly”, meaning something will never happen, or “you’re being unrealistic” (Source):<br />
– Thai: ชาติหน้าตอนบ่าย ๆ – literally, ‘One afternoon in your next reincarnation’<br />
– French: Quand les poules auront des dents – literally, ‘When hens have teeth’<br />
– Russian, Когда рак на горе свистнет – literally, ‘When a lobster whistles on top of a mountain’<br />
– Dutch, Als de koeien op het ijs dansen – literally, ‘When the cows dance on the ice’<br />
– Chinese: 除非太陽從西邊出來！– literally, ‘Only if the sun rises in the west’<br />
<br />
50. Adaptation<br />
What is it?<br />
A translation that substitutes a culturally-specific reference with something that’s more relevant or meaningful in the target language.<br />
<br />
It’s also known as cultural substitution or cultural equivalence.<br />
<br />
It’s a useful technique when a reference wouldn’t be understood at all, or the associated nuances or connotations would be lost in the target language.<br />
<br />
Note: the translation method of the same name is a similar concept but applied to the text as a whole.<br />
<br />
Examples<br />
Different cultures celebrate different coming of age birthdays – 21 in many cultures, 20, 15 or 16 in others. A translator might consider changing the age to the target culture custom where the coming of age implications were important in the original text.<br />
Animals have different connotations across languages and cultures. Owls for example are associated with wisdom in English, but are a bad omen to Vietnamese. A translator might want to remove or amend an animal reference where this would create a different image in the target language.<br />
<br />
51. Compensation<br />
What is it?<br />
A meaning or nuance that can’t be directly translated is expressed in another way in the text.<br />
Example<br />
Many languages have ways of expressing social status (honorifics) encoded into their grammatical structures.<br />
<br />
So you can convey different levels of respect, politeness, humility, etc simply by choosing different forms of words or grammatical elements.<br />
But these nuances will be lost when translating into languages that don’t have these structures.<br />
Then translating into languages that don’t have these structures<br />
Then translating into languages that don’t have these structures.<br />
<br />
===Conclusion ===<br />
<br />
In the light of above mentioned facts and figures here by we would say that machine translation is a challenge for human translators because it can reduce the workload of translation but can't give accurate and exact translation of the target language.It can be less reliable than human translation..</div>Chen Huinihttps://bou.de/u/index.php?title=Machine_translation&diff=133235Machine translation2021-12-15T05:00:15Z<p>Chen Huini: /* 12 蔡珠凤=(The Mistranslation of C-J Machine Translation of Political Statements) */</p>
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<div>'''Machine Translation - A challenge or a chance for human translators?'''<br />
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[[Machine_translation|Overview Page of Machine Translation]]<br />
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30 Chapters（0/30)<br />
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[[Machine_Trans_EN_1]] [[Machine_Trans_EN_2]] [[Machine_Trans_EN_3]] [[Machine_Trans_EN_4]] [[Machine_Trans_EN_5]] [[Machine_Trans_EN_6]] [[Machine_Trans_EN_7]] [[Machine_Trans_EN_8]] [[Machine_Trans_EN_9]] [[Machine_Trans_EN_10]] [[Machine_Trans_EN_11]] [[Machine_Trans_EN_12]] [[Machine_Trans_EN_13]] [[Machine_Trans_EN_14]] [[Machine_Trans_EN_15]] [[Machine_Trans_EN_16]] [[Machine_Trans_EN_17]] [[Machine_Trans_EN_18]] [[Machine_Trans_EN_19]] [[Machine_Trans_EN_20]] [[Machine_Trans_EN_21]] [[Machine_Trans_EN_22]] [[Machine_Trans_EN_23]] [[Machine_Trans_EN_24]] [[Machine_Trans_EN_25]] [[Machine_Trans_EN_26]] [[Machine_Trans_EN_27]] [[Machine_Trans_EN_28]] [[Machine_Trans_EN_29]] [[Machine_Trans_EN_30]] ...<br />
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[[Book_projects|Back to translation project overview]]<br />
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[[DCG-To-Do|To the To Do list]]<br />
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=Chapter 1:A Comparison Between the Quality of Machine Translation and Human Translation——A Case Study of the Application of artificial intelligence in Sports Events=<br />
'''论机器翻译与人工翻译的质量对比——以人工智能在体育赛事领域的应用为例'''<br />
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卫怡雯 Wei Yiwen, Hunan Normal University, China<br />
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[[Machine_Trans_EN_1]]<br />
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=Chapter 3：On the Realm Advantages And Mutual Development of Machine Translation And Huamn Translation)=<br />
"'论机器翻译与人工翻译的领域优势及共同发展'"<br />
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肖毅瑶 Xiao Yiyao, Hunan Normal University, China<br />
[[Machine_Trans_EN_3]]<br />
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=Chapter 4 : A Comparison Between Machine Translation of Netease and Traditional Human Translation—A Case Study of The Economist Articles)= <br />
'''网易有道机器翻译与人工翻译的译文对比——以经济学人语料为例'''<br />
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王李菲 Wang Lifei, Hunan Normal University<br />
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[[Machine_Trans_EN_4]]<br />
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=Chapter 5: Muhammad Saqib Mehran - Problems in translation studies=<br />
[[Machine_Trans_EN_5]]<br />
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=6 徐敏赟(Machine Translation Based on Neural Network --Challenge or Chance?)=<br />
[[Machine_Trans_EN_6]]<br />
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=Chapter 7: The Cultivation of artificial intelligence and translation talents in the Belt and Road Initiative=<br />
[[Machine_Trans_EN_7]]<br />
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一带一路背景下人工智能与翻译人才的培养<br />
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颜莉莉 Yan Lili, Hunan Normal University, China<br />
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=Chapter 8: On Machine Translation Under Lanuguage Intelligence——An Option and Opportunity for Human Translators=<br />
'''论语言智能下的机器翻译——译者的选择与机遇'''<br />
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颜静 Yan Jing, Hunan Normal University, China<br />
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[[Machine_Trans_EN_8]]<br />
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=9 谢佳芬（ Machine Translation and Artificial Translation in the Era of Artificial Intelligence）=<br />
[[Machine_Trans_EN_9]]<br />
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=10 熊敏(Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts)=<br />
机器翻译对各类型文本的英汉翻译能力探究<br />
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熊敏, Xiong Min, Hunan Normal University<br />
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[[Machine_Trans_EN_10]]<br />
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===Abstract===<br />
The history of machine translation can be traced to 1940s, undergoing germination, recession, revival and flourish. Nowadays, with the rapid development of information technology, machine translation technology emerged and is gradually becoming mature. Whether manual translation can be replaced by machine translation becomes a widely-disputed topic. So in order to explore the ability of machine translation, I adopts two versions of translation, which are manual translation and machine translation(this paper uses Youdao translation) for different types of texts(according to Peter Newmark's types of text：informative text, expressive text and vocative text). The results are quite different in terms of quality and accuracy. The results show that machine translation is more suitable for informative text for its brevity, while the expressive texts especially literary works and vocative texts are difficult to be translated, because there are too many loaded words and cultural images in expressive text and dependence on the readers. To draw a conclusion, the relationship between machine translation and manual translation should be complementary rather than competitive.<br />
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===Key words===<br />
machine translation; manual translation; Newmark's type of texts<br />
<br />
===题目===<br />
Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts<br />
<br />
===摘要===<br />
机器翻译的历史可以追溯到上个世纪40年代，经历了萌芽期、萧条期、复兴期和繁荣期四个阶段。如今，随着信息技术的高速发展，机器翻译技术日益成熟，于是机器翻译能不能替代人工翻译这个话题引起了广泛热议。为了探究机器翻译的能力水平是否足以替代人工翻译，本人根据皮特纽马克的文本类型分类理论（信息类文本，表达文本，呼吁文本），选择了相应的译文类型，并且将其机器翻译的版本以及人工翻译的版本进行对比。结果发现，就质量和准确度而言，译文的水平大相径庭。因为其简洁的特性，机器比较适合翻译信息文本。而就表达文本，尤其是文学作品，因其存在文化负载词及相应的文化现象，所以机器翻译这类文本存在难度。而呼唤文本因其目的性以及对读者的依赖性较强，翻译难度较大。由此得知，机器翻译和人工翻译的关系应该是互补的，而不是竞争的。<br />
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===关键词===<br />
机器翻译；人工翻译；纽马克文本类型<br />
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===1. Introduction===<br />
====1.1.Introduction to machine translation====<br />
Machine translation, also known as computer translation is a technique to translating through machine translator, such as Google translation and Youdao translation. Machine translation is one of the branches of computational linguistics, ranging from computer science, statistics, information science and so on. Machine translation plays an important role in all aspects.<br />
Machine translation can be traced back to 1940s, when British engineer Booth and American engineer Weaver proposed using computer to translate and started to study machines used for translation. And the first machine translating system launched in 1954, used in English and Russian translation. And this means machine translation becomes a reality. However, the arrival of new things always accompanies barriers and setbacks. In the 1960s, reports from ALPAC (Automated Language Processing Advisory Committee) showed studies on machine translation had stagnated for a decade. At that time, due to the high cost of machine, choosing human translators to finish translating works was more economical for most companies. What’s worse, machine had difficulty in understanding semantic and pragmatic meanings. Fortunately, in the 1970s, with the advance and popularity of computer, machine translation was gradually back on track. With the development of science and technology, machine translation has better technological guarantee, such as artificial intelligence and computer technology. In the last decades, from the late 90s till now, machine translation is becoming more and more mature. The initial rule-oriented machine translation has been updated and Corpus-aided machine translation appears. And nowadays, technology in voice recognition has been further developed. This technique is frequently applied in speech translation products. Users only need to speak source language to the online translator and instantly it will speak out the target version. But machine can’t fully provide precise and accurate translation without any grammatical or semantic problems. Machine can understand the literal meaning of texts, but not the meaning in context. For example, there is polysemy in English, which refers to words having multiple meanings. So when translating these words, translators should take contexts into consideration. But machine has troubles in this way. In addition, machine has no feeling or intention. Hence, it is also difficult to convey the feelings from the source language.(Wei 2021:5)#<br />
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====1.2.Process of machine translation====<br />
The process of manual translation is different from that of machine translation. Here is the process of the former. (1) Understand source language. (2) Use target language to organize language. (3) Generate translation. Unlike manual translation, machine translation tends to analyze and code source language first, then look for related codes in corpus, and work out the code that represents target language, generating translation. But they share a common feature, which is that Lexicon, grammatical rules and syntactic structure are taken into consideration. This is one of the biggest challenges for machine translation.<br />
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===2.Theorectical framework===<br />
====2.1Newmark’s text typology====<br />
Text typology is a theory from linguistics. It undergoes several phrases. Karl Buhler, a German psychologist and linguist defines communicative functions into expressive, information and vocative. Then Roman Jakobson proposes categorization of texts, which are intralingual translation, interlingual translation and intersemiotic translation. Accordingly, he defines six main functions of language, including the referential function, conative function, emotive function, poetic function, metalingual function and the phatic function. Based on the two theories, Peter Newmark, a translation theorist, summarizes the language function into six parts: the informative function, the vocative function, the expressive function, the phatic function, the aesthetic function, and the metalingual function. Later, he further divided texts into informative type, expressive text and vocative type according to the linguistic functions of various texts. (Newmark 2002:2)#<br />
The core of the informative texts is the truth. It is to convey facts, information, knowledge of certain topics, reality and theories. The language style of the text is objective, logical and standardized. Reports, papers, scientific and technological textbooks are all attributed to informative texts. Translators are required to take format into account for different styles.<br />
The core of the expressive text is the sender’s emotions and attitudes. It is to express sender’s preferences, feelings, views and so on. The language style of it is subjective. Imaginative literature, including fictions, poems and dramas, autobiography and authoritative statements belong to expressive text. It requires translators to be faithful to the source language and maintain the style.<br />
The core of the vocative text is readership. It is to call upon readers to act, think, feel and react in the way intended by the text. So it is reader-oriented. Such texts as advertisement, propaganda and notices are of vocative text. Newmark noted that translators need to consider cultural background of the source language and the semantic and pragmatic effect of target language. If readers can comprehend the meaning at once and do as the text says, then the goal of information communication is achieved. (Liu 2021:3)#<br />
To draw a conclusion, informative texts focus on the information and facts. Expressive texts center on the mind of addressers, including feelings, prejudices and so on. And vocative texts are oriented on readers and call on them to practice as the texts say.<br />
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====2.2Study method====<br />
Manual translations of the three texts are selected from authoritative versions and universally acknowledged. And machine translations of those come from Youdao Translator. And in this thesis I will compare and evaluate the two methods in word diction, sentence structure, word order and redundancy.<br />
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===3.Comparison and analysis of machine translation and manual translation ===<br />
====3.1Informative text ====<br />
（1）English into Chinese<br />
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①Source language:<br />
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Keep the tip of Apple Pencil clean, as dirt and other small particles may cause excessive wear to the tip or damage the screen of i-pad.<br />
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Target language:<br />
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Machine translation: Apple Pencil笔尖应保持清洁，灰尘等小颗粒可能会导致笔尖过度磨损或损坏ipad屏幕。<br />
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Manual translation: 保持Apple Pencil铅笔的笔尖干净，因为灰尘和其他微粒可能会导致笔尖的过度磨损或损坏iPad屏幕。<br />
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Analysis: Here is the instruction of Apple Pencil. And the manual translation is the Chinese version on the instruction.Product instruction tends to be professional, since there are many terms for some concepts. Machine can easily identify these terms and provide related words to translate. The machine version is faithful and expressive to the source language. So it is well-qualified and readable for readers to understand the instruction. So we can use machine to translate informative text.<br />
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②Source language:<br />
<br />
China on Saturday launched a rocket carrying three astronauts-two men and one woman - to the core module of a future space station where they will live and work for six months, the longest orbit for Chinese astronauts.<br />
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Target language:<br />
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Machine translation: 周六，中国发射了一枚运载三名宇航员(两男一女)的火箭，进入未来空间站的核心舱，他们将在那里生活和工作6个月，这是中国宇航员最长的轨道。<br />
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Manual translation: 周六，中国发射了一枚搭载三名宇航员(两男一女)的火箭，进入未来空间站的核心舱，他们将在那里生活和工作6个月，这是中国宇航员最漫长的一次轨道飞行。<br />
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Analysis: This is a news from Reuters, reporting that China has launched a rocket.The meaning of the two translations is almost the same, except for some word diction. But there are some details dealt with different choice. For example, the last sentence of the machine translation is a bit of obscure and direct. There are some ambiguous words and expressions.<br />
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(2)Chinese into English<br />
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Source language:湖南省博物馆是湖南省最大的历史艺术类博物馆，占地面积4.9万平方米，总建筑面积为9.1万平方米，是首批国家一级博物馆，中央地方共建的八个国家级重点博物馆之一、全国文化系统先进集体、文化强省建设有突出贡献先进集体。<br />
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Target language:<br />
Manual translation: As the largest history and art museum in Hunan province, the Hunan Museum covers an area of 49,000㎡, with the building area reaching 91,000㎡. It is one of the first batch of national first-level museums and one of the first eight national museums co-funded by central and local governments.<br />
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Machine translation: Museum in hunan province is one of the largest historical art museum in hunan province, covers an area of 49000 square meters, a total construction area of 91000 square meters, is the first national museum, the central place to build one of the eight national key museum, national cultural system advanced collectives, strong culture began with outstanding contribution of advanced collective.<br />
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Analysis: Machine translation is not faithful enough in content. For instance, “首批国家一级博物馆” is translated into “first national museum”, which is not the meaning of the source language. And there are some obvious grammar mistakes in the machine translation. For example, machine translates it into just one sentence but there are multiple predicates in it. So it is not grammatically permissible. What’s more, the sentence structure of machine translation is confusing and the focus is not specific enough.<br />
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====3.2Expressive text ====<br />
(1)English into Chinese<br />
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Source language:<br />
<br />
An individual human existence should be like a river- small at first, narrowly contained within its banks, and rushing passionately past rocks and over waterfalls. Gradually the river grows wider, the banks recede, the waters flow more quietly, and in the end, without any visible breaks, they become merged in the sea, and painlessly lose their individual being.()<br />
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Target language:<br />
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Machine translation: 一个人的存在应该像一条河流——开始很小，被紧紧地夹在两岸中间，然后热情奔放地冲过岩石，飞下瀑布。渐渐地，河面变宽，两岸后退，水流更加平缓，最后，没有任何明显的停顿，它们汇入大海，毫无痛苦地失去了自己的存在。<br />
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Manual translation:人生在世，如若河流；河口初始狭窄，河岸虬曲，而后狂涛击石，飞泻成瀑。河道渐趋开阔，峡岸退去，水流潺缓，终了，一马平川，汇于大海，消逝无影。<br />
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Analysis: Here is a well-known metaphor in the prose How to Grow Old written by Bertrand Russell. The manual translation is written by Tian Rongchang.This is a philosophical prose with graceful language. Literary translation is a most important and difficult branch of translation. Translator should focus on the literal meaning, culture, writing style and so on. It is a combination of beauty and elegance. Therefore, translators find it in a dilemma of beauty and faithfulness, let alone translating machine. Compared with manual translation, machine translation has difficulty in word choice. It is faithful and expressive, but not elegant enough.<br />
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(2)Chinese into English<br />
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Source language:没有一个人将小草叫做“大力士”，但是它的力量之大，的确是世界无比。这种力，是一般人看不见的生命力，只要生命存在，这种力就要显现，上面的石块，丝毫不足以阻挡。因为它是一种“长期抗战”的力，有弹性，能屈能伸的力，有韧性，不达目的不止的力。(Zhang, 2007:186)#<br />
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Target language:<br />
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Machine translation: No one calls the little grass "hercules", but its power is truly matchless in the world. This force is invisible life force. As long as there is life, this force will show itself. The stone above is not strong enough to stop it. Because it is a "long-term resistance" of the force, elastic, can bend and extend force, tenacity, not to achieve the purpose of the force.<br />
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Manual translation: Though nobody describes the little grass as a “husky”, yet its herculean strength is unrivalled. It is the force of life invisible to naked eye. It will display itself so long as there is life. The rock is utterly helpless before this force- a force that will forever remain militant, a force that is resilient and can take temporary setbacks calmly, a force that is tenacity itself and will never give up until the goal is reached. (by Zhang Peiji)<br />
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Analysis:This is the excerpt of a well-known Chinese prose written by Xia Yan. It is written during the war of Resistance Against Japan. So the prose holds symbolic meaning, eulogizing the invisible tenacious vitality so as to encourage Chinese to have confidence in the anti-aggression war. Compared with manual translation, machine translation is much more abstract and confusing, especially for the word diction. For example, “大力士” is translated into “hercules” which is a man of exceptional strength and size in Greek and Roman Mythology, making it difficult to understand if readers of target language have no idea of the allusion. What’s worse, the machine version doesn’t reveal the symbolic meaning of the text, which is the core of this prose.<br />
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====3.3Vocative text ====<br />
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(1)English into Chinese<br />
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①Source language:<br />
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iPhone went to film school, so you don’t have to. (Advertisement of iPhone13)<br />
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Target language:<br />
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Machine translation: iPhone上的是电影学院，所以你不用去。<br />
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Manual translation:电影专业课，iPhone同学替你上完了。<br />
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Analysis：Here are advertisements of iPhone on Apple official website. There is a personification in the source language. It is used to stress the advancement and proficiency in camera, which is an appealing selling point to potential buyers. Compared with manual translation, machine translation is plain and not eye-catching enough for customers.<br />
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②Source language: <br />
<br />
5G speed OMGGGGG<br />
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Machine language: 5克的速度 OMGGGGG<br />
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Manual translation:<br />
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iPhone的5G 巨巨巨巨巨5G<br />
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Analysis: The “G” in the source language is the unit of speed, standing for generation. However, it is mistaken as a unit of weight, representing gram in the machine translation. So the meaning is not faithful to the source language at all. As for manual translation, it complies with the source in form. Specifically speaking, five “G”s in the former complies with five characters “巨”in the latter. And the pronunciation of the two is similar. There are two layers of meaning for the 5 “G”s. One exclaims the fast speed of 5 generation network and the other new technology. In the manual version, “巨”can be used to show degree, meaning “quite” or “very”. <br />
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③Source language: <br />
<br />
History, faith and reason show the way, the way of unity. We can see each other not as adversaries but as neighbors. We can treat each other with dignity and respect, we can join forces, stop the shouting and lower the temperature. For without unity, there is no peace, only bitterness and fury."<br />
<br />
Target language:<br />
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Machine translation: 历史、信仰和理性指明了团结的道路。我们可以把彼此视为邻居，而不是对手。我们可以尊严地对待彼此，我们可以联合起来，停止大喊大叫，降低温度。因为没有团结，就没有和平，只有痛苦和愤怒。<br />
<br />
Manual translation:历史、信仰和理性为我们指明道路。那是团结之路。我们可以把彼此视为邻居，而不是对手。我们可以有尊严地相互尊重。我们可以联合起来，停止喊叫，减少愤怒。因为没有团结就没有和平，只有痛苦和愤怒<br />
<br />
Analysis: Speech is a way to propagate some activity in public. It is an art to inspire emotion of the audience. The source language is the excerpt of Joe Biden’s inaugural speech. The speech should be inspiring and logic. The machine translation has some misunderstanding. Taking the translation of “lower the temperature” for example, machine only translates its literal meaning, relating to the temperature itself, without considering the context. What’s more, it is less logic than the manual one. Therefore, it adds difficulty to inspire the audience and infect their emotion.<br />
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===4.Common mistakes in machine translation ===<br />
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====4.1 lexical mistakes ====<br />
<br />
Common lexical mistakes include misunderstandings in word category, lexical meaning and emotive and evaluative meaning. Misunderstanding in word category shows in the classification of word in the source language. As for misunderstanding in lexical meaning, machine has difficulty in precisely reflecting the meaning of the original texts, due to different cultural background and different language system. And for misunderstanding in emotive meaning, machine has no intention and emotion like human-beings. Therefore, it’s impossible for it to know writers’ feelings and their writing purposes. So sometimes, it may translate something negative into something positive. (Wang 2008:45)#<br />
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====4.2 grammatical mistakes====<br />
<br />
Grammatical analysis plays an important part in translation. Normally speaking, every language has its own unique grammatical rules. So in the process of translation, if translators don’t know the formation rule well, the sentence meaning will be affected. Even though all the lexical meanings are well-known by translators, the lack of consciousness of grammaticality makes it harder to arrange words according to sequential rule. English tends to be hypotactic, while Chinese tends to be paratactic. English sentences are connected through syntactic devices and lexical devices. While Chinese sentences are semantically connected, which means there are limited logical words and connection words in Chinese. So when translating English sentence, we should first analyze its grammaticality and logical structure and then rearrange its sequence. However, online translating machine has troubles in grammatical analysis, which makes its improvement more difficult.<br />
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====4.3 other mistakes====<br />
<br />
The two mistakes above are the internal ones. Apart from mistakes in linguistic system, there are some mistakes in other aspects, such as cultural background.<br />
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===5.Reasons for its common mistakes ===<br />
<br />
====5.1 Difference in two linguistic system====<br />
<br />
With different history, English and Chinese have different ways of expression. Commonly speaking, English is synthetic language which expresses grammatical meaning through inflection such as tense and Chinese is analytic language which expresses grammatical meaning through word order and function word. In addition, English is more compact with full sentences. Subordinate sentence is one of the most important features in modern English. Chinese, on the other hand, is more diffusive with minor sentences.<br />
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====5.2 Difference in thinking patterns and cultural background====<br />
<br />
According to Sapir-Whorf’s Hypothesis, our language helps mould our way of thinking and consequently, different languages may probably express their unique ways of understanding the world. For two different speech communities, the greater their structural differentiations are, the more diverse their conceptualization of the world will be. For example, western culture is more direct and eastern culture more euphemistic. What’s more, English culture tends to be individualism, focusing on detail, through which it reflects the whole, while Chinese culture tends to be collective. Different thinking patterns will add difficulty for machine to translate texts.<br />
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====5.3 Limitation of computer====<br />
<br />
Recently, there are some breakthroughs and innovation in machine translation. However, due to its own limitation, online translation has limitation in some ways. Firstly, compared with machine, human brain is much more complicated, consisting of ten billions of neuron, each of which has different function to affect human’s daily activities and help humans avoid some errors. However, computer can only function according to preset programming has no intention or consciousness. Until now, countless related scholars have invested much time in machine translation. They upload massive language database, which include almost all linguistic rules. But computers still fail to precisely reflect the meaning of source language for many times due to the complexity and flexibility of language. On the other hand, computers can’t take context into consideration. During translation, it is often the case that machine chooses the most-frequently used meaning of one word. So without the correct and exact meaning, readers are easier to feel confused and even misunderstand the meaning of source language. (Qiu 2021:4)#<br />
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===6.Conclusion===<br />
From the analysis above, we can draw a conclusion that machine deals with informative text best, followed by non-literary translation of expressive text. What’s more, machine can be a useful tool to get to know the gist and main idea of a specific topic, for the simple sentence structure and numerous terms. And it can improve translating efficiency with high speed. But machine has difficulty in translating literary works, especially proses and poems.<br />
<br />
Machine translation has mixed future. From the perspective of commercial, machine translation boasts a bright future. With the process of globalization, the demand for translation is increasing accordingly. On one hand, if we only depend on human translator to deal with translating works, the quality and accuracy of translation can be greatly affected. On the other hand, if machine is used properly to do some basic work, human translators only need to make preparation before translating, progress, polish and other advanced work, contributing to highly-qualified translation and high working efficiency.<br />
<br />
However, compared with manual translation, machine translation has a bleak future. It is still impossible for machine to replace interpreter or translator in a short term. With intelligence and initiative, humans are able to learn new knowledge constantly, which machine will never accomplish. Besides, machine is not used to replace translators but to assist them in work. In other words, translators and machine carry out their own duties and they are not incompatible.(He 2021:5)#<br />
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To draw a conclusion, although there are certain limitations of machine translation, it can serve as a catalyst for translating works. Therefore, with the rapid development of artificial intelligence and related technology, there are still many opportunities for machine translation.<br />
<br />
===Reference ===<br />
<br />
Chen Cheng陈诚.机器翻译技术的综述[J][Overview of Machine Translation Technology].Electronic Techonology 电子技术,2021,50(11):290-291.<br />
<br />
Cui Zihan 崔子涵.机器翻译译文质量对比——以谷歌翻译和DeepL为例[J] [Comparison among Machine Translation--Taking Google Translation and Deepl for Example].Overseas English 海外英语,2021(15):182-183.<br />
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He Xinyu何馨宇.机器翻译的发展及其对翻译职业化的影响研究[J] [The Development of Machine Translation and its Effect on Professional Transltors].Overseas English 海外英语,2021(20):48-49.<br />
<br />
He Wen 何雯, Wang Xiufeng 王秀峰.信息型文本的在线机器翻译错误研究[J][Research on Errors in Online Machine Translation of Informative text ].Overseas English海外英语,2021(15):188-189.<br />
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Li Deyi 李德毅. (2018). 人工智能导论 [Introduction to Artificial Intelligence]. Beijing: China Science and Technology Press 中国科学技术出版社.<br />
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Liu Qin刘琴.功能目的论对于不同文本类型的翻译解读[J][Analysis of Translations in Different Types of Text based on Functionalist Approaches].Overseas Engliosh 海外英语,2021(17):8-9.<br />
<br />
Li Hanji 李晗佶. (2021). 人工智能时代翻译技术与译者关系演变与重构 [Evolution and reconstruction of the relationship between translation technology and translators in the era of artificial intelligence]. 西华师范大学学报(哲学社会科学版) Journal of West China Normal University (PHILOSOPHY AND SOCIAL SCIENCES EDITION) (2021-12-04) 1-6.<br />
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(英) Peter Newmark A Textbook of Translation[M] Shanghai Foreign Education Press, 2002<br />
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Qiu Quanju 仇全菊.大数据时代背景下机器翻译及其发展趋势[J][Machine Translation and its Development Trend under the Background of Big Data Era]. English Teachers 英语教师,2021,21(16):60-62.<br />
<br />
Wang Hongqi 王红旗. (2008). 语言学概论 [Introduction to Linguistics]. Beijing: Peking University Press 北京大学出版社.<br />
<br />
Wei Guang魏光. 人工翻译与机器翻译译文编辑比较研究[J][Comparative Study of Translation Editing between Manual Translation and Machine Translation]. Overseas English 海外英语,2021(19):18-19+21.<br />
<br />
Zhuo Jianbin 卓键滨,Liu Wenxian 刘文娴,Peng Zili 彭子莉.机器翻译对各类型文本的德汉翻译能力探究[J][Research on the German Chinese Translation Ability of Machine Translation for Various Types of Texts]. Comparative Study of Cultural innovation 文化创新比较研究,2021,5(28):122-125.<br />
<br />
Zhang Peiji 张培基.英译中国现代散文选[M][Selected Modern Chinese Prose Writings]. Shanghai Foreign Languages Education Press 上海外语教育出版社, 2002.<br />
<br />
--[[User:Xiong Min|Xiong Min]] ([[User talk:Xiong Min|talk]]) 01:36, 15 December 2021 (UTC)<br />
<br />
=Chapter 11 陈惠妮 Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts=<br />
<br />
机器翻译的译前编辑研究——以医学类文摘为例<br />
<br />
陈惠妮 Chen Huini, Hunan Normal University<br />
<br />
[[Machine_Trans_EN_11]]<br />
<br />
===Abstract===<br />
At present, globalization is accelerating and the market demand for language services is rapidly increasing . Machine translation, as an important translation method, can greatly improve translation efficiency due to its low cost and high speed. However, because of the limitations of machine translation and the differences between Chinese and English language, machine translation is not accurate enough. In order to balance translation efficiency and translation quality, a great number of manual revisions in translation are required for the machine translating texts. Medical papers are specialized, special and purposeful, so it requires accurate,qualified and professional translation. However, the quality of translations by machine is inefficient to meet the high-quality requirements of medical papers translation. Therefore, the introduction of pre-editing can greatly improve the efficiency and quality of machine translation.<br />
<br />
===Key words===<br />
Pre-editing, Machine translation, Medical texts<br />
<br />
===题目===<br />
Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts<br />
<br />
===摘要===<br />
在全球化加速发展的今天，市场对语言服务的需求迅速增加。机器翻译作为一种重要的翻译途径，由于其成本低、速度快，可以大大提高翻译效率。然而，由于机器翻译的局限性以及中英文语言的差异，机器翻译的准确性不高。为了平衡翻译效率和翻译质量，机器翻译文本需要大量的手工修改。<br />
医学论文具有专业性、特殊性和目的性，要求其译文准确、合格、专业。然而，机器翻译的质量较低，无法满足医学论文对翻译的高质量要求。因此，译前编辑的引入可以大大提高机器翻译的效率和质量。<br />
<br />
===关键词===<br />
译前编辑；机器翻译；医学文本<br />
<br />
===1. Introduction===<br />
===1.1 Definition of Machine Translation===<br />
As Cronin(2013) revealed: "Translation is undergoing a revolutionary upheaval. The influence of digital technology and the Internet on translation is continuous, extensive and profound. From the popularity of automatic online translation applications, translation revolution is everywhere (Cronin,2013) However,the concept of machine translation was firstly proposed in the 1930s. Since 1940s, the machine translation technology has been evolving from rule-based machine translation (RBMT) to statistical machine translation (SMT), and to neural machine translation (NMT). Machine translation refers to the automatic translation of source language into target language by using a computer system. That is, machine translation refers to the automatic translation of text from one language into another natural language by computer software or other online translation webs. Machine translation is also defined as the process of “using a computer system to automatically translate text or speech from one natural language to another” according to the definition by ISO (Cui 2014:68-73).O 'Brien (2002) defines it as "the behavior of modifying errors in machine translation to ensure that the target translation meets certain quality requirements". On the basic level, machine translation performs mechanical substitution of words in one language for words in another language, but that rarely produces a good translation, therefore, recognition of the whole phrases and their closest counterparts in the target language is needed. Not all words in one language have equivalents in another language, and many words have more than one meaning. A huge demand for translation is greatly needed in today’s global world, which creates new opportunities for the development of machine translation, attracts more and more attention and becomes one of the current research focuses.<br />
<br />
As Cronin(2013) revealed: "Translation is undergoing a revolutionary upheaval. The influence of digital technology and the Internet on translation is continuous, extensive and profound. From the popularity of automatic online translation applications, translation revolution is everywhere (Cronin,2013) However,the concept of machine translation was firstly proposed in the 1930s. Since 1940s, the machine translation technology has been evolving from rule-based machine translation (RBMT) to statistical machine translation (SMT), and to neural machine translation (NMT). Machine translation refers to the automatic translation of source language into target language by using a computer system. That is, machine translation refers to the automatic translation of text from one language into another natural language by computer software or other online translation webs. Machine translation is also defined as the process of “using a computer system to automatically translate text or speech from one natural language to another” according to the definition by ISO (Cui 2014:68-73).O 'Brien (2002) defines it as "the behavior of modifying errors in machine translation to ensure that the target translation meets certain quality requirements". On the basic level, machine translation performs mechanical substitution of words in one language for words in another language, but that rarely produces a good translation, therefore, recognition of the whole phrases and their closest counterparts in the target language is needed. Not all words in one language have equivalents in another language, and many words have more than one meaning. A huge demand for translation is greatly needed in today’s global world, which creates new opportunities for the development of machine translation, attracts more and more attention and becomes one of the current research focuses.<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:34, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===1.2 Definition of Pre-editing===<br />
Pre-editing means to adjust and modify the source language to make it fit more with the characteristics of the machine translation software before putting the source language before the into machine translation, so as to improve the quality of the translation machine translation (Wei Changhong 2008:93-94). Pre-editing is to modify the original text before putting it into machine translation software, in order to improve the recognition rate of machine translation, optimize the output quality of translated text and reduce the workload of post-editing. Because pre-translation editing only needs to be modified in one language, the operation is simpler than post-translation editing, which can realize the double improvement of quality and efficiency. A good pre-editing translation can help machine translation more smoothly, thus improving the machine readability and quality of the output translation. Pre-editing is mostly applied in the following situations: one is when the original text is of poor quality and the machine is difficult to recognize the meaning of the sentence, such as user generated content with poor readability and translatability (Gerlach et al 2003:45-53); Documents that need to be published in multiple languages; Next is when the original text contains a lot of jargon; The last is the original text has a corresponding translation memory bank. If the original text is edited, it can better match the content of the translation memory bank. Pre-editing is a process of identifying problems. It requires to pre-edit the source texts before putting it into machine translation according to the requirements, listing the expressions or sentences that may have trouble in machine translation and then pre-edit it by human. The purpose is to enable the computer to translate better, improve the translatability of machine translation. (Slype G V & Guinet J F & Seitz F 1984:115)<br />
<br />
Pre-editing means to adjust and modify the source language to make it fit more with the characteristics of the machine translation software before putting the source language before the into machine translation, so as to improve the quality of the translation machine translation (Wei Changhong 2008:93-94). Pre-editing is to modify the original text before putting it into machine translation software, in order to improve the recognition rate of machine translation, optimize the output quality of translated text and reduce the workload of post-editing. Because pre-translation editing only needs to be modified in one language, the operation is simpler than post-translation editing, which can realize the double improvement of quality and efficiency. A good pre-editing translation can help machine translation more smoothly, thus improving the machine readability and quality of the output translation. Pre-editing is mostly applied in the following situations: one is when the original text is of poor quality and the machine is difficult to recognize the meaning of the sentence, such as user generated content with poor readability and translatability (Gerlach et al 2003:45-53); Documents that need to be published in multiple languages; Next is when the original text contains a lot of jargon; The last is the original text has a corresponding translation memory bank. If the original text is edited, it can better match the content of the translation memory bank. Pre-editing is a process of identifying problems. It requires to pre-edit the source texts before putting it into machine translation according to the requirements, listing the expressions or sentences that may have trouble in machine translation and then pre-edit it by human. The purpose is to enable the computer to translate better, improve the translatability of machine translation. (Slype G V & Guinet J F & Seitz F 1984:115)<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:36, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===1.3 Machine Translation Mode===<br />
According to the different knowledge acquisition methods, machine translation modes can be classified as follows: one is rule-based machine translation, which is based on bilingual dictionaries and a library of language rules for each language. The quality of translation depends on whether the source language conforms to the existing rules, but the inexhaustible rules are the hinders of this model. The second mode is machine translation based on statistics. This translation model relies on the principles of mathematics and statistics to find various existing translations corresponding to the translation tasks through the employment of corpus, analyzing the frequency of their occurrence and selecting the translation with the highest frequency for output. The disadvantage of this translation model is that it ignores the flexibility of language and the importance of context. The last one is neural network language model. This model is different from previous translation models in that it uses end-to-end neural network to realize automatic translation between natural languages. At present, the quality of its translation is much higher than that of the previous translation models.<br />
<br />
According to the different knowledge acquisition methods, machine translation modes can be classified as follows: one is rule-based machine translation, which is based on bilingual dictionaries and a library of language rules for each language. The quality of translation depends on whether the source language conforms to the existing rules, but the inexhaustible rules are the hinders of this model. The second mode is machine translation based on statistics. This translation model relies on the principles of mathematics and statistics to find various existing translations corresponding to the translation tasks through the employment of corpus, analyzing the frequency of their occurrence and selecting the translation with the highest frequency for output. The disadvantage of this translation model is that it ignores the flexibility of language and the importance of context. The last one is neural network language model. This model is different from previous translation models in that it uses end-to-end neural network to realize automatic translation between natural languages. At present, the quality of its translation is much higher than that of the previous translation models.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===1.4 Source of Study Abstracts===<br />
The core medical journals from domestic are selected in order to make the paper more representative and authoritative, such as National Medical Journal of China, Journal of Peking University (Health Science), and Journal of Third Military Medical University. All of them include basic medical science, biomedical technology, laboratory medical science and other fields. In this way, the pre-editing approaches included are applicable enough to machine translation of medical abstracts.<br />
<br />
The core medical journals from domestic are selected in order to make the paper more representative and authoritative, such as National Medical Journal of China, Journal of Peking University (Health Science), and Journal of Third Military Medical University. All of them include basic medical science, biomedical technology, laboratory medical science and other fields. In this way, the pre-editing approaches included are applicable enough to machine translation of medical abstracts.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===1.5 Selection of Translation Software===<br />
Generally, machine translation software includes Google Translation, Youdao Translator and Niu Translation, which has their own special use in translation. Take Google as an example, it is more like neurons in human brain, enabling to learn and collect information to establish connections with its neural machine translator’s neurons. However, it also causes many errors because of the lack of enough information. In this paper, contrastive analysis will be carried on by using Google translation. On the one hand, being a pioneer in the translation of NET, it is inevitably to sue Google as the translation software to translate medical texts in this paper. Comparing the program called “Google brain”, other NMT of translation software are relatively disadvantages. On the other hand, the Google translation enjoys the largest users in the world, with its downloads of more than one billion. The output quality of Google translation is more correct and complete than other machine translation software. With years of development and improvement, Google Translation has been greatly promoted. In this paper, Chinese medical abstract will be automatically translated by Google translation, and then the translation output will be compared with the translation by human and the publication of English abstracts. The main purpose is to prove that the improvement and promotion of quality and accuracy in the medical abstracts will be obtained through the pre-editing approaches.<br />
<br />
Generally, machine translation software includes Google Translation, Youdao Translator and Niu Translation, which has their own special use in translation. Take Google as an example, it is more like neurons in human brain, enabling to learn and collect information to establish connections with its neural machine translator’s neurons. However, it also causes many errors because of the lack of enough information. In this paper, contrastive analysis will be carried on by using Google translation. On the one hand, being a pioneer in the translation of NET, it is inevitably to sue Google as the translation software to translate medical texts in this paper. Comparing the program called “Google brain”, other NMT of translation software are relatively disadvantages. On the other hand, the Google translation enjoys the largest users in the world, with its downloads of more than one billion. The output quality of Google translation is more correct and complete than other machine translation software. With years of development and improvement, Google Translation has been greatly promoted. In this paper, Chinese medical abstract will be automatically translated by Google translation, and then the translation output will be compared with the translation by human and the publication of English abstracts. The main purpose is to prove that the improvement and promotion of quality and accuracy in the medical abstracts will be obtained through the pre-editing approaches.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===2.Language Characteristics and Error Division===<br />
Since Chinese and English are two different languages, it is quite neccessary to identify their own characteristics so as to better analyze and understand the two languages. Tytler (1978: 118-119) argued in his Essay on the Principles of Translation that there are three principles of translation that in all the translation should give readers the same feelings as the source text, except for complete transcript of ideas. There actually also exit some mistakes of these two languages. So the following will make some clarrifications of these two languages to ensure more accurate translation.<br />
<br />
Since Chinese and English are two different languages, it is quite neccessary to identify their own characteristics so as to better analyze and understand the two languages. Tytler (1978: 118-119) argued in his Essay on the Principles of Translation that there are three principles of translation that in all the translation should give readers the same feelings as the source text, except for complete transcript of ideas. There actually also exit some mistakes of these two languages. So the following will make some clarrifications of these two languages to ensure more accurate translation.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===2.1 Language Characteristics of Medical Abstracts===<br />
Chinese and English belong to different language systems, so there are differences in their language structure and the way users think of the languages. When using machine translation from Chinese to English, due to the unequal language levels, there will be many mistakes in the translation process, especially for ESP texts, such as medical papers.Here, these differences mainly refer to the linguistic characteristics of medical abstracts. In medical abstracts, it usually includes structured and unstructured abstracts. Although in different forms, they both describe the purpose, methods and conclusions of the research.<br />
<br />
In the method section of medial abstract, several Chinese sentences can be connected with commas, but each sentence may convey different information. In contrast, English sentences contain a great deal of information, but in order to ensure clarity, some modifiers need to be isolated and then reconstructed. However, in Chinese-English machine translation, a lot of information is put into the sentence because the machine segments the sentence on the basis of the full comma.<br />
<br />
In addition, subjectless sentence is used in the objective and method parts of Chinese medical abstracts. The subjectless sentence means the sentence without or free from subject and is usually employed in two contexts. The first is "needless to say". In Chinese sentences, it is common to omit the subject of the sentence. Chinese sentences can convey meanings by using incomplete sentence structures, so Chinese speakers can understand the meanings of the sentences even though the sentence subject is omitted. The second is "emphasis of action". In this context, subjectless sentences are used to describe behaviors, especially in the study of traditional Chinese medicine abstracts. Comparatively speaking, it should be avoided in English medical abstracts. Abstract sentences are more subjective when describing the learning process, while the essential requirement of English medical abstract is objectivity. From this point, sentences without subject should be avoided in English medical abstracts. Another feature is voice. Few words with passive meanings appear in Chinese abstracts. English sentences are more favoured in using passive voice. When translating from Chinese to English, passive voice should be used to make the contents objective, which is also the basic requirement of medical papers.<br />
<br />
These are the linguistic features of Chinese medical abstracts. There are great differences in sentence structure and expression between Chinese and English medical abstracts. These differences may reduce the accuracy of machine translation, so it is necessary to introduce pre-editing to edit the source text to ensure that the source text can be accurately recognized by the machine and fully translated into English.<br />
<br />
Chinese and English belong to different language systems, so there are differences in their language structure and the way users think of the languages. When using machine translation from Chinese to English, due to the unequal language levels, there will be many mistakes in the translation process, especially for ESP texts, such as medical papers.Here, these differences mainly refer to the linguistic characteristics of medical abstracts. In medical abstracts, it usually includes structured and unstructured abstracts. Although in different forms, they both describe the purpose, methods and conclusions of the research.<br />
<br />
In the method section of medial abstract, several Chinese sentences can be connected with commas, but each sentence may convey different information. In contrast, English sentences contain a great deal of information, but in order to ensure clarity, some modifiers need to be isolated and then reconstructed. However, in Chinese-English machine translation, a lot of information is put into the sentence because the machine segments the sentence on the basis of the full comma.<br />
<br />
In addition, subjectless sentence is used in the objective and method parts of Chinese medical abstracts. The subjectless sentence means the sentence without or free from subject and is usually employed in two contexts. The first is "needless to say". In Chinese sentences, it is common to omit the subject of the sentence. Chinese sentences can convey meanings by using incomplete sentence structures, so Chinese speakers can understand the meanings of the sentences even though the sentence subject is omitted. The second is "emphasis of action". In this context, subjectless sentences are used to describe behaviors, especially in the study of traditional Chinese medicine abstracts. Comparatively speaking, it should be avoided in English medical abstracts. Abstract sentences are more subjective when describing the learning process, while the essential requirement of English medical abstract is objectivity. From this point, sentences without subject should be avoided in English medical abstracts. Another feature is voice. Few words with passive meanings appear in Chinese abstracts. English sentences are more favoured in using passive voice. When translating from Chinese to English, passive voice should be used to make the contents objective, which is also the basic requirement of medical papers.<br />
<br />
These are the linguistic features of Chinese medical abstracts. There are great differences in sentence structure and expression between Chinese and English medical abstracts. These differences may reduce the accuracy of machine translation, so it is necessary to introduce pre-editing to edit the source text to ensure that the source text can be accurately recognized by the machine and fully translated into English.<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===2.2 Error Division of Machine Translation in Translating Abstracts of Medical Papers from Chinese to English===<br />
In this paper, errors in machine translation are listed out after analyzing. Some are due to the principles, such as statistical-based MT and NMT, employed by machine translation. Based on the original errors, they can be studied from two levels. One is from the macro-level, referring to mistakes caused by objective factors. These are not human factors, but the disadvantage of machine translation and the limitations of language. The limitations of machine translation are derived from the principles and models adopted and manifests itself as a reliance on reference sources. In this case, semantic ambiguity and textual incoherence may occur in the absence of reference sources.<br />
<br />
However, for ESP texts such as medical papers, the requirements for terms and sentence patterns are far beyond the existing corpora. For unfamiliar texts (that is, the corresponding texts cannot be found in the corpora). The quality of output translation will be relatively low, which is determined by the principles of machine translation. As mentioned above, machine translation has evolved over the past few decades from rules-based to corp-based statistics to NMT. However, there still exist some limitations in machine translation.<br />
<br />
Mistakes on micro-level are mainly caused by the variations and differences of linguistic structure between Chinese and English. Chinese is an implicit language, while English is an explicit one (Lian, 2010) To put it in another way, Chinese expression does not depend on language structures, while English does the opposite. This may result in mismatches between the original and machine translated sentences. This kind of error is mainly divided into two types: component fragment and component missing. For a medical paper, such errors are very serious. As a kind of ESP discourse, medical paper has the characteristics of fixed, objective and accurate language structure. In order to reproduce the characteristics of medical abstracts in translation, it is necessary to avoid errors at the level of words and sentences and pay attention to logic and consistency.<br />
<br />
Lexical errors here refer to the inconsistency of fixed expressions in the translation of terms. Although these terms are machine translation based on the corpus, the corpus may not be perfect for ESP texts such as medical papers. Therefore, fixed expressions are not used in term translation. This is also the most common mistake in machine translation (the term here includes but is not limited to nouns). For ESP texts, medical text in particular, the fixed expressions and the accuracy of terms are of great importance.<br />
<br />
In this paper, errors in machine translation are listed out after analyzing. Some are due to the principles, such as statistical-based MT and NMT, employed by machine translation. Based on the original errors, they can be studied from two levels. One is from the macro-level, referring to mistakes caused by objective factors. These are not human factors, but the disadvantage of machine translation and the limitations of language. The limitations of machine translation are derived from the principles and models adopted and manifests itself as a reliance on reference sources. In this case, semantic ambiguity and textual incoherence may occur in the absence of reference sources.<br />
<br />
However, for ESP texts such as medical papers, the requirements for terms and sentence patterns are far beyond the existing corpora. For unfamiliar texts (that is, the corresponding texts cannot be found in the corpora). The quality of output translation will be relatively low, which is determined by the principles of machine translation. As mentioned above, machine translation has evolved over the past few decades from rules-based to corp-based statistics to NMT. However, there still exist some limitations in machine translation.<br />
<br />
Mistakes on micro-level are mainly caused by the variations and differences of linguistic structure between Chinese and English. Chinese is an implicit language, while English is an explicit one (Lian, 2010) To put it in another way, Chinese expression does not depend on language structures, while English does the opposite. This may result in mismatches between the original and machine translated sentences. This kind of error is mainly divided into two types: component fragment and component missing. For a medical paper, such errors are very serious. As a kind of ESP discourse, medical paper has the characteristics of fixed, objective and accurate language structure. In order to reproduce the characteristics of medical abstracts in translation, it is necessary to avoid errors at the level of words and sentences and pay attention to logic and consistency.<br />
<br />
Lexical errors here refer to the inconsistency of fixed expressions in the translation of terms. Although these terms are machine translation based on the corpus, the corpus may not be perfect for ESP texts such as medical papers. Therefore, fixed expressions are not used in term translation. This is also the most common mistake in machine translation (the term here includes but is not limited to nouns). For ESP texts, medical text in particular, the fixed expressions and the accuracy of terms are of great importance.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===3.Approaches Proposed for Pre-editing ===<br />
Generally speaking, there is a close relationship between pre-editing and post-editing, both of which aim to convey information and ensure high or publishable translation quality. Proper pre-editing can improve the quality of machine translation in terms of adequacy and consistency. <br />
Complexity of natural language and people use language arbitrarily, bringing many difficulties to Chinese-English machine translation, Hu Qingping (2005:24) has proposed "the research of translation software and the development of the controlled language are the two directions to improve the quality of machine translation : the former aims at the difficulty in the natural language processing, the latter overcomes the arbitrariness of natural language". Feng Quangong and Gao Lin (2017: 63-68) put forward: "The writing principles of controlled language can be applied to pre-editing of machine translation. Pre-editing based on controlled language can effectively reduce the complexity and ambiguity of source text, improve the identifiability of machine translation (the translatability of source text itself), and thus reduce (fully) the workload of post-editing".<br />
The central task of pre-editing is to transform human-friendly content into machine-friendly content, so words and sentences need to be repositioned or even changed. Based on the analysis of the language characteristics of Chinese and English, the Chinese ideographic group should be split before the Chinese source text is input into the machine software to translate so that the sentence structure is complete which can be easily recognized by the machine translation software.<br />
<br />
Generally speaking, there is a close relationship between pre-editing and post-editing, both of which aim to convey information and ensure high or publishable translation quality. Proper pre-editing can improve the quality of machine translation in terms of adequacy and consistency. <br />
<br />
Complexity of natural language and people use language arbitrarily, bringing many difficulties to Chinese-English machine translation, Hu Qingping (2005:24) has proposed "the research of translation software and the development of the controlled language are the two directions to improve the quality of machine translation : the former aims at the difficulty in the natural language processing, the latter overcomes the arbitrariness of natural language". Feng Quangong and Gao Lin (2017: 63-68) put forward: "The writing principles of controlled language can be applied to pre-editing of machine translation. Pre-editing based on controlled language can effectively reduce the complexity and ambiguity of source text, improve the identifiability of machine translation (the translatability of source text itself), and thus reduce (fully) the workload of post-editing".<br />
<br />
The central task of pre-editing is to transform human-friendly content into machine-friendly content, so words and sentences need to be repositioned or even changed. Based on the analysis of the language characteristics of Chinese and English, the Chinese ideographic group should be split before the Chinese source text is input into the machine software to translate so that the sentence structure is complete which can be easily recognized by the machine translation software.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufefng<br />
<br />
===3.1 Extraction and Replacement of Terms in the Source Text===<br />
As a branch of applied English, medical English is the product of the combination of English language knowledge and medical knowledge. The terms in medical papers have the characteristics of fixed and complex language structure. Although Google Translation is based on a corpus, the language structure is not fixed due to the limited size of the corpus. Therefore, the following two steps should be followed before machine translation. The first is to extract terms and create a glossary, and then replace the Chinese terms in the source text with the corresponding English expressions in the glossary. Of course, the terms of extraction should be searched and verified according to the actual situation. All of these words are verified before they can be replaced. This method can not only ensure the accuracy of term translation, but also maintain the consistency of expression, especially when dealing with long texts.<br />
Example1<br />
Source abstract: 口腔白斑病癌变相关缺氧应答基因和微小RNA的芯片及表达验证。<br />
Google translation: Microarray detection/Chip detection and expression verification of hypoxia response genes and microRNAs related to oral leukoplakia canceration.<br />
Published translation:Transcriptome array screening and verification of oral leukoplakia carcinogenesis-related hypoxia-responsive gene and microRNA. <br />
Analysis: The expression “ Affymetrix GeneChip” empolyed in this thesis is a human transcription array for transcriptome array. In the source abstract, “芯片检测“ can be meant “screening” but not detection, so the proper and appropriate translation of these expression should be “transcription array screening”, but the Google translates it into “microarry detection of chip detection”. Therefore, term extraction is essential and inevitable before putting the source abstracts into the machine translation software.<br />
After pre-editing source abstracts: 对 oral leukoplakia carcinogenesis 相关 hypoxia-responsive gene 和微小RNA进行 transcriptome array screening 及表达验证。<br />
After pre-editing translation: Transcriptome array screening and expression- verification of hypoxia-responsive genes and microRNAs related to oral leukoplakia carcinogenesis.<br />
<br />
As a branch of applied English, medical English is the product of the combination of English language knowledge and medical knowledge. The terms in medical papers have the characteristics of fixed and complex language structure. Although Google Translation is based on a corpus, the language structure is not fixed due to the limited size of the corpus. Therefore, the following two steps should be followed before machine translation. The first is to extract terms and create a glossary, and then replace the Chinese terms in the source text with the corresponding English expressions in the glossary. Of course, the terms of extraction should be searched and verified according to the actual situation. All of these words are verified before they can be replaced. This method can not only ensure the accuracy of term translation, but also maintain the consistency of expression, especially when dealing with long texts.<br />
<br />
Example1<br />
Source abstract: 口腔白斑病癌变相关缺氧应答基因和微小RNA的芯片及表达验证。<br />
Google translation: Microarray detection/Chip detection and expression verification of hypoxia response genes and microRNAs related to oral leukoplakia canceration.<br />
Published translation:Transcriptome array screening and verification of oral leukoplakia carcinogenesis-related hypoxia-responsive gene and microRNA. <br />
Analysis: The expression “ Affymetrix GeneChip” empolyed in this thesis is a human transcription array for transcriptome array. In the source abstract, “芯片检测“ can be meant “screening” but not detection, so the proper and appropriate translation of these expression should be “transcription array screening”, but the Google translates it into “microarry detection of chip detection”. Therefore, term extraction is essential and inevitable before putting the source abstracts into the machine translation software.<br />
After pre-editing source abstracts: 对 oral leukoplakia carcinogenesis 相关 hypoxia-responsive gene 和微小RNA进行 transcriptome array screening 及表达验证。<br />
After pre-editing translation: Transcriptome array screening and expression- verification of hypoxia-responsive genes and microRNAs related to oral leukoplakia carcinogenesis.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===3.2 Explication of Subordination===<br />
As mentioned above, the subordination of Chinese sentences is judged by certain specific words in machine translation . Chinese is a paratactic language, and sentences are often connected by internal logical relationships; while English depends on sentence structure, so sentences are often closely linked by various language forms. In order to improve the accuracy of machine translation, we can adjust the sentence structure and adjust the position of adjectives and their modifiers. <br />
<br />
Example 2<br />
Source abstracts:回顾性分析南京医科大学附属儿童医院中重度HIE患儿49例及同期就诊的无神经系统症状体征的足月新生儿为对照组31例的头颅磁共振成像（MRI）资料。<br />
Google translation: A retrospective analysis that the brain magnetic resonance imaging (MRI) data of 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University and full-term neonates with no neurological symptoms and signs during the same period were included in the control group.<br />
Published translation: A total of 49 children with moderate to severe HIE admitted to the children’s Hospital Affiliated to Nanjing Medical University were retrospectively analyzed. Cranial magnetic resonance imaging (MRI) date of 31 full-term neonates without neurological symptoms and signs who visited the hospital during the same period were recruited as the control group. <br />
<br />
Analysis: As the above example shows that “中重度HIE患儿” and “足月新生儿” in the source abstracts are from the Children’s Hospital Affiliated Nanjing Medical University. The Google translation shows that 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University. There is an error due to the misuse of subordination. There are some advice in order to solve the problem. That is, first to segment the sentence and then reconstruct the sentence. For instance, the Chinese expression “南京医科大学附属儿童医院” carries many modifiers, which requires to cut the modifiers and reconstruct the sentence. <br />
Therefore, the Chinese expression “南京医科大学附属儿童医院’can be divided into abstractions, leaving two sentences instead of one long sentence with modifiers..<br />
After pre-editing source abstracts: 对49例中重度HIE患儿以及31例无神经系统症状体征的足月新生儿的MRI资料进行回顾性分析。这些患者收治于南京医科大学儿童附属医院。<br />
After pre-editing translation: The MRI data of 49 children with moderate to severe HIE and 31 full-term newborns without neurological symptoms and signs were retrospectively analyzed. These patients were admitted to the Children’s Hospital of Nanjing Medical University.<br />
<br />
As mentioned above, the subordination of Chinese sentences is judged by certain specific words in machine translation . Chinese is a paratactic language, and sentences are often connected by internal logical relationships; while English depends on sentence structure, so sentences are often closely linked by various language forms. In order to improve the accuracy of machine translation, we can adjust the sentence structure and adjust the position of adjectives and their modifiers. <br />
<br />
Example 2<br />
Source abstracts:回顾性分析南京医科大学附属儿童医院中重度HIE患儿49例及同期就诊的无神经系统症状体征的足月新生儿为对照组31例的头颅磁共振成像（MRI）资料。<br />
Google translation: A retrospective analysis that the brain magnetic resonance imaging (MRI) data of 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University and full-term neonates with no neurological symptoms and signs during the same period were included in the control group.<br />
Published translation: A total of 49 children with moderate to severe HIE admitted to the children’s Hospital Affiliated to Nanjing Medical University were retrospectively analyzed. Cranial magnetic resonance imaging (MRI) date of 31 full-term neonates without neurological symptoms and signs who visited the hospital during the same period were recruited as the control group. <br />
<br />
Analysis: As the above example shows that “中重度HIE患儿” and “足月新生儿” in the source abstracts are from the Children’s Hospital Affiliated Nanjing Medical University. The Google translation shows that 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University. There is an error due to the misuse of subordination. There are some advice in order to solve the problem. That is, first to segment the sentence and then reconstruct the sentence. For instance, the Chinese expression “南京医科大学附属儿童医院” carries many modifiers, which requires to cut the modifiers and reconstruct the sentence. <br />
<br />
Therefore, the Chinese expression “南京医科大学附属儿童医院’can be divided into abstractions, leaving two sentences instead of one long sentence with modifiers..<br />
After pre-editing source abstracts: 对49例中重度HIE患儿以及31例无神经系统症状体征的足月新生儿的MRI资料进行回顾性分析。这些患者收治于南京医科大学儿童附属医院。<br />
After pre-editing translation: The MRI data of 49 children with moderate to severe HIE and 31 full-term newborns without neurological symptoms and signs were retrospectively analyzed. These patients were admitted to the Children’s Hospital of Nanjing Medical University.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===3.3 Explication of Subject through Voice Changing===<br />
The extensive use of subjectless sentences are quite common in the Chinese abstracts, while English prefers to employ passive voice in the sentences so as to make them object and accurate. In order to take the characteristics of English sentences into great and careful consideration, the sentences written in passive voice in particular, it will be helpful for machine translation to find out the subject and reconstruct a sentence in passive voice (Wang Yan: 2008). The first is to discover the “doee” in a sentence and then is to reconstruct the sentence structure and put the “doee” before the verb. The last is to explicate the passive relation between the noun and the verb.<br />
Example 3<br />
Source abstract: 回顾性分析2018年1月至2020年12月解放军总医院第七医学中心收治的500例老年髋部骨折患者的资料。<br />
Google translation: A retrospective analysis of the data of 500 elderly patients with hip fractures admitted to the Seventh Medical Center of the PLA General Hospital from January 2018 to December 2020.<br />
Published translation: From January 2018 to December 2020, the data of 500 elderly patients with hip fracture treated in the Seventh Medical Center of PLA General Hospital were analyzed retrospectively.<br />
Analysis: The above example indicates that the “回顾性分析”in the Google Translate is a noun phrase, but the source abstracts actually describes an action or a behavior. The reason for such a mistake is that the machine can’t recognize the Chinese subjetless sentences. To find the subject of the sentence, the source abstracts can be revised and adjusted. Finding the “doee” is the first step, which refers to “500例老年髋部骨折患者的资料”in the source abstracts. And next is to put it in front of the verb, that is to place it in the beginning of the sentence. Last but not least, the passive voice should be explicated in the sentence. <br />
After pre-editing source abstract: 500例老年髋部骨折患者的资料被回顾性分析，他们在2018年1月之2020年12月期间收治于解放军总医院第七医学中心。<br />
After pre-editing translation: The data of 500 elderly hip fracture patients were retrospectively analyzed. They were admitted to the Seventh Medical Center of the PLA General Hospital between January 2018 and December 2020.<br />
<br />
The extensive use of subjectless sentences are quite common in the Chinese abstracts, while English prefers to employ passive voice in the sentences so as to make them object and accurate. In order to take the characteristics of English sentences into great and careful consideration, the sentences written in passive voice in particular, it will be helpful for machine translation to find out the subject and reconstruct a sentence in passive voice (Wang Yan: 2008). The first is to discover the “doee” in a sentence and then is to reconstruct the sentence structure and put the “doee” before the verb. The last is to explicate the passive relation between the noun and the verb.<br />
<br />
Example 3<br />
Source abstract: 回顾性分析2018年1月至2020年12月解放军总医院第七医学中心收治的500例老年髋部骨折患者的资料。<br />
Google translation: A retrospective analysis of the data of 500 elderly patients with hip fractures admitted to the Seventh Medical Center of the PLA General Hospital from January 2018 to December 2020.<br />
Published translation: From January 2018 to December 2020, the data of 500 elderly patients with hip fracture treated in the Seventh Medical Center of PLA General Hospital were analyzed retrospectively.<br />
<br />
Analysis: The above example indicates that the “回顾性分析”in the Google Translate is a noun phrase, but the source abstracts actually describes an action or a behavior. The reason for such a mistake is that the machine can’t recognize the Chinese subjetless sentences. To find the subject of the sentence, the source abstracts can be revised and adjusted. Finding the “doee” is the first step, which refers to “500例老年髋部骨折患者的资料”in the source abstracts. And next is to put it in front of the verb, that is to place it in the beginning of the sentence. Last but not least, the passive voice should be explicated in the sentence. <br />
After pre-editing source abstract: 500例老年髋部骨折患者的资料被回顾性分析，他们在2018年1月之2020年12月期间收治于解放军总医院第七医学中心。<br />
After pre-editing translation: The data of 500 elderly hip fracture patients were retrospectively analyzed. They were admitted to the Seventh Medical Center of the PLA General Hospital between January 2018 and December 2020.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===3.4 Relocation of Modifiers===<br />
Modifiers, including noun, adverbial and attributive are constantly employed in the Chinese medical papers in order to add information to subject and object in the sentence. By doing this, complicated sentences can be structured, thus causing obstacles to machine translation for recognizing this complex sentence structures when translating Chinese-English sentences. To make the machine translation successfully recognize the sentence structure, simplifying the sentence structure is quite necessary. The key is to moving the location of the modifiers and thus making the modifiers an independent sentence. In other words, the modifiers ought to be put before or behind the main part of the sentence to satisfy the common use of English. <br />
Usually, the modifiers in Chinese sentence can only be placed in front of the core word, while in English, modifiers are very flexible. It is all right to place the modifiers in front of or behind the major part of the sentences with adjective or a connective noun.<br />
<br />
Example 4<br />
Source abstracts: 利用DNA重组技术以pET-28a表达系统在E.coli BL21(DE3) 中重组表达Hepl。<br />
Google Translation: Hepl is recombined in E.coli BL21 (DE3) using DNA recombination technology with pET-28a expression system.<br />
Published translation: The recombinant Hcpl protein was expressed by using DNA recombination technology through pET-28a expression system in E. coli BL21 (De3).<br />
Analysis: The Chinese medical text includes two modifiers, “利用DNA”and “以pET-28a)表达系统”. These two modifiers will be translated by machine, which catches more attention to the two constituents. From the Google translation above, one failure is obvious that it misplaces the location of the two modifiers and presents it in not accurate form. To make it translate correctly by machine translation, dividing the sentences is very important so that the source abstracts can be correctly recognized by machine translation.<br />
<br />
After pre-editing source abstract: 利用DNA重组技术，Hcp1重组表达在E.coli BL21 (DE3)中， 通过pET-28a表达系统在E. coli BL21 (DE3)中。<br />
Google translation: Using DNA recombination technology, Hcp1 recombination is expressed in E.coli BL21 (DE3), via pET-28a expression system in E. coli BL21 (DE3).<br />
The second is the independence of modifiers, that is, modifiers can also be reconstructed into clauses to modify core words. Compared with English, There is no subordinate clause in Chinese, so redundant Chinese modifiers need to be reconstructed into subordinate clauses in Chinese-English translation to meet the characteristics of English. English, especially sentences with multiple modifiers. Otherwise, sentence structure may be confused, such as scattered modifiers of core words. To avoid this mistake, it is necessary to separate the modifier from the main part of the sentence. These modifiers should be reconstituted into clauses. Also, keep your sentences simple and easy to understand.<br />
<br />
Modifiers, including noun, adverbial and attributive are constantly employed in the Chinese medical papers in order to add information to subject and object in the sentence. By doing this, complicated sentences can be structured, thus causing obstacles to machine translation for recognizing this complex sentence structures when translating Chinese-English sentences. To make the machine translation successfully recognize the sentence structure, simplifying the sentence structure is quite necessary. The key is to moving the location of the modifiers and thus making the modifiers an independent sentence. In other words, the modifiers ought to be put before or behind the main part of the sentence to satisfy the common use of English. <br />
Usually, the modifiers in Chinese sentence can only be placed in front of the core word, while in English, modifiers are very flexible. It is all right to place the modifiers in front of or behind the major part of the sentences with adjective or a connective noun.<br />
<br />
Example 4<br />
Source abstracts: 利用DNA重组技术以pET-28a表达系统在E.coli BL21(DE3) 中重组表达Hepl。<br />
Google Translation: Hepl is recombined in E.coli BL21 (DE3) using DNA recombination technology with pET-28a expression system.<br />
Published translation: The recombinant Hcpl protein was expressed by using DNA recombination technology through pET-28a expression system in E. coli BL21 (De3).<br />
Analysis: The Chinese medical text includes two modifiers, “利用DNA”and “以pET-28a)表达系统”. These two modifiers will be translated by machine, which catches more attention to the two constituents. From the Google translation above, one failure is obvious that it misplaces the location of the two modifiers and presents it in not accurate form. To make it translate correctly by machine translation, dividing the sentences is very important so that the source abstracts can be correctly recognized by machine translation.<br />
<br />
After pre-editing source abstract: 利用DNA重组技术，Hcp1重组表达在E.coli BL21 (DE3)中， 通过pET-28a表达系统在E. coli BL21 (DE3)中。<br />
Google translation: Using DNA recombination technology, Hcp1 recombination is expressed in E.coli BL21 (DE3), via pET-28a expression system in E. coli BL21 (DE3).<br />
The second is the independence of modifiers, that is, modifiers can also be reconstructed into clauses to modify core words. Compared with English, There is no subordinate clause in Chinese, so redundant Chinese modifiers need to be reconstructed into subordinate clauses in Chinese-English translation to meet the characteristics of English. English, especially sentences with multiple modifiers. Otherwise, sentence structure may be confused, such as scattered modifiers of core words. To avoid this mistake, it is necessary to separate the modifier from the main part of the sentence. These modifiers should be reconstituted into clauses. Also, keep your sentences simple and easy to understand.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===3.5 Proper Omission and Deletion of Category Words===<br />
Category words are commonly used in Chinese. Category words complement the meaning of words, including problems, positions, situations and jobs. Adding this supplementary word is more in line with Chinese custom. In many cases, it has no real meaning, so it can be omitted in translation. In Chinese, category words are frequently used. However, it is rarely used in English, which is one of the differences between Chinese and English. There are many kinds of category words. Considering objectivity and the fixed structure of language, redundant category words should be deleted in Chinese-English translation. In the general, the most commonly used category of words in the Chinese medical abstracts are "process", "behavior", and "situation". These categories of words hinder the language conversion of Chinese and English, resulting in redundancy. <br />
<br />
Example 5<br />
Source abstract: 探讨口腔白斑病癌变进程中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia response genes and associated microRNAs in the process of oral leukoplakia cancer was discussed.<br />
Published translation: To study the hypoxia response gene and microRNA (miRNA)expression profiles in the pathogenesis and progression of oral leukoplakia (OLK).<br />
Analysis: As the above example shows, the Chinese words “进程” belongs to a category word. However, the Chinese expression “癌变”contains the process, so the “进程” expression can be deleted before placing it into the machine translation, because the meaning of it has been overlapping between the expression “癌变”. Therefore, its place can be replaced with preposition “during”.<br />
<br />
After pre-editing source abstract: 探讨口腔白斑病癌变中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia responsive genes and miRNA in oral leukoplakia cancer was investigated.<br />
<br />
Category words are commonly used in Chinese. Category words complement the meaning of words, including problems, positions, situations and jobs. Adding this supplementary word is more in line with Chinese custom. In many cases, it has no real meaning, so it can be omitted in translation. In Chinese, category words are frequently used. However, it is rarely used in English, which is one of the differences between Chinese and English. There are many kinds of category words. Considering objectivity and the fixed structure of language, redundant category words should be deleted in Chinese-English translation. In the general, the most commonly used category of words in the Chinese medical abstracts are "process", "behavior", and "situation". These categories of words hinder the language conversion of Chinese and English, resulting in redundancy. <br />
<br />
Example 5<br />
Source abstract: 探讨口腔白斑病癌变进程中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia response genes and associated microRNAs in the process of oral leukoplakia cancer was discussed.<br />
Published translation: To study the hypoxia response gene and microRNA (miRNA)expression profiles in the pathogenesis and progression of oral leukoplakia (OLK).<br />
Analysis: As the above example shows, the Chinese words “进程” belongs to a category word. However, the Chinese expression “癌变”contains the process, so the “进程” expression can be deleted before placing it into the machine translation, because the meaning of it has been overlapping between the expression “癌变”. Therefore, its place can be replaced with preposition “during”.<br />
<br />
After pre-editing source abstract: 探讨口腔白斑病癌变中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia responsive genes and miRNA in oral leukoplakia cancer was investigated.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===Conclusion===<br />
After years of development, machine translation has made great progress. The accuracy of machine translation has been greatly improved in both text recognition and sentence pattern conversion. However, machine translation has its own limitations. In other words, it needs to rely on the parallel corpus as a reference source for improving its accuracy. ESP text, in particular, is harder to get the high quality by machine translation. <br />
<br />
As one of the research papers, the characteristics of medical abstracts are fixed language structures, objectivity and accuracy (Qin Yi 2004:421-423). Therefore, medical translation must be accurate, object and understandable to follow the specific demands of the medical paper. Being an important field in the human society, medical paper translation is on a great demand, which means that it needs a huge demand for human labor. However, with the machine translation promoting, it will be more efficient to translate medical papers combining the effort by human and machine. The improvement and development of machine translation requires the joint efforts of computer science, information science, statistics, linguistics and other academic circles to achieve more mature human-computer mutual assistance translation (Li Yafei, Zhang Ruihua 2019:38-45).<br />
<br />
However, errors can occur during the process of machine translation of Chinese- English, because of the differences of the Chinese and English and the processing of the machine. Errors from the perspective of linguistic or grammar can affect the machine translation a lot. After division and recognition of errors, some pre-editing approaches are put forward to help the machine translation more accurate and readable, that are, extraction and replacement of terms in the source text, relocation of modifiers, explication of subordination, proper omission, deletion of category words and explication of subject through voice changing. <br />
<br />
<br />
The paper mainly focuses on the pre-editing machine translation by using medical papers as a case study. The errors of machine translation occurring in the translation of medical abstracts and pre-editing approaches for machine translation. The quality of machine translation of medical papers is greatly improved after employing the pre-editing methods. However, machine translation is not as flexible and accurate as human brain, so it is of importance to combine pre-editing and post-editing approaches with machine translation in order to produce more accurate, more object machine translation of medical papers.<br />
<br />
After years of development, machine translation has made great progress. The accuracy of machine translation has been greatly improved in both text recognition and sentence pattern conversion. However, machine translation has its own limitations. In other words, it needs to rely on the parallel corpus as a reference source for improving its accuracy. ESP text, in particular, is harder to get the high quality by machine translation. <br />
<br />
As one of the research papers, the characteristics of medical abstracts are fixed language structures, objectivity and accuracy (Qin Yi 2004:421-423). Therefore, medical translation must be accurate, object and understandable to follow the specific demands of the medical paper. Being an important field in the human society, medical paper translation is on a great demand, which means that it needs a huge demand for human labor. However, with the machine translation promoting, it will be more efficient to translate medical papers combining the effort by human and machine. The improvement and development of machine translation requires the joint efforts of computer science, information science, statistics, linguistics and other academic circles to achieve more mature human-computer mutual assistance translation (Li Yafei, Zhang Ruihua 2019:38-45).<br />
<br />
However, errors can occur during the process of machine translation of Chinese- English, because of the differences of the Chinese and English and the processing of the machine. Errors from the perspective of linguistic or grammar can affect the machine translation a lot. After division and recognition of errors, some pre-editing approaches are put forward to help the machine translation more accurate and readable, that are, extraction and replacement of terms in the source text, relocation of modifiers, explication of subordination, proper omission, deletion of category words and explication of subject through voice changing. <br />
<br />
The paper mainly focuses on the pre-editing machine translation by using medical papers as a case study. The errors of machine translation occurring in the translation of medical abstracts and pre-editing approaches for machine translation. The quality of machine translation of medical papers is greatly improved after employing the pre-editing methods. However, machine translation is not as flexible and accurate as human brain, so it is of importance to combine pre-editing and post-editing approaches with machine translation in order to produce more accurate, more object machine translation of medical papers.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
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Lian Shuneng连淑能 (2010). 英汉对比研究增订本[M]''An Updated Version of English-Chinese Contrastive Studies'' . 北京:高等教育出版社Beijing: Higher Education Publishing House. 35-36.<br />
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Li Yafei, Zhang Ruihua黎亚飞,张瑞华 (2019). 机器翻译发展与现状[J]''The Development and Current Situation of Machine Translation''. 中国轻工教育 China Light Industry Education, (5):38-45. <br />
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Qin Yi秦毅(2004),从翻译基本标准议医学英语的翻译[J] ''On the Translation of Medical English from the Basic Standard of Translation''. 遵义医学院学报 Journal of Zunyi Medical College,27 (4): 421-423. <br />
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Slype G V & Guinet J F & Seitz F (1984). ''Better Translation for Better Communication'' [M] . Oxford: Pergamon Press Ltd (U.K.). 90-93<br />
<br />
O'Brien S (2002). Teaching Post-editing: A Proposal for Course Con-tent [EB/OL]. http://mt-archive. Info/EAMT-2002-0brien. Pdf.<br />
<br />
Tytler, A. F. (1978). ''Essay On The Principles of Translation''[M]. Amsterdam: JohnBenjamins Publishing. 118-119<br />
<br />
Wang Yan王燕 (2008). 医学英语翻译与写作教程[M] ''Medical English Translation and Writing Course''. 重庆:重庆大学出版社 Chongqing: Chongqing University Press. 60-61<br />
<br />
Written by --[[User:Chen Huini|Chen Huini]] ([[User talk:Chen Huini|talk]]) 04:58, 15 December 2021 (UTC)Chen Huini<br />
<br />
=12 蔡珠凤 The Mistranslation of C-J Machine Translation of Political Statements=<br />
<br />
机器翻译中政治发言中译日的误译<br />
<br />
蔡珠凤 Cai Zhufeng, Hunan Normal University<br />
<br />
[[Machine_Trans_EN_12]]<br />
===Abstract===<br />
Language is the main way of communication between people. With the continuous development of globalization, the scale of cross-border exchanges is also expanding. However, due to cultural differences and diversity, the languages of different countries and regions are very different, which seriously hinders people's communication. The demand for efficient and convenient translation tools is increasing. At the same time, with the development of network technology and artificial intelligence, recognition technology based on deep learning is more and more widely used in English, Japanese and other fields.<br />
<br />
===Key words===<br />
machine translation; political statements; mistranslation of C-J machine translation<br />
===题目===<br />
The Mistranslation of C-J Machine Translation of Political Statements<br />
===摘要===<br />
语言是人与人之间交流的主要方式。随着全球化的不断发展，跨境交流的规模也在不断扩大。然而，由于文化的差异和多样性，不同国家和地区的语言差异很大，这严重阻碍了人们的交流。对高效便捷的翻译工具的需求正在增加。同时，随着网络技术和人工智能的发展，基于深度学习的识别技术在英语、日语等领域的应用越来越广泛。<br />
<br />
===关键词===<br />
机器翻译；政治发言；政治发言中译日的误译<br />
===1. Introduction===<br />
===Introduction to machine translation===<br />
Machine translation, also known as automatic translation, is a process of using computers to convert one natural language (source language) into another natural language (target language). It is a branch of computational linguistics, one of the ultimate goals of artificial intelligence, and has important scientific research value.At the same time, machine translation has important practical value. With the rapid development of economic globalization and the Internet, machine translation technology plays a more and more important role in promoting political, economic and cultural exchanges.The development of machine translation technology has been closely accompanied by the development of computer technology, information theory, linguistics and other disciplines. From the early dictionary matching, to the rule translation of dictionaries combined with linguistic expert knowledge, and then to the statistical machine translation based on corpus, with the improvement of computer computing power and the explosive growth of multilingual information, machine translation technology gradually stepped out of the ivory tower and began to provide real-time and convenient translation services for general users.（Zhang 2019:5-6)<br />
<br />
=== C-J machine translation software===<br />
Today's online machine translation software includes Baidu translation, Tencent translation, Google translation, Youdao translation, Bing translation and so on. Google was the first company to launch the machine translation system, and Baidu was the first company to import the machine translation system in China. In addition, Tencent and Youdao have attracted much attention.Machine translation is the process of using computers to convert one natural language into another. It usually refers to sentence and full-text translation between natural languages. In order to continuously improve the translation quality, R & D personnel have added artificial intelligence technologies such as speech recognition, image processing and deep neural network to machine translation on the basis of traditional machine translation based on rules, statistics and examples.With the increase of using machine translation, the joint cooperation between manual translation and machine translation will also increase significantly in the future. What criteria should be used to evaluate the quality of machine translation? In the evolving field of machine translation, there is an urgent need to clarify the unsolvable questions and solved problems.(Lv 1996:3)<br />
<br />
===The history of machine translation===<br />
The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. alchuni put forward the idea of using machines for translation. In 1933, Soviet inventor П.П. Trojansky designed a machine to translate one language into another, and registered his invention on September 5 of the same year; However, due to the low technical level in the 1930s, his translation machine was not made. In 1946, the first modern electronic computer ENIAC was born. Shortly after that, W. weaver, an American scientist and A. D. booth, a British engineer, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947 when discussing the application scope of electronic computer. In 1949, W. Weaver published the translation memorandum, which formally put forward the idea of machine translation. After 60 years of ups and downs, machine translation has experienced a tortuous and long development path. The academic community generally divides it into the following four stages:<br />
<br />
Pioneering period（1947-1964）<br />
<br />
In 1954, with the cooperation of IBM, Georgetown University completed the English Russian machine translation experiment with ibm-701 computer for the first time, showing the feasibility of machine translation to the public and the scientific community, thus opening the prelude to the study of machine translation.<br />
It is not too late for China to start this research. As early as 1956, the state included this research in the national scientific work development plan. The topic name is "machine translation, the construction of natural language translation rules and the mathematical theory of natural language". In 1957, the Institute of language and the Institute of computing technology of the Chinese Academy of Sciences cooperated in the Russian Chinese machine translation experiment, translating 9 different types of more complex sentences.From the 1950s to the first half of the 1960s, machine translation research has been on the rise. The United States and the former Soviet Union, two superpowers, have provided a lot of financial support for machine translation projects for military, political and economic purposes, while European countries have also paid considerable attention to machine translation research due to geopolitical and economic needs, and machine translation has become an upsurge for a time. In this period, although machine translation is just in the pioneering stage, it has entered an optimistic period of prosperity.<br />
<br />
Frustrated period（1964-1975）<br />
<br />
In 1964, in order to evaluate the research progress of machine translation, the American Academy of Sciences established the automatic language processing Advisory Committee (Alpac Committee) and began a two-year comprehensive investigation, analysis and test.In November 1966, the committee published a report entitled "language and machine" (Alpac report for short), which comprehensively denied the feasibility of machine translation and suggested stopping the financial support for machine translation projects. The publication of this report has dealt a blow to the booming machine translation, and the research of machine translation has fallen into a standstill. Coincidentally, during this period, China broke out the "ten-year Cultural Revolution", and basically these studies also stagnated. Machine translation has entered a depression.<br />
<br />
convalescence（1975-1989）<br />
<br />
Since the 1970s, with the development of science and technology and the increasingly frequent exchange of scientific and technological information among countries, the language barriers between countries have become more serious. The traditional manual operation mode has been far from meeting the needs, and there is an urgent need for computers to engage in translation. At the same time, the development of computer science and linguistics, especially the substantial improvement of computer hardware technology and the application of artificial intelligence in natural language processing, have promoted the recovery of machine translation research from the technical level. Machine translation projects have begun to develop again, and various practical and experimental systems have been launched successively, such as weinder system Eurpotra multilingual translation system, taum-meteo system, etc.However, after the end of the "ten-year holocaust", China has perked up again, and machine translation research has been put on the agenda again. The "784" project has paid enough attention to machine translation research. After the mid-1980s, the development of machine translation research in China has further accelerated. Firstly, two English Chinese machine translation systems, ky-1 and MT / ec863, have been successfully developed, indicating that China has made great progress in machine translation technology.(Chen 2016:5)<br />
<br />
New period(1990 present)<br />
<br />
With the universal application of the Internet, the acceleration of the process of world economic integration and the increasingly frequent exchanges in the international community, the traditional way of manual operation is far from meeting the rapidly growing needs of translation. People's demand for machine translation has increased unprecedentedly, and machine translation has ushered in a new development opportunity. International conferences on machine translation research have been held frequently, and China has made unprecedented achievements. A series of machine translation software have been launched, such as "Yixing", "Yaxin", "Tongyi", "Huajian", etc. Driven by the market demand, the commercial machine translation system has entered the practical stage, entered the market and came to the users.Since the new century, with the emergence and popularization of the Internet, the amount of data has increased sharply, and statistical methods have been fully applied. Internet companies have set up machine translation research groups and developed machine translation systems based on Internet big data, so as to make machine translation really practical, such as "Baidu translation", "Google translation", etc. In recent years, with the progress of in-depth learning, machine translation technology has further developed, which has promoted the rapid improvement of translation quality, and the translation in oral and other fields is more authentic and fluent.(Liu 2014:6)<br />
<br />
===The problem of machine translation at present===<br />
Error is inevitable<br />
Many people have misunderstandings about machine translation. They think that machine translation has great deviation and can't help people solve any problems. In fact, the error is inevitable. The reason is that machine translation uses linguistic principles. The machine automatically recognizes grammar, calls the stored thesaurus and automatically performs corresponding translation. However, errors are inevitable due to changes or irregularities in grammar, morphology and syntax, such as sentences with adverbials after "give me a reason to kill you first" in Dahua journey to the West. After all, a machine is a machine. No one has special feelings for language. How can it feel the lasting charm of "the tenderness of lowering its head, like the shame of a water lotus? After all, the meaning of Chinese is very different due to the changes of morphology, grammar and syntax and the change of context. Even many Chinese people are zhanger monks - they can't touch their heads, let alone machines.(Liu 2014：3）<br />
<br />
Bottleneck<br />
In fact, no matter which method, the biggest factor affecting the development of machine translation lies in the quality of translation. Judging from the achievements, the quality of machine translation is still far from the ultimate goal.<br />
Chinese mathematician and linguist Zhou Haizhong once pointed out in his paper "fifty years of machine translation": to improve the quality of machine translation, the first thing to solve is the problem of language itself rather than programming; It is certainly impossible to improve the quality of machine translation by relying on several programs alone. At the same time, he also pointed out that it is impossible for machine translation to achieve the degree of "faithfulness, expressiveness and elegance" when human beings have not yet understood how the brain performs fuzzy recognition and logical judgment of language. This view may reveal the bottleneck restricting the quality of translation. <br />
It is worth mentioning that American inventor and futurist ray cozwell predicted in an interview with Huffington Post that the quality of machine translation will reach the level of human translation by 2029. There are still many disputes about this thesis in the academic circles.（Cui 2019：4）<br />
<br />
===2.Mistranslation of Chinese Japanese machine translation===<br />
===2.1Vocabulary mistranslation in Chinese Japanese machine translation===<br />
===2.1.1Mistranslation of proper nouns===<br />
In political materials, there are often political dignitaries' names, place names or a large number of proper nouns in the political field. The morphemes of such words are definite and inseparable. Mistranslation will make the source language lose its specific meaning. <br />
<br />
Japanese translation into Chinese Chinese translation into Japanese<br />
<br />
original text translation by Youdao reference translation original text translation by Youdao reference translation<br />
<br />
朱鎔基 朱基 朱镕基 栗战书 栗戰史書 栗戰書<br />
<br />
労安 劳安 劳安 李克强 李克強 李克強 <br />
<br />
筑紫哲也 筑紫哲也 筑紫哲也 习近平 習近平 習近平<br />
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山口百惠 山口百惠 山口百惠 韩正 韓中 韓正<br />
<br />
田中角栄 田中角荣 田中角荣 王沪宁 王上海氏 王滬寧<br />
<br />
東条英機 东条英社 东条英机 汪洋 汪洋 汪洋<br />
<br />
毛沢东 毛泽东 毛泽东 赵乐际 趙樂南 趙樂際<br />
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トウ・ショウヘイ 大酱 邓小平 江泽民 江沢民 江沢民<br />
<br />
周恩来 周恩来 周恩来<br />
<br />
クリントン 克林顿 克林顿<br />
<br />
The above table counts 18 special names in the two texts, and 7 machine translation errors. In terms of mistranslation, there are not only "战书" but also "戰史" and "史書" in Chinese. "沪" is the abbreviation of Shanghai. In other words, since "战书" and "沪" are originally common nouns, this disrupts the choice of target language in machine translation. Except Katakana interference (except for トウシゃウヘイ, most of the mistranslations appear in the new Standing Committee. It can be seen that the machine translation system does not update the thesaurus in time. Different from other words, people's names have particularity. Especially as members of the Standing Committee of the Political Bureau of the CPC Central Committee, the translation of their names is officially unified. In this regard, public figures such as political dignitaries, stars, famous hosts and important. In addition, the machine also translates Japanese surnames such as "タ二モト" (谷本), "アンドウ" (安藤) into "塔尼莫特" and "龙胆". It can be found that the language feature that Japanese will be written together with Chinese characters and Hiragana also directly affects the translation quality of Japanese Chinese machine translation. Japanese people often use Katakana pronunciation. For example, when Japanese people talk with Premier Zhu, they often use "二ーハウ" (Hello). However, the machine only recognizes the two pseudonyms "二" and "ハ" and transliterates them into "尼哈" , ignoring the long sound after "二" and "ハ".(Guan 2018:10-12)<br />
<br />
original text Translation by Youdao reference translation<br />
<br />
日美安全体制 日米の安全体制 日米安保体制<br />
<br />
中国共产党第十九次全国代表大会 中国共産党第19回全国代表大会 中国共産党第19回全国代表大会（第19回党大会）<br />
<br />
十八大 十八大 第18回党大会中国特色社会主义<br />
<br />
中国特色社会主義 中国の特色ある社会主義 第18回党大会<br />
<br />
中国共产党中央委员会 中国共産党中央委員会 中国共産党中央委員会<br />
<br />
中国共産党中央委員会十八届中共中央政治局常委 第18代中国共產党中央政治局常務委員 第18期中共中央政治局常務委員<br />
<br />
十八届中共中央政治局委员 18期の中国共產党中央政治局委員 第18期中共中央政治局委員<br />
<br />
十九届中共中央政治局常委 十九回中国共產党中央政治局常務委員 第19期中央政治局常務委員<br />
<br />
中共十九届一中全会 中国共產党第十九回一中央委員会 第19期中央委員会第1回全体会議<br />
<br />
The above table is a comparison of the original and translated versions of some proper nouns. As shown in the table, the mistranslation problems are mainly reflected in the mismatch of numerals + quantifiers, the wrong addition of case auxiliary word "の", the lack of connectors, the Mistranslation of abbreviations, dead translation, and the writing errors of Chinese characters. The following is a specific analysis one by one.<br />
<br />
"十八届中共中央政治局常委", "十八届中共中央政治局委员", "十九届中共中央政治局常委" and "中共十九届一中全会" all have quantifiers "届", which are translated into "代", "期" and "回" respectively. The meaning is vague and should be uniformly translated into "期"; Among them, the translation of the last three proper nouns lacks the Chinese conjunction "第". The case auxiliary word "の" was added by mistake in the translation of "十八届中共中央政治局委员" and "日美安全体制". The "中国共产党中央委员会" did not write in the form of Japanese Ming Dynasty characters, but directly used simplified Chinese characters.<br />
<br />
The full name of "中共十九届一中全会" is "中国共产党第十九届中央委员会第一次全体会议", in which "一" stands for "第一次", which is an ordinal word rather than a cardinal word. Machine translation does not produce a correct translation. The fundamental reason is that there are no "规则" for translating such words in machine translation. If we can formulate corresponding rules for such words (for example, 中共m'1届m2 中全会→第m1期中央委员会第m2回全体会议), the translation system must be able to translate well no matter how many plenary sessions of the CPC Central Committee.(Guan 2018:6-7)<br />
<br />
===2.1.2Mistranslation of Polysemy===<br />
original text Translation by Youdao reference translation<br />
<br />
スタジオ 摄影棚/工作室 直播现场/演播厅<br />
<br />
日中関係の話 中日关系的故事 就中日关系(话题)<br />
<br />
溝 水沟 鸿沟<br />
<br />
それでは日中の問題について質問のある方。 那么对白天的问题有提问的人。 关于中日问题的话题，举手提问。<br />
<br />
私たちのクラスは20人ちょっとですが、 我们班有20人左右， 我们班二十多人的意见统一很难，<br />
<br />
いろいろな意見が出て、まとめるのは大変です。 但是又各种各样的意见，总结起来很困难。 <br />
<br />
一体どうやって、13億人もの人をまとめているんですか。 到底是怎么处理13亿的人的呢？ 中国是怎样把13亿人凝聚在一起的？<br />
<br />
In the original text, the word "スタジオ" appeared four times and was translated into "摄影棚" or "工作室" respectively, but the context is on-site interview, not the production site of photography or film. "話" has many semantics, such as "说话", "事情", "道理", etc. In accordance with the practice of the interview program, Premier Zhu Rongji was invited to answer five questions on the topic of China Japan relations. Obviously, the meaning of the word "故事" is very abrupt. The inherent Japanese word "日中" means "晌午、白天". At the same time, it is also the abbreviation of the names of the two countries. The machine failed to deal with it correctly according to the context. "溝" refers to the gap between China and Japan, not a "水沟". The former "まとめる" appears in the original text with the word "意见", which is intended to describe that it is difficult to unify everyone's opinions. The latter "まとめている" also refers to unifying the thoughts of 1.3 billion people, not "处理". Therefore, it is more appropriate to use "统一" and "处理" in human translation, rather than "总结意见" or "处理意见".(Zhang 2019:5)<br />
<br />
Mistranslation of polysemy has always been a difficult problem in machine translation research. The translation of each word is correct, but it is often very different from the original expression in the context. Zhang Zhengzeng said that ambiguity is a common phenomenon in natural language. Its essence is that the same language form may have different meanings, which is also one of the differences between natural language and artificial language. Therefore, one of the difficulties faced by machine translation is language disambiguation (Zhang Zheng, 2005:60). In this regard, we can mark all the meanings of polysemous words and judge which meaning to choose by common collocation with other words. At the same time, strengthen the text recognition ability of the machine to avoid the translation inconsistent with the current context. In this way, we can avoid the mistake of "大酱" in the place where famous figures such as Deng Xiaoping should have appeared in the previous political articles.(Wang 2020:7-9)<br />
<br />
===2.1.3Mistranslation of compound words===<br />
Multi class words refer to a word with two or more parts of speech, also known as the same word and different classes.<br />
<br />
original text Translation by Youdao reference translation<br />
<br />
1998年江泽民主席曾经访问日本, 1998年の江沢民国家主席の日本訪問し、 1998年、江沢民総書記が日本を訪問し、かつ<br />
<br />
同已故小渊首相签署了联合宣言。 かつて同じ故小渕首相が署名した共同宣言 亡くなられた小渕総理と宣言に調印されました<br />
<br />
Chinese "同" has two parts of speech: prepositions and conjunctions: "故" has many parts of speech, such as nouns, verbs, adjectives and conjunctions. This directly affects the judgment of the machine at the source language level. The word "同" in the above table is used as a conjunction to indicate the other party of a common act. "故" is used as a verb with the semantic meaning of "死亡", which is a modifier of "小渊首相". The machine regards "同" as an adjective and "故" as a noun "原因", which leads to confusion in the structure and unclear semantics of the translation. Different from the polysemy problem, multi category words have at least two parts of speech, and there is often not only one meaning under each part of speech. In this regard, software R & D personnel should fully consider the existence of multi category words, so that the translation machine can distinguish the meaning of words on the basis of marking the part of speech, so as to select the translation through the context and the components of the word in the sentence. Of course, the realization of this function is difficult, and we need to give full play to the wisdom of R & D personnel.(Guan 2018:9-12)<br />
<br />
===2.2 Syntactic mistranslation in Chinese Japanese machine translation===<br />
===2.2.1Mistranslation of tenses===<br />
original text：History is written by the people, and all achievements are attributed to the people. <br />
<br />
Translation by Youdao：歴史は人民が書いたものであり、すべての成果は人民のためである。<br />
<br />
reference translation：歴史は人民が綴っていくものであり、すべての成果は人民に帰することとなります。<br />
<br />
The original sentence meaning is "历史是人民书写的历史" or "历史是人民书写的东西". When translated into Japanese, due to the influence of Japanese language habits, the formal noun "もの" should be supplemented accordingly. In this sentence, both the machine and the interpreter have translated correctly. However, the machine's recognition of "的" is biased, resulting in tense translation errors. The past, present and future will become "history", and this is a continuous action. The form translated as "~ ていく" not only reflects that the action is a continuous action, but also conforms to the tense and semantic information of the original text. In addition, the machine's treatment of the preposition "于" is also inappropriate. In the original text, "人民" is the recipient of action, not the target language. As the most obvious feature of isolated language, function words in Chinese play an important role in semantic expression, and their translation should be regarded as a focus of machine translation research.(Zuo 2021:8)<br />
<br />
original text：李克强同志是十六届中共中央政治局常委,其他五位同志都是十六届中共中央政治局委员。<br />
<br />
Translation by Youdao：李克強総理は第16代中国共産党中央政治局常務委員であり、他の５人の同志はいずれも16期の中国共産党中央政治局委員である。<br />
<br />
reference translation：李克強同志は第16期中国共産党中央政治局常務委員を務め、他の５人は第16期中共中央政治局委員を務めました。<br />
<br />
Judging the verb "是" in the original text plays its most basic positive role, but due to the complexity of Chinese language, "是" often can not be completely transformed into the form of "だ / である" in the process of translation. This sentence is a judgment of what has happened. Compared with the 19th session, the 18th session has become history. This is an implicit temporal information. It is very difficult for machines without human brain to recognize this implicit information. "中共中央政治局常委" is the abbreviation of "中共中央政治局常务委员会委员" and it is a kind of position. In Japanese, often use it with verbs such as "務める","担当する",etc. The translator adopts the past tense of "務める", which conforms to the expression habit of Japanese and deals with the problem of tense at the same time. However, machine translation only mechanically translates this sentence into judgment sentence, and fails to correctly deal with the past information implied by the word "十八届".(Guan 2018:4)<br />
<br />
===2.2.2Mistranslation of honorifics===<br />
Honorific language is a language means to show respect to the listener. Different from Japanese, there is no grammatical category of honorifics in Chinese. There is no specific fixed grammatical form to express honorifics, self modesty and politeness. Instead, specific words such as "您", "请" and "劳驾" are used to express all kinds of respect or self modesty. (Yang 2020:5-9)<br />
<br />
Original text translation by Youdao reference translation<br />
<br />
女士们，先生们，同志们，朋友们 さんたち、先生たち、同志たち、お友达さん ご列席の皆さん<br />
<br />
谢谢大家！ ありがとうございます！ ご清聴ありがとうございました。<br />
<br />
これはどうされますか。 这是怎么回事呢? 您将如何解决这一问题？<br />
<br />
こうした問題をどうお考えでしょうか。 我们会如何考虑这些问题呢？ 您如何看待这一问题？<br />
<br />
For example, for the processing of "女士们，先生们，同志们，朋友们", the machine makes the translation correspond to the original one by one based on the principle of unchanged format, but there are errors in semantic communication and pragmatic habits. When speaking on formal occasions, Chinese expression tends to be comprehensive and detailed, as well as the address of the audience. In contrast, Japanese usually uses general terms such as "皆様", "ご臨席の皆さん" or "代表団の方々" as the opening remarks. In terms of Japanese Chinese translation, the machine also failed to recognize the usage of Japanese honorifics such as "さ れ る" and "お考え". It can be seen that Chinese Japanese machine translation has a low ability to deal with honorific expressions. The occasion is more formal. A good translation should not only be fluent in meaning, but also conform to the expression habits of the target language and match the current translation environment.(Che 2021:3-7)<br />
<br />
===Conclusion===<br />
In the process of discussing the Mistranslation of machine translation, this paper mainly cites the Mistranslation of Youdao translator. When I studied the problem of machine translation mistranslation, I also used a large number of translation software such as Google and Baidu for parallel comparison, and found that these translation software also had similar problems with Youdao translator. For example, the "新世界新未来" in Chinese ABAC structural phrases is translated by Baidu as "新し世界の新しい未来", which, like Youdao, is not translated in the form of parallel phrases; Google online translation translates the word "“全心全意" into a completely wrong "全面的に"; The original sentence "我吃了很多亏" in the Mistranslation of the unique expression in the language is translated by Google online as "私はたくさんの損失を食べました". Because the "吃" and "亏" in the original text are not closely adjacent, the machine can not recognize this echo and mistakenly treats "亏" as a kind of food, so the machine translates the predicate as "食べました"; Japanese "こうした問題をどう考えでしょうか", Google's online translation is "你如何看待这些问题?", "你" tone can not reflect the tone of Japanese honorific, while Baidu translates as "你怎么想这样的问题呢?" Although the meaning can be understood, it is an irregular spoken language. Google and Baidu also made the same mistakes as Youdao in the translation of proper nouns. For example, they translated "十七届(中共中央政治局常委)”" into "第17回..." .In short, similar mistranslations of Youdao are also common in Baidu and Google. Due to space constraints, they will not be listed one by one here.(Cui 2019:7)<br />
<br />
Mr. Liu Yongquan, Institute of language, Chinese Academy of Social Sciences (1997) It has been pointed out that machine translation is a linguistic problem in the final analysis. Although corpus based machine translation does not require a lot of linguistic knowledge, language expression is ever-changing. Machine translation based solely on statistical ideas can not avoid the translation quality problems caused by the lack of language rules. The following is based on the analysis of lexical, syntactic and other mistranslations From the perspective of the characteristics of the source language, this paper summarizes some difficulties of Chinese and Japanese in machine translation.(Liu 2014:8)<br />
<br />
(1) The difficulties of Chinese in machine translation <br />
<br />
Chinese is a typical isolated language. The relationship between words needs to be reflected by word order and function words. We should pay attention to the transformation of function words such as "的", "在", "向" and "了". Some special verbs, such as "是", "做" and "作", are widely used. How to translate on the basis of conforming to Japanese pragmatic habits and expressions is a difficulty in machine translation research. In terms of word order, Chinese is basically "subject→predicate→object". At the same time, Chinese pays attention to parataxis, and there is no need to be clear in expression with meaningful cohesion, which increases the difficulty of Chinese Japanese machine translation. When there are multiple verbs or modifier components in complex long sentences, machine translation usually can not accurately divide the components of the sentence, resulting in the result that the translation is completely unreadable.(Guan 2018:6-12) <br />
<br />
(2) Difficulties of Japanese in machine translation <br />
<br />
Both Chinese and Japanese languages use Chinese characters, and most machines produce the target language through the corresponding translation of Chinese characters. However, pseudonyms in Japanese play an important role in judging the part of speech and meaning, and can not only recognize Chinese characters and judge the structure and semantics of sentences. Japanese vocabulary is composed of Chinese vocabulary, inherent vocabulary and foreign vocabulary. Among them, the first two have a great impact on Chinese Japanese machine translation. For example "提出" is translated as "提出する" or "打ち出す"; and "重视" is translated as "重視する" or "大切にする". Japanese is an adhesive language, which contains a lot of "けど", "が" and "けれども" Some of them are transitional and progressive structures, but there are also some sequential expressions that do not need translation, and these translation software often can not accurately grasp them.(Che 2021:10)<br />
<br />
===References===<br />
[1] Navroz Kaur Kahlon,(2021(prepublish));Williamjeet Singh.Machine translation from text to sign language: a systematic review[J].Universal Access in the Information Society,1-35.<br />
<br />
[2] Cao Qianyu;Hao Hanmei,(2021);Ahmed Syed Hassan.A Chaotic Neural Network Model for English Machine Translation Based on Big Data Analysis[J].Computational Intelligence and Neuroscience,3274326-3274326.<br />
<br />
[3]Hwang Yongkeun;Kim Yanghoon;Jung Kyomin.(2021)Context-Aware Neural Machine Translation for Korean Honorific Expressions[J].Electronics,10(13):1589-1589.<br />
<br />
[4]Zakaryia Almahasees.(2021)Analysing English-Arabic Machine Translation:Google Translate, Microsoft Translator and Sakhr.<br />
<br />
[5](2021)Machine learning in translation[J].Nature Biomedical Engineering,5(6):485-486.<br />
<br />
[6]Shaimaa Marzouk.(2021(prepublish))An in-depth analysis of the individual impact of controlled language rules on machine translation output: a mixed-methods approach[J].Machine Translation,1-37.<br />
<br />
[7]Welnitzová Katarína;Munková Daša.(2021)Sentence-structure errors of machine translation into Slovak[J].Topics in Linguistics,22(1):78-92.<br />
<br />
[8]Xu Xueyuan.(2021).Machine learning-based prediction of urban soil environment and corpus translation teaching[J].Arabian Journal of Geosciences,14(11). <br />
<br />
[9]Chen Bingchang 陈丙昌(2016).機械翻訳の誤訳分析【D】.Error analysis of mechanical translation.贵州大学.2016(05) <br />
<br />
[10]Lv Yinqiu 呂寅秋(1996).機械翻訳の言語規則と伝統文法との相違点.【D】The language rules of mechanical translation, the traditional grammar, and the points of contradiction.日本学研究.Japanese Studies.1996(00):21-22 <br />
<br />
[11]Liu Jun 刘君(2014).基于语料库的中日同形词词义用法对比及其日中机器翻译研究【D】.A Corpus-based Comparison of the Meanings of Chinese and Japanese Homographs and Research on Japanese-Chinese Machine Translation.广西大学.(03) <br />
<br />
[12]Cun Qianqian 崔倩倩(2019).机器翻译错误与译后编辑策略研究【D】.Research on Machine Translation Errors and Post-Editing Strategies.北京外国语大学.(09) <br />
<br />
[13]Zhang Yi 张义(2019).机器翻译的译文分析【D】.Translation analysis of machine translation.西安外国语大学.(10) <br />
<br />
[14]Zhang Linqian 张琳婧(2019).在线机器翻译中日翻译错误原因及对策【D】.Causes and countermeasures of online machine translation errors in Chinese-Japanese translation.山西大学.(02)<br />
<br />
[15]Wang Dan 王丹(2020).基于机器翻译的专利文本译后编辑对策研究【D】.Research on countermeasures for post-translational editing of patent texts based on machine translation.大连理工大学.(06)<br />
<br />
[16]Yang Xiaokun 杨晓琨(2020).日中机器翻译中的前编辑规则与效果验证【D】.Pre-editing rules and effect verification in Japanese-Chinese machine translation.大连理工大学.(06)<br />
<br />
[17]Zuo Jia 左嘉(2021). 机器翻译日译汉误译研究【D】. Research on Mistranslation of Machine Translation from Japanese to Chinese.北京第二外国语学院.<br />
<br />
[18]Guan Biying 关碧莹(2018).关于政治类发言的汉日机器翻译误译分析【D】.Analysis of Chinese-Japanese Machine Translation Mistranslations of Political Speeches.哈尔滨理工大学.<br />
<br />
[19]Che Tong 车彤(2021).汉译日机器翻译质量评估及译后编辑策略研究【D】.Research on Quality Evaluation of Chinese-Japanese Machine Translation and Post-translation Editing Strategies.北京外国语大学.(09)<br />
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Networking Linking<br />
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http://www.elecfans.com/rengongzhineng/692245.html<br />
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https://baike.baidu.com/item/%E6%9C%BA%E5%99%A8%E7%BF%BB%E8%AF%91/411793<br />
<br />
=13 陈湘琼Chen Xiangqiong（Study on Post-editing from the Perspective of Functional Equivalence Theory ）=<br />
[[Machine_Trans_EN_13]]<br />
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=14 Bi bi Nadia（Machine Translation a Challenge for Human Translators)=<br />
[[Machine_Trans_EN_14]]<br />
<br />
===Abstract===<br />
Machine translation is a big obstacle in the way of Human translators or interpreters although it is quick and less time consuming.People are trying to get translation of their target language using through source language.For this purpose they are using digital apps like Google translation Google translation does not give accurate and exact interpretation.Google translator is translating word to word but it doesn't clarify it's true and actual meaning.On the other hand human translators can give exact and accurate translation ,they take care of grammatical mistakes , diction and sentence structure.They clarify the purpose of target language through using source language.<br />
<br />
===Keywords===<br />
Descriptive translation, academic, interdiscipline, comparative literature, localization,Translatology,school of thought,translation , studies, linguistics, corresponding.<br />
<br />
===Body of article===<br />
Translation is the process of reworking text from one language into another to maintain the original message and communication. But, like anything else, there are different methods of translation, and they vary in form and function. What is translation? The term has become widely used among knowledge transferring researchers and practitioners, especially in the fields of health and health care. In a landmark review, Jonathan Lomas began to argue that 'The tasks… may be defined as to establish and maintain links between researchers and their audience, via the appropriate translation of research findings' (Lomas 1997, p 4). In 2004, World Health Organization's World Report on Knowledge for Better Health suggested that 'One of the key contributions of research to health systems is the translation of knowledge into actions' (WHO 2004, p 33 and p 100). By 2006, special issues of WHO's Bulletin as well as the journals Evaluation and the Health Professions and the Journal of Continuing Education in the Health Professions were dedicated to translation.But what does 'translation' mean? It may be a new word for an old problem, meaning nothing more than 'transfer'. The rapidly emerging field of 'translational medicine' seems to take translation to mean generally what transfer might have meant, that is the transmission of knowledge and evidence 'from bench to bedside'. As one commentator has observed, "Translational medicine" as a fashionable term is being increasingly used to describe the wish of biomedical researchers to ultimately help patients' (Wehling 2008, abstract). <br />
Semantic uncertainty persists, not least because of the different interests of scientists, clinicians, patients and commercial firms (Littman et al 2007): 'Translational research means different things to different people, but it seems important to almost everyone' (Woolf 2008, p 211).<br />
In related fields, such as public health (Armstrong et al 2006), 'translation' seems to signify dissatisfaction with 'transfer'. It wants to move away from thinking of knowledge transfer as a form of technology transfer or dissemination, rejecting if only by implication its mechanistic assumptions and its model of linear messaging from A to B. But still, what does it signify?<br />
<br />
===Why translation?===<br />
'Translation' indicates a closer attention to the problem of shared meaning and how it might be developed. It seems to represent some new epistemological lubricant, facilitating the dissemination of texts and the application and use of the knowledge and information they in. Simply, translation might be the key to transfer. And yet, when we stop to think, we are more ambivalent. What is translated often seems somehow inferior, not real or original. Note how readily commentators reach for the idea that things might be 'lost in translation'. Knowing at a distance – made in and mediated by translation - makes for incomplete renditions, blurred images, partial truths. So what might 'translation' really mean? The purpose of this paper is to set out, for policy makers and practitioners, the theoretical and conceptual resources translation holds and seems to represent. In doing so, it explores understandings of translation in the fields of literature and linguistics and in the sociology of science and technology. It begins by setting out just why this idea of translation should make immediate, intuitive sense in relation to research, policy and practice.<br />
1translation in research, policy and practice research as translation<br />
Research often entails translation from one language to another: where data is collected from more than one ethnic group, for example, or where the language of the researcher is other than that of the research subject. It may draw on a secondary literature or source documents written in different languages, and may be published and disseminated in languages other than the one in which it is first written up.<br />
2In a different way, to conduct an interview is to ask for an account of experience and its meanings, but it is also to construct and translate that experience in terms defined at least in part by the researcher. In representing what is said, transcripts then select data, usually excluding significant gesture and eye-contact, for example. Often, certain characteristics of speech-acts (such as hesitations) will be edited out. In turn, the format of the transcript shapes the analytic use the researcher may make of it. The basis of research 'findings', then, is an artefact, a transcript or translation, not an original interaction (Ochs 1979, Barnes, Bloor and Henry 1996, Ross 2009).<br />
3In this way, the researcher recasts aspects of his or her problem or topic in new, scientific form: 'All researchers "translate" the experiences of others' (Temple 1997, p 609). Research is invariably conducted in a sort of 'metalanguage' (Hantrais and Ager 1985): the research process can be conceived as one of successive translations (from theoretical formulation to operationalization, transcription, interpretation and dissemination). Theorization is a process of reciprocal back and forth between theory and fact, in which conceptions of each are revised in order that one fit the other (Baldamus 1974).<br />
4 It is a kind of translation: a rereading, re-use, re-application or re-representation of what we know in new terms (Turner 1980). Referencing, too, is an act of translation, a form of appropriation and incorporation of one text by another (Gilbert 1977).policy as translation Each of the fields covered by the paper is diverse and ill-defined, and there is no intention here to provide a comprehensive account of any them. Sources have been chosen for their relevance: main references are cited in the text, and additional sources listed in footnotes.<br />
<br />
2For a brief introduction to the technical issues involved in social research in more than one language, see Birbili (2000). For translation issues in survey research and question design in general, see Ervin and Bower (1952) and Deutscher (1968); on translating survey research instruments (in this instance health-related quality of life measures), Bowden and Fox-Rushby (2003). On the use of translators and interpreters, see Temple (1997) and Jentsch (1998).<br />
3For an interesting discussion of this problem, see Bourdieu (1999), esp pp 621-626, 'The risks of writing'. <br />
===Difference between machine translation and human translators===<br />
Few people disagree on the differences between the two, but many argue over the quality of the translations. How accurate are machine translations? How reliable are human translations? Some say machine translation produces near-perfect translations while others are adamant that translations are incomprehensible and cause more problems than they solve. Results will, of course, vary depending on the source and target languages, the machine translation service used (e.g. Google Translate), and the complexity of the original text.<br />
Machine translation, love it or hate it, is here to stay. In fact, the machine translation market is growing at such a fast pace that it is predicted to reach $980 million by 2022.<br />
===Machine translation: the pros and cons===<br />
The advantages of machine translation generally come down to two factors: it’s faster and cheaper. The downside to this is the standard of translation can be anywhere from inaccurate, to incomprehensible, and potentially dangerous (more on that shortly).<br />
==The advantages of machine translation==<br />
Many free tools are readily available (Google Translate, Skype Translator, etc.)<br />
Quick turnaround time <br />
You can translate between multiple languages using one tool<br />
Translation technology is constantly improving<br />
The disadvantages of machine translation<br />
Level of accuracy can be very low <br />
Accuracy is also very inconsistent across different languages<br />
==Machines can't translate context ==<br />
Mistakes are sometimes costly <br />
Sometimes translation simply doesn’t work<br />
The most important thing to consider with any kind of translation is the cost of potential mistakes. Translating instructions for medical equipment, aviation manuals, legal documents and many other kinds of content require 100% accuracy. In such cases, mistakes can cost lives, huge amounts of money and irreparable damage to your company’s image. So choose carefully!<br />
Human translation: the pros and cons<br />
Human translation essentially switches the table in terms of pros and cons. A higher standard of accuracy comes at the price of longer turnaround times and higher costs. What you have to decide is whether that initial investment outweighs the potential cost of mistakes. Alternatively, whether mistakes simply aren’t an option, like the cases we looked at in the previous section.<br />
<br />
==The advantages of human translation ==<br />
It’s a translator’s job to ensure the highest accuracy<br />
Humans can interpret context and capture the same meaning, rather than simply translating words<br />
Human translators can review their work and provide a quality process<br />
Humans can interpret the creative use of language, e.g. puns, metaphors, slogans, etc.<br />
Professional translators understand the idiomatic differences between their languages<br />
Humans can spot pieces of content where literal translation isn’t possible and find the most suitable alternative<br />
The disadvantages of human translation<br />
Turnaround time is longer <br />
Translators rarely work for free <br />
Unless you use a translation agency, with access to thousands of translators, you’re limited to the languages any one translator can work with<br />
Simply put, human translation is your best option when accuracy is even remotely important. Other considerations to make are the complexity of your source material and the two languages you’re translating between – both of which can render machines pretty useless.<br />
<br />
When to use machine and human translation<br />
<br />
The truth is, the debate over machine vs human translation is an unnecessary distraction. What we should really be talking about is when to use these two different types of translation services, because they both serve a very valid purpose.<br />
<br />
Examples of when to use machine translation<br />
<br />
When you have a large bulk of content to translate and the general meaning is enough<br />
When your translation never reaches the final audience, e.g. you’re translating a resource as research for another piece of content<br />
Translating documents for internal use within a company, provided 100% accuracy isn’t needed<br />
To partially translate large chunks of content for a human translator to improve upon<br />
Examples of when to use human translation<br />
When accuracy is important<br />
Most cases where your translated content is received by a consumer audience<br />
When you have a duty of care to provide accurate translations (e.g. legal documents, product instructions, medical guidelines or health and safety content)<br />
When translating marketing material or other texts for creative language uses.<br />
<br />
types of machine translation.<br />
<br />
What is Machine Translation? Rule Based Machine Translation vs. Statistical Machine Translation. Machine translation (MT) is automated translation. It is the process by which computer software is used to translate a text from one natural language (such as English) to another (such as Spanish).<br />
<br />
To process any translation, human or automated, the meaning of a text in the original (source) language must be fully restored in the target language, i.e. the translation. While on the surface this seems straightforward, it is far more complex. Translation is not a mere word-for-word substitution. A translator must interpret and analyze all of the elements in the text and know how each word may influence another. This requires extensive expertise in grammar, syntax (sentence structure), semantics (meanings), etc., in the source and target languages, as well as familiarity with each local region.<br />
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Human and machine translation each have their share of challenges. For example, no two individual translators can produce identical translations of the same text in the same language pair, and it may take several rounds of revisions to meet customer satisfaction. But the greater challenge lies in how machine translation can produce publishable quality translations.<br />
<br />
Rule-Based Machine Translation Technology<br />
Rule-based machine translation relies on countless built-in linguistic rules and millions of bilingual dictionaries for each language pair.<br />
The software parses text and creates a transitional representation from which the text in the target language is generated. This process requires extensive lexicons with morphological, syntactic, and semantic information, and large sets of rules. The software uses these complex rule sets and then transfers the grammatical structure of the source language into the target language.<br />
Translations are built on gigantic dictionaries and sophisticated linguistic rules. Users can improve the out-of-the-box translation quality by adding their terminology into the translation process. They create user-defined dictionaries which override the system’s default settings.<br />
In most cases, there are two steps: an initial investment that significantly increases the quality at a limited cost, and an ongoing investment to increase quality incrementally. While rule-based MT brings companies to the quality threshold and beyond, the quality improvement process may be long and expensive.<br />
<br />
Statistical Machine Translation Technology<br />
Statistical machine translation utilizes statistical translation models whose parameters stem from the analysis of monolingual and bilingual corpora. Building statistical translation models is a quick process, but the technology relies heavily on existing multilingual corpora. A minimum of 2 million words for a specific domain and even more for general language are required. Theoretically it is possible to reach the quality threshold but most companies do not have such large amounts of existing multilingual corpora to build the necessary translation models. Additionally, statistical machine translation is CPU intensive and requires an extensive hardware configuration to run translation models for average performance levels.<br />
<br />
Rule-Based MT vs. Statistical MT<br />
Rule-based MT provides good out-of-domain quality and is by nature predictable. Dictionary-based customization guarantees improved quality and compliance with corporate terminology. But translation results may lack the fluency readers expect. In terms of investment, the customization cycle needed to reach the quality threshold can be long and costly. The performance is high even on standard hardware.<br />
<br />
Statistical MT provides good quality when large and qualified corpora are available. The translation is fluent, meaning it reads well and therefore meets user expectations. However, the translation is neither predictable nor consistent. Training from good corpora is automated and cheaper. But training on general language corpora, meaning text other than the specified domain, is poor. Furthermore, statistical MT requires significant hardware to build and manage large translation models.<br />
<br />
Rule-Based MT Statistical MT<br />
+ Consistent and predictable quality – Unpredictable translation quality<br />
+ Out-of-domain translation quality – Poor out-of-domain quality<br />
+ Knows grammatical rules – Does not know grammar <br />
+ High performance and robustness – High CPU and disk space requirements<br />
+ Consistency between versions – Inconsistency between versions <br />
– Lack of fluency + Good fluency<br />
– Hard to handle exceptions to rules + Good for catching exceptions to rules <br />
– High development and customization costs + Rapid and cost-effective development costs provided the required corpus exists<br />
Given the overall requirements, there is a clear need for a third approach through which users would reach better translation quality and high performance (similar to rule-based MT), with less investment (similar to statistical MT).<br />
Post-Edited Machine Translation (PEMT)<br />
Often, PEMT is used to bridge the gap between the speed of machine translation and the quality of human translation, as translators review, edit and improve machine-translated texts. PEMT services cost more than plain machine translations but less than 100% human translation, especially since the post-editors don’t have to be fluently bilingual—they just have to be skilled proofreaders with some experience in the language and target region.<br />
Successful translation is about more than just the words, which is why we advocate for not just human translation by skilled linguists, but for translation by people deeply familiar with the cultures they’re writing for. Life experience, study and the knowledge that only comes from living in a geographic region can make the difference between words that are understandable and language that is capable of having real, positive impact. <br />
<br />
PacTranz<br />
The HUGE list of 51 translation types, methods and techniques<br />
Upper section of infographic of 51 common types of translation classified in 4 broad categoriesThere are a bewildering number of different types of translation.<br />
So we’ve identified the 51 types you’re most likely to come across, and explain exactly what each one means.<br />
This includes all the main translation methods, techniques, strategies, procedures and areas of specialisation.<br />
It’s our way of helping you make sense of the many different kinds of translation – and deciding which ones are right for you.<br />
Don’t miss our free summary pdf download later in the article!<br />
The 51 types of translation we’ve identified fall neatly into four distinct categories.<br />
Translation Category A: 15 types of translation based on the technical field or subject area of the text<br />
Icons representing 15 types of translation categorised by the technical field or subject area of the textTranslation companies often define the various kinds of translation they provide according to the subject area of the text.<br />
This is a useful way of classifying translation types because specialist texts normally require translators with specialist knowledge.<br />
Here are the most common types you’re like to come across in this category.<br />
<br />
1. General Translation<br />
What is it?<br />
The translation of non-specialised text. That is, text that we can all understand without needing specialist knowledge in some area.<br />
The text may still contain some technical terms and jargon, but these will either be widely understood, or easily researched.<br />
What this means<br />
The implication is that you don’t need someone with specialist knowledge for this type of translation – any professional translator can handle them.<br />
Translators who only do this kind of translation (don’t have a specialist field) are sometimes referred to as ‘generalist’ or ‘general purpose’ translators.<br />
Examples<br />
Most business correspondence, website content, company and product/service info, non-technical reports.<br />
Most of the rest of the translation types in this Category do require specialist translators.<br />
Check out our video on 13 types of translation requiring special translator expertise:<br />
<br />
2. Technical Translation<br />
What is it?<br />
We use the term “technical translation” in two different ways:<br />
Broad meaning: any translation where the translator needs specialist knowledge in some domain or area.<br />
This definition would include almost all the translation types described in this section.<br />
Narrow meaning: limited to the translation of engineering (in all its forms), IT and industrial texts.<br />
This narrower meaning would exclude legal, financial and medical translations for example, where these would be included in the broader definition.<br />
What this means<br />
Technical translations require knowledge of the specialist field or domain of the text.<br />
That’s because without it translators won’t completely understand the text and its implications. And this is essential if we want a fully accurate and appropriate translation.Good to know Many technical translation projects also have a typesetting/dtp requirement. Be sure your translation provider can handle this component, and that you’ve allowed for it in your project costings and time frames.<br />
Examples<br />
Manuals, specialist reports, product brochures<br />
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3. Scientific Translation<br />
What is it?<br />
The translation of scientific research or documents relating to it.<br />
What this means<br />
These texts invariably contain domain-specific terminology, and often involve cutting edge research.<br />
So it’s imperative the translator has the necessary knowledge of the field to fully understand the text. That’s why scientific translators are typically either experts in the field who have turned to translation, or professionally qualified translators who also have qualifications and/or experience in that domain.<br />
On occasion the translator may have to consult either with the author or other domain experts to fully comprehend the material and so translate it appropriately.<br />
Examples<br />
Research papers, journal articles, experiment/trial results<br />
<br />
4. Medical Translation<br />
What is it?<br />
The translation of healthcare, medical product, pharmaceutical and biotechnology materials.<br />
Medical translation is a very broad term covering a wide variety of specialist areas and materials – everything from patient information to regulatory, marketing and technical documents.<br />
As a result, this translation type has numerous potential sub-categories – ‘medical device translations’ and ‘clinical trial translations’, for example.<br />
What this means<br />
As with any text, the translators need to fully understand the materials they’re translating. That means sound knowledge of medical terminology and they’ll often also need specific subject-matter expertise.<br />
Good to know<br />
Many countries have specific requirements governing the translation of medical device and pharmaceutical documentation. This includes both your client-facing and product-related materials.<br />
Examples<br />
Medical reports, product instructions, labeling, clinical trial documentation<br />
<br />
5. Financial Translation<br />
What is it?<br />
In broad terms, the translation of banking, stock exchange, forex, financing and financial reporting documents.<br />
However, the term is generally used only for the more technical of these documents that require translators with knowledge of the field.<br />
Any competent translator could translate a bank statement, for example, so that wouldn’t typically be considered a financial translation.<br />
What this means<br />
You need translators with domain expertise to correctly understand and translate the financial terminology in these texts.<br />
Examples<br />
Company accounts, annual reports, fund or product prospectuses, audit reports, IPO documentation<br />
<br />
6. Economic Translations<br />
What is it?<br />
1. Sometimes used as a synonym for financial translations.<br />
2. Other times used somewhat loosely to refer to any area of economic activity – so combining business/commercial, financial and some types of technical translations.<br />
3. More narrowly, the translation of documents relating specifically to the economy and the field of economics.<br />
What this means<br />
As always, you need translators with the relevant expertise and knowledge for this type of translation.<br />
<br />
7. Legal Translation<br />
What is it?<br />
The translation of documents relating to the law and legal process.<br />
What this means<br />
Legal texts require translators with a legal background.<br />
That’s because without it, a translator may not:<br />
– fully understand the legal concepts<br />
– write in legal style<br />
– understand the differences between legal systems, and how best to translate concepts that don’t correspond.<br />
And we need all that to produce professional quality legal translations – translations that are accurate, terminologically correct and stylistically appropriate.<br />
Examples<br />
Contracts, legal reports, court judgments, expert opinions, legislation<br />
<br />
8. Juridical Translation<br />
What is it?<br />
1. Generally used as a synonym for legal translations.<br />
2. Alternatively, can refer to translations requiring some form of legal verification, certification or notarization that is common in many jurisdictions.<br />
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9. Judicial Translation<br />
What is it?<br />
1. Most commonly a synonym for legal translations.<br />
2. Rarely, used to refer specifically to the translation of court proceeding documentation – so judgments, minutes, testimonies, etc. <br />
<br />
10. Patent Translation<br />
What is it?<br />
The translation of intellectual property and patent-related documents.<br />
Key features<br />
Patents have a specific structure, established terminology and a requirement for complete consistency throughout – read more on this here. These are key aspects to patent translations that translators need to get right.<br />
In addition, subject matter can be highly technical.<br />
What this means<br />
You need translators who have been trained in the specific requirements for translating patent documents. And with the domain expertise needed to handle any technical content.<br />
Examples<br />
Patent specifications, prior art documents, oppositions, opinions<br />
<br />
11. Literary Translation<br />
What is it?<br />
The translation of literary works – novels, short stories, plays, essays, poems.<br />
Key features<br />
Literary translation is widely regarded as the most difficult form of translation.<br />
That’s because it involves much more than simply conveying all meaning in an appropriate style. The translator’s challenge is to also reproduce the character, subtlety and impact of the original – the essence of what makes that work unique.<br />
This is a monumental task, and why it’s often said that the translation of a literary work should be a literary work in its own right.<br />
What this means<br />
Literary translators must be talented wordsmiths with exceptional creative writing skills.<br />
Because few translators have this skillset, you should only consider dedicated literary translators for this type of translation.<br />
<br />
<br />
12. Commercial Translation<br />
What is it?<br />
The translation of documents relating to the world of business.<br />
This is a very generic, wide-reaching translation type. It includes other more specialised forms of translation – legal, financial and technical, for example. And all types of more general business documentation.<br />
Also, some documents will require familiarity with business jargon and an ability to write in that style.<br />
What this means<br />
Different translators will be required for different document types – specialists should handle materials involving technical and specialist fields, whereas generalist translators can translate non-specialist materials.<br />
Examples<br />
Business correspondence, reports, marketing and promotional materials, sales proposals<br />
<br />
13. Business Translations<br />
What is it?<br />
A synonym for Commercial Translations.<br />
<br />
14. Administrative Translations<br />
What is it?<br />
The translation of business management and administration documents.<br />
So it’s a subset of business / commercial translations.<br />
What this means<br />
The implication is these documents will include business jargon and ‘management speak’, so require a translator familiar with, and practised at, writing in that style.<br />
Examples<br />
Management reports and proposals<br />
<br />
15. Marketing Translations<br />
What is it?<br />
The translation of advertising, marketing and promotional materials.<br />
This is a subset of business or commercial translations.<br />
Key features<br />
Marketing copy is designed to have a specific impact on the audience – to appeal and persuade.<br />
So the translated copy must do this too.<br />
But a direct translation will seldom achieve this – so translators need to adapt their wording to produce the impact the text is seeking.<br />
And sometimes a completely new message might be needed – see transcreation in our next category of translation types.<br />
What this means<br />
Marketing translations require translators who are skilled writers with a flair for producing persuasive, impactful copy.<br />
As relatively few translators have these skills, engaging the right translator is key.<br />
Good to know<br />
This type of translation often comes with a typesetting or dtp requirement – particularly for adverts, posters, brochures, etc.<br />
Its best for your translation provider to handle this component. That’s because multilingual typesetters understand the design and aesthetic conventions in other languages/cultures. And these are essential to ensure your materials have the desired impact and appeal in your target markets.<br />
Examples<br />
Advertising, brochures, some website/social media text.<br />
Translation Category B: 14 types of translation based on the end product or use of the translation<br />
This category is all about how the translation is going to be used or the end product that’s produced.<br />
Most of these types involve either adapting or processing a completed translation in some way, or converting or incorporating it into another program or format.<br />
You’ll see that some are very specialised, and complex.<br />
It’s another way translation providers refer to the range of services they provide.<br />
Check out our video of the most specialised of these types of translation:<br />
<br />
16. Document Translations<br />
What is it?<br />
The translation of documents of all sorts.<br />
Here the translation itself is the end product and needs no further processing beyond standard formatting and layout.<br />
<br />
17. Text Translations<br />
What is it?<br />
A synonym for document translation.<br />
<br />
18. Certified Translations<br />
What is it?<br />
A translation with some form of certification.<br />
Key features<br />
The certification can take many forms. It can be a statement by the translation company, signed and dated, and optionally with their company seal. Or a similar certification by the translator.<br />
The exact format and wording will depend on what clients and authorities require – here’s an example.<br />
<br />
19. Official Translations<br />
What is it?<br />
1. Generally used as a synonym for certified translations.<br />
2. Can also refer to the translation of ‘official’ documents issued by the authorities in a foreign country. These will almost always need to be certified.<br />
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20. Software Localisation<br />
What is it?<br />
Adapting software for another language/culture.<br />
Key features<br />
The goal of software localisation is not just to make the program or product available in other languages. It’s also about ensuring the user experience in those languages is as natural and effective as possible.<br />
Translating the user interface, messaging, documentation, etc is a major part of the process.<br />
Also key is a customisation process to ensure everything matches the conventions, norms and expectations of the target cultures.<br />
Adjusting time, date and currency formats are examples of simple customisations. Others might involve adapting symbols, graphics, colours and even concepts and ideas.<br />
Localisation is often preceded by internationalisation – a review process to ensure the software is optimally designed to handle other languages.<br />
And it’s almost always followed by thorough testing – to ensure all text is in the correct place and fits the space, and that everything makes sense, functions as intended and is culturally appropriate.<br />
Localisation is often abbreviated to L10N, internationalisation to i18n.<br />
What this means<br />
Software localisation is a specialised kind of translation, and you should always engage a company that specialises in it.<br />
They’ll have the systems, tools, personnel and experience needed to achieve top quality outcomes for your product.<br />
<br />
21. Game Localisation<br />
What is it?<br />
Adapting games for other languages and markets.<br />
<br />
It’s a subset of software localisation.<br />
Key features<br />
The goal of game localisation is to provide an engaging and fun gaming experience for speakers of other languages.<br />
<br />
It involves translating all text and recording any required foreign language audio.<br />
<br />
But also adapting anything that would clash with the target culture’s customs, sensibilities and regulations.<br />
<br />
For example, content involving alcohol, violence or gambling may either be censored or inappropriate in the target market.<br />
<br />
And at a more basic level, anything that makes users feel uncomfortable or awkward will detract from their experience and thus the success of the game in that market.<br />
<br />
So portions of the game may have to be removed, added to or re-worked.<br />
<br />
Game localisation involves at least the steps of translation, adaptation, integrating the translations and adaptations into the game, and testing.<br />
<br />
What this means<br />
Game localisation is a very specialised type of translation best left to those with specific expertise and experience in this area.<br />
<br />
22. Multimedia Localisation<br />
What is it?<br />
Adapting multimedia for other languages and cultures.<br />
<br />
Multimedia refers to any material that combines visual, audio and/or interactive elements. So videos and movies, on-line presentations, e-Learning courses, etc.<br />
Key features<br />
Anything a user can see or hear may need localising.<br />
<br />
That means the audio and any text appearing on screen or in images and animations.<br />
<br />
Plus it can mean reviewing and adapting the visuals and/or script if these aren’t suitable for the target culture.<br />
<br />
The localisation process will typical involve:<br />
– Translation<br />
– Modifying the translation for cultural reasons and/or to meet technical requirements<br />
– Producing the other language versions<br />
<br />
Audio output may be voice-overs, dubbing or subtitling.<br />
<br />
And output for visuals can involve re-creating elements, or supplying the translated text for the designers/engineers to incorporate.<br />
<br />
What this means<br />
Multimedia localisation projects vary hugely, and it’s essential your translation providers have the specific expertise needed for your materials.<br />
<br />
<br />
<br />
23. Script Translations<br />
What is it?<br />
Preparing the text of recorded material for recording in other languages.<br />
Key features<br />
There are several issues with script translation.<br />
<br />
One is that translations typically end up longer than the original script. So voicing the translation would take up more space/time on the video than the original language.<br />
<br />
Sometimes that space will be available and this will be OK.<br />
<br />
But generally it won’t be. So the translation has to be edited back until it can be comfortably voiced within the time available on the video.<br />
<br />
Another challenge is the translation may have to synchronise with specific actions, animations or text on screen.<br />
<br />
Also, some scripts also deal with technical subject areas involving specialist technical terminology.<br />
<br />
Finally, some scripts may be very culture-specific – featuring humour, customs or activities that won’t work well in another language. Here the script, and sometimes also the associated visuals, may need to be adjusted before beginning the translation process.<br />
<br />
It goes without saying that a script translation must be done well. If it’s not, there’ll be problems producing a good foreign language audio, which will compromise the effectiveness of the video.<br />
<br />
Translators typically work from a time-coded transcript. This is the original script marked to show the time available for each section of the translation.<br />
<br />
What this means<br />
There are several potential pitfalls in script translations. So it’s vital your translation provider is practiced at this type of translation and able to handle any technical content.<br />
<br />
<br />
<br />
24. Voice-over and Dubbing Projects<br />
What is it?<br />
Translation and recording of scripts in other languages.<br />
<br />
Voice-overs vs dubbing<br />
There is a technical difference.<br />
A voice-over adds a new track to the production, dubbing replaces an existing one.<br />
<br />
<br />
Key features<br />
These projects involve two parts:<br />
– a script translation (as described above), and<br />
– producing the audio<br />
<br />
So they involve the combined efforts of translators and voice artists.<br />
The task for the voice artist is to produce a high quality read. That’s one that matches the style, tone and richness of the original.<br />
<br />
Often each section of the new audio will need to be the same length as the original.<br />
<br />
But sometimes the segments will need to be shorter – for example where the voice-over lags the original by a second or two. This is common in interviews etc, where the original voice is heard initially then drops out.<br />
<br />
The most difficult form of dubbing is lip-syncing – where the new audio needs to synchronise with the original speaker’s lip movements, gestures and actions.<br />
<br />
Lip-syncing requires an exceptionally skilled voice talent and considerable time spent rehearsing and fine tuning the translation.<br />
<br />
What this means<br />
You need to use experienced professionals every step of the way in this type of project.<br />
<br />
That’s to ensure firstly that your foreign-language scripts are first class, then that the voicing is of high professional standard.<br />
<br />
Anything less will mean your foreign language versions will be way less effective and appealing to your target audience.<br />
<br />
<br />
<br />
25. Subtitle Translations<br />
What is it?<br />
Producing foreign language captions for sub or surtitles.<br />
Key features<br />
The goal with subtitling is to produce captions that viewers can comfortably read in the time available and still follow what’s happening on the video.<br />
<br />
To achieve this, languages have “rules” governing the number of characters per line and the minimum time each subtitle should display.<br />
<br />
Sticking to these guidelines is essential if your subtitles are to be effective.<br />
<br />
But this is no easy task – it requires simple language, short words, and a very succinct style. Translators will spend considerable time mulling over and re-working their translation to get it just right.<br />
<br />
Most subtitle translators use specialised software that will output the captions in the format sound engineers need for incorporation into the video.<br />
<br />
What this means<br />
As with other specialised types of translation, you should only use translators with specific expertise and experience in subtitling.<br />
<br />
<br />
<br />
26. Website Localisation<br />
What is it?<br />
The translation and adapting of relevant content on a website to best suit the target language and culture.<br />
<br />
Note: Many providers use the term website translation as a synonym for localisation. Strictly speaking though, translation is just one part of localisation.<br />
Key features<br />
<br />
Not all pages on a website may need to be localised – clients should review their content to identify what’s relevant for the other language versions.<br />
Some content may need specialist translators – legal and technical pages for example.<br />
There may also be videos, linked documents, and text or captions in graphics to translate.<br />
Adaptation can mean changing date, time, currency and number formats, units of measure, etc.<br />
But also images, colours and even the overall site design and style if these won’t have the desired impact in the target culture.<br />
Translated files can be supplied in a wide range of formats – translators usually coordinate output with the site webmasters.<br />
New language versions are normally thoroughly reviewed and tested before going live to confirm everything is displaying correctly, works as intended and is cultural appropriate.<br />
What this means<br />
The first step should be to review your content and identify what needs to be translated. This might lead you to modify some pages for the foreign language versions.<br />
<br />
In choosing your translation providers be sure they can:<br />
– handle any technical or legal content,<br />
– provide your webmaster with the file types they want.<br />
<br />
And you should always get your translators to systematically review the foreign language versions before going live.<br />
<br />
<br />
<br />
27. Transcreation<br />
What is it?<br />
Adapting a message to elicit the same emotional response in another language and culture.<br />
Translation is all about conveying the message or meaning of a text in another language. But sometimes that message or meaning won’t have the desired effect in the target culture.<br />
<br />
This is where transcreation comes in. Transcreation creates a new message that will get the desired emotional response in that culture, while preserving the style and tone of the original.<br />
<br />
So it’s a sort of creative translation – which is where the word comes from, a combination of ‘translation’ and ‘creation’.<br />
<br />
At one level transcreation may be as simple as choosing an appropriate idiom to convey the same intent in the target language – something translators do all the time.<br />
<br />
But mostly the term is used to refer to adapting key advertising and marketing messaging. Which requires copywriting skills, cultural awareness and an excellent knowledge of the target market.<br />
<br />
Who does it?<br />
Some translation companies have suitably skilled personnel and offer transcreation services.<br />
<br />
Often though it’s done in the target country by specialist copywriters or an advertising or marketing agency – particularly for significant campaigns and to establish a brand in the target marketplace.<br />
<br />
What this means<br />
Most general marketing and promotional texts won’t need transcreation – they can be handled by a translator with excellent creative writing skills.<br />
<br />
But slogans, by-lines, advertising copy and branding statements often do.<br />
<br />
Whether you should opt for a translation company or an in-market agency will depend on the nature and importance of the material, and of course your budget.<br />
<br />
<br />
<br />
28. Audio Translations<br />
What is it?<br />
Broad meaning: the translation of any type of recorded material into another language.<br />
<br />
More commonly: the translation of a foreign language video or audio recording into your own language. So this is where you want to know and document what a recording says.<br />
Key features<br />
The first challenge with audio translations is it’s often impossible to pick up every word that’s said. That’s because audio quality, speech clarity and speaking speed can all vary enormously.<br />
<br />
It’s also a mentally challenging task to listen to an audio and translate it directly into another language. It’s easy to miss a word or an aspect of meaning.<br />
<br />
So best practice is to first transcribe the audio (type up exactly what is said in the language it is spoken in), then translate that transcription.<br />
<br />
However, this is time consuming and therefore costly, and there are other options if lesser precision is acceptable.<br />
<br />
What this means<br />
It’s best to discuss your requirements for this kind of translation with your translation provider. They’ll be able to suggest the best translation process for your needs.<br />
<br />
Examples<br />
Interviews, product videos, police recordings, social media videos.<br />
<br />
<br />
<br />
29. Translations with DTP<br />
What is it?<br />
Translation incorporated into graphic design files.multilingual dtp example in the form of a Rubik's Cube with foreign text on each square<br />
Key features<br />
Graphic design programs are used by professional designers and graphic artists to combine text and images to create brochures, books, posters, packaging, etc.<br />
<br />
Translation plus dtp projects involve 3 steps – translation, typesetting, output.<br />
<br />
The typesetting component requires specific expertise and resources – software and fonts, typesetting know-how, an appreciation of foreign language display conventions and aesthetics.<br />
<br />
What this means<br />
Make sure your translation company has the required multilingual typesetting/desktop publishing expertise whenever you’re translating a document created in a graphic design program.<br />
<br />
Translation Category C: 13 types of translation based on the translation method employed<br />
This category has two sub-groups:<br />
– the practical methods translation providers use to produce their translations, and<br />
– the translation strategies/methods identified and discussed within academia.<br />
<br />
The translation methods translation providers use<br />
There are 4 main methods used in the translation industry today. We have an overview of each below, but for more detail, including when to use each one, see our comprehensive blog article.<br />
<br />
Or watch our video.<br />
<br />
Important: If you’re a client you need to understand these 4 methods – choose the wrong one and the translation you end up with may not meet your needs!<br />
<br />
<br />
30. Machine Translation (MT)<br />
What is it?<br />
A translation produced entirely by a software program with no human intervention.<br />
<br />
A widely used, and free, example is Google Translate. And there are also commercial MT engines, generally tailored to specific domains, languages and/or clients.<br />
<br />
Pros and cons<br />
There are two limitations to MT:<br />
– they make mistakes (incorrect translations), and<br />
– quality of wording is patchy (some parts good, others unnatural or even nonsensical)<br />
<br />
On they positive side they are virtually instantaneous and many are free.<br />
<br />
Best suited for:<br />
Getting the general idea of what a text says.<br />
<br />
This method should never be relied on when high accuracy and/or good quality wording is needed.<br />
<br />
<br />
<br />
31. Machine Translation plus Human Editing (PEMT)<br />
What is it?<br />
A machine translation subsequently edited by a human translator or editor (often called Post-editing Machine Translation = PEMT).<br />
<br />
The editing process is designed to rectify some of the deficiencies of a machine translation.<br />
<br />
This process can take different forms, with different desired outcomes. Probably most common is a ‘light editing’ process where the editor ensures the text is understandable, without trying to fix quality of expression.<br />
<br />
Pros and cons<br />
This method won’t necessarily eliminate all translation mistakes. That’s because the program may have chosen a wrong word (meaning) that wasn’t obvious to the editor.<br />
<br />
And wording won’t generally be as good as a professional human translator would produce.<br />
<br />
Its advantage is it’s generally quicker and a little cheaper than a full translation by a professional translator.<br />
<br />
Best suited for:<br />
Translations for information purposes only.<br />
<br />
Again, this method shouldn’t be used when full accuracy and/or consistent, natural wording is needed.<br />
<br />
<br />
<br />
32. Human Translation<br />
What is it?<br />
Translation by a professional human translator.<br />
Pros and cons<br />
Professional translators should produce translations that are fully accurate and well-worded.<br />
<br />
That said, there is always the possibility of ‘human error’, which is why translation companies like us typically offer an additional review process – see next method.<br />
<br />
This method will take a little longer and likely cost more than the PEMT method.<br />
<br />
Best suited for:<br />
Most if not all translation purposes.<br />
<br />
<br />
<br />
33. Human Translation + Revision<br />
What is it?<br />
A human translation with an additional review by a second translator.<br />
<br />
The review is essentially a safety check – designed to pick up any translation errors and refine wording if need be.<br />
<br />
Pros and cons<br />
This produces the highest level of translation quality.<br />
<br />
It’s also the most expensive of the 4 methods, and takes the longest.<br />
<br />
Best suited for:<br />
All translation purposes.<br />
<br />
Gearwheel with 5 practical translation methods written on the teeth <br />
There’s also one other common term used by practitioners and academics alike to describe a type (method) of translation:<br />
<br />
34. Computer-Assisted Translation (CAT)<br />
What is it?<br />
A human translator using computer tools to aid the translation process.<br />
<br />
Key features<br />
Virtually all translators use such tools these days.<br />
<br />
The most prevalent tool is Translation Memory (TM) software. This creates a database of previous translations that can be accessed for future work.<br />
<br />
TM software is particularly useful when dealing with repeated and closely-matching text, and for ensuring consistency of terminology. For certain projects it can speed up the translation process.<br />
<br />
<br />
<br />
The translation methods described by academia<br />
A great deal has been written within academia analysing how human translators go about their craft.<br />
<br />
Seminal has been the work of Newmark, and the following methods of translation attributed to him are widely discussed in the literature.Gearwheel with Newmark's 8 translation methods written on the teeth <br />
These methods are approaches and strategies for translating the text as a whole, not techniques for handling smaller text units, which we discuss in our final translation category.<br />
<br />
<br />
<br />
35. Word-for-word Translation<br />
This method translates each word into the other language using its most common meaning and keeping the word order of the original language.<br />
<br />
So the translator deliberately ignores context and target language grammar and syntax.<br />
<br />
Its main purpose is to help understand the source language structure and word use.<br />
<br />
Often the translation will be placed below the original text to aid comparison.<br />
<br />
<br />
<br />
36. Literal Translation<br />
Words are again translated independently using their most common meanings and out of context, but word order changed to the closest acceptable target language grammatical structure to the original.<br />
<br />
Its main suggested purpose is to help someone read the original text.<br />
<br />
<br />
<br />
37. Faithful Translation<br />
Faithful translation focuses on the intention of the author and seeks to convey the precise meaning of the original text.<br />
<br />
It uses correct target language structures, but structure is less important than meaning.<br />
<br />
<br />
<br />
38. Semantic Translation<br />
Semantic translation is also author-focused and seeks to convey the exact meaning.<br />
<br />
Where it differs from faithful translation is that it places equal emphasis on aesthetics, ie the ‘sounds’ of the text – repetition, word play, assonance, etc.<br />
<br />
In this method form is as important as meaning as it seeks to “recreate the precise flavour and tone of the original” (Newmark).slide showing definition of semantic translation as a translation method<br />
<br />
39. Communicative Translation<br />
Seeks to communicate the message and meaning of the text in a natural and easily understood way.<br />
<br />
It’s described as reader-focused, seeking to produce the same effect on the reader as the original text.<br />
<br />
A good comparison of Communicative and Semantic translation can be found here.<br />
<br />
40. Free Translation<br />
Here conveying the meaning and effect of the original are all important.<br />
<br />
There are no constraints on grammatical form or word choice to achieve this.<br />
<br />
Often the translation will paraphrase, so may be of markedly different length to the original.<br />
<br />
41. Adaptation<br />
Mainly used for poetry and plays, this method involves re-writing the text where the translation would otherwise lack the same resonance and impact on the audience.<br />
<br />
Themes, storylines and characters will generally be retained, but cultural references, acts and situations adapted to relevant target culture ones.<br />
<br />
So this is effectively a re-creation of the work for the target culture.<br />
<br />
42. Idiomatic Translation<br />
Reproduces the meaning or message of the text using idioms and colloquial expressions and language wherever possible.<br />
<br />
The goal is to produce a translation with language that is as natural as possible.<br />
<br />
Translation Category D: 9 types of translation based on the translation technique used<br />
These translation types are specific strategies, techniques and procedures for dealing with short chunks of text – generally words or phrases.<br />
<br />
They’re often thought of as techniques for solving translation problems.<br />
<br />
They differ from the translation methods of the previous category which deal with the text as a whole.<br />
9 translation techniques as titles of books in a bookcase<br />
<br />
43. Borrowing<br />
What is it?<br />
Using a word or phrase from the original text unchanged in the translation.<br />
<br />
Key features<br />
With this procedure we don’t translate the word or phrase at all – we simply ‘borrow’ it from the source language.<br />
<br />
Borrowing is a very common strategy across languages. Initially, borrowed words seem clearly ‘foreign’, but as they become more familiar, they can lose that ‘foreignness’.<br />
<br />
Translators use this technique:<br />
– when it’s the best word to use – either because it has become the standard, or it’s the most precise term, or<br />
– for stylist effect – borrowings can add a prestigious or scholarly flavour.<br />
<br />
Borrowed words or phrases are often italicised in English.<br />
<br />
Examples of borrowings in English<br />
grand prix, kindergarten, tango, perestroika, barista, sampan, karaoke, tofu<br />
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<br />
44. Transliteration<br />
What is it?<br />
Reproducing the approximate sounds of a name or term from a language with a different writing system.<br />
<br />
Key features<br />
In English we use the Roman (Latin) alphabet in common with many other languages including almost all European languages.<br />
<br />
Other writing systems include Arabic, Cyrillic, Chinese, Japanese, Korean, Thai, and the Indian languages.<br />
<br />
Transliteration from such systems into the Roman alphabet is also called romanisation.<br />
<br />
There are accepted systems for how individual letters/sounds should be romanised from most other languages – there are three common systems for Chinese, for example.<br />
<br />
English borrowings from languages using non-Roman writing systems also require transliteration – perestroika, sampan, karaoke, tofu are examples from the above list.<br />
<br />
Translators mostly use transliteration as a procedure for translating proper names.<br />
<br />
Examples<br />
毛泽东 Mao Tse-tung or Mao Zedong<br />
Владимир Путин Vladimir Putin<br />
서울 Seoul<br />
ភ្នំពេញ Phnom Penh<br />
<br />
45. Calque or Loan Translation<br />
What is it?<br />
A literal translation of a foreign word or phrase to create a new term with the same meaning in the target language.<br />
<br />
So a calque is a borrowing with translation if you like. The new term may be changed slightly to reflect target language structures.<br />
<br />
Examples<br />
German ‘Kindergarten’ has been calqued as детский сад in Russian, literally ‘children garden’ in both languages.<br />
<br />
Chinese 洗腦 ‘wash’ + ‘brain’ is the origin of ‘brainwash’ in English.<br />
<br />
English skyscraper is calqued as gratte-ciel in French and rascacielos in Spanish, literally ‘scratches sky’ in both languages.<br />
<br />
46. Word-for-word translation<br />
What is it?<br />
A literal translation that is natural and correct in the target language.<br />
<br />
Alternative names are ‘literal translation’ or ‘metaphrase’.<br />
<br />
Note: this technique is different to the translation method of the same name, which does not produce correct and natural text and has a different purpose.<br />
<br />
Key features<br />
This translation strategy will only work between languages that have very similar grammatical structures.<br />
<br />
And even then, only sometimes.<br />
<br />
For example, standard word order in Turkish is Subject-Object-Verb whereas in English it’s Subject-Verb-Object. So a literal translation between these two will seldom work:<br />
– Yusuf elmayı yedi is literally ‘Joseph the apple ate’.<br />
<br />
When word-for-word translations don’t produce natural and correct text, translators resort to some of the other techniques described below.<br />
Examples<br />
French ‘Quelle heure est-il?’ works into English as ‘What time is it?’.<br />
<br />
Russian ‘Oн хочет что-нибудь поесть’ is ‘He wants something to eat’.<br />
<br />
47. Transposition<br />
What is it?<br />
Translation with a change of grammatical structure.<br />
<br />
This technique gives the translation more natural wording and/or makes it grammatically correct.<br />
<br />
Examples<br />
A change in word order:<br />
Our Turkish example Yusuf elmayı yedi (literally ‘Joseph the apple ate’) –> Joseph ate the apple.<br />
<br />
Spanish La Casa Blanca (literally ‘The House White’) –> The White House<br />
<br />
A change in grammatical category:<br />
German Er hört gerne Musik (literally ‘he listens gladly [to] music’)<br />
= subject pronoun + verb + adverb + noun<br />
becomes Spanish Le gusta escuchar música (literally ‘[to] him [it] pleases to listen [to] music’)<br />
= indirect object pronoun + verb + infinitive + noun<br />
and English He likes listening to music<br />
= subject pronoun + verb + gerund + noun.<br />
<br />
48. Modulation<br />
What is it?<br />
Translation with a change of focus or point of view in the target language.<br />
<br />
This technique makes the translation more idiomatic – how people would normally say it in the language.<br />
<br />
Examples<br />
English talks of the ‘top floor’ of a building, French the dernier étage = last floor. ‘Last floor’ would be unnatural in English, so too ‘top floor’ in French.<br />
<br />
German uses the term Lebensgefahr (literally ‘danger to life’) where in English we’d be more likely to say ‘risk of death’.<br />
In English we’d say ‘I dropped the key’, in Spanish se me cayó la llave, literally ‘the key fell from me’. The English perspective is that I did something (dropped the key), whereas in Spanish something happened to me – I’m the recipient of the action.<br />
<br />
49. Equivalence or Reformulation<br />
What is it?<br />
Translating the underlying concept or meaning using a totally different expression.<br />
<br />
This technique is widely used when translating idioms and proverbs.<br />
<br />
And it’s common in titles and advertising slogans.<br />
<br />
It’s a common strategy where a direct translation either wouldn’t make sense or wouldn’t resonate in the same way.<br />
<br />
Examples<br />
Here are some equivalents of the English saying “Pigs may fly”, meaning something will never happen, or “you’re being unrealistic” (Source):<br />
– Thai: ชาติหน้าตอนบ่าย ๆ – literally, ‘One afternoon in your next reincarnation’<br />
– French: Quand les poules auront des dents – literally, ‘When hens have teeth’<br />
– Russian, Когда рак на горе свистнет – literally, ‘When a lobster whistles on top of a mountain’<br />
– Dutch, Als de koeien op het ijs dansen – literally, ‘When the cows dance on the ice’<br />
– Chinese: 除非太陽從西邊出來！– literally, ‘Only if the sun rises in the west’<br />
<br />
50. Adaptation<br />
What is it?<br />
A translation that substitutes a culturally-specific reference with something that’s more relevant or meaningful in the target language.<br />
<br />
It’s also known as cultural substitution or cultural equivalence.<br />
<br />
It’s a useful technique when a reference wouldn’t be understood at all, or the associated nuances or connotations would be lost in the target language.<br />
<br />
Note: the translation method of the same name is a similar concept but applied to the text as a whole.<br />
<br />
Examples<br />
Different cultures celebrate different coming of age birthdays – 21 in many cultures, 20, 15 or 16 in others. A translator might consider changing the age to the target culture custom where the coming of age implications were important in the original text.<br />
Animals have different connotations across languages and cultures. Owls for example are associated with wisdom in English, but are a bad omen to Vietnamese. A translator might want to remove or amend an animal reference where this would create a different image in the target language.<br />
<br />
51. Compensation<br />
What is it?<br />
A meaning or nuance that can’t be directly translated is expressed in another way in the text.<br />
Example<br />
Many languages have ways of expressing social status (honorifics) encoded into their grammatical structures.<br />
<br />
So you can convey different levels of respect, politeness, humility, etc simply by choosing different forms of words or grammatical elements.<br />
But these nuances will be lost when translating into languages that don’t have these structures.<br />
Then translating into languages that don’t have these structures<br />
Then translating into languages that don’t have these structures.<br />
<br />
===Conclusion ===<br />
<br />
In the light of above mentioned facts and figures here by we would say that machine translation is a challenge for human translators because it can reduce the workload of translation but can't give accurate and exact translation of the target language.It can be less reliable than human translation..</div>Chen Huinihttps://bou.de/u/index.php?title=Machine_translation&diff=133233Machine translation2021-12-15T04:59:27Z<p>Chen Huini: /* Chapter 11 陈惠妮 Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts */</p>
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<div>'''Machine Translation - A challenge or a chance for human translators?'''<br />
<br />
[[Machine_translation|Overview Page of Machine Translation]]<br />
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30 Chapters（0/30)<br />
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[[Machine_Trans_EN_1]] [[Machine_Trans_EN_2]] [[Machine_Trans_EN_3]] [[Machine_Trans_EN_4]] [[Machine_Trans_EN_5]] [[Machine_Trans_EN_6]] [[Machine_Trans_EN_7]] [[Machine_Trans_EN_8]] [[Machine_Trans_EN_9]] [[Machine_Trans_EN_10]] [[Machine_Trans_EN_11]] [[Machine_Trans_EN_12]] [[Machine_Trans_EN_13]] [[Machine_Trans_EN_14]] [[Machine_Trans_EN_15]] [[Machine_Trans_EN_16]] [[Machine_Trans_EN_17]] [[Machine_Trans_EN_18]] [[Machine_Trans_EN_19]] [[Machine_Trans_EN_20]] [[Machine_Trans_EN_21]] [[Machine_Trans_EN_22]] [[Machine_Trans_EN_23]] [[Machine_Trans_EN_24]] [[Machine_Trans_EN_25]] [[Machine_Trans_EN_26]] [[Machine_Trans_EN_27]] [[Machine_Trans_EN_28]] [[Machine_Trans_EN_29]] [[Machine_Trans_EN_30]] ...<br />
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[[Book_projects|Back to translation project overview]]<br />
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[[DCG-To-Do|To the To Do list]]<br />
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=Chapter 1:A Comparison Between the Quality of Machine Translation and Human Translation——A Case Study of the Application of artificial intelligence in Sports Events=<br />
'''论机器翻译与人工翻译的质量对比——以人工智能在体育赛事领域的应用为例'''<br />
<br />
卫怡雯 Wei Yiwen, Hunan Normal University, China<br />
<br />
[[Machine_Trans_EN_1]]<br />
<br />
=Chapter 3：On the Realm Advantages And Mutual Development of Machine Translation And Huamn Translation)=<br />
"'论机器翻译与人工翻译的领域优势及共同发展'"<br />
<br />
肖毅瑶 Xiao Yiyao, Hunan Normal University, China<br />
[[Machine_Trans_EN_3]]<br />
<br />
=Chapter 4 : A Comparison Between Machine Translation of Netease and Traditional Human Translation—A Case Study of The Economist Articles)= <br />
'''网易有道机器翻译与人工翻译的译文对比——以经济学人语料为例'''<br />
<br />
王李菲 Wang Lifei, Hunan Normal University<br />
<br />
[[Machine_Trans_EN_4]]<br />
<br />
=Chapter 5: Muhammad Saqib Mehran - Problems in translation studies=<br />
[[Machine_Trans_EN_5]]<br />
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=6 徐敏赟(Machine Translation Based on Neural Network --Challenge or Chance?)=<br />
[[Machine_Trans_EN_6]]<br />
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=Chapter 7: The Cultivation of artificial intelligence and translation talents in the Belt and Road Initiative=<br />
[[Machine_Trans_EN_7]]<br />
<br />
一带一路背景下人工智能与翻译人才的培养<br />
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颜莉莉 Yan Lili, Hunan Normal University, China<br />
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=Chapter 8: On Machine Translation Under Lanuguage Intelligence——An Option and Opportunity for Human Translators=<br />
'''论语言智能下的机器翻译——译者的选择与机遇'''<br />
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颜静 Yan Jing, Hunan Normal University, China<br />
<br />
[[Machine_Trans_EN_8]]<br />
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=9 谢佳芬（ Machine Translation and Artificial Translation in the Era of Artificial Intelligence）=<br />
[[Machine_Trans_EN_9]]<br />
<br />
=10 熊敏(Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts)=<br />
机器翻译对各类型文本的英汉翻译能力探究<br />
<br />
熊敏, Xiong Min, Hunan Normal University<br />
<br />
[[Machine_Trans_EN_10]]<br />
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<br />
===Abstract===<br />
The history of machine translation can be traced to 1940s, undergoing germination, recession, revival and flourish. Nowadays, with the rapid development of information technology, machine translation technology emerged and is gradually becoming mature. Whether manual translation can be replaced by machine translation becomes a widely-disputed topic. So in order to explore the ability of machine translation, I adopts two versions of translation, which are manual translation and machine translation(this paper uses Youdao translation) for different types of texts(according to Peter Newmark's types of text：informative text, expressive text and vocative text). The results are quite different in terms of quality and accuracy. The results show that machine translation is more suitable for informative text for its brevity, while the expressive texts especially literary works and vocative texts are difficult to be translated, because there are too many loaded words and cultural images in expressive text and dependence on the readers. To draw a conclusion, the relationship between machine translation and manual translation should be complementary rather than competitive.<br />
<br />
===Key words===<br />
machine translation; manual translation; Newmark's type of texts<br />
<br />
===题目===<br />
Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts<br />
<br />
===摘要===<br />
机器翻译的历史可以追溯到上个世纪40年代，经历了萌芽期、萧条期、复兴期和繁荣期四个阶段。如今，随着信息技术的高速发展，机器翻译技术日益成熟，于是机器翻译能不能替代人工翻译这个话题引起了广泛热议。为了探究机器翻译的能力水平是否足以替代人工翻译，本人根据皮特纽马克的文本类型分类理论（信息类文本，表达文本，呼吁文本），选择了相应的译文类型，并且将其机器翻译的版本以及人工翻译的版本进行对比。结果发现，就质量和准确度而言，译文的水平大相径庭。因为其简洁的特性，机器比较适合翻译信息文本。而就表达文本，尤其是文学作品，因其存在文化负载词及相应的文化现象，所以机器翻译这类文本存在难度。而呼唤文本因其目的性以及对读者的依赖性较强，翻译难度较大。由此得知，机器翻译和人工翻译的关系应该是互补的，而不是竞争的。<br />
<br />
===关键词===<br />
机器翻译；人工翻译；纽马克文本类型<br />
<br />
===1. Introduction===<br />
====1.1.Introduction to machine translation====<br />
Machine translation, also known as computer translation is a technique to translating through machine translator, such as Google translation and Youdao translation. Machine translation is one of the branches of computational linguistics, ranging from computer science, statistics, information science and so on. Machine translation plays an important role in all aspects.<br />
Machine translation can be traced back to 1940s, when British engineer Booth and American engineer Weaver proposed using computer to translate and started to study machines used for translation. And the first machine translating system launched in 1954, used in English and Russian translation. And this means machine translation becomes a reality. However, the arrival of new things always accompanies barriers and setbacks. In the 1960s, reports from ALPAC (Automated Language Processing Advisory Committee) showed studies on machine translation had stagnated for a decade. At that time, due to the high cost of machine, choosing human translators to finish translating works was more economical for most companies. What’s worse, machine had difficulty in understanding semantic and pragmatic meanings. Fortunately, in the 1970s, with the advance and popularity of computer, machine translation was gradually back on track. With the development of science and technology, machine translation has better technological guarantee, such as artificial intelligence and computer technology. In the last decades, from the late 90s till now, machine translation is becoming more and more mature. The initial rule-oriented machine translation has been updated and Corpus-aided machine translation appears. And nowadays, technology in voice recognition has been further developed. This technique is frequently applied in speech translation products. Users only need to speak source language to the online translator and instantly it will speak out the target version. But machine can’t fully provide precise and accurate translation without any grammatical or semantic problems. Machine can understand the literal meaning of texts, but not the meaning in context. For example, there is polysemy in English, which refers to words having multiple meanings. So when translating these words, translators should take contexts into consideration. But machine has troubles in this way. In addition, machine has no feeling or intention. Hence, it is also difficult to convey the feelings from the source language.(Wei 2021:5)#<br />
<br />
====1.2.Process of machine translation====<br />
The process of manual translation is different from that of machine translation. Here is the process of the former. (1) Understand source language. (2) Use target language to organize language. (3) Generate translation. Unlike manual translation, machine translation tends to analyze and code source language first, then look for related codes in corpus, and work out the code that represents target language, generating translation. But they share a common feature, which is that Lexicon, grammatical rules and syntactic structure are taken into consideration. This is one of the biggest challenges for machine translation.<br />
<br />
===2.Theorectical framework===<br />
====2.1Newmark’s text typology====<br />
Text typology is a theory from linguistics. It undergoes several phrases. Karl Buhler, a German psychologist and linguist defines communicative functions into expressive, information and vocative. Then Roman Jakobson proposes categorization of texts, which are intralingual translation, interlingual translation and intersemiotic translation. Accordingly, he defines six main functions of language, including the referential function, conative function, emotive function, poetic function, metalingual function and the phatic function. Based on the two theories, Peter Newmark, a translation theorist, summarizes the language function into six parts: the informative function, the vocative function, the expressive function, the phatic function, the aesthetic function, and the metalingual function. Later, he further divided texts into informative type, expressive text and vocative type according to the linguistic functions of various texts. (Newmark 2002:2)#<br />
The core of the informative texts is the truth. It is to convey facts, information, knowledge of certain topics, reality and theories. The language style of the text is objective, logical and standardized. Reports, papers, scientific and technological textbooks are all attributed to informative texts. Translators are required to take format into account for different styles.<br />
The core of the expressive text is the sender’s emotions and attitudes. It is to express sender’s preferences, feelings, views and so on. The language style of it is subjective. Imaginative literature, including fictions, poems and dramas, autobiography and authoritative statements belong to expressive text. It requires translators to be faithful to the source language and maintain the style.<br />
The core of the vocative text is readership. It is to call upon readers to act, think, feel and react in the way intended by the text. So it is reader-oriented. Such texts as advertisement, propaganda and notices are of vocative text. Newmark noted that translators need to consider cultural background of the source language and the semantic and pragmatic effect of target language. If readers can comprehend the meaning at once and do as the text says, then the goal of information communication is achieved. (Liu 2021:3)#<br />
To draw a conclusion, informative texts focus on the information and facts. Expressive texts center on the mind of addressers, including feelings, prejudices and so on. And vocative texts are oriented on readers and call on them to practice as the texts say.<br />
<br />
====2.2Study method====<br />
Manual translations of the three texts are selected from authoritative versions and universally acknowledged. And machine translations of those come from Youdao Translator. And in this thesis I will compare and evaluate the two methods in word diction, sentence structure, word order and redundancy.<br />
<br />
===3.Comparison and analysis of machine translation and manual translation ===<br />
====3.1Informative text ====<br />
（1）English into Chinese<br />
<br />
①Source language:<br />
<br />
Keep the tip of Apple Pencil clean, as dirt and other small particles may cause excessive wear to the tip or damage the screen of i-pad.<br />
<br />
Target language:<br />
<br />
Machine translation: Apple Pencil笔尖应保持清洁，灰尘等小颗粒可能会导致笔尖过度磨损或损坏ipad屏幕。<br />
<br />
Manual translation: 保持Apple Pencil铅笔的笔尖干净，因为灰尘和其他微粒可能会导致笔尖的过度磨损或损坏iPad屏幕。<br />
<br />
Analysis: Here is the instruction of Apple Pencil. And the manual translation is the Chinese version on the instruction.Product instruction tends to be professional, since there are many terms for some concepts. Machine can easily identify these terms and provide related words to translate. The machine version is faithful and expressive to the source language. So it is well-qualified and readable for readers to understand the instruction. So we can use machine to translate informative text.<br />
<br />
②Source language:<br />
<br />
China on Saturday launched a rocket carrying three astronauts-two men and one woman - to the core module of a future space station where they will live and work for six months, the longest orbit for Chinese astronauts.<br />
<br />
Target language:<br />
<br />
Machine translation: 周六，中国发射了一枚运载三名宇航员(两男一女)的火箭，进入未来空间站的核心舱，他们将在那里生活和工作6个月，这是中国宇航员最长的轨道。<br />
<br />
Manual translation: 周六，中国发射了一枚搭载三名宇航员(两男一女)的火箭，进入未来空间站的核心舱，他们将在那里生活和工作6个月，这是中国宇航员最漫长的一次轨道飞行。<br />
<br />
Analysis: This is a news from Reuters, reporting that China has launched a rocket.The meaning of the two translations is almost the same, except for some word diction. But there are some details dealt with different choice. For example, the last sentence of the machine translation is a bit of obscure and direct. There are some ambiguous words and expressions.<br />
<br />
(2)Chinese into English<br />
<br />
Source language:湖南省博物馆是湖南省最大的历史艺术类博物馆，占地面积4.9万平方米，总建筑面积为9.1万平方米，是首批国家一级博物馆，中央地方共建的八个国家级重点博物馆之一、全国文化系统先进集体、文化强省建设有突出贡献先进集体。<br />
<br />
Target language:<br />
Manual translation: As the largest history and art museum in Hunan province, the Hunan Museum covers an area of 49,000㎡, with the building area reaching 91,000㎡. It is one of the first batch of national first-level museums and one of the first eight national museums co-funded by central and local governments.<br />
<br />
Machine translation: Museum in hunan province is one of the largest historical art museum in hunan province, covers an area of 49000 square meters, a total construction area of 91000 square meters, is the first national museum, the central place to build one of the eight national key museum, national cultural system advanced collectives, strong culture began with outstanding contribution of advanced collective.<br />
<br />
Analysis: Machine translation is not faithful enough in content. For instance, “首批国家一级博物馆” is translated into “first national museum”, which is not the meaning of the source language. And there are some obvious grammar mistakes in the machine translation. For example, machine translates it into just one sentence but there are multiple predicates in it. So it is not grammatically permissible. What’s more, the sentence structure of machine translation is confusing and the focus is not specific enough.<br />
<br />
====3.2Expressive text ====<br />
(1)English into Chinese<br />
<br />
Source language:<br />
<br />
An individual human existence should be like a river- small at first, narrowly contained within its banks, and rushing passionately past rocks and over waterfalls. Gradually the river grows wider, the banks recede, the waters flow more quietly, and in the end, without any visible breaks, they become merged in the sea, and painlessly lose their individual being.()<br />
<br />
Target language:<br />
<br />
Machine translation: 一个人的存在应该像一条河流——开始很小，被紧紧地夹在两岸中间，然后热情奔放地冲过岩石，飞下瀑布。渐渐地，河面变宽，两岸后退，水流更加平缓，最后，没有任何明显的停顿，它们汇入大海，毫无痛苦地失去了自己的存在。<br />
<br />
Manual translation:人生在世，如若河流；河口初始狭窄，河岸虬曲，而后狂涛击石，飞泻成瀑。河道渐趋开阔，峡岸退去，水流潺缓，终了，一马平川，汇于大海，消逝无影。<br />
<br />
Analysis: Here is a well-known metaphor in the prose How to Grow Old written by Bertrand Russell. The manual translation is written by Tian Rongchang.This is a philosophical prose with graceful language. Literary translation is a most important and difficult branch of translation. Translator should focus on the literal meaning, culture, writing style and so on. It is a combination of beauty and elegance. Therefore, translators find it in a dilemma of beauty and faithfulness, let alone translating machine. Compared with manual translation, machine translation has difficulty in word choice. It is faithful and expressive, but not elegant enough.<br />
<br />
(2)Chinese into English<br />
<br />
Source language:没有一个人将小草叫做“大力士”，但是它的力量之大，的确是世界无比。这种力，是一般人看不见的生命力，只要生命存在，这种力就要显现，上面的石块，丝毫不足以阻挡。因为它是一种“长期抗战”的力，有弹性，能屈能伸的力，有韧性，不达目的不止的力。(Zhang, 2007:186)#<br />
<br />
Target language:<br />
<br />
Machine translation: No one calls the little grass "hercules", but its power is truly matchless in the world. This force is invisible life force. As long as there is life, this force will show itself. The stone above is not strong enough to stop it. Because it is a "long-term resistance" of the force, elastic, can bend and extend force, tenacity, not to achieve the purpose of the force.<br />
<br />
Manual translation: Though nobody describes the little grass as a “husky”, yet its herculean strength is unrivalled. It is the force of life invisible to naked eye. It will display itself so long as there is life. The rock is utterly helpless before this force- a force that will forever remain militant, a force that is resilient and can take temporary setbacks calmly, a force that is tenacity itself and will never give up until the goal is reached. (by Zhang Peiji)<br />
<br />
Analysis:This is the excerpt of a well-known Chinese prose written by Xia Yan. It is written during the war of Resistance Against Japan. So the prose holds symbolic meaning, eulogizing the invisible tenacious vitality so as to encourage Chinese to have confidence in the anti-aggression war. Compared with manual translation, machine translation is much more abstract and confusing, especially for the word diction. For example, “大力士” is translated into “hercules” which is a man of exceptional strength and size in Greek and Roman Mythology, making it difficult to understand if readers of target language have no idea of the allusion. What’s worse, the machine version doesn’t reveal the symbolic meaning of the text, which is the core of this prose.<br />
<br />
====3.3Vocative text ====<br />
<br />
(1)English into Chinese<br />
<br />
①Source language:<br />
<br />
iPhone went to film school, so you don’t have to. (Advertisement of iPhone13)<br />
<br />
Target language:<br />
<br />
Machine translation: iPhone上的是电影学院，所以你不用去。<br />
<br />
Manual translation:电影专业课，iPhone同学替你上完了。<br />
<br />
Analysis：Here are advertisements of iPhone on Apple official website. There is a personification in the source language. It is used to stress the advancement and proficiency in camera, which is an appealing selling point to potential buyers. Compared with manual translation, machine translation is plain and not eye-catching enough for customers.<br />
<br />
②Source language: <br />
<br />
5G speed OMGGGGG<br />
<br />
Machine language: 5克的速度 OMGGGGG<br />
<br />
Manual translation:<br />
<br />
iPhone的5G 巨巨巨巨巨5G<br />
<br />
Analysis: The “G” in the source language is the unit of speed, standing for generation. However, it is mistaken as a unit of weight, representing gram in the machine translation. So the meaning is not faithful to the source language at all. As for manual translation, it complies with the source in form. Specifically speaking, five “G”s in the former complies with five characters “巨”in the latter. And the pronunciation of the two is similar. There are two layers of meaning for the 5 “G”s. One exclaims the fast speed of 5 generation network and the other new technology. In the manual version, “巨”can be used to show degree, meaning “quite” or “very”. <br />
<br />
③Source language: <br />
<br />
History, faith and reason show the way, the way of unity. We can see each other not as adversaries but as neighbors. We can treat each other with dignity and respect, we can join forces, stop the shouting and lower the temperature. For without unity, there is no peace, only bitterness and fury."<br />
<br />
Target language:<br />
<br />
Machine translation: 历史、信仰和理性指明了团结的道路。我们可以把彼此视为邻居，而不是对手。我们可以尊严地对待彼此，我们可以联合起来，停止大喊大叫，降低温度。因为没有团结，就没有和平，只有痛苦和愤怒。<br />
<br />
Manual translation:历史、信仰和理性为我们指明道路。那是团结之路。我们可以把彼此视为邻居，而不是对手。我们可以有尊严地相互尊重。我们可以联合起来，停止喊叫，减少愤怒。因为没有团结就没有和平，只有痛苦和愤怒<br />
<br />
Analysis: Speech is a way to propagate some activity in public. It is an art to inspire emotion of the audience. The source language is the excerpt of Joe Biden’s inaugural speech. The speech should be inspiring and logic. The machine translation has some misunderstanding. Taking the translation of “lower the temperature” for example, machine only translates its literal meaning, relating to the temperature itself, without considering the context. What’s more, it is less logic than the manual one. Therefore, it adds difficulty to inspire the audience and infect their emotion.<br />
<br />
===4.Common mistakes in machine translation ===<br />
<br />
====4.1 lexical mistakes ====<br />
<br />
Common lexical mistakes include misunderstandings in word category, lexical meaning and emotive and evaluative meaning. Misunderstanding in word category shows in the classification of word in the source language. As for misunderstanding in lexical meaning, machine has difficulty in precisely reflecting the meaning of the original texts, due to different cultural background and different language system. And for misunderstanding in emotive meaning, machine has no intention and emotion like human-beings. Therefore, it’s impossible for it to know writers’ feelings and their writing purposes. So sometimes, it may translate something negative into something positive. (Wang 2008:45)#<br />
<br />
====4.2 grammatical mistakes====<br />
<br />
Grammatical analysis plays an important part in translation. Normally speaking, every language has its own unique grammatical rules. So in the process of translation, if translators don’t know the formation rule well, the sentence meaning will be affected. Even though all the lexical meanings are well-known by translators, the lack of consciousness of grammaticality makes it harder to arrange words according to sequential rule. English tends to be hypotactic, while Chinese tends to be paratactic. English sentences are connected through syntactic devices and lexical devices. While Chinese sentences are semantically connected, which means there are limited logical words and connection words in Chinese. So when translating English sentence, we should first analyze its grammaticality and logical structure and then rearrange its sequence. However, online translating machine has troubles in grammatical analysis, which makes its improvement more difficult.<br />
<br />
====4.3 other mistakes====<br />
<br />
The two mistakes above are the internal ones. Apart from mistakes in linguistic system, there are some mistakes in other aspects, such as cultural background.<br />
<br />
===5.Reasons for its common mistakes ===<br />
<br />
====5.1 Difference in two linguistic system====<br />
<br />
With different history, English and Chinese have different ways of expression. Commonly speaking, English is synthetic language which expresses grammatical meaning through inflection such as tense and Chinese is analytic language which expresses grammatical meaning through word order and function word. In addition, English is more compact with full sentences. Subordinate sentence is one of the most important features in modern English. Chinese, on the other hand, is more diffusive with minor sentences.<br />
<br />
====5.2 Difference in thinking patterns and cultural background====<br />
<br />
According to Sapir-Whorf’s Hypothesis, our language helps mould our way of thinking and consequently, different languages may probably express their unique ways of understanding the world. For two different speech communities, the greater their structural differentiations are, the more diverse their conceptualization of the world will be. For example, western culture is more direct and eastern culture more euphemistic. What’s more, English culture tends to be individualism, focusing on detail, through which it reflects the whole, while Chinese culture tends to be collective. Different thinking patterns will add difficulty for machine to translate texts.<br />
<br />
====5.3 Limitation of computer====<br />
<br />
Recently, there are some breakthroughs and innovation in machine translation. However, due to its own limitation, online translation has limitation in some ways. Firstly, compared with machine, human brain is much more complicated, consisting of ten billions of neuron, each of which has different function to affect human’s daily activities and help humans avoid some errors. However, computer can only function according to preset programming has no intention or consciousness. Until now, countless related scholars have invested much time in machine translation. They upload massive language database, which include almost all linguistic rules. But computers still fail to precisely reflect the meaning of source language for many times due to the complexity and flexibility of language. On the other hand, computers can’t take context into consideration. During translation, it is often the case that machine chooses the most-frequently used meaning of one word. So without the correct and exact meaning, readers are easier to feel confused and even misunderstand the meaning of source language. (Qiu 2021:4)#<br />
<br />
===6.Conclusion===<br />
From the analysis above, we can draw a conclusion that machine deals with informative text best, followed by non-literary translation of expressive text. What’s more, machine can be a useful tool to get to know the gist and main idea of a specific topic, for the simple sentence structure and numerous terms. And it can improve translating efficiency with high speed. But machine has difficulty in translating literary works, especially proses and poems.<br />
<br />
Machine translation has mixed future. From the perspective of commercial, machine translation boasts a bright future. With the process of globalization, the demand for translation is increasing accordingly. On one hand, if we only depend on human translator to deal with translating works, the quality and accuracy of translation can be greatly affected. On the other hand, if machine is used properly to do some basic work, human translators only need to make preparation before translating, progress, polish and other advanced work, contributing to highly-qualified translation and high working efficiency.<br />
<br />
However, compared with manual translation, machine translation has a bleak future. It is still impossible for machine to replace interpreter or translator in a short term. With intelligence and initiative, humans are able to learn new knowledge constantly, which machine will never accomplish. Besides, machine is not used to replace translators but to assist them in work. In other words, translators and machine carry out their own duties and they are not incompatible.(He 2021:5)#<br />
<br />
To draw a conclusion, although there are certain limitations of machine translation, it can serve as a catalyst for translating works. Therefore, with the rapid development of artificial intelligence and related technology, there are still many opportunities for machine translation.<br />
<br />
===Reference ===<br />
<br />
Chen Cheng陈诚.机器翻译技术的综述[J][Overview of Machine Translation Technology].Electronic Techonology 电子技术,2021,50(11):290-291.<br />
<br />
Cui Zihan 崔子涵.机器翻译译文质量对比——以谷歌翻译和DeepL为例[J] [Comparison among Machine Translation--Taking Google Translation and Deepl for Example].Overseas English 海外英语,2021(15):182-183.<br />
<br />
He Xinyu何馨宇.机器翻译的发展及其对翻译职业化的影响研究[J] [The Development of Machine Translation and its Effect on Professional Transltors].Overseas English 海外英语,2021(20):48-49.<br />
<br />
He Wen 何雯, Wang Xiufeng 王秀峰.信息型文本的在线机器翻译错误研究[J][Research on Errors in Online Machine Translation of Informative text ].Overseas English海外英语,2021(15):188-189.<br />
<br />
Li Deyi 李德毅. (2018). 人工智能导论 [Introduction to Artificial Intelligence]. Beijing: China Science and Technology Press 中国科学技术出版社.<br />
<br />
Liu Qin刘琴.功能目的论对于不同文本类型的翻译解读[J][Analysis of Translations in Different Types of Text based on Functionalist Approaches].Overseas Engliosh 海外英语,2021(17):8-9.<br />
<br />
Li Hanji 李晗佶. (2021). 人工智能时代翻译技术与译者关系演变与重构 [Evolution and reconstruction of the relationship between translation technology and translators in the era of artificial intelligence]. 西华师范大学学报(哲学社会科学版) Journal of West China Normal University (PHILOSOPHY AND SOCIAL SCIENCES EDITION) (2021-12-04) 1-6.<br />
<br />
(英) Peter Newmark A Textbook of Translation[M] Shanghai Foreign Education Press, 2002<br />
<br />
Qiu Quanju 仇全菊.大数据时代背景下机器翻译及其发展趋势[J][Machine Translation and its Development Trend under the Background of Big Data Era]. English Teachers 英语教师,2021,21(16):60-62.<br />
<br />
Wang Hongqi 王红旗. (2008). 语言学概论 [Introduction to Linguistics]. Beijing: Peking University Press 北京大学出版社.<br />
<br />
Wei Guang魏光. 人工翻译与机器翻译译文编辑比较研究[J][Comparative Study of Translation Editing between Manual Translation and Machine Translation]. Overseas English 海外英语,2021(19):18-19+21.<br />
<br />
Zhuo Jianbin 卓键滨,Liu Wenxian 刘文娴,Peng Zili 彭子莉.机器翻译对各类型文本的德汉翻译能力探究[J][Research on the German Chinese Translation Ability of Machine Translation for Various Types of Texts]. Comparative Study of Cultural innovation 文化创新比较研究,2021,5(28):122-125.<br />
<br />
Zhang Peiji 张培基.英译中国现代散文选[M][Selected Modern Chinese Prose Writings]. Shanghai Foreign Languages Education Press 上海外语教育出版社, 2002.<br />
<br />
--[[User:Xiong Min|Xiong Min]] ([[User talk:Xiong Min|talk]]) 01:36, 15 December 2021 (UTC)<br />
<br />
=Chapter 11 陈惠妮 Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts=<br />
<br />
机器翻译的译前编辑研究——以医学类文摘为例<br />
<br />
陈惠妮 Chen Huini, Hunan Normal University<br />
<br />
[[Machine_Trans_EN_11]]<br />
<br />
===Abstract===<br />
At present, globalization is accelerating and the market demand for language services is rapidly increasing . Machine translation, as an important translation method, can greatly improve translation efficiency due to its low cost and high speed. However, because of the limitations of machine translation and the differences between Chinese and English language, machine translation is not accurate enough. In order to balance translation efficiency and translation quality, a great number of manual revisions in translation are required for the machine translating texts. Medical papers are specialized, special and purposeful, so it requires accurate,qualified and professional translation. However, the quality of translations by machine is inefficient to meet the high-quality requirements of medical papers translation. Therefore, the introduction of pre-editing can greatly improve the efficiency and quality of machine translation.<br />
<br />
===Key words===<br />
Pre-editing, Machine translation, Medical texts<br />
<br />
===题目===<br />
Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts<br />
<br />
===摘要===<br />
在全球化加速发展的今天，市场对语言服务的需求迅速增加。机器翻译作为一种重要的翻译途径，由于其成本低、速度快，可以大大提高翻译效率。然而，由于机器翻译的局限性以及中英文语言的差异，机器翻译的准确性不高。为了平衡翻译效率和翻译质量，机器翻译文本需要大量的手工修改。<br />
医学论文具有专业性、特殊性和目的性，要求其译文准确、合格、专业。然而，机器翻译的质量较低，无法满足医学论文对翻译的高质量要求。因此，译前编辑的引入可以大大提高机器翻译的效率和质量。<br />
<br />
===关键词===<br />
译前编辑；机器翻译；医学文本<br />
<br />
===1. Introduction===<br />
===1.1 Definition of Machine Translation===<br />
As Cronin(2013) revealed: "Translation is undergoing a revolutionary upheaval. The influence of digital technology and the Internet on translation is continuous, extensive and profound. From the popularity of automatic online translation applications, translation revolution is everywhere (Cronin,2013) However,the concept of machine translation was firstly proposed in the 1930s. Since 1940s, the machine translation technology has been evolving from rule-based machine translation (RBMT) to statistical machine translation (SMT), and to neural machine translation (NMT). Machine translation refers to the automatic translation of source language into target language by using a computer system. That is, machine translation refers to the automatic translation of text from one language into another natural language by computer software or other online translation webs. Machine translation is also defined as the process of “using a computer system to automatically translate text or speech from one natural language to another” according to the definition by ISO (Cui 2014:68-73).O 'Brien (2002) defines it as "the behavior of modifying errors in machine translation to ensure that the target translation meets certain quality requirements". On the basic level, machine translation performs mechanical substitution of words in one language for words in another language, but that rarely produces a good translation, therefore, recognition of the whole phrases and their closest counterparts in the target language is needed. Not all words in one language have equivalents in another language, and many words have more than one meaning. A huge demand for translation is greatly needed in today’s global world, which creates new opportunities for the development of machine translation, attracts more and more attention and becomes one of the current research focuses.<br />
<br />
As Cronin(2013) revealed: "Translation is undergoing a revolutionary upheaval. The influence of digital technology and the Internet on translation is continuous, extensive and profound. From the popularity of automatic online translation applications, translation revolution is everywhere (Cronin,2013) However,the concept of machine translation was firstly proposed in the 1930s. Since 1940s, the machine translation technology has been evolving from rule-based machine translation (RBMT) to statistical machine translation (SMT), and to neural machine translation (NMT). Machine translation refers to the automatic translation of source language into target language by using a computer system. That is, machine translation refers to the automatic translation of text from one language into another natural language by computer software or other online translation webs. Machine translation is also defined as the process of “using a computer system to automatically translate text or speech from one natural language to another” according to the definition by ISO (Cui 2014:68-73).O 'Brien (2002) defines it as "the behavior of modifying errors in machine translation to ensure that the target translation meets certain quality requirements". On the basic level, machine translation performs mechanical substitution of words in one language for words in another language, but that rarely produces a good translation, therefore, recognition of the whole phrases and their closest counterparts in the target language is needed. Not all words in one language have equivalents in another language, and many words have more than one meaning. A huge demand for translation is greatly needed in today’s global world, which creates new opportunities for the development of machine translation, attracts more and more attention and becomes one of the current research focuses.<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:34, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===1.2 Definition of Pre-editing===<br />
Pre-editing means to adjust and modify the source language to make it fit more with the characteristics of the machine translation software before putting the source language before the into machine translation, so as to improve the quality of the translation machine translation (Wei Changhong 2008:93-94). Pre-editing is to modify the original text before putting it into machine translation software, in order to improve the recognition rate of machine translation, optimize the output quality of translated text and reduce the workload of post-editing. Because pre-translation editing only needs to be modified in one language, the operation is simpler than post-translation editing, which can realize the double improvement of quality and efficiency. A good pre-editing translation can help machine translation more smoothly, thus improving the machine readability and quality of the output translation. Pre-editing is mostly applied in the following situations: one is when the original text is of poor quality and the machine is difficult to recognize the meaning of the sentence, such as user generated content with poor readability and translatability (Gerlach et al 2003:45-53); Documents that need to be published in multiple languages; Next is when the original text contains a lot of jargon; The last is the original text has a corresponding translation memory bank. If the original text is edited, it can better match the content of the translation memory bank. Pre-editing is a process of identifying problems. It requires to pre-edit the source texts before putting it into machine translation according to the requirements, listing the expressions or sentences that may have trouble in machine translation and then pre-edit it by human. The purpose is to enable the computer to translate better, improve the translatability of machine translation. (Slype G V & Guinet J F & Seitz F 1984:115)<br />
<br />
Pre-editing means to adjust and modify the source language to make it fit more with the characteristics of the machine translation software before putting the source language before the into machine translation, so as to improve the quality of the translation machine translation (Wei Changhong 2008:93-94). Pre-editing is to modify the original text before putting it into machine translation software, in order to improve the recognition rate of machine translation, optimize the output quality of translated text and reduce the workload of post-editing. Because pre-translation editing only needs to be modified in one language, the operation is simpler than post-translation editing, which can realize the double improvement of quality and efficiency. A good pre-editing translation can help machine translation more smoothly, thus improving the machine readability and quality of the output translation. Pre-editing is mostly applied in the following situations: one is when the original text is of poor quality and the machine is difficult to recognize the meaning of the sentence, such as user generated content with poor readability and translatability (Gerlach et al 2003:45-53); Documents that need to be published in multiple languages; Next is when the original text contains a lot of jargon; The last is the original text has a corresponding translation memory bank. If the original text is edited, it can better match the content of the translation memory bank. Pre-editing is a process of identifying problems. It requires to pre-edit the source texts before putting it into machine translation according to the requirements, listing the expressions or sentences that may have trouble in machine translation and then pre-edit it by human. The purpose is to enable the computer to translate better, improve the translatability of machine translation. (Slype G V & Guinet J F & Seitz F 1984:115)<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:36, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===1.3 Machine Translation Mode===<br />
According to the different knowledge acquisition methods, machine translation modes can be classified as follows: one is rule-based machine translation, which is based on bilingual dictionaries and a library of language rules for each language. The quality of translation depends on whether the source language conforms to the existing rules, but the inexhaustible rules are the hinders of this model. The second mode is machine translation based on statistics. This translation model relies on the principles of mathematics and statistics to find various existing translations corresponding to the translation tasks through the employment of corpus, analyzing the frequency of their occurrence and selecting the translation with the highest frequency for output. The disadvantage of this translation model is that it ignores the flexibility of language and the importance of context. The last one is neural network language model. This model is different from previous translation models in that it uses end-to-end neural network to realize automatic translation between natural languages. At present, the quality of its translation is much higher than that of the previous translation models.<br />
<br />
According to the different knowledge acquisition methods, machine translation modes can be classified as follows: one is rule-based machine translation, which is based on bilingual dictionaries and a library of language rules for each language. The quality of translation depends on whether the source language conforms to the existing rules, but the inexhaustible rules are the hinders of this model. The second mode is machine translation based on statistics. This translation model relies on the principles of mathematics and statistics to find various existing translations corresponding to the translation tasks through the employment of corpus, analyzing the frequency of their occurrence and selecting the translation with the highest frequency for output. The disadvantage of this translation model is that it ignores the flexibility of language and the importance of context. The last one is neural network language model. This model is different from previous translation models in that it uses end-to-end neural network to realize automatic translation between natural languages. At present, the quality of its translation is much higher than that of the previous translation models.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===1.4 Source of Study Abstracts===<br />
The core medical journals from domestic are selected in order to make the paper more representative and authoritative, such as National Medical Journal of China, Journal of Peking University (Health Science), and Journal of Third Military Medical University. All of them include basic medical science, biomedical technology, laboratory medical science and other fields. In this way, the pre-editing approaches included are applicable enough to machine translation of medical abstracts.<br />
<br />
The core medical journals from domestic are selected in order to make the paper more representative and authoritative, such as National Medical Journal of China, Journal of Peking University (Health Science), and Journal of Third Military Medical University. All of them include basic medical science, biomedical technology, laboratory medical science and other fields. In this way, the pre-editing approaches included are applicable enough to machine translation of medical abstracts.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===1.5 Selection of Translation Software===<br />
Generally, machine translation software includes Google Translation, Youdao Translator and Niu Translation, which has their own special use in translation. Take Google as an example, it is more like neurons in human brain, enabling to learn and collect information to establish connections with its neural machine translator’s neurons. However, it also causes many errors because of the lack of enough information. In this paper, contrastive analysis will be carried on by using Google translation. On the one hand, being a pioneer in the translation of NET, it is inevitably to sue Google as the translation software to translate medical texts in this paper. Comparing the program called “Google brain”, other NMT of translation software are relatively disadvantages. On the other hand, the Google translation enjoys the largest users in the world, with its downloads of more than one billion. The output quality of Google translation is more correct and complete than other machine translation software. With years of development and improvement, Google Translation has been greatly promoted. In this paper, Chinese medical abstract will be automatically translated by Google translation, and then the translation output will be compared with the translation by human and the publication of English abstracts. The main purpose is to prove that the improvement and promotion of quality and accuracy in the medical abstracts will be obtained through the pre-editing approaches.<br />
<br />
Generally, machine translation software includes Google Translation, Youdao Translator and Niu Translation, which has their own special use in translation. Take Google as an example, it is more like neurons in human brain, enabling to learn and collect information to establish connections with its neural machine translator’s neurons. However, it also causes many errors because of the lack of enough information. In this paper, contrastive analysis will be carried on by using Google translation. On the one hand, being a pioneer in the translation of NET, it is inevitably to sue Google as the translation software to translate medical texts in this paper. Comparing the program called “Google brain”, other NMT of translation software are relatively disadvantages. On the other hand, the Google translation enjoys the largest users in the world, with its downloads of more than one billion. The output quality of Google translation is more correct and complete than other machine translation software. With years of development and improvement, Google Translation has been greatly promoted. In this paper, Chinese medical abstract will be automatically translated by Google translation, and then the translation output will be compared with the translation by human and the publication of English abstracts. The main purpose is to prove that the improvement and promotion of quality and accuracy in the medical abstracts will be obtained through the pre-editing approaches.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===2.Language Characteristics and Error Division===<br />
Since Chinese and English are two different languages, it is quite neccessary to identify their own characteristics so as to better analyze and understand the two languages. Tytler (1978: 118-119) argued in his Essay on the Principles of Translation that there are three principles of translation that in all the translation should give readers the same feelings as the source text, except for complete transcript of ideas. There actually also exit some mistakes of these two languages. So the following will make some clarrifications of these two languages to ensure more accurate translation.<br />
<br />
Since Chinese and English are two different languages, it is quite neccessary to identify their own characteristics so as to better analyze and understand the two languages. Tytler (1978: 118-119) argued in his Essay on the Principles of Translation that there are three principles of translation that in all the translation should give readers the same feelings as the source text, except for complete transcript of ideas. There actually also exit some mistakes of these two languages. So the following will make some clarrifications of these two languages to ensure more accurate translation.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===2.1 Language Characteristics of Medical Abstracts===<br />
Chinese and English belong to different language systems, so there are differences in their language structure and the way users think of the languages. When using machine translation from Chinese to English, due to the unequal language levels, there will be many mistakes in the translation process, especially for ESP texts, such as medical papers.Here, these differences mainly refer to the linguistic characteristics of medical abstracts. In medical abstracts, it usually includes structured and unstructured abstracts. Although in different forms, they both describe the purpose, methods and conclusions of the research.<br />
<br />
In the method section of medial abstract, several Chinese sentences can be connected with commas, but each sentence may convey different information. In contrast, English sentences contain a great deal of information, but in order to ensure clarity, some modifiers need to be isolated and then reconstructed. However, in Chinese-English machine translation, a lot of information is put into the sentence because the machine segments the sentence on the basis of the full comma.<br />
<br />
In addition, subjectless sentence is used in the objective and method parts of Chinese medical abstracts. The subjectless sentence means the sentence without or free from subject and is usually employed in two contexts. The first is "needless to say". In Chinese sentences, it is common to omit the subject of the sentence. Chinese sentences can convey meanings by using incomplete sentence structures, so Chinese speakers can understand the meanings of the sentences even though the sentence subject is omitted. The second is "emphasis of action". In this context, subjectless sentences are used to describe behaviors, especially in the study of traditional Chinese medicine abstracts. Comparatively speaking, it should be avoided in English medical abstracts. Abstract sentences are more subjective when describing the learning process, while the essential requirement of English medical abstract is objectivity. From this point, sentences without subject should be avoided in English medical abstracts. Another feature is voice. Few words with passive meanings appear in Chinese abstracts. English sentences are more favoured in using passive voice. When translating from Chinese to English, passive voice should be used to make the contents objective, which is also the basic requirement of medical papers.<br />
<br />
These are the linguistic features of Chinese medical abstracts. There are great differences in sentence structure and expression between Chinese and English medical abstracts. These differences may reduce the accuracy of machine translation, so it is necessary to introduce pre-editing to edit the source text to ensure that the source text can be accurately recognized by the machine and fully translated into English.<br />
<br />
Chinese and English belong to different language systems, so there are differences in their language structure and the way users think of the languages. When using machine translation from Chinese to English, due to the unequal language levels, there will be many mistakes in the translation process, especially for ESP texts, such as medical papers.Here, these differences mainly refer to the linguistic characteristics of medical abstracts. In medical abstracts, it usually includes structured and unstructured abstracts. Although in different forms, they both describe the purpose, methods and conclusions of the research.<br />
<br />
In the method section of medial abstract, several Chinese sentences can be connected with commas, but each sentence may convey different information. In contrast, English sentences contain a great deal of information, but in order to ensure clarity, some modifiers need to be isolated and then reconstructed. However, in Chinese-English machine translation, a lot of information is put into the sentence because the machine segments the sentence on the basis of the full comma.<br />
<br />
In addition, subjectless sentence is used in the objective and method parts of Chinese medical abstracts. The subjectless sentence means the sentence without or free from subject and is usually employed in two contexts. The first is "needless to say". In Chinese sentences, it is common to omit the subject of the sentence. Chinese sentences can convey meanings by using incomplete sentence structures, so Chinese speakers can understand the meanings of the sentences even though the sentence subject is omitted. The second is "emphasis of action". In this context, subjectless sentences are used to describe behaviors, especially in the study of traditional Chinese medicine abstracts. Comparatively speaking, it should be avoided in English medical abstracts. Abstract sentences are more subjective when describing the learning process, while the essential requirement of English medical abstract is objectivity. From this point, sentences without subject should be avoided in English medical abstracts. Another feature is voice. Few words with passive meanings appear in Chinese abstracts. English sentences are more favoured in using passive voice. When translating from Chinese to English, passive voice should be used to make the contents objective, which is also the basic requirement of medical papers.<br />
<br />
These are the linguistic features of Chinese medical abstracts. There are great differences in sentence structure and expression between Chinese and English medical abstracts. These differences may reduce the accuracy of machine translation, so it is necessary to introduce pre-editing to edit the source text to ensure that the source text can be accurately recognized by the machine and fully translated into English.<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===2.2 Error Division of Machine Translation in Translating Abstracts of Medical Papers from Chinese to English===<br />
In this paper, errors in machine translation are listed out after analyzing. Some are due to the principles, such as statistical-based MT and NMT, employed by machine translation. Based on the original errors, they can be studied from two levels. One is from the macro-level, referring to mistakes caused by objective factors. These are not human factors, but the disadvantage of machine translation and the limitations of language. The limitations of machine translation are derived from the principles and models adopted and manifests itself as a reliance on reference sources. In this case, semantic ambiguity and textual incoherence may occur in the absence of reference sources.<br />
<br />
However, for ESP texts such as medical papers, the requirements for terms and sentence patterns are far beyond the existing corpora. For unfamiliar texts (that is, the corresponding texts cannot be found in the corpora). The quality of output translation will be relatively low, which is determined by the principles of machine translation. As mentioned above, machine translation has evolved over the past few decades from rules-based to corp-based statistics to NMT. However, there still exist some limitations in machine translation.<br />
<br />
Mistakes on micro-level are mainly caused by the variations and differences of linguistic structure between Chinese and English. Chinese is an implicit language, while English is an explicit one (Lian, 2010) To put it in another way, Chinese expression does not depend on language structures, while English does the opposite. This may result in mismatches between the original and machine translated sentences. This kind of error is mainly divided into two types: component fragment and component missing. For a medical paper, such errors are very serious. As a kind of ESP discourse, medical paper has the characteristics of fixed, objective and accurate language structure. In order to reproduce the characteristics of medical abstracts in translation, it is necessary to avoid errors at the level of words and sentences and pay attention to logic and consistency.<br />
<br />
Lexical errors here refer to the inconsistency of fixed expressions in the translation of terms. Although these terms are machine translation based on the corpus, the corpus may not be perfect for ESP texts such as medical papers. Therefore, fixed expressions are not used in term translation. This is also the most common mistake in machine translation (the term here includes but is not limited to nouns). For ESP texts, medical text in particular, the fixed expressions and the accuracy of terms are of great importance.<br />
<br />
In this paper, errors in machine translation are listed out after analyzing. Some are due to the principles, such as statistical-based MT and NMT, employed by machine translation. Based on the original errors, they can be studied from two levels. One is from the macro-level, referring to mistakes caused by objective factors. These are not human factors, but the disadvantage of machine translation and the limitations of language. The limitations of machine translation are derived from the principles and models adopted and manifests itself as a reliance on reference sources. In this case, semantic ambiguity and textual incoherence may occur in the absence of reference sources.<br />
<br />
However, for ESP texts such as medical papers, the requirements for terms and sentence patterns are far beyond the existing corpora. For unfamiliar texts (that is, the corresponding texts cannot be found in the corpora). The quality of output translation will be relatively low, which is determined by the principles of machine translation. As mentioned above, machine translation has evolved over the past few decades from rules-based to corp-based statistics to NMT. However, there still exist some limitations in machine translation.<br />
<br />
Mistakes on micro-level are mainly caused by the variations and differences of linguistic structure between Chinese and English. Chinese is an implicit language, while English is an explicit one (Lian, 2010) To put it in another way, Chinese expression does not depend on language structures, while English does the opposite. This may result in mismatches between the original and machine translated sentences. This kind of error is mainly divided into two types: component fragment and component missing. For a medical paper, such errors are very serious. As a kind of ESP discourse, medical paper has the characteristics of fixed, objective and accurate language structure. In order to reproduce the characteristics of medical abstracts in translation, it is necessary to avoid errors at the level of words and sentences and pay attention to logic and consistency.<br />
<br />
Lexical errors here refer to the inconsistency of fixed expressions in the translation of terms. Although these terms are machine translation based on the corpus, the corpus may not be perfect for ESP texts such as medical papers. Therefore, fixed expressions are not used in term translation. This is also the most common mistake in machine translation (the term here includes but is not limited to nouns). For ESP texts, medical text in particular, the fixed expressions and the accuracy of terms are of great importance.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===3.Approaches Proposed for Pre-editing ===<br />
Generally speaking, there is a close relationship between pre-editing and post-editing, both of which aim to convey information and ensure high or publishable translation quality. Proper pre-editing can improve the quality of machine translation in terms of adequacy and consistency. <br />
Complexity of natural language and people use language arbitrarily, bringing many difficulties to Chinese-English machine translation, Hu Qingping (2005:24) has proposed "the research of translation software and the development of the controlled language are the two directions to improve the quality of machine translation : the former aims at the difficulty in the natural language processing, the latter overcomes the arbitrariness of natural language". Feng Quangong and Gao Lin (2017: 63-68) put forward: "The writing principles of controlled language can be applied to pre-editing of machine translation. Pre-editing based on controlled language can effectively reduce the complexity and ambiguity of source text, improve the identifiability of machine translation (the translatability of source text itself), and thus reduce (fully) the workload of post-editing".<br />
The central task of pre-editing is to transform human-friendly content into machine-friendly content, so words and sentences need to be repositioned or even changed. Based on the analysis of the language characteristics of Chinese and English, the Chinese ideographic group should be split before the Chinese source text is input into the machine software to translate so that the sentence structure is complete which can be easily recognized by the machine translation software.<br />
<br />
Generally speaking, there is a close relationship between pre-editing and post-editing, both of which aim to convey information and ensure high or publishable translation quality. Proper pre-editing can improve the quality of machine translation in terms of adequacy and consistency. <br />
<br />
Complexity of natural language and people use language arbitrarily, bringing many difficulties to Chinese-English machine translation, Hu Qingping (2005:24) has proposed "the research of translation software and the development of the controlled language are the two directions to improve the quality of machine translation : the former aims at the difficulty in the natural language processing, the latter overcomes the arbitrariness of natural language". Feng Quangong and Gao Lin (2017: 63-68) put forward: "The writing principles of controlled language can be applied to pre-editing of machine translation. Pre-editing based on controlled language can effectively reduce the complexity and ambiguity of source text, improve the identifiability of machine translation (the translatability of source text itself), and thus reduce (fully) the workload of post-editing".<br />
<br />
The central task of pre-editing is to transform human-friendly content into machine-friendly content, so words and sentences need to be repositioned or even changed. Based on the analysis of the language characteristics of Chinese and English, the Chinese ideographic group should be split before the Chinese source text is input into the machine software to translate so that the sentence structure is complete which can be easily recognized by the machine translation software.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufefng<br />
<br />
===3.1 Extraction and Replacement of Terms in the Source Text===<br />
As a branch of applied English, medical English is the product of the combination of English language knowledge and medical knowledge. The terms in medical papers have the characteristics of fixed and complex language structure. Although Google Translation is based on a corpus, the language structure is not fixed due to the limited size of the corpus. Therefore, the following two steps should be followed before machine translation. The first is to extract terms and create a glossary, and then replace the Chinese terms in the source text with the corresponding English expressions in the glossary. Of course, the terms of extraction should be searched and verified according to the actual situation. All of these words are verified before they can be replaced. This method can not only ensure the accuracy of term translation, but also maintain the consistency of expression, especially when dealing with long texts.<br />
Example1<br />
Source abstract: 口腔白斑病癌变相关缺氧应答基因和微小RNA的芯片及表达验证。<br />
Google translation: Microarray detection/Chip detection and expression verification of hypoxia response genes and microRNAs related to oral leukoplakia canceration.<br />
Published translation:Transcriptome array screening and verification of oral leukoplakia carcinogenesis-related hypoxia-responsive gene and microRNA. <br />
Analysis: The expression “ Affymetrix GeneChip” empolyed in this thesis is a human transcription array for transcriptome array. In the source abstract, “芯片检测“ can be meant “screening” but not detection, so the proper and appropriate translation of these expression should be “transcription array screening”, but the Google translates it into “microarry detection of chip detection”. Therefore, term extraction is essential and inevitable before putting the source abstracts into the machine translation software.<br />
After pre-editing source abstracts: 对 oral leukoplakia carcinogenesis 相关 hypoxia-responsive gene 和微小RNA进行 transcriptome array screening 及表达验证。<br />
After pre-editing translation: Transcriptome array screening and expression- verification of hypoxia-responsive genes and microRNAs related to oral leukoplakia carcinogenesis.<br />
<br />
As a branch of applied English, medical English is the product of the combination of English language knowledge and medical knowledge. The terms in medical papers have the characteristics of fixed and complex language structure. Although Google Translation is based on a corpus, the language structure is not fixed due to the limited size of the corpus. Therefore, the following two steps should be followed before machine translation. The first is to extract terms and create a glossary, and then replace the Chinese terms in the source text with the corresponding English expressions in the glossary. Of course, the terms of extraction should be searched and verified according to the actual situation. All of these words are verified before they can be replaced. This method can not only ensure the accuracy of term translation, but also maintain the consistency of expression, especially when dealing with long texts.<br />
<br />
Example1<br />
Source abstract: 口腔白斑病癌变相关缺氧应答基因和微小RNA的芯片及表达验证。<br />
Google translation: Microarray detection/Chip detection and expression verification of hypoxia response genes and microRNAs related to oral leukoplakia canceration.<br />
Published translation:Transcriptome array screening and verification of oral leukoplakia carcinogenesis-related hypoxia-responsive gene and microRNA. <br />
Analysis: The expression “ Affymetrix GeneChip” empolyed in this thesis is a human transcription array for transcriptome array. In the source abstract, “芯片检测“ can be meant “screening” but not detection, so the proper and appropriate translation of these expression should be “transcription array screening”, but the Google translates it into “microarry detection of chip detection”. Therefore, term extraction is essential and inevitable before putting the source abstracts into the machine translation software.<br />
After pre-editing source abstracts: 对 oral leukoplakia carcinogenesis 相关 hypoxia-responsive gene 和微小RNA进行 transcriptome array screening 及表达验证。<br />
After pre-editing translation: Transcriptome array screening and expression- verification of hypoxia-responsive genes and microRNAs related to oral leukoplakia carcinogenesis.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===3.2 Explication of Subordination===<br />
As mentioned above, the subordination of Chinese sentences is judged by certain specific words in machine translation . Chinese is a paratactic language, and sentences are often connected by internal logical relationships; while English depends on sentence structure, so sentences are often closely linked by various language forms. In order to improve the accuracy of machine translation, we can adjust the sentence structure and adjust the position of adjectives and their modifiers. <br />
<br />
Example 2<br />
Source abstracts:回顾性分析南京医科大学附属儿童医院中重度HIE患儿49例及同期就诊的无神经系统症状体征的足月新生儿为对照组31例的头颅磁共振成像（MRI）资料。<br />
Google translation: A retrospective analysis that the brain magnetic resonance imaging (MRI) data of 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University and full-term neonates with no neurological symptoms and signs during the same period were included in the control group.<br />
Published translation: A total of 49 children with moderate to severe HIE admitted to the children’s Hospital Affiliated to Nanjing Medical University were retrospectively analyzed. Cranial magnetic resonance imaging (MRI) date of 31 full-term neonates without neurological symptoms and signs who visited the hospital during the same period were recruited as the control group. <br />
<br />
Analysis: As the above example shows that “中重度HIE患儿” and “足月新生儿” in the source abstracts are from the Children’s Hospital Affiliated Nanjing Medical University. The Google translation shows that 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University. There is an error due to the misuse of subordination. There are some advice in order to solve the problem. That is, first to segment the sentence and then reconstruct the sentence. For instance, the Chinese expression “南京医科大学附属儿童医院” carries many modifiers, which requires to cut the modifiers and reconstruct the sentence. <br />
Therefore, the Chinese expression “南京医科大学附属儿童医院’can be divided into abstractions, leaving two sentences instead of one long sentence with modifiers..<br />
After pre-editing source abstracts: 对49例中重度HIE患儿以及31例无神经系统症状体征的足月新生儿的MRI资料进行回顾性分析。这些患者收治于南京医科大学儿童附属医院。<br />
After pre-editing translation: The MRI data of 49 children with moderate to severe HIE and 31 full-term newborns without neurological symptoms and signs were retrospectively analyzed. These patients were admitted to the Children’s Hospital of Nanjing Medical University.<br />
<br />
As mentioned above, the subordination of Chinese sentences is judged by certain specific words in machine translation . Chinese is a paratactic language, and sentences are often connected by internal logical relationships; while English depends on sentence structure, so sentences are often closely linked by various language forms. In order to improve the accuracy of machine translation, we can adjust the sentence structure and adjust the position of adjectives and their modifiers. <br />
<br />
Example 2<br />
Source abstracts:回顾性分析南京医科大学附属儿童医院中重度HIE患儿49例及同期就诊的无神经系统症状体征的足月新生儿为对照组31例的头颅磁共振成像（MRI）资料。<br />
Google translation: A retrospective analysis that the brain magnetic resonance imaging (MRI) data of 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University and full-term neonates with no neurological symptoms and signs during the same period were included in the control group.<br />
Published translation: A total of 49 children with moderate to severe HIE admitted to the children’s Hospital Affiliated to Nanjing Medical University were retrospectively analyzed. Cranial magnetic resonance imaging (MRI) date of 31 full-term neonates without neurological symptoms and signs who visited the hospital during the same period were recruited as the control group. <br />
<br />
Analysis: As the above example shows that “中重度HIE患儿” and “足月新生儿” in the source abstracts are from the Children’s Hospital Affiliated Nanjing Medical University. The Google translation shows that 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University. There is an error due to the misuse of subordination. There are some advice in order to solve the problem. That is, first to segment the sentence and then reconstruct the sentence. For instance, the Chinese expression “南京医科大学附属儿童医院” carries many modifiers, which requires to cut the modifiers and reconstruct the sentence. <br />
<br />
Therefore, the Chinese expression “南京医科大学附属儿童医院’can be divided into abstractions, leaving two sentences instead of one long sentence with modifiers..<br />
After pre-editing source abstracts: 对49例中重度HIE患儿以及31例无神经系统症状体征的足月新生儿的MRI资料进行回顾性分析。这些患者收治于南京医科大学儿童附属医院。<br />
After pre-editing translation: The MRI data of 49 children with moderate to severe HIE and 31 full-term newborns without neurological symptoms and signs were retrospectively analyzed. These patients were admitted to the Children’s Hospital of Nanjing Medical University.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===3.3 Explication of Subject through Voice Changing===<br />
The extensive use of subjectless sentences are quite common in the Chinese abstracts, while English prefers to employ passive voice in the sentences so as to make them object and accurate. In order to take the characteristics of English sentences into great and careful consideration, the sentences written in passive voice in particular, it will be helpful for machine translation to find out the subject and reconstruct a sentence in passive voice (Wang Yan: 2008). The first is to discover the “doee” in a sentence and then is to reconstruct the sentence structure and put the “doee” before the verb. The last is to explicate the passive relation between the noun and the verb.<br />
Example 3<br />
Source abstract: 回顾性分析2018年1月至2020年12月解放军总医院第七医学中心收治的500例老年髋部骨折患者的资料。<br />
Google translation: A retrospective analysis of the data of 500 elderly patients with hip fractures admitted to the Seventh Medical Center of the PLA General Hospital from January 2018 to December 2020.<br />
Published translation: From January 2018 to December 2020, the data of 500 elderly patients with hip fracture treated in the Seventh Medical Center of PLA General Hospital were analyzed retrospectively.<br />
Analysis: The above example indicates that the “回顾性分析”in the Google Translate is a noun phrase, but the source abstracts actually describes an action or a behavior. The reason for such a mistake is that the machine can’t recognize the Chinese subjetless sentences. To find the subject of the sentence, the source abstracts can be revised and adjusted. Finding the “doee” is the first step, which refers to “500例老年髋部骨折患者的资料”in the source abstracts. And next is to put it in front of the verb, that is to place it in the beginning of the sentence. Last but not least, the passive voice should be explicated in the sentence. <br />
After pre-editing source abstract: 500例老年髋部骨折患者的资料被回顾性分析，他们在2018年1月之2020年12月期间收治于解放军总医院第七医学中心。<br />
After pre-editing translation: The data of 500 elderly hip fracture patients were retrospectively analyzed. They were admitted to the Seventh Medical Center of the PLA General Hospital between January 2018 and December 2020.<br />
<br />
The extensive use of subjectless sentences are quite common in the Chinese abstracts, while English prefers to employ passive voice in the sentences so as to make them object and accurate. In order to take the characteristics of English sentences into great and careful consideration, the sentences written in passive voice in particular, it will be helpful for machine translation to find out the subject and reconstruct a sentence in passive voice (Wang Yan: 2008). The first is to discover the “doee” in a sentence and then is to reconstruct the sentence structure and put the “doee” before the verb. The last is to explicate the passive relation between the noun and the verb.<br />
<br />
Example 3<br />
Source abstract: 回顾性分析2018年1月至2020年12月解放军总医院第七医学中心收治的500例老年髋部骨折患者的资料。<br />
Google translation: A retrospective analysis of the data of 500 elderly patients with hip fractures admitted to the Seventh Medical Center of the PLA General Hospital from January 2018 to December 2020.<br />
Published translation: From January 2018 to December 2020, the data of 500 elderly patients with hip fracture treated in the Seventh Medical Center of PLA General Hospital were analyzed retrospectively.<br />
<br />
Analysis: The above example indicates that the “回顾性分析”in the Google Translate is a noun phrase, but the source abstracts actually describes an action or a behavior. The reason for such a mistake is that the machine can’t recognize the Chinese subjetless sentences. To find the subject of the sentence, the source abstracts can be revised and adjusted. Finding the “doee” is the first step, which refers to “500例老年髋部骨折患者的资料”in the source abstracts. And next is to put it in front of the verb, that is to place it in the beginning of the sentence. Last but not least, the passive voice should be explicated in the sentence. <br />
After pre-editing source abstract: 500例老年髋部骨折患者的资料被回顾性分析，他们在2018年1月之2020年12月期间收治于解放军总医院第七医学中心。<br />
After pre-editing translation: The data of 500 elderly hip fracture patients were retrospectively analyzed. They were admitted to the Seventh Medical Center of the PLA General Hospital between January 2018 and December 2020.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===3.4 Relocation of Modifiers===<br />
Modifiers, including noun, adverbial and attributive are constantly employed in the Chinese medical papers in order to add information to subject and object in the sentence. By doing this, complicated sentences can be structured, thus causing obstacles to machine translation for recognizing this complex sentence structures when translating Chinese-English sentences. To make the machine translation successfully recognize the sentence structure, simplifying the sentence structure is quite necessary. The key is to moving the location of the modifiers and thus making the modifiers an independent sentence. In other words, the modifiers ought to be put before or behind the main part of the sentence to satisfy the common use of English. <br />
Usually, the modifiers in Chinese sentence can only be placed in front of the core word, while in English, modifiers are very flexible. It is all right to place the modifiers in front of or behind the major part of the sentences with adjective or a connective noun.<br />
<br />
Example 4<br />
Source abstracts: 利用DNA重组技术以pET-28a表达系统在E.coli BL21(DE3) 中重组表达Hepl。<br />
Google Translation: Hepl is recombined in E.coli BL21 (DE3) using DNA recombination technology with pET-28a expression system.<br />
Published translation: The recombinant Hcpl protein was expressed by using DNA recombination technology through pET-28a expression system in E. coli BL21 (De3).<br />
Analysis: The Chinese medical text includes two modifiers, “利用DNA”and “以pET-28a)表达系统”. These two modifiers will be translated by machine, which catches more attention to the two constituents. From the Google translation above, one failure is obvious that it misplaces the location of the two modifiers and presents it in not accurate form. To make it translate correctly by machine translation, dividing the sentences is very important so that the source abstracts can be correctly recognized by machine translation.<br />
<br />
After pre-editing source abstract: 利用DNA重组技术，Hcp1重组表达在E.coli BL21 (DE3)中， 通过pET-28a表达系统在E. coli BL21 (DE3)中。<br />
Google translation: Using DNA recombination technology, Hcp1 recombination is expressed in E.coli BL21 (DE3), via pET-28a expression system in E. coli BL21 (DE3).<br />
The second is the independence of modifiers, that is, modifiers can also be reconstructed into clauses to modify core words. Compared with English, There is no subordinate clause in Chinese, so redundant Chinese modifiers need to be reconstructed into subordinate clauses in Chinese-English translation to meet the characteristics of English. English, especially sentences with multiple modifiers. Otherwise, sentence structure may be confused, such as scattered modifiers of core words. To avoid this mistake, it is necessary to separate the modifier from the main part of the sentence. These modifiers should be reconstituted into clauses. Also, keep your sentences simple and easy to understand.<br />
<br />
Modifiers, including noun, adverbial and attributive are constantly employed in the Chinese medical papers in order to add information to subject and object in the sentence. By doing this, complicated sentences can be structured, thus causing obstacles to machine translation for recognizing this complex sentence structures when translating Chinese-English sentences. To make the machine translation successfully recognize the sentence structure, simplifying the sentence structure is quite necessary. The key is to moving the location of the modifiers and thus making the modifiers an independent sentence. In other words, the modifiers ought to be put before or behind the main part of the sentence to satisfy the common use of English. <br />
Usually, the modifiers in Chinese sentence can only be placed in front of the core word, while in English, modifiers are very flexible. It is all right to place the modifiers in front of or behind the major part of the sentences with adjective or a connective noun.<br />
<br />
Example 4<br />
Source abstracts: 利用DNA重组技术以pET-28a表达系统在E.coli BL21(DE3) 中重组表达Hepl。<br />
Google Translation: Hepl is recombined in E.coli BL21 (DE3) using DNA recombination technology with pET-28a expression system.<br />
Published translation: The recombinant Hcpl protein was expressed by using DNA recombination technology through pET-28a expression system in E. coli BL21 (De3).<br />
Analysis: The Chinese medical text includes two modifiers, “利用DNA”and “以pET-28a)表达系统”. These two modifiers will be translated by machine, which catches more attention to the two constituents. From the Google translation above, one failure is obvious that it misplaces the location of the two modifiers and presents it in not accurate form. To make it translate correctly by machine translation, dividing the sentences is very important so that the source abstracts can be correctly recognized by machine translation.<br />
<br />
After pre-editing source abstract: 利用DNA重组技术，Hcp1重组表达在E.coli BL21 (DE3)中， 通过pET-28a表达系统在E. coli BL21 (DE3)中。<br />
Google translation: Using DNA recombination technology, Hcp1 recombination is expressed in E.coli BL21 (DE3), via pET-28a expression system in E. coli BL21 (DE3).<br />
The second is the independence of modifiers, that is, modifiers can also be reconstructed into clauses to modify core words. Compared with English, There is no subordinate clause in Chinese, so redundant Chinese modifiers need to be reconstructed into subordinate clauses in Chinese-English translation to meet the characteristics of English. English, especially sentences with multiple modifiers. Otherwise, sentence structure may be confused, such as scattered modifiers of core words. To avoid this mistake, it is necessary to separate the modifier from the main part of the sentence. These modifiers should be reconstituted into clauses. Also, keep your sentences simple and easy to understand.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===3.5 Proper Omission and Deletion of Category Words===<br />
Category words are commonly used in Chinese. Category words complement the meaning of words, including problems, positions, situations and jobs. Adding this supplementary word is more in line with Chinese custom. In many cases, it has no real meaning, so it can be omitted in translation. In Chinese, category words are frequently used. However, it is rarely used in English, which is one of the differences between Chinese and English. There are many kinds of category words. Considering objectivity and the fixed structure of language, redundant category words should be deleted in Chinese-English translation. In the general, the most commonly used category of words in the Chinese medical abstracts are "process", "behavior", and "situation". These categories of words hinder the language conversion of Chinese and English, resulting in redundancy. <br />
<br />
Example 5<br />
Source abstract: 探讨口腔白斑病癌变进程中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia response genes and associated microRNAs in the process of oral leukoplakia cancer was discussed.<br />
Published translation: To study the hypoxia response gene and microRNA (miRNA)expression profiles in the pathogenesis and progression of oral leukoplakia (OLK).<br />
Analysis: As the above example shows, the Chinese words “进程” belongs to a category word. However, the Chinese expression “癌变”contains the process, so the “进程” expression can be deleted before placing it into the machine translation, because the meaning of it has been overlapping between the expression “癌变”. Therefore, its place can be replaced with preposition “during”.<br />
<br />
After pre-editing source abstract: 探讨口腔白斑病癌变中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia responsive genes and miRNA in oral leukoplakia cancer was investigated.<br />
<br />
Category words are commonly used in Chinese. Category words complement the meaning of words, including problems, positions, situations and jobs. Adding this supplementary word is more in line with Chinese custom. In many cases, it has no real meaning, so it can be omitted in translation. In Chinese, category words are frequently used. However, it is rarely used in English, which is one of the differences between Chinese and English. There are many kinds of category words. Considering objectivity and the fixed structure of language, redundant category words should be deleted in Chinese-English translation. In the general, the most commonly used category of words in the Chinese medical abstracts are "process", "behavior", and "situation". These categories of words hinder the language conversion of Chinese and English, resulting in redundancy. <br />
<br />
Example 5<br />
Source abstract: 探讨口腔白斑病癌变进程中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia response genes and associated microRNAs in the process of oral leukoplakia cancer was discussed.<br />
Published translation: To study the hypoxia response gene and microRNA (miRNA)expression profiles in the pathogenesis and progression of oral leukoplakia (OLK).<br />
Analysis: As the above example shows, the Chinese words “进程” belongs to a category word. However, the Chinese expression “癌变”contains the process, so the “进程” expression can be deleted before placing it into the machine translation, because the meaning of it has been overlapping between the expression “癌变”. Therefore, its place can be replaced with preposition “during”.<br />
<br />
After pre-editing source abstract: 探讨口腔白斑病癌变中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia responsive genes and miRNA in oral leukoplakia cancer was investigated.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===Conclusion===<br />
After years of development, machine translation has made great progress. The accuracy of machine translation has been greatly improved in both text recognition and sentence pattern conversion. However, machine translation has its own limitations. In other words, it needs to rely on the parallel corpus as a reference source for improving its accuracy. ESP text, in particular, is harder to get the high quality by machine translation. <br />
<br />
As one of the research papers, the characteristics of medical abstracts are fixed language structures, objectivity and accuracy (Qin Yi 2004:421-423). Therefore, medical translation must be accurate, object and understandable to follow the specific demands of the medical paper. Being an important field in the human society, medical paper translation is on a great demand, which means that it needs a huge demand for human labor. However, with the machine translation promoting, it will be more efficient to translate medical papers combining the effort by human and machine. The improvement and development of machine translation requires the joint efforts of computer science, information science, statistics, linguistics and other academic circles to achieve more mature human-computer mutual assistance translation (Li Yafei, Zhang Ruihua 2019:38-45).<br />
<br />
However, errors can occur during the process of machine translation of Chinese- English, because of the differences of the Chinese and English and the processing of the machine. Errors from the perspective of linguistic or grammar can affect the machine translation a lot. After division and recognition of errors, some pre-editing approaches are put forward to help the machine translation more accurate and readable, that are, extraction and replacement of terms in the source text, relocation of modifiers, explication of subordination, proper omission, deletion of category words and explication of subject through voice changing. <br />
<br />
<br />
The paper mainly focuses on the pre-editing machine translation by using medical papers as a case study. The errors of machine translation occurring in the translation of medical abstracts and pre-editing approaches for machine translation. The quality of machine translation of medical papers is greatly improved after employing the pre-editing methods. However, machine translation is not as flexible and accurate as human brain, so it is of importance to combine pre-editing and post-editing approaches with machine translation in order to produce more accurate, more object machine translation of medical papers.<br />
<br />
After years of development, machine translation has made great progress. The accuracy of machine translation has been greatly improved in both text recognition and sentence pattern conversion. However, machine translation has its own limitations. In other words, it needs to rely on the parallel corpus as a reference source for improving its accuracy. ESP text, in particular, is harder to get the high quality by machine translation. <br />
<br />
As one of the research papers, the characteristics of medical abstracts are fixed language structures, objectivity and accuracy (Qin Yi 2004:421-423). Therefore, medical translation must be accurate, object and understandable to follow the specific demands of the medical paper. Being an important field in the human society, medical paper translation is on a great demand, which means that it needs a huge demand for human labor. However, with the machine translation promoting, it will be more efficient to translate medical papers combining the effort by human and machine. The improvement and development of machine translation requires the joint efforts of computer science, information science, statistics, linguistics and other academic circles to achieve more mature human-computer mutual assistance translation (Li Yafei, Zhang Ruihua 2019:38-45).<br />
<br />
However, errors can occur during the process of machine translation of Chinese- English, because of the differences of the Chinese and English and the processing of the machine. Errors from the perspective of linguistic or grammar can affect the machine translation a lot. After division and recognition of errors, some pre-editing approaches are put forward to help the machine translation more accurate and readable, that are, extraction and replacement of terms in the source text, relocation of modifiers, explication of subordination, proper omission, deletion of category words and explication of subject through voice changing. <br />
<br />
The paper mainly focuses on the pre-editing machine translation by using medical papers as a case study. The errors of machine translation occurring in the translation of medical abstracts and pre-editing approaches for machine translation. The quality of machine translation of medical papers is greatly improved after employing the pre-editing methods. However, machine translation is not as flexible and accurate as human brain, so it is of importance to combine pre-editing and post-editing approaches with machine translation in order to produce more accurate, more object machine translation of medical papers.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===References===<br />
<br />
Cui Qiliang崔启亮(2014).论机器翻译的译后编辑[J] ''On Post-Editing of Machine Translatio''. 中国翻译 Chinese Translators Journal, 035(006):68-73<br />
<br />
Feng Quangong, Gao Lin冯全功,高琳 (2017). 基于受控语言的译前编辑对机器翻译的影响[J] ''Influence of Pre-editing Based on Controlled Language on Machine Translation''. 当代外语研究Contemporary Foreign Language Research,(2): 63-68+87+110.<br />
<br />
GERLACH J, et al ( 2013). ''Combining Pre-editing and Post-editing to Improve SMT of User-generated Content''[M]// Proceedings of MT Summit XIV Workshop on Post-editing Technology and Practice. 45-53<br />
<br />
Hu Qingping胡清平(2005). 机器翻译中的受控语言[J] ''Controlled Language in Machine Translation''. 中国科技翻译 Chinese Science and Technology Translation, (03): 24-27. <br />
<br />
Lian Shuneng连淑能 (2010). 英汉对比研究增订本[M]''An Updated Version of English-Chinese Contrastive Studies'' . 北京:高等教育出版社Beijing: Higher Education Publishing House. 35-36.<br />
<br />
Li Yafei, Zhang Ruihua黎亚飞,张瑞华 (2019). 机器翻译发展与现状[J]''The Development and Current Situation of Machine Translation''. 中国轻工教育 China Light Industry Education, (5):38-45. <br />
<br />
Qin Yi秦毅(2004),从翻译基本标准议医学英语的翻译[J] ''On the Translation of Medical English from the Basic Standard of Translation''. 遵义医学院学报 Journal of Zunyi Medical College,27 (4): 421-423. <br />
<br />
Slype G V & Guinet J F & Seitz F (1984). ''Better Translation for Better Communication'' [M] . Oxford: Pergamon Press Ltd (U.K.). 90-93<br />
<br />
O'Brien S (2002). Teaching Post-editing: A Proposal for Course Con-tent [EB/OL]. http://mt-archive. Info/EAMT-2002-0brien. Pdf.<br />
<br />
Tytler, A. F. (1978). ''Essay On The Principles of Translation''[M]. Amsterdam: JohnBenjamins Publishing. 118-119<br />
<br />
Wang Yan王燕 (2008). 医学英语翻译与写作教程[M] ''Medical English Translation and Writing Course''. 重庆:重庆大学出版社 Chongqing: Chongqing University Press. 60-61<br />
<br />
Written by --[[User:Chen Huini|Chen Huini]] ([[User talk:Chen Huini|talk]]) 04:58, 15 December 2021 (UTC)Chen Huini<br />
<br />
=12 蔡珠凤=(The Mistranslation of C-J Machine Translation of Political Statements)=<br />
<br />
机器翻译中政治发言中译日的误译<br />
<br />
蔡珠凤 Cai Zhufeng, Hunan Normal University<br />
<br />
[[Machine_Trans_EN_12]]<br />
===Abstract===<br />
Language is the main way of communication between people. With the continuous development of globalization, the scale of cross-border exchanges is also expanding. However, due to cultural differences and diversity, the languages of different countries and regions are very different, which seriously hinders people's communication. The demand for efficient and convenient translation tools is increasing. At the same time, with the development of network technology and artificial intelligence, recognition technology based on deep learning is more and more widely used in English, Japanese and other fields.<br />
<br />
===Key words===<br />
machine translation; political statements; mistranslation of C-J machine translation<br />
===题目===<br />
The Mistranslation of C-J Machine Translation of Political Statements<br />
===摘要===<br />
语言是人与人之间交流的主要方式。随着全球化的不断发展，跨境交流的规模也在不断扩大。然而，由于文化的差异和多样性，不同国家和地区的语言差异很大，这严重阻碍了人们的交流。对高效便捷的翻译工具的需求正在增加。同时，随着网络技术和人工智能的发展，基于深度学习的识别技术在英语、日语等领域的应用越来越广泛。<br />
<br />
===关键词===<br />
机器翻译；政治发言；政治发言中译日的误译<br />
===1. Introduction===<br />
===Introduction to machine translation===<br />
Machine translation, also known as automatic translation, is a process of using computers to convert one natural language (source language) into another natural language (target language). It is a branch of computational linguistics, one of the ultimate goals of artificial intelligence, and has important scientific research value.At the same time, machine translation has important practical value. With the rapid development of economic globalization and the Internet, machine translation technology plays a more and more important role in promoting political, economic and cultural exchanges.The development of machine translation technology has been closely accompanied by the development of computer technology, information theory, linguistics and other disciplines. From the early dictionary matching, to the rule translation of dictionaries combined with linguistic expert knowledge, and then to the statistical machine translation based on corpus, with the improvement of computer computing power and the explosive growth of multilingual information, machine translation technology gradually stepped out of the ivory tower and began to provide real-time and convenient translation services for general users.（Zhang 2019:5-6)<br />
<br />
=== C-J machine translation software===<br />
Today's online machine translation software includes Baidu translation, Tencent translation, Google translation, Youdao translation, Bing translation and so on. Google was the first company to launch the machine translation system, and Baidu was the first company to import the machine translation system in China. In addition, Tencent and Youdao have attracted much attention.Machine translation is the process of using computers to convert one natural language into another. It usually refers to sentence and full-text translation between natural languages. In order to continuously improve the translation quality, R & D personnel have added artificial intelligence technologies such as speech recognition, image processing and deep neural network to machine translation on the basis of traditional machine translation based on rules, statistics and examples.With the increase of using machine translation, the joint cooperation between manual translation and machine translation will also increase significantly in the future. What criteria should be used to evaluate the quality of machine translation? In the evolving field of machine translation, there is an urgent need to clarify the unsolvable questions and solved problems.(Lv 1996:3)<br />
<br />
===The history of machine translation===<br />
The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. alchuni put forward the idea of using machines for translation. In 1933, Soviet inventor П.П. Trojansky designed a machine to translate one language into another, and registered his invention on September 5 of the same year; However, due to the low technical level in the 1930s, his translation machine was not made. In 1946, the first modern electronic computer ENIAC was born. Shortly after that, W. weaver, an American scientist and A. D. booth, a British engineer, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947 when discussing the application scope of electronic computer. In 1949, W. Weaver published the translation memorandum, which formally put forward the idea of machine translation. After 60 years of ups and downs, machine translation has experienced a tortuous and long development path. The academic community generally divides it into the following four stages:<br />
<br />
Pioneering period（1947-1964）<br />
<br />
In 1954, with the cooperation of IBM, Georgetown University completed the English Russian machine translation experiment with ibm-701 computer for the first time, showing the feasibility of machine translation to the public and the scientific community, thus opening the prelude to the study of machine translation.<br />
It is not too late for China to start this research. As early as 1956, the state included this research in the national scientific work development plan. The topic name is "machine translation, the construction of natural language translation rules and the mathematical theory of natural language". In 1957, the Institute of language and the Institute of computing technology of the Chinese Academy of Sciences cooperated in the Russian Chinese machine translation experiment, translating 9 different types of more complex sentences.From the 1950s to the first half of the 1960s, machine translation research has been on the rise. The United States and the former Soviet Union, two superpowers, have provided a lot of financial support for machine translation projects for military, political and economic purposes, while European countries have also paid considerable attention to machine translation research due to geopolitical and economic needs, and machine translation has become an upsurge for a time. In this period, although machine translation is just in the pioneering stage, it has entered an optimistic period of prosperity.<br />
<br />
Frustrated period（1964-1975）<br />
<br />
In 1964, in order to evaluate the research progress of machine translation, the American Academy of Sciences established the automatic language processing Advisory Committee (Alpac Committee) and began a two-year comprehensive investigation, analysis and test.In November 1966, the committee published a report entitled "language and machine" (Alpac report for short), which comprehensively denied the feasibility of machine translation and suggested stopping the financial support for machine translation projects. The publication of this report has dealt a blow to the booming machine translation, and the research of machine translation has fallen into a standstill. Coincidentally, during this period, China broke out the "ten-year Cultural Revolution", and basically these studies also stagnated. Machine translation has entered a depression.<br />
<br />
convalescence（1975-1989）<br />
<br />
Since the 1970s, with the development of science and technology and the increasingly frequent exchange of scientific and technological information among countries, the language barriers between countries have become more serious. The traditional manual operation mode has been far from meeting the needs, and there is an urgent need for computers to engage in translation. At the same time, the development of computer science and linguistics, especially the substantial improvement of computer hardware technology and the application of artificial intelligence in natural language processing, have promoted the recovery of machine translation research from the technical level. Machine translation projects have begun to develop again, and various practical and experimental systems have been launched successively, such as weinder system Eurpotra multilingual translation system, taum-meteo system, etc.However, after the end of the "ten-year holocaust", China has perked up again, and machine translation research has been put on the agenda again. The "784" project has paid enough attention to machine translation research. After the mid-1980s, the development of machine translation research in China has further accelerated. Firstly, two English Chinese machine translation systems, ky-1 and MT / ec863, have been successfully developed, indicating that China has made great progress in machine translation technology.(Chen 2016:5)<br />
<br />
New period(1990 present)<br />
<br />
With the universal application of the Internet, the acceleration of the process of world economic integration and the increasingly frequent exchanges in the international community, the traditional way of manual operation is far from meeting the rapidly growing needs of translation. People's demand for machine translation has increased unprecedentedly, and machine translation has ushered in a new development opportunity. International conferences on machine translation research have been held frequently, and China has made unprecedented achievements. A series of machine translation software have been launched, such as "Yixing", "Yaxin", "Tongyi", "Huajian", etc. Driven by the market demand, the commercial machine translation system has entered the practical stage, entered the market and came to the users.Since the new century, with the emergence and popularization of the Internet, the amount of data has increased sharply, and statistical methods have been fully applied. Internet companies have set up machine translation research groups and developed machine translation systems based on Internet big data, so as to make machine translation really practical, such as "Baidu translation", "Google translation", etc. In recent years, with the progress of in-depth learning, machine translation technology has further developed, which has promoted the rapid improvement of translation quality, and the translation in oral and other fields is more authentic and fluent.(Liu 2014:6)<br />
<br />
===The problem of machine translation at present===<br />
Error is inevitable<br />
Many people have misunderstandings about machine translation. They think that machine translation has great deviation and can't help people solve any problems. In fact, the error is inevitable. The reason is that machine translation uses linguistic principles. The machine automatically recognizes grammar, calls the stored thesaurus and automatically performs corresponding translation. However, errors are inevitable due to changes or irregularities in grammar, morphology and syntax, such as sentences with adverbials after "give me a reason to kill you first" in Dahua journey to the West. After all, a machine is a machine. No one has special feelings for language. How can it feel the lasting charm of "the tenderness of lowering its head, like the shame of a water lotus? After all, the meaning of Chinese is very different due to the changes of morphology, grammar and syntax and the change of context. Even many Chinese people are zhanger monks - they can't touch their heads, let alone machines.(Liu 2014：3）<br />
<br />
Bottleneck<br />
In fact, no matter which method, the biggest factor affecting the development of machine translation lies in the quality of translation. Judging from the achievements, the quality of machine translation is still far from the ultimate goal.<br />
Chinese mathematician and linguist Zhou Haizhong once pointed out in his paper "fifty years of machine translation": to improve the quality of machine translation, the first thing to solve is the problem of language itself rather than programming; It is certainly impossible to improve the quality of machine translation by relying on several programs alone. At the same time, he also pointed out that it is impossible for machine translation to achieve the degree of "faithfulness, expressiveness and elegance" when human beings have not yet understood how the brain performs fuzzy recognition and logical judgment of language. This view may reveal the bottleneck restricting the quality of translation. <br />
It is worth mentioning that American inventor and futurist ray cozwell predicted in an interview with Huffington Post that the quality of machine translation will reach the level of human translation by 2029. There are still many disputes about this thesis in the academic circles.（Cui 2019：4）<br />
<br />
===2.Mistranslation of Chinese Japanese machine translation===<br />
===2.1Vocabulary mistranslation in Chinese Japanese machine translation===<br />
===2.1.1Mistranslation of proper nouns===<br />
In political materials, there are often political dignitaries' names, place names or a large number of proper nouns in the political field. The morphemes of such words are definite and inseparable. Mistranslation will make the source language lose its specific meaning. <br />
<br />
Japanese translation into Chinese Chinese translation into Japanese<br />
<br />
original text translation by Youdao reference translation original text translation by Youdao reference translation<br />
<br />
朱鎔基 朱基 朱镕基 栗战书 栗戰史書 栗戰書<br />
<br />
労安 劳安 劳安 李克强 李克強 李克強 <br />
<br />
筑紫哲也 筑紫哲也 筑紫哲也 习近平 習近平 習近平<br />
<br />
山口百惠 山口百惠 山口百惠 韩正 韓中 韓正<br />
<br />
田中角栄 田中角荣 田中角荣 王沪宁 王上海氏 王滬寧<br />
<br />
東条英機 东条英社 东条英机 汪洋 汪洋 汪洋<br />
<br />
毛沢东 毛泽东 毛泽东 赵乐际 趙樂南 趙樂際<br />
<br />
トウ・ショウヘイ 大酱 邓小平 江泽民 江沢民 江沢民<br />
<br />
周恩来 周恩来 周恩来<br />
<br />
クリントン 克林顿 克林顿<br />
<br />
The above table counts 18 special names in the two texts, and 7 machine translation errors. In terms of mistranslation, there are not only "战书" but also "戰史" and "史書" in Chinese. "沪" is the abbreviation of Shanghai. In other words, since "战书" and "沪" are originally common nouns, this disrupts the choice of target language in machine translation. Except Katakana interference (except for トウシゃウヘイ, most of the mistranslations appear in the new Standing Committee. It can be seen that the machine translation system does not update the thesaurus in time. Different from other words, people's names have particularity. Especially as members of the Standing Committee of the Political Bureau of the CPC Central Committee, the translation of their names is officially unified. In this regard, public figures such as political dignitaries, stars, famous hosts and important. In addition, the machine also translates Japanese surnames such as "タ二モト" (谷本), "アンドウ" (安藤) into "塔尼莫特" and "龙胆". It can be found that the language feature that Japanese will be written together with Chinese characters and Hiragana also directly affects the translation quality of Japanese Chinese machine translation. Japanese people often use Katakana pronunciation. For example, when Japanese people talk with Premier Zhu, they often use "二ーハウ" (Hello). However, the machine only recognizes the two pseudonyms "二" and "ハ" and transliterates them into "尼哈" , ignoring the long sound after "二" and "ハ".(Guan 2018:10-12)<br />
<br />
original text Translation by Youdao reference translation<br />
<br />
日美安全体制 日米の安全体制 日米安保体制<br />
<br />
中国共产党第十九次全国代表大会 中国共産党第19回全国代表大会 中国共産党第19回全国代表大会（第19回党大会）<br />
<br />
十八大 十八大 第18回党大会中国特色社会主义<br />
<br />
中国特色社会主義 中国の特色ある社会主義 第18回党大会<br />
<br />
中国共产党中央委员会 中国共産党中央委員会 中国共産党中央委員会<br />
<br />
中国共産党中央委員会十八届中共中央政治局常委 第18代中国共產党中央政治局常務委員 第18期中共中央政治局常務委員<br />
<br />
十八届中共中央政治局委员 18期の中国共產党中央政治局委員 第18期中共中央政治局委員<br />
<br />
十九届中共中央政治局常委 十九回中国共產党中央政治局常務委員 第19期中央政治局常務委員<br />
<br />
中共十九届一中全会 中国共產党第十九回一中央委員会 第19期中央委員会第1回全体会議<br />
<br />
The above table is a comparison of the original and translated versions of some proper nouns. As shown in the table, the mistranslation problems are mainly reflected in the mismatch of numerals + quantifiers, the wrong addition of case auxiliary word "の", the lack of connectors, the Mistranslation of abbreviations, dead translation, and the writing errors of Chinese characters. The following is a specific analysis one by one.<br />
<br />
"十八届中共中央政治局常委", "十八届中共中央政治局委员", "十九届中共中央政治局常委" and "中共十九届一中全会" all have quantifiers "届", which are translated into "代", "期" and "回" respectively. The meaning is vague and should be uniformly translated into "期"; Among them, the translation of the last three proper nouns lacks the Chinese conjunction "第". The case auxiliary word "の" was added by mistake in the translation of "十八届中共中央政治局委员" and "日美安全体制". The "中国共产党中央委员会" did not write in the form of Japanese Ming Dynasty characters, but directly used simplified Chinese characters.<br />
<br />
The full name of "中共十九届一中全会" is "中国共产党第十九届中央委员会第一次全体会议", in which "一" stands for "第一次", which is an ordinal word rather than a cardinal word. Machine translation does not produce a correct translation. The fundamental reason is that there are no "规则" for translating such words in machine translation. If we can formulate corresponding rules for such words (for example, 中共m'1届m2 中全会→第m1期中央委员会第m2回全体会议), the translation system must be able to translate well no matter how many plenary sessions of the CPC Central Committee.(Guan 2018:6-7)<br />
<br />
===2.1.2Mistranslation of Polysemy===<br />
original text Translation by Youdao reference translation<br />
<br />
スタジオ 摄影棚/工作室 直播现场/演播厅<br />
<br />
日中関係の話 中日关系的故事 就中日关系(话题)<br />
<br />
溝 水沟 鸿沟<br />
<br />
それでは日中の問題について質問のある方。 那么对白天的问题有提问的人。 关于中日问题的话题，举手提问。<br />
<br />
私たちのクラスは20人ちょっとですが、 我们班有20人左右， 我们班二十多人的意见统一很难，<br />
<br />
いろいろな意見が出て、まとめるのは大変です。 但是又各种各样的意见，总结起来很困难。 <br />
<br />
一体どうやって、13億人もの人をまとめているんですか。 到底是怎么处理13亿的人的呢？ 中国是怎样把13亿人凝聚在一起的？<br />
<br />
In the original text, the word "スタジオ" appeared four times and was translated into "摄影棚" or "工作室" respectively, but the context is on-site interview, not the production site of photography or film. "話" has many semantics, such as "说话", "事情", "道理", etc. In accordance with the practice of the interview program, Premier Zhu Rongji was invited to answer five questions on the topic of China Japan relations. Obviously, the meaning of the word "故事" is very abrupt. The inherent Japanese word "日中" means "晌午、白天". At the same time, it is also the abbreviation of the names of the two countries. The machine failed to deal with it correctly according to the context. "溝" refers to the gap between China and Japan, not a "水沟". The former "まとめる" appears in the original text with the word "意见", which is intended to describe that it is difficult to unify everyone's opinions. The latter "まとめている" also refers to unifying the thoughts of 1.3 billion people, not "处理". Therefore, it is more appropriate to use "统一" and "处理" in human translation, rather than "总结意见" or "处理意见".(Zhang 2019:5)<br />
<br />
Mistranslation of polysemy has always been a difficult problem in machine translation research. The translation of each word is correct, but it is often very different from the original expression in the context. Zhang Zhengzeng said that ambiguity is a common phenomenon in natural language. Its essence is that the same language form may have different meanings, which is also one of the differences between natural language and artificial language. Therefore, one of the difficulties faced by machine translation is language disambiguation (Zhang Zheng, 2005:60). In this regard, we can mark all the meanings of polysemous words and judge which meaning to choose by common collocation with other words. At the same time, strengthen the text recognition ability of the machine to avoid the translation inconsistent with the current context. In this way, we can avoid the mistake of "大酱" in the place where famous figures such as Deng Xiaoping should have appeared in the previous political articles.(Wang 2020:7-9)<br />
<br />
===2.1.3Mistranslation of compound words===<br />
Multi class words refer to a word with two or more parts of speech, also known as the same word and different classes.<br />
<br />
original text Translation by Youdao reference translation<br />
<br />
1998年江泽民主席曾经访问日本, 1998年の江沢民国家主席の日本訪問し、 1998年、江沢民総書記が日本を訪問し、かつ<br />
<br />
同已故小渊首相签署了联合宣言。 かつて同じ故小渕首相が署名した共同宣言 亡くなられた小渕総理と宣言に調印されました<br />
<br />
Chinese "同" has two parts of speech: prepositions and conjunctions: "故" has many parts of speech, such as nouns, verbs, adjectives and conjunctions. This directly affects the judgment of the machine at the source language level. The word "同" in the above table is used as a conjunction to indicate the other party of a common act. "故" is used as a verb with the semantic meaning of "死亡", which is a modifier of "小渊首相". The machine regards "同" as an adjective and "故" as a noun "原因", which leads to confusion in the structure and unclear semantics of the translation. Different from the polysemy problem, multi category words have at least two parts of speech, and there is often not only one meaning under each part of speech. In this regard, software R & D personnel should fully consider the existence of multi category words, so that the translation machine can distinguish the meaning of words on the basis of marking the part of speech, so as to select the translation through the context and the components of the word in the sentence. Of course, the realization of this function is difficult, and we need to give full play to the wisdom of R & D personnel.(Guan 2018:9-12)<br />
<br />
===2.2 Syntactic mistranslation in Chinese Japanese machine translation===<br />
===2.2.1Mistranslation of tenses===<br />
original text：History is written by the people, and all achievements are attributed to the people. <br />
<br />
Translation by Youdao：歴史は人民が書いたものであり、すべての成果は人民のためである。<br />
<br />
reference translation：歴史は人民が綴っていくものであり、すべての成果は人民に帰することとなります。<br />
<br />
The original sentence meaning is "历史是人民书写的历史" or "历史是人民书写的东西". When translated into Japanese, due to the influence of Japanese language habits, the formal noun "もの" should be supplemented accordingly. In this sentence, both the machine and the interpreter have translated correctly. However, the machine's recognition of "的" is biased, resulting in tense translation errors. The past, present and future will become "history", and this is a continuous action. The form translated as "~ ていく" not only reflects that the action is a continuous action, but also conforms to the tense and semantic information of the original text. In addition, the machine's treatment of the preposition "于" is also inappropriate. In the original text, "人民" is the recipient of action, not the target language. As the most obvious feature of isolated language, function words in Chinese play an important role in semantic expression, and their translation should be regarded as a focus of machine translation research.(Zuo 2021:8)<br />
<br />
original text：李克强同志是十六届中共中央政治局常委,其他五位同志都是十六届中共中央政治局委员。<br />
<br />
Translation by Youdao：李克強総理は第16代中国共産党中央政治局常務委員であり、他の５人の同志はいずれも16期の中国共産党中央政治局委員である。<br />
<br />
reference translation：李克強同志は第16期中国共産党中央政治局常務委員を務め、他の５人は第16期中共中央政治局委員を務めました。<br />
<br />
Judging the verb "是" in the original text plays its most basic positive role, but due to the complexity of Chinese language, "是" often can not be completely transformed into the form of "だ / である" in the process of translation. This sentence is a judgment of what has happened. Compared with the 19th session, the 18th session has become history. This is an implicit temporal information. It is very difficult for machines without human brain to recognize this implicit information. "中共中央政治局常委" is the abbreviation of "中共中央政治局常务委员会委员" and it is a kind of position. In Japanese, often use it with verbs such as "務める","担当する",etc. The translator adopts the past tense of "務める", which conforms to the expression habit of Japanese and deals with the problem of tense at the same time. However, machine translation only mechanically translates this sentence into judgment sentence, and fails to correctly deal with the past information implied by the word "十八届".(Guan 2018:4)<br />
<br />
===2.2.2Mistranslation of honorifics===<br />
Honorific language is a language means to show respect to the listener. Different from Japanese, there is no grammatical category of honorifics in Chinese. There is no specific fixed grammatical form to express honorifics, self modesty and politeness. Instead, specific words such as "您", "请" and "劳驾" are used to express all kinds of respect or self modesty. (Yang 2020:5-9)<br />
<br />
Original text translation by Youdao reference translation<br />
<br />
女士们，先生们，同志们，朋友们 さんたち、先生たち、同志たち、お友达さん ご列席の皆さん<br />
<br />
谢谢大家！ ありがとうございます！ ご清聴ありがとうございました。<br />
<br />
これはどうされますか。 这是怎么回事呢? 您将如何解决这一问题？<br />
<br />
こうした問題をどうお考えでしょうか。 我们会如何考虑这些问题呢？ 您如何看待这一问题？<br />
<br />
For example, for the processing of "女士们，先生们，同志们，朋友们", the machine makes the translation correspond to the original one by one based on the principle of unchanged format, but there are errors in semantic communication and pragmatic habits. When speaking on formal occasions, Chinese expression tends to be comprehensive and detailed, as well as the address of the audience. In contrast, Japanese usually uses general terms such as "皆様", "ご臨席の皆さん" or "代表団の方々" as the opening remarks. In terms of Japanese Chinese translation, the machine also failed to recognize the usage of Japanese honorifics such as "さ れ る" and "お考え". It can be seen that Chinese Japanese machine translation has a low ability to deal with honorific expressions. The occasion is more formal. A good translation should not only be fluent in meaning, but also conform to the expression habits of the target language and match the current translation environment.(Che 2021:3-7)<br />
<br />
===Conclusion===<br />
In the process of discussing the Mistranslation of machine translation, this paper mainly cites the Mistranslation of Youdao translator. When I studied the problem of machine translation mistranslation, I also used a large number of translation software such as Google and Baidu for parallel comparison, and found that these translation software also had similar problems with Youdao translator. For example, the "新世界新未来" in Chinese ABAC structural phrases is translated by Baidu as "新し世界の新しい未来", which, like Youdao, is not translated in the form of parallel phrases; Google online translation translates the word "“全心全意" into a completely wrong "全面的に"; The original sentence "我吃了很多亏" in the Mistranslation of the unique expression in the language is translated by Google online as "私はたくさんの損失を食べました". Because the "吃" and "亏" in the original text are not closely adjacent, the machine can not recognize this echo and mistakenly treats "亏" as a kind of food, so the machine translates the predicate as "食べました"; Japanese "こうした問題をどう考えでしょうか", Google's online translation is "你如何看待这些问题?", "你" tone can not reflect the tone of Japanese honorific, while Baidu translates as "你怎么想这样的问题呢?" Although the meaning can be understood, it is an irregular spoken language. Google and Baidu also made the same mistakes as Youdao in the translation of proper nouns. For example, they translated "十七届(中共中央政治局常委)”" into "第17回..." .In short, similar mistranslations of Youdao are also common in Baidu and Google. Due to space constraints, they will not be listed one by one here.(Cui 2019:7)<br />
<br />
Mr. Liu Yongquan, Institute of language, Chinese Academy of Social Sciences (1997) It has been pointed out that machine translation is a linguistic problem in the final analysis. Although corpus based machine translation does not require a lot of linguistic knowledge, language expression is ever-changing. Machine translation based solely on statistical ideas can not avoid the translation quality problems caused by the lack of language rules. The following is based on the analysis of lexical, syntactic and other mistranslations From the perspective of the characteristics of the source language, this paper summarizes some difficulties of Chinese and Japanese in machine translation.(Liu 2014:8)<br />
<br />
(1) The difficulties of Chinese in machine translation <br />
<br />
Chinese is a typical isolated language. The relationship between words needs to be reflected by word order and function words. We should pay attention to the transformation of function words such as "的", "在", "向" and "了". Some special verbs, such as "是", "做" and "作", are widely used. How to translate on the basis of conforming to Japanese pragmatic habits and expressions is a difficulty in machine translation research. In terms of word order, Chinese is basically "subject→predicate→object". At the same time, Chinese pays attention to parataxis, and there is no need to be clear in expression with meaningful cohesion, which increases the difficulty of Chinese Japanese machine translation. When there are multiple verbs or modifier components in complex long sentences, machine translation usually can not accurately divide the components of the sentence, resulting in the result that the translation is completely unreadable.(Guan 2018:6-12) <br />
<br />
(2) Difficulties of Japanese in machine translation <br />
<br />
Both Chinese and Japanese languages use Chinese characters, and most machines produce the target language through the corresponding translation of Chinese characters. However, pseudonyms in Japanese play an important role in judging the part of speech and meaning, and can not only recognize Chinese characters and judge the structure and semantics of sentences. Japanese vocabulary is composed of Chinese vocabulary, inherent vocabulary and foreign vocabulary. Among them, the first two have a great impact on Chinese Japanese machine translation. For example "提出" is translated as "提出する" or "打ち出す"; and "重视" is translated as "重視する" or "大切にする". Japanese is an adhesive language, which contains a lot of "けど", "が" and "けれども" Some of them are transitional and progressive structures, but there are also some sequential expressions that do not need translation, and these translation software often can not accurately grasp them.(Che 2021:10)<br />
<br />
===References===<br />
[1] Navroz Kaur Kahlon,(2021(prepublish));Williamjeet Singh.Machine translation from text to sign language: a systematic review[J].Universal Access in the Information Society,1-35.<br />
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[2] Cao Qianyu;Hao Hanmei,(2021);Ahmed Syed Hassan.A Chaotic Neural Network Model for English Machine Translation Based on Big Data Analysis[J].Computational Intelligence and Neuroscience,3274326-3274326.<br />
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[3]Hwang Yongkeun;Kim Yanghoon;Jung Kyomin.(2021)Context-Aware Neural Machine Translation for Korean Honorific Expressions[J].Electronics,10(13):1589-1589.<br />
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[4]Zakaryia Almahasees.(2021)Analysing English-Arabic Machine Translation:Google Translate, Microsoft Translator and Sakhr.<br />
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[5](2021)Machine learning in translation[J].Nature Biomedical Engineering,5(6):485-486.<br />
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[6]Shaimaa Marzouk.(2021(prepublish))An in-depth analysis of the individual impact of controlled language rules on machine translation output: a mixed-methods approach[J].Machine Translation,1-37.<br />
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[7]Welnitzová Katarína;Munková Daša.(2021)Sentence-structure errors of machine translation into Slovak[J].Topics in Linguistics,22(1):78-92.<br />
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[8]Xu Xueyuan.(2021).Machine learning-based prediction of urban soil environment and corpus translation teaching[J].Arabian Journal of Geosciences,14(11). <br />
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[9]Chen Bingchang 陈丙昌(2016).機械翻訳の誤訳分析【D】.Error analysis of mechanical translation.贵州大学.2016(05) <br />
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[10]Lv Yinqiu 呂寅秋(1996).機械翻訳の言語規則と伝統文法との相違点.【D】The language rules of mechanical translation, the traditional grammar, and the points of contradiction.日本学研究.Japanese Studies.1996(00):21-22 <br />
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[11]Liu Jun 刘君(2014).基于语料库的中日同形词词义用法对比及其日中机器翻译研究【D】.A Corpus-based Comparison of the Meanings of Chinese and Japanese Homographs and Research on Japanese-Chinese Machine Translation.广西大学.(03) <br />
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[12]Cun Qianqian 崔倩倩(2019).机器翻译错误与译后编辑策略研究【D】.Research on Machine Translation Errors and Post-Editing Strategies.北京外国语大学.(09) <br />
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[13]Zhang Yi 张义(2019).机器翻译的译文分析【D】.Translation analysis of machine translation.西安外国语大学.(10) <br />
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[14]Zhang Linqian 张琳婧(2019).在线机器翻译中日翻译错误原因及对策【D】.Causes and countermeasures of online machine translation errors in Chinese-Japanese translation.山西大学.(02)<br />
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[15]Wang Dan 王丹(2020).基于机器翻译的专利文本译后编辑对策研究【D】.Research on countermeasures for post-translational editing of patent texts based on machine translation.大连理工大学.(06)<br />
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[16]Yang Xiaokun 杨晓琨(2020).日中机器翻译中的前编辑规则与效果验证【D】.Pre-editing rules and effect verification in Japanese-Chinese machine translation.大连理工大学.(06)<br />
<br />
[17]Zuo Jia 左嘉(2021). 机器翻译日译汉误译研究【D】. Research on Mistranslation of Machine Translation from Japanese to Chinese.北京第二外国语学院.<br />
<br />
[18]Guan Biying 关碧莹(2018).关于政治类发言的汉日机器翻译误译分析【D】.Analysis of Chinese-Japanese Machine Translation Mistranslations of Political Speeches.哈尔滨理工大学.<br />
<br />
[19]Che Tong 车彤(2021).汉译日机器翻译质量评估及译后编辑策略研究【D】.Research on Quality Evaluation of Chinese-Japanese Machine Translation and Post-translation Editing Strategies.北京外国语大学.(09)<br />
<br />
Networking Linking<br />
<br />
http://www.elecfans.com/rengongzhineng/692245.html<br />
<br />
https://baike.baidu.com/item/%E6%9C%BA%E5%99%A8%E7%BF%BB%E8%AF%91/411793<br />
<br />
=13 陈湘琼Chen Xiangqiong（Study on Post-editing from the Perspective of Functional Equivalence Theory ）=<br />
[[Machine_Trans_EN_13]]<br />
<br />
=14 Bi bi Nadia（Machine Translation a Challenge for Human Translators)=<br />
[[Machine_Trans_EN_14]]<br />
<br />
===Abstract===<br />
Machine translation is a big obstacle in the way of Human translators or interpreters although it is quick and less time consuming.People are trying to get translation of their target language using through source language.For this purpose they are using digital apps like Google translation Google translation does not give accurate and exact interpretation.Google translator is translating word to word but it doesn't clarify it's true and actual meaning.On the other hand human translators can give exact and accurate translation ,they take care of grammatical mistakes , diction and sentence structure.They clarify the purpose of target language through using source language.<br />
<br />
===Keywords===<br />
Descriptive translation, academic, interdiscipline, comparative literature, localization,Translatology,school of thought,translation , studies, linguistics, corresponding.<br />
<br />
===Body of article===<br />
Translation is the process of reworking text from one language into another to maintain the original message and communication. But, like anything else, there are different methods of translation, and they vary in form and function. What is translation? The term has become widely used among knowledge transferring researchers and practitioners, especially in the fields of health and health care. In a landmark review, Jonathan Lomas began to argue that 'The tasks… may be defined as to establish and maintain links between researchers and their audience, via the appropriate translation of research findings' (Lomas 1997, p 4). In 2004, World Health Organization's World Report on Knowledge for Better Health suggested that 'One of the key contributions of research to health systems is the translation of knowledge into actions' (WHO 2004, p 33 and p 100). By 2006, special issues of WHO's Bulletin as well as the journals Evaluation and the Health Professions and the Journal of Continuing Education in the Health Professions were dedicated to translation.But what does 'translation' mean? It may be a new word for an old problem, meaning nothing more than 'transfer'. The rapidly emerging field of 'translational medicine' seems to take translation to mean generally what transfer might have meant, that is the transmission of knowledge and evidence 'from bench to bedside'. As one commentator has observed, "Translational medicine" as a fashionable term is being increasingly used to describe the wish of biomedical researchers to ultimately help patients' (Wehling 2008, abstract). <br />
Semantic uncertainty persists, not least because of the different interests of scientists, clinicians, patients and commercial firms (Littman et al 2007): 'Translational research means different things to different people, but it seems important to almost everyone' (Woolf 2008, p 211).<br />
In related fields, such as public health (Armstrong et al 2006), 'translation' seems to signify dissatisfaction with 'transfer'. It wants to move away from thinking of knowledge transfer as a form of technology transfer or dissemination, rejecting if only by implication its mechanistic assumptions and its model of linear messaging from A to B. But still, what does it signify?<br />
<br />
===Why translation?===<br />
'Translation' indicates a closer attention to the problem of shared meaning and how it might be developed. It seems to represent some new epistemological lubricant, facilitating the dissemination of texts and the application and use of the knowledge and information they in. Simply, translation might be the key to transfer. And yet, when we stop to think, we are more ambivalent. What is translated often seems somehow inferior, not real or original. Note how readily commentators reach for the idea that things might be 'lost in translation'. Knowing at a distance – made in and mediated by translation - makes for incomplete renditions, blurred images, partial truths. So what might 'translation' really mean? The purpose of this paper is to set out, for policy makers and practitioners, the theoretical and conceptual resources translation holds and seems to represent. In doing so, it explores understandings of translation in the fields of literature and linguistics and in the sociology of science and technology. It begins by setting out just why this idea of translation should make immediate, intuitive sense in relation to research, policy and practice.<br />
1translation in research, policy and practice research as translation<br />
Research often entails translation from one language to another: where data is collected from more than one ethnic group, for example, or where the language of the researcher is other than that of the research subject. It may draw on a secondary literature or source documents written in different languages, and may be published and disseminated in languages other than the one in which it is first written up.<br />
2In a different way, to conduct an interview is to ask for an account of experience and its meanings, but it is also to construct and translate that experience in terms defined at least in part by the researcher. In representing what is said, transcripts then select data, usually excluding significant gesture and eye-contact, for example. Often, certain characteristics of speech-acts (such as hesitations) will be edited out. In turn, the format of the transcript shapes the analytic use the researcher may make of it. The basis of research 'findings', then, is an artefact, a transcript or translation, not an original interaction (Ochs 1979, Barnes, Bloor and Henry 1996, Ross 2009).<br />
3In this way, the researcher recasts aspects of his or her problem or topic in new, scientific form: 'All researchers "translate" the experiences of others' (Temple 1997, p 609). Research is invariably conducted in a sort of 'metalanguage' (Hantrais and Ager 1985): the research process can be conceived as one of successive translations (from theoretical formulation to operationalization, transcription, interpretation and dissemination). Theorization is a process of reciprocal back and forth between theory and fact, in which conceptions of each are revised in order that one fit the other (Baldamus 1974).<br />
4 It is a kind of translation: a rereading, re-use, re-application or re-representation of what we know in new terms (Turner 1980). Referencing, too, is an act of translation, a form of appropriation and incorporation of one text by another (Gilbert 1977).policy as translation Each of the fields covered by the paper is diverse and ill-defined, and there is no intention here to provide a comprehensive account of any them. Sources have been chosen for their relevance: main references are cited in the text, and additional sources listed in footnotes.<br />
<br />
2For a brief introduction to the technical issues involved in social research in more than one language, see Birbili (2000). For translation issues in survey research and question design in general, see Ervin and Bower (1952) and Deutscher (1968); on translating survey research instruments (in this instance health-related quality of life measures), Bowden and Fox-Rushby (2003). On the use of translators and interpreters, see Temple (1997) and Jentsch (1998).<br />
3For an interesting discussion of this problem, see Bourdieu (1999), esp pp 621-626, 'The risks of writing'. <br />
===Difference between machine translation and human translators===<br />
Few people disagree on the differences between the two, but many argue over the quality of the translations. How accurate are machine translations? How reliable are human translations? Some say machine translation produces near-perfect translations while others are adamant that translations are incomprehensible and cause more problems than they solve. Results will, of course, vary depending on the source and target languages, the machine translation service used (e.g. Google Translate), and the complexity of the original text.<br />
Machine translation, love it or hate it, is here to stay. In fact, the machine translation market is growing at such a fast pace that it is predicted to reach $980 million by 2022.<br />
===Machine translation: the pros and cons===<br />
The advantages of machine translation generally come down to two factors: it’s faster and cheaper. The downside to this is the standard of translation can be anywhere from inaccurate, to incomprehensible, and potentially dangerous (more on that shortly).<br />
==The advantages of machine translation==<br />
Many free tools are readily available (Google Translate, Skype Translator, etc.)<br />
Quick turnaround time <br />
You can translate between multiple languages using one tool<br />
Translation technology is constantly improving<br />
The disadvantages of machine translation<br />
Level of accuracy can be very low <br />
Accuracy is also very inconsistent across different languages<br />
==Machines can't translate context ==<br />
Mistakes are sometimes costly <br />
Sometimes translation simply doesn’t work<br />
The most important thing to consider with any kind of translation is the cost of potential mistakes. Translating instructions for medical equipment, aviation manuals, legal documents and many other kinds of content require 100% accuracy. In such cases, mistakes can cost lives, huge amounts of money and irreparable damage to your company’s image. So choose carefully!<br />
Human translation: the pros and cons<br />
Human translation essentially switches the table in terms of pros and cons. A higher standard of accuracy comes at the price of longer turnaround times and higher costs. What you have to decide is whether that initial investment outweighs the potential cost of mistakes. Alternatively, whether mistakes simply aren’t an option, like the cases we looked at in the previous section.<br />
<br />
==The advantages of human translation ==<br />
It’s a translator’s job to ensure the highest accuracy<br />
Humans can interpret context and capture the same meaning, rather than simply translating words<br />
Human translators can review their work and provide a quality process<br />
Humans can interpret the creative use of language, e.g. puns, metaphors, slogans, etc.<br />
Professional translators understand the idiomatic differences between their languages<br />
Humans can spot pieces of content where literal translation isn’t possible and find the most suitable alternative<br />
The disadvantages of human translation<br />
Turnaround time is longer <br />
Translators rarely work for free <br />
Unless you use a translation agency, with access to thousands of translators, you’re limited to the languages any one translator can work with<br />
Simply put, human translation is your best option when accuracy is even remotely important. Other considerations to make are the complexity of your source material and the two languages you’re translating between – both of which can render machines pretty useless.<br />
<br />
When to use machine and human translation<br />
<br />
The truth is, the debate over machine vs human translation is an unnecessary distraction. What we should really be talking about is when to use these two different types of translation services, because they both serve a very valid purpose.<br />
<br />
Examples of when to use machine translation<br />
<br />
When you have a large bulk of content to translate and the general meaning is enough<br />
When your translation never reaches the final audience, e.g. you’re translating a resource as research for another piece of content<br />
Translating documents for internal use within a company, provided 100% accuracy isn’t needed<br />
To partially translate large chunks of content for a human translator to improve upon<br />
Examples of when to use human translation<br />
When accuracy is important<br />
Most cases where your translated content is received by a consumer audience<br />
When you have a duty of care to provide accurate translations (e.g. legal documents, product instructions, medical guidelines or health and safety content)<br />
When translating marketing material or other texts for creative language uses.<br />
<br />
types of machine translation.<br />
<br />
What is Machine Translation? Rule Based Machine Translation vs. Statistical Machine Translation. Machine translation (MT) is automated translation. It is the process by which computer software is used to translate a text from one natural language (such as English) to another (such as Spanish).<br />
<br />
To process any translation, human or automated, the meaning of a text in the original (source) language must be fully restored in the target language, i.e. the translation. While on the surface this seems straightforward, it is far more complex. Translation is not a mere word-for-word substitution. A translator must interpret and analyze all of the elements in the text and know how each word may influence another. This requires extensive expertise in grammar, syntax (sentence structure), semantics (meanings), etc., in the source and target languages, as well as familiarity with each local region.<br />
<br />
Human and machine translation each have their share of challenges. For example, no two individual translators can produce identical translations of the same text in the same language pair, and it may take several rounds of revisions to meet customer satisfaction. But the greater challenge lies in how machine translation can produce publishable quality translations.<br />
<br />
Rule-Based Machine Translation Technology<br />
Rule-based machine translation relies on countless built-in linguistic rules and millions of bilingual dictionaries for each language pair.<br />
The software parses text and creates a transitional representation from which the text in the target language is generated. This process requires extensive lexicons with morphological, syntactic, and semantic information, and large sets of rules. The software uses these complex rule sets and then transfers the grammatical structure of the source language into the target language.<br />
Translations are built on gigantic dictionaries and sophisticated linguistic rules. Users can improve the out-of-the-box translation quality by adding their terminology into the translation process. They create user-defined dictionaries which override the system’s default settings.<br />
In most cases, there are two steps: an initial investment that significantly increases the quality at a limited cost, and an ongoing investment to increase quality incrementally. While rule-based MT brings companies to the quality threshold and beyond, the quality improvement process may be long and expensive.<br />
<br />
Statistical Machine Translation Technology<br />
Statistical machine translation utilizes statistical translation models whose parameters stem from the analysis of monolingual and bilingual corpora. Building statistical translation models is a quick process, but the technology relies heavily on existing multilingual corpora. A minimum of 2 million words for a specific domain and even more for general language are required. Theoretically it is possible to reach the quality threshold but most companies do not have such large amounts of existing multilingual corpora to build the necessary translation models. Additionally, statistical machine translation is CPU intensive and requires an extensive hardware configuration to run translation models for average performance levels.<br />
<br />
Rule-Based MT vs. Statistical MT<br />
Rule-based MT provides good out-of-domain quality and is by nature predictable. Dictionary-based customization guarantees improved quality and compliance with corporate terminology. But translation results may lack the fluency readers expect. In terms of investment, the customization cycle needed to reach the quality threshold can be long and costly. The performance is high even on standard hardware.<br />
<br />
Statistical MT provides good quality when large and qualified corpora are available. The translation is fluent, meaning it reads well and therefore meets user expectations. However, the translation is neither predictable nor consistent. Training from good corpora is automated and cheaper. But training on general language corpora, meaning text other than the specified domain, is poor. Furthermore, statistical MT requires significant hardware to build and manage large translation models.<br />
<br />
Rule-Based MT Statistical MT<br />
+ Consistent and predictable quality – Unpredictable translation quality<br />
+ Out-of-domain translation quality – Poor out-of-domain quality<br />
+ Knows grammatical rules – Does not know grammar <br />
+ High performance and robustness – High CPU and disk space requirements<br />
+ Consistency between versions – Inconsistency between versions <br />
– Lack of fluency + Good fluency<br />
– Hard to handle exceptions to rules + Good for catching exceptions to rules <br />
– High development and customization costs + Rapid and cost-effective development costs provided the required corpus exists<br />
Given the overall requirements, there is a clear need for a third approach through which users would reach better translation quality and high performance (similar to rule-based MT), with less investment (similar to statistical MT).<br />
Post-Edited Machine Translation (PEMT)<br />
Often, PEMT is used to bridge the gap between the speed of machine translation and the quality of human translation, as translators review, edit and improve machine-translated texts. PEMT services cost more than plain machine translations but less than 100% human translation, especially since the post-editors don’t have to be fluently bilingual—they just have to be skilled proofreaders with some experience in the language and target region.<br />
Successful translation is about more than just the words, which is why we advocate for not just human translation by skilled linguists, but for translation by people deeply familiar with the cultures they’re writing for. Life experience, study and the knowledge that only comes from living in a geographic region can make the difference between words that are understandable and language that is capable of having real, positive impact. <br />
<br />
PacTranz<br />
The HUGE list of 51 translation types, methods and techniques<br />
Upper section of infographic of 51 common types of translation classified in 4 broad categoriesThere are a bewildering number of different types of translation.<br />
So we’ve identified the 51 types you’re most likely to come across, and explain exactly what each one means.<br />
This includes all the main translation methods, techniques, strategies, procedures and areas of specialisation.<br />
It’s our way of helping you make sense of the many different kinds of translation – and deciding which ones are right for you.<br />
Don’t miss our free summary pdf download later in the article!<br />
The 51 types of translation we’ve identified fall neatly into four distinct categories.<br />
Translation Category A: 15 types of translation based on the technical field or subject area of the text<br />
Icons representing 15 types of translation categorised by the technical field or subject area of the textTranslation companies often define the various kinds of translation they provide according to the subject area of the text.<br />
This is a useful way of classifying translation types because specialist texts normally require translators with specialist knowledge.<br />
Here are the most common types you’re like to come across in this category.<br />
<br />
1. General Translation<br />
What is it?<br />
The translation of non-specialised text. That is, text that we can all understand without needing specialist knowledge in some area.<br />
The text may still contain some technical terms and jargon, but these will either be widely understood, or easily researched.<br />
What this means<br />
The implication is that you don’t need someone with specialist knowledge for this type of translation – any professional translator can handle them.<br />
Translators who only do this kind of translation (don’t have a specialist field) are sometimes referred to as ‘generalist’ or ‘general purpose’ translators.<br />
Examples<br />
Most business correspondence, website content, company and product/service info, non-technical reports.<br />
Most of the rest of the translation types in this Category do require specialist translators.<br />
Check out our video on 13 types of translation requiring special translator expertise:<br />
<br />
2. Technical Translation<br />
What is it?<br />
We use the term “technical translation” in two different ways:<br />
Broad meaning: any translation where the translator needs specialist knowledge in some domain or area.<br />
This definition would include almost all the translation types described in this section.<br />
Narrow meaning: limited to the translation of engineering (in all its forms), IT and industrial texts.<br />
This narrower meaning would exclude legal, financial and medical translations for example, where these would be included in the broader definition.<br />
What this means<br />
Technical translations require knowledge of the specialist field or domain of the text.<br />
That’s because without it translators won’t completely understand the text and its implications. And this is essential if we want a fully accurate and appropriate translation.Good to know Many technical translation projects also have a typesetting/dtp requirement. Be sure your translation provider can handle this component, and that you’ve allowed for it in your project costings and time frames.<br />
Examples<br />
Manuals, specialist reports, product brochures<br />
<br />
3. Scientific Translation<br />
What is it?<br />
The translation of scientific research or documents relating to it.<br />
What this means<br />
These texts invariably contain domain-specific terminology, and often involve cutting edge research.<br />
So it’s imperative the translator has the necessary knowledge of the field to fully understand the text. That’s why scientific translators are typically either experts in the field who have turned to translation, or professionally qualified translators who also have qualifications and/or experience in that domain.<br />
On occasion the translator may have to consult either with the author or other domain experts to fully comprehend the material and so translate it appropriately.<br />
Examples<br />
Research papers, journal articles, experiment/trial results<br />
<br />
4. Medical Translation<br />
What is it?<br />
The translation of healthcare, medical product, pharmaceutical and biotechnology materials.<br />
Medical translation is a very broad term covering a wide variety of specialist areas and materials – everything from patient information to regulatory, marketing and technical documents.<br />
As a result, this translation type has numerous potential sub-categories – ‘medical device translations’ and ‘clinical trial translations’, for example.<br />
What this means<br />
As with any text, the translators need to fully understand the materials they’re translating. That means sound knowledge of medical terminology and they’ll often also need specific subject-matter expertise.<br />
Good to know<br />
Many countries have specific requirements governing the translation of medical device and pharmaceutical documentation. This includes both your client-facing and product-related materials.<br />
Examples<br />
Medical reports, product instructions, labeling, clinical trial documentation<br />
<br />
5. Financial Translation<br />
What is it?<br />
In broad terms, the translation of banking, stock exchange, forex, financing and financial reporting documents.<br />
However, the term is generally used only for the more technical of these documents that require translators with knowledge of the field.<br />
Any competent translator could translate a bank statement, for example, so that wouldn’t typically be considered a financial translation.<br />
What this means<br />
You need translators with domain expertise to correctly understand and translate the financial terminology in these texts.<br />
Examples<br />
Company accounts, annual reports, fund or product prospectuses, audit reports, IPO documentation<br />
<br />
6. Economic Translations<br />
What is it?<br />
1. Sometimes used as a synonym for financial translations.<br />
2. Other times used somewhat loosely to refer to any area of economic activity – so combining business/commercial, financial and some types of technical translations.<br />
3. More narrowly, the translation of documents relating specifically to the economy and the field of economics.<br />
What this means<br />
As always, you need translators with the relevant expertise and knowledge for this type of translation.<br />
<br />
7. Legal Translation<br />
What is it?<br />
The translation of documents relating to the law and legal process.<br />
What this means<br />
Legal texts require translators with a legal background.<br />
That’s because without it, a translator may not:<br />
– fully understand the legal concepts<br />
– write in legal style<br />
– understand the differences between legal systems, and how best to translate concepts that don’t correspond.<br />
And we need all that to produce professional quality legal translations – translations that are accurate, terminologically correct and stylistically appropriate.<br />
Examples<br />
Contracts, legal reports, court judgments, expert opinions, legislation<br />
<br />
8. Juridical Translation<br />
What is it?<br />
1. Generally used as a synonym for legal translations.<br />
2. Alternatively, can refer to translations requiring some form of legal verification, certification or notarization that is common in many jurisdictions.<br />
<br />
9. Judicial Translation<br />
What is it?<br />
1. Most commonly a synonym for legal translations.<br />
2. Rarely, used to refer specifically to the translation of court proceeding documentation – so judgments, minutes, testimonies, etc. <br />
<br />
10. Patent Translation<br />
What is it?<br />
The translation of intellectual property and patent-related documents.<br />
Key features<br />
Patents have a specific structure, established terminology and a requirement for complete consistency throughout – read more on this here. These are key aspects to patent translations that translators need to get right.<br />
In addition, subject matter can be highly technical.<br />
What this means<br />
You need translators who have been trained in the specific requirements for translating patent documents. And with the domain expertise needed to handle any technical content.<br />
Examples<br />
Patent specifications, prior art documents, oppositions, opinions<br />
<br />
11. Literary Translation<br />
What is it?<br />
The translation of literary works – novels, short stories, plays, essays, poems.<br />
Key features<br />
Literary translation is widely regarded as the most difficult form of translation.<br />
That’s because it involves much more than simply conveying all meaning in an appropriate style. The translator’s challenge is to also reproduce the character, subtlety and impact of the original – the essence of what makes that work unique.<br />
This is a monumental task, and why it’s often said that the translation of a literary work should be a literary work in its own right.<br />
What this means<br />
Literary translators must be talented wordsmiths with exceptional creative writing skills.<br />
Because few translators have this skillset, you should only consider dedicated literary translators for this type of translation.<br />
<br />
<br />
12. Commercial Translation<br />
What is it?<br />
The translation of documents relating to the world of business.<br />
This is a very generic, wide-reaching translation type. It includes other more specialised forms of translation – legal, financial and technical, for example. And all types of more general business documentation.<br />
Also, some documents will require familiarity with business jargon and an ability to write in that style.<br />
What this means<br />
Different translators will be required for different document types – specialists should handle materials involving technical and specialist fields, whereas generalist translators can translate non-specialist materials.<br />
Examples<br />
Business correspondence, reports, marketing and promotional materials, sales proposals<br />
<br />
13. Business Translations<br />
What is it?<br />
A synonym for Commercial Translations.<br />
<br />
14. Administrative Translations<br />
What is it?<br />
The translation of business management and administration documents.<br />
So it’s a subset of business / commercial translations.<br />
What this means<br />
The implication is these documents will include business jargon and ‘management speak’, so require a translator familiar with, and practised at, writing in that style.<br />
Examples<br />
Management reports and proposals<br />
<br />
15. Marketing Translations<br />
What is it?<br />
The translation of advertising, marketing and promotional materials.<br />
This is a subset of business or commercial translations.<br />
Key features<br />
Marketing copy is designed to have a specific impact on the audience – to appeal and persuade.<br />
So the translated copy must do this too.<br />
But a direct translation will seldom achieve this – so translators need to adapt their wording to produce the impact the text is seeking.<br />
And sometimes a completely new message might be needed – see transcreation in our next category of translation types.<br />
What this means<br />
Marketing translations require translators who are skilled writers with a flair for producing persuasive, impactful copy.<br />
As relatively few translators have these skills, engaging the right translator is key.<br />
Good to know<br />
This type of translation often comes with a typesetting or dtp requirement – particularly for adverts, posters, brochures, etc.<br />
Its best for your translation provider to handle this component. That’s because multilingual typesetters understand the design and aesthetic conventions in other languages/cultures. And these are essential to ensure your materials have the desired impact and appeal in your target markets.<br />
Examples<br />
Advertising, brochures, some website/social media text.<br />
Translation Category B: 14 types of translation based on the end product or use of the translation<br />
This category is all about how the translation is going to be used or the end product that’s produced.<br />
Most of these types involve either adapting or processing a completed translation in some way, or converting or incorporating it into another program or format.<br />
You’ll see that some are very specialised, and complex.<br />
It’s another way translation providers refer to the range of services they provide.<br />
Check out our video of the most specialised of these types of translation:<br />
<br />
16. Document Translations<br />
What is it?<br />
The translation of documents of all sorts.<br />
Here the translation itself is the end product and needs no further processing beyond standard formatting and layout.<br />
<br />
17. Text Translations<br />
What is it?<br />
A synonym for document translation.<br />
<br />
18. Certified Translations<br />
What is it?<br />
A translation with some form of certification.<br />
Key features<br />
The certification can take many forms. It can be a statement by the translation company, signed and dated, and optionally with their company seal. Or a similar certification by the translator.<br />
The exact format and wording will depend on what clients and authorities require – here’s an example.<br />
<br />
19. Official Translations<br />
What is it?<br />
1. Generally used as a synonym for certified translations.<br />
2. Can also refer to the translation of ‘official’ documents issued by the authorities in a foreign country. These will almost always need to be certified.<br />
<br />
20. Software Localisation<br />
What is it?<br />
Adapting software for another language/culture.<br />
Key features<br />
The goal of software localisation is not just to make the program or product available in other languages. It’s also about ensuring the user experience in those languages is as natural and effective as possible.<br />
Translating the user interface, messaging, documentation, etc is a major part of the process.<br />
Also key is a customisation process to ensure everything matches the conventions, norms and expectations of the target cultures.<br />
Adjusting time, date and currency formats are examples of simple customisations. Others might involve adapting symbols, graphics, colours and even concepts and ideas.<br />
Localisation is often preceded by internationalisation – a review process to ensure the software is optimally designed to handle other languages.<br />
And it’s almost always followed by thorough testing – to ensure all text is in the correct place and fits the space, and that everything makes sense, functions as intended and is culturally appropriate.<br />
Localisation is often abbreviated to L10N, internationalisation to i18n.<br />
What this means<br />
Software localisation is a specialised kind of translation, and you should always engage a company that specialises in it.<br />
They’ll have the systems, tools, personnel and experience needed to achieve top quality outcomes for your product.<br />
<br />
21. Game Localisation<br />
What is it?<br />
Adapting games for other languages and markets.<br />
<br />
It’s a subset of software localisation.<br />
Key features<br />
The goal of game localisation is to provide an engaging and fun gaming experience for speakers of other languages.<br />
<br />
It involves translating all text and recording any required foreign language audio.<br />
<br />
But also adapting anything that would clash with the target culture’s customs, sensibilities and regulations.<br />
<br />
For example, content involving alcohol, violence or gambling may either be censored or inappropriate in the target market.<br />
<br />
And at a more basic level, anything that makes users feel uncomfortable or awkward will detract from their experience and thus the success of the game in that market.<br />
<br />
So portions of the game may have to be removed, added to or re-worked.<br />
<br />
Game localisation involves at least the steps of translation, adaptation, integrating the translations and adaptations into the game, and testing.<br />
<br />
What this means<br />
Game localisation is a very specialised type of translation best left to those with specific expertise and experience in this area.<br />
<br />
22. Multimedia Localisation<br />
What is it?<br />
Adapting multimedia for other languages and cultures.<br />
<br />
Multimedia refers to any material that combines visual, audio and/or interactive elements. So videos and movies, on-line presentations, e-Learning courses, etc.<br />
Key features<br />
Anything a user can see or hear may need localising.<br />
<br />
That means the audio and any text appearing on screen or in images and animations.<br />
<br />
Plus it can mean reviewing and adapting the visuals and/or script if these aren’t suitable for the target culture.<br />
<br />
The localisation process will typical involve:<br />
– Translation<br />
– Modifying the translation for cultural reasons and/or to meet technical requirements<br />
– Producing the other language versions<br />
<br />
Audio output may be voice-overs, dubbing or subtitling.<br />
<br />
And output for visuals can involve re-creating elements, or supplying the translated text for the designers/engineers to incorporate.<br />
<br />
What this means<br />
Multimedia localisation projects vary hugely, and it’s essential your translation providers have the specific expertise needed for your materials.<br />
<br />
<br />
<br />
23. Script Translations<br />
What is it?<br />
Preparing the text of recorded material for recording in other languages.<br />
Key features<br />
There are several issues with script translation.<br />
<br />
One is that translations typically end up longer than the original script. So voicing the translation would take up more space/time on the video than the original language.<br />
<br />
Sometimes that space will be available and this will be OK.<br />
<br />
But generally it won’t be. So the translation has to be edited back until it can be comfortably voiced within the time available on the video.<br />
<br />
Another challenge is the translation may have to synchronise with specific actions, animations or text on screen.<br />
<br />
Also, some scripts also deal with technical subject areas involving specialist technical terminology.<br />
<br />
Finally, some scripts may be very culture-specific – featuring humour, customs or activities that won’t work well in another language. Here the script, and sometimes also the associated visuals, may need to be adjusted before beginning the translation process.<br />
<br />
It goes without saying that a script translation must be done well. If it’s not, there’ll be problems producing a good foreign language audio, which will compromise the effectiveness of the video.<br />
<br />
Translators typically work from a time-coded transcript. This is the original script marked to show the time available for each section of the translation.<br />
<br />
What this means<br />
There are several potential pitfalls in script translations. So it’s vital your translation provider is practiced at this type of translation and able to handle any technical content.<br />
<br />
<br />
<br />
24. Voice-over and Dubbing Projects<br />
What is it?<br />
Translation and recording of scripts in other languages.<br />
<br />
Voice-overs vs dubbing<br />
There is a technical difference.<br />
A voice-over adds a new track to the production, dubbing replaces an existing one.<br />
<br />
<br />
Key features<br />
These projects involve two parts:<br />
– a script translation (as described above), and<br />
– producing the audio<br />
<br />
So they involve the combined efforts of translators and voice artists.<br />
The task for the voice artist is to produce a high quality read. That’s one that matches the style, tone and richness of the original.<br />
<br />
Often each section of the new audio will need to be the same length as the original.<br />
<br />
But sometimes the segments will need to be shorter – for example where the voice-over lags the original by a second or two. This is common in interviews etc, where the original voice is heard initially then drops out.<br />
<br />
The most difficult form of dubbing is lip-syncing – where the new audio needs to synchronise with the original speaker’s lip movements, gestures and actions.<br />
<br />
Lip-syncing requires an exceptionally skilled voice talent and considerable time spent rehearsing and fine tuning the translation.<br />
<br />
What this means<br />
You need to use experienced professionals every step of the way in this type of project.<br />
<br />
That’s to ensure firstly that your foreign-language scripts are first class, then that the voicing is of high professional standard.<br />
<br />
Anything less will mean your foreign language versions will be way less effective and appealing to your target audience.<br />
<br />
<br />
<br />
25. Subtitle Translations<br />
What is it?<br />
Producing foreign language captions for sub or surtitles.<br />
Key features<br />
The goal with subtitling is to produce captions that viewers can comfortably read in the time available and still follow what’s happening on the video.<br />
<br />
To achieve this, languages have “rules” governing the number of characters per line and the minimum time each subtitle should display.<br />
<br />
Sticking to these guidelines is essential if your subtitles are to be effective.<br />
<br />
But this is no easy task – it requires simple language, short words, and a very succinct style. Translators will spend considerable time mulling over and re-working their translation to get it just right.<br />
<br />
Most subtitle translators use specialised software that will output the captions in the format sound engineers need for incorporation into the video.<br />
<br />
What this means<br />
As with other specialised types of translation, you should only use translators with specific expertise and experience in subtitling.<br />
<br />
<br />
<br />
26. Website Localisation<br />
What is it?<br />
The translation and adapting of relevant content on a website to best suit the target language and culture.<br />
<br />
Note: Many providers use the term website translation as a synonym for localisation. Strictly speaking though, translation is just one part of localisation.<br />
Key features<br />
<br />
Not all pages on a website may need to be localised – clients should review their content to identify what’s relevant for the other language versions.<br />
Some content may need specialist translators – legal and technical pages for example.<br />
There may also be videos, linked documents, and text or captions in graphics to translate.<br />
Adaptation can mean changing date, time, currency and number formats, units of measure, etc.<br />
But also images, colours and even the overall site design and style if these won’t have the desired impact in the target culture.<br />
Translated files can be supplied in a wide range of formats – translators usually coordinate output with the site webmasters.<br />
New language versions are normally thoroughly reviewed and tested before going live to confirm everything is displaying correctly, works as intended and is cultural appropriate.<br />
What this means<br />
The first step should be to review your content and identify what needs to be translated. This might lead you to modify some pages for the foreign language versions.<br />
<br />
In choosing your translation providers be sure they can:<br />
– handle any technical or legal content,<br />
– provide your webmaster with the file types they want.<br />
<br />
And you should always get your translators to systematically review the foreign language versions before going live.<br />
<br />
<br />
<br />
27. Transcreation<br />
What is it?<br />
Adapting a message to elicit the same emotional response in another language and culture.<br />
Translation is all about conveying the message or meaning of a text in another language. But sometimes that message or meaning won’t have the desired effect in the target culture.<br />
<br />
This is where transcreation comes in. Transcreation creates a new message that will get the desired emotional response in that culture, while preserving the style and tone of the original.<br />
<br />
So it’s a sort of creative translation – which is where the word comes from, a combination of ‘translation’ and ‘creation’.<br />
<br />
At one level transcreation may be as simple as choosing an appropriate idiom to convey the same intent in the target language – something translators do all the time.<br />
<br />
But mostly the term is used to refer to adapting key advertising and marketing messaging. Which requires copywriting skills, cultural awareness and an excellent knowledge of the target market.<br />
<br />
Who does it?<br />
Some translation companies have suitably skilled personnel and offer transcreation services.<br />
<br />
Often though it’s done in the target country by specialist copywriters or an advertising or marketing agency – particularly for significant campaigns and to establish a brand in the target marketplace.<br />
<br />
What this means<br />
Most general marketing and promotional texts won’t need transcreation – they can be handled by a translator with excellent creative writing skills.<br />
<br />
But slogans, by-lines, advertising copy and branding statements often do.<br />
<br />
Whether you should opt for a translation company or an in-market agency will depend on the nature and importance of the material, and of course your budget.<br />
<br />
<br />
<br />
28. Audio Translations<br />
What is it?<br />
Broad meaning: the translation of any type of recorded material into another language.<br />
<br />
More commonly: the translation of a foreign language video or audio recording into your own language. So this is where you want to know and document what a recording says.<br />
Key features<br />
The first challenge with audio translations is it’s often impossible to pick up every word that’s said. That’s because audio quality, speech clarity and speaking speed can all vary enormously.<br />
<br />
It’s also a mentally challenging task to listen to an audio and translate it directly into another language. It’s easy to miss a word or an aspect of meaning.<br />
<br />
So best practice is to first transcribe the audio (type up exactly what is said in the language it is spoken in), then translate that transcription.<br />
<br />
However, this is time consuming and therefore costly, and there are other options if lesser precision is acceptable.<br />
<br />
What this means<br />
It’s best to discuss your requirements for this kind of translation with your translation provider. They’ll be able to suggest the best translation process for your needs.<br />
<br />
Examples<br />
Interviews, product videos, police recordings, social media videos.<br />
<br />
<br />
<br />
29. Translations with DTP<br />
What is it?<br />
Translation incorporated into graphic design files.multilingual dtp example in the form of a Rubik's Cube with foreign text on each square<br />
Key features<br />
Graphic design programs are used by professional designers and graphic artists to combine text and images to create brochures, books, posters, packaging, etc.<br />
<br />
Translation plus dtp projects involve 3 steps – translation, typesetting, output.<br />
<br />
The typesetting component requires specific expertise and resources – software and fonts, typesetting know-how, an appreciation of foreign language display conventions and aesthetics.<br />
<br />
What this means<br />
Make sure your translation company has the required multilingual typesetting/desktop publishing expertise whenever you’re translating a document created in a graphic design program.<br />
<br />
Translation Category C: 13 types of translation based on the translation method employed<br />
This category has two sub-groups:<br />
– the practical methods translation providers use to produce their translations, and<br />
– the translation strategies/methods identified and discussed within academia.<br />
<br />
The translation methods translation providers use<br />
There are 4 main methods used in the translation industry today. We have an overview of each below, but for more detail, including when to use each one, see our comprehensive blog article.<br />
<br />
Or watch our video.<br />
<br />
Important: If you’re a client you need to understand these 4 methods – choose the wrong one and the translation you end up with may not meet your needs!<br />
<br />
<br />
30. Machine Translation (MT)<br />
What is it?<br />
A translation produced entirely by a software program with no human intervention.<br />
<br />
A widely used, and free, example is Google Translate. And there are also commercial MT engines, generally tailored to specific domains, languages and/or clients.<br />
<br />
Pros and cons<br />
There are two limitations to MT:<br />
– they make mistakes (incorrect translations), and<br />
– quality of wording is patchy (some parts good, others unnatural or even nonsensical)<br />
<br />
On they positive side they are virtually instantaneous and many are free.<br />
<br />
Best suited for:<br />
Getting the general idea of what a text says.<br />
<br />
This method should never be relied on when high accuracy and/or good quality wording is needed.<br />
<br />
<br />
<br />
31. Machine Translation plus Human Editing (PEMT)<br />
What is it?<br />
A machine translation subsequently edited by a human translator or editor (often called Post-editing Machine Translation = PEMT).<br />
<br />
The editing process is designed to rectify some of the deficiencies of a machine translation.<br />
<br />
This process can take different forms, with different desired outcomes. Probably most common is a ‘light editing’ process where the editor ensures the text is understandable, without trying to fix quality of expression.<br />
<br />
Pros and cons<br />
This method won’t necessarily eliminate all translation mistakes. That’s because the program may have chosen a wrong word (meaning) that wasn’t obvious to the editor.<br />
<br />
And wording won’t generally be as good as a professional human translator would produce.<br />
<br />
Its advantage is it’s generally quicker and a little cheaper than a full translation by a professional translator.<br />
<br />
Best suited for:<br />
Translations for information purposes only.<br />
<br />
Again, this method shouldn’t be used when full accuracy and/or consistent, natural wording is needed.<br />
<br />
<br />
<br />
32. Human Translation<br />
What is it?<br />
Translation by a professional human translator.<br />
Pros and cons<br />
Professional translators should produce translations that are fully accurate and well-worded.<br />
<br />
That said, there is always the possibility of ‘human error’, which is why translation companies like us typically offer an additional review process – see next method.<br />
<br />
This method will take a little longer and likely cost more than the PEMT method.<br />
<br />
Best suited for:<br />
Most if not all translation purposes.<br />
<br />
<br />
<br />
33. Human Translation + Revision<br />
What is it?<br />
A human translation with an additional review by a second translator.<br />
<br />
The review is essentially a safety check – designed to pick up any translation errors and refine wording if need be.<br />
<br />
Pros and cons<br />
This produces the highest level of translation quality.<br />
<br />
It’s also the most expensive of the 4 methods, and takes the longest.<br />
<br />
Best suited for:<br />
All translation purposes.<br />
<br />
Gearwheel with 5 practical translation methods written on the teeth <br />
There’s also one other common term used by practitioners and academics alike to describe a type (method) of translation:<br />
<br />
34. Computer-Assisted Translation (CAT)<br />
What is it?<br />
A human translator using computer tools to aid the translation process.<br />
<br />
Key features<br />
Virtually all translators use such tools these days.<br />
<br />
The most prevalent tool is Translation Memory (TM) software. This creates a database of previous translations that can be accessed for future work.<br />
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TM software is particularly useful when dealing with repeated and closely-matching text, and for ensuring consistency of terminology. For certain projects it can speed up the translation process.<br />
<br />
<br />
<br />
The translation methods described by academia<br />
A great deal has been written within academia analysing how human translators go about their craft.<br />
<br />
Seminal has been the work of Newmark, and the following methods of translation attributed to him are widely discussed in the literature.Gearwheel with Newmark's 8 translation methods written on the teeth <br />
These methods are approaches and strategies for translating the text as a whole, not techniques for handling smaller text units, which we discuss in our final translation category.<br />
<br />
<br />
<br />
35. Word-for-word Translation<br />
This method translates each word into the other language using its most common meaning and keeping the word order of the original language.<br />
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So the translator deliberately ignores context and target language grammar and syntax.<br />
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Its main purpose is to help understand the source language structure and word use.<br />
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Often the translation will be placed below the original text to aid comparison.<br />
<br />
<br />
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36. Literal Translation<br />
Words are again translated independently using their most common meanings and out of context, but word order changed to the closest acceptable target language grammatical structure to the original.<br />
<br />
Its main suggested purpose is to help someone read the original text.<br />
<br />
<br />
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37. Faithful Translation<br />
Faithful translation focuses on the intention of the author and seeks to convey the precise meaning of the original text.<br />
<br />
It uses correct target language structures, but structure is less important than meaning.<br />
<br />
<br />
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38. Semantic Translation<br />
Semantic translation is also author-focused and seeks to convey the exact meaning.<br />
<br />
Where it differs from faithful translation is that it places equal emphasis on aesthetics, ie the ‘sounds’ of the text – repetition, word play, assonance, etc.<br />
<br />
In this method form is as important as meaning as it seeks to “recreate the precise flavour and tone of the original” (Newmark).slide showing definition of semantic translation as a translation method<br />
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39. Communicative Translation<br />
Seeks to communicate the message and meaning of the text in a natural and easily understood way.<br />
<br />
It’s described as reader-focused, seeking to produce the same effect on the reader as the original text.<br />
<br />
A good comparison of Communicative and Semantic translation can be found here.<br />
<br />
40. Free Translation<br />
Here conveying the meaning and effect of the original are all important.<br />
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There are no constraints on grammatical form or word choice to achieve this.<br />
<br />
Often the translation will paraphrase, so may be of markedly different length to the original.<br />
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41. Adaptation<br />
Mainly used for poetry and plays, this method involves re-writing the text where the translation would otherwise lack the same resonance and impact on the audience.<br />
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Themes, storylines and characters will generally be retained, but cultural references, acts and situations adapted to relevant target culture ones.<br />
<br />
So this is effectively a re-creation of the work for the target culture.<br />
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42. Idiomatic Translation<br />
Reproduces the meaning or message of the text using idioms and colloquial expressions and language wherever possible.<br />
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The goal is to produce a translation with language that is as natural as possible.<br />
<br />
Translation Category D: 9 types of translation based on the translation technique used<br />
These translation types are specific strategies, techniques and procedures for dealing with short chunks of text – generally words or phrases.<br />
<br />
They’re often thought of as techniques for solving translation problems.<br />
<br />
They differ from the translation methods of the previous category which deal with the text as a whole.<br />
9 translation techniques as titles of books in a bookcase<br />
<br />
43. Borrowing<br />
What is it?<br />
Using a word or phrase from the original text unchanged in the translation.<br />
<br />
Key features<br />
With this procedure we don’t translate the word or phrase at all – we simply ‘borrow’ it from the source language.<br />
<br />
Borrowing is a very common strategy across languages. Initially, borrowed words seem clearly ‘foreign’, but as they become more familiar, they can lose that ‘foreignness’.<br />
<br />
Translators use this technique:<br />
– when it’s the best word to use – either because it has become the standard, or it’s the most precise term, or<br />
– for stylist effect – borrowings can add a prestigious or scholarly flavour.<br />
<br />
Borrowed words or phrases are often italicised in English.<br />
<br />
Examples of borrowings in English<br />
grand prix, kindergarten, tango, perestroika, barista, sampan, karaoke, tofu<br />
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44. Transliteration<br />
What is it?<br />
Reproducing the approximate sounds of a name or term from a language with a different writing system.<br />
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Key features<br />
In English we use the Roman (Latin) alphabet in common with many other languages including almost all European languages.<br />
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Other writing systems include Arabic, Cyrillic, Chinese, Japanese, Korean, Thai, and the Indian languages.<br />
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Transliteration from such systems into the Roman alphabet is also called romanisation.<br />
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There are accepted systems for how individual letters/sounds should be romanised from most other languages – there are three common systems for Chinese, for example.<br />
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English borrowings from languages using non-Roman writing systems also require transliteration – perestroika, sampan, karaoke, tofu are examples from the above list.<br />
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Translators mostly use transliteration as a procedure for translating proper names.<br />
<br />
Examples<br />
毛泽东 Mao Tse-tung or Mao Zedong<br />
Владимир Путин Vladimir Putin<br />
서울 Seoul<br />
ភ្នំពេញ Phnom Penh<br />
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45. Calque or Loan Translation<br />
What is it?<br />
A literal translation of a foreign word or phrase to create a new term with the same meaning in the target language.<br />
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So a calque is a borrowing with translation if you like. The new term may be changed slightly to reflect target language structures.<br />
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Examples<br />
German ‘Kindergarten’ has been calqued as детский сад in Russian, literally ‘children garden’ in both languages.<br />
<br />
Chinese 洗腦 ‘wash’ + ‘brain’ is the origin of ‘brainwash’ in English.<br />
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English skyscraper is calqued as gratte-ciel in French and rascacielos in Spanish, literally ‘scratches sky’ in both languages.<br />
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46. Word-for-word translation<br />
What is it?<br />
A literal translation that is natural and correct in the target language.<br />
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Alternative names are ‘literal translation’ or ‘metaphrase’.<br />
<br />
Note: this technique is different to the translation method of the same name, which does not produce correct and natural text and has a different purpose.<br />
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Key features<br />
This translation strategy will only work between languages that have very similar grammatical structures.<br />
<br />
And even then, only sometimes.<br />
<br />
For example, standard word order in Turkish is Subject-Object-Verb whereas in English it’s Subject-Verb-Object. So a literal translation between these two will seldom work:<br />
– Yusuf elmayı yedi is literally ‘Joseph the apple ate’.<br />
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When word-for-word translations don’t produce natural and correct text, translators resort to some of the other techniques described below.<br />
Examples<br />
French ‘Quelle heure est-il?’ works into English as ‘What time is it?’.<br />
<br />
Russian ‘Oн хочет что-нибудь поесть’ is ‘He wants something to eat’.<br />
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47. Transposition<br />
What is it?<br />
Translation with a change of grammatical structure.<br />
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This technique gives the translation more natural wording and/or makes it grammatically correct.<br />
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Examples<br />
A change in word order:<br />
Our Turkish example Yusuf elmayı yedi (literally ‘Joseph the apple ate’) –> Joseph ate the apple.<br />
<br />
Spanish La Casa Blanca (literally ‘The House White’) –> The White House<br />
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A change in grammatical category:<br />
German Er hört gerne Musik (literally ‘he listens gladly [to] music’)<br />
= subject pronoun + verb + adverb + noun<br />
becomes Spanish Le gusta escuchar música (literally ‘[to] him [it] pleases to listen [to] music’)<br />
= indirect object pronoun + verb + infinitive + noun<br />
and English He likes listening to music<br />
= subject pronoun + verb + gerund + noun.<br />
<br />
48. Modulation<br />
What is it?<br />
Translation with a change of focus or point of view in the target language.<br />
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This technique makes the translation more idiomatic – how people would normally say it in the language.<br />
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Examples<br />
English talks of the ‘top floor’ of a building, French the dernier étage = last floor. ‘Last floor’ would be unnatural in English, so too ‘top floor’ in French.<br />
<br />
German uses the term Lebensgefahr (literally ‘danger to life’) where in English we’d be more likely to say ‘risk of death’.<br />
In English we’d say ‘I dropped the key’, in Spanish se me cayó la llave, literally ‘the key fell from me’. The English perspective is that I did something (dropped the key), whereas in Spanish something happened to me – I’m the recipient of the action.<br />
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49. Equivalence or Reformulation<br />
What is it?<br />
Translating the underlying concept or meaning using a totally different expression.<br />
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This technique is widely used when translating idioms and proverbs.<br />
<br />
And it’s common in titles and advertising slogans.<br />
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It’s a common strategy where a direct translation either wouldn’t make sense or wouldn’t resonate in the same way.<br />
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Examples<br />
Here are some equivalents of the English saying “Pigs may fly”, meaning something will never happen, or “you’re being unrealistic” (Source):<br />
– Thai: ชาติหน้าตอนบ่าย ๆ – literally, ‘One afternoon in your next reincarnation’<br />
– French: Quand les poules auront des dents – literally, ‘When hens have teeth’<br />
– Russian, Когда рак на горе свистнет – literally, ‘When a lobster whistles on top of a mountain’<br />
– Dutch, Als de koeien op het ijs dansen – literally, ‘When the cows dance on the ice’<br />
– Chinese: 除非太陽從西邊出來！– literally, ‘Only if the sun rises in the west’<br />
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50. Adaptation<br />
What is it?<br />
A translation that substitutes a culturally-specific reference with something that’s more relevant or meaningful in the target language.<br />
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It’s also known as cultural substitution or cultural equivalence.<br />
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It’s a useful technique when a reference wouldn’t be understood at all, or the associated nuances or connotations would be lost in the target language.<br />
<br />
Note: the translation method of the same name is a similar concept but applied to the text as a whole.<br />
<br />
Examples<br />
Different cultures celebrate different coming of age birthdays – 21 in many cultures, 20, 15 or 16 in others. A translator might consider changing the age to the target culture custom where the coming of age implications were important in the original text.<br />
Animals have different connotations across languages and cultures. Owls for example are associated with wisdom in English, but are a bad omen to Vietnamese. A translator might want to remove or amend an animal reference where this would create a different image in the target language.<br />
<br />
51. Compensation<br />
What is it?<br />
A meaning or nuance that can’t be directly translated is expressed in another way in the text.<br />
Example<br />
Many languages have ways of expressing social status (honorifics) encoded into their grammatical structures.<br />
<br />
So you can convey different levels of respect, politeness, humility, etc simply by choosing different forms of words or grammatical elements.<br />
But these nuances will be lost when translating into languages that don’t have these structures.<br />
Then translating into languages that don’t have these structures<br />
Then translating into languages that don’t have these structures.<br />
<br />
===Conclusion ===<br />
<br />
In the light of above mentioned facts and figures here by we would say that machine translation is a challenge for human translators because it can reduce the workload of translation but can't give accurate and exact translation of the target language.It can be less reliable than human translation..</div>Chen Huinihttps://bou.de/u/index.php?title=Machine_translation&diff=133232Machine translation2021-12-15T04:59:00Z<p>Chen Huini: /* Chapter 11 陈惠妮=Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts */</p>
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<div>'''Machine Translation - A challenge or a chance for human translators?'''<br />
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[[Machine_translation|Overview Page of Machine Translation]]<br />
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30 Chapters（0/30)<br />
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[[Machine_Trans_EN_1]] [[Machine_Trans_EN_2]] [[Machine_Trans_EN_3]] [[Machine_Trans_EN_4]] [[Machine_Trans_EN_5]] [[Machine_Trans_EN_6]] [[Machine_Trans_EN_7]] [[Machine_Trans_EN_8]] [[Machine_Trans_EN_9]] [[Machine_Trans_EN_10]] [[Machine_Trans_EN_11]] [[Machine_Trans_EN_12]] [[Machine_Trans_EN_13]] [[Machine_Trans_EN_14]] [[Machine_Trans_EN_15]] [[Machine_Trans_EN_16]] [[Machine_Trans_EN_17]] [[Machine_Trans_EN_18]] [[Machine_Trans_EN_19]] [[Machine_Trans_EN_20]] [[Machine_Trans_EN_21]] [[Machine_Trans_EN_22]] [[Machine_Trans_EN_23]] [[Machine_Trans_EN_24]] [[Machine_Trans_EN_25]] [[Machine_Trans_EN_26]] [[Machine_Trans_EN_27]] [[Machine_Trans_EN_28]] [[Machine_Trans_EN_29]] [[Machine_Trans_EN_30]] ...<br />
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[[Book_projects|Back to translation project overview]]<br />
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[[DCG-To-Do|To the To Do list]]<br />
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=Chapter 1:A Comparison Between the Quality of Machine Translation and Human Translation——A Case Study of the Application of artificial intelligence in Sports Events=<br />
'''论机器翻译与人工翻译的质量对比——以人工智能在体育赛事领域的应用为例'''<br />
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卫怡雯 Wei Yiwen, Hunan Normal University, China<br />
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[[Machine_Trans_EN_1]]<br />
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=Chapter 3：On the Realm Advantages And Mutual Development of Machine Translation And Huamn Translation)=<br />
"'论机器翻译与人工翻译的领域优势及共同发展'"<br />
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肖毅瑶 Xiao Yiyao, Hunan Normal University, China<br />
[[Machine_Trans_EN_3]]<br />
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=Chapter 4 : A Comparison Between Machine Translation of Netease and Traditional Human Translation—A Case Study of The Economist Articles)= <br />
'''网易有道机器翻译与人工翻译的译文对比——以经济学人语料为例'''<br />
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王李菲 Wang Lifei, Hunan Normal University<br />
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[[Machine_Trans_EN_4]]<br />
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=Chapter 5: Muhammad Saqib Mehran - Problems in translation studies=<br />
[[Machine_Trans_EN_5]]<br />
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=6 徐敏赟(Machine Translation Based on Neural Network --Challenge or Chance?)=<br />
[[Machine_Trans_EN_6]]<br />
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=Chapter 7: The Cultivation of artificial intelligence and translation talents in the Belt and Road Initiative=<br />
[[Machine_Trans_EN_7]]<br />
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一带一路背景下人工智能与翻译人才的培养<br />
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颜莉莉 Yan Lili, Hunan Normal University, China<br />
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=Chapter 8: On Machine Translation Under Lanuguage Intelligence——An Option and Opportunity for Human Translators=<br />
'''论语言智能下的机器翻译——译者的选择与机遇'''<br />
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颜静 Yan Jing, Hunan Normal University, China<br />
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[[Machine_Trans_EN_8]]<br />
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=9 谢佳芬（ Machine Translation and Artificial Translation in the Era of Artificial Intelligence）=<br />
[[Machine_Trans_EN_9]]<br />
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=10 熊敏(Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts)=<br />
机器翻译对各类型文本的英汉翻译能力探究<br />
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熊敏, Xiong Min, Hunan Normal University<br />
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[[Machine_Trans_EN_10]]<br />
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===Abstract===<br />
The history of machine translation can be traced to 1940s, undergoing germination, recession, revival and flourish. Nowadays, with the rapid development of information technology, machine translation technology emerged and is gradually becoming mature. Whether manual translation can be replaced by machine translation becomes a widely-disputed topic. So in order to explore the ability of machine translation, I adopts two versions of translation, which are manual translation and machine translation(this paper uses Youdao translation) for different types of texts(according to Peter Newmark's types of text：informative text, expressive text and vocative text). The results are quite different in terms of quality and accuracy. The results show that machine translation is more suitable for informative text for its brevity, while the expressive texts especially literary works and vocative texts are difficult to be translated, because there are too many loaded words and cultural images in expressive text and dependence on the readers. To draw a conclusion, the relationship between machine translation and manual translation should be complementary rather than competitive.<br />
<br />
===Key words===<br />
machine translation; manual translation; Newmark's type of texts<br />
<br />
===题目===<br />
Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts<br />
<br />
===摘要===<br />
机器翻译的历史可以追溯到上个世纪40年代，经历了萌芽期、萧条期、复兴期和繁荣期四个阶段。如今，随着信息技术的高速发展，机器翻译技术日益成熟，于是机器翻译能不能替代人工翻译这个话题引起了广泛热议。为了探究机器翻译的能力水平是否足以替代人工翻译，本人根据皮特纽马克的文本类型分类理论（信息类文本，表达文本，呼吁文本），选择了相应的译文类型，并且将其机器翻译的版本以及人工翻译的版本进行对比。结果发现，就质量和准确度而言，译文的水平大相径庭。因为其简洁的特性，机器比较适合翻译信息文本。而就表达文本，尤其是文学作品，因其存在文化负载词及相应的文化现象，所以机器翻译这类文本存在难度。而呼唤文本因其目的性以及对读者的依赖性较强，翻译难度较大。由此得知，机器翻译和人工翻译的关系应该是互补的，而不是竞争的。<br />
<br />
===关键词===<br />
机器翻译；人工翻译；纽马克文本类型<br />
<br />
===1. Introduction===<br />
====1.1.Introduction to machine translation====<br />
Machine translation, also known as computer translation is a technique to translating through machine translator, such as Google translation and Youdao translation. Machine translation is one of the branches of computational linguistics, ranging from computer science, statistics, information science and so on. Machine translation plays an important role in all aspects.<br />
Machine translation can be traced back to 1940s, when British engineer Booth and American engineer Weaver proposed using computer to translate and started to study machines used for translation. And the first machine translating system launched in 1954, used in English and Russian translation. And this means machine translation becomes a reality. However, the arrival of new things always accompanies barriers and setbacks. In the 1960s, reports from ALPAC (Automated Language Processing Advisory Committee) showed studies on machine translation had stagnated for a decade. At that time, due to the high cost of machine, choosing human translators to finish translating works was more economical for most companies. What’s worse, machine had difficulty in understanding semantic and pragmatic meanings. Fortunately, in the 1970s, with the advance and popularity of computer, machine translation was gradually back on track. With the development of science and technology, machine translation has better technological guarantee, such as artificial intelligence and computer technology. In the last decades, from the late 90s till now, machine translation is becoming more and more mature. The initial rule-oriented machine translation has been updated and Corpus-aided machine translation appears. And nowadays, technology in voice recognition has been further developed. This technique is frequently applied in speech translation products. Users only need to speak source language to the online translator and instantly it will speak out the target version. But machine can’t fully provide precise and accurate translation without any grammatical or semantic problems. Machine can understand the literal meaning of texts, but not the meaning in context. For example, there is polysemy in English, which refers to words having multiple meanings. So when translating these words, translators should take contexts into consideration. But machine has troubles in this way. In addition, machine has no feeling or intention. Hence, it is also difficult to convey the feelings from the source language.(Wei 2021:5)#<br />
<br />
====1.2.Process of machine translation====<br />
The process of manual translation is different from that of machine translation. Here is the process of the former. (1) Understand source language. (2) Use target language to organize language. (3) Generate translation. Unlike manual translation, machine translation tends to analyze and code source language first, then look for related codes in corpus, and work out the code that represents target language, generating translation. But they share a common feature, which is that Lexicon, grammatical rules and syntactic structure are taken into consideration. This is one of the biggest challenges for machine translation.<br />
<br />
===2.Theorectical framework===<br />
====2.1Newmark’s text typology====<br />
Text typology is a theory from linguistics. It undergoes several phrases. Karl Buhler, a German psychologist and linguist defines communicative functions into expressive, information and vocative. Then Roman Jakobson proposes categorization of texts, which are intralingual translation, interlingual translation and intersemiotic translation. Accordingly, he defines six main functions of language, including the referential function, conative function, emotive function, poetic function, metalingual function and the phatic function. Based on the two theories, Peter Newmark, a translation theorist, summarizes the language function into six parts: the informative function, the vocative function, the expressive function, the phatic function, the aesthetic function, and the metalingual function. Later, he further divided texts into informative type, expressive text and vocative type according to the linguistic functions of various texts. (Newmark 2002:2)#<br />
The core of the informative texts is the truth. It is to convey facts, information, knowledge of certain topics, reality and theories. The language style of the text is objective, logical and standardized. Reports, papers, scientific and technological textbooks are all attributed to informative texts. Translators are required to take format into account for different styles.<br />
The core of the expressive text is the sender’s emotions and attitudes. It is to express sender’s preferences, feelings, views and so on. The language style of it is subjective. Imaginative literature, including fictions, poems and dramas, autobiography and authoritative statements belong to expressive text. It requires translators to be faithful to the source language and maintain the style.<br />
The core of the vocative text is readership. It is to call upon readers to act, think, feel and react in the way intended by the text. So it is reader-oriented. Such texts as advertisement, propaganda and notices are of vocative text. Newmark noted that translators need to consider cultural background of the source language and the semantic and pragmatic effect of target language. If readers can comprehend the meaning at once and do as the text says, then the goal of information communication is achieved. (Liu 2021:3)#<br />
To draw a conclusion, informative texts focus on the information and facts. Expressive texts center on the mind of addressers, including feelings, prejudices and so on. And vocative texts are oriented on readers and call on them to practice as the texts say.<br />
<br />
====2.2Study method====<br />
Manual translations of the three texts are selected from authoritative versions and universally acknowledged. And machine translations of those come from Youdao Translator. And in this thesis I will compare and evaluate the two methods in word diction, sentence structure, word order and redundancy.<br />
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===3.Comparison and analysis of machine translation and manual translation ===<br />
====3.1Informative text ====<br />
（1）English into Chinese<br />
<br />
①Source language:<br />
<br />
Keep the tip of Apple Pencil clean, as dirt and other small particles may cause excessive wear to the tip or damage the screen of i-pad.<br />
<br />
Target language:<br />
<br />
Machine translation: Apple Pencil笔尖应保持清洁，灰尘等小颗粒可能会导致笔尖过度磨损或损坏ipad屏幕。<br />
<br />
Manual translation: 保持Apple Pencil铅笔的笔尖干净，因为灰尘和其他微粒可能会导致笔尖的过度磨损或损坏iPad屏幕。<br />
<br />
Analysis: Here is the instruction of Apple Pencil. And the manual translation is the Chinese version on the instruction.Product instruction tends to be professional, since there are many terms for some concepts. Machine can easily identify these terms and provide related words to translate. The machine version is faithful and expressive to the source language. So it is well-qualified and readable for readers to understand the instruction. So we can use machine to translate informative text.<br />
<br />
②Source language:<br />
<br />
China on Saturday launched a rocket carrying three astronauts-two men and one woman - to the core module of a future space station where they will live and work for six months, the longest orbit for Chinese astronauts.<br />
<br />
Target language:<br />
<br />
Machine translation: 周六，中国发射了一枚运载三名宇航员(两男一女)的火箭，进入未来空间站的核心舱，他们将在那里生活和工作6个月，这是中国宇航员最长的轨道。<br />
<br />
Manual translation: 周六，中国发射了一枚搭载三名宇航员(两男一女)的火箭，进入未来空间站的核心舱，他们将在那里生活和工作6个月，这是中国宇航员最漫长的一次轨道飞行。<br />
<br />
Analysis: This is a news from Reuters, reporting that China has launched a rocket.The meaning of the two translations is almost the same, except for some word diction. But there are some details dealt with different choice. For example, the last sentence of the machine translation is a bit of obscure and direct. There are some ambiguous words and expressions.<br />
<br />
(2)Chinese into English<br />
<br />
Source language:湖南省博物馆是湖南省最大的历史艺术类博物馆，占地面积4.9万平方米，总建筑面积为9.1万平方米，是首批国家一级博物馆，中央地方共建的八个国家级重点博物馆之一、全国文化系统先进集体、文化强省建设有突出贡献先进集体。<br />
<br />
Target language:<br />
Manual translation: As the largest history and art museum in Hunan province, the Hunan Museum covers an area of 49,000㎡, with the building area reaching 91,000㎡. It is one of the first batch of national first-level museums and one of the first eight national museums co-funded by central and local governments.<br />
<br />
Machine translation: Museum in hunan province is one of the largest historical art museum in hunan province, covers an area of 49000 square meters, a total construction area of 91000 square meters, is the first national museum, the central place to build one of the eight national key museum, national cultural system advanced collectives, strong culture began with outstanding contribution of advanced collective.<br />
<br />
Analysis: Machine translation is not faithful enough in content. For instance, “首批国家一级博物馆” is translated into “first national museum”, which is not the meaning of the source language. And there are some obvious grammar mistakes in the machine translation. For example, machine translates it into just one sentence but there are multiple predicates in it. So it is not grammatically permissible. What’s more, the sentence structure of machine translation is confusing and the focus is not specific enough.<br />
<br />
====3.2Expressive text ====<br />
(1)English into Chinese<br />
<br />
Source language:<br />
<br />
An individual human existence should be like a river- small at first, narrowly contained within its banks, and rushing passionately past rocks and over waterfalls. Gradually the river grows wider, the banks recede, the waters flow more quietly, and in the end, without any visible breaks, they become merged in the sea, and painlessly lose their individual being.()<br />
<br />
Target language:<br />
<br />
Machine translation: 一个人的存在应该像一条河流——开始很小，被紧紧地夹在两岸中间，然后热情奔放地冲过岩石，飞下瀑布。渐渐地，河面变宽，两岸后退，水流更加平缓，最后，没有任何明显的停顿，它们汇入大海，毫无痛苦地失去了自己的存在。<br />
<br />
Manual translation:人生在世，如若河流；河口初始狭窄，河岸虬曲，而后狂涛击石，飞泻成瀑。河道渐趋开阔，峡岸退去，水流潺缓，终了，一马平川，汇于大海，消逝无影。<br />
<br />
Analysis: Here is a well-known metaphor in the prose How to Grow Old written by Bertrand Russell. The manual translation is written by Tian Rongchang.This is a philosophical prose with graceful language. Literary translation is a most important and difficult branch of translation. Translator should focus on the literal meaning, culture, writing style and so on. It is a combination of beauty and elegance. Therefore, translators find it in a dilemma of beauty and faithfulness, let alone translating machine. Compared with manual translation, machine translation has difficulty in word choice. It is faithful and expressive, but not elegant enough.<br />
<br />
(2)Chinese into English<br />
<br />
Source language:没有一个人将小草叫做“大力士”，但是它的力量之大，的确是世界无比。这种力，是一般人看不见的生命力，只要生命存在，这种力就要显现，上面的石块，丝毫不足以阻挡。因为它是一种“长期抗战”的力，有弹性，能屈能伸的力，有韧性，不达目的不止的力。(Zhang, 2007:186)#<br />
<br />
Target language:<br />
<br />
Machine translation: No one calls the little grass "hercules", but its power is truly matchless in the world. This force is invisible life force. As long as there is life, this force will show itself. The stone above is not strong enough to stop it. Because it is a "long-term resistance" of the force, elastic, can bend and extend force, tenacity, not to achieve the purpose of the force.<br />
<br />
Manual translation: Though nobody describes the little grass as a “husky”, yet its herculean strength is unrivalled. It is the force of life invisible to naked eye. It will display itself so long as there is life. The rock is utterly helpless before this force- a force that will forever remain militant, a force that is resilient and can take temporary setbacks calmly, a force that is tenacity itself and will never give up until the goal is reached. (by Zhang Peiji)<br />
<br />
Analysis:This is the excerpt of a well-known Chinese prose written by Xia Yan. It is written during the war of Resistance Against Japan. So the prose holds symbolic meaning, eulogizing the invisible tenacious vitality so as to encourage Chinese to have confidence in the anti-aggression war. Compared with manual translation, machine translation is much more abstract and confusing, especially for the word diction. For example, “大力士” is translated into “hercules” which is a man of exceptional strength and size in Greek and Roman Mythology, making it difficult to understand if readers of target language have no idea of the allusion. What’s worse, the machine version doesn’t reveal the symbolic meaning of the text, which is the core of this prose.<br />
<br />
====3.3Vocative text ====<br />
<br />
(1)English into Chinese<br />
<br />
①Source language:<br />
<br />
iPhone went to film school, so you don’t have to. (Advertisement of iPhone13)<br />
<br />
Target language:<br />
<br />
Machine translation: iPhone上的是电影学院，所以你不用去。<br />
<br />
Manual translation:电影专业课，iPhone同学替你上完了。<br />
<br />
Analysis：Here are advertisements of iPhone on Apple official website. There is a personification in the source language. It is used to stress the advancement and proficiency in camera, which is an appealing selling point to potential buyers. Compared with manual translation, machine translation is plain and not eye-catching enough for customers.<br />
<br />
②Source language: <br />
<br />
5G speed OMGGGGG<br />
<br />
Machine language: 5克的速度 OMGGGGG<br />
<br />
Manual translation:<br />
<br />
iPhone的5G 巨巨巨巨巨5G<br />
<br />
Analysis: The “G” in the source language is the unit of speed, standing for generation. However, it is mistaken as a unit of weight, representing gram in the machine translation. So the meaning is not faithful to the source language at all. As for manual translation, it complies with the source in form. Specifically speaking, five “G”s in the former complies with five characters “巨”in the latter. And the pronunciation of the two is similar. There are two layers of meaning for the 5 “G”s. One exclaims the fast speed of 5 generation network and the other new technology. In the manual version, “巨”can be used to show degree, meaning “quite” or “very”. <br />
<br />
③Source language: <br />
<br />
History, faith and reason show the way, the way of unity. We can see each other not as adversaries but as neighbors. We can treat each other with dignity and respect, we can join forces, stop the shouting and lower the temperature. For without unity, there is no peace, only bitterness and fury."<br />
<br />
Target language:<br />
<br />
Machine translation: 历史、信仰和理性指明了团结的道路。我们可以把彼此视为邻居，而不是对手。我们可以尊严地对待彼此，我们可以联合起来，停止大喊大叫，降低温度。因为没有团结，就没有和平，只有痛苦和愤怒。<br />
<br />
Manual translation:历史、信仰和理性为我们指明道路。那是团结之路。我们可以把彼此视为邻居，而不是对手。我们可以有尊严地相互尊重。我们可以联合起来，停止喊叫，减少愤怒。因为没有团结就没有和平，只有痛苦和愤怒<br />
<br />
Analysis: Speech is a way to propagate some activity in public. It is an art to inspire emotion of the audience. The source language is the excerpt of Joe Biden’s inaugural speech. The speech should be inspiring and logic. The machine translation has some misunderstanding. Taking the translation of “lower the temperature” for example, machine only translates its literal meaning, relating to the temperature itself, without considering the context. What’s more, it is less logic than the manual one. Therefore, it adds difficulty to inspire the audience and infect their emotion.<br />
<br />
===4.Common mistakes in machine translation ===<br />
<br />
====4.1 lexical mistakes ====<br />
<br />
Common lexical mistakes include misunderstandings in word category, lexical meaning and emotive and evaluative meaning. Misunderstanding in word category shows in the classification of word in the source language. As for misunderstanding in lexical meaning, machine has difficulty in precisely reflecting the meaning of the original texts, due to different cultural background and different language system. And for misunderstanding in emotive meaning, machine has no intention and emotion like human-beings. Therefore, it’s impossible for it to know writers’ feelings and their writing purposes. So sometimes, it may translate something negative into something positive. (Wang 2008:45)#<br />
<br />
====4.2 grammatical mistakes====<br />
<br />
Grammatical analysis plays an important part in translation. Normally speaking, every language has its own unique grammatical rules. So in the process of translation, if translators don’t know the formation rule well, the sentence meaning will be affected. Even though all the lexical meanings are well-known by translators, the lack of consciousness of grammaticality makes it harder to arrange words according to sequential rule. English tends to be hypotactic, while Chinese tends to be paratactic. English sentences are connected through syntactic devices and lexical devices. While Chinese sentences are semantically connected, which means there are limited logical words and connection words in Chinese. So when translating English sentence, we should first analyze its grammaticality and logical structure and then rearrange its sequence. However, online translating machine has troubles in grammatical analysis, which makes its improvement more difficult.<br />
<br />
====4.3 other mistakes====<br />
<br />
The two mistakes above are the internal ones. Apart from mistakes in linguistic system, there are some mistakes in other aspects, such as cultural background.<br />
<br />
===5.Reasons for its common mistakes ===<br />
<br />
====5.1 Difference in two linguistic system====<br />
<br />
With different history, English and Chinese have different ways of expression. Commonly speaking, English is synthetic language which expresses grammatical meaning through inflection such as tense and Chinese is analytic language which expresses grammatical meaning through word order and function word. In addition, English is more compact with full sentences. Subordinate sentence is one of the most important features in modern English. Chinese, on the other hand, is more diffusive with minor sentences.<br />
<br />
====5.2 Difference in thinking patterns and cultural background====<br />
<br />
According to Sapir-Whorf’s Hypothesis, our language helps mould our way of thinking and consequently, different languages may probably express their unique ways of understanding the world. For two different speech communities, the greater their structural differentiations are, the more diverse their conceptualization of the world will be. For example, western culture is more direct and eastern culture more euphemistic. What’s more, English culture tends to be individualism, focusing on detail, through which it reflects the whole, while Chinese culture tends to be collective. Different thinking patterns will add difficulty for machine to translate texts.<br />
<br />
====5.3 Limitation of computer====<br />
<br />
Recently, there are some breakthroughs and innovation in machine translation. However, due to its own limitation, online translation has limitation in some ways. Firstly, compared with machine, human brain is much more complicated, consisting of ten billions of neuron, each of which has different function to affect human’s daily activities and help humans avoid some errors. However, computer can only function according to preset programming has no intention or consciousness. Until now, countless related scholars have invested much time in machine translation. They upload massive language database, which include almost all linguistic rules. But computers still fail to precisely reflect the meaning of source language for many times due to the complexity and flexibility of language. On the other hand, computers can’t take context into consideration. During translation, it is often the case that machine chooses the most-frequently used meaning of one word. So without the correct and exact meaning, readers are easier to feel confused and even misunderstand the meaning of source language. (Qiu 2021:4)#<br />
<br />
===6.Conclusion===<br />
From the analysis above, we can draw a conclusion that machine deals with informative text best, followed by non-literary translation of expressive text. What’s more, machine can be a useful tool to get to know the gist and main idea of a specific topic, for the simple sentence structure and numerous terms. And it can improve translating efficiency with high speed. But machine has difficulty in translating literary works, especially proses and poems.<br />
<br />
Machine translation has mixed future. From the perspective of commercial, machine translation boasts a bright future. With the process of globalization, the demand for translation is increasing accordingly. On one hand, if we only depend on human translator to deal with translating works, the quality and accuracy of translation can be greatly affected. On the other hand, if machine is used properly to do some basic work, human translators only need to make preparation before translating, progress, polish and other advanced work, contributing to highly-qualified translation and high working efficiency.<br />
<br />
However, compared with manual translation, machine translation has a bleak future. It is still impossible for machine to replace interpreter or translator in a short term. With intelligence and initiative, humans are able to learn new knowledge constantly, which machine will never accomplish. Besides, machine is not used to replace translators but to assist them in work. In other words, translators and machine carry out their own duties and they are not incompatible.(He 2021:5)#<br />
<br />
To draw a conclusion, although there are certain limitations of machine translation, it can serve as a catalyst for translating works. Therefore, with the rapid development of artificial intelligence and related technology, there are still many opportunities for machine translation.<br />
<br />
===Reference ===<br />
<br />
Chen Cheng陈诚.机器翻译技术的综述[J][Overview of Machine Translation Technology].Electronic Techonology 电子技术,2021,50(11):290-291.<br />
<br />
Cui Zihan 崔子涵.机器翻译译文质量对比——以谷歌翻译和DeepL为例[J] [Comparison among Machine Translation--Taking Google Translation and Deepl for Example].Overseas English 海外英语,2021(15):182-183.<br />
<br />
He Xinyu何馨宇.机器翻译的发展及其对翻译职业化的影响研究[J] [The Development of Machine Translation and its Effect on Professional Transltors].Overseas English 海外英语,2021(20):48-49.<br />
<br />
He Wen 何雯, Wang Xiufeng 王秀峰.信息型文本的在线机器翻译错误研究[J][Research on Errors in Online Machine Translation of Informative text ].Overseas English海外英语,2021(15):188-189.<br />
<br />
Li Deyi 李德毅. (2018). 人工智能导论 [Introduction to Artificial Intelligence]. Beijing: China Science and Technology Press 中国科学技术出版社.<br />
<br />
Liu Qin刘琴.功能目的论对于不同文本类型的翻译解读[J][Analysis of Translations in Different Types of Text based on Functionalist Approaches].Overseas Engliosh 海外英语,2021(17):8-9.<br />
<br />
Li Hanji 李晗佶. (2021). 人工智能时代翻译技术与译者关系演变与重构 [Evolution and reconstruction of the relationship between translation technology and translators in the era of artificial intelligence]. 西华师范大学学报(哲学社会科学版) Journal of West China Normal University (PHILOSOPHY AND SOCIAL SCIENCES EDITION) (2021-12-04) 1-6.<br />
<br />
(英) Peter Newmark A Textbook of Translation[M] Shanghai Foreign Education Press, 2002<br />
<br />
Qiu Quanju 仇全菊.大数据时代背景下机器翻译及其发展趋势[J][Machine Translation and its Development Trend under the Background of Big Data Era]. English Teachers 英语教师,2021,21(16):60-62.<br />
<br />
Wang Hongqi 王红旗. (2008). 语言学概论 [Introduction to Linguistics]. Beijing: Peking University Press 北京大学出版社.<br />
<br />
Wei Guang魏光. 人工翻译与机器翻译译文编辑比较研究[J][Comparative Study of Translation Editing between Manual Translation and Machine Translation]. Overseas English 海外英语,2021(19):18-19+21.<br />
<br />
Zhuo Jianbin 卓键滨,Liu Wenxian 刘文娴,Peng Zili 彭子莉.机器翻译对各类型文本的德汉翻译能力探究[J][Research on the German Chinese Translation Ability of Machine Translation for Various Types of Texts]. Comparative Study of Cultural innovation 文化创新比较研究,2021,5(28):122-125.<br />
<br />
Zhang Peiji 张培基.英译中国现代散文选[M][Selected Modern Chinese Prose Writings]. Shanghai Foreign Languages Education Press 上海外语教育出版社, 2002.<br />
<br />
--[[User:Xiong Min|Xiong Min]] ([[User talk:Xiong Min|talk]]) 01:36, 15 December 2021 (UTC)<br />
<br />
=Chapter 11 陈惠妮Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts=<br />
<br />
机器翻译的译前编辑研究——以医学类文摘为例<br />
<br />
陈惠妮 Chen Huini, Hunan Normal University<br />
<br />
[[Machine_Trans_EN_11]]<br />
<br />
===Abstract===<br />
At present, globalization is accelerating and the market demand for language services is rapidly increasing . Machine translation, as an important translation method, can greatly improve translation efficiency due to its low cost and high speed. However, because of the limitations of machine translation and the differences between Chinese and English language, machine translation is not accurate enough. In order to balance translation efficiency and translation quality, a great number of manual revisions in translation are required for the machine translating texts. Medical papers are specialized, special and purposeful, so it requires accurate,qualified and professional translation. However, the quality of translations by machine is inefficient to meet the high-quality requirements of medical papers translation. Therefore, the introduction of pre-editing can greatly improve the efficiency and quality of machine translation.<br />
<br />
===Key words===<br />
Pre-editing, Machine translation, Medical texts<br />
<br />
===题目===<br />
Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts<br />
<br />
===摘要===<br />
在全球化加速发展的今天，市场对语言服务的需求迅速增加。机器翻译作为一种重要的翻译途径，由于其成本低、速度快，可以大大提高翻译效率。然而，由于机器翻译的局限性以及中英文语言的差异，机器翻译的准确性不高。为了平衡翻译效率和翻译质量，机器翻译文本需要大量的手工修改。<br />
医学论文具有专业性、特殊性和目的性，要求其译文准确、合格、专业。然而，机器翻译的质量较低，无法满足医学论文对翻译的高质量要求。因此，译前编辑的引入可以大大提高机器翻译的效率和质量。<br />
<br />
===关键词===<br />
译前编辑；机器翻译；医学文本<br />
<br />
===1. Introduction===<br />
===1.1 Definition of Machine Translation===<br />
As Cronin(2013) revealed: "Translation is undergoing a revolutionary upheaval. The influence of digital technology and the Internet on translation is continuous, extensive and profound. From the popularity of automatic online translation applications, translation revolution is everywhere (Cronin,2013) However,the concept of machine translation was firstly proposed in the 1930s. Since 1940s, the machine translation technology has been evolving from rule-based machine translation (RBMT) to statistical machine translation (SMT), and to neural machine translation (NMT). Machine translation refers to the automatic translation of source language into target language by using a computer system. That is, machine translation refers to the automatic translation of text from one language into another natural language by computer software or other online translation webs. Machine translation is also defined as the process of “using a computer system to automatically translate text or speech from one natural language to another” according to the definition by ISO (Cui 2014:68-73).O 'Brien (2002) defines it as "the behavior of modifying errors in machine translation to ensure that the target translation meets certain quality requirements". On the basic level, machine translation performs mechanical substitution of words in one language for words in another language, but that rarely produces a good translation, therefore, recognition of the whole phrases and their closest counterparts in the target language is needed. Not all words in one language have equivalents in another language, and many words have more than one meaning. A huge demand for translation is greatly needed in today’s global world, which creates new opportunities for the development of machine translation, attracts more and more attention and becomes one of the current research focuses.<br />
<br />
As Cronin(2013) revealed: "Translation is undergoing a revolutionary upheaval. The influence of digital technology and the Internet on translation is continuous, extensive and profound. From the popularity of automatic online translation applications, translation revolution is everywhere (Cronin,2013) However,the concept of machine translation was firstly proposed in the 1930s. Since 1940s, the machine translation technology has been evolving from rule-based machine translation (RBMT) to statistical machine translation (SMT), and to neural machine translation (NMT). Machine translation refers to the automatic translation of source language into target language by using a computer system. That is, machine translation refers to the automatic translation of text from one language into another natural language by computer software or other online translation webs. Machine translation is also defined as the process of “using a computer system to automatically translate text or speech from one natural language to another” according to the definition by ISO (Cui 2014:68-73).O 'Brien (2002) defines it as "the behavior of modifying errors in machine translation to ensure that the target translation meets certain quality requirements". On the basic level, machine translation performs mechanical substitution of words in one language for words in another language, but that rarely produces a good translation, therefore, recognition of the whole phrases and their closest counterparts in the target language is needed. Not all words in one language have equivalents in another language, and many words have more than one meaning. A huge demand for translation is greatly needed in today’s global world, which creates new opportunities for the development of machine translation, attracts more and more attention and becomes one of the current research focuses.<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:34, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===1.2 Definition of Pre-editing===<br />
Pre-editing means to adjust and modify the source language to make it fit more with the characteristics of the machine translation software before putting the source language before the into machine translation, so as to improve the quality of the translation machine translation (Wei Changhong 2008:93-94). Pre-editing is to modify the original text before putting it into machine translation software, in order to improve the recognition rate of machine translation, optimize the output quality of translated text and reduce the workload of post-editing. Because pre-translation editing only needs to be modified in one language, the operation is simpler than post-translation editing, which can realize the double improvement of quality and efficiency. A good pre-editing translation can help machine translation more smoothly, thus improving the machine readability and quality of the output translation. Pre-editing is mostly applied in the following situations: one is when the original text is of poor quality and the machine is difficult to recognize the meaning of the sentence, such as user generated content with poor readability and translatability (Gerlach et al 2003:45-53); Documents that need to be published in multiple languages; Next is when the original text contains a lot of jargon; The last is the original text has a corresponding translation memory bank. If the original text is edited, it can better match the content of the translation memory bank. Pre-editing is a process of identifying problems. It requires to pre-edit the source texts before putting it into machine translation according to the requirements, listing the expressions or sentences that may have trouble in machine translation and then pre-edit it by human. The purpose is to enable the computer to translate better, improve the translatability of machine translation. (Slype G V & Guinet J F & Seitz F 1984:115)<br />
<br />
Pre-editing means to adjust and modify the source language to make it fit more with the characteristics of the machine translation software before putting the source language before the into machine translation, so as to improve the quality of the translation machine translation (Wei Changhong 2008:93-94). Pre-editing is to modify the original text before putting it into machine translation software, in order to improve the recognition rate of machine translation, optimize the output quality of translated text and reduce the workload of post-editing. Because pre-translation editing only needs to be modified in one language, the operation is simpler than post-translation editing, which can realize the double improvement of quality and efficiency. A good pre-editing translation can help machine translation more smoothly, thus improving the machine readability and quality of the output translation. Pre-editing is mostly applied in the following situations: one is when the original text is of poor quality and the machine is difficult to recognize the meaning of the sentence, such as user generated content with poor readability and translatability (Gerlach et al 2003:45-53); Documents that need to be published in multiple languages; Next is when the original text contains a lot of jargon; The last is the original text has a corresponding translation memory bank. If the original text is edited, it can better match the content of the translation memory bank. Pre-editing is a process of identifying problems. It requires to pre-edit the source texts before putting it into machine translation according to the requirements, listing the expressions or sentences that may have trouble in machine translation and then pre-edit it by human. The purpose is to enable the computer to translate better, improve the translatability of machine translation. (Slype G V & Guinet J F & Seitz F 1984:115)<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:36, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===1.3 Machine Translation Mode===<br />
According to the different knowledge acquisition methods, machine translation modes can be classified as follows: one is rule-based machine translation, which is based on bilingual dictionaries and a library of language rules for each language. The quality of translation depends on whether the source language conforms to the existing rules, but the inexhaustible rules are the hinders of this model. The second mode is machine translation based on statistics. This translation model relies on the principles of mathematics and statistics to find various existing translations corresponding to the translation tasks through the employment of corpus, analyzing the frequency of their occurrence and selecting the translation with the highest frequency for output. The disadvantage of this translation model is that it ignores the flexibility of language and the importance of context. The last one is neural network language model. This model is different from previous translation models in that it uses end-to-end neural network to realize automatic translation between natural languages. At present, the quality of its translation is much higher than that of the previous translation models.<br />
<br />
According to the different knowledge acquisition methods, machine translation modes can be classified as follows: one is rule-based machine translation, which is based on bilingual dictionaries and a library of language rules for each language. The quality of translation depends on whether the source language conforms to the existing rules, but the inexhaustible rules are the hinders of this model. The second mode is machine translation based on statistics. This translation model relies on the principles of mathematics and statistics to find various existing translations corresponding to the translation tasks through the employment of corpus, analyzing the frequency of their occurrence and selecting the translation with the highest frequency for output. The disadvantage of this translation model is that it ignores the flexibility of language and the importance of context. The last one is neural network language model. This model is different from previous translation models in that it uses end-to-end neural network to realize automatic translation between natural languages. At present, the quality of its translation is much higher than that of the previous translation models.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===1.4 Source of Study Abstracts===<br />
The core medical journals from domestic are selected in order to make the paper more representative and authoritative, such as National Medical Journal of China, Journal of Peking University (Health Science), and Journal of Third Military Medical University. All of them include basic medical science, biomedical technology, laboratory medical science and other fields. In this way, the pre-editing approaches included are applicable enough to machine translation of medical abstracts.<br />
<br />
The core medical journals from domestic are selected in order to make the paper more representative and authoritative, such as National Medical Journal of China, Journal of Peking University (Health Science), and Journal of Third Military Medical University. All of them include basic medical science, biomedical technology, laboratory medical science and other fields. In this way, the pre-editing approaches included are applicable enough to machine translation of medical abstracts.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===1.5 Selection of Translation Software===<br />
Generally, machine translation software includes Google Translation, Youdao Translator and Niu Translation, which has their own special use in translation. Take Google as an example, it is more like neurons in human brain, enabling to learn and collect information to establish connections with its neural machine translator’s neurons. However, it also causes many errors because of the lack of enough information. In this paper, contrastive analysis will be carried on by using Google translation. On the one hand, being a pioneer in the translation of NET, it is inevitably to sue Google as the translation software to translate medical texts in this paper. Comparing the program called “Google brain”, other NMT of translation software are relatively disadvantages. On the other hand, the Google translation enjoys the largest users in the world, with its downloads of more than one billion. The output quality of Google translation is more correct and complete than other machine translation software. With years of development and improvement, Google Translation has been greatly promoted. In this paper, Chinese medical abstract will be automatically translated by Google translation, and then the translation output will be compared with the translation by human and the publication of English abstracts. The main purpose is to prove that the improvement and promotion of quality and accuracy in the medical abstracts will be obtained through the pre-editing approaches.<br />
<br />
Generally, machine translation software includes Google Translation, Youdao Translator and Niu Translation, which has their own special use in translation. Take Google as an example, it is more like neurons in human brain, enabling to learn and collect information to establish connections with its neural machine translator’s neurons. However, it also causes many errors because of the lack of enough information. In this paper, contrastive analysis will be carried on by using Google translation. On the one hand, being a pioneer in the translation of NET, it is inevitably to sue Google as the translation software to translate medical texts in this paper. Comparing the program called “Google brain”, other NMT of translation software are relatively disadvantages. On the other hand, the Google translation enjoys the largest users in the world, with its downloads of more than one billion. The output quality of Google translation is more correct and complete than other machine translation software. With years of development and improvement, Google Translation has been greatly promoted. In this paper, Chinese medical abstract will be automatically translated by Google translation, and then the translation output will be compared with the translation by human and the publication of English abstracts. The main purpose is to prove that the improvement and promotion of quality and accuracy in the medical abstracts will be obtained through the pre-editing approaches.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===2.Language Characteristics and Error Division===<br />
Since Chinese and English are two different languages, it is quite neccessary to identify their own characteristics so as to better analyze and understand the two languages. Tytler (1978: 118-119) argued in his Essay on the Principles of Translation that there are three principles of translation that in all the translation should give readers the same feelings as the source text, except for complete transcript of ideas. There actually also exit some mistakes of these two languages. So the following will make some clarrifications of these two languages to ensure more accurate translation.<br />
<br />
Since Chinese and English are two different languages, it is quite neccessary to identify their own characteristics so as to better analyze and understand the two languages. Tytler (1978: 118-119) argued in his Essay on the Principles of Translation that there are three principles of translation that in all the translation should give readers the same feelings as the source text, except for complete transcript of ideas. There actually also exit some mistakes of these two languages. So the following will make some clarrifications of these two languages to ensure more accurate translation.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===2.1 Language Characteristics of Medical Abstracts===<br />
Chinese and English belong to different language systems, so there are differences in their language structure and the way users think of the languages. When using machine translation from Chinese to English, due to the unequal language levels, there will be many mistakes in the translation process, especially for ESP texts, such as medical papers.Here, these differences mainly refer to the linguistic characteristics of medical abstracts. In medical abstracts, it usually includes structured and unstructured abstracts. Although in different forms, they both describe the purpose, methods and conclusions of the research.<br />
<br />
In the method section of medial abstract, several Chinese sentences can be connected with commas, but each sentence may convey different information. In contrast, English sentences contain a great deal of information, but in order to ensure clarity, some modifiers need to be isolated and then reconstructed. However, in Chinese-English machine translation, a lot of information is put into the sentence because the machine segments the sentence on the basis of the full comma.<br />
<br />
In addition, subjectless sentence is used in the objective and method parts of Chinese medical abstracts. The subjectless sentence means the sentence without or free from subject and is usually employed in two contexts. The first is "needless to say". In Chinese sentences, it is common to omit the subject of the sentence. Chinese sentences can convey meanings by using incomplete sentence structures, so Chinese speakers can understand the meanings of the sentences even though the sentence subject is omitted. The second is "emphasis of action". In this context, subjectless sentences are used to describe behaviors, especially in the study of traditional Chinese medicine abstracts. Comparatively speaking, it should be avoided in English medical abstracts. Abstract sentences are more subjective when describing the learning process, while the essential requirement of English medical abstract is objectivity. From this point, sentences without subject should be avoided in English medical abstracts. Another feature is voice. Few words with passive meanings appear in Chinese abstracts. English sentences are more favoured in using passive voice. When translating from Chinese to English, passive voice should be used to make the contents objective, which is also the basic requirement of medical papers.<br />
<br />
These are the linguistic features of Chinese medical abstracts. There are great differences in sentence structure and expression between Chinese and English medical abstracts. These differences may reduce the accuracy of machine translation, so it is necessary to introduce pre-editing to edit the source text to ensure that the source text can be accurately recognized by the machine and fully translated into English.<br />
<br />
Chinese and English belong to different language systems, so there are differences in their language structure and the way users think of the languages. When using machine translation from Chinese to English, due to the unequal language levels, there will be many mistakes in the translation process, especially for ESP texts, such as medical papers.Here, these differences mainly refer to the linguistic characteristics of medical abstracts. In medical abstracts, it usually includes structured and unstructured abstracts. Although in different forms, they both describe the purpose, methods and conclusions of the research.<br />
<br />
In the method section of medial abstract, several Chinese sentences can be connected with commas, but each sentence may convey different information. In contrast, English sentences contain a great deal of information, but in order to ensure clarity, some modifiers need to be isolated and then reconstructed. However, in Chinese-English machine translation, a lot of information is put into the sentence because the machine segments the sentence on the basis of the full comma.<br />
<br />
In addition, subjectless sentence is used in the objective and method parts of Chinese medical abstracts. The subjectless sentence means the sentence without or free from subject and is usually employed in two contexts. The first is "needless to say". In Chinese sentences, it is common to omit the subject of the sentence. Chinese sentences can convey meanings by using incomplete sentence structures, so Chinese speakers can understand the meanings of the sentences even though the sentence subject is omitted. The second is "emphasis of action". In this context, subjectless sentences are used to describe behaviors, especially in the study of traditional Chinese medicine abstracts. Comparatively speaking, it should be avoided in English medical abstracts. Abstract sentences are more subjective when describing the learning process, while the essential requirement of English medical abstract is objectivity. From this point, sentences without subject should be avoided in English medical abstracts. Another feature is voice. Few words with passive meanings appear in Chinese abstracts. English sentences are more favoured in using passive voice. When translating from Chinese to English, passive voice should be used to make the contents objective, which is also the basic requirement of medical papers.<br />
<br />
These are the linguistic features of Chinese medical abstracts. There are great differences in sentence structure and expression between Chinese and English medical abstracts. These differences may reduce the accuracy of machine translation, so it is necessary to introduce pre-editing to edit the source text to ensure that the source text can be accurately recognized by the machine and fully translated into English.<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===2.2 Error Division of Machine Translation in Translating Abstracts of Medical Papers from Chinese to English===<br />
In this paper, errors in machine translation are listed out after analyzing. Some are due to the principles, such as statistical-based MT and NMT, employed by machine translation. Based on the original errors, they can be studied from two levels. One is from the macro-level, referring to mistakes caused by objective factors. These are not human factors, but the disadvantage of machine translation and the limitations of language. The limitations of machine translation are derived from the principles and models adopted and manifests itself as a reliance on reference sources. In this case, semantic ambiguity and textual incoherence may occur in the absence of reference sources.<br />
<br />
However, for ESP texts such as medical papers, the requirements for terms and sentence patterns are far beyond the existing corpora. For unfamiliar texts (that is, the corresponding texts cannot be found in the corpora). The quality of output translation will be relatively low, which is determined by the principles of machine translation. As mentioned above, machine translation has evolved over the past few decades from rules-based to corp-based statistics to NMT. However, there still exist some limitations in machine translation.<br />
<br />
Mistakes on micro-level are mainly caused by the variations and differences of linguistic structure between Chinese and English. Chinese is an implicit language, while English is an explicit one (Lian, 2010) To put it in another way, Chinese expression does not depend on language structures, while English does the opposite. This may result in mismatches between the original and machine translated sentences. This kind of error is mainly divided into two types: component fragment and component missing. For a medical paper, such errors are very serious. As a kind of ESP discourse, medical paper has the characteristics of fixed, objective and accurate language structure. In order to reproduce the characteristics of medical abstracts in translation, it is necessary to avoid errors at the level of words and sentences and pay attention to logic and consistency.<br />
<br />
Lexical errors here refer to the inconsistency of fixed expressions in the translation of terms. Although these terms are machine translation based on the corpus, the corpus may not be perfect for ESP texts such as medical papers. Therefore, fixed expressions are not used in term translation. This is also the most common mistake in machine translation (the term here includes but is not limited to nouns). For ESP texts, medical text in particular, the fixed expressions and the accuracy of terms are of great importance.<br />
<br />
In this paper, errors in machine translation are listed out after analyzing. Some are due to the principles, such as statistical-based MT and NMT, employed by machine translation. Based on the original errors, they can be studied from two levels. One is from the macro-level, referring to mistakes caused by objective factors. These are not human factors, but the disadvantage of machine translation and the limitations of language. The limitations of machine translation are derived from the principles and models adopted and manifests itself as a reliance on reference sources. In this case, semantic ambiguity and textual incoherence may occur in the absence of reference sources.<br />
<br />
However, for ESP texts such as medical papers, the requirements for terms and sentence patterns are far beyond the existing corpora. For unfamiliar texts (that is, the corresponding texts cannot be found in the corpora). The quality of output translation will be relatively low, which is determined by the principles of machine translation. As mentioned above, machine translation has evolved over the past few decades from rules-based to corp-based statistics to NMT. However, there still exist some limitations in machine translation.<br />
<br />
Mistakes on micro-level are mainly caused by the variations and differences of linguistic structure between Chinese and English. Chinese is an implicit language, while English is an explicit one (Lian, 2010) To put it in another way, Chinese expression does not depend on language structures, while English does the opposite. This may result in mismatches between the original and machine translated sentences. This kind of error is mainly divided into two types: component fragment and component missing. For a medical paper, such errors are very serious. As a kind of ESP discourse, medical paper has the characteristics of fixed, objective and accurate language structure. In order to reproduce the characteristics of medical abstracts in translation, it is necessary to avoid errors at the level of words and sentences and pay attention to logic and consistency.<br />
<br />
Lexical errors here refer to the inconsistency of fixed expressions in the translation of terms. Although these terms are machine translation based on the corpus, the corpus may not be perfect for ESP texts such as medical papers. Therefore, fixed expressions are not used in term translation. This is also the most common mistake in machine translation (the term here includes but is not limited to nouns). For ESP texts, medical text in particular, the fixed expressions and the accuracy of terms are of great importance.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===3.Approaches Proposed for Pre-editing ===<br />
Generally speaking, there is a close relationship between pre-editing and post-editing, both of which aim to convey information and ensure high or publishable translation quality. Proper pre-editing can improve the quality of machine translation in terms of adequacy and consistency. <br />
Complexity of natural language and people use language arbitrarily, bringing many difficulties to Chinese-English machine translation, Hu Qingping (2005:24) has proposed "the research of translation software and the development of the controlled language are the two directions to improve the quality of machine translation : the former aims at the difficulty in the natural language processing, the latter overcomes the arbitrariness of natural language". Feng Quangong and Gao Lin (2017: 63-68) put forward: "The writing principles of controlled language can be applied to pre-editing of machine translation. Pre-editing based on controlled language can effectively reduce the complexity and ambiguity of source text, improve the identifiability of machine translation (the translatability of source text itself), and thus reduce (fully) the workload of post-editing".<br />
The central task of pre-editing is to transform human-friendly content into machine-friendly content, so words and sentences need to be repositioned or even changed. Based on the analysis of the language characteristics of Chinese and English, the Chinese ideographic group should be split before the Chinese source text is input into the machine software to translate so that the sentence structure is complete which can be easily recognized by the machine translation software.<br />
<br />
Generally speaking, there is a close relationship between pre-editing and post-editing, both of which aim to convey information and ensure high or publishable translation quality. Proper pre-editing can improve the quality of machine translation in terms of adequacy and consistency. <br />
<br />
Complexity of natural language and people use language arbitrarily, bringing many difficulties to Chinese-English machine translation, Hu Qingping (2005:24) has proposed "the research of translation software and the development of the controlled language are the two directions to improve the quality of machine translation : the former aims at the difficulty in the natural language processing, the latter overcomes the arbitrariness of natural language". Feng Quangong and Gao Lin (2017: 63-68) put forward: "The writing principles of controlled language can be applied to pre-editing of machine translation. Pre-editing based on controlled language can effectively reduce the complexity and ambiguity of source text, improve the identifiability of machine translation (the translatability of source text itself), and thus reduce (fully) the workload of post-editing".<br />
<br />
The central task of pre-editing is to transform human-friendly content into machine-friendly content, so words and sentences need to be repositioned or even changed. Based on the analysis of the language characteristics of Chinese and English, the Chinese ideographic group should be split before the Chinese source text is input into the machine software to translate so that the sentence structure is complete which can be easily recognized by the machine translation software.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufefng<br />
<br />
===3.1 Extraction and Replacement of Terms in the Source Text===<br />
As a branch of applied English, medical English is the product of the combination of English language knowledge and medical knowledge. The terms in medical papers have the characteristics of fixed and complex language structure. Although Google Translation is based on a corpus, the language structure is not fixed due to the limited size of the corpus. Therefore, the following two steps should be followed before machine translation. The first is to extract terms and create a glossary, and then replace the Chinese terms in the source text with the corresponding English expressions in the glossary. Of course, the terms of extraction should be searched and verified according to the actual situation. All of these words are verified before they can be replaced. This method can not only ensure the accuracy of term translation, but also maintain the consistency of expression, especially when dealing with long texts.<br />
Example1<br />
Source abstract: 口腔白斑病癌变相关缺氧应答基因和微小RNA的芯片及表达验证。<br />
Google translation: Microarray detection/Chip detection and expression verification of hypoxia response genes and microRNAs related to oral leukoplakia canceration.<br />
Published translation:Transcriptome array screening and verification of oral leukoplakia carcinogenesis-related hypoxia-responsive gene and microRNA. <br />
Analysis: The expression “ Affymetrix GeneChip” empolyed in this thesis is a human transcription array for transcriptome array. In the source abstract, “芯片检测“ can be meant “screening” but not detection, so the proper and appropriate translation of these expression should be “transcription array screening”, but the Google translates it into “microarry detection of chip detection”. Therefore, term extraction is essential and inevitable before putting the source abstracts into the machine translation software.<br />
After pre-editing source abstracts: 对 oral leukoplakia carcinogenesis 相关 hypoxia-responsive gene 和微小RNA进行 transcriptome array screening 及表达验证。<br />
After pre-editing translation: Transcriptome array screening and expression- verification of hypoxia-responsive genes and microRNAs related to oral leukoplakia carcinogenesis.<br />
<br />
As a branch of applied English, medical English is the product of the combination of English language knowledge and medical knowledge. The terms in medical papers have the characteristics of fixed and complex language structure. Although Google Translation is based on a corpus, the language structure is not fixed due to the limited size of the corpus. Therefore, the following two steps should be followed before machine translation. The first is to extract terms and create a glossary, and then replace the Chinese terms in the source text with the corresponding English expressions in the glossary. Of course, the terms of extraction should be searched and verified according to the actual situation. All of these words are verified before they can be replaced. This method can not only ensure the accuracy of term translation, but also maintain the consistency of expression, especially when dealing with long texts.<br />
<br />
Example1<br />
Source abstract: 口腔白斑病癌变相关缺氧应答基因和微小RNA的芯片及表达验证。<br />
Google translation: Microarray detection/Chip detection and expression verification of hypoxia response genes and microRNAs related to oral leukoplakia canceration.<br />
Published translation:Transcriptome array screening and verification of oral leukoplakia carcinogenesis-related hypoxia-responsive gene and microRNA. <br />
Analysis: The expression “ Affymetrix GeneChip” empolyed in this thesis is a human transcription array for transcriptome array. In the source abstract, “芯片检测“ can be meant “screening” but not detection, so the proper and appropriate translation of these expression should be “transcription array screening”, but the Google translates it into “microarry detection of chip detection”. Therefore, term extraction is essential and inevitable before putting the source abstracts into the machine translation software.<br />
After pre-editing source abstracts: 对 oral leukoplakia carcinogenesis 相关 hypoxia-responsive gene 和微小RNA进行 transcriptome array screening 及表达验证。<br />
After pre-editing translation: Transcriptome array screening and expression- verification of hypoxia-responsive genes and microRNAs related to oral leukoplakia carcinogenesis.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===3.2 Explication of Subordination===<br />
As mentioned above, the subordination of Chinese sentences is judged by certain specific words in machine translation . Chinese is a paratactic language, and sentences are often connected by internal logical relationships; while English depends on sentence structure, so sentences are often closely linked by various language forms. In order to improve the accuracy of machine translation, we can adjust the sentence structure and adjust the position of adjectives and their modifiers. <br />
<br />
Example 2<br />
Source abstracts:回顾性分析南京医科大学附属儿童医院中重度HIE患儿49例及同期就诊的无神经系统症状体征的足月新生儿为对照组31例的头颅磁共振成像（MRI）资料。<br />
Google translation: A retrospective analysis that the brain magnetic resonance imaging (MRI) data of 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University and full-term neonates with no neurological symptoms and signs during the same period were included in the control group.<br />
Published translation: A total of 49 children with moderate to severe HIE admitted to the children’s Hospital Affiliated to Nanjing Medical University were retrospectively analyzed. Cranial magnetic resonance imaging (MRI) date of 31 full-term neonates without neurological symptoms and signs who visited the hospital during the same period were recruited as the control group. <br />
<br />
Analysis: As the above example shows that “中重度HIE患儿” and “足月新生儿” in the source abstracts are from the Children’s Hospital Affiliated Nanjing Medical University. The Google translation shows that 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University. There is an error due to the misuse of subordination. There are some advice in order to solve the problem. That is, first to segment the sentence and then reconstruct the sentence. For instance, the Chinese expression “南京医科大学附属儿童医院” carries many modifiers, which requires to cut the modifiers and reconstruct the sentence. <br />
Therefore, the Chinese expression “南京医科大学附属儿童医院’can be divided into abstractions, leaving two sentences instead of one long sentence with modifiers..<br />
After pre-editing source abstracts: 对49例中重度HIE患儿以及31例无神经系统症状体征的足月新生儿的MRI资料进行回顾性分析。这些患者收治于南京医科大学儿童附属医院。<br />
After pre-editing translation: The MRI data of 49 children with moderate to severe HIE and 31 full-term newborns without neurological symptoms and signs were retrospectively analyzed. These patients were admitted to the Children’s Hospital of Nanjing Medical University.<br />
<br />
As mentioned above, the subordination of Chinese sentences is judged by certain specific words in machine translation . Chinese is a paratactic language, and sentences are often connected by internal logical relationships; while English depends on sentence structure, so sentences are often closely linked by various language forms. In order to improve the accuracy of machine translation, we can adjust the sentence structure and adjust the position of adjectives and their modifiers. <br />
<br />
Example 2<br />
Source abstracts:回顾性分析南京医科大学附属儿童医院中重度HIE患儿49例及同期就诊的无神经系统症状体征的足月新生儿为对照组31例的头颅磁共振成像（MRI）资料。<br />
Google translation: A retrospective analysis that the brain magnetic resonance imaging (MRI) data of 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University and full-term neonates with no neurological symptoms and signs during the same period were included in the control group.<br />
Published translation: A total of 49 children with moderate to severe HIE admitted to the children’s Hospital Affiliated to Nanjing Medical University were retrospectively analyzed. Cranial magnetic resonance imaging (MRI) date of 31 full-term neonates without neurological symptoms and signs who visited the hospital during the same period were recruited as the control group. <br />
<br />
Analysis: As the above example shows that “中重度HIE患儿” and “足月新生儿” in the source abstracts are from the Children’s Hospital Affiliated Nanjing Medical University. The Google translation shows that 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University. There is an error due to the misuse of subordination. There are some advice in order to solve the problem. That is, first to segment the sentence and then reconstruct the sentence. For instance, the Chinese expression “南京医科大学附属儿童医院” carries many modifiers, which requires to cut the modifiers and reconstruct the sentence. <br />
<br />
Therefore, the Chinese expression “南京医科大学附属儿童医院’can be divided into abstractions, leaving two sentences instead of one long sentence with modifiers..<br />
After pre-editing source abstracts: 对49例中重度HIE患儿以及31例无神经系统症状体征的足月新生儿的MRI资料进行回顾性分析。这些患者收治于南京医科大学儿童附属医院。<br />
After pre-editing translation: The MRI data of 49 children with moderate to severe HIE and 31 full-term newborns without neurological symptoms and signs were retrospectively analyzed. These patients were admitted to the Children’s Hospital of Nanjing Medical University.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===3.3 Explication of Subject through Voice Changing===<br />
The extensive use of subjectless sentences are quite common in the Chinese abstracts, while English prefers to employ passive voice in the sentences so as to make them object and accurate. In order to take the characteristics of English sentences into great and careful consideration, the sentences written in passive voice in particular, it will be helpful for machine translation to find out the subject and reconstruct a sentence in passive voice (Wang Yan: 2008). The first is to discover the “doee” in a sentence and then is to reconstruct the sentence structure and put the “doee” before the verb. The last is to explicate the passive relation between the noun and the verb.<br />
Example 3<br />
Source abstract: 回顾性分析2018年1月至2020年12月解放军总医院第七医学中心收治的500例老年髋部骨折患者的资料。<br />
Google translation: A retrospective analysis of the data of 500 elderly patients with hip fractures admitted to the Seventh Medical Center of the PLA General Hospital from January 2018 to December 2020.<br />
Published translation: From January 2018 to December 2020, the data of 500 elderly patients with hip fracture treated in the Seventh Medical Center of PLA General Hospital were analyzed retrospectively.<br />
Analysis: The above example indicates that the “回顾性分析”in the Google Translate is a noun phrase, but the source abstracts actually describes an action or a behavior. The reason for such a mistake is that the machine can’t recognize the Chinese subjetless sentences. To find the subject of the sentence, the source abstracts can be revised and adjusted. Finding the “doee” is the first step, which refers to “500例老年髋部骨折患者的资料”in the source abstracts. And next is to put it in front of the verb, that is to place it in the beginning of the sentence. Last but not least, the passive voice should be explicated in the sentence. <br />
After pre-editing source abstract: 500例老年髋部骨折患者的资料被回顾性分析，他们在2018年1月之2020年12月期间收治于解放军总医院第七医学中心。<br />
After pre-editing translation: The data of 500 elderly hip fracture patients were retrospectively analyzed. They were admitted to the Seventh Medical Center of the PLA General Hospital between January 2018 and December 2020.<br />
<br />
The extensive use of subjectless sentences are quite common in the Chinese abstracts, while English prefers to employ passive voice in the sentences so as to make them object and accurate. In order to take the characteristics of English sentences into great and careful consideration, the sentences written in passive voice in particular, it will be helpful for machine translation to find out the subject and reconstruct a sentence in passive voice (Wang Yan: 2008). The first is to discover the “doee” in a sentence and then is to reconstruct the sentence structure and put the “doee” before the verb. The last is to explicate the passive relation between the noun and the verb.<br />
<br />
Example 3<br />
Source abstract: 回顾性分析2018年1月至2020年12月解放军总医院第七医学中心收治的500例老年髋部骨折患者的资料。<br />
Google translation: A retrospective analysis of the data of 500 elderly patients with hip fractures admitted to the Seventh Medical Center of the PLA General Hospital from January 2018 to December 2020.<br />
Published translation: From January 2018 to December 2020, the data of 500 elderly patients with hip fracture treated in the Seventh Medical Center of PLA General Hospital were analyzed retrospectively.<br />
<br />
Analysis: The above example indicates that the “回顾性分析”in the Google Translate is a noun phrase, but the source abstracts actually describes an action or a behavior. The reason for such a mistake is that the machine can’t recognize the Chinese subjetless sentences. To find the subject of the sentence, the source abstracts can be revised and adjusted. Finding the “doee” is the first step, which refers to “500例老年髋部骨折患者的资料”in the source abstracts. And next is to put it in front of the verb, that is to place it in the beginning of the sentence. Last but not least, the passive voice should be explicated in the sentence. <br />
After pre-editing source abstract: 500例老年髋部骨折患者的资料被回顾性分析，他们在2018年1月之2020年12月期间收治于解放军总医院第七医学中心。<br />
After pre-editing translation: The data of 500 elderly hip fracture patients were retrospectively analyzed. They were admitted to the Seventh Medical Center of the PLA General Hospital between January 2018 and December 2020.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===3.4 Relocation of Modifiers===<br />
Modifiers, including noun, adverbial and attributive are constantly employed in the Chinese medical papers in order to add information to subject and object in the sentence. By doing this, complicated sentences can be structured, thus causing obstacles to machine translation for recognizing this complex sentence structures when translating Chinese-English sentences. To make the machine translation successfully recognize the sentence structure, simplifying the sentence structure is quite necessary. The key is to moving the location of the modifiers and thus making the modifiers an independent sentence. In other words, the modifiers ought to be put before or behind the main part of the sentence to satisfy the common use of English. <br />
Usually, the modifiers in Chinese sentence can only be placed in front of the core word, while in English, modifiers are very flexible. It is all right to place the modifiers in front of or behind the major part of the sentences with adjective or a connective noun.<br />
<br />
Example 4<br />
Source abstracts: 利用DNA重组技术以pET-28a表达系统在E.coli BL21(DE3) 中重组表达Hepl。<br />
Google Translation: Hepl is recombined in E.coli BL21 (DE3) using DNA recombination technology with pET-28a expression system.<br />
Published translation: The recombinant Hcpl protein was expressed by using DNA recombination technology through pET-28a expression system in E. coli BL21 (De3).<br />
Analysis: The Chinese medical text includes two modifiers, “利用DNA”and “以pET-28a)表达系统”. These two modifiers will be translated by machine, which catches more attention to the two constituents. From the Google translation above, one failure is obvious that it misplaces the location of the two modifiers and presents it in not accurate form. To make it translate correctly by machine translation, dividing the sentences is very important so that the source abstracts can be correctly recognized by machine translation.<br />
<br />
After pre-editing source abstract: 利用DNA重组技术，Hcp1重组表达在E.coli BL21 (DE3)中， 通过pET-28a表达系统在E. coli BL21 (DE3)中。<br />
Google translation: Using DNA recombination technology, Hcp1 recombination is expressed in E.coli BL21 (DE3), via pET-28a expression system in E. coli BL21 (DE3).<br />
The second is the independence of modifiers, that is, modifiers can also be reconstructed into clauses to modify core words. Compared with English, There is no subordinate clause in Chinese, so redundant Chinese modifiers need to be reconstructed into subordinate clauses in Chinese-English translation to meet the characteristics of English. English, especially sentences with multiple modifiers. Otherwise, sentence structure may be confused, such as scattered modifiers of core words. To avoid this mistake, it is necessary to separate the modifier from the main part of the sentence. These modifiers should be reconstituted into clauses. Also, keep your sentences simple and easy to understand.<br />
<br />
Modifiers, including noun, adverbial and attributive are constantly employed in the Chinese medical papers in order to add information to subject and object in the sentence. By doing this, complicated sentences can be structured, thus causing obstacles to machine translation for recognizing this complex sentence structures when translating Chinese-English sentences. To make the machine translation successfully recognize the sentence structure, simplifying the sentence structure is quite necessary. The key is to moving the location of the modifiers and thus making the modifiers an independent sentence. In other words, the modifiers ought to be put before or behind the main part of the sentence to satisfy the common use of English. <br />
Usually, the modifiers in Chinese sentence can only be placed in front of the core word, while in English, modifiers are very flexible. It is all right to place the modifiers in front of or behind the major part of the sentences with adjective or a connective noun.<br />
<br />
Example 4<br />
Source abstracts: 利用DNA重组技术以pET-28a表达系统在E.coli BL21(DE3) 中重组表达Hepl。<br />
Google Translation: Hepl is recombined in E.coli BL21 (DE3) using DNA recombination technology with pET-28a expression system.<br />
Published translation: The recombinant Hcpl protein was expressed by using DNA recombination technology through pET-28a expression system in E. coli BL21 (De3).<br />
Analysis: The Chinese medical text includes two modifiers, “利用DNA”and “以pET-28a)表达系统”. These two modifiers will be translated by machine, which catches more attention to the two constituents. From the Google translation above, one failure is obvious that it misplaces the location of the two modifiers and presents it in not accurate form. To make it translate correctly by machine translation, dividing the sentences is very important so that the source abstracts can be correctly recognized by machine translation.<br />
<br />
After pre-editing source abstract: 利用DNA重组技术，Hcp1重组表达在E.coli BL21 (DE3)中， 通过pET-28a表达系统在E. coli BL21 (DE3)中。<br />
Google translation: Using DNA recombination technology, Hcp1 recombination is expressed in E.coli BL21 (DE3), via pET-28a expression system in E. coli BL21 (DE3).<br />
The second is the independence of modifiers, that is, modifiers can also be reconstructed into clauses to modify core words. Compared with English, There is no subordinate clause in Chinese, so redundant Chinese modifiers need to be reconstructed into subordinate clauses in Chinese-English translation to meet the characteristics of English. English, especially sentences with multiple modifiers. Otherwise, sentence structure may be confused, such as scattered modifiers of core words. To avoid this mistake, it is necessary to separate the modifier from the main part of the sentence. These modifiers should be reconstituted into clauses. Also, keep your sentences simple and easy to understand.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===3.5 Proper Omission and Deletion of Category Words===<br />
Category words are commonly used in Chinese. Category words complement the meaning of words, including problems, positions, situations and jobs. Adding this supplementary word is more in line with Chinese custom. In many cases, it has no real meaning, so it can be omitted in translation. In Chinese, category words are frequently used. However, it is rarely used in English, which is one of the differences between Chinese and English. There are many kinds of category words. Considering objectivity and the fixed structure of language, redundant category words should be deleted in Chinese-English translation. In the general, the most commonly used category of words in the Chinese medical abstracts are "process", "behavior", and "situation". These categories of words hinder the language conversion of Chinese and English, resulting in redundancy. <br />
<br />
Example 5<br />
Source abstract: 探讨口腔白斑病癌变进程中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia response genes and associated microRNAs in the process of oral leukoplakia cancer was discussed.<br />
Published translation: To study the hypoxia response gene and microRNA (miRNA)expression profiles in the pathogenesis and progression of oral leukoplakia (OLK).<br />
Analysis: As the above example shows, the Chinese words “进程” belongs to a category word. However, the Chinese expression “癌变”contains the process, so the “进程” expression can be deleted before placing it into the machine translation, because the meaning of it has been overlapping between the expression “癌变”. Therefore, its place can be replaced with preposition “during”.<br />
<br />
After pre-editing source abstract: 探讨口腔白斑病癌变中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia responsive genes and miRNA in oral leukoplakia cancer was investigated.<br />
<br />
Category words are commonly used in Chinese. Category words complement the meaning of words, including problems, positions, situations and jobs. Adding this supplementary word is more in line with Chinese custom. In many cases, it has no real meaning, so it can be omitted in translation. In Chinese, category words are frequently used. However, it is rarely used in English, which is one of the differences between Chinese and English. There are many kinds of category words. Considering objectivity and the fixed structure of language, redundant category words should be deleted in Chinese-English translation. In the general, the most commonly used category of words in the Chinese medical abstracts are "process", "behavior", and "situation". These categories of words hinder the language conversion of Chinese and English, resulting in redundancy. <br />
<br />
Example 5<br />
Source abstract: 探讨口腔白斑病癌变进程中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia response genes and associated microRNAs in the process of oral leukoplakia cancer was discussed.<br />
Published translation: To study the hypoxia response gene and microRNA (miRNA)expression profiles in the pathogenesis and progression of oral leukoplakia (OLK).<br />
Analysis: As the above example shows, the Chinese words “进程” belongs to a category word. However, the Chinese expression “癌变”contains the process, so the “进程” expression can be deleted before placing it into the machine translation, because the meaning of it has been overlapping between the expression “癌变”. Therefore, its place can be replaced with preposition “during”.<br />
<br />
After pre-editing source abstract: 探讨口腔白斑病癌变中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia responsive genes and miRNA in oral leukoplakia cancer was investigated.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===Conclusion===<br />
After years of development, machine translation has made great progress. The accuracy of machine translation has been greatly improved in both text recognition and sentence pattern conversion. However, machine translation has its own limitations. In other words, it needs to rely on the parallel corpus as a reference source for improving its accuracy. ESP text, in particular, is harder to get the high quality by machine translation. <br />
<br />
As one of the research papers, the characteristics of medical abstracts are fixed language structures, objectivity and accuracy (Qin Yi 2004:421-423). Therefore, medical translation must be accurate, object and understandable to follow the specific demands of the medical paper. Being an important field in the human society, medical paper translation is on a great demand, which means that it needs a huge demand for human labor. However, with the machine translation promoting, it will be more efficient to translate medical papers combining the effort by human and machine. The improvement and development of machine translation requires the joint efforts of computer science, information science, statistics, linguistics and other academic circles to achieve more mature human-computer mutual assistance translation (Li Yafei, Zhang Ruihua 2019:38-45).<br />
<br />
However, errors can occur during the process of machine translation of Chinese- English, because of the differences of the Chinese and English and the processing of the machine. Errors from the perspective of linguistic or grammar can affect the machine translation a lot. After division and recognition of errors, some pre-editing approaches are put forward to help the machine translation more accurate and readable, that are, extraction and replacement of terms in the source text, relocation of modifiers, explication of subordination, proper omission, deletion of category words and explication of subject through voice changing. <br />
<br />
<br />
The paper mainly focuses on the pre-editing machine translation by using medical papers as a case study. The errors of machine translation occurring in the translation of medical abstracts and pre-editing approaches for machine translation. The quality of machine translation of medical papers is greatly improved after employing the pre-editing methods. However, machine translation is not as flexible and accurate as human brain, so it is of importance to combine pre-editing and post-editing approaches with machine translation in order to produce more accurate, more object machine translation of medical papers.<br />
<br />
After years of development, machine translation has made great progress. The accuracy of machine translation has been greatly improved in both text recognition and sentence pattern conversion. However, machine translation has its own limitations. In other words, it needs to rely on the parallel corpus as a reference source for improving its accuracy. ESP text, in particular, is harder to get the high quality by machine translation. <br />
<br />
As one of the research papers, the characteristics of medical abstracts are fixed language structures, objectivity and accuracy (Qin Yi 2004:421-423). Therefore, medical translation must be accurate, object and understandable to follow the specific demands of the medical paper. Being an important field in the human society, medical paper translation is on a great demand, which means that it needs a huge demand for human labor. However, with the machine translation promoting, it will be more efficient to translate medical papers combining the effort by human and machine. The improvement and development of machine translation requires the joint efforts of computer science, information science, statistics, linguistics and other academic circles to achieve more mature human-computer mutual assistance translation (Li Yafei, Zhang Ruihua 2019:38-45).<br />
<br />
However, errors can occur during the process of machine translation of Chinese- English, because of the differences of the Chinese and English and the processing of the machine. Errors from the perspective of linguistic or grammar can affect the machine translation a lot. After division and recognition of errors, some pre-editing approaches are put forward to help the machine translation more accurate and readable, that are, extraction and replacement of terms in the source text, relocation of modifiers, explication of subordination, proper omission, deletion of category words and explication of subject through voice changing. <br />
<br />
The paper mainly focuses on the pre-editing machine translation by using medical papers as a case study. The errors of machine translation occurring in the translation of medical abstracts and pre-editing approaches for machine translation. The quality of machine translation of medical papers is greatly improved after employing the pre-editing methods. However, machine translation is not as flexible and accurate as human brain, so it is of importance to combine pre-editing and post-editing approaches with machine translation in order to produce more accurate, more object machine translation of medical papers.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===References===<br />
<br />
Cui Qiliang崔启亮(2014).论机器翻译的译后编辑[J] ''On Post-Editing of Machine Translatio''. 中国翻译 Chinese Translators Journal, 035(006):68-73<br />
<br />
Feng Quangong, Gao Lin冯全功,高琳 (2017). 基于受控语言的译前编辑对机器翻译的影响[J] ''Influence of Pre-editing Based on Controlled Language on Machine Translation''. 当代外语研究Contemporary Foreign Language Research,(2): 63-68+87+110.<br />
<br />
GERLACH J, et al ( 2013). ''Combining Pre-editing and Post-editing to Improve SMT of User-generated Content''[M]// Proceedings of MT Summit XIV Workshop on Post-editing Technology and Practice. 45-53<br />
<br />
Hu Qingping胡清平(2005). 机器翻译中的受控语言[J] ''Controlled Language in Machine Translation''. 中国科技翻译 Chinese Science and Technology Translation, (03): 24-27. <br />
<br />
Lian Shuneng连淑能 (2010). 英汉对比研究增订本[M]''An Updated Version of English-Chinese Contrastive Studies'' . 北京:高等教育出版社Beijing: Higher Education Publishing House. 35-36.<br />
<br />
Li Yafei, Zhang Ruihua黎亚飞,张瑞华 (2019). 机器翻译发展与现状[J]''The Development and Current Situation of Machine Translation''. 中国轻工教育 China Light Industry Education, (5):38-45. <br />
<br />
Qin Yi秦毅(2004),从翻译基本标准议医学英语的翻译[J] ''On the Translation of Medical English from the Basic Standard of Translation''. 遵义医学院学报 Journal of Zunyi Medical College,27 (4): 421-423. <br />
<br />
Slype G V & Guinet J F & Seitz F (1984). ''Better Translation for Better Communication'' [M] . Oxford: Pergamon Press Ltd (U.K.). 90-93<br />
<br />
O'Brien S (2002). Teaching Post-editing: A Proposal for Course Con-tent [EB/OL]. http://mt-archive. Info/EAMT-2002-0brien. Pdf.<br />
<br />
Tytler, A. F. (1978). ''Essay On The Principles of Translation''[M]. Amsterdam: JohnBenjamins Publishing. 118-119<br />
<br />
Wang Yan王燕 (2008). 医学英语翻译与写作教程[M] ''Medical English Translation and Writing Course''. 重庆:重庆大学出版社 Chongqing: Chongqing University Press. 60-61<br />
<br />
Written by --[[User:Chen Huini|Chen Huini]] ([[User talk:Chen Huini|talk]]) 04:58, 15 December 2021 (UTC)Chen Huini<br />
<br />
=12 蔡珠凤=(The Mistranslation of C-J Machine Translation of Political Statements)=<br />
<br />
机器翻译中政治发言中译日的误译<br />
<br />
蔡珠凤 Cai Zhufeng, Hunan Normal University<br />
<br />
[[Machine_Trans_EN_12]]<br />
===Abstract===<br />
Language is the main way of communication between people. With the continuous development of globalization, the scale of cross-border exchanges is also expanding. However, due to cultural differences and diversity, the languages of different countries and regions are very different, which seriously hinders people's communication. The demand for efficient and convenient translation tools is increasing. At the same time, with the development of network technology and artificial intelligence, recognition technology based on deep learning is more and more widely used in English, Japanese and other fields.<br />
<br />
===Key words===<br />
machine translation; political statements; mistranslation of C-J machine translation<br />
===题目===<br />
The Mistranslation of C-J Machine Translation of Political Statements<br />
===摘要===<br />
语言是人与人之间交流的主要方式。随着全球化的不断发展，跨境交流的规模也在不断扩大。然而，由于文化的差异和多样性，不同国家和地区的语言差异很大，这严重阻碍了人们的交流。对高效便捷的翻译工具的需求正在增加。同时，随着网络技术和人工智能的发展，基于深度学习的识别技术在英语、日语等领域的应用越来越广泛。<br />
<br />
===关键词===<br />
机器翻译；政治发言；政治发言中译日的误译<br />
===1. Introduction===<br />
===Introduction to machine translation===<br />
Machine translation, also known as automatic translation, is a process of using computers to convert one natural language (source language) into another natural language (target language). It is a branch of computational linguistics, one of the ultimate goals of artificial intelligence, and has important scientific research value.At the same time, machine translation has important practical value. With the rapid development of economic globalization and the Internet, machine translation technology plays a more and more important role in promoting political, economic and cultural exchanges.The development of machine translation technology has been closely accompanied by the development of computer technology, information theory, linguistics and other disciplines. From the early dictionary matching, to the rule translation of dictionaries combined with linguistic expert knowledge, and then to the statistical machine translation based on corpus, with the improvement of computer computing power and the explosive growth of multilingual information, machine translation technology gradually stepped out of the ivory tower and began to provide real-time and convenient translation services for general users.（Zhang 2019:5-6)<br />
<br />
=== C-J machine translation software===<br />
Today's online machine translation software includes Baidu translation, Tencent translation, Google translation, Youdao translation, Bing translation and so on. Google was the first company to launch the machine translation system, and Baidu was the first company to import the machine translation system in China. In addition, Tencent and Youdao have attracted much attention.Machine translation is the process of using computers to convert one natural language into another. It usually refers to sentence and full-text translation between natural languages. In order to continuously improve the translation quality, R & D personnel have added artificial intelligence technologies such as speech recognition, image processing and deep neural network to machine translation on the basis of traditional machine translation based on rules, statistics and examples.With the increase of using machine translation, the joint cooperation between manual translation and machine translation will also increase significantly in the future. What criteria should be used to evaluate the quality of machine translation? In the evolving field of machine translation, there is an urgent need to clarify the unsolvable questions and solved problems.(Lv 1996:3)<br />
<br />
===The history of machine translation===<br />
The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. alchuni put forward the idea of using machines for translation. In 1933, Soviet inventor П.П. Trojansky designed a machine to translate one language into another, and registered his invention on September 5 of the same year; However, due to the low technical level in the 1930s, his translation machine was not made. In 1946, the first modern electronic computer ENIAC was born. Shortly after that, W. weaver, an American scientist and A. D. booth, a British engineer, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947 when discussing the application scope of electronic computer. In 1949, W. Weaver published the translation memorandum, which formally put forward the idea of machine translation. After 60 years of ups and downs, machine translation has experienced a tortuous and long development path. The academic community generally divides it into the following four stages:<br />
<br />
Pioneering period（1947-1964）<br />
<br />
In 1954, with the cooperation of IBM, Georgetown University completed the English Russian machine translation experiment with ibm-701 computer for the first time, showing the feasibility of machine translation to the public and the scientific community, thus opening the prelude to the study of machine translation.<br />
It is not too late for China to start this research. As early as 1956, the state included this research in the national scientific work development plan. The topic name is "machine translation, the construction of natural language translation rules and the mathematical theory of natural language". In 1957, the Institute of language and the Institute of computing technology of the Chinese Academy of Sciences cooperated in the Russian Chinese machine translation experiment, translating 9 different types of more complex sentences.From the 1950s to the first half of the 1960s, machine translation research has been on the rise. The United States and the former Soviet Union, two superpowers, have provided a lot of financial support for machine translation projects for military, political and economic purposes, while European countries have also paid considerable attention to machine translation research due to geopolitical and economic needs, and machine translation has become an upsurge for a time. In this period, although machine translation is just in the pioneering stage, it has entered an optimistic period of prosperity.<br />
<br />
Frustrated period（1964-1975）<br />
<br />
In 1964, in order to evaluate the research progress of machine translation, the American Academy of Sciences established the automatic language processing Advisory Committee (Alpac Committee) and began a two-year comprehensive investigation, analysis and test.In November 1966, the committee published a report entitled "language and machine" (Alpac report for short), which comprehensively denied the feasibility of machine translation and suggested stopping the financial support for machine translation projects. The publication of this report has dealt a blow to the booming machine translation, and the research of machine translation has fallen into a standstill. Coincidentally, during this period, China broke out the "ten-year Cultural Revolution", and basically these studies also stagnated. Machine translation has entered a depression.<br />
<br />
convalescence（1975-1989）<br />
<br />
Since the 1970s, with the development of science and technology and the increasingly frequent exchange of scientific and technological information among countries, the language barriers between countries have become more serious. The traditional manual operation mode has been far from meeting the needs, and there is an urgent need for computers to engage in translation. At the same time, the development of computer science and linguistics, especially the substantial improvement of computer hardware technology and the application of artificial intelligence in natural language processing, have promoted the recovery of machine translation research from the technical level. Machine translation projects have begun to develop again, and various practical and experimental systems have been launched successively, such as weinder system Eurpotra multilingual translation system, taum-meteo system, etc.However, after the end of the "ten-year holocaust", China has perked up again, and machine translation research has been put on the agenda again. The "784" project has paid enough attention to machine translation research. After the mid-1980s, the development of machine translation research in China has further accelerated. Firstly, two English Chinese machine translation systems, ky-1 and MT / ec863, have been successfully developed, indicating that China has made great progress in machine translation technology.(Chen 2016:5)<br />
<br />
New period(1990 present)<br />
<br />
With the universal application of the Internet, the acceleration of the process of world economic integration and the increasingly frequent exchanges in the international community, the traditional way of manual operation is far from meeting the rapidly growing needs of translation. People's demand for machine translation has increased unprecedentedly, and machine translation has ushered in a new development opportunity. International conferences on machine translation research have been held frequently, and China has made unprecedented achievements. A series of machine translation software have been launched, such as "Yixing", "Yaxin", "Tongyi", "Huajian", etc. Driven by the market demand, the commercial machine translation system has entered the practical stage, entered the market and came to the users.Since the new century, with the emergence and popularization of the Internet, the amount of data has increased sharply, and statistical methods have been fully applied. Internet companies have set up machine translation research groups and developed machine translation systems based on Internet big data, so as to make machine translation really practical, such as "Baidu translation", "Google translation", etc. In recent years, with the progress of in-depth learning, machine translation technology has further developed, which has promoted the rapid improvement of translation quality, and the translation in oral and other fields is more authentic and fluent.(Liu 2014:6)<br />
<br />
===The problem of machine translation at present===<br />
Error is inevitable<br />
Many people have misunderstandings about machine translation. They think that machine translation has great deviation and can't help people solve any problems. In fact, the error is inevitable. The reason is that machine translation uses linguistic principles. The machine automatically recognizes grammar, calls the stored thesaurus and automatically performs corresponding translation. However, errors are inevitable due to changes or irregularities in grammar, morphology and syntax, such as sentences with adverbials after "give me a reason to kill you first" in Dahua journey to the West. After all, a machine is a machine. No one has special feelings for language. How can it feel the lasting charm of "the tenderness of lowering its head, like the shame of a water lotus? After all, the meaning of Chinese is very different due to the changes of morphology, grammar and syntax and the change of context. Even many Chinese people are zhanger monks - they can't touch their heads, let alone machines.(Liu 2014：3）<br />
<br />
Bottleneck<br />
In fact, no matter which method, the biggest factor affecting the development of machine translation lies in the quality of translation. Judging from the achievements, the quality of machine translation is still far from the ultimate goal.<br />
Chinese mathematician and linguist Zhou Haizhong once pointed out in his paper "fifty years of machine translation": to improve the quality of machine translation, the first thing to solve is the problem of language itself rather than programming; It is certainly impossible to improve the quality of machine translation by relying on several programs alone. At the same time, he also pointed out that it is impossible for machine translation to achieve the degree of "faithfulness, expressiveness and elegance" when human beings have not yet understood how the brain performs fuzzy recognition and logical judgment of language. This view may reveal the bottleneck restricting the quality of translation. <br />
It is worth mentioning that American inventor and futurist ray cozwell predicted in an interview with Huffington Post that the quality of machine translation will reach the level of human translation by 2029. There are still many disputes about this thesis in the academic circles.（Cui 2019：4）<br />
<br />
===2.Mistranslation of Chinese Japanese machine translation===<br />
===2.1Vocabulary mistranslation in Chinese Japanese machine translation===<br />
===2.1.1Mistranslation of proper nouns===<br />
In political materials, there are often political dignitaries' names, place names or a large number of proper nouns in the political field. The morphemes of such words are definite and inseparable. Mistranslation will make the source language lose its specific meaning. <br />
<br />
Japanese translation into Chinese Chinese translation into Japanese<br />
<br />
original text translation by Youdao reference translation original text translation by Youdao reference translation<br />
<br />
朱鎔基 朱基 朱镕基 栗战书 栗戰史書 栗戰書<br />
<br />
労安 劳安 劳安 李克强 李克強 李克強 <br />
<br />
筑紫哲也 筑紫哲也 筑紫哲也 习近平 習近平 習近平<br />
<br />
山口百惠 山口百惠 山口百惠 韩正 韓中 韓正<br />
<br />
田中角栄 田中角荣 田中角荣 王沪宁 王上海氏 王滬寧<br />
<br />
東条英機 东条英社 东条英机 汪洋 汪洋 汪洋<br />
<br />
毛沢东 毛泽东 毛泽东 赵乐际 趙樂南 趙樂際<br />
<br />
トウ・ショウヘイ 大酱 邓小平 江泽民 江沢民 江沢民<br />
<br />
周恩来 周恩来 周恩来<br />
<br />
クリントン 克林顿 克林顿<br />
<br />
The above table counts 18 special names in the two texts, and 7 machine translation errors. In terms of mistranslation, there are not only "战书" but also "戰史" and "史書" in Chinese. "沪" is the abbreviation of Shanghai. In other words, since "战书" and "沪" are originally common nouns, this disrupts the choice of target language in machine translation. Except Katakana interference (except for トウシゃウヘイ, most of the mistranslations appear in the new Standing Committee. It can be seen that the machine translation system does not update the thesaurus in time. Different from other words, people's names have particularity. Especially as members of the Standing Committee of the Political Bureau of the CPC Central Committee, the translation of their names is officially unified. In this regard, public figures such as political dignitaries, stars, famous hosts and important. In addition, the machine also translates Japanese surnames such as "タ二モト" (谷本), "アンドウ" (安藤) into "塔尼莫特" and "龙胆". It can be found that the language feature that Japanese will be written together with Chinese characters and Hiragana also directly affects the translation quality of Japanese Chinese machine translation. Japanese people often use Katakana pronunciation. For example, when Japanese people talk with Premier Zhu, they often use "二ーハウ" (Hello). However, the machine only recognizes the two pseudonyms "二" and "ハ" and transliterates them into "尼哈" , ignoring the long sound after "二" and "ハ".(Guan 2018:10-12)<br />
<br />
original text Translation by Youdao reference translation<br />
<br />
日美安全体制 日米の安全体制 日米安保体制<br />
<br />
中国共产党第十九次全国代表大会 中国共産党第19回全国代表大会 中国共産党第19回全国代表大会（第19回党大会）<br />
<br />
十八大 十八大 第18回党大会中国特色社会主义<br />
<br />
中国特色社会主義 中国の特色ある社会主義 第18回党大会<br />
<br />
中国共产党中央委员会 中国共産党中央委員会 中国共産党中央委員会<br />
<br />
中国共産党中央委員会十八届中共中央政治局常委 第18代中国共產党中央政治局常務委員 第18期中共中央政治局常務委員<br />
<br />
十八届中共中央政治局委员 18期の中国共產党中央政治局委員 第18期中共中央政治局委員<br />
<br />
十九届中共中央政治局常委 十九回中国共產党中央政治局常務委員 第19期中央政治局常務委員<br />
<br />
中共十九届一中全会 中国共產党第十九回一中央委員会 第19期中央委員会第1回全体会議<br />
<br />
The above table is a comparison of the original and translated versions of some proper nouns. As shown in the table, the mistranslation problems are mainly reflected in the mismatch of numerals + quantifiers, the wrong addition of case auxiliary word "の", the lack of connectors, the Mistranslation of abbreviations, dead translation, and the writing errors of Chinese characters. The following is a specific analysis one by one.<br />
<br />
"十八届中共中央政治局常委", "十八届中共中央政治局委员", "十九届中共中央政治局常委" and "中共十九届一中全会" all have quantifiers "届", which are translated into "代", "期" and "回" respectively. The meaning is vague and should be uniformly translated into "期"; Among them, the translation of the last three proper nouns lacks the Chinese conjunction "第". The case auxiliary word "の" was added by mistake in the translation of "十八届中共中央政治局委员" and "日美安全体制". The "中国共产党中央委员会" did not write in the form of Japanese Ming Dynasty characters, but directly used simplified Chinese characters.<br />
<br />
The full name of "中共十九届一中全会" is "中国共产党第十九届中央委员会第一次全体会议", in which "一" stands for "第一次", which is an ordinal word rather than a cardinal word. Machine translation does not produce a correct translation. The fundamental reason is that there are no "规则" for translating such words in machine translation. If we can formulate corresponding rules for such words (for example, 中共m'1届m2 中全会→第m1期中央委员会第m2回全体会议), the translation system must be able to translate well no matter how many plenary sessions of the CPC Central Committee.(Guan 2018:6-7)<br />
<br />
===2.1.2Mistranslation of Polysemy===<br />
original text Translation by Youdao reference translation<br />
<br />
スタジオ 摄影棚/工作室 直播现场/演播厅<br />
<br />
日中関係の話 中日关系的故事 就中日关系(话题)<br />
<br />
溝 水沟 鸿沟<br />
<br />
それでは日中の問題について質問のある方。 那么对白天的问题有提问的人。 关于中日问题的话题，举手提问。<br />
<br />
私たちのクラスは20人ちょっとですが、 我们班有20人左右， 我们班二十多人的意见统一很难，<br />
<br />
いろいろな意見が出て、まとめるのは大変です。 但是又各种各样的意见，总结起来很困难。 <br />
<br />
一体どうやって、13億人もの人をまとめているんですか。 到底是怎么处理13亿的人的呢？ 中国是怎样把13亿人凝聚在一起的？<br />
<br />
In the original text, the word "スタジオ" appeared four times and was translated into "摄影棚" or "工作室" respectively, but the context is on-site interview, not the production site of photography or film. "話" has many semantics, such as "说话", "事情", "道理", etc. In accordance with the practice of the interview program, Premier Zhu Rongji was invited to answer five questions on the topic of China Japan relations. Obviously, the meaning of the word "故事" is very abrupt. The inherent Japanese word "日中" means "晌午、白天". At the same time, it is also the abbreviation of the names of the two countries. The machine failed to deal with it correctly according to the context. "溝" refers to the gap between China and Japan, not a "水沟". The former "まとめる" appears in the original text with the word "意见", which is intended to describe that it is difficult to unify everyone's opinions. The latter "まとめている" also refers to unifying the thoughts of 1.3 billion people, not "处理". Therefore, it is more appropriate to use "统一" and "处理" in human translation, rather than "总结意见" or "处理意见".(Zhang 2019:5)<br />
<br />
Mistranslation of polysemy has always been a difficult problem in machine translation research. The translation of each word is correct, but it is often very different from the original expression in the context. Zhang Zhengzeng said that ambiguity is a common phenomenon in natural language. Its essence is that the same language form may have different meanings, which is also one of the differences between natural language and artificial language. Therefore, one of the difficulties faced by machine translation is language disambiguation (Zhang Zheng, 2005:60). In this regard, we can mark all the meanings of polysemous words and judge which meaning to choose by common collocation with other words. At the same time, strengthen the text recognition ability of the machine to avoid the translation inconsistent with the current context. In this way, we can avoid the mistake of "大酱" in the place where famous figures such as Deng Xiaoping should have appeared in the previous political articles.(Wang 2020:7-9)<br />
<br />
===2.1.3Mistranslation of compound words===<br />
Multi class words refer to a word with two or more parts of speech, also known as the same word and different classes.<br />
<br />
original text Translation by Youdao reference translation<br />
<br />
1998年江泽民主席曾经访问日本, 1998年の江沢民国家主席の日本訪問し、 1998年、江沢民総書記が日本を訪問し、かつ<br />
<br />
同已故小渊首相签署了联合宣言。 かつて同じ故小渕首相が署名した共同宣言 亡くなられた小渕総理と宣言に調印されました<br />
<br />
Chinese "同" has two parts of speech: prepositions and conjunctions: "故" has many parts of speech, such as nouns, verbs, adjectives and conjunctions. This directly affects the judgment of the machine at the source language level. The word "同" in the above table is used as a conjunction to indicate the other party of a common act. "故" is used as a verb with the semantic meaning of "死亡", which is a modifier of "小渊首相". The machine regards "同" as an adjective and "故" as a noun "原因", which leads to confusion in the structure and unclear semantics of the translation. Different from the polysemy problem, multi category words have at least two parts of speech, and there is often not only one meaning under each part of speech. In this regard, software R & D personnel should fully consider the existence of multi category words, so that the translation machine can distinguish the meaning of words on the basis of marking the part of speech, so as to select the translation through the context and the components of the word in the sentence. Of course, the realization of this function is difficult, and we need to give full play to the wisdom of R & D personnel.(Guan 2018:9-12)<br />
<br />
===2.2 Syntactic mistranslation in Chinese Japanese machine translation===<br />
===2.2.1Mistranslation of tenses===<br />
original text：History is written by the people, and all achievements are attributed to the people. <br />
<br />
Translation by Youdao：歴史は人民が書いたものであり、すべての成果は人民のためである。<br />
<br />
reference translation：歴史は人民が綴っていくものであり、すべての成果は人民に帰することとなります。<br />
<br />
The original sentence meaning is "历史是人民书写的历史" or "历史是人民书写的东西". When translated into Japanese, due to the influence of Japanese language habits, the formal noun "もの" should be supplemented accordingly. In this sentence, both the machine and the interpreter have translated correctly. However, the machine's recognition of "的" is biased, resulting in tense translation errors. The past, present and future will become "history", and this is a continuous action. The form translated as "~ ていく" not only reflects that the action is a continuous action, but also conforms to the tense and semantic information of the original text. In addition, the machine's treatment of the preposition "于" is also inappropriate. In the original text, "人民" is the recipient of action, not the target language. As the most obvious feature of isolated language, function words in Chinese play an important role in semantic expression, and their translation should be regarded as a focus of machine translation research.(Zuo 2021:8)<br />
<br />
original text：李克强同志是十六届中共中央政治局常委,其他五位同志都是十六届中共中央政治局委员。<br />
<br />
Translation by Youdao：李克強総理は第16代中国共産党中央政治局常務委員であり、他の５人の同志はいずれも16期の中国共産党中央政治局委員である。<br />
<br />
reference translation：李克強同志は第16期中国共産党中央政治局常務委員を務め、他の５人は第16期中共中央政治局委員を務めました。<br />
<br />
Judging the verb "是" in the original text plays its most basic positive role, but due to the complexity of Chinese language, "是" often can not be completely transformed into the form of "だ / である" in the process of translation. This sentence is a judgment of what has happened. Compared with the 19th session, the 18th session has become history. This is an implicit temporal information. It is very difficult for machines without human brain to recognize this implicit information. "中共中央政治局常委" is the abbreviation of "中共中央政治局常务委员会委员" and it is a kind of position. In Japanese, often use it with verbs such as "務める","担当する",etc. The translator adopts the past tense of "務める", which conforms to the expression habit of Japanese and deals with the problem of tense at the same time. However, machine translation only mechanically translates this sentence into judgment sentence, and fails to correctly deal with the past information implied by the word "十八届".(Guan 2018:4)<br />
<br />
===2.2.2Mistranslation of honorifics===<br />
Honorific language is a language means to show respect to the listener. Different from Japanese, there is no grammatical category of honorifics in Chinese. There is no specific fixed grammatical form to express honorifics, self modesty and politeness. Instead, specific words such as "您", "请" and "劳驾" are used to express all kinds of respect or self modesty. (Yang 2020:5-9)<br />
<br />
Original text translation by Youdao reference translation<br />
<br />
女士们，先生们，同志们，朋友们 さんたち、先生たち、同志たち、お友达さん ご列席の皆さん<br />
<br />
谢谢大家！ ありがとうございます！ ご清聴ありがとうございました。<br />
<br />
これはどうされますか。 这是怎么回事呢? 您将如何解决这一问题？<br />
<br />
こうした問題をどうお考えでしょうか。 我们会如何考虑这些问题呢？ 您如何看待这一问题？<br />
<br />
For example, for the processing of "女士们，先生们，同志们，朋友们", the machine makes the translation correspond to the original one by one based on the principle of unchanged format, but there are errors in semantic communication and pragmatic habits. When speaking on formal occasions, Chinese expression tends to be comprehensive and detailed, as well as the address of the audience. In contrast, Japanese usually uses general terms such as "皆様", "ご臨席の皆さん" or "代表団の方々" as the opening remarks. In terms of Japanese Chinese translation, the machine also failed to recognize the usage of Japanese honorifics such as "さ れ る" and "お考え". It can be seen that Chinese Japanese machine translation has a low ability to deal with honorific expressions. The occasion is more formal. A good translation should not only be fluent in meaning, but also conform to the expression habits of the target language and match the current translation environment.(Che 2021:3-7)<br />
<br />
===Conclusion===<br />
In the process of discussing the Mistranslation of machine translation, this paper mainly cites the Mistranslation of Youdao translator. When I studied the problem of machine translation mistranslation, I also used a large number of translation software such as Google and Baidu for parallel comparison, and found that these translation software also had similar problems with Youdao translator. For example, the "新世界新未来" in Chinese ABAC structural phrases is translated by Baidu as "新し世界の新しい未来", which, like Youdao, is not translated in the form of parallel phrases; Google online translation translates the word "“全心全意" into a completely wrong "全面的に"; The original sentence "我吃了很多亏" in the Mistranslation of the unique expression in the language is translated by Google online as "私はたくさんの損失を食べました". Because the "吃" and "亏" in the original text are not closely adjacent, the machine can not recognize this echo and mistakenly treats "亏" as a kind of food, so the machine translates the predicate as "食べました"; Japanese "こうした問題をどう考えでしょうか", Google's online translation is "你如何看待这些问题?", "你" tone can not reflect the tone of Japanese honorific, while Baidu translates as "你怎么想这样的问题呢?" Although the meaning can be understood, it is an irregular spoken language. Google and Baidu also made the same mistakes as Youdao in the translation of proper nouns. For example, they translated "十七届(中共中央政治局常委)”" into "第17回..." .In short, similar mistranslations of Youdao are also common in Baidu and Google. Due to space constraints, they will not be listed one by one here.(Cui 2019:7)<br />
<br />
Mr. Liu Yongquan, Institute of language, Chinese Academy of Social Sciences (1997) It has been pointed out that machine translation is a linguistic problem in the final analysis. Although corpus based machine translation does not require a lot of linguistic knowledge, language expression is ever-changing. Machine translation based solely on statistical ideas can not avoid the translation quality problems caused by the lack of language rules. The following is based on the analysis of lexical, syntactic and other mistranslations From the perspective of the characteristics of the source language, this paper summarizes some difficulties of Chinese and Japanese in machine translation.(Liu 2014:8)<br />
<br />
(1) The difficulties of Chinese in machine translation <br />
<br />
Chinese is a typical isolated language. The relationship between words needs to be reflected by word order and function words. We should pay attention to the transformation of function words such as "的", "在", "向" and "了". Some special verbs, such as "是", "做" and "作", are widely used. How to translate on the basis of conforming to Japanese pragmatic habits and expressions is a difficulty in machine translation research. In terms of word order, Chinese is basically "subject→predicate→object". At the same time, Chinese pays attention to parataxis, and there is no need to be clear in expression with meaningful cohesion, which increases the difficulty of Chinese Japanese machine translation. When there are multiple verbs or modifier components in complex long sentences, machine translation usually can not accurately divide the components of the sentence, resulting in the result that the translation is completely unreadable.(Guan 2018:6-12) <br />
<br />
(2) Difficulties of Japanese in machine translation <br />
<br />
Both Chinese and Japanese languages use Chinese characters, and most machines produce the target language through the corresponding translation of Chinese characters. However, pseudonyms in Japanese play an important role in judging the part of speech and meaning, and can not only recognize Chinese characters and judge the structure and semantics of sentences. Japanese vocabulary is composed of Chinese vocabulary, inherent vocabulary and foreign vocabulary. Among them, the first two have a great impact on Chinese Japanese machine translation. For example "提出" is translated as "提出する" or "打ち出す"; and "重视" is translated as "重視する" or "大切にする". Japanese is an adhesive language, which contains a lot of "けど", "が" and "けれども" Some of them are transitional and progressive structures, but there are also some sequential expressions that do not need translation, and these translation software often can not accurately grasp them.(Che 2021:10)<br />
<br />
===References===<br />
[1] Navroz Kaur Kahlon,(2021(prepublish));Williamjeet Singh.Machine translation from text to sign language: a systematic review[J].Universal Access in the Information Society,1-35.<br />
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[2] Cao Qianyu;Hao Hanmei,(2021);Ahmed Syed Hassan.A Chaotic Neural Network Model for English Machine Translation Based on Big Data Analysis[J].Computational Intelligence and Neuroscience,3274326-3274326.<br />
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[4]Zakaryia Almahasees.(2021)Analysing English-Arabic Machine Translation:Google Translate, Microsoft Translator and Sakhr.<br />
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[5](2021)Machine learning in translation[J].Nature Biomedical Engineering,5(6):485-486.<br />
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[6]Shaimaa Marzouk.(2021(prepublish))An in-depth analysis of the individual impact of controlled language rules on machine translation output: a mixed-methods approach[J].Machine Translation,1-37.<br />
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[7]Welnitzová Katarína;Munková Daša.(2021)Sentence-structure errors of machine translation into Slovak[J].Topics in Linguistics,22(1):78-92.<br />
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[8]Xu Xueyuan.(2021).Machine learning-based prediction of urban soil environment and corpus translation teaching[J].Arabian Journal of Geosciences,14(11). <br />
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[9]Chen Bingchang 陈丙昌(2016).機械翻訳の誤訳分析【D】.Error analysis of mechanical translation.贵州大学.2016(05) <br />
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[10]Lv Yinqiu 呂寅秋(1996).機械翻訳の言語規則と伝統文法との相違点.【D】The language rules of mechanical translation, the traditional grammar, and the points of contradiction.日本学研究.Japanese Studies.1996(00):21-22 <br />
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[11]Liu Jun 刘君(2014).基于语料库的中日同形词词义用法对比及其日中机器翻译研究【D】.A Corpus-based Comparison of the Meanings of Chinese and Japanese Homographs and Research on Japanese-Chinese Machine Translation.广西大学.(03) <br />
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[12]Cun Qianqian 崔倩倩(2019).机器翻译错误与译后编辑策略研究【D】.Research on Machine Translation Errors and Post-Editing Strategies.北京外国语大学.(09) <br />
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[13]Zhang Yi 张义(2019).机器翻译的译文分析【D】.Translation analysis of machine translation.西安外国语大学.(10) <br />
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[14]Zhang Linqian 张琳婧(2019).在线机器翻译中日翻译错误原因及对策【D】.Causes and countermeasures of online machine translation errors in Chinese-Japanese translation.山西大学.(02)<br />
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[15]Wang Dan 王丹(2020).基于机器翻译的专利文本译后编辑对策研究【D】.Research on countermeasures for post-translational editing of patent texts based on machine translation.大连理工大学.(06)<br />
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[16]Yang Xiaokun 杨晓琨(2020).日中机器翻译中的前编辑规则与效果验证【D】.Pre-editing rules and effect verification in Japanese-Chinese machine translation.大连理工大学.(06)<br />
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[17]Zuo Jia 左嘉(2021). 机器翻译日译汉误译研究【D】. Research on Mistranslation of Machine Translation from Japanese to Chinese.北京第二外国语学院.<br />
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[18]Guan Biying 关碧莹(2018).关于政治类发言的汉日机器翻译误译分析【D】.Analysis of Chinese-Japanese Machine Translation Mistranslations of Political Speeches.哈尔滨理工大学.<br />
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[19]Che Tong 车彤(2021).汉译日机器翻译质量评估及译后编辑策略研究【D】.Research on Quality Evaluation of Chinese-Japanese Machine Translation and Post-translation Editing Strategies.北京外国语大学.(09)<br />
<br />
Networking Linking<br />
<br />
http://www.elecfans.com/rengongzhineng/692245.html<br />
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https://baike.baidu.com/item/%E6%9C%BA%E5%99%A8%E7%BF%BB%E8%AF%91/411793<br />
<br />
=13 陈湘琼Chen Xiangqiong（Study on Post-editing from the Perspective of Functional Equivalence Theory ）=<br />
[[Machine_Trans_EN_13]]<br />
<br />
=14 Bi bi Nadia（Machine Translation a Challenge for Human Translators)=<br />
[[Machine_Trans_EN_14]]<br />
<br />
===Abstract===<br />
Machine translation is a big obstacle in the way of Human translators or interpreters although it is quick and less time consuming.People are trying to get translation of their target language using through source language.For this purpose they are using digital apps like Google translation Google translation does not give accurate and exact interpretation.Google translator is translating word to word but it doesn't clarify it's true and actual meaning.On the other hand human translators can give exact and accurate translation ,they take care of grammatical mistakes , diction and sentence structure.They clarify the purpose of target language through using source language.<br />
<br />
===Keywords===<br />
Descriptive translation, academic, interdiscipline, comparative literature, localization,Translatology,school of thought,translation , studies, linguistics, corresponding.<br />
<br />
===Body of article===<br />
Translation is the process of reworking text from one language into another to maintain the original message and communication. But, like anything else, there are different methods of translation, and they vary in form and function. What is translation? The term has become widely used among knowledge transferring researchers and practitioners, especially in the fields of health and health care. In a landmark review, Jonathan Lomas began to argue that 'The tasks… may be defined as to establish and maintain links between researchers and their audience, via the appropriate translation of research findings' (Lomas 1997, p 4). In 2004, World Health Organization's World Report on Knowledge for Better Health suggested that 'One of the key contributions of research to health systems is the translation of knowledge into actions' (WHO 2004, p 33 and p 100). By 2006, special issues of WHO's Bulletin as well as the journals Evaluation and the Health Professions and the Journal of Continuing Education in the Health Professions were dedicated to translation.But what does 'translation' mean? It may be a new word for an old problem, meaning nothing more than 'transfer'. The rapidly emerging field of 'translational medicine' seems to take translation to mean generally what transfer might have meant, that is the transmission of knowledge and evidence 'from bench to bedside'. As one commentator has observed, "Translational medicine" as a fashionable term is being increasingly used to describe the wish of biomedical researchers to ultimately help patients' (Wehling 2008, abstract). <br />
Semantic uncertainty persists, not least because of the different interests of scientists, clinicians, patients and commercial firms (Littman et al 2007): 'Translational research means different things to different people, but it seems important to almost everyone' (Woolf 2008, p 211).<br />
In related fields, such as public health (Armstrong et al 2006), 'translation' seems to signify dissatisfaction with 'transfer'. It wants to move away from thinking of knowledge transfer as a form of technology transfer or dissemination, rejecting if only by implication its mechanistic assumptions and its model of linear messaging from A to B. But still, what does it signify?<br />
<br />
===Why translation?===<br />
'Translation' indicates a closer attention to the problem of shared meaning and how it might be developed. It seems to represent some new epistemological lubricant, facilitating the dissemination of texts and the application and use of the knowledge and information they in. Simply, translation might be the key to transfer. And yet, when we stop to think, we are more ambivalent. What is translated often seems somehow inferior, not real or original. Note how readily commentators reach for the idea that things might be 'lost in translation'. Knowing at a distance – made in and mediated by translation - makes for incomplete renditions, blurred images, partial truths. So what might 'translation' really mean? The purpose of this paper is to set out, for policy makers and practitioners, the theoretical and conceptual resources translation holds and seems to represent. In doing so, it explores understandings of translation in the fields of literature and linguistics and in the sociology of science and technology. It begins by setting out just why this idea of translation should make immediate, intuitive sense in relation to research, policy and practice.<br />
1translation in research, policy and practice research as translation<br />
Research often entails translation from one language to another: where data is collected from more than one ethnic group, for example, or where the language of the researcher is other than that of the research subject. It may draw on a secondary literature or source documents written in different languages, and may be published and disseminated in languages other than the one in which it is first written up.<br />
2In a different way, to conduct an interview is to ask for an account of experience and its meanings, but it is also to construct and translate that experience in terms defined at least in part by the researcher. In representing what is said, transcripts then select data, usually excluding significant gesture and eye-contact, for example. Often, certain characteristics of speech-acts (such as hesitations) will be edited out. In turn, the format of the transcript shapes the analytic use the researcher may make of it. The basis of research 'findings', then, is an artefact, a transcript or translation, not an original interaction (Ochs 1979, Barnes, Bloor and Henry 1996, Ross 2009).<br />
3In this way, the researcher recasts aspects of his or her problem or topic in new, scientific form: 'All researchers "translate" the experiences of others' (Temple 1997, p 609). Research is invariably conducted in a sort of 'metalanguage' (Hantrais and Ager 1985): the research process can be conceived as one of successive translations (from theoretical formulation to operationalization, transcription, interpretation and dissemination). Theorization is a process of reciprocal back and forth between theory and fact, in which conceptions of each are revised in order that one fit the other (Baldamus 1974).<br />
4 It is a kind of translation: a rereading, re-use, re-application or re-representation of what we know in new terms (Turner 1980). Referencing, too, is an act of translation, a form of appropriation and incorporation of one text by another (Gilbert 1977).policy as translation Each of the fields covered by the paper is diverse and ill-defined, and there is no intention here to provide a comprehensive account of any them. Sources have been chosen for their relevance: main references are cited in the text, and additional sources listed in footnotes.<br />
<br />
2For a brief introduction to the technical issues involved in social research in more than one language, see Birbili (2000). For translation issues in survey research and question design in general, see Ervin and Bower (1952) and Deutscher (1968); on translating survey research instruments (in this instance health-related quality of life measures), Bowden and Fox-Rushby (2003). On the use of translators and interpreters, see Temple (1997) and Jentsch (1998).<br />
3For an interesting discussion of this problem, see Bourdieu (1999), esp pp 621-626, 'The risks of writing'. <br />
===Difference between machine translation and human translators===<br />
Few people disagree on the differences between the two, but many argue over the quality of the translations. How accurate are machine translations? How reliable are human translations? Some say machine translation produces near-perfect translations while others are adamant that translations are incomprehensible and cause more problems than they solve. Results will, of course, vary depending on the source and target languages, the machine translation service used (e.g. Google Translate), and the complexity of the original text.<br />
Machine translation, love it or hate it, is here to stay. In fact, the machine translation market is growing at such a fast pace that it is predicted to reach $980 million by 2022.<br />
===Machine translation: the pros and cons===<br />
The advantages of machine translation generally come down to two factors: it’s faster and cheaper. The downside to this is the standard of translation can be anywhere from inaccurate, to incomprehensible, and potentially dangerous (more on that shortly).<br />
==The advantages of machine translation==<br />
Many free tools are readily available (Google Translate, Skype Translator, etc.)<br />
Quick turnaround time <br />
You can translate between multiple languages using one tool<br />
Translation technology is constantly improving<br />
The disadvantages of machine translation<br />
Level of accuracy can be very low <br />
Accuracy is also very inconsistent across different languages<br />
==Machines can't translate context ==<br />
Mistakes are sometimes costly <br />
Sometimes translation simply doesn’t work<br />
The most important thing to consider with any kind of translation is the cost of potential mistakes. Translating instructions for medical equipment, aviation manuals, legal documents and many other kinds of content require 100% accuracy. In such cases, mistakes can cost lives, huge amounts of money and irreparable damage to your company’s image. So choose carefully!<br />
Human translation: the pros and cons<br />
Human translation essentially switches the table in terms of pros and cons. A higher standard of accuracy comes at the price of longer turnaround times and higher costs. What you have to decide is whether that initial investment outweighs the potential cost of mistakes. Alternatively, whether mistakes simply aren’t an option, like the cases we looked at in the previous section.<br />
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==The advantages of human translation ==<br />
It’s a translator’s job to ensure the highest accuracy<br />
Humans can interpret context and capture the same meaning, rather than simply translating words<br />
Human translators can review their work and provide a quality process<br />
Humans can interpret the creative use of language, e.g. puns, metaphors, slogans, etc.<br />
Professional translators understand the idiomatic differences between their languages<br />
Humans can spot pieces of content where literal translation isn’t possible and find the most suitable alternative<br />
The disadvantages of human translation<br />
Turnaround time is longer <br />
Translators rarely work for free <br />
Unless you use a translation agency, with access to thousands of translators, you’re limited to the languages any one translator can work with<br />
Simply put, human translation is your best option when accuracy is even remotely important. Other considerations to make are the complexity of your source material and the two languages you’re translating between – both of which can render machines pretty useless.<br />
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When to use machine and human translation<br />
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The truth is, the debate over machine vs human translation is an unnecessary distraction. What we should really be talking about is when to use these two different types of translation services, because they both serve a very valid purpose.<br />
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Examples of when to use machine translation<br />
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When you have a large bulk of content to translate and the general meaning is enough<br />
When your translation never reaches the final audience, e.g. you’re translating a resource as research for another piece of content<br />
Translating documents for internal use within a company, provided 100% accuracy isn’t needed<br />
To partially translate large chunks of content for a human translator to improve upon<br />
Examples of when to use human translation<br />
When accuracy is important<br />
Most cases where your translated content is received by a consumer audience<br />
When you have a duty of care to provide accurate translations (e.g. legal documents, product instructions, medical guidelines or health and safety content)<br />
When translating marketing material or other texts for creative language uses.<br />
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types of machine translation.<br />
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What is Machine Translation? Rule Based Machine Translation vs. Statistical Machine Translation. Machine translation (MT) is automated translation. It is the process by which computer software is used to translate a text from one natural language (such as English) to another (such as Spanish).<br />
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To process any translation, human or automated, the meaning of a text in the original (source) language must be fully restored in the target language, i.e. the translation. While on the surface this seems straightforward, it is far more complex. Translation is not a mere word-for-word substitution. A translator must interpret and analyze all of the elements in the text and know how each word may influence another. This requires extensive expertise in grammar, syntax (sentence structure), semantics (meanings), etc., in the source and target languages, as well as familiarity with each local region.<br />
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Human and machine translation each have their share of challenges. For example, no two individual translators can produce identical translations of the same text in the same language pair, and it may take several rounds of revisions to meet customer satisfaction. But the greater challenge lies in how machine translation can produce publishable quality translations.<br />
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Rule-Based Machine Translation Technology<br />
Rule-based machine translation relies on countless built-in linguistic rules and millions of bilingual dictionaries for each language pair.<br />
The software parses text and creates a transitional representation from which the text in the target language is generated. This process requires extensive lexicons with morphological, syntactic, and semantic information, and large sets of rules. The software uses these complex rule sets and then transfers the grammatical structure of the source language into the target language.<br />
Translations are built on gigantic dictionaries and sophisticated linguistic rules. Users can improve the out-of-the-box translation quality by adding their terminology into the translation process. They create user-defined dictionaries which override the system’s default settings.<br />
In most cases, there are two steps: an initial investment that significantly increases the quality at a limited cost, and an ongoing investment to increase quality incrementally. While rule-based MT brings companies to the quality threshold and beyond, the quality improvement process may be long and expensive.<br />
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Statistical Machine Translation Technology<br />
Statistical machine translation utilizes statistical translation models whose parameters stem from the analysis of monolingual and bilingual corpora. Building statistical translation models is a quick process, but the technology relies heavily on existing multilingual corpora. A minimum of 2 million words for a specific domain and even more for general language are required. Theoretically it is possible to reach the quality threshold but most companies do not have such large amounts of existing multilingual corpora to build the necessary translation models. Additionally, statistical machine translation is CPU intensive and requires an extensive hardware configuration to run translation models for average performance levels.<br />
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Rule-Based MT vs. Statistical MT<br />
Rule-based MT provides good out-of-domain quality and is by nature predictable. Dictionary-based customization guarantees improved quality and compliance with corporate terminology. But translation results may lack the fluency readers expect. In terms of investment, the customization cycle needed to reach the quality threshold can be long and costly. The performance is high even on standard hardware.<br />
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Statistical MT provides good quality when large and qualified corpora are available. The translation is fluent, meaning it reads well and therefore meets user expectations. However, the translation is neither predictable nor consistent. Training from good corpora is automated and cheaper. But training on general language corpora, meaning text other than the specified domain, is poor. Furthermore, statistical MT requires significant hardware to build and manage large translation models.<br />
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Rule-Based MT Statistical MT<br />
+ Consistent and predictable quality – Unpredictable translation quality<br />
+ Out-of-domain translation quality – Poor out-of-domain quality<br />
+ Knows grammatical rules – Does not know grammar <br />
+ High performance and robustness – High CPU and disk space requirements<br />
+ Consistency between versions – Inconsistency between versions <br />
– Lack of fluency + Good fluency<br />
– Hard to handle exceptions to rules + Good for catching exceptions to rules <br />
– High development and customization costs + Rapid and cost-effective development costs provided the required corpus exists<br />
Given the overall requirements, there is a clear need for a third approach through which users would reach better translation quality and high performance (similar to rule-based MT), with less investment (similar to statistical MT).<br />
Post-Edited Machine Translation (PEMT)<br />
Often, PEMT is used to bridge the gap between the speed of machine translation and the quality of human translation, as translators review, edit and improve machine-translated texts. PEMT services cost more than plain machine translations but less than 100% human translation, especially since the post-editors don’t have to be fluently bilingual—they just have to be skilled proofreaders with some experience in the language and target region.<br />
Successful translation is about more than just the words, which is why we advocate for not just human translation by skilled linguists, but for translation by people deeply familiar with the cultures they’re writing for. Life experience, study and the knowledge that only comes from living in a geographic region can make the difference between words that are understandable and language that is capable of having real, positive impact. <br />
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PacTranz<br />
The HUGE list of 51 translation types, methods and techniques<br />
Upper section of infographic of 51 common types of translation classified in 4 broad categoriesThere are a bewildering number of different types of translation.<br />
So we’ve identified the 51 types you’re most likely to come across, and explain exactly what each one means.<br />
This includes all the main translation methods, techniques, strategies, procedures and areas of specialisation.<br />
It’s our way of helping you make sense of the many different kinds of translation – and deciding which ones are right for you.<br />
Don’t miss our free summary pdf download later in the article!<br />
The 51 types of translation we’ve identified fall neatly into four distinct categories.<br />
Translation Category A: 15 types of translation based on the technical field or subject area of the text<br />
Icons representing 15 types of translation categorised by the technical field or subject area of the textTranslation companies often define the various kinds of translation they provide according to the subject area of the text.<br />
This is a useful way of classifying translation types because specialist texts normally require translators with specialist knowledge.<br />
Here are the most common types you’re like to come across in this category.<br />
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1. General Translation<br />
What is it?<br />
The translation of non-specialised text. That is, text that we can all understand without needing specialist knowledge in some area.<br />
The text may still contain some technical terms and jargon, but these will either be widely understood, or easily researched.<br />
What this means<br />
The implication is that you don’t need someone with specialist knowledge for this type of translation – any professional translator can handle them.<br />
Translators who only do this kind of translation (don’t have a specialist field) are sometimes referred to as ‘generalist’ or ‘general purpose’ translators.<br />
Examples<br />
Most business correspondence, website content, company and product/service info, non-technical reports.<br />
Most of the rest of the translation types in this Category do require specialist translators.<br />
Check out our video on 13 types of translation requiring special translator expertise:<br />
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2. Technical Translation<br />
What is it?<br />
We use the term “technical translation” in two different ways:<br />
Broad meaning: any translation where the translator needs specialist knowledge in some domain or area.<br />
This definition would include almost all the translation types described in this section.<br />
Narrow meaning: limited to the translation of engineering (in all its forms), IT and industrial texts.<br />
This narrower meaning would exclude legal, financial and medical translations for example, where these would be included in the broader definition.<br />
What this means<br />
Technical translations require knowledge of the specialist field or domain of the text.<br />
That’s because without it translators won’t completely understand the text and its implications. And this is essential if we want a fully accurate and appropriate translation.Good to know Many technical translation projects also have a typesetting/dtp requirement. Be sure your translation provider can handle this component, and that you’ve allowed for it in your project costings and time frames.<br />
Examples<br />
Manuals, specialist reports, product brochures<br />
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3. Scientific Translation<br />
What is it?<br />
The translation of scientific research or documents relating to it.<br />
What this means<br />
These texts invariably contain domain-specific terminology, and often involve cutting edge research.<br />
So it’s imperative the translator has the necessary knowledge of the field to fully understand the text. That’s why scientific translators are typically either experts in the field who have turned to translation, or professionally qualified translators who also have qualifications and/or experience in that domain.<br />
On occasion the translator may have to consult either with the author or other domain experts to fully comprehend the material and so translate it appropriately.<br />
Examples<br />
Research papers, journal articles, experiment/trial results<br />
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4. Medical Translation<br />
What is it?<br />
The translation of healthcare, medical product, pharmaceutical and biotechnology materials.<br />
Medical translation is a very broad term covering a wide variety of specialist areas and materials – everything from patient information to regulatory, marketing and technical documents.<br />
As a result, this translation type has numerous potential sub-categories – ‘medical device translations’ and ‘clinical trial translations’, for example.<br />
What this means<br />
As with any text, the translators need to fully understand the materials they’re translating. That means sound knowledge of medical terminology and they’ll often also need specific subject-matter expertise.<br />
Good to know<br />
Many countries have specific requirements governing the translation of medical device and pharmaceutical documentation. This includes both your client-facing and product-related materials.<br />
Examples<br />
Medical reports, product instructions, labeling, clinical trial documentation<br />
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5. Financial Translation<br />
What is it?<br />
In broad terms, the translation of banking, stock exchange, forex, financing and financial reporting documents.<br />
However, the term is generally used only for the more technical of these documents that require translators with knowledge of the field.<br />
Any competent translator could translate a bank statement, for example, so that wouldn’t typically be considered a financial translation.<br />
What this means<br />
You need translators with domain expertise to correctly understand and translate the financial terminology in these texts.<br />
Examples<br />
Company accounts, annual reports, fund or product prospectuses, audit reports, IPO documentation<br />
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6. Economic Translations<br />
What is it?<br />
1. Sometimes used as a synonym for financial translations.<br />
2. Other times used somewhat loosely to refer to any area of economic activity – so combining business/commercial, financial and some types of technical translations.<br />
3. More narrowly, the translation of documents relating specifically to the economy and the field of economics.<br />
What this means<br />
As always, you need translators with the relevant expertise and knowledge for this type of translation.<br />
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7. Legal Translation<br />
What is it?<br />
The translation of documents relating to the law and legal process.<br />
What this means<br />
Legal texts require translators with a legal background.<br />
That’s because without it, a translator may not:<br />
– fully understand the legal concepts<br />
– write in legal style<br />
– understand the differences between legal systems, and how best to translate concepts that don’t correspond.<br />
And we need all that to produce professional quality legal translations – translations that are accurate, terminologically correct and stylistically appropriate.<br />
Examples<br />
Contracts, legal reports, court judgments, expert opinions, legislation<br />
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8. Juridical Translation<br />
What is it?<br />
1. Generally used as a synonym for legal translations.<br />
2. Alternatively, can refer to translations requiring some form of legal verification, certification or notarization that is common in many jurisdictions.<br />
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9. Judicial Translation<br />
What is it?<br />
1. Most commonly a synonym for legal translations.<br />
2. Rarely, used to refer specifically to the translation of court proceeding documentation – so judgments, minutes, testimonies, etc. <br />
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10. Patent Translation<br />
What is it?<br />
The translation of intellectual property and patent-related documents.<br />
Key features<br />
Patents have a specific structure, established terminology and a requirement for complete consistency throughout – read more on this here. These are key aspects to patent translations that translators need to get right.<br />
In addition, subject matter can be highly technical.<br />
What this means<br />
You need translators who have been trained in the specific requirements for translating patent documents. And with the domain expertise needed to handle any technical content.<br />
Examples<br />
Patent specifications, prior art documents, oppositions, opinions<br />
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11. Literary Translation<br />
What is it?<br />
The translation of literary works – novels, short stories, plays, essays, poems.<br />
Key features<br />
Literary translation is widely regarded as the most difficult form of translation.<br />
That’s because it involves much more than simply conveying all meaning in an appropriate style. The translator’s challenge is to also reproduce the character, subtlety and impact of the original – the essence of what makes that work unique.<br />
This is a monumental task, and why it’s often said that the translation of a literary work should be a literary work in its own right.<br />
What this means<br />
Literary translators must be talented wordsmiths with exceptional creative writing skills.<br />
Because few translators have this skillset, you should only consider dedicated literary translators for this type of translation.<br />
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12. Commercial Translation<br />
What is it?<br />
The translation of documents relating to the world of business.<br />
This is a very generic, wide-reaching translation type. It includes other more specialised forms of translation – legal, financial and technical, for example. And all types of more general business documentation.<br />
Also, some documents will require familiarity with business jargon and an ability to write in that style.<br />
What this means<br />
Different translators will be required for different document types – specialists should handle materials involving technical and specialist fields, whereas generalist translators can translate non-specialist materials.<br />
Examples<br />
Business correspondence, reports, marketing and promotional materials, sales proposals<br />
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13. Business Translations<br />
What is it?<br />
A synonym for Commercial Translations.<br />
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14. Administrative Translations<br />
What is it?<br />
The translation of business management and administration documents.<br />
So it’s a subset of business / commercial translations.<br />
What this means<br />
The implication is these documents will include business jargon and ‘management speak’, so require a translator familiar with, and practised at, writing in that style.<br />
Examples<br />
Management reports and proposals<br />
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15. Marketing Translations<br />
What is it?<br />
The translation of advertising, marketing and promotional materials.<br />
This is a subset of business or commercial translations.<br />
Key features<br />
Marketing copy is designed to have a specific impact on the audience – to appeal and persuade.<br />
So the translated copy must do this too.<br />
But a direct translation will seldom achieve this – so translators need to adapt their wording to produce the impact the text is seeking.<br />
And sometimes a completely new message might be needed – see transcreation in our next category of translation types.<br />
What this means<br />
Marketing translations require translators who are skilled writers with a flair for producing persuasive, impactful copy.<br />
As relatively few translators have these skills, engaging the right translator is key.<br />
Good to know<br />
This type of translation often comes with a typesetting or dtp requirement – particularly for adverts, posters, brochures, etc.<br />
Its best for your translation provider to handle this component. That’s because multilingual typesetters understand the design and aesthetic conventions in other languages/cultures. And these are essential to ensure your materials have the desired impact and appeal in your target markets.<br />
Examples<br />
Advertising, brochures, some website/social media text.<br />
Translation Category B: 14 types of translation based on the end product or use of the translation<br />
This category is all about how the translation is going to be used or the end product that’s produced.<br />
Most of these types involve either adapting or processing a completed translation in some way, or converting or incorporating it into another program or format.<br />
You’ll see that some are very specialised, and complex.<br />
It’s another way translation providers refer to the range of services they provide.<br />
Check out our video of the most specialised of these types of translation:<br />
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16. Document Translations<br />
What is it?<br />
The translation of documents of all sorts.<br />
Here the translation itself is the end product and needs no further processing beyond standard formatting and layout.<br />
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17. Text Translations<br />
What is it?<br />
A synonym for document translation.<br />
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18. Certified Translations<br />
What is it?<br />
A translation with some form of certification.<br />
Key features<br />
The certification can take many forms. It can be a statement by the translation company, signed and dated, and optionally with their company seal. Or a similar certification by the translator.<br />
The exact format and wording will depend on what clients and authorities require – here’s an example.<br />
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19. Official Translations<br />
What is it?<br />
1. Generally used as a synonym for certified translations.<br />
2. Can also refer to the translation of ‘official’ documents issued by the authorities in a foreign country. These will almost always need to be certified.<br />
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20. Software Localisation<br />
What is it?<br />
Adapting software for another language/culture.<br />
Key features<br />
The goal of software localisation is not just to make the program or product available in other languages. It’s also about ensuring the user experience in those languages is as natural and effective as possible.<br />
Translating the user interface, messaging, documentation, etc is a major part of the process.<br />
Also key is a customisation process to ensure everything matches the conventions, norms and expectations of the target cultures.<br />
Adjusting time, date and currency formats are examples of simple customisations. Others might involve adapting symbols, graphics, colours and even concepts and ideas.<br />
Localisation is often preceded by internationalisation – a review process to ensure the software is optimally designed to handle other languages.<br />
And it’s almost always followed by thorough testing – to ensure all text is in the correct place and fits the space, and that everything makes sense, functions as intended and is culturally appropriate.<br />
Localisation is often abbreviated to L10N, internationalisation to i18n.<br />
What this means<br />
Software localisation is a specialised kind of translation, and you should always engage a company that specialises in it.<br />
They’ll have the systems, tools, personnel and experience needed to achieve top quality outcomes for your product.<br />
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21. Game Localisation<br />
What is it?<br />
Adapting games for other languages and markets.<br />
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It’s a subset of software localisation.<br />
Key features<br />
The goal of game localisation is to provide an engaging and fun gaming experience for speakers of other languages.<br />
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It involves translating all text and recording any required foreign language audio.<br />
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But also adapting anything that would clash with the target culture’s customs, sensibilities and regulations.<br />
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For example, content involving alcohol, violence or gambling may either be censored or inappropriate in the target market.<br />
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And at a more basic level, anything that makes users feel uncomfortable or awkward will detract from their experience and thus the success of the game in that market.<br />
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So portions of the game may have to be removed, added to or re-worked.<br />
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Game localisation involves at least the steps of translation, adaptation, integrating the translations and adaptations into the game, and testing.<br />
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What this means<br />
Game localisation is a very specialised type of translation best left to those with specific expertise and experience in this area.<br />
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22. Multimedia Localisation<br />
What is it?<br />
Adapting multimedia for other languages and cultures.<br />
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Multimedia refers to any material that combines visual, audio and/or interactive elements. So videos and movies, on-line presentations, e-Learning courses, etc.<br />
Key features<br />
Anything a user can see or hear may need localising.<br />
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That means the audio and any text appearing on screen or in images and animations.<br />
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Plus it can mean reviewing and adapting the visuals and/or script if these aren’t suitable for the target culture.<br />
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The localisation process will typical involve:<br />
– Translation<br />
– Modifying the translation for cultural reasons and/or to meet technical requirements<br />
– Producing the other language versions<br />
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Audio output may be voice-overs, dubbing or subtitling.<br />
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And output for visuals can involve re-creating elements, or supplying the translated text for the designers/engineers to incorporate.<br />
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What this means<br />
Multimedia localisation projects vary hugely, and it’s essential your translation providers have the specific expertise needed for your materials.<br />
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23. Script Translations<br />
What is it?<br />
Preparing the text of recorded material for recording in other languages.<br />
Key features<br />
There are several issues with script translation.<br />
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One is that translations typically end up longer than the original script. So voicing the translation would take up more space/time on the video than the original language.<br />
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Sometimes that space will be available and this will be OK.<br />
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But generally it won’t be. So the translation has to be edited back until it can be comfortably voiced within the time available on the video.<br />
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Another challenge is the translation may have to synchronise with specific actions, animations or text on screen.<br />
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Also, some scripts also deal with technical subject areas involving specialist technical terminology.<br />
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Finally, some scripts may be very culture-specific – featuring humour, customs or activities that won’t work well in another language. Here the script, and sometimes also the associated visuals, may need to be adjusted before beginning the translation process.<br />
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It goes without saying that a script translation must be done well. If it’s not, there’ll be problems producing a good foreign language audio, which will compromise the effectiveness of the video.<br />
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Translators typically work from a time-coded transcript. This is the original script marked to show the time available for each section of the translation.<br />
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What this means<br />
There are several potential pitfalls in script translations. So it’s vital your translation provider is practiced at this type of translation and able to handle any technical content.<br />
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24. Voice-over and Dubbing Projects<br />
What is it?<br />
Translation and recording of scripts in other languages.<br />
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Voice-overs vs dubbing<br />
There is a technical difference.<br />
A voice-over adds a new track to the production, dubbing replaces an existing one.<br />
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Key features<br />
These projects involve two parts:<br />
– a script translation (as described above), and<br />
– producing the audio<br />
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So they involve the combined efforts of translators and voice artists.<br />
The task for the voice artist is to produce a high quality read. That’s one that matches the style, tone and richness of the original.<br />
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Often each section of the new audio will need to be the same length as the original.<br />
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But sometimes the segments will need to be shorter – for example where the voice-over lags the original by a second or two. This is common in interviews etc, where the original voice is heard initially then drops out.<br />
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The most difficult form of dubbing is lip-syncing – where the new audio needs to synchronise with the original speaker’s lip movements, gestures and actions.<br />
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Lip-syncing requires an exceptionally skilled voice talent and considerable time spent rehearsing and fine tuning the translation.<br />
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What this means<br />
You need to use experienced professionals every step of the way in this type of project.<br />
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That’s to ensure firstly that your foreign-language scripts are first class, then that the voicing is of high professional standard.<br />
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Anything less will mean your foreign language versions will be way less effective and appealing to your target audience.<br />
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25. Subtitle Translations<br />
What is it?<br />
Producing foreign language captions for sub or surtitles.<br />
Key features<br />
The goal with subtitling is to produce captions that viewers can comfortably read in the time available and still follow what’s happening on the video.<br />
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To achieve this, languages have “rules” governing the number of characters per line and the minimum time each subtitle should display.<br />
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Sticking to these guidelines is essential if your subtitles are to be effective.<br />
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But this is no easy task – it requires simple language, short words, and a very succinct style. Translators will spend considerable time mulling over and re-working their translation to get it just right.<br />
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Most subtitle translators use specialised software that will output the captions in the format sound engineers need for incorporation into the video.<br />
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What this means<br />
As with other specialised types of translation, you should only use translators with specific expertise and experience in subtitling.<br />
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26. Website Localisation<br />
What is it?<br />
The translation and adapting of relevant content on a website to best suit the target language and culture.<br />
<br />
Note: Many providers use the term website translation as a synonym for localisation. Strictly speaking though, translation is just one part of localisation.<br />
Key features<br />
<br />
Not all pages on a website may need to be localised – clients should review their content to identify what’s relevant for the other language versions.<br />
Some content may need specialist translators – legal and technical pages for example.<br />
There may also be videos, linked documents, and text or captions in graphics to translate.<br />
Adaptation can mean changing date, time, currency and number formats, units of measure, etc.<br />
But also images, colours and even the overall site design and style if these won’t have the desired impact in the target culture.<br />
Translated files can be supplied in a wide range of formats – translators usually coordinate output with the site webmasters.<br />
New language versions are normally thoroughly reviewed and tested before going live to confirm everything is displaying correctly, works as intended and is cultural appropriate.<br />
What this means<br />
The first step should be to review your content and identify what needs to be translated. This might lead you to modify some pages for the foreign language versions.<br />
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In choosing your translation providers be sure they can:<br />
– handle any technical or legal content,<br />
– provide your webmaster with the file types they want.<br />
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And you should always get your translators to systematically review the foreign language versions before going live.<br />
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27. Transcreation<br />
What is it?<br />
Adapting a message to elicit the same emotional response in another language and culture.<br />
Translation is all about conveying the message or meaning of a text in another language. But sometimes that message or meaning won’t have the desired effect in the target culture.<br />
<br />
This is where transcreation comes in. Transcreation creates a new message that will get the desired emotional response in that culture, while preserving the style and tone of the original.<br />
<br />
So it’s a sort of creative translation – which is where the word comes from, a combination of ‘translation’ and ‘creation’.<br />
<br />
At one level transcreation may be as simple as choosing an appropriate idiom to convey the same intent in the target language – something translators do all the time.<br />
<br />
But mostly the term is used to refer to adapting key advertising and marketing messaging. Which requires copywriting skills, cultural awareness and an excellent knowledge of the target market.<br />
<br />
Who does it?<br />
Some translation companies have suitably skilled personnel and offer transcreation services.<br />
<br />
Often though it’s done in the target country by specialist copywriters or an advertising or marketing agency – particularly for significant campaigns and to establish a brand in the target marketplace.<br />
<br />
What this means<br />
Most general marketing and promotional texts won’t need transcreation – they can be handled by a translator with excellent creative writing skills.<br />
<br />
But slogans, by-lines, advertising copy and branding statements often do.<br />
<br />
Whether you should opt for a translation company or an in-market agency will depend on the nature and importance of the material, and of course your budget.<br />
<br />
<br />
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28. Audio Translations<br />
What is it?<br />
Broad meaning: the translation of any type of recorded material into another language.<br />
<br />
More commonly: the translation of a foreign language video or audio recording into your own language. So this is where you want to know and document what a recording says.<br />
Key features<br />
The first challenge with audio translations is it’s often impossible to pick up every word that’s said. That’s because audio quality, speech clarity and speaking speed can all vary enormously.<br />
<br />
It’s also a mentally challenging task to listen to an audio and translate it directly into another language. It’s easy to miss a word or an aspect of meaning.<br />
<br />
So best practice is to first transcribe the audio (type up exactly what is said in the language it is spoken in), then translate that transcription.<br />
<br />
However, this is time consuming and therefore costly, and there are other options if lesser precision is acceptable.<br />
<br />
What this means<br />
It’s best to discuss your requirements for this kind of translation with your translation provider. They’ll be able to suggest the best translation process for your needs.<br />
<br />
Examples<br />
Interviews, product videos, police recordings, social media videos.<br />
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<br />
<br />
29. Translations with DTP<br />
What is it?<br />
Translation incorporated into graphic design files.multilingual dtp example in the form of a Rubik's Cube with foreign text on each square<br />
Key features<br />
Graphic design programs are used by professional designers and graphic artists to combine text and images to create brochures, books, posters, packaging, etc.<br />
<br />
Translation plus dtp projects involve 3 steps – translation, typesetting, output.<br />
<br />
The typesetting component requires specific expertise and resources – software and fonts, typesetting know-how, an appreciation of foreign language display conventions and aesthetics.<br />
<br />
What this means<br />
Make sure your translation company has the required multilingual typesetting/desktop publishing expertise whenever you’re translating a document created in a graphic design program.<br />
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Translation Category C: 13 types of translation based on the translation method employed<br />
This category has two sub-groups:<br />
– the practical methods translation providers use to produce their translations, and<br />
– the translation strategies/methods identified and discussed within academia.<br />
<br />
The translation methods translation providers use<br />
There are 4 main methods used in the translation industry today. We have an overview of each below, but for more detail, including when to use each one, see our comprehensive blog article.<br />
<br />
Or watch our video.<br />
<br />
Important: If you’re a client you need to understand these 4 methods – choose the wrong one and the translation you end up with may not meet your needs!<br />
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<br />
30. Machine Translation (MT)<br />
What is it?<br />
A translation produced entirely by a software program with no human intervention.<br />
<br />
A widely used, and free, example is Google Translate. And there are also commercial MT engines, generally tailored to specific domains, languages and/or clients.<br />
<br />
Pros and cons<br />
There are two limitations to MT:<br />
– they make mistakes (incorrect translations), and<br />
– quality of wording is patchy (some parts good, others unnatural or even nonsensical)<br />
<br />
On they positive side they are virtually instantaneous and many are free.<br />
<br />
Best suited for:<br />
Getting the general idea of what a text says.<br />
<br />
This method should never be relied on when high accuracy and/or good quality wording is needed.<br />
<br />
<br />
<br />
31. Machine Translation plus Human Editing (PEMT)<br />
What is it?<br />
A machine translation subsequently edited by a human translator or editor (often called Post-editing Machine Translation = PEMT).<br />
<br />
The editing process is designed to rectify some of the deficiencies of a machine translation.<br />
<br />
This process can take different forms, with different desired outcomes. Probably most common is a ‘light editing’ process where the editor ensures the text is understandable, without trying to fix quality of expression.<br />
<br />
Pros and cons<br />
This method won’t necessarily eliminate all translation mistakes. That’s because the program may have chosen a wrong word (meaning) that wasn’t obvious to the editor.<br />
<br />
And wording won’t generally be as good as a professional human translator would produce.<br />
<br />
Its advantage is it’s generally quicker and a little cheaper than a full translation by a professional translator.<br />
<br />
Best suited for:<br />
Translations for information purposes only.<br />
<br />
Again, this method shouldn’t be used when full accuracy and/or consistent, natural wording is needed.<br />
<br />
<br />
<br />
32. Human Translation<br />
What is it?<br />
Translation by a professional human translator.<br />
Pros and cons<br />
Professional translators should produce translations that are fully accurate and well-worded.<br />
<br />
That said, there is always the possibility of ‘human error’, which is why translation companies like us typically offer an additional review process – see next method.<br />
<br />
This method will take a little longer and likely cost more than the PEMT method.<br />
<br />
Best suited for:<br />
Most if not all translation purposes.<br />
<br />
<br />
<br />
33. Human Translation + Revision<br />
What is it?<br />
A human translation with an additional review by a second translator.<br />
<br />
The review is essentially a safety check – designed to pick up any translation errors and refine wording if need be.<br />
<br />
Pros and cons<br />
This produces the highest level of translation quality.<br />
<br />
It’s also the most expensive of the 4 methods, and takes the longest.<br />
<br />
Best suited for:<br />
All translation purposes.<br />
<br />
Gearwheel with 5 practical translation methods written on the teeth <br />
There’s also one other common term used by practitioners and academics alike to describe a type (method) of translation:<br />
<br />
34. Computer-Assisted Translation (CAT)<br />
What is it?<br />
A human translator using computer tools to aid the translation process.<br />
<br />
Key features<br />
Virtually all translators use such tools these days.<br />
<br />
The most prevalent tool is Translation Memory (TM) software. This creates a database of previous translations that can be accessed for future work.<br />
<br />
TM software is particularly useful when dealing with repeated and closely-matching text, and for ensuring consistency of terminology. For certain projects it can speed up the translation process.<br />
<br />
<br />
<br />
The translation methods described by academia<br />
A great deal has been written within academia analysing how human translators go about their craft.<br />
<br />
Seminal has been the work of Newmark, and the following methods of translation attributed to him are widely discussed in the literature.Gearwheel with Newmark's 8 translation methods written on the teeth <br />
These methods are approaches and strategies for translating the text as a whole, not techniques for handling smaller text units, which we discuss in our final translation category.<br />
<br />
<br />
<br />
35. Word-for-word Translation<br />
This method translates each word into the other language using its most common meaning and keeping the word order of the original language.<br />
<br />
So the translator deliberately ignores context and target language grammar and syntax.<br />
<br />
Its main purpose is to help understand the source language structure and word use.<br />
<br />
Often the translation will be placed below the original text to aid comparison.<br />
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<br />
<br />
36. Literal Translation<br />
Words are again translated independently using their most common meanings and out of context, but word order changed to the closest acceptable target language grammatical structure to the original.<br />
<br />
Its main suggested purpose is to help someone read the original text.<br />
<br />
<br />
<br />
37. Faithful Translation<br />
Faithful translation focuses on the intention of the author and seeks to convey the precise meaning of the original text.<br />
<br />
It uses correct target language structures, but structure is less important than meaning.<br />
<br />
<br />
<br />
38. Semantic Translation<br />
Semantic translation is also author-focused and seeks to convey the exact meaning.<br />
<br />
Where it differs from faithful translation is that it places equal emphasis on aesthetics, ie the ‘sounds’ of the text – repetition, word play, assonance, etc.<br />
<br />
In this method form is as important as meaning as it seeks to “recreate the precise flavour and tone of the original” (Newmark).slide showing definition of semantic translation as a translation method<br />
<br />
39. Communicative Translation<br />
Seeks to communicate the message and meaning of the text in a natural and easily understood way.<br />
<br />
It’s described as reader-focused, seeking to produce the same effect on the reader as the original text.<br />
<br />
A good comparison of Communicative and Semantic translation can be found here.<br />
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40. Free Translation<br />
Here conveying the meaning and effect of the original are all important.<br />
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There are no constraints on grammatical form or word choice to achieve this.<br />
<br />
Often the translation will paraphrase, so may be of markedly different length to the original.<br />
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41. Adaptation<br />
Mainly used for poetry and plays, this method involves re-writing the text where the translation would otherwise lack the same resonance and impact on the audience.<br />
<br />
Themes, storylines and characters will generally be retained, but cultural references, acts and situations adapted to relevant target culture ones.<br />
<br />
So this is effectively a re-creation of the work for the target culture.<br />
<br />
42. Idiomatic Translation<br />
Reproduces the meaning or message of the text using idioms and colloquial expressions and language wherever possible.<br />
<br />
The goal is to produce a translation with language that is as natural as possible.<br />
<br />
Translation Category D: 9 types of translation based on the translation technique used<br />
These translation types are specific strategies, techniques and procedures for dealing with short chunks of text – generally words or phrases.<br />
<br />
They’re often thought of as techniques for solving translation problems.<br />
<br />
They differ from the translation methods of the previous category which deal with the text as a whole.<br />
9 translation techniques as titles of books in a bookcase<br />
<br />
43. Borrowing<br />
What is it?<br />
Using a word or phrase from the original text unchanged in the translation.<br />
<br />
Key features<br />
With this procedure we don’t translate the word or phrase at all – we simply ‘borrow’ it from the source language.<br />
<br />
Borrowing is a very common strategy across languages. Initially, borrowed words seem clearly ‘foreign’, but as they become more familiar, they can lose that ‘foreignness’.<br />
<br />
Translators use this technique:<br />
– when it’s the best word to use – either because it has become the standard, or it’s the most precise term, or<br />
– for stylist effect – borrowings can add a prestigious or scholarly flavour.<br />
<br />
Borrowed words or phrases are often italicised in English.<br />
<br />
Examples of borrowings in English<br />
grand prix, kindergarten, tango, perestroika, barista, sampan, karaoke, tofu<br />
<br />
<br />
44. Transliteration<br />
What is it?<br />
Reproducing the approximate sounds of a name or term from a language with a different writing system.<br />
<br />
Key features<br />
In English we use the Roman (Latin) alphabet in common with many other languages including almost all European languages.<br />
<br />
Other writing systems include Arabic, Cyrillic, Chinese, Japanese, Korean, Thai, and the Indian languages.<br />
<br />
Transliteration from such systems into the Roman alphabet is also called romanisation.<br />
<br />
There are accepted systems for how individual letters/sounds should be romanised from most other languages – there are three common systems for Chinese, for example.<br />
<br />
English borrowings from languages using non-Roman writing systems also require transliteration – perestroika, sampan, karaoke, tofu are examples from the above list.<br />
<br />
Translators mostly use transliteration as a procedure for translating proper names.<br />
<br />
Examples<br />
毛泽东 Mao Tse-tung or Mao Zedong<br />
Владимир Путин Vladimir Putin<br />
서울 Seoul<br />
ភ្នំពេញ Phnom Penh<br />
<br />
45. Calque or Loan Translation<br />
What is it?<br />
A literal translation of a foreign word or phrase to create a new term with the same meaning in the target language.<br />
<br />
So a calque is a borrowing with translation if you like. The new term may be changed slightly to reflect target language structures.<br />
<br />
Examples<br />
German ‘Kindergarten’ has been calqued as детский сад in Russian, literally ‘children garden’ in both languages.<br />
<br />
Chinese 洗腦 ‘wash’ + ‘brain’ is the origin of ‘brainwash’ in English.<br />
<br />
English skyscraper is calqued as gratte-ciel in French and rascacielos in Spanish, literally ‘scratches sky’ in both languages.<br />
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46. Word-for-word translation<br />
What is it?<br />
A literal translation that is natural and correct in the target language.<br />
<br />
Alternative names are ‘literal translation’ or ‘metaphrase’.<br />
<br />
Note: this technique is different to the translation method of the same name, which does not produce correct and natural text and has a different purpose.<br />
<br />
Key features<br />
This translation strategy will only work between languages that have very similar grammatical structures.<br />
<br />
And even then, only sometimes.<br />
<br />
For example, standard word order in Turkish is Subject-Object-Verb whereas in English it’s Subject-Verb-Object. So a literal translation between these two will seldom work:<br />
– Yusuf elmayı yedi is literally ‘Joseph the apple ate’.<br />
<br />
When word-for-word translations don’t produce natural and correct text, translators resort to some of the other techniques described below.<br />
Examples<br />
French ‘Quelle heure est-il?’ works into English as ‘What time is it?’.<br />
<br />
Russian ‘Oн хочет что-нибудь поесть’ is ‘He wants something to eat’.<br />
<br />
47. Transposition<br />
What is it?<br />
Translation with a change of grammatical structure.<br />
<br />
This technique gives the translation more natural wording and/or makes it grammatically correct.<br />
<br />
Examples<br />
A change in word order:<br />
Our Turkish example Yusuf elmayı yedi (literally ‘Joseph the apple ate’) –> Joseph ate the apple.<br />
<br />
Spanish La Casa Blanca (literally ‘The House White’) –> The White House<br />
<br />
A change in grammatical category:<br />
German Er hört gerne Musik (literally ‘he listens gladly [to] music’)<br />
= subject pronoun + verb + adverb + noun<br />
becomes Spanish Le gusta escuchar música (literally ‘[to] him [it] pleases to listen [to] music’)<br />
= indirect object pronoun + verb + infinitive + noun<br />
and English He likes listening to music<br />
= subject pronoun + verb + gerund + noun.<br />
<br />
48. Modulation<br />
What is it?<br />
Translation with a change of focus or point of view in the target language.<br />
<br />
This technique makes the translation more idiomatic – how people would normally say it in the language.<br />
<br />
Examples<br />
English talks of the ‘top floor’ of a building, French the dernier étage = last floor. ‘Last floor’ would be unnatural in English, so too ‘top floor’ in French.<br />
<br />
German uses the term Lebensgefahr (literally ‘danger to life’) where in English we’d be more likely to say ‘risk of death’.<br />
In English we’d say ‘I dropped the key’, in Spanish se me cayó la llave, literally ‘the key fell from me’. The English perspective is that I did something (dropped the key), whereas in Spanish something happened to me – I’m the recipient of the action.<br />
<br />
49. Equivalence or Reformulation<br />
What is it?<br />
Translating the underlying concept or meaning using a totally different expression.<br />
<br />
This technique is widely used when translating idioms and proverbs.<br />
<br />
And it’s common in titles and advertising slogans.<br />
<br />
It’s a common strategy where a direct translation either wouldn’t make sense or wouldn’t resonate in the same way.<br />
<br />
Examples<br />
Here are some equivalents of the English saying “Pigs may fly”, meaning something will never happen, or “you’re being unrealistic” (Source):<br />
– Thai: ชาติหน้าตอนบ่าย ๆ – literally, ‘One afternoon in your next reincarnation’<br />
– French: Quand les poules auront des dents – literally, ‘When hens have teeth’<br />
– Russian, Когда рак на горе свистнет – literally, ‘When a lobster whistles on top of a mountain’<br />
– Dutch, Als de koeien op het ijs dansen – literally, ‘When the cows dance on the ice’<br />
– Chinese: 除非太陽從西邊出來！– literally, ‘Only if the sun rises in the west’<br />
<br />
50. Adaptation<br />
What is it?<br />
A translation that substitutes a culturally-specific reference with something that’s more relevant or meaningful in the target language.<br />
<br />
It’s also known as cultural substitution or cultural equivalence.<br />
<br />
It’s a useful technique when a reference wouldn’t be understood at all, or the associated nuances or connotations would be lost in the target language.<br />
<br />
Note: the translation method of the same name is a similar concept but applied to the text as a whole.<br />
<br />
Examples<br />
Different cultures celebrate different coming of age birthdays – 21 in many cultures, 20, 15 or 16 in others. A translator might consider changing the age to the target culture custom where the coming of age implications were important in the original text.<br />
Animals have different connotations across languages and cultures. Owls for example are associated with wisdom in English, but are a bad omen to Vietnamese. A translator might want to remove or amend an animal reference where this would create a different image in the target language.<br />
<br />
51. Compensation<br />
What is it?<br />
A meaning or nuance that can’t be directly translated is expressed in another way in the text.<br />
Example<br />
Many languages have ways of expressing social status (honorifics) encoded into their grammatical structures.<br />
<br />
So you can convey different levels of respect, politeness, humility, etc simply by choosing different forms of words or grammatical elements.<br />
But these nuances will be lost when translating into languages that don’t have these structures.<br />
Then translating into languages that don’t have these structures<br />
Then translating into languages that don’t have these structures.<br />
<br />
===Conclusion ===<br />
<br />
In the light of above mentioned facts and figures here by we would say that machine translation is a challenge for human translators because it can reduce the workload of translation but can't give accurate and exact translation of the target language.It can be less reliable than human translation..</div>Chen Huinihttps://bou.de/u/index.php?title=Machine_translation&diff=133231Machine translation2021-12-15T04:58:22Z<p>Chen Huini: /* References */</p>
<hr />
<div>'''Machine Translation - A challenge or a chance for human translators?'''<br />
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[[Machine_translation|Overview Page of Machine Translation]]<br />
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30 Chapters（0/30)<br />
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[[Machine_Trans_EN_1]] [[Machine_Trans_EN_2]] [[Machine_Trans_EN_3]] [[Machine_Trans_EN_4]] [[Machine_Trans_EN_5]] [[Machine_Trans_EN_6]] [[Machine_Trans_EN_7]] [[Machine_Trans_EN_8]] [[Machine_Trans_EN_9]] [[Machine_Trans_EN_10]] [[Machine_Trans_EN_11]] [[Machine_Trans_EN_12]] [[Machine_Trans_EN_13]] [[Machine_Trans_EN_14]] [[Machine_Trans_EN_15]] [[Machine_Trans_EN_16]] [[Machine_Trans_EN_17]] [[Machine_Trans_EN_18]] [[Machine_Trans_EN_19]] [[Machine_Trans_EN_20]] [[Machine_Trans_EN_21]] [[Machine_Trans_EN_22]] [[Machine_Trans_EN_23]] [[Machine_Trans_EN_24]] [[Machine_Trans_EN_25]] [[Machine_Trans_EN_26]] [[Machine_Trans_EN_27]] [[Machine_Trans_EN_28]] [[Machine_Trans_EN_29]] [[Machine_Trans_EN_30]] ...<br />
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[[Book_projects|Back to translation project overview]]<br />
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[[DCG-To-Do|To the To Do list]]<br />
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=Chapter 1:A Comparison Between the Quality of Machine Translation and Human Translation——A Case Study of the Application of artificial intelligence in Sports Events=<br />
'''论机器翻译与人工翻译的质量对比——以人工智能在体育赛事领域的应用为例'''<br />
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卫怡雯 Wei Yiwen, Hunan Normal University, China<br />
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[[Machine_Trans_EN_1]]<br />
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=Chapter 3：On the Realm Advantages And Mutual Development of Machine Translation And Huamn Translation)=<br />
"'论机器翻译与人工翻译的领域优势及共同发展'"<br />
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肖毅瑶 Xiao Yiyao, Hunan Normal University, China<br />
[[Machine_Trans_EN_3]]<br />
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=Chapter 4 : A Comparison Between Machine Translation of Netease and Traditional Human Translation—A Case Study of The Economist Articles)= <br />
'''网易有道机器翻译与人工翻译的译文对比——以经济学人语料为例'''<br />
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王李菲 Wang Lifei, Hunan Normal University<br />
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[[Machine_Trans_EN_4]]<br />
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=Chapter 5: Muhammad Saqib Mehran - Problems in translation studies=<br />
[[Machine_Trans_EN_5]]<br />
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=6 徐敏赟(Machine Translation Based on Neural Network --Challenge or Chance?)=<br />
[[Machine_Trans_EN_6]]<br />
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=Chapter 7: The Cultivation of artificial intelligence and translation talents in the Belt and Road Initiative=<br />
[[Machine_Trans_EN_7]]<br />
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一带一路背景下人工智能与翻译人才的培养<br />
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颜莉莉 Yan Lili, Hunan Normal University, China<br />
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=Chapter 8: On Machine Translation Under Lanuguage Intelligence——An Option and Opportunity for Human Translators=<br />
'''论语言智能下的机器翻译——译者的选择与机遇'''<br />
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颜静 Yan Jing, Hunan Normal University, China<br />
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[[Machine_Trans_EN_8]]<br />
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=9 谢佳芬（ Machine Translation and Artificial Translation in the Era of Artificial Intelligence）=<br />
[[Machine_Trans_EN_9]]<br />
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=10 熊敏(Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts)=<br />
机器翻译对各类型文本的英汉翻译能力探究<br />
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熊敏, Xiong Min, Hunan Normal University<br />
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[[Machine_Trans_EN_10]]<br />
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===Abstract===<br />
The history of machine translation can be traced to 1940s, undergoing germination, recession, revival and flourish. Nowadays, with the rapid development of information technology, machine translation technology emerged and is gradually becoming mature. Whether manual translation can be replaced by machine translation becomes a widely-disputed topic. So in order to explore the ability of machine translation, I adopts two versions of translation, which are manual translation and machine translation(this paper uses Youdao translation) for different types of texts(according to Peter Newmark's types of text：informative text, expressive text and vocative text). The results are quite different in terms of quality and accuracy. The results show that machine translation is more suitable for informative text for its brevity, while the expressive texts especially literary works and vocative texts are difficult to be translated, because there are too many loaded words and cultural images in expressive text and dependence on the readers. To draw a conclusion, the relationship between machine translation and manual translation should be complementary rather than competitive.<br />
<br />
===Key words===<br />
machine translation; manual translation; Newmark's type of texts<br />
<br />
===题目===<br />
Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts<br />
<br />
===摘要===<br />
机器翻译的历史可以追溯到上个世纪40年代，经历了萌芽期、萧条期、复兴期和繁荣期四个阶段。如今，随着信息技术的高速发展，机器翻译技术日益成熟，于是机器翻译能不能替代人工翻译这个话题引起了广泛热议。为了探究机器翻译的能力水平是否足以替代人工翻译，本人根据皮特纽马克的文本类型分类理论（信息类文本，表达文本，呼吁文本），选择了相应的译文类型，并且将其机器翻译的版本以及人工翻译的版本进行对比。结果发现，就质量和准确度而言，译文的水平大相径庭。因为其简洁的特性，机器比较适合翻译信息文本。而就表达文本，尤其是文学作品，因其存在文化负载词及相应的文化现象，所以机器翻译这类文本存在难度。而呼唤文本因其目的性以及对读者的依赖性较强，翻译难度较大。由此得知，机器翻译和人工翻译的关系应该是互补的，而不是竞争的。<br />
<br />
===关键词===<br />
机器翻译；人工翻译；纽马克文本类型<br />
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===1. Introduction===<br />
====1.1.Introduction to machine translation====<br />
Machine translation, also known as computer translation is a technique to translating through machine translator, such as Google translation and Youdao translation. Machine translation is one of the branches of computational linguistics, ranging from computer science, statistics, information science and so on. Machine translation plays an important role in all aspects.<br />
Machine translation can be traced back to 1940s, when British engineer Booth and American engineer Weaver proposed using computer to translate and started to study machines used for translation. And the first machine translating system launched in 1954, used in English and Russian translation. And this means machine translation becomes a reality. However, the arrival of new things always accompanies barriers and setbacks. In the 1960s, reports from ALPAC (Automated Language Processing Advisory Committee) showed studies on machine translation had stagnated for a decade. At that time, due to the high cost of machine, choosing human translators to finish translating works was more economical for most companies. What’s worse, machine had difficulty in understanding semantic and pragmatic meanings. Fortunately, in the 1970s, with the advance and popularity of computer, machine translation was gradually back on track. With the development of science and technology, machine translation has better technological guarantee, such as artificial intelligence and computer technology. In the last decades, from the late 90s till now, machine translation is becoming more and more mature. The initial rule-oriented machine translation has been updated and Corpus-aided machine translation appears. And nowadays, technology in voice recognition has been further developed. This technique is frequently applied in speech translation products. Users only need to speak source language to the online translator and instantly it will speak out the target version. But machine can’t fully provide precise and accurate translation without any grammatical or semantic problems. Machine can understand the literal meaning of texts, but not the meaning in context. For example, there is polysemy in English, which refers to words having multiple meanings. So when translating these words, translators should take contexts into consideration. But machine has troubles in this way. In addition, machine has no feeling or intention. Hence, it is also difficult to convey the feelings from the source language.(Wei 2021:5)#<br />
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====1.2.Process of machine translation====<br />
The process of manual translation is different from that of machine translation. Here is the process of the former. (1) Understand source language. (2) Use target language to organize language. (3) Generate translation. Unlike manual translation, machine translation tends to analyze and code source language first, then look for related codes in corpus, and work out the code that represents target language, generating translation. But they share a common feature, which is that Lexicon, grammatical rules and syntactic structure are taken into consideration. This is one of the biggest challenges for machine translation.<br />
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===2.Theorectical framework===<br />
====2.1Newmark’s text typology====<br />
Text typology is a theory from linguistics. It undergoes several phrases. Karl Buhler, a German psychologist and linguist defines communicative functions into expressive, information and vocative. Then Roman Jakobson proposes categorization of texts, which are intralingual translation, interlingual translation and intersemiotic translation. Accordingly, he defines six main functions of language, including the referential function, conative function, emotive function, poetic function, metalingual function and the phatic function. Based on the two theories, Peter Newmark, a translation theorist, summarizes the language function into six parts: the informative function, the vocative function, the expressive function, the phatic function, the aesthetic function, and the metalingual function. Later, he further divided texts into informative type, expressive text and vocative type according to the linguistic functions of various texts. (Newmark 2002:2)#<br />
The core of the informative texts is the truth. It is to convey facts, information, knowledge of certain topics, reality and theories. The language style of the text is objective, logical and standardized. Reports, papers, scientific and technological textbooks are all attributed to informative texts. Translators are required to take format into account for different styles.<br />
The core of the expressive text is the sender’s emotions and attitudes. It is to express sender’s preferences, feelings, views and so on. The language style of it is subjective. Imaginative literature, including fictions, poems and dramas, autobiography and authoritative statements belong to expressive text. It requires translators to be faithful to the source language and maintain the style.<br />
The core of the vocative text is readership. It is to call upon readers to act, think, feel and react in the way intended by the text. So it is reader-oriented. Such texts as advertisement, propaganda and notices are of vocative text. Newmark noted that translators need to consider cultural background of the source language and the semantic and pragmatic effect of target language. If readers can comprehend the meaning at once and do as the text says, then the goal of information communication is achieved. (Liu 2021:3)#<br />
To draw a conclusion, informative texts focus on the information and facts. Expressive texts center on the mind of addressers, including feelings, prejudices and so on. And vocative texts are oriented on readers and call on them to practice as the texts say.<br />
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====2.2Study method====<br />
Manual translations of the three texts are selected from authoritative versions and universally acknowledged. And machine translations of those come from Youdao Translator. And in this thesis I will compare and evaluate the two methods in word diction, sentence structure, word order and redundancy.<br />
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===3.Comparison and analysis of machine translation and manual translation ===<br />
====3.1Informative text ====<br />
（1）English into Chinese<br />
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①Source language:<br />
<br />
Keep the tip of Apple Pencil clean, as dirt and other small particles may cause excessive wear to the tip or damage the screen of i-pad.<br />
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Target language:<br />
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Machine translation: Apple Pencil笔尖应保持清洁，灰尘等小颗粒可能会导致笔尖过度磨损或损坏ipad屏幕。<br />
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Manual translation: 保持Apple Pencil铅笔的笔尖干净，因为灰尘和其他微粒可能会导致笔尖的过度磨损或损坏iPad屏幕。<br />
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Analysis: Here is the instruction of Apple Pencil. And the manual translation is the Chinese version on the instruction.Product instruction tends to be professional, since there are many terms for some concepts. Machine can easily identify these terms and provide related words to translate. The machine version is faithful and expressive to the source language. So it is well-qualified and readable for readers to understand the instruction. So we can use machine to translate informative text.<br />
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②Source language:<br />
<br />
China on Saturday launched a rocket carrying three astronauts-two men and one woman - to the core module of a future space station where they will live and work for six months, the longest orbit for Chinese astronauts.<br />
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Target language:<br />
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Machine translation: 周六，中国发射了一枚运载三名宇航员(两男一女)的火箭，进入未来空间站的核心舱，他们将在那里生活和工作6个月，这是中国宇航员最长的轨道。<br />
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Manual translation: 周六，中国发射了一枚搭载三名宇航员(两男一女)的火箭，进入未来空间站的核心舱，他们将在那里生活和工作6个月，这是中国宇航员最漫长的一次轨道飞行。<br />
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Analysis: This is a news from Reuters, reporting that China has launched a rocket.The meaning of the two translations is almost the same, except for some word diction. But there are some details dealt with different choice. For example, the last sentence of the machine translation is a bit of obscure and direct. There are some ambiguous words and expressions.<br />
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(2)Chinese into English<br />
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Source language:湖南省博物馆是湖南省最大的历史艺术类博物馆，占地面积4.9万平方米，总建筑面积为9.1万平方米，是首批国家一级博物馆，中央地方共建的八个国家级重点博物馆之一、全国文化系统先进集体、文化强省建设有突出贡献先进集体。<br />
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Target language:<br />
Manual translation: As the largest history and art museum in Hunan province, the Hunan Museum covers an area of 49,000㎡, with the building area reaching 91,000㎡. It is one of the first batch of national first-level museums and one of the first eight national museums co-funded by central and local governments.<br />
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Machine translation: Museum in hunan province is one of the largest historical art museum in hunan province, covers an area of 49000 square meters, a total construction area of 91000 square meters, is the first national museum, the central place to build one of the eight national key museum, national cultural system advanced collectives, strong culture began with outstanding contribution of advanced collective.<br />
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Analysis: Machine translation is not faithful enough in content. For instance, “首批国家一级博物馆” is translated into “first national museum”, which is not the meaning of the source language. And there are some obvious grammar mistakes in the machine translation. For example, machine translates it into just one sentence but there are multiple predicates in it. So it is not grammatically permissible. What’s more, the sentence structure of machine translation is confusing and the focus is not specific enough.<br />
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====3.2Expressive text ====<br />
(1)English into Chinese<br />
<br />
Source language:<br />
<br />
An individual human existence should be like a river- small at first, narrowly contained within its banks, and rushing passionately past rocks and over waterfalls. Gradually the river grows wider, the banks recede, the waters flow more quietly, and in the end, without any visible breaks, they become merged in the sea, and painlessly lose their individual being.()<br />
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Target language:<br />
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Machine translation: 一个人的存在应该像一条河流——开始很小，被紧紧地夹在两岸中间，然后热情奔放地冲过岩石，飞下瀑布。渐渐地，河面变宽，两岸后退，水流更加平缓，最后，没有任何明显的停顿，它们汇入大海，毫无痛苦地失去了自己的存在。<br />
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Manual translation:人生在世，如若河流；河口初始狭窄，河岸虬曲，而后狂涛击石，飞泻成瀑。河道渐趋开阔，峡岸退去，水流潺缓，终了，一马平川，汇于大海，消逝无影。<br />
<br />
Analysis: Here is a well-known metaphor in the prose How to Grow Old written by Bertrand Russell. The manual translation is written by Tian Rongchang.This is a philosophical prose with graceful language. Literary translation is a most important and difficult branch of translation. Translator should focus on the literal meaning, culture, writing style and so on. It is a combination of beauty and elegance. Therefore, translators find it in a dilemma of beauty and faithfulness, let alone translating machine. Compared with manual translation, machine translation has difficulty in word choice. It is faithful and expressive, but not elegant enough.<br />
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(2)Chinese into English<br />
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Source language:没有一个人将小草叫做“大力士”，但是它的力量之大，的确是世界无比。这种力，是一般人看不见的生命力，只要生命存在，这种力就要显现，上面的石块，丝毫不足以阻挡。因为它是一种“长期抗战”的力，有弹性，能屈能伸的力，有韧性，不达目的不止的力。(Zhang, 2007:186)#<br />
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Target language:<br />
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Machine translation: No one calls the little grass "hercules", but its power is truly matchless in the world. This force is invisible life force. As long as there is life, this force will show itself. The stone above is not strong enough to stop it. Because it is a "long-term resistance" of the force, elastic, can bend and extend force, tenacity, not to achieve the purpose of the force.<br />
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Manual translation: Though nobody describes the little grass as a “husky”, yet its herculean strength is unrivalled. It is the force of life invisible to naked eye. It will display itself so long as there is life. The rock is utterly helpless before this force- a force that will forever remain militant, a force that is resilient and can take temporary setbacks calmly, a force that is tenacity itself and will never give up until the goal is reached. (by Zhang Peiji)<br />
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Analysis:This is the excerpt of a well-known Chinese prose written by Xia Yan. It is written during the war of Resistance Against Japan. So the prose holds symbolic meaning, eulogizing the invisible tenacious vitality so as to encourage Chinese to have confidence in the anti-aggression war. Compared with manual translation, machine translation is much more abstract and confusing, especially for the word diction. For example, “大力士” is translated into “hercules” which is a man of exceptional strength and size in Greek and Roman Mythology, making it difficult to understand if readers of target language have no idea of the allusion. What’s worse, the machine version doesn’t reveal the symbolic meaning of the text, which is the core of this prose.<br />
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====3.3Vocative text ====<br />
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(1)English into Chinese<br />
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①Source language:<br />
<br />
iPhone went to film school, so you don’t have to. (Advertisement of iPhone13)<br />
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Target language:<br />
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Machine translation: iPhone上的是电影学院，所以你不用去。<br />
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Manual translation:电影专业课，iPhone同学替你上完了。<br />
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Analysis：Here are advertisements of iPhone on Apple official website. There is a personification in the source language. It is used to stress the advancement and proficiency in camera, which is an appealing selling point to potential buyers. Compared with manual translation, machine translation is plain and not eye-catching enough for customers.<br />
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②Source language: <br />
<br />
5G speed OMGGGGG<br />
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Machine language: 5克的速度 OMGGGGG<br />
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Manual translation:<br />
<br />
iPhone的5G 巨巨巨巨巨5G<br />
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Analysis: The “G” in the source language is the unit of speed, standing for generation. However, it is mistaken as a unit of weight, representing gram in the machine translation. So the meaning is not faithful to the source language at all. As for manual translation, it complies with the source in form. Specifically speaking, five “G”s in the former complies with five characters “巨”in the latter. And the pronunciation of the two is similar. There are two layers of meaning for the 5 “G”s. One exclaims the fast speed of 5 generation network and the other new technology. In the manual version, “巨”can be used to show degree, meaning “quite” or “very”. <br />
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③Source language: <br />
<br />
History, faith and reason show the way, the way of unity. We can see each other not as adversaries but as neighbors. We can treat each other with dignity and respect, we can join forces, stop the shouting and lower the temperature. For without unity, there is no peace, only bitterness and fury."<br />
<br />
Target language:<br />
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Machine translation: 历史、信仰和理性指明了团结的道路。我们可以把彼此视为邻居，而不是对手。我们可以尊严地对待彼此，我们可以联合起来，停止大喊大叫，降低温度。因为没有团结，就没有和平，只有痛苦和愤怒。<br />
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Manual translation:历史、信仰和理性为我们指明道路。那是团结之路。我们可以把彼此视为邻居，而不是对手。我们可以有尊严地相互尊重。我们可以联合起来，停止喊叫，减少愤怒。因为没有团结就没有和平，只有痛苦和愤怒<br />
<br />
Analysis: Speech is a way to propagate some activity in public. It is an art to inspire emotion of the audience. The source language is the excerpt of Joe Biden’s inaugural speech. The speech should be inspiring and logic. The machine translation has some misunderstanding. Taking the translation of “lower the temperature” for example, machine only translates its literal meaning, relating to the temperature itself, without considering the context. What’s more, it is less logic than the manual one. Therefore, it adds difficulty to inspire the audience and infect their emotion.<br />
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===4.Common mistakes in machine translation ===<br />
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====4.1 lexical mistakes ====<br />
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Common lexical mistakes include misunderstandings in word category, lexical meaning and emotive and evaluative meaning. Misunderstanding in word category shows in the classification of word in the source language. As for misunderstanding in lexical meaning, machine has difficulty in precisely reflecting the meaning of the original texts, due to different cultural background and different language system. And for misunderstanding in emotive meaning, machine has no intention and emotion like human-beings. Therefore, it’s impossible for it to know writers’ feelings and their writing purposes. So sometimes, it may translate something negative into something positive. (Wang 2008:45)#<br />
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====4.2 grammatical mistakes====<br />
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Grammatical analysis plays an important part in translation. Normally speaking, every language has its own unique grammatical rules. So in the process of translation, if translators don’t know the formation rule well, the sentence meaning will be affected. Even though all the lexical meanings are well-known by translators, the lack of consciousness of grammaticality makes it harder to arrange words according to sequential rule. English tends to be hypotactic, while Chinese tends to be paratactic. English sentences are connected through syntactic devices and lexical devices. While Chinese sentences are semantically connected, which means there are limited logical words and connection words in Chinese. So when translating English sentence, we should first analyze its grammaticality and logical structure and then rearrange its sequence. However, online translating machine has troubles in grammatical analysis, which makes its improvement more difficult.<br />
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====4.3 other mistakes====<br />
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The two mistakes above are the internal ones. Apart from mistakes in linguistic system, there are some mistakes in other aspects, such as cultural background.<br />
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===5.Reasons for its common mistakes ===<br />
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====5.1 Difference in two linguistic system====<br />
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With different history, English and Chinese have different ways of expression. Commonly speaking, English is synthetic language which expresses grammatical meaning through inflection such as tense and Chinese is analytic language which expresses grammatical meaning through word order and function word. In addition, English is more compact with full sentences. Subordinate sentence is one of the most important features in modern English. Chinese, on the other hand, is more diffusive with minor sentences.<br />
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====5.2 Difference in thinking patterns and cultural background====<br />
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According to Sapir-Whorf’s Hypothesis, our language helps mould our way of thinking and consequently, different languages may probably express their unique ways of understanding the world. For two different speech communities, the greater their structural differentiations are, the more diverse their conceptualization of the world will be. For example, western culture is more direct and eastern culture more euphemistic. What’s more, English culture tends to be individualism, focusing on detail, through which it reflects the whole, while Chinese culture tends to be collective. Different thinking patterns will add difficulty for machine to translate texts.<br />
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====5.3 Limitation of computer====<br />
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Recently, there are some breakthroughs and innovation in machine translation. However, due to its own limitation, online translation has limitation in some ways. Firstly, compared with machine, human brain is much more complicated, consisting of ten billions of neuron, each of which has different function to affect human’s daily activities and help humans avoid some errors. However, computer can only function according to preset programming has no intention or consciousness. Until now, countless related scholars have invested much time in machine translation. They upload massive language database, which include almost all linguistic rules. But computers still fail to precisely reflect the meaning of source language for many times due to the complexity and flexibility of language. On the other hand, computers can’t take context into consideration. During translation, it is often the case that machine chooses the most-frequently used meaning of one word. So without the correct and exact meaning, readers are easier to feel confused and even misunderstand the meaning of source language. (Qiu 2021:4)#<br />
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===6.Conclusion===<br />
From the analysis above, we can draw a conclusion that machine deals with informative text best, followed by non-literary translation of expressive text. What’s more, machine can be a useful tool to get to know the gist and main idea of a specific topic, for the simple sentence structure and numerous terms. And it can improve translating efficiency with high speed. But machine has difficulty in translating literary works, especially proses and poems.<br />
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Machine translation has mixed future. From the perspective of commercial, machine translation boasts a bright future. With the process of globalization, the demand for translation is increasing accordingly. On one hand, if we only depend on human translator to deal with translating works, the quality and accuracy of translation can be greatly affected. On the other hand, if machine is used properly to do some basic work, human translators only need to make preparation before translating, progress, polish and other advanced work, contributing to highly-qualified translation and high working efficiency.<br />
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However, compared with manual translation, machine translation has a bleak future. It is still impossible for machine to replace interpreter or translator in a short term. With intelligence and initiative, humans are able to learn new knowledge constantly, which machine will never accomplish. Besides, machine is not used to replace translators but to assist them in work. In other words, translators and machine carry out their own duties and they are not incompatible.(He 2021:5)#<br />
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To draw a conclusion, although there are certain limitations of machine translation, it can serve as a catalyst for translating works. Therefore, with the rapid development of artificial intelligence and related technology, there are still many opportunities for machine translation.<br />
<br />
===Reference ===<br />
<br />
Chen Cheng陈诚.机器翻译技术的综述[J][Overview of Machine Translation Technology].Electronic Techonology 电子技术,2021,50(11):290-291.<br />
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Cui Zihan 崔子涵.机器翻译译文质量对比——以谷歌翻译和DeepL为例[J] [Comparison among Machine Translation--Taking Google Translation and Deepl for Example].Overseas English 海外英语,2021(15):182-183.<br />
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He Xinyu何馨宇.机器翻译的发展及其对翻译职业化的影响研究[J] [The Development of Machine Translation and its Effect on Professional Transltors].Overseas English 海外英语,2021(20):48-49.<br />
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He Wen 何雯, Wang Xiufeng 王秀峰.信息型文本的在线机器翻译错误研究[J][Research on Errors in Online Machine Translation of Informative text ].Overseas English海外英语,2021(15):188-189.<br />
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Li Deyi 李德毅. (2018). 人工智能导论 [Introduction to Artificial Intelligence]. Beijing: China Science and Technology Press 中国科学技术出版社.<br />
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Liu Qin刘琴.功能目的论对于不同文本类型的翻译解读[J][Analysis of Translations in Different Types of Text based on Functionalist Approaches].Overseas Engliosh 海外英语,2021(17):8-9.<br />
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Li Hanji 李晗佶. (2021). 人工智能时代翻译技术与译者关系演变与重构 [Evolution and reconstruction of the relationship between translation technology and translators in the era of artificial intelligence]. 西华师范大学学报(哲学社会科学版) Journal of West China Normal University (PHILOSOPHY AND SOCIAL SCIENCES EDITION) (2021-12-04) 1-6.<br />
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(英) Peter Newmark A Textbook of Translation[M] Shanghai Foreign Education Press, 2002<br />
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Qiu Quanju 仇全菊.大数据时代背景下机器翻译及其发展趋势[J][Machine Translation and its Development Trend under the Background of Big Data Era]. English Teachers 英语教师,2021,21(16):60-62.<br />
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Wang Hongqi 王红旗. (2008). 语言学概论 [Introduction to Linguistics]. Beijing: Peking University Press 北京大学出版社.<br />
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Wei Guang魏光. 人工翻译与机器翻译译文编辑比较研究[J][Comparative Study of Translation Editing between Manual Translation and Machine Translation]. Overseas English 海外英语,2021(19):18-19+21.<br />
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Zhuo Jianbin 卓键滨,Liu Wenxian 刘文娴,Peng Zili 彭子莉.机器翻译对各类型文本的德汉翻译能力探究[J][Research on the German Chinese Translation Ability of Machine Translation for Various Types of Texts]. Comparative Study of Cultural innovation 文化创新比较研究,2021,5(28):122-125.<br />
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--[[User:Xiong Min|Xiong Min]] ([[User talk:Xiong Min|talk]]) 01:36, 15 December 2021 (UTC)<br />
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=Chapter 11 陈惠妮=Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts=<br />
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机器翻译的译前编辑研究——以医学类文摘为例<br />
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陈惠妮 Chen Huini, Hunan Normal University<br />
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[[Machine_Trans_EN_11]]<br />
<br />
===Abstract===<br />
At present, globalization is accelerating and the market demand for language services is rapidly increasing . Machine translation, as an important translation method, can greatly improve translation efficiency due to its low cost and high speed. However, because of the limitations of machine translation and the differences between Chinese and English language, machine translation is not accurate enough. In order to balance translation efficiency and translation quality, a great number of manual revisions in translation are required for the machine translating texts. Medical papers are specialized, special and purposeful, so it requires accurate,qualified and professional translation. However, the quality of translations by machine is inefficient to meet the high-quality requirements of medical papers translation. Therefore, the introduction of pre-editing can greatly improve the efficiency and quality of machine translation.<br />
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===Key words===<br />
Pre-editing, Machine translation, Medical texts<br />
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===题目===<br />
Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts<br />
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===摘要===<br />
在全球化加速发展的今天，市场对语言服务的需求迅速增加。机器翻译作为一种重要的翻译途径，由于其成本低、速度快，可以大大提高翻译效率。然而，由于机器翻译的局限性以及中英文语言的差异，机器翻译的准确性不高。为了平衡翻译效率和翻译质量，机器翻译文本需要大量的手工修改。<br />
医学论文具有专业性、特殊性和目的性，要求其译文准确、合格、专业。然而，机器翻译的质量较低，无法满足医学论文对翻译的高质量要求。因此，译前编辑的引入可以大大提高机器翻译的效率和质量。<br />
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===关键词===<br />
译前编辑；机器翻译；医学文本<br />
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===1. Introduction===<br />
===1.1 Definition of Machine Translation===<br />
As Cronin(2013) revealed: "Translation is undergoing a revolutionary upheaval. The influence of digital technology and the Internet on translation is continuous, extensive and profound. From the popularity of automatic online translation applications, translation revolution is everywhere (Cronin,2013) However,the concept of machine translation was firstly proposed in the 1930s. Since 1940s, the machine translation technology has been evolving from rule-based machine translation (RBMT) to statistical machine translation (SMT), and to neural machine translation (NMT). Machine translation refers to the automatic translation of source language into target language by using a computer system. That is, machine translation refers to the automatic translation of text from one language into another natural language by computer software or other online translation webs. Machine translation is also defined as the process of “using a computer system to automatically translate text or speech from one natural language to another” according to the definition by ISO (Cui 2014:68-73).O 'Brien (2002) defines it as "the behavior of modifying errors in machine translation to ensure that the target translation meets certain quality requirements". On the basic level, machine translation performs mechanical substitution of words in one language for words in another language, but that rarely produces a good translation, therefore, recognition of the whole phrases and their closest counterparts in the target language is needed. Not all words in one language have equivalents in another language, and many words have more than one meaning. A huge demand for translation is greatly needed in today’s global world, which creates new opportunities for the development of machine translation, attracts more and more attention and becomes one of the current research focuses.<br />
<br />
As Cronin(2013) revealed: "Translation is undergoing a revolutionary upheaval. The influence of digital technology and the Internet on translation is continuous, extensive and profound. From the popularity of automatic online translation applications, translation revolution is everywhere (Cronin,2013) However,the concept of machine translation was firstly proposed in the 1930s. Since 1940s, the machine translation technology has been evolving from rule-based machine translation (RBMT) to statistical machine translation (SMT), and to neural machine translation (NMT). Machine translation refers to the automatic translation of source language into target language by using a computer system. That is, machine translation refers to the automatic translation of text from one language into another natural language by computer software or other online translation webs. Machine translation is also defined as the process of “using a computer system to automatically translate text or speech from one natural language to another” according to the definition by ISO (Cui 2014:68-73).O 'Brien (2002) defines it as "the behavior of modifying errors in machine translation to ensure that the target translation meets certain quality requirements". On the basic level, machine translation performs mechanical substitution of words in one language for words in another language, but that rarely produces a good translation, therefore, recognition of the whole phrases and their closest counterparts in the target language is needed. Not all words in one language have equivalents in another language, and many words have more than one meaning. A huge demand for translation is greatly needed in today’s global world, which creates new opportunities for the development of machine translation, attracts more and more attention and becomes one of the current research focuses.<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:34, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===1.2 Definition of Pre-editing===<br />
Pre-editing means to adjust and modify the source language to make it fit more with the characteristics of the machine translation software before putting the source language before the into machine translation, so as to improve the quality of the translation machine translation (Wei Changhong 2008:93-94). Pre-editing is to modify the original text before putting it into machine translation software, in order to improve the recognition rate of machine translation, optimize the output quality of translated text and reduce the workload of post-editing. Because pre-translation editing only needs to be modified in one language, the operation is simpler than post-translation editing, which can realize the double improvement of quality and efficiency. A good pre-editing translation can help machine translation more smoothly, thus improving the machine readability and quality of the output translation. Pre-editing is mostly applied in the following situations: one is when the original text is of poor quality and the machine is difficult to recognize the meaning of the sentence, such as user generated content with poor readability and translatability (Gerlach et al 2003:45-53); Documents that need to be published in multiple languages; Next is when the original text contains a lot of jargon; The last is the original text has a corresponding translation memory bank. If the original text is edited, it can better match the content of the translation memory bank. Pre-editing is a process of identifying problems. It requires to pre-edit the source texts before putting it into machine translation according to the requirements, listing the expressions or sentences that may have trouble in machine translation and then pre-edit it by human. The purpose is to enable the computer to translate better, improve the translatability of machine translation. (Slype G V & Guinet J F & Seitz F 1984:115)<br />
<br />
Pre-editing means to adjust and modify the source language to make it fit more with the characteristics of the machine translation software before putting the source language before the into machine translation, so as to improve the quality of the translation machine translation (Wei Changhong 2008:93-94). Pre-editing is to modify the original text before putting it into machine translation software, in order to improve the recognition rate of machine translation, optimize the output quality of translated text and reduce the workload of post-editing. Because pre-translation editing only needs to be modified in one language, the operation is simpler than post-translation editing, which can realize the double improvement of quality and efficiency. A good pre-editing translation can help machine translation more smoothly, thus improving the machine readability and quality of the output translation. Pre-editing is mostly applied in the following situations: one is when the original text is of poor quality and the machine is difficult to recognize the meaning of the sentence, such as user generated content with poor readability and translatability (Gerlach et al 2003:45-53); Documents that need to be published in multiple languages; Next is when the original text contains a lot of jargon; The last is the original text has a corresponding translation memory bank. If the original text is edited, it can better match the content of the translation memory bank. Pre-editing is a process of identifying problems. It requires to pre-edit the source texts before putting it into machine translation according to the requirements, listing the expressions or sentences that may have trouble in machine translation and then pre-edit it by human. The purpose is to enable the computer to translate better, improve the translatability of machine translation. (Slype G V & Guinet J F & Seitz F 1984:115)<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:36, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===1.3 Machine Translation Mode===<br />
According to the different knowledge acquisition methods, machine translation modes can be classified as follows: one is rule-based machine translation, which is based on bilingual dictionaries and a library of language rules for each language. The quality of translation depends on whether the source language conforms to the existing rules, but the inexhaustible rules are the hinders of this model. The second mode is machine translation based on statistics. This translation model relies on the principles of mathematics and statistics to find various existing translations corresponding to the translation tasks through the employment of corpus, analyzing the frequency of their occurrence and selecting the translation with the highest frequency for output. The disadvantage of this translation model is that it ignores the flexibility of language and the importance of context. The last one is neural network language model. This model is different from previous translation models in that it uses end-to-end neural network to realize automatic translation between natural languages. At present, the quality of its translation is much higher than that of the previous translation models.<br />
<br />
According to the different knowledge acquisition methods, machine translation modes can be classified as follows: one is rule-based machine translation, which is based on bilingual dictionaries and a library of language rules for each language. The quality of translation depends on whether the source language conforms to the existing rules, but the inexhaustible rules are the hinders of this model. The second mode is machine translation based on statistics. This translation model relies on the principles of mathematics and statistics to find various existing translations corresponding to the translation tasks through the employment of corpus, analyzing the frequency of their occurrence and selecting the translation with the highest frequency for output. The disadvantage of this translation model is that it ignores the flexibility of language and the importance of context. The last one is neural network language model. This model is different from previous translation models in that it uses end-to-end neural network to realize automatic translation between natural languages. At present, the quality of its translation is much higher than that of the previous translation models.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===1.4 Source of Study Abstracts===<br />
The core medical journals from domestic are selected in order to make the paper more representative and authoritative, such as National Medical Journal of China, Journal of Peking University (Health Science), and Journal of Third Military Medical University. All of them include basic medical science, biomedical technology, laboratory medical science and other fields. In this way, the pre-editing approaches included are applicable enough to machine translation of medical abstracts.<br />
<br />
The core medical journals from domestic are selected in order to make the paper more representative and authoritative, such as National Medical Journal of China, Journal of Peking University (Health Science), and Journal of Third Military Medical University. All of them include basic medical science, biomedical technology, laboratory medical science and other fields. In this way, the pre-editing approaches included are applicable enough to machine translation of medical abstracts.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===1.5 Selection of Translation Software===<br />
Generally, machine translation software includes Google Translation, Youdao Translator and Niu Translation, which has their own special use in translation. Take Google as an example, it is more like neurons in human brain, enabling to learn and collect information to establish connections with its neural machine translator’s neurons. However, it also causes many errors because of the lack of enough information. In this paper, contrastive analysis will be carried on by using Google translation. On the one hand, being a pioneer in the translation of NET, it is inevitably to sue Google as the translation software to translate medical texts in this paper. Comparing the program called “Google brain”, other NMT of translation software are relatively disadvantages. On the other hand, the Google translation enjoys the largest users in the world, with its downloads of more than one billion. The output quality of Google translation is more correct and complete than other machine translation software. With years of development and improvement, Google Translation has been greatly promoted. In this paper, Chinese medical abstract will be automatically translated by Google translation, and then the translation output will be compared with the translation by human and the publication of English abstracts. The main purpose is to prove that the improvement and promotion of quality and accuracy in the medical abstracts will be obtained through the pre-editing approaches.<br />
<br />
Generally, machine translation software includes Google Translation, Youdao Translator and Niu Translation, which has their own special use in translation. Take Google as an example, it is more like neurons in human brain, enabling to learn and collect information to establish connections with its neural machine translator’s neurons. However, it also causes many errors because of the lack of enough information. In this paper, contrastive analysis will be carried on by using Google translation. On the one hand, being a pioneer in the translation of NET, it is inevitably to sue Google as the translation software to translate medical texts in this paper. Comparing the program called “Google brain”, other NMT of translation software are relatively disadvantages. On the other hand, the Google translation enjoys the largest users in the world, with its downloads of more than one billion. The output quality of Google translation is more correct and complete than other machine translation software. With years of development and improvement, Google Translation has been greatly promoted. In this paper, Chinese medical abstract will be automatically translated by Google translation, and then the translation output will be compared with the translation by human and the publication of English abstracts. The main purpose is to prove that the improvement and promotion of quality and accuracy in the medical abstracts will be obtained through the pre-editing approaches.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===2.Language Characteristics and Error Division===<br />
Since Chinese and English are two different languages, it is quite neccessary to identify their own characteristics so as to better analyze and understand the two languages. Tytler (1978: 118-119) argued in his Essay on the Principles of Translation that there are three principles of translation that in all the translation should give readers the same feelings as the source text, except for complete transcript of ideas. There actually also exit some mistakes of these two languages. So the following will make some clarrifications of these two languages to ensure more accurate translation.<br />
<br />
Since Chinese and English are two different languages, it is quite neccessary to identify their own characteristics so as to better analyze and understand the two languages. Tytler (1978: 118-119) argued in his Essay on the Principles of Translation that there are three principles of translation that in all the translation should give readers the same feelings as the source text, except for complete transcript of ideas. There actually also exit some mistakes of these two languages. So the following will make some clarrifications of these two languages to ensure more accurate translation.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===2.1 Language Characteristics of Medical Abstracts===<br />
Chinese and English belong to different language systems, so there are differences in their language structure and the way users think of the languages. When using machine translation from Chinese to English, due to the unequal language levels, there will be many mistakes in the translation process, especially for ESP texts, such as medical papers.Here, these differences mainly refer to the linguistic characteristics of medical abstracts. In medical abstracts, it usually includes structured and unstructured abstracts. Although in different forms, they both describe the purpose, methods and conclusions of the research.<br />
<br />
In the method section of medial abstract, several Chinese sentences can be connected with commas, but each sentence may convey different information. In contrast, English sentences contain a great deal of information, but in order to ensure clarity, some modifiers need to be isolated and then reconstructed. However, in Chinese-English machine translation, a lot of information is put into the sentence because the machine segments the sentence on the basis of the full comma.<br />
<br />
In addition, subjectless sentence is used in the objective and method parts of Chinese medical abstracts. The subjectless sentence means the sentence without or free from subject and is usually employed in two contexts. The first is "needless to say". In Chinese sentences, it is common to omit the subject of the sentence. Chinese sentences can convey meanings by using incomplete sentence structures, so Chinese speakers can understand the meanings of the sentences even though the sentence subject is omitted. The second is "emphasis of action". In this context, subjectless sentences are used to describe behaviors, especially in the study of traditional Chinese medicine abstracts. Comparatively speaking, it should be avoided in English medical abstracts. Abstract sentences are more subjective when describing the learning process, while the essential requirement of English medical abstract is objectivity. From this point, sentences without subject should be avoided in English medical abstracts. Another feature is voice. Few words with passive meanings appear in Chinese abstracts. English sentences are more favoured in using passive voice. When translating from Chinese to English, passive voice should be used to make the contents objective, which is also the basic requirement of medical papers.<br />
<br />
These are the linguistic features of Chinese medical abstracts. There are great differences in sentence structure and expression between Chinese and English medical abstracts. These differences may reduce the accuracy of machine translation, so it is necessary to introduce pre-editing to edit the source text to ensure that the source text can be accurately recognized by the machine and fully translated into English.<br />
<br />
Chinese and English belong to different language systems, so there are differences in their language structure and the way users think of the languages. When using machine translation from Chinese to English, due to the unequal language levels, there will be many mistakes in the translation process, especially for ESP texts, such as medical papers.Here, these differences mainly refer to the linguistic characteristics of medical abstracts. In medical abstracts, it usually includes structured and unstructured abstracts. Although in different forms, they both describe the purpose, methods and conclusions of the research.<br />
<br />
In the method section of medial abstract, several Chinese sentences can be connected with commas, but each sentence may convey different information. In contrast, English sentences contain a great deal of information, but in order to ensure clarity, some modifiers need to be isolated and then reconstructed. However, in Chinese-English machine translation, a lot of information is put into the sentence because the machine segments the sentence on the basis of the full comma.<br />
<br />
In addition, subjectless sentence is used in the objective and method parts of Chinese medical abstracts. The subjectless sentence means the sentence without or free from subject and is usually employed in two contexts. The first is "needless to say". In Chinese sentences, it is common to omit the subject of the sentence. Chinese sentences can convey meanings by using incomplete sentence structures, so Chinese speakers can understand the meanings of the sentences even though the sentence subject is omitted. The second is "emphasis of action". In this context, subjectless sentences are used to describe behaviors, especially in the study of traditional Chinese medicine abstracts. Comparatively speaking, it should be avoided in English medical abstracts. Abstract sentences are more subjective when describing the learning process, while the essential requirement of English medical abstract is objectivity. From this point, sentences without subject should be avoided in English medical abstracts. Another feature is voice. Few words with passive meanings appear in Chinese abstracts. English sentences are more favoured in using passive voice. When translating from Chinese to English, passive voice should be used to make the contents objective, which is also the basic requirement of medical papers.<br />
<br />
These are the linguistic features of Chinese medical abstracts. There are great differences in sentence structure and expression between Chinese and English medical abstracts. These differences may reduce the accuracy of machine translation, so it is necessary to introduce pre-editing to edit the source text to ensure that the source text can be accurately recognized by the machine and fully translated into English.<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===2.2 Error Division of Machine Translation in Translating Abstracts of Medical Papers from Chinese to English===<br />
In this paper, errors in machine translation are listed out after analyzing. Some are due to the principles, such as statistical-based MT and NMT, employed by machine translation. Based on the original errors, they can be studied from two levels. One is from the macro-level, referring to mistakes caused by objective factors. These are not human factors, but the disadvantage of machine translation and the limitations of language. The limitations of machine translation are derived from the principles and models adopted and manifests itself as a reliance on reference sources. In this case, semantic ambiguity and textual incoherence may occur in the absence of reference sources.<br />
<br />
However, for ESP texts such as medical papers, the requirements for terms and sentence patterns are far beyond the existing corpora. For unfamiliar texts (that is, the corresponding texts cannot be found in the corpora). The quality of output translation will be relatively low, which is determined by the principles of machine translation. As mentioned above, machine translation has evolved over the past few decades from rules-based to corp-based statistics to NMT. However, there still exist some limitations in machine translation.<br />
<br />
Mistakes on micro-level are mainly caused by the variations and differences of linguistic structure between Chinese and English. Chinese is an implicit language, while English is an explicit one (Lian, 2010) To put it in another way, Chinese expression does not depend on language structures, while English does the opposite. This may result in mismatches between the original and machine translated sentences. This kind of error is mainly divided into two types: component fragment and component missing. For a medical paper, such errors are very serious. As a kind of ESP discourse, medical paper has the characteristics of fixed, objective and accurate language structure. In order to reproduce the characteristics of medical abstracts in translation, it is necessary to avoid errors at the level of words and sentences and pay attention to logic and consistency.<br />
<br />
Lexical errors here refer to the inconsistency of fixed expressions in the translation of terms. Although these terms are machine translation based on the corpus, the corpus may not be perfect for ESP texts such as medical papers. Therefore, fixed expressions are not used in term translation. This is also the most common mistake in machine translation (the term here includes but is not limited to nouns). For ESP texts, medical text in particular, the fixed expressions and the accuracy of terms are of great importance.<br />
<br />
In this paper, errors in machine translation are listed out after analyzing. Some are due to the principles, such as statistical-based MT and NMT, employed by machine translation. Based on the original errors, they can be studied from two levels. One is from the macro-level, referring to mistakes caused by objective factors. These are not human factors, but the disadvantage of machine translation and the limitations of language. The limitations of machine translation are derived from the principles and models adopted and manifests itself as a reliance on reference sources. In this case, semantic ambiguity and textual incoherence may occur in the absence of reference sources.<br />
<br />
However, for ESP texts such as medical papers, the requirements for terms and sentence patterns are far beyond the existing corpora. For unfamiliar texts (that is, the corresponding texts cannot be found in the corpora). The quality of output translation will be relatively low, which is determined by the principles of machine translation. As mentioned above, machine translation has evolved over the past few decades from rules-based to corp-based statistics to NMT. However, there still exist some limitations in machine translation.<br />
<br />
Mistakes on micro-level are mainly caused by the variations and differences of linguistic structure between Chinese and English. Chinese is an implicit language, while English is an explicit one (Lian, 2010) To put it in another way, Chinese expression does not depend on language structures, while English does the opposite. This may result in mismatches between the original and machine translated sentences. This kind of error is mainly divided into two types: component fragment and component missing. For a medical paper, such errors are very serious. As a kind of ESP discourse, medical paper has the characteristics of fixed, objective and accurate language structure. In order to reproduce the characteristics of medical abstracts in translation, it is necessary to avoid errors at the level of words and sentences and pay attention to logic and consistency.<br />
<br />
Lexical errors here refer to the inconsistency of fixed expressions in the translation of terms. Although these terms are machine translation based on the corpus, the corpus may not be perfect for ESP texts such as medical papers. Therefore, fixed expressions are not used in term translation. This is also the most common mistake in machine translation (the term here includes but is not limited to nouns). For ESP texts, medical text in particular, the fixed expressions and the accuracy of terms are of great importance.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===3.Approaches Proposed for Pre-editing ===<br />
Generally speaking, there is a close relationship between pre-editing and post-editing, both of which aim to convey information and ensure high or publishable translation quality. Proper pre-editing can improve the quality of machine translation in terms of adequacy and consistency. <br />
Complexity of natural language and people use language arbitrarily, bringing many difficulties to Chinese-English machine translation, Hu Qingping (2005:24) has proposed "the research of translation software and the development of the controlled language are the two directions to improve the quality of machine translation : the former aims at the difficulty in the natural language processing, the latter overcomes the arbitrariness of natural language". Feng Quangong and Gao Lin (2017: 63-68) put forward: "The writing principles of controlled language can be applied to pre-editing of machine translation. Pre-editing based on controlled language can effectively reduce the complexity and ambiguity of source text, improve the identifiability of machine translation (the translatability of source text itself), and thus reduce (fully) the workload of post-editing".<br />
The central task of pre-editing is to transform human-friendly content into machine-friendly content, so words and sentences need to be repositioned or even changed. Based on the analysis of the language characteristics of Chinese and English, the Chinese ideographic group should be split before the Chinese source text is input into the machine software to translate so that the sentence structure is complete which can be easily recognized by the machine translation software.<br />
<br />
Generally speaking, there is a close relationship between pre-editing and post-editing, both of which aim to convey information and ensure high or publishable translation quality. Proper pre-editing can improve the quality of machine translation in terms of adequacy and consistency. <br />
<br />
Complexity of natural language and people use language arbitrarily, bringing many difficulties to Chinese-English machine translation, Hu Qingping (2005:24) has proposed "the research of translation software and the development of the controlled language are the two directions to improve the quality of machine translation : the former aims at the difficulty in the natural language processing, the latter overcomes the arbitrariness of natural language". Feng Quangong and Gao Lin (2017: 63-68) put forward: "The writing principles of controlled language can be applied to pre-editing of machine translation. Pre-editing based on controlled language can effectively reduce the complexity and ambiguity of source text, improve the identifiability of machine translation (the translatability of source text itself), and thus reduce (fully) the workload of post-editing".<br />
<br />
The central task of pre-editing is to transform human-friendly content into machine-friendly content, so words and sentences need to be repositioned or even changed. Based on the analysis of the language characteristics of Chinese and English, the Chinese ideographic group should be split before the Chinese source text is input into the machine software to translate so that the sentence structure is complete which can be easily recognized by the machine translation software.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufefng<br />
<br />
===3.1 Extraction and Replacement of Terms in the Source Text===<br />
As a branch of applied English, medical English is the product of the combination of English language knowledge and medical knowledge. The terms in medical papers have the characteristics of fixed and complex language structure. Although Google Translation is based on a corpus, the language structure is not fixed due to the limited size of the corpus. Therefore, the following two steps should be followed before machine translation. The first is to extract terms and create a glossary, and then replace the Chinese terms in the source text with the corresponding English expressions in the glossary. Of course, the terms of extraction should be searched and verified according to the actual situation. All of these words are verified before they can be replaced. This method can not only ensure the accuracy of term translation, but also maintain the consistency of expression, especially when dealing with long texts.<br />
Example1<br />
Source abstract: 口腔白斑病癌变相关缺氧应答基因和微小RNA的芯片及表达验证。<br />
Google translation: Microarray detection/Chip detection and expression verification of hypoxia response genes and microRNAs related to oral leukoplakia canceration.<br />
Published translation:Transcriptome array screening and verification of oral leukoplakia carcinogenesis-related hypoxia-responsive gene and microRNA. <br />
Analysis: The expression “ Affymetrix GeneChip” empolyed in this thesis is a human transcription array for transcriptome array. In the source abstract, “芯片检测“ can be meant “screening” but not detection, so the proper and appropriate translation of these expression should be “transcription array screening”, but the Google translates it into “microarry detection of chip detection”. Therefore, term extraction is essential and inevitable before putting the source abstracts into the machine translation software.<br />
After pre-editing source abstracts: 对 oral leukoplakia carcinogenesis 相关 hypoxia-responsive gene 和微小RNA进行 transcriptome array screening 及表达验证。<br />
After pre-editing translation: Transcriptome array screening and expression- verification of hypoxia-responsive genes and microRNAs related to oral leukoplakia carcinogenesis.<br />
<br />
As a branch of applied English, medical English is the product of the combination of English language knowledge and medical knowledge. The terms in medical papers have the characteristics of fixed and complex language structure. Although Google Translation is based on a corpus, the language structure is not fixed due to the limited size of the corpus. Therefore, the following two steps should be followed before machine translation. The first is to extract terms and create a glossary, and then replace the Chinese terms in the source text with the corresponding English expressions in the glossary. Of course, the terms of extraction should be searched and verified according to the actual situation. All of these words are verified before they can be replaced. This method can not only ensure the accuracy of term translation, but also maintain the consistency of expression, especially when dealing with long texts.<br />
<br />
Example1<br />
Source abstract: 口腔白斑病癌变相关缺氧应答基因和微小RNA的芯片及表达验证。<br />
Google translation: Microarray detection/Chip detection and expression verification of hypoxia response genes and microRNAs related to oral leukoplakia canceration.<br />
Published translation:Transcriptome array screening and verification of oral leukoplakia carcinogenesis-related hypoxia-responsive gene and microRNA. <br />
Analysis: The expression “ Affymetrix GeneChip” empolyed in this thesis is a human transcription array for transcriptome array. In the source abstract, “芯片检测“ can be meant “screening” but not detection, so the proper and appropriate translation of these expression should be “transcription array screening”, but the Google translates it into “microarry detection of chip detection”. Therefore, term extraction is essential and inevitable before putting the source abstracts into the machine translation software.<br />
After pre-editing source abstracts: 对 oral leukoplakia carcinogenesis 相关 hypoxia-responsive gene 和微小RNA进行 transcriptome array screening 及表达验证。<br />
After pre-editing translation: Transcriptome array screening and expression- verification of hypoxia-responsive genes and microRNAs related to oral leukoplakia carcinogenesis.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===3.2 Explication of Subordination===<br />
As mentioned above, the subordination of Chinese sentences is judged by certain specific words in machine translation . Chinese is a paratactic language, and sentences are often connected by internal logical relationships; while English depends on sentence structure, so sentences are often closely linked by various language forms. In order to improve the accuracy of machine translation, we can adjust the sentence structure and adjust the position of adjectives and their modifiers. <br />
<br />
Example 2<br />
Source abstracts:回顾性分析南京医科大学附属儿童医院中重度HIE患儿49例及同期就诊的无神经系统症状体征的足月新生儿为对照组31例的头颅磁共振成像（MRI）资料。<br />
Google translation: A retrospective analysis that the brain magnetic resonance imaging (MRI) data of 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University and full-term neonates with no neurological symptoms and signs during the same period were included in the control group.<br />
Published translation: A total of 49 children with moderate to severe HIE admitted to the children’s Hospital Affiliated to Nanjing Medical University were retrospectively analyzed. Cranial magnetic resonance imaging (MRI) date of 31 full-term neonates without neurological symptoms and signs who visited the hospital during the same period were recruited as the control group. <br />
<br />
Analysis: As the above example shows that “中重度HIE患儿” and “足月新生儿” in the source abstracts are from the Children’s Hospital Affiliated Nanjing Medical University. The Google translation shows that 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University. There is an error due to the misuse of subordination. There are some advice in order to solve the problem. That is, first to segment the sentence and then reconstruct the sentence. For instance, the Chinese expression “南京医科大学附属儿童医院” carries many modifiers, which requires to cut the modifiers and reconstruct the sentence. <br />
Therefore, the Chinese expression “南京医科大学附属儿童医院’can be divided into abstractions, leaving two sentences instead of one long sentence with modifiers..<br />
After pre-editing source abstracts: 对49例中重度HIE患儿以及31例无神经系统症状体征的足月新生儿的MRI资料进行回顾性分析。这些患者收治于南京医科大学儿童附属医院。<br />
After pre-editing translation: The MRI data of 49 children with moderate to severe HIE and 31 full-term newborns without neurological symptoms and signs were retrospectively analyzed. These patients were admitted to the Children’s Hospital of Nanjing Medical University.<br />
<br />
As mentioned above, the subordination of Chinese sentences is judged by certain specific words in machine translation . Chinese is a paratactic language, and sentences are often connected by internal logical relationships; while English depends on sentence structure, so sentences are often closely linked by various language forms. In order to improve the accuracy of machine translation, we can adjust the sentence structure and adjust the position of adjectives and their modifiers. <br />
<br />
Example 2<br />
Source abstracts:回顾性分析南京医科大学附属儿童医院中重度HIE患儿49例及同期就诊的无神经系统症状体征的足月新生儿为对照组31例的头颅磁共振成像（MRI）资料。<br />
Google translation: A retrospective analysis that the brain magnetic resonance imaging (MRI) data of 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University and full-term neonates with no neurological symptoms and signs during the same period were included in the control group.<br />
Published translation: A total of 49 children with moderate to severe HIE admitted to the children’s Hospital Affiliated to Nanjing Medical University were retrospectively analyzed. Cranial magnetic resonance imaging (MRI) date of 31 full-term neonates without neurological symptoms and signs who visited the hospital during the same period were recruited as the control group. <br />
<br />
Analysis: As the above example shows that “中重度HIE患儿” and “足月新生儿” in the source abstracts are from the Children’s Hospital Affiliated Nanjing Medical University. The Google translation shows that 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University. There is an error due to the misuse of subordination. There are some advice in order to solve the problem. That is, first to segment the sentence and then reconstruct the sentence. For instance, the Chinese expression “南京医科大学附属儿童医院” carries many modifiers, which requires to cut the modifiers and reconstruct the sentence. <br />
<br />
Therefore, the Chinese expression “南京医科大学附属儿童医院’can be divided into abstractions, leaving two sentences instead of one long sentence with modifiers..<br />
After pre-editing source abstracts: 对49例中重度HIE患儿以及31例无神经系统症状体征的足月新生儿的MRI资料进行回顾性分析。这些患者收治于南京医科大学儿童附属医院。<br />
After pre-editing translation: The MRI data of 49 children with moderate to severe HIE and 31 full-term newborns without neurological symptoms and signs were retrospectively analyzed. These patients were admitted to the Children’s Hospital of Nanjing Medical University.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===3.3 Explication of Subject through Voice Changing===<br />
The extensive use of subjectless sentences are quite common in the Chinese abstracts, while English prefers to employ passive voice in the sentences so as to make them object and accurate. In order to take the characteristics of English sentences into great and careful consideration, the sentences written in passive voice in particular, it will be helpful for machine translation to find out the subject and reconstruct a sentence in passive voice (Wang Yan: 2008). The first is to discover the “doee” in a sentence and then is to reconstruct the sentence structure and put the “doee” before the verb. The last is to explicate the passive relation between the noun and the verb.<br />
Example 3<br />
Source abstract: 回顾性分析2018年1月至2020年12月解放军总医院第七医学中心收治的500例老年髋部骨折患者的资料。<br />
Google translation: A retrospective analysis of the data of 500 elderly patients with hip fractures admitted to the Seventh Medical Center of the PLA General Hospital from January 2018 to December 2020.<br />
Published translation: From January 2018 to December 2020, the data of 500 elderly patients with hip fracture treated in the Seventh Medical Center of PLA General Hospital were analyzed retrospectively.<br />
Analysis: The above example indicates that the “回顾性分析”in the Google Translate is a noun phrase, but the source abstracts actually describes an action or a behavior. The reason for such a mistake is that the machine can’t recognize the Chinese subjetless sentences. To find the subject of the sentence, the source abstracts can be revised and adjusted. Finding the “doee” is the first step, which refers to “500例老年髋部骨折患者的资料”in the source abstracts. And next is to put it in front of the verb, that is to place it in the beginning of the sentence. Last but not least, the passive voice should be explicated in the sentence. <br />
After pre-editing source abstract: 500例老年髋部骨折患者的资料被回顾性分析，他们在2018年1月之2020年12月期间收治于解放军总医院第七医学中心。<br />
After pre-editing translation: The data of 500 elderly hip fracture patients were retrospectively analyzed. They were admitted to the Seventh Medical Center of the PLA General Hospital between January 2018 and December 2020.<br />
<br />
The extensive use of subjectless sentences are quite common in the Chinese abstracts, while English prefers to employ passive voice in the sentences so as to make them object and accurate. In order to take the characteristics of English sentences into great and careful consideration, the sentences written in passive voice in particular, it will be helpful for machine translation to find out the subject and reconstruct a sentence in passive voice (Wang Yan: 2008). The first is to discover the “doee” in a sentence and then is to reconstruct the sentence structure and put the “doee” before the verb. The last is to explicate the passive relation between the noun and the verb.<br />
<br />
Example 3<br />
Source abstract: 回顾性分析2018年1月至2020年12月解放军总医院第七医学中心收治的500例老年髋部骨折患者的资料。<br />
Google translation: A retrospective analysis of the data of 500 elderly patients with hip fractures admitted to the Seventh Medical Center of the PLA General Hospital from January 2018 to December 2020.<br />
Published translation: From January 2018 to December 2020, the data of 500 elderly patients with hip fracture treated in the Seventh Medical Center of PLA General Hospital were analyzed retrospectively.<br />
<br />
Analysis: The above example indicates that the “回顾性分析”in the Google Translate is a noun phrase, but the source abstracts actually describes an action or a behavior. The reason for such a mistake is that the machine can’t recognize the Chinese subjetless sentences. To find the subject of the sentence, the source abstracts can be revised and adjusted. Finding the “doee” is the first step, which refers to “500例老年髋部骨折患者的资料”in the source abstracts. And next is to put it in front of the verb, that is to place it in the beginning of the sentence. Last but not least, the passive voice should be explicated in the sentence. <br />
After pre-editing source abstract: 500例老年髋部骨折患者的资料被回顾性分析，他们在2018年1月之2020年12月期间收治于解放军总医院第七医学中心。<br />
After pre-editing translation: The data of 500 elderly hip fracture patients were retrospectively analyzed. They were admitted to the Seventh Medical Center of the PLA General Hospital between January 2018 and December 2020.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===3.4 Relocation of Modifiers===<br />
Modifiers, including noun, adverbial and attributive are constantly employed in the Chinese medical papers in order to add information to subject and object in the sentence. By doing this, complicated sentences can be structured, thus causing obstacles to machine translation for recognizing this complex sentence structures when translating Chinese-English sentences. To make the machine translation successfully recognize the sentence structure, simplifying the sentence structure is quite necessary. The key is to moving the location of the modifiers and thus making the modifiers an independent sentence. In other words, the modifiers ought to be put before or behind the main part of the sentence to satisfy the common use of English. <br />
Usually, the modifiers in Chinese sentence can only be placed in front of the core word, while in English, modifiers are very flexible. It is all right to place the modifiers in front of or behind the major part of the sentences with adjective or a connective noun.<br />
<br />
Example 4<br />
Source abstracts: 利用DNA重组技术以pET-28a表达系统在E.coli BL21(DE3) 中重组表达Hepl。<br />
Google Translation: Hepl is recombined in E.coli BL21 (DE3) using DNA recombination technology with pET-28a expression system.<br />
Published translation: The recombinant Hcpl protein was expressed by using DNA recombination technology through pET-28a expression system in E. coli BL21 (De3).<br />
Analysis: The Chinese medical text includes two modifiers, “利用DNA”and “以pET-28a)表达系统”. These two modifiers will be translated by machine, which catches more attention to the two constituents. From the Google translation above, one failure is obvious that it misplaces the location of the two modifiers and presents it in not accurate form. To make it translate correctly by machine translation, dividing the sentences is very important so that the source abstracts can be correctly recognized by machine translation.<br />
<br />
After pre-editing source abstract: 利用DNA重组技术，Hcp1重组表达在E.coli BL21 (DE3)中， 通过pET-28a表达系统在E. coli BL21 (DE3)中。<br />
Google translation: Using DNA recombination technology, Hcp1 recombination is expressed in E.coli BL21 (DE3), via pET-28a expression system in E. coli BL21 (DE3).<br />
The second is the independence of modifiers, that is, modifiers can also be reconstructed into clauses to modify core words. Compared with English, There is no subordinate clause in Chinese, so redundant Chinese modifiers need to be reconstructed into subordinate clauses in Chinese-English translation to meet the characteristics of English. English, especially sentences with multiple modifiers. Otherwise, sentence structure may be confused, such as scattered modifiers of core words. To avoid this mistake, it is necessary to separate the modifier from the main part of the sentence. These modifiers should be reconstituted into clauses. Also, keep your sentences simple and easy to understand.<br />
<br />
Modifiers, including noun, adverbial and attributive are constantly employed in the Chinese medical papers in order to add information to subject and object in the sentence. By doing this, complicated sentences can be structured, thus causing obstacles to machine translation for recognizing this complex sentence structures when translating Chinese-English sentences. To make the machine translation successfully recognize the sentence structure, simplifying the sentence structure is quite necessary. The key is to moving the location of the modifiers and thus making the modifiers an independent sentence. In other words, the modifiers ought to be put before or behind the main part of the sentence to satisfy the common use of English. <br />
Usually, the modifiers in Chinese sentence can only be placed in front of the core word, while in English, modifiers are very flexible. It is all right to place the modifiers in front of or behind the major part of the sentences with adjective or a connective noun.<br />
<br />
Example 4<br />
Source abstracts: 利用DNA重组技术以pET-28a表达系统在E.coli BL21(DE3) 中重组表达Hepl。<br />
Google Translation: Hepl is recombined in E.coli BL21 (DE3) using DNA recombination technology with pET-28a expression system.<br />
Published translation: The recombinant Hcpl protein was expressed by using DNA recombination technology through pET-28a expression system in E. coli BL21 (De3).<br />
Analysis: The Chinese medical text includes two modifiers, “利用DNA”and “以pET-28a)表达系统”. These two modifiers will be translated by machine, which catches more attention to the two constituents. From the Google translation above, one failure is obvious that it misplaces the location of the two modifiers and presents it in not accurate form. To make it translate correctly by machine translation, dividing the sentences is very important so that the source abstracts can be correctly recognized by machine translation.<br />
<br />
After pre-editing source abstract: 利用DNA重组技术，Hcp1重组表达在E.coli BL21 (DE3)中， 通过pET-28a表达系统在E. coli BL21 (DE3)中。<br />
Google translation: Using DNA recombination technology, Hcp1 recombination is expressed in E.coli BL21 (DE3), via pET-28a expression system in E. coli BL21 (DE3).<br />
The second is the independence of modifiers, that is, modifiers can also be reconstructed into clauses to modify core words. Compared with English, There is no subordinate clause in Chinese, so redundant Chinese modifiers need to be reconstructed into subordinate clauses in Chinese-English translation to meet the characteristics of English. English, especially sentences with multiple modifiers. Otherwise, sentence structure may be confused, such as scattered modifiers of core words. To avoid this mistake, it is necessary to separate the modifier from the main part of the sentence. These modifiers should be reconstituted into clauses. Also, keep your sentences simple and easy to understand.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===3.5 Proper Omission and Deletion of Category Words===<br />
Category words are commonly used in Chinese. Category words complement the meaning of words, including problems, positions, situations and jobs. Adding this supplementary word is more in line with Chinese custom. In many cases, it has no real meaning, so it can be omitted in translation. In Chinese, category words are frequently used. However, it is rarely used in English, which is one of the differences between Chinese and English. There are many kinds of category words. Considering objectivity and the fixed structure of language, redundant category words should be deleted in Chinese-English translation. In the general, the most commonly used category of words in the Chinese medical abstracts are "process", "behavior", and "situation". These categories of words hinder the language conversion of Chinese and English, resulting in redundancy. <br />
<br />
Example 5<br />
Source abstract: 探讨口腔白斑病癌变进程中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia response genes and associated microRNAs in the process of oral leukoplakia cancer was discussed.<br />
Published translation: To study the hypoxia response gene and microRNA (miRNA)expression profiles in the pathogenesis and progression of oral leukoplakia (OLK).<br />
Analysis: As the above example shows, the Chinese words “进程” belongs to a category word. However, the Chinese expression “癌变”contains the process, so the “进程” expression can be deleted before placing it into the machine translation, because the meaning of it has been overlapping between the expression “癌变”. Therefore, its place can be replaced with preposition “during”.<br />
<br />
After pre-editing source abstract: 探讨口腔白斑病癌变中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia responsive genes and miRNA in oral leukoplakia cancer was investigated.<br />
<br />
Category words are commonly used in Chinese. Category words complement the meaning of words, including problems, positions, situations and jobs. Adding this supplementary word is more in line with Chinese custom. In many cases, it has no real meaning, so it can be omitted in translation. In Chinese, category words are frequently used. However, it is rarely used in English, which is one of the differences between Chinese and English. There are many kinds of category words. Considering objectivity and the fixed structure of language, redundant category words should be deleted in Chinese-English translation. In the general, the most commonly used category of words in the Chinese medical abstracts are "process", "behavior", and "situation". These categories of words hinder the language conversion of Chinese and English, resulting in redundancy. <br />
<br />
Example 5<br />
Source abstract: 探讨口腔白斑病癌变进程中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia response genes and associated microRNAs in the process of oral leukoplakia cancer was discussed.<br />
Published translation: To study the hypoxia response gene and microRNA (miRNA)expression profiles in the pathogenesis and progression of oral leukoplakia (OLK).<br />
Analysis: As the above example shows, the Chinese words “进程” belongs to a category word. However, the Chinese expression “癌变”contains the process, so the “进程” expression can be deleted before placing it into the machine translation, because the meaning of it has been overlapping between the expression “癌变”. Therefore, its place can be replaced with preposition “during”.<br />
<br />
After pre-editing source abstract: 探讨口腔白斑病癌变中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia responsive genes and miRNA in oral leukoplakia cancer was investigated.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===Conclusion===<br />
After years of development, machine translation has made great progress. The accuracy of machine translation has been greatly improved in both text recognition and sentence pattern conversion. However, machine translation has its own limitations. In other words, it needs to rely on the parallel corpus as a reference source for improving its accuracy. ESP text, in particular, is harder to get the high quality by machine translation. <br />
<br />
As one of the research papers, the characteristics of medical abstracts are fixed language structures, objectivity and accuracy (Qin Yi 2004:421-423). Therefore, medical translation must be accurate, object and understandable to follow the specific demands of the medical paper. Being an important field in the human society, medical paper translation is on a great demand, which means that it needs a huge demand for human labor. However, with the machine translation promoting, it will be more efficient to translate medical papers combining the effort by human and machine. The improvement and development of machine translation requires the joint efforts of computer science, information science, statistics, linguistics and other academic circles to achieve more mature human-computer mutual assistance translation (Li Yafei, Zhang Ruihua 2019:38-45).<br />
<br />
However, errors can occur during the process of machine translation of Chinese- English, because of the differences of the Chinese and English and the processing of the machine. Errors from the perspective of linguistic or grammar can affect the machine translation a lot. After division and recognition of errors, some pre-editing approaches are put forward to help the machine translation more accurate and readable, that are, extraction and replacement of terms in the source text, relocation of modifiers, explication of subordination, proper omission, deletion of category words and explication of subject through voice changing. <br />
<br />
<br />
The paper mainly focuses on the pre-editing machine translation by using medical papers as a case study. The errors of machine translation occurring in the translation of medical abstracts and pre-editing approaches for machine translation. The quality of machine translation of medical papers is greatly improved after employing the pre-editing methods. However, machine translation is not as flexible and accurate as human brain, so it is of importance to combine pre-editing and post-editing approaches with machine translation in order to produce more accurate, more object machine translation of medical papers.<br />
<br />
After years of development, machine translation has made great progress. The accuracy of machine translation has been greatly improved in both text recognition and sentence pattern conversion. However, machine translation has its own limitations. In other words, it needs to rely on the parallel corpus as a reference source for improving its accuracy. ESP text, in particular, is harder to get the high quality by machine translation. <br />
<br />
As one of the research papers, the characteristics of medical abstracts are fixed language structures, objectivity and accuracy (Qin Yi 2004:421-423). Therefore, medical translation must be accurate, object and understandable to follow the specific demands of the medical paper. Being an important field in the human society, medical paper translation is on a great demand, which means that it needs a huge demand for human labor. However, with the machine translation promoting, it will be more efficient to translate medical papers combining the effort by human and machine. The improvement and development of machine translation requires the joint efforts of computer science, information science, statistics, linguistics and other academic circles to achieve more mature human-computer mutual assistance translation (Li Yafei, Zhang Ruihua 2019:38-45).<br />
<br />
However, errors can occur during the process of machine translation of Chinese- English, because of the differences of the Chinese and English and the processing of the machine. Errors from the perspective of linguistic or grammar can affect the machine translation a lot. After division and recognition of errors, some pre-editing approaches are put forward to help the machine translation more accurate and readable, that are, extraction and replacement of terms in the source text, relocation of modifiers, explication of subordination, proper omission, deletion of category words and explication of subject through voice changing. <br />
<br />
The paper mainly focuses on the pre-editing machine translation by using medical papers as a case study. The errors of machine translation occurring in the translation of medical abstracts and pre-editing approaches for machine translation. The quality of machine translation of medical papers is greatly improved after employing the pre-editing methods. However, machine translation is not as flexible and accurate as human brain, so it is of importance to combine pre-editing and post-editing approaches with machine translation in order to produce more accurate, more object machine translation of medical papers.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===References===<br />
<br />
Cui Qiliang崔启亮(2014).论机器翻译的译后编辑[J] ''On Post-Editing of Machine Translatio''. 中国翻译 Chinese Translators Journal, 035(006):68-73<br />
<br />
Feng Quangong, Gao Lin冯全功,高琳 (2017). 基于受控语言的译前编辑对机器翻译的影响[J] ''Influence of Pre-editing Based on Controlled Language on Machine Translation''. 当代外语研究Contemporary Foreign Language Research,(2): 63-68+87+110.<br />
<br />
GERLACH J, et al ( 2013). ''Combining Pre-editing and Post-editing to Improve SMT of User-generated Content''[M]// Proceedings of MT Summit XIV Workshop on Post-editing Technology and Practice. 45-53<br />
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Hu Qingping胡清平(2005). 机器翻译中的受控语言[J] ''Controlled Language in Machine Translation''. 中国科技翻译 Chinese Science and Technology Translation, (03): 24-27. <br />
<br />
Lian Shuneng连淑能 (2010). 英汉对比研究增订本[M]''An Updated Version of English-Chinese Contrastive Studies'' . 北京:高等教育出版社Beijing: Higher Education Publishing House. 35-36.<br />
<br />
Li Yafei, Zhang Ruihua黎亚飞,张瑞华 (2019). 机器翻译发展与现状[J]''The Development and Current Situation of Machine Translation''. 中国轻工教育 China Light Industry Education, (5):38-45. <br />
<br />
Qin Yi秦毅(2004),从翻译基本标准议医学英语的翻译[J] ''On the Translation of Medical English from the Basic Standard of Translation''. 遵义医学院学报 Journal of Zunyi Medical College,27 (4): 421-423. <br />
<br />
Slype G V & Guinet J F & Seitz F (1984). ''Better Translation for Better Communication'' [M] . Oxford: Pergamon Press Ltd (U.K.). 90-93<br />
<br />
O'Brien S (2002). Teaching Post-editing: A Proposal for Course Con-tent [EB/OL]. http://mt-archive. Info/EAMT-2002-0brien. Pdf.<br />
<br />
Tytler, A. F. (1978). ''Essay On The Principles of Translation''[M]. Amsterdam: JohnBenjamins Publishing. 118-119<br />
<br />
Wang Yan王燕 (2008). 医学英语翻译与写作教程[M] ''Medical English Translation and Writing Course''. 重庆:重庆大学出版社 Chongqing: Chongqing University Press. 60-61<br />
<br />
Written by --[[User:Chen Huini|Chen Huini]] ([[User talk:Chen Huini|talk]]) 04:58, 15 December 2021 (UTC)Chen Huini<br />
<br />
=12 蔡珠凤=(The Mistranslation of C-J Machine Translation of Political Statements)=<br />
<br />
机器翻译中政治发言中译日的误译<br />
<br />
蔡珠凤 Cai Zhufeng, Hunan Normal University<br />
<br />
[[Machine_Trans_EN_12]]<br />
===Abstract===<br />
Language is the main way of communication between people. With the continuous development of globalization, the scale of cross-border exchanges is also expanding. However, due to cultural differences and diversity, the languages of different countries and regions are very different, which seriously hinders people's communication. The demand for efficient and convenient translation tools is increasing. At the same time, with the development of network technology and artificial intelligence, recognition technology based on deep learning is more and more widely used in English, Japanese and other fields.<br />
<br />
===Key words===<br />
machine translation; political statements; mistranslation of C-J machine translation<br />
===题目===<br />
The Mistranslation of C-J Machine Translation of Political Statements<br />
===摘要===<br />
语言是人与人之间交流的主要方式。随着全球化的不断发展，跨境交流的规模也在不断扩大。然而，由于文化的差异和多样性，不同国家和地区的语言差异很大，这严重阻碍了人们的交流。对高效便捷的翻译工具的需求正在增加。同时，随着网络技术和人工智能的发展，基于深度学习的识别技术在英语、日语等领域的应用越来越广泛。<br />
<br />
===关键词===<br />
机器翻译；政治发言；政治发言中译日的误译<br />
===1. Introduction===<br />
===Introduction to machine translation===<br />
Machine translation, also known as automatic translation, is a process of using computers to convert one natural language (source language) into another natural language (target language). It is a branch of computational linguistics, one of the ultimate goals of artificial intelligence, and has important scientific research value.At the same time, machine translation has important practical value. With the rapid development of economic globalization and the Internet, machine translation technology plays a more and more important role in promoting political, economic and cultural exchanges.The development of machine translation technology has been closely accompanied by the development of computer technology, information theory, linguistics and other disciplines. From the early dictionary matching, to the rule translation of dictionaries combined with linguistic expert knowledge, and then to the statistical machine translation based on corpus, with the improvement of computer computing power and the explosive growth of multilingual information, machine translation technology gradually stepped out of the ivory tower and began to provide real-time and convenient translation services for general users.（Zhang 2019:5-6)<br />
<br />
=== C-J machine translation software===<br />
Today's online machine translation software includes Baidu translation, Tencent translation, Google translation, Youdao translation, Bing translation and so on. Google was the first company to launch the machine translation system, and Baidu was the first company to import the machine translation system in China. In addition, Tencent and Youdao have attracted much attention.Machine translation is the process of using computers to convert one natural language into another. It usually refers to sentence and full-text translation between natural languages. In order to continuously improve the translation quality, R & D personnel have added artificial intelligence technologies such as speech recognition, image processing and deep neural network to machine translation on the basis of traditional machine translation based on rules, statistics and examples.With the increase of using machine translation, the joint cooperation between manual translation and machine translation will also increase significantly in the future. What criteria should be used to evaluate the quality of machine translation? In the evolving field of machine translation, there is an urgent need to clarify the unsolvable questions and solved problems.(Lv 1996:3)<br />
<br />
===The history of machine translation===<br />
The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. alchuni put forward the idea of using machines for translation. In 1933, Soviet inventor П.П. Trojansky designed a machine to translate one language into another, and registered his invention on September 5 of the same year; However, due to the low technical level in the 1930s, his translation machine was not made. In 1946, the first modern electronic computer ENIAC was born. Shortly after that, W. weaver, an American scientist and A. D. booth, a British engineer, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947 when discussing the application scope of electronic computer. In 1949, W. Weaver published the translation memorandum, which formally put forward the idea of machine translation. After 60 years of ups and downs, machine translation has experienced a tortuous and long development path. The academic community generally divides it into the following four stages:<br />
<br />
Pioneering period（1947-1964）<br />
<br />
In 1954, with the cooperation of IBM, Georgetown University completed the English Russian machine translation experiment with ibm-701 computer for the first time, showing the feasibility of machine translation to the public and the scientific community, thus opening the prelude to the study of machine translation.<br />
It is not too late for China to start this research. As early as 1956, the state included this research in the national scientific work development plan. The topic name is "machine translation, the construction of natural language translation rules and the mathematical theory of natural language". In 1957, the Institute of language and the Institute of computing technology of the Chinese Academy of Sciences cooperated in the Russian Chinese machine translation experiment, translating 9 different types of more complex sentences.From the 1950s to the first half of the 1960s, machine translation research has been on the rise. The United States and the former Soviet Union, two superpowers, have provided a lot of financial support for machine translation projects for military, political and economic purposes, while European countries have also paid considerable attention to machine translation research due to geopolitical and economic needs, and machine translation has become an upsurge for a time. In this period, although machine translation is just in the pioneering stage, it has entered an optimistic period of prosperity.<br />
<br />
Frustrated period（1964-1975）<br />
<br />
In 1964, in order to evaluate the research progress of machine translation, the American Academy of Sciences established the automatic language processing Advisory Committee (Alpac Committee) and began a two-year comprehensive investigation, analysis and test.In November 1966, the committee published a report entitled "language and machine" (Alpac report for short), which comprehensively denied the feasibility of machine translation and suggested stopping the financial support for machine translation projects. The publication of this report has dealt a blow to the booming machine translation, and the research of machine translation has fallen into a standstill. Coincidentally, during this period, China broke out the "ten-year Cultural Revolution", and basically these studies also stagnated. Machine translation has entered a depression.<br />
<br />
convalescence（1975-1989）<br />
<br />
Since the 1970s, with the development of science and technology and the increasingly frequent exchange of scientific and technological information among countries, the language barriers between countries have become more serious. The traditional manual operation mode has been far from meeting the needs, and there is an urgent need for computers to engage in translation. At the same time, the development of computer science and linguistics, especially the substantial improvement of computer hardware technology and the application of artificial intelligence in natural language processing, have promoted the recovery of machine translation research from the technical level. Machine translation projects have begun to develop again, and various practical and experimental systems have been launched successively, such as weinder system Eurpotra multilingual translation system, taum-meteo system, etc.However, after the end of the "ten-year holocaust", China has perked up again, and machine translation research has been put on the agenda again. The "784" project has paid enough attention to machine translation research. After the mid-1980s, the development of machine translation research in China has further accelerated. Firstly, two English Chinese machine translation systems, ky-1 and MT / ec863, have been successfully developed, indicating that China has made great progress in machine translation technology.(Chen 2016:5)<br />
<br />
New period(1990 present)<br />
<br />
With the universal application of the Internet, the acceleration of the process of world economic integration and the increasingly frequent exchanges in the international community, the traditional way of manual operation is far from meeting the rapidly growing needs of translation. People's demand for machine translation has increased unprecedentedly, and machine translation has ushered in a new development opportunity. International conferences on machine translation research have been held frequently, and China has made unprecedented achievements. A series of machine translation software have been launched, such as "Yixing", "Yaxin", "Tongyi", "Huajian", etc. Driven by the market demand, the commercial machine translation system has entered the practical stage, entered the market and came to the users.Since the new century, with the emergence and popularization of the Internet, the amount of data has increased sharply, and statistical methods have been fully applied. Internet companies have set up machine translation research groups and developed machine translation systems based on Internet big data, so as to make machine translation really practical, such as "Baidu translation", "Google translation", etc. In recent years, with the progress of in-depth learning, machine translation technology has further developed, which has promoted the rapid improvement of translation quality, and the translation in oral and other fields is more authentic and fluent.(Liu 2014:6)<br />
<br />
===The problem of machine translation at present===<br />
Error is inevitable<br />
Many people have misunderstandings about machine translation. They think that machine translation has great deviation and can't help people solve any problems. In fact, the error is inevitable. The reason is that machine translation uses linguistic principles. The machine automatically recognizes grammar, calls the stored thesaurus and automatically performs corresponding translation. However, errors are inevitable due to changes or irregularities in grammar, morphology and syntax, such as sentences with adverbials after "give me a reason to kill you first" in Dahua journey to the West. After all, a machine is a machine. No one has special feelings for language. How can it feel the lasting charm of "the tenderness of lowering its head, like the shame of a water lotus? After all, the meaning of Chinese is very different due to the changes of morphology, grammar and syntax and the change of context. Even many Chinese people are zhanger monks - they can't touch their heads, let alone machines.(Liu 2014：3）<br />
<br />
Bottleneck<br />
In fact, no matter which method, the biggest factor affecting the development of machine translation lies in the quality of translation. Judging from the achievements, the quality of machine translation is still far from the ultimate goal.<br />
Chinese mathematician and linguist Zhou Haizhong once pointed out in his paper "fifty years of machine translation": to improve the quality of machine translation, the first thing to solve is the problem of language itself rather than programming; It is certainly impossible to improve the quality of machine translation by relying on several programs alone. At the same time, he also pointed out that it is impossible for machine translation to achieve the degree of "faithfulness, expressiveness and elegance" when human beings have not yet understood how the brain performs fuzzy recognition and logical judgment of language. This view may reveal the bottleneck restricting the quality of translation. <br />
It is worth mentioning that American inventor and futurist ray cozwell predicted in an interview with Huffington Post that the quality of machine translation will reach the level of human translation by 2029. There are still many disputes about this thesis in the academic circles.（Cui 2019：4）<br />
<br />
===2.Mistranslation of Chinese Japanese machine translation===<br />
===2.1Vocabulary mistranslation in Chinese Japanese machine translation===<br />
===2.1.1Mistranslation of proper nouns===<br />
In political materials, there are often political dignitaries' names, place names or a large number of proper nouns in the political field. The morphemes of such words are definite and inseparable. Mistranslation will make the source language lose its specific meaning. <br />
<br />
Japanese translation into Chinese Chinese translation into Japanese<br />
<br />
original text translation by Youdao reference translation original text translation by Youdao reference translation<br />
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朱鎔基 朱基 朱镕基 栗战书 栗戰史書 栗戰書<br />
<br />
労安 劳安 劳安 李克强 李克強 李克強 <br />
<br />
筑紫哲也 筑紫哲也 筑紫哲也 习近平 習近平 習近平<br />
<br />
山口百惠 山口百惠 山口百惠 韩正 韓中 韓正<br />
<br />
田中角栄 田中角荣 田中角荣 王沪宁 王上海氏 王滬寧<br />
<br />
東条英機 东条英社 东条英机 汪洋 汪洋 汪洋<br />
<br />
毛沢东 毛泽东 毛泽东 赵乐际 趙樂南 趙樂際<br />
<br />
トウ・ショウヘイ 大酱 邓小平 江泽民 江沢民 江沢民<br />
<br />
周恩来 周恩来 周恩来<br />
<br />
クリントン 克林顿 克林顿<br />
<br />
The above table counts 18 special names in the two texts, and 7 machine translation errors. In terms of mistranslation, there are not only "战书" but also "戰史" and "史書" in Chinese. "沪" is the abbreviation of Shanghai. In other words, since "战书" and "沪" are originally common nouns, this disrupts the choice of target language in machine translation. Except Katakana interference (except for トウシゃウヘイ, most of the mistranslations appear in the new Standing Committee. It can be seen that the machine translation system does not update the thesaurus in time. Different from other words, people's names have particularity. Especially as members of the Standing Committee of the Political Bureau of the CPC Central Committee, the translation of their names is officially unified. In this regard, public figures such as political dignitaries, stars, famous hosts and important. In addition, the machine also translates Japanese surnames such as "タ二モト" (谷本), "アンドウ" (安藤) into "塔尼莫特" and "龙胆". It can be found that the language feature that Japanese will be written together with Chinese characters and Hiragana also directly affects the translation quality of Japanese Chinese machine translation. Japanese people often use Katakana pronunciation. For example, when Japanese people talk with Premier Zhu, they often use "二ーハウ" (Hello). However, the machine only recognizes the two pseudonyms "二" and "ハ" and transliterates them into "尼哈" , ignoring the long sound after "二" and "ハ".(Guan 2018:10-12)<br />
<br />
original text Translation by Youdao reference translation<br />
<br />
日美安全体制 日米の安全体制 日米安保体制<br />
<br />
中国共产党第十九次全国代表大会 中国共産党第19回全国代表大会 中国共産党第19回全国代表大会（第19回党大会）<br />
<br />
十八大 十八大 第18回党大会中国特色社会主义<br />
<br />
中国特色社会主義 中国の特色ある社会主義 第18回党大会<br />
<br />
中国共产党中央委员会 中国共産党中央委員会 中国共産党中央委員会<br />
<br />
中国共産党中央委員会十八届中共中央政治局常委 第18代中国共產党中央政治局常務委員 第18期中共中央政治局常務委員<br />
<br />
十八届中共中央政治局委员 18期の中国共產党中央政治局委員 第18期中共中央政治局委員<br />
<br />
十九届中共中央政治局常委 十九回中国共產党中央政治局常務委員 第19期中央政治局常務委員<br />
<br />
中共十九届一中全会 中国共產党第十九回一中央委員会 第19期中央委員会第1回全体会議<br />
<br />
The above table is a comparison of the original and translated versions of some proper nouns. As shown in the table, the mistranslation problems are mainly reflected in the mismatch of numerals + quantifiers, the wrong addition of case auxiliary word "の", the lack of connectors, the Mistranslation of abbreviations, dead translation, and the writing errors of Chinese characters. The following is a specific analysis one by one.<br />
<br />
"十八届中共中央政治局常委", "十八届中共中央政治局委员", "十九届中共中央政治局常委" and "中共十九届一中全会" all have quantifiers "届", which are translated into "代", "期" and "回" respectively. The meaning is vague and should be uniformly translated into "期"; Among them, the translation of the last three proper nouns lacks the Chinese conjunction "第". The case auxiliary word "の" was added by mistake in the translation of "十八届中共中央政治局委员" and "日美安全体制". The "中国共产党中央委员会" did not write in the form of Japanese Ming Dynasty characters, but directly used simplified Chinese characters.<br />
<br />
The full name of "中共十九届一中全会" is "中国共产党第十九届中央委员会第一次全体会议", in which "一" stands for "第一次", which is an ordinal word rather than a cardinal word. Machine translation does not produce a correct translation. The fundamental reason is that there are no "规则" for translating such words in machine translation. If we can formulate corresponding rules for such words (for example, 中共m'1届m2 中全会→第m1期中央委员会第m2回全体会议), the translation system must be able to translate well no matter how many plenary sessions of the CPC Central Committee.(Guan 2018:6-7)<br />
<br />
===2.1.2Mistranslation of Polysemy===<br />
original text Translation by Youdao reference translation<br />
<br />
スタジオ 摄影棚/工作室 直播现场/演播厅<br />
<br />
日中関係の話 中日关系的故事 就中日关系(话题)<br />
<br />
溝 水沟 鸿沟<br />
<br />
それでは日中の問題について質問のある方。 那么对白天的问题有提问的人。 关于中日问题的话题，举手提问。<br />
<br />
私たちのクラスは20人ちょっとですが、 我们班有20人左右， 我们班二十多人的意见统一很难，<br />
<br />
いろいろな意見が出て、まとめるのは大変です。 但是又各种各样的意见，总结起来很困难。 <br />
<br />
一体どうやって、13億人もの人をまとめているんですか。 到底是怎么处理13亿的人的呢？ 中国是怎样把13亿人凝聚在一起的？<br />
<br />
In the original text, the word "スタジオ" appeared four times and was translated into "摄影棚" or "工作室" respectively, but the context is on-site interview, not the production site of photography or film. "話" has many semantics, such as "说话", "事情", "道理", etc. In accordance with the practice of the interview program, Premier Zhu Rongji was invited to answer five questions on the topic of China Japan relations. Obviously, the meaning of the word "故事" is very abrupt. The inherent Japanese word "日中" means "晌午、白天". At the same time, it is also the abbreviation of the names of the two countries. The machine failed to deal with it correctly according to the context. "溝" refers to the gap between China and Japan, not a "水沟". The former "まとめる" appears in the original text with the word "意见", which is intended to describe that it is difficult to unify everyone's opinions. The latter "まとめている" also refers to unifying the thoughts of 1.3 billion people, not "处理". Therefore, it is more appropriate to use "统一" and "处理" in human translation, rather than "总结意见" or "处理意见".(Zhang 2019:5)<br />
<br />
Mistranslation of polysemy has always been a difficult problem in machine translation research. The translation of each word is correct, but it is often very different from the original expression in the context. Zhang Zhengzeng said that ambiguity is a common phenomenon in natural language. Its essence is that the same language form may have different meanings, which is also one of the differences between natural language and artificial language. Therefore, one of the difficulties faced by machine translation is language disambiguation (Zhang Zheng, 2005:60). In this regard, we can mark all the meanings of polysemous words and judge which meaning to choose by common collocation with other words. At the same time, strengthen the text recognition ability of the machine to avoid the translation inconsistent with the current context. In this way, we can avoid the mistake of "大酱" in the place where famous figures such as Deng Xiaoping should have appeared in the previous political articles.(Wang 2020:7-9)<br />
<br />
===2.1.3Mistranslation of compound words===<br />
Multi class words refer to a word with two or more parts of speech, also known as the same word and different classes.<br />
<br />
original text Translation by Youdao reference translation<br />
<br />
1998年江泽民主席曾经访问日本, 1998年の江沢民国家主席の日本訪問し、 1998年、江沢民総書記が日本を訪問し、かつ<br />
<br />
同已故小渊首相签署了联合宣言。 かつて同じ故小渕首相が署名した共同宣言 亡くなられた小渕総理と宣言に調印されました<br />
<br />
Chinese "同" has two parts of speech: prepositions and conjunctions: "故" has many parts of speech, such as nouns, verbs, adjectives and conjunctions. This directly affects the judgment of the machine at the source language level. The word "同" in the above table is used as a conjunction to indicate the other party of a common act. "故" is used as a verb with the semantic meaning of "死亡", which is a modifier of "小渊首相". The machine regards "同" as an adjective and "故" as a noun "原因", which leads to confusion in the structure and unclear semantics of the translation. Different from the polysemy problem, multi category words have at least two parts of speech, and there is often not only one meaning under each part of speech. In this regard, software R & D personnel should fully consider the existence of multi category words, so that the translation machine can distinguish the meaning of words on the basis of marking the part of speech, so as to select the translation through the context and the components of the word in the sentence. Of course, the realization of this function is difficult, and we need to give full play to the wisdom of R & D personnel.(Guan 2018:9-12)<br />
<br />
===2.2 Syntactic mistranslation in Chinese Japanese machine translation===<br />
===2.2.1Mistranslation of tenses===<br />
original text：History is written by the people, and all achievements are attributed to the people. <br />
<br />
Translation by Youdao：歴史は人民が書いたものであり、すべての成果は人民のためである。<br />
<br />
reference translation：歴史は人民が綴っていくものであり、すべての成果は人民に帰することとなります。<br />
<br />
The original sentence meaning is "历史是人民书写的历史" or "历史是人民书写的东西". When translated into Japanese, due to the influence of Japanese language habits, the formal noun "もの" should be supplemented accordingly. In this sentence, both the machine and the interpreter have translated correctly. However, the machine's recognition of "的" is biased, resulting in tense translation errors. The past, present and future will become "history", and this is a continuous action. The form translated as "~ ていく" not only reflects that the action is a continuous action, but also conforms to the tense and semantic information of the original text. In addition, the machine's treatment of the preposition "于" is also inappropriate. In the original text, "人民" is the recipient of action, not the target language. As the most obvious feature of isolated language, function words in Chinese play an important role in semantic expression, and their translation should be regarded as a focus of machine translation research.(Zuo 2021:8)<br />
<br />
original text：李克强同志是十六届中共中央政治局常委,其他五位同志都是十六届中共中央政治局委员。<br />
<br />
Translation by Youdao：李克強総理は第16代中国共産党中央政治局常務委員であり、他の５人の同志はいずれも16期の中国共産党中央政治局委員である。<br />
<br />
reference translation：李克強同志は第16期中国共産党中央政治局常務委員を務め、他の５人は第16期中共中央政治局委員を務めました。<br />
<br />
Judging the verb "是" in the original text plays its most basic positive role, but due to the complexity of Chinese language, "是" often can not be completely transformed into the form of "だ / である" in the process of translation. This sentence is a judgment of what has happened. Compared with the 19th session, the 18th session has become history. This is an implicit temporal information. It is very difficult for machines without human brain to recognize this implicit information. "中共中央政治局常委" is the abbreviation of "中共中央政治局常务委员会委员" and it is a kind of position. In Japanese, often use it with verbs such as "務める","担当する",etc. The translator adopts the past tense of "務める", which conforms to the expression habit of Japanese and deals with the problem of tense at the same time. However, machine translation only mechanically translates this sentence into judgment sentence, and fails to correctly deal with the past information implied by the word "十八届".(Guan 2018:4)<br />
<br />
===2.2.2Mistranslation of honorifics===<br />
Honorific language is a language means to show respect to the listener. Different from Japanese, there is no grammatical category of honorifics in Chinese. There is no specific fixed grammatical form to express honorifics, self modesty and politeness. Instead, specific words such as "您", "请" and "劳驾" are used to express all kinds of respect or self modesty. (Yang 2020:5-9)<br />
<br />
Original text translation by Youdao reference translation<br />
<br />
女士们，先生们，同志们，朋友们 さんたち、先生たち、同志たち、お友达さん ご列席の皆さん<br />
<br />
谢谢大家！ ありがとうございます！ ご清聴ありがとうございました。<br />
<br />
これはどうされますか。 这是怎么回事呢? 您将如何解决这一问题？<br />
<br />
こうした問題をどうお考えでしょうか。 我们会如何考虑这些问题呢？ 您如何看待这一问题？<br />
<br />
For example, for the processing of "女士们，先生们，同志们，朋友们", the machine makes the translation correspond to the original one by one based on the principle of unchanged format, but there are errors in semantic communication and pragmatic habits. When speaking on formal occasions, Chinese expression tends to be comprehensive and detailed, as well as the address of the audience. In contrast, Japanese usually uses general terms such as "皆様", "ご臨席の皆さん" or "代表団の方々" as the opening remarks. In terms of Japanese Chinese translation, the machine also failed to recognize the usage of Japanese honorifics such as "さ れ る" and "お考え". It can be seen that Chinese Japanese machine translation has a low ability to deal with honorific expressions. The occasion is more formal. A good translation should not only be fluent in meaning, but also conform to the expression habits of the target language and match the current translation environment.(Che 2021:3-7)<br />
<br />
===Conclusion===<br />
In the process of discussing the Mistranslation of machine translation, this paper mainly cites the Mistranslation of Youdao translator. When I studied the problem of machine translation mistranslation, I also used a large number of translation software such as Google and Baidu for parallel comparison, and found that these translation software also had similar problems with Youdao translator. For example, the "新世界新未来" in Chinese ABAC structural phrases is translated by Baidu as "新し世界の新しい未来", which, like Youdao, is not translated in the form of parallel phrases; Google online translation translates the word "“全心全意" into a completely wrong "全面的に"; The original sentence "我吃了很多亏" in the Mistranslation of the unique expression in the language is translated by Google online as "私はたくさんの損失を食べました". Because the "吃" and "亏" in the original text are not closely adjacent, the machine can not recognize this echo and mistakenly treats "亏" as a kind of food, so the machine translates the predicate as "食べました"; Japanese "こうした問題をどう考えでしょうか", Google's online translation is "你如何看待这些问题?", "你" tone can not reflect the tone of Japanese honorific, while Baidu translates as "你怎么想这样的问题呢?" Although the meaning can be understood, it is an irregular spoken language. Google and Baidu also made the same mistakes as Youdao in the translation of proper nouns. For example, they translated "十七届(中共中央政治局常委)”" into "第17回..." .In short, similar mistranslations of Youdao are also common in Baidu and Google. Due to space constraints, they will not be listed one by one here.(Cui 2019:7)<br />
<br />
Mr. Liu Yongquan, Institute of language, Chinese Academy of Social Sciences (1997) It has been pointed out that machine translation is a linguistic problem in the final analysis. Although corpus based machine translation does not require a lot of linguistic knowledge, language expression is ever-changing. Machine translation based solely on statistical ideas can not avoid the translation quality problems caused by the lack of language rules. The following is based on the analysis of lexical, syntactic and other mistranslations From the perspective of the characteristics of the source language, this paper summarizes some difficulties of Chinese and Japanese in machine translation.(Liu 2014:8)<br />
<br />
(1) The difficulties of Chinese in machine translation <br />
<br />
Chinese is a typical isolated language. The relationship between words needs to be reflected by word order and function words. We should pay attention to the transformation of function words such as "的", "在", "向" and "了". Some special verbs, such as "是", "做" and "作", are widely used. How to translate on the basis of conforming to Japanese pragmatic habits and expressions is a difficulty in machine translation research. In terms of word order, Chinese is basically "subject→predicate→object". At the same time, Chinese pays attention to parataxis, and there is no need to be clear in expression with meaningful cohesion, which increases the difficulty of Chinese Japanese machine translation. When there are multiple verbs or modifier components in complex long sentences, machine translation usually can not accurately divide the components of the sentence, resulting in the result that the translation is completely unreadable.(Guan 2018:6-12) <br />
<br />
(2) Difficulties of Japanese in machine translation <br />
<br />
Both Chinese and Japanese languages use Chinese characters, and most machines produce the target language through the corresponding translation of Chinese characters. However, pseudonyms in Japanese play an important role in judging the part of speech and meaning, and can not only recognize Chinese characters and judge the structure and semantics of sentences. Japanese vocabulary is composed of Chinese vocabulary, inherent vocabulary and foreign vocabulary. Among them, the first two have a great impact on Chinese Japanese machine translation. For example "提出" is translated as "提出する" or "打ち出す"; and "重视" is translated as "重視する" or "大切にする". Japanese is an adhesive language, which contains a lot of "けど", "が" and "けれども" Some of them are transitional and progressive structures, but there are also some sequential expressions that do not need translation, and these translation software often can not accurately grasp them.(Che 2021:10)<br />
<br />
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[13]Zhang Yi 张义(2019).机器翻译的译文分析【D】.Translation analysis of machine translation.西安外国语大学.(10) <br />
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[14]Zhang Linqian 张琳婧(2019).在线机器翻译中日翻译错误原因及对策【D】.Causes and countermeasures of online machine translation errors in Chinese-Japanese translation.山西大学.(02)<br />
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[15]Wang Dan 王丹(2020).基于机器翻译的专利文本译后编辑对策研究【D】.Research on countermeasures for post-translational editing of patent texts based on machine translation.大连理工大学.(06)<br />
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[16]Yang Xiaokun 杨晓琨(2020).日中机器翻译中的前编辑规则与效果验证【D】.Pre-editing rules and effect verification in Japanese-Chinese machine translation.大连理工大学.(06)<br />
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[17]Zuo Jia 左嘉(2021). 机器翻译日译汉误译研究【D】. Research on Mistranslation of Machine Translation from Japanese to Chinese.北京第二外国语学院.<br />
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[18]Guan Biying 关碧莹(2018).关于政治类发言的汉日机器翻译误译分析【D】.Analysis of Chinese-Japanese Machine Translation Mistranslations of Political Speeches.哈尔滨理工大学.<br />
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[19]Che Tong 车彤(2021).汉译日机器翻译质量评估及译后编辑策略研究【D】.Research on Quality Evaluation of Chinese-Japanese Machine Translation and Post-translation Editing Strategies.北京外国语大学.(09)<br />
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Networking Linking<br />
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http://www.elecfans.com/rengongzhineng/692245.html<br />
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https://baike.baidu.com/item/%E6%9C%BA%E5%99%A8%E7%BF%BB%E8%AF%91/411793<br />
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=13 陈湘琼Chen Xiangqiong（Study on Post-editing from the Perspective of Functional Equivalence Theory ）=<br />
[[Machine_Trans_EN_13]]<br />
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=14 Bi bi Nadia（Machine Translation a Challenge for Human Translators)=<br />
[[Machine_Trans_EN_14]]<br />
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===Abstract===<br />
Machine translation is a big obstacle in the way of Human translators or interpreters although it is quick and less time consuming.People are trying to get translation of their target language using through source language.For this purpose they are using digital apps like Google translation Google translation does not give accurate and exact interpretation.Google translator is translating word to word but it doesn't clarify it's true and actual meaning.On the other hand human translators can give exact and accurate translation ,they take care of grammatical mistakes , diction and sentence structure.They clarify the purpose of target language through using source language.<br />
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===Keywords===<br />
Descriptive translation, academic, interdiscipline, comparative literature, localization,Translatology,school of thought,translation , studies, linguistics, corresponding.<br />
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===Body of article===<br />
Translation is the process of reworking text from one language into another to maintain the original message and communication. But, like anything else, there are different methods of translation, and they vary in form and function. What is translation? The term has become widely used among knowledge transferring researchers and practitioners, especially in the fields of health and health care. In a landmark review, Jonathan Lomas began to argue that 'The tasks… may be defined as to establish and maintain links between researchers and their audience, via the appropriate translation of research findings' (Lomas 1997, p 4). In 2004, World Health Organization's World Report on Knowledge for Better Health suggested that 'One of the key contributions of research to health systems is the translation of knowledge into actions' (WHO 2004, p 33 and p 100). By 2006, special issues of WHO's Bulletin as well as the journals Evaluation and the Health Professions and the Journal of Continuing Education in the Health Professions were dedicated to translation.But what does 'translation' mean? It may be a new word for an old problem, meaning nothing more than 'transfer'. The rapidly emerging field of 'translational medicine' seems to take translation to mean generally what transfer might have meant, that is the transmission of knowledge and evidence 'from bench to bedside'. As one commentator has observed, "Translational medicine" as a fashionable term is being increasingly used to describe the wish of biomedical researchers to ultimately help patients' (Wehling 2008, abstract). <br />
Semantic uncertainty persists, not least because of the different interests of scientists, clinicians, patients and commercial firms (Littman et al 2007): 'Translational research means different things to different people, but it seems important to almost everyone' (Woolf 2008, p 211).<br />
In related fields, such as public health (Armstrong et al 2006), 'translation' seems to signify dissatisfaction with 'transfer'. It wants to move away from thinking of knowledge transfer as a form of technology transfer or dissemination, rejecting if only by implication its mechanistic assumptions and its model of linear messaging from A to B. But still, what does it signify?<br />
<br />
===Why translation?===<br />
'Translation' indicates a closer attention to the problem of shared meaning and how it might be developed. It seems to represent some new epistemological lubricant, facilitating the dissemination of texts and the application and use of the knowledge and information they in. Simply, translation might be the key to transfer. And yet, when we stop to think, we are more ambivalent. What is translated often seems somehow inferior, not real or original. Note how readily commentators reach for the idea that things might be 'lost in translation'. Knowing at a distance – made in and mediated by translation - makes for incomplete renditions, blurred images, partial truths. So what might 'translation' really mean? The purpose of this paper is to set out, for policy makers and practitioners, the theoretical and conceptual resources translation holds and seems to represent. In doing so, it explores understandings of translation in the fields of literature and linguistics and in the sociology of science and technology. It begins by setting out just why this idea of translation should make immediate, intuitive sense in relation to research, policy and practice.<br />
1translation in research, policy and practice research as translation<br />
Research often entails translation from one language to another: where data is collected from more than one ethnic group, for example, or where the language of the researcher is other than that of the research subject. It may draw on a secondary literature or source documents written in different languages, and may be published and disseminated in languages other than the one in which it is first written up.<br />
2In a different way, to conduct an interview is to ask for an account of experience and its meanings, but it is also to construct and translate that experience in terms defined at least in part by the researcher. In representing what is said, transcripts then select data, usually excluding significant gesture and eye-contact, for example. Often, certain characteristics of speech-acts (such as hesitations) will be edited out. In turn, the format of the transcript shapes the analytic use the researcher may make of it. The basis of research 'findings', then, is an artefact, a transcript or translation, not an original interaction (Ochs 1979, Barnes, Bloor and Henry 1996, Ross 2009).<br />
3In this way, the researcher recasts aspects of his or her problem or topic in new, scientific form: 'All researchers "translate" the experiences of others' (Temple 1997, p 609). Research is invariably conducted in a sort of 'metalanguage' (Hantrais and Ager 1985): the research process can be conceived as one of successive translations (from theoretical formulation to operationalization, transcription, interpretation and dissemination). Theorization is a process of reciprocal back and forth between theory and fact, in which conceptions of each are revised in order that one fit the other (Baldamus 1974).<br />
4 It is a kind of translation: a rereading, re-use, re-application or re-representation of what we know in new terms (Turner 1980). Referencing, too, is an act of translation, a form of appropriation and incorporation of one text by another (Gilbert 1977).policy as translation Each of the fields covered by the paper is diverse and ill-defined, and there is no intention here to provide a comprehensive account of any them. Sources have been chosen for their relevance: main references are cited in the text, and additional sources listed in footnotes.<br />
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2For a brief introduction to the technical issues involved in social research in more than one language, see Birbili (2000). For translation issues in survey research and question design in general, see Ervin and Bower (1952) and Deutscher (1968); on translating survey research instruments (in this instance health-related quality of life measures), Bowden and Fox-Rushby (2003). On the use of translators and interpreters, see Temple (1997) and Jentsch (1998).<br />
3For an interesting discussion of this problem, see Bourdieu (1999), esp pp 621-626, 'The risks of writing'. <br />
===Difference between machine translation and human translators===<br />
Few people disagree on the differences between the two, but many argue over the quality of the translations. How accurate are machine translations? How reliable are human translations? Some say machine translation produces near-perfect translations while others are adamant that translations are incomprehensible and cause more problems than they solve. Results will, of course, vary depending on the source and target languages, the machine translation service used (e.g. Google Translate), and the complexity of the original text.<br />
Machine translation, love it or hate it, is here to stay. In fact, the machine translation market is growing at such a fast pace that it is predicted to reach $980 million by 2022.<br />
===Machine translation: the pros and cons===<br />
The advantages of machine translation generally come down to two factors: it’s faster and cheaper. The downside to this is the standard of translation can be anywhere from inaccurate, to incomprehensible, and potentially dangerous (more on that shortly).<br />
==The advantages of machine translation==<br />
Many free tools are readily available (Google Translate, Skype Translator, etc.)<br />
Quick turnaround time <br />
You can translate between multiple languages using one tool<br />
Translation technology is constantly improving<br />
The disadvantages of machine translation<br />
Level of accuracy can be very low <br />
Accuracy is also very inconsistent across different languages<br />
==Machines can't translate context ==<br />
Mistakes are sometimes costly <br />
Sometimes translation simply doesn’t work<br />
The most important thing to consider with any kind of translation is the cost of potential mistakes. Translating instructions for medical equipment, aviation manuals, legal documents and many other kinds of content require 100% accuracy. In such cases, mistakes can cost lives, huge amounts of money and irreparable damage to your company’s image. So choose carefully!<br />
Human translation: the pros and cons<br />
Human translation essentially switches the table in terms of pros and cons. A higher standard of accuracy comes at the price of longer turnaround times and higher costs. What you have to decide is whether that initial investment outweighs the potential cost of mistakes. Alternatively, whether mistakes simply aren’t an option, like the cases we looked at in the previous section.<br />
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==The advantages of human translation ==<br />
It’s a translator’s job to ensure the highest accuracy<br />
Humans can interpret context and capture the same meaning, rather than simply translating words<br />
Human translators can review their work and provide a quality process<br />
Humans can interpret the creative use of language, e.g. puns, metaphors, slogans, etc.<br />
Professional translators understand the idiomatic differences between their languages<br />
Humans can spot pieces of content where literal translation isn’t possible and find the most suitable alternative<br />
The disadvantages of human translation<br />
Turnaround time is longer <br />
Translators rarely work for free <br />
Unless you use a translation agency, with access to thousands of translators, you’re limited to the languages any one translator can work with<br />
Simply put, human translation is your best option when accuracy is even remotely important. Other considerations to make are the complexity of your source material and the two languages you’re translating between – both of which can render machines pretty useless.<br />
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When to use machine and human translation<br />
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The truth is, the debate over machine vs human translation is an unnecessary distraction. What we should really be talking about is when to use these two different types of translation services, because they both serve a very valid purpose.<br />
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Examples of when to use machine translation<br />
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When you have a large bulk of content to translate and the general meaning is enough<br />
When your translation never reaches the final audience, e.g. you’re translating a resource as research for another piece of content<br />
Translating documents for internal use within a company, provided 100% accuracy isn’t needed<br />
To partially translate large chunks of content for a human translator to improve upon<br />
Examples of when to use human translation<br />
When accuracy is important<br />
Most cases where your translated content is received by a consumer audience<br />
When you have a duty of care to provide accurate translations (e.g. legal documents, product instructions, medical guidelines or health and safety content)<br />
When translating marketing material or other texts for creative language uses.<br />
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types of machine translation.<br />
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What is Machine Translation? Rule Based Machine Translation vs. Statistical Machine Translation. Machine translation (MT) is automated translation. It is the process by which computer software is used to translate a text from one natural language (such as English) to another (such as Spanish).<br />
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To process any translation, human or automated, the meaning of a text in the original (source) language must be fully restored in the target language, i.e. the translation. While on the surface this seems straightforward, it is far more complex. Translation is not a mere word-for-word substitution. A translator must interpret and analyze all of the elements in the text and know how each word may influence another. This requires extensive expertise in grammar, syntax (sentence structure), semantics (meanings), etc., in the source and target languages, as well as familiarity with each local region.<br />
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Human and machine translation each have their share of challenges. For example, no two individual translators can produce identical translations of the same text in the same language pair, and it may take several rounds of revisions to meet customer satisfaction. But the greater challenge lies in how machine translation can produce publishable quality translations.<br />
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Rule-Based Machine Translation Technology<br />
Rule-based machine translation relies on countless built-in linguistic rules and millions of bilingual dictionaries for each language pair.<br />
The software parses text and creates a transitional representation from which the text in the target language is generated. This process requires extensive lexicons with morphological, syntactic, and semantic information, and large sets of rules. The software uses these complex rule sets and then transfers the grammatical structure of the source language into the target language.<br />
Translations are built on gigantic dictionaries and sophisticated linguistic rules. Users can improve the out-of-the-box translation quality by adding their terminology into the translation process. They create user-defined dictionaries which override the system’s default settings.<br />
In most cases, there are two steps: an initial investment that significantly increases the quality at a limited cost, and an ongoing investment to increase quality incrementally. While rule-based MT brings companies to the quality threshold and beyond, the quality improvement process may be long and expensive.<br />
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Statistical Machine Translation Technology<br />
Statistical machine translation utilizes statistical translation models whose parameters stem from the analysis of monolingual and bilingual corpora. Building statistical translation models is a quick process, but the technology relies heavily on existing multilingual corpora. A minimum of 2 million words for a specific domain and even more for general language are required. Theoretically it is possible to reach the quality threshold but most companies do not have such large amounts of existing multilingual corpora to build the necessary translation models. Additionally, statistical machine translation is CPU intensive and requires an extensive hardware configuration to run translation models for average performance levels.<br />
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Rule-Based MT vs. Statistical MT<br />
Rule-based MT provides good out-of-domain quality and is by nature predictable. Dictionary-based customization guarantees improved quality and compliance with corporate terminology. But translation results may lack the fluency readers expect. In terms of investment, the customization cycle needed to reach the quality threshold can be long and costly. The performance is high even on standard hardware.<br />
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Statistical MT provides good quality when large and qualified corpora are available. The translation is fluent, meaning it reads well and therefore meets user expectations. However, the translation is neither predictable nor consistent. Training from good corpora is automated and cheaper. But training on general language corpora, meaning text other than the specified domain, is poor. Furthermore, statistical MT requires significant hardware to build and manage large translation models.<br />
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Rule-Based MT Statistical MT<br />
+ Consistent and predictable quality – Unpredictable translation quality<br />
+ Out-of-domain translation quality – Poor out-of-domain quality<br />
+ Knows grammatical rules – Does not know grammar <br />
+ High performance and robustness – High CPU and disk space requirements<br />
+ Consistency between versions – Inconsistency between versions <br />
– Lack of fluency + Good fluency<br />
– Hard to handle exceptions to rules + Good for catching exceptions to rules <br />
– High development and customization costs + Rapid and cost-effective development costs provided the required corpus exists<br />
Given the overall requirements, there is a clear need for a third approach through which users would reach better translation quality and high performance (similar to rule-based MT), with less investment (similar to statistical MT).<br />
Post-Edited Machine Translation (PEMT)<br />
Often, PEMT is used to bridge the gap between the speed of machine translation and the quality of human translation, as translators review, edit and improve machine-translated texts. PEMT services cost more than plain machine translations but less than 100% human translation, especially since the post-editors don’t have to be fluently bilingual—they just have to be skilled proofreaders with some experience in the language and target region.<br />
Successful translation is about more than just the words, which is why we advocate for not just human translation by skilled linguists, but for translation by people deeply familiar with the cultures they’re writing for. Life experience, study and the knowledge that only comes from living in a geographic region can make the difference between words that are understandable and language that is capable of having real, positive impact. <br />
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PacTranz<br />
The HUGE list of 51 translation types, methods and techniques<br />
Upper section of infographic of 51 common types of translation classified in 4 broad categoriesThere are a bewildering number of different types of translation.<br />
So we’ve identified the 51 types you’re most likely to come across, and explain exactly what each one means.<br />
This includes all the main translation methods, techniques, strategies, procedures and areas of specialisation.<br />
It’s our way of helping you make sense of the many different kinds of translation – and deciding which ones are right for you.<br />
Don’t miss our free summary pdf download later in the article!<br />
The 51 types of translation we’ve identified fall neatly into four distinct categories.<br />
Translation Category A: 15 types of translation based on the technical field or subject area of the text<br />
Icons representing 15 types of translation categorised by the technical field or subject area of the textTranslation companies often define the various kinds of translation they provide according to the subject area of the text.<br />
This is a useful way of classifying translation types because specialist texts normally require translators with specialist knowledge.<br />
Here are the most common types you’re like to come across in this category.<br />
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1. General Translation<br />
What is it?<br />
The translation of non-specialised text. That is, text that we can all understand without needing specialist knowledge in some area.<br />
The text may still contain some technical terms and jargon, but these will either be widely understood, or easily researched.<br />
What this means<br />
The implication is that you don’t need someone with specialist knowledge for this type of translation – any professional translator can handle them.<br />
Translators who only do this kind of translation (don’t have a specialist field) are sometimes referred to as ‘generalist’ or ‘general purpose’ translators.<br />
Examples<br />
Most business correspondence, website content, company and product/service info, non-technical reports.<br />
Most of the rest of the translation types in this Category do require specialist translators.<br />
Check out our video on 13 types of translation requiring special translator expertise:<br />
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2. Technical Translation<br />
What is it?<br />
We use the term “technical translation” in two different ways:<br />
Broad meaning: any translation where the translator needs specialist knowledge in some domain or area.<br />
This definition would include almost all the translation types described in this section.<br />
Narrow meaning: limited to the translation of engineering (in all its forms), IT and industrial texts.<br />
This narrower meaning would exclude legal, financial and medical translations for example, where these would be included in the broader definition.<br />
What this means<br />
Technical translations require knowledge of the specialist field or domain of the text.<br />
That’s because without it translators won’t completely understand the text and its implications. And this is essential if we want a fully accurate and appropriate translation.Good to know Many technical translation projects also have a typesetting/dtp requirement. Be sure your translation provider can handle this component, and that you’ve allowed for it in your project costings and time frames.<br />
Examples<br />
Manuals, specialist reports, product brochures<br />
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3. Scientific Translation<br />
What is it?<br />
The translation of scientific research or documents relating to it.<br />
What this means<br />
These texts invariably contain domain-specific terminology, and often involve cutting edge research.<br />
So it’s imperative the translator has the necessary knowledge of the field to fully understand the text. That’s why scientific translators are typically either experts in the field who have turned to translation, or professionally qualified translators who also have qualifications and/or experience in that domain.<br />
On occasion the translator may have to consult either with the author or other domain experts to fully comprehend the material and so translate it appropriately.<br />
Examples<br />
Research papers, journal articles, experiment/trial results<br />
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4. Medical Translation<br />
What is it?<br />
The translation of healthcare, medical product, pharmaceutical and biotechnology materials.<br />
Medical translation is a very broad term covering a wide variety of specialist areas and materials – everything from patient information to regulatory, marketing and technical documents.<br />
As a result, this translation type has numerous potential sub-categories – ‘medical device translations’ and ‘clinical trial translations’, for example.<br />
What this means<br />
As with any text, the translators need to fully understand the materials they’re translating. That means sound knowledge of medical terminology and they’ll often also need specific subject-matter expertise.<br />
Good to know<br />
Many countries have specific requirements governing the translation of medical device and pharmaceutical documentation. This includes both your client-facing and product-related materials.<br />
Examples<br />
Medical reports, product instructions, labeling, clinical trial documentation<br />
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5. Financial Translation<br />
What is it?<br />
In broad terms, the translation of banking, stock exchange, forex, financing and financial reporting documents.<br />
However, the term is generally used only for the more technical of these documents that require translators with knowledge of the field.<br />
Any competent translator could translate a bank statement, for example, so that wouldn’t typically be considered a financial translation.<br />
What this means<br />
You need translators with domain expertise to correctly understand and translate the financial terminology in these texts.<br />
Examples<br />
Company accounts, annual reports, fund or product prospectuses, audit reports, IPO documentation<br />
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6. Economic Translations<br />
What is it?<br />
1. Sometimes used as a synonym for financial translations.<br />
2. Other times used somewhat loosely to refer to any area of economic activity – so combining business/commercial, financial and some types of technical translations.<br />
3. More narrowly, the translation of documents relating specifically to the economy and the field of economics.<br />
What this means<br />
As always, you need translators with the relevant expertise and knowledge for this type of translation.<br />
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7. Legal Translation<br />
What is it?<br />
The translation of documents relating to the law and legal process.<br />
What this means<br />
Legal texts require translators with a legal background.<br />
That’s because without it, a translator may not:<br />
– fully understand the legal concepts<br />
– write in legal style<br />
– understand the differences between legal systems, and how best to translate concepts that don’t correspond.<br />
And we need all that to produce professional quality legal translations – translations that are accurate, terminologically correct and stylistically appropriate.<br />
Examples<br />
Contracts, legal reports, court judgments, expert opinions, legislation<br />
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8. Juridical Translation<br />
What is it?<br />
1. Generally used as a synonym for legal translations.<br />
2. Alternatively, can refer to translations requiring some form of legal verification, certification or notarization that is common in many jurisdictions.<br />
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9. Judicial Translation<br />
What is it?<br />
1. Most commonly a synonym for legal translations.<br />
2. Rarely, used to refer specifically to the translation of court proceeding documentation – so judgments, minutes, testimonies, etc. <br />
<br />
10. Patent Translation<br />
What is it?<br />
The translation of intellectual property and patent-related documents.<br />
Key features<br />
Patents have a specific structure, established terminology and a requirement for complete consistency throughout – read more on this here. These are key aspects to patent translations that translators need to get right.<br />
In addition, subject matter can be highly technical.<br />
What this means<br />
You need translators who have been trained in the specific requirements for translating patent documents. And with the domain expertise needed to handle any technical content.<br />
Examples<br />
Patent specifications, prior art documents, oppositions, opinions<br />
<br />
11. Literary Translation<br />
What is it?<br />
The translation of literary works – novels, short stories, plays, essays, poems.<br />
Key features<br />
Literary translation is widely regarded as the most difficult form of translation.<br />
That’s because it involves much more than simply conveying all meaning in an appropriate style. The translator’s challenge is to also reproduce the character, subtlety and impact of the original – the essence of what makes that work unique.<br />
This is a monumental task, and why it’s often said that the translation of a literary work should be a literary work in its own right.<br />
What this means<br />
Literary translators must be talented wordsmiths with exceptional creative writing skills.<br />
Because few translators have this skillset, you should only consider dedicated literary translators for this type of translation.<br />
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<br />
12. Commercial Translation<br />
What is it?<br />
The translation of documents relating to the world of business.<br />
This is a very generic, wide-reaching translation type. It includes other more specialised forms of translation – legal, financial and technical, for example. And all types of more general business documentation.<br />
Also, some documents will require familiarity with business jargon and an ability to write in that style.<br />
What this means<br />
Different translators will be required for different document types – specialists should handle materials involving technical and specialist fields, whereas generalist translators can translate non-specialist materials.<br />
Examples<br />
Business correspondence, reports, marketing and promotional materials, sales proposals<br />
<br />
13. Business Translations<br />
What is it?<br />
A synonym for Commercial Translations.<br />
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14. Administrative Translations<br />
What is it?<br />
The translation of business management and administration documents.<br />
So it’s a subset of business / commercial translations.<br />
What this means<br />
The implication is these documents will include business jargon and ‘management speak’, so require a translator familiar with, and practised at, writing in that style.<br />
Examples<br />
Management reports and proposals<br />
<br />
15. Marketing Translations<br />
What is it?<br />
The translation of advertising, marketing and promotional materials.<br />
This is a subset of business or commercial translations.<br />
Key features<br />
Marketing copy is designed to have a specific impact on the audience – to appeal and persuade.<br />
So the translated copy must do this too.<br />
But a direct translation will seldom achieve this – so translators need to adapt their wording to produce the impact the text is seeking.<br />
And sometimes a completely new message might be needed – see transcreation in our next category of translation types.<br />
What this means<br />
Marketing translations require translators who are skilled writers with a flair for producing persuasive, impactful copy.<br />
As relatively few translators have these skills, engaging the right translator is key.<br />
Good to know<br />
This type of translation often comes with a typesetting or dtp requirement – particularly for adverts, posters, brochures, etc.<br />
Its best for your translation provider to handle this component. That’s because multilingual typesetters understand the design and aesthetic conventions in other languages/cultures. And these are essential to ensure your materials have the desired impact and appeal in your target markets.<br />
Examples<br />
Advertising, brochures, some website/social media text.<br />
Translation Category B: 14 types of translation based on the end product or use of the translation<br />
This category is all about how the translation is going to be used or the end product that’s produced.<br />
Most of these types involve either adapting or processing a completed translation in some way, or converting or incorporating it into another program or format.<br />
You’ll see that some are very specialised, and complex.<br />
It’s another way translation providers refer to the range of services they provide.<br />
Check out our video of the most specialised of these types of translation:<br />
<br />
16. Document Translations<br />
What is it?<br />
The translation of documents of all sorts.<br />
Here the translation itself is the end product and needs no further processing beyond standard formatting and layout.<br />
<br />
17. Text Translations<br />
What is it?<br />
A synonym for document translation.<br />
<br />
18. Certified Translations<br />
What is it?<br />
A translation with some form of certification.<br />
Key features<br />
The certification can take many forms. It can be a statement by the translation company, signed and dated, and optionally with their company seal. Or a similar certification by the translator.<br />
The exact format and wording will depend on what clients and authorities require – here’s an example.<br />
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19. Official Translations<br />
What is it?<br />
1. Generally used as a synonym for certified translations.<br />
2. Can also refer to the translation of ‘official’ documents issued by the authorities in a foreign country. These will almost always need to be certified.<br />
<br />
20. Software Localisation<br />
What is it?<br />
Adapting software for another language/culture.<br />
Key features<br />
The goal of software localisation is not just to make the program or product available in other languages. It’s also about ensuring the user experience in those languages is as natural and effective as possible.<br />
Translating the user interface, messaging, documentation, etc is a major part of the process.<br />
Also key is a customisation process to ensure everything matches the conventions, norms and expectations of the target cultures.<br />
Adjusting time, date and currency formats are examples of simple customisations. Others might involve adapting symbols, graphics, colours and even concepts and ideas.<br />
Localisation is often preceded by internationalisation – a review process to ensure the software is optimally designed to handle other languages.<br />
And it’s almost always followed by thorough testing – to ensure all text is in the correct place and fits the space, and that everything makes sense, functions as intended and is culturally appropriate.<br />
Localisation is often abbreviated to L10N, internationalisation to i18n.<br />
What this means<br />
Software localisation is a specialised kind of translation, and you should always engage a company that specialises in it.<br />
They’ll have the systems, tools, personnel and experience needed to achieve top quality outcomes for your product.<br />
<br />
21. Game Localisation<br />
What is it?<br />
Adapting games for other languages and markets.<br />
<br />
It’s a subset of software localisation.<br />
Key features<br />
The goal of game localisation is to provide an engaging and fun gaming experience for speakers of other languages.<br />
<br />
It involves translating all text and recording any required foreign language audio.<br />
<br />
But also adapting anything that would clash with the target culture’s customs, sensibilities and regulations.<br />
<br />
For example, content involving alcohol, violence or gambling may either be censored or inappropriate in the target market.<br />
<br />
And at a more basic level, anything that makes users feel uncomfortable or awkward will detract from their experience and thus the success of the game in that market.<br />
<br />
So portions of the game may have to be removed, added to or re-worked.<br />
<br />
Game localisation involves at least the steps of translation, adaptation, integrating the translations and adaptations into the game, and testing.<br />
<br />
What this means<br />
Game localisation is a very specialised type of translation best left to those with specific expertise and experience in this area.<br />
<br />
22. Multimedia Localisation<br />
What is it?<br />
Adapting multimedia for other languages and cultures.<br />
<br />
Multimedia refers to any material that combines visual, audio and/or interactive elements. So videos and movies, on-line presentations, e-Learning courses, etc.<br />
Key features<br />
Anything a user can see or hear may need localising.<br />
<br />
That means the audio and any text appearing on screen or in images and animations.<br />
<br />
Plus it can mean reviewing and adapting the visuals and/or script if these aren’t suitable for the target culture.<br />
<br />
The localisation process will typical involve:<br />
– Translation<br />
– Modifying the translation for cultural reasons and/or to meet technical requirements<br />
– Producing the other language versions<br />
<br />
Audio output may be voice-overs, dubbing or subtitling.<br />
<br />
And output for visuals can involve re-creating elements, or supplying the translated text for the designers/engineers to incorporate.<br />
<br />
What this means<br />
Multimedia localisation projects vary hugely, and it’s essential your translation providers have the specific expertise needed for your materials.<br />
<br />
<br />
<br />
23. Script Translations<br />
What is it?<br />
Preparing the text of recorded material for recording in other languages.<br />
Key features<br />
There are several issues with script translation.<br />
<br />
One is that translations typically end up longer than the original script. So voicing the translation would take up more space/time on the video than the original language.<br />
<br />
Sometimes that space will be available and this will be OK.<br />
<br />
But generally it won’t be. So the translation has to be edited back until it can be comfortably voiced within the time available on the video.<br />
<br />
Another challenge is the translation may have to synchronise with specific actions, animations or text on screen.<br />
<br />
Also, some scripts also deal with technical subject areas involving specialist technical terminology.<br />
<br />
Finally, some scripts may be very culture-specific – featuring humour, customs or activities that won’t work well in another language. Here the script, and sometimes also the associated visuals, may need to be adjusted before beginning the translation process.<br />
<br />
It goes without saying that a script translation must be done well. If it’s not, there’ll be problems producing a good foreign language audio, which will compromise the effectiveness of the video.<br />
<br />
Translators typically work from a time-coded transcript. This is the original script marked to show the time available for each section of the translation.<br />
<br />
What this means<br />
There are several potential pitfalls in script translations. So it’s vital your translation provider is practiced at this type of translation and able to handle any technical content.<br />
<br />
<br />
<br />
24. Voice-over and Dubbing Projects<br />
What is it?<br />
Translation and recording of scripts in other languages.<br />
<br />
Voice-overs vs dubbing<br />
There is a technical difference.<br />
A voice-over adds a new track to the production, dubbing replaces an existing one.<br />
<br />
<br />
Key features<br />
These projects involve two parts:<br />
– a script translation (as described above), and<br />
– producing the audio<br />
<br />
So they involve the combined efforts of translators and voice artists.<br />
The task for the voice artist is to produce a high quality read. That’s one that matches the style, tone and richness of the original.<br />
<br />
Often each section of the new audio will need to be the same length as the original.<br />
<br />
But sometimes the segments will need to be shorter – for example where the voice-over lags the original by a second or two. This is common in interviews etc, where the original voice is heard initially then drops out.<br />
<br />
The most difficult form of dubbing is lip-syncing – where the new audio needs to synchronise with the original speaker’s lip movements, gestures and actions.<br />
<br />
Lip-syncing requires an exceptionally skilled voice talent and considerable time spent rehearsing and fine tuning the translation.<br />
<br />
What this means<br />
You need to use experienced professionals every step of the way in this type of project.<br />
<br />
That’s to ensure firstly that your foreign-language scripts are first class, then that the voicing is of high professional standard.<br />
<br />
Anything less will mean your foreign language versions will be way less effective and appealing to your target audience.<br />
<br />
<br />
<br />
25. Subtitle Translations<br />
What is it?<br />
Producing foreign language captions for sub or surtitles.<br />
Key features<br />
The goal with subtitling is to produce captions that viewers can comfortably read in the time available and still follow what’s happening on the video.<br />
<br />
To achieve this, languages have “rules” governing the number of characters per line and the minimum time each subtitle should display.<br />
<br />
Sticking to these guidelines is essential if your subtitles are to be effective.<br />
<br />
But this is no easy task – it requires simple language, short words, and a very succinct style. Translators will spend considerable time mulling over and re-working their translation to get it just right.<br />
<br />
Most subtitle translators use specialised software that will output the captions in the format sound engineers need for incorporation into the video.<br />
<br />
What this means<br />
As with other specialised types of translation, you should only use translators with specific expertise and experience in subtitling.<br />
<br />
<br />
<br />
26. Website Localisation<br />
What is it?<br />
The translation and adapting of relevant content on a website to best suit the target language and culture.<br />
<br />
Note: Many providers use the term website translation as a synonym for localisation. Strictly speaking though, translation is just one part of localisation.<br />
Key features<br />
<br />
Not all pages on a website may need to be localised – clients should review their content to identify what’s relevant for the other language versions.<br />
Some content may need specialist translators – legal and technical pages for example.<br />
There may also be videos, linked documents, and text or captions in graphics to translate.<br />
Adaptation can mean changing date, time, currency and number formats, units of measure, etc.<br />
But also images, colours and even the overall site design and style if these won’t have the desired impact in the target culture.<br />
Translated files can be supplied in a wide range of formats – translators usually coordinate output with the site webmasters.<br />
New language versions are normally thoroughly reviewed and tested before going live to confirm everything is displaying correctly, works as intended and is cultural appropriate.<br />
What this means<br />
The first step should be to review your content and identify what needs to be translated. This might lead you to modify some pages for the foreign language versions.<br />
<br />
In choosing your translation providers be sure they can:<br />
– handle any technical or legal content,<br />
– provide your webmaster with the file types they want.<br />
<br />
And you should always get your translators to systematically review the foreign language versions before going live.<br />
<br />
<br />
<br />
27. Transcreation<br />
What is it?<br />
Adapting a message to elicit the same emotional response in another language and culture.<br />
Translation is all about conveying the message or meaning of a text in another language. But sometimes that message or meaning won’t have the desired effect in the target culture.<br />
<br />
This is where transcreation comes in. Transcreation creates a new message that will get the desired emotional response in that culture, while preserving the style and tone of the original.<br />
<br />
So it’s a sort of creative translation – which is where the word comes from, a combination of ‘translation’ and ‘creation’.<br />
<br />
At one level transcreation may be as simple as choosing an appropriate idiom to convey the same intent in the target language – something translators do all the time.<br />
<br />
But mostly the term is used to refer to adapting key advertising and marketing messaging. Which requires copywriting skills, cultural awareness and an excellent knowledge of the target market.<br />
<br />
Who does it?<br />
Some translation companies have suitably skilled personnel and offer transcreation services.<br />
<br />
Often though it’s done in the target country by specialist copywriters or an advertising or marketing agency – particularly for significant campaigns and to establish a brand in the target marketplace.<br />
<br />
What this means<br />
Most general marketing and promotional texts won’t need transcreation – they can be handled by a translator with excellent creative writing skills.<br />
<br />
But slogans, by-lines, advertising copy and branding statements often do.<br />
<br />
Whether you should opt for a translation company or an in-market agency will depend on the nature and importance of the material, and of course your budget.<br />
<br />
<br />
<br />
28. Audio Translations<br />
What is it?<br />
Broad meaning: the translation of any type of recorded material into another language.<br />
<br />
More commonly: the translation of a foreign language video or audio recording into your own language. So this is where you want to know and document what a recording says.<br />
Key features<br />
The first challenge with audio translations is it’s often impossible to pick up every word that’s said. That’s because audio quality, speech clarity and speaking speed can all vary enormously.<br />
<br />
It’s also a mentally challenging task to listen to an audio and translate it directly into another language. It’s easy to miss a word or an aspect of meaning.<br />
<br />
So best practice is to first transcribe the audio (type up exactly what is said in the language it is spoken in), then translate that transcription.<br />
<br />
However, this is time consuming and therefore costly, and there are other options if lesser precision is acceptable.<br />
<br />
What this means<br />
It’s best to discuss your requirements for this kind of translation with your translation provider. They’ll be able to suggest the best translation process for your needs.<br />
<br />
Examples<br />
Interviews, product videos, police recordings, social media videos.<br />
<br />
<br />
<br />
29. Translations with DTP<br />
What is it?<br />
Translation incorporated into graphic design files.multilingual dtp example in the form of a Rubik's Cube with foreign text on each square<br />
Key features<br />
Graphic design programs are used by professional designers and graphic artists to combine text and images to create brochures, books, posters, packaging, etc.<br />
<br />
Translation plus dtp projects involve 3 steps – translation, typesetting, output.<br />
<br />
The typesetting component requires specific expertise and resources – software and fonts, typesetting know-how, an appreciation of foreign language display conventions and aesthetics.<br />
<br />
What this means<br />
Make sure your translation company has the required multilingual typesetting/desktop publishing expertise whenever you’re translating a document created in a graphic design program.<br />
<br />
Translation Category C: 13 types of translation based on the translation method employed<br />
This category has two sub-groups:<br />
– the practical methods translation providers use to produce their translations, and<br />
– the translation strategies/methods identified and discussed within academia.<br />
<br />
The translation methods translation providers use<br />
There are 4 main methods used in the translation industry today. We have an overview of each below, but for more detail, including when to use each one, see our comprehensive blog article.<br />
<br />
Or watch our video.<br />
<br />
Important: If you’re a client you need to understand these 4 methods – choose the wrong one and the translation you end up with may not meet your needs!<br />
<br />
<br />
30. Machine Translation (MT)<br />
What is it?<br />
A translation produced entirely by a software program with no human intervention.<br />
<br />
A widely used, and free, example is Google Translate. And there are also commercial MT engines, generally tailored to specific domains, languages and/or clients.<br />
<br />
Pros and cons<br />
There are two limitations to MT:<br />
– they make mistakes (incorrect translations), and<br />
– quality of wording is patchy (some parts good, others unnatural or even nonsensical)<br />
<br />
On they positive side they are virtually instantaneous and many are free.<br />
<br />
Best suited for:<br />
Getting the general idea of what a text says.<br />
<br />
This method should never be relied on when high accuracy and/or good quality wording is needed.<br />
<br />
<br />
<br />
31. Machine Translation plus Human Editing (PEMT)<br />
What is it?<br />
A machine translation subsequently edited by a human translator or editor (often called Post-editing Machine Translation = PEMT).<br />
<br />
The editing process is designed to rectify some of the deficiencies of a machine translation.<br />
<br />
This process can take different forms, with different desired outcomes. Probably most common is a ‘light editing’ process where the editor ensures the text is understandable, without trying to fix quality of expression.<br />
<br />
Pros and cons<br />
This method won’t necessarily eliminate all translation mistakes. That’s because the program may have chosen a wrong word (meaning) that wasn’t obvious to the editor.<br />
<br />
And wording won’t generally be as good as a professional human translator would produce.<br />
<br />
Its advantage is it’s generally quicker and a little cheaper than a full translation by a professional translator.<br />
<br />
Best suited for:<br />
Translations for information purposes only.<br />
<br />
Again, this method shouldn’t be used when full accuracy and/or consistent, natural wording is needed.<br />
<br />
<br />
<br />
32. Human Translation<br />
What is it?<br />
Translation by a professional human translator.<br />
Pros and cons<br />
Professional translators should produce translations that are fully accurate and well-worded.<br />
<br />
That said, there is always the possibility of ‘human error’, which is why translation companies like us typically offer an additional review process – see next method.<br />
<br />
This method will take a little longer and likely cost more than the PEMT method.<br />
<br />
Best suited for:<br />
Most if not all translation purposes.<br />
<br />
<br />
<br />
33. Human Translation + Revision<br />
What is it?<br />
A human translation with an additional review by a second translator.<br />
<br />
The review is essentially a safety check – designed to pick up any translation errors and refine wording if need be.<br />
<br />
Pros and cons<br />
This produces the highest level of translation quality.<br />
<br />
It’s also the most expensive of the 4 methods, and takes the longest.<br />
<br />
Best suited for:<br />
All translation purposes.<br />
<br />
Gearwheel with 5 practical translation methods written on the teeth <br />
There’s also one other common term used by practitioners and academics alike to describe a type (method) of translation:<br />
<br />
34. Computer-Assisted Translation (CAT)<br />
What is it?<br />
A human translator using computer tools to aid the translation process.<br />
<br />
Key features<br />
Virtually all translators use such tools these days.<br />
<br />
The most prevalent tool is Translation Memory (TM) software. This creates a database of previous translations that can be accessed for future work.<br />
<br />
TM software is particularly useful when dealing with repeated and closely-matching text, and for ensuring consistency of terminology. For certain projects it can speed up the translation process.<br />
<br />
<br />
<br />
The translation methods described by academia<br />
A great deal has been written within academia analysing how human translators go about their craft.<br />
<br />
Seminal has been the work of Newmark, and the following methods of translation attributed to him are widely discussed in the literature.Gearwheel with Newmark's 8 translation methods written on the teeth <br />
These methods are approaches and strategies for translating the text as a whole, not techniques for handling smaller text units, which we discuss in our final translation category.<br />
<br />
<br />
<br />
35. Word-for-word Translation<br />
This method translates each word into the other language using its most common meaning and keeping the word order of the original language.<br />
<br />
So the translator deliberately ignores context and target language grammar and syntax.<br />
<br />
Its main purpose is to help understand the source language structure and word use.<br />
<br />
Often the translation will be placed below the original text to aid comparison.<br />
<br />
<br />
<br />
36. Literal Translation<br />
Words are again translated independently using their most common meanings and out of context, but word order changed to the closest acceptable target language grammatical structure to the original.<br />
<br />
Its main suggested purpose is to help someone read the original text.<br />
<br />
<br />
<br />
37. Faithful Translation<br />
Faithful translation focuses on the intention of the author and seeks to convey the precise meaning of the original text.<br />
<br />
It uses correct target language structures, but structure is less important than meaning.<br />
<br />
<br />
<br />
38. Semantic Translation<br />
Semantic translation is also author-focused and seeks to convey the exact meaning.<br />
<br />
Where it differs from faithful translation is that it places equal emphasis on aesthetics, ie the ‘sounds’ of the text – repetition, word play, assonance, etc.<br />
<br />
In this method form is as important as meaning as it seeks to “recreate the precise flavour and tone of the original” (Newmark).slide showing definition of semantic translation as a translation method<br />
<br />
39. Communicative Translation<br />
Seeks to communicate the message and meaning of the text in a natural and easily understood way.<br />
<br />
It’s described as reader-focused, seeking to produce the same effect on the reader as the original text.<br />
<br />
A good comparison of Communicative and Semantic translation can be found here.<br />
<br />
40. Free Translation<br />
Here conveying the meaning and effect of the original are all important.<br />
<br />
There are no constraints on grammatical form or word choice to achieve this.<br />
<br />
Often the translation will paraphrase, so may be of markedly different length to the original.<br />
<br />
41. Adaptation<br />
Mainly used for poetry and plays, this method involves re-writing the text where the translation would otherwise lack the same resonance and impact on the audience.<br />
<br />
Themes, storylines and characters will generally be retained, but cultural references, acts and situations adapted to relevant target culture ones.<br />
<br />
So this is effectively a re-creation of the work for the target culture.<br />
<br />
42. Idiomatic Translation<br />
Reproduces the meaning or message of the text using idioms and colloquial expressions and language wherever possible.<br />
<br />
The goal is to produce a translation with language that is as natural as possible.<br />
<br />
Translation Category D: 9 types of translation based on the translation technique used<br />
These translation types are specific strategies, techniques and procedures for dealing with short chunks of text – generally words or phrases.<br />
<br />
They’re often thought of as techniques for solving translation problems.<br />
<br />
They differ from the translation methods of the previous category which deal with the text as a whole.<br />
9 translation techniques as titles of books in a bookcase<br />
<br />
43. Borrowing<br />
What is it?<br />
Using a word or phrase from the original text unchanged in the translation.<br />
<br />
Key features<br />
With this procedure we don’t translate the word or phrase at all – we simply ‘borrow’ it from the source language.<br />
<br />
Borrowing is a very common strategy across languages. Initially, borrowed words seem clearly ‘foreign’, but as they become more familiar, they can lose that ‘foreignness’.<br />
<br />
Translators use this technique:<br />
– when it’s the best word to use – either because it has become the standard, or it’s the most precise term, or<br />
– for stylist effect – borrowings can add a prestigious or scholarly flavour.<br />
<br />
Borrowed words or phrases are often italicised in English.<br />
<br />
Examples of borrowings in English<br />
grand prix, kindergarten, tango, perestroika, barista, sampan, karaoke, tofu<br />
<br />
<br />
44. Transliteration<br />
What is it?<br />
Reproducing the approximate sounds of a name or term from a language with a different writing system.<br />
<br />
Key features<br />
In English we use the Roman (Latin) alphabet in common with many other languages including almost all European languages.<br />
<br />
Other writing systems include Arabic, Cyrillic, Chinese, Japanese, Korean, Thai, and the Indian languages.<br />
<br />
Transliteration from such systems into the Roman alphabet is also called romanisation.<br />
<br />
There are accepted systems for how individual letters/sounds should be romanised from most other languages – there are three common systems for Chinese, for example.<br />
<br />
English borrowings from languages using non-Roman writing systems also require transliteration – perestroika, sampan, karaoke, tofu are examples from the above list.<br />
<br />
Translators mostly use transliteration as a procedure for translating proper names.<br />
<br />
Examples<br />
毛泽东 Mao Tse-tung or Mao Zedong<br />
Владимир Путин Vladimir Putin<br />
서울 Seoul<br />
ភ្នំពេញ Phnom Penh<br />
<br />
45. Calque or Loan Translation<br />
What is it?<br />
A literal translation of a foreign word or phrase to create a new term with the same meaning in the target language.<br />
<br />
So a calque is a borrowing with translation if you like. The new term may be changed slightly to reflect target language structures.<br />
<br />
Examples<br />
German ‘Kindergarten’ has been calqued as детский сад in Russian, literally ‘children garden’ in both languages.<br />
<br />
Chinese 洗腦 ‘wash’ + ‘brain’ is the origin of ‘brainwash’ in English.<br />
<br />
English skyscraper is calqued as gratte-ciel in French and rascacielos in Spanish, literally ‘scratches sky’ in both languages.<br />
<br />
46. Word-for-word translation<br />
What is it?<br />
A literal translation that is natural and correct in the target language.<br />
<br />
Alternative names are ‘literal translation’ or ‘metaphrase’.<br />
<br />
Note: this technique is different to the translation method of the same name, which does not produce correct and natural text and has a different purpose.<br />
<br />
Key features<br />
This translation strategy will only work between languages that have very similar grammatical structures.<br />
<br />
And even then, only sometimes.<br />
<br />
For example, standard word order in Turkish is Subject-Object-Verb whereas in English it’s Subject-Verb-Object. So a literal translation between these two will seldom work:<br />
– Yusuf elmayı yedi is literally ‘Joseph the apple ate’.<br />
<br />
When word-for-word translations don’t produce natural and correct text, translators resort to some of the other techniques described below.<br />
Examples<br />
French ‘Quelle heure est-il?’ works into English as ‘What time is it?’.<br />
<br />
Russian ‘Oн хочет что-нибудь поесть’ is ‘He wants something to eat’.<br />
<br />
47. Transposition<br />
What is it?<br />
Translation with a change of grammatical structure.<br />
<br />
This technique gives the translation more natural wording and/or makes it grammatically correct.<br />
<br />
Examples<br />
A change in word order:<br />
Our Turkish example Yusuf elmayı yedi (literally ‘Joseph the apple ate’) –> Joseph ate the apple.<br />
<br />
Spanish La Casa Blanca (literally ‘The House White’) –> The White House<br />
<br />
A change in grammatical category:<br />
German Er hört gerne Musik (literally ‘he listens gladly [to] music’)<br />
= subject pronoun + verb + adverb + noun<br />
becomes Spanish Le gusta escuchar música (literally ‘[to] him [it] pleases to listen [to] music’)<br />
= indirect object pronoun + verb + infinitive + noun<br />
and English He likes listening to music<br />
= subject pronoun + verb + gerund + noun.<br />
<br />
48. Modulation<br />
What is it?<br />
Translation with a change of focus or point of view in the target language.<br />
<br />
This technique makes the translation more idiomatic – how people would normally say it in the language.<br />
<br />
Examples<br />
English talks of the ‘top floor’ of a building, French the dernier étage = last floor. ‘Last floor’ would be unnatural in English, so too ‘top floor’ in French.<br />
<br />
German uses the term Lebensgefahr (literally ‘danger to life’) where in English we’d be more likely to say ‘risk of death’.<br />
In English we’d say ‘I dropped the key’, in Spanish se me cayó la llave, literally ‘the key fell from me’. The English perspective is that I did something (dropped the key), whereas in Spanish something happened to me – I’m the recipient of the action.<br />
<br />
49. Equivalence or Reformulation<br />
What is it?<br />
Translating the underlying concept or meaning using a totally different expression.<br />
<br />
This technique is widely used when translating idioms and proverbs.<br />
<br />
And it’s common in titles and advertising slogans.<br />
<br />
It’s a common strategy where a direct translation either wouldn’t make sense or wouldn’t resonate in the same way.<br />
<br />
Examples<br />
Here are some equivalents of the English saying “Pigs may fly”, meaning something will never happen, or “you’re being unrealistic” (Source):<br />
– Thai: ชาติหน้าตอนบ่าย ๆ – literally, ‘One afternoon in your next reincarnation’<br />
– French: Quand les poules auront des dents – literally, ‘When hens have teeth’<br />
– Russian, Когда рак на горе свистнет – literally, ‘When a lobster whistles on top of a mountain’<br />
– Dutch, Als de koeien op het ijs dansen – literally, ‘When the cows dance on the ice’<br />
– Chinese: 除非太陽從西邊出來！– literally, ‘Only if the sun rises in the west’<br />
<br />
50. Adaptation<br />
What is it?<br />
A translation that substitutes a culturally-specific reference with something that’s more relevant or meaningful in the target language.<br />
<br />
It’s also known as cultural substitution or cultural equivalence.<br />
<br />
It’s a useful technique when a reference wouldn’t be understood at all, or the associated nuances or connotations would be lost in the target language.<br />
<br />
Note: the translation method of the same name is a similar concept but applied to the text as a whole.<br />
<br />
Examples<br />
Different cultures celebrate different coming of age birthdays – 21 in many cultures, 20, 15 or 16 in others. A translator might consider changing the age to the target culture custom where the coming of age implications were important in the original text.<br />
Animals have different connotations across languages and cultures. Owls for example are associated with wisdom in English, but are a bad omen to Vietnamese. A translator might want to remove or amend an animal reference where this would create a different image in the target language.<br />
<br />
51. Compensation<br />
What is it?<br />
A meaning or nuance that can’t be directly translated is expressed in another way in the text.<br />
Example<br />
Many languages have ways of expressing social status (honorifics) encoded into their grammatical structures.<br />
<br />
So you can convey different levels of respect, politeness, humility, etc simply by choosing different forms of words or grammatical elements.<br />
But these nuances will be lost when translating into languages that don’t have these structures.<br />
Then translating into languages that don’t have these structures<br />
Then translating into languages that don’t have these structures.<br />
<br />
===Conclusion ===<br />
<br />
In the light of above mentioned facts and figures here by we would say that machine translation is a challenge for human translators because it can reduce the workload of translation but can't give accurate and exact translation of the target language.It can be less reliable than human translation..</div>Chen Huinihttps://bou.de/u/index.php?title=Machine_translation&diff=133228Machine translation2021-12-15T04:57:28Z<p>Chen Huini: /* Conclusion */</p>
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<div>'''Machine Translation - A challenge or a chance for human translators?'''<br />
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[[Machine_translation|Overview Page of Machine Translation]]<br />
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30 Chapters（0/30)<br />
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[[Machine_Trans_EN_1]] [[Machine_Trans_EN_2]] [[Machine_Trans_EN_3]] [[Machine_Trans_EN_4]] [[Machine_Trans_EN_5]] [[Machine_Trans_EN_6]] [[Machine_Trans_EN_7]] [[Machine_Trans_EN_8]] [[Machine_Trans_EN_9]] [[Machine_Trans_EN_10]] [[Machine_Trans_EN_11]] [[Machine_Trans_EN_12]] [[Machine_Trans_EN_13]] [[Machine_Trans_EN_14]] [[Machine_Trans_EN_15]] [[Machine_Trans_EN_16]] [[Machine_Trans_EN_17]] [[Machine_Trans_EN_18]] [[Machine_Trans_EN_19]] [[Machine_Trans_EN_20]] [[Machine_Trans_EN_21]] [[Machine_Trans_EN_22]] [[Machine_Trans_EN_23]] [[Machine_Trans_EN_24]] [[Machine_Trans_EN_25]] [[Machine_Trans_EN_26]] [[Machine_Trans_EN_27]] [[Machine_Trans_EN_28]] [[Machine_Trans_EN_29]] [[Machine_Trans_EN_30]] ...<br />
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[[Book_projects|Back to translation project overview]]<br />
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=Chapter 1:A Comparison Between the Quality of Machine Translation and Human Translation——A Case Study of the Application of artificial intelligence in Sports Events=<br />
'''论机器翻译与人工翻译的质量对比——以人工智能在体育赛事领域的应用为例'''<br />
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卫怡雯 Wei Yiwen, Hunan Normal University, China<br />
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[[Machine_Trans_EN_1]]<br />
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=Chapter 3：On the Realm Advantages And Mutual Development of Machine Translation And Huamn Translation)=<br />
"'论机器翻译与人工翻译的领域优势及共同发展'"<br />
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肖毅瑶 Xiao Yiyao, Hunan Normal University, China<br />
[[Machine_Trans_EN_3]]<br />
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=Chapter 4 : A Comparison Between Machine Translation of Netease and Traditional Human Translation—A Case Study of The Economist Articles)= <br />
'''网易有道机器翻译与人工翻译的译文对比——以经济学人语料为例'''<br />
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王李菲 Wang Lifei, Hunan Normal University<br />
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[[Machine_Trans_EN_4]]<br />
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=Chapter 5: Muhammad Saqib Mehran - Problems in translation studies=<br />
[[Machine_Trans_EN_5]]<br />
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=6 徐敏赟(Machine Translation Based on Neural Network --Challenge or Chance?)=<br />
[[Machine_Trans_EN_6]]<br />
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=Chapter 7: The Cultivation of artificial intelligence and translation talents in the Belt and Road Initiative=<br />
[[Machine_Trans_EN_7]]<br />
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一带一路背景下人工智能与翻译人才的培养<br />
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颜莉莉 Yan Lili, Hunan Normal University, China<br />
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=Chapter 8: On Machine Translation Under Lanuguage Intelligence——An Option and Opportunity for Human Translators=<br />
'''论语言智能下的机器翻译——译者的选择与机遇'''<br />
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颜静 Yan Jing, Hunan Normal University, China<br />
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[[Machine_Trans_EN_8]]<br />
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=9 谢佳芬（ Machine Translation and Artificial Translation in the Era of Artificial Intelligence）=<br />
[[Machine_Trans_EN_9]]<br />
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=10 熊敏(Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts)=<br />
机器翻译对各类型文本的英汉翻译能力探究<br />
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熊敏, Xiong Min, Hunan Normal University<br />
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[[Machine_Trans_EN_10]]<br />
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===Abstract===<br />
The history of machine translation can be traced to 1940s, undergoing germination, recession, revival and flourish. Nowadays, with the rapid development of information technology, machine translation technology emerged and is gradually becoming mature. Whether manual translation can be replaced by machine translation becomes a widely-disputed topic. So in order to explore the ability of machine translation, I adopts two versions of translation, which are manual translation and machine translation(this paper uses Youdao translation) for different types of texts(according to Peter Newmark's types of text：informative text, expressive text and vocative text). The results are quite different in terms of quality and accuracy. The results show that machine translation is more suitable for informative text for its brevity, while the expressive texts especially literary works and vocative texts are difficult to be translated, because there are too many loaded words and cultural images in expressive text and dependence on the readers. To draw a conclusion, the relationship between machine translation and manual translation should be complementary rather than competitive.<br />
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===Key words===<br />
machine translation; manual translation; Newmark's type of texts<br />
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===题目===<br />
Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts<br />
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===摘要===<br />
机器翻译的历史可以追溯到上个世纪40年代，经历了萌芽期、萧条期、复兴期和繁荣期四个阶段。如今，随着信息技术的高速发展，机器翻译技术日益成熟，于是机器翻译能不能替代人工翻译这个话题引起了广泛热议。为了探究机器翻译的能力水平是否足以替代人工翻译，本人根据皮特纽马克的文本类型分类理论（信息类文本，表达文本，呼吁文本），选择了相应的译文类型，并且将其机器翻译的版本以及人工翻译的版本进行对比。结果发现，就质量和准确度而言，译文的水平大相径庭。因为其简洁的特性，机器比较适合翻译信息文本。而就表达文本，尤其是文学作品，因其存在文化负载词及相应的文化现象，所以机器翻译这类文本存在难度。而呼唤文本因其目的性以及对读者的依赖性较强，翻译难度较大。由此得知，机器翻译和人工翻译的关系应该是互补的，而不是竞争的。<br />
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===关键词===<br />
机器翻译；人工翻译；纽马克文本类型<br />
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===1. Introduction===<br />
====1.1.Introduction to machine translation====<br />
Machine translation, also known as computer translation is a technique to translating through machine translator, such as Google translation and Youdao translation. Machine translation is one of the branches of computational linguistics, ranging from computer science, statistics, information science and so on. Machine translation plays an important role in all aspects.<br />
Machine translation can be traced back to 1940s, when British engineer Booth and American engineer Weaver proposed using computer to translate and started to study machines used for translation. And the first machine translating system launched in 1954, used in English and Russian translation. And this means machine translation becomes a reality. However, the arrival of new things always accompanies barriers and setbacks. In the 1960s, reports from ALPAC (Automated Language Processing Advisory Committee) showed studies on machine translation had stagnated for a decade. At that time, due to the high cost of machine, choosing human translators to finish translating works was more economical for most companies. What’s worse, machine had difficulty in understanding semantic and pragmatic meanings. Fortunately, in the 1970s, with the advance and popularity of computer, machine translation was gradually back on track. With the development of science and technology, machine translation has better technological guarantee, such as artificial intelligence and computer technology. In the last decades, from the late 90s till now, machine translation is becoming more and more mature. The initial rule-oriented machine translation has been updated and Corpus-aided machine translation appears. And nowadays, technology in voice recognition has been further developed. This technique is frequently applied in speech translation products. Users only need to speak source language to the online translator and instantly it will speak out the target version. But machine can’t fully provide precise and accurate translation without any grammatical or semantic problems. Machine can understand the literal meaning of texts, but not the meaning in context. For example, there is polysemy in English, which refers to words having multiple meanings. So when translating these words, translators should take contexts into consideration. But machine has troubles in this way. In addition, machine has no feeling or intention. Hence, it is also difficult to convey the feelings from the source language.(Wei 2021:5)#<br />
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====1.2.Process of machine translation====<br />
The process of manual translation is different from that of machine translation. Here is the process of the former. (1) Understand source language. (2) Use target language to organize language. (3) Generate translation. Unlike manual translation, machine translation tends to analyze and code source language first, then look for related codes in corpus, and work out the code that represents target language, generating translation. But they share a common feature, which is that Lexicon, grammatical rules and syntactic structure are taken into consideration. This is one of the biggest challenges for machine translation.<br />
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===2.Theorectical framework===<br />
====2.1Newmark’s text typology====<br />
Text typology is a theory from linguistics. It undergoes several phrases. Karl Buhler, a German psychologist and linguist defines communicative functions into expressive, information and vocative. Then Roman Jakobson proposes categorization of texts, which are intralingual translation, interlingual translation and intersemiotic translation. Accordingly, he defines six main functions of language, including the referential function, conative function, emotive function, poetic function, metalingual function and the phatic function. Based on the two theories, Peter Newmark, a translation theorist, summarizes the language function into six parts: the informative function, the vocative function, the expressive function, the phatic function, the aesthetic function, and the metalingual function. Later, he further divided texts into informative type, expressive text and vocative type according to the linguistic functions of various texts. (Newmark 2002:2)#<br />
The core of the informative texts is the truth. It is to convey facts, information, knowledge of certain topics, reality and theories. The language style of the text is objective, logical and standardized. Reports, papers, scientific and technological textbooks are all attributed to informative texts. Translators are required to take format into account for different styles.<br />
The core of the expressive text is the sender’s emotions and attitudes. It is to express sender’s preferences, feelings, views and so on. The language style of it is subjective. Imaginative literature, including fictions, poems and dramas, autobiography and authoritative statements belong to expressive text. It requires translators to be faithful to the source language and maintain the style.<br />
The core of the vocative text is readership. It is to call upon readers to act, think, feel and react in the way intended by the text. So it is reader-oriented. Such texts as advertisement, propaganda and notices are of vocative text. Newmark noted that translators need to consider cultural background of the source language and the semantic and pragmatic effect of target language. If readers can comprehend the meaning at once and do as the text says, then the goal of information communication is achieved. (Liu 2021:3)#<br />
To draw a conclusion, informative texts focus on the information and facts. Expressive texts center on the mind of addressers, including feelings, prejudices and so on. And vocative texts are oriented on readers and call on them to practice as the texts say.<br />
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====2.2Study method====<br />
Manual translations of the three texts are selected from authoritative versions and universally acknowledged. And machine translations of those come from Youdao Translator. And in this thesis I will compare and evaluate the two methods in word diction, sentence structure, word order and redundancy.<br />
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===3.Comparison and analysis of machine translation and manual translation ===<br />
====3.1Informative text ====<br />
（1）English into Chinese<br />
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①Source language:<br />
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Keep the tip of Apple Pencil clean, as dirt and other small particles may cause excessive wear to the tip or damage the screen of i-pad.<br />
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Target language:<br />
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Machine translation: Apple Pencil笔尖应保持清洁，灰尘等小颗粒可能会导致笔尖过度磨损或损坏ipad屏幕。<br />
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Manual translation: 保持Apple Pencil铅笔的笔尖干净，因为灰尘和其他微粒可能会导致笔尖的过度磨损或损坏iPad屏幕。<br />
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Analysis: Here is the instruction of Apple Pencil. And the manual translation is the Chinese version on the instruction.Product instruction tends to be professional, since there are many terms for some concepts. Machine can easily identify these terms and provide related words to translate. The machine version is faithful and expressive to the source language. So it is well-qualified and readable for readers to understand the instruction. So we can use machine to translate informative text.<br />
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②Source language:<br />
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China on Saturday launched a rocket carrying three astronauts-two men and one woman - to the core module of a future space station where they will live and work for six months, the longest orbit for Chinese astronauts.<br />
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Target language:<br />
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Machine translation: 周六，中国发射了一枚运载三名宇航员(两男一女)的火箭，进入未来空间站的核心舱，他们将在那里生活和工作6个月，这是中国宇航员最长的轨道。<br />
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Manual translation: 周六，中国发射了一枚搭载三名宇航员(两男一女)的火箭，进入未来空间站的核心舱，他们将在那里生活和工作6个月，这是中国宇航员最漫长的一次轨道飞行。<br />
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Analysis: This is a news from Reuters, reporting that China has launched a rocket.The meaning of the two translations is almost the same, except for some word diction. But there are some details dealt with different choice. For example, the last sentence of the machine translation is a bit of obscure and direct. There are some ambiguous words and expressions.<br />
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(2)Chinese into English<br />
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Source language:湖南省博物馆是湖南省最大的历史艺术类博物馆，占地面积4.9万平方米，总建筑面积为9.1万平方米，是首批国家一级博物馆，中央地方共建的八个国家级重点博物馆之一、全国文化系统先进集体、文化强省建设有突出贡献先进集体。<br />
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Target language:<br />
Manual translation: As the largest history and art museum in Hunan province, the Hunan Museum covers an area of 49,000㎡, with the building area reaching 91,000㎡. It is one of the first batch of national first-level museums and one of the first eight national museums co-funded by central and local governments.<br />
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Machine translation: Museum in hunan province is one of the largest historical art museum in hunan province, covers an area of 49000 square meters, a total construction area of 91000 square meters, is the first national museum, the central place to build one of the eight national key museum, national cultural system advanced collectives, strong culture began with outstanding contribution of advanced collective.<br />
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Analysis: Machine translation is not faithful enough in content. For instance, “首批国家一级博物馆” is translated into “first national museum”, which is not the meaning of the source language. And there are some obvious grammar mistakes in the machine translation. For example, machine translates it into just one sentence but there are multiple predicates in it. So it is not grammatically permissible. What’s more, the sentence structure of machine translation is confusing and the focus is not specific enough.<br />
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====3.2Expressive text ====<br />
(1)English into Chinese<br />
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Source language:<br />
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An individual human existence should be like a river- small at first, narrowly contained within its banks, and rushing passionately past rocks and over waterfalls. Gradually the river grows wider, the banks recede, the waters flow more quietly, and in the end, without any visible breaks, they become merged in the sea, and painlessly lose their individual being.()<br />
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Target language:<br />
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Machine translation: 一个人的存在应该像一条河流——开始很小，被紧紧地夹在两岸中间，然后热情奔放地冲过岩石，飞下瀑布。渐渐地，河面变宽，两岸后退，水流更加平缓，最后，没有任何明显的停顿，它们汇入大海，毫无痛苦地失去了自己的存在。<br />
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Manual translation:人生在世，如若河流；河口初始狭窄，河岸虬曲，而后狂涛击石，飞泻成瀑。河道渐趋开阔，峡岸退去，水流潺缓，终了，一马平川，汇于大海，消逝无影。<br />
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Analysis: Here is a well-known metaphor in the prose How to Grow Old written by Bertrand Russell. The manual translation is written by Tian Rongchang.This is a philosophical prose with graceful language. Literary translation is a most important and difficult branch of translation. Translator should focus on the literal meaning, culture, writing style and so on. It is a combination of beauty and elegance. Therefore, translators find it in a dilemma of beauty and faithfulness, let alone translating machine. Compared with manual translation, machine translation has difficulty in word choice. It is faithful and expressive, but not elegant enough.<br />
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(2)Chinese into English<br />
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Source language:没有一个人将小草叫做“大力士”，但是它的力量之大，的确是世界无比。这种力，是一般人看不见的生命力，只要生命存在，这种力就要显现，上面的石块，丝毫不足以阻挡。因为它是一种“长期抗战”的力，有弹性，能屈能伸的力，有韧性，不达目的不止的力。(Zhang, 2007:186)#<br />
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Target language:<br />
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Machine translation: No one calls the little grass "hercules", but its power is truly matchless in the world. This force is invisible life force. As long as there is life, this force will show itself. The stone above is not strong enough to stop it. Because it is a "long-term resistance" of the force, elastic, can bend and extend force, tenacity, not to achieve the purpose of the force.<br />
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Manual translation: Though nobody describes the little grass as a “husky”, yet its herculean strength is unrivalled. It is the force of life invisible to naked eye. It will display itself so long as there is life. The rock is utterly helpless before this force- a force that will forever remain militant, a force that is resilient and can take temporary setbacks calmly, a force that is tenacity itself and will never give up until the goal is reached. (by Zhang Peiji)<br />
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Analysis:This is the excerpt of a well-known Chinese prose written by Xia Yan. It is written during the war of Resistance Against Japan. So the prose holds symbolic meaning, eulogizing the invisible tenacious vitality so as to encourage Chinese to have confidence in the anti-aggression war. Compared with manual translation, machine translation is much more abstract and confusing, especially for the word diction. For example, “大力士” is translated into “hercules” which is a man of exceptional strength and size in Greek and Roman Mythology, making it difficult to understand if readers of target language have no idea of the allusion. What’s worse, the machine version doesn’t reveal the symbolic meaning of the text, which is the core of this prose.<br />
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====3.3Vocative text ====<br />
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(1)English into Chinese<br />
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①Source language:<br />
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iPhone went to film school, so you don’t have to. (Advertisement of iPhone13)<br />
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Target language:<br />
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Machine translation: iPhone上的是电影学院，所以你不用去。<br />
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Manual translation:电影专业课，iPhone同学替你上完了。<br />
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Analysis：Here are advertisements of iPhone on Apple official website. There is a personification in the source language. It is used to stress the advancement and proficiency in camera, which is an appealing selling point to potential buyers. Compared with manual translation, machine translation is plain and not eye-catching enough for customers.<br />
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②Source language: <br />
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5G speed OMGGGGG<br />
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Machine language: 5克的速度 OMGGGGG<br />
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Manual translation:<br />
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iPhone的5G 巨巨巨巨巨5G<br />
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Analysis: The “G” in the source language is the unit of speed, standing for generation. However, it is mistaken as a unit of weight, representing gram in the machine translation. So the meaning is not faithful to the source language at all. As for manual translation, it complies with the source in form. Specifically speaking, five “G”s in the former complies with five characters “巨”in the latter. And the pronunciation of the two is similar. There are two layers of meaning for the 5 “G”s. One exclaims the fast speed of 5 generation network and the other new technology. In the manual version, “巨”can be used to show degree, meaning “quite” or “very”. <br />
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③Source language: <br />
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History, faith and reason show the way, the way of unity. We can see each other not as adversaries but as neighbors. We can treat each other with dignity and respect, we can join forces, stop the shouting and lower the temperature. For without unity, there is no peace, only bitterness and fury."<br />
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Target language:<br />
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Machine translation: 历史、信仰和理性指明了团结的道路。我们可以把彼此视为邻居，而不是对手。我们可以尊严地对待彼此，我们可以联合起来，停止大喊大叫，降低温度。因为没有团结，就没有和平，只有痛苦和愤怒。<br />
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Manual translation:历史、信仰和理性为我们指明道路。那是团结之路。我们可以把彼此视为邻居，而不是对手。我们可以有尊严地相互尊重。我们可以联合起来，停止喊叫，减少愤怒。因为没有团结就没有和平，只有痛苦和愤怒<br />
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Analysis: Speech is a way to propagate some activity in public. It is an art to inspire emotion of the audience. The source language is the excerpt of Joe Biden’s inaugural speech. The speech should be inspiring and logic. The machine translation has some misunderstanding. Taking the translation of “lower the temperature” for example, machine only translates its literal meaning, relating to the temperature itself, without considering the context. What’s more, it is less logic than the manual one. Therefore, it adds difficulty to inspire the audience and infect their emotion.<br />
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===4.Common mistakes in machine translation ===<br />
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====4.1 lexical mistakes ====<br />
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Common lexical mistakes include misunderstandings in word category, lexical meaning and emotive and evaluative meaning. Misunderstanding in word category shows in the classification of word in the source language. As for misunderstanding in lexical meaning, machine has difficulty in precisely reflecting the meaning of the original texts, due to different cultural background and different language system. And for misunderstanding in emotive meaning, machine has no intention and emotion like human-beings. Therefore, it’s impossible for it to know writers’ feelings and their writing purposes. So sometimes, it may translate something negative into something positive. (Wang 2008:45)#<br />
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====4.2 grammatical mistakes====<br />
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Grammatical analysis plays an important part in translation. Normally speaking, every language has its own unique grammatical rules. So in the process of translation, if translators don’t know the formation rule well, the sentence meaning will be affected. Even though all the lexical meanings are well-known by translators, the lack of consciousness of grammaticality makes it harder to arrange words according to sequential rule. English tends to be hypotactic, while Chinese tends to be paratactic. English sentences are connected through syntactic devices and lexical devices. While Chinese sentences are semantically connected, which means there are limited logical words and connection words in Chinese. So when translating English sentence, we should first analyze its grammaticality and logical structure and then rearrange its sequence. However, online translating machine has troubles in grammatical analysis, which makes its improvement more difficult.<br />
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====4.3 other mistakes====<br />
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The two mistakes above are the internal ones. Apart from mistakes in linguistic system, there are some mistakes in other aspects, such as cultural background.<br />
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===5.Reasons for its common mistakes ===<br />
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====5.1 Difference in two linguistic system====<br />
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With different history, English and Chinese have different ways of expression. Commonly speaking, English is synthetic language which expresses grammatical meaning through inflection such as tense and Chinese is analytic language which expresses grammatical meaning through word order and function word. In addition, English is more compact with full sentences. Subordinate sentence is one of the most important features in modern English. Chinese, on the other hand, is more diffusive with minor sentences.<br />
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====5.2 Difference in thinking patterns and cultural background====<br />
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According to Sapir-Whorf’s Hypothesis, our language helps mould our way of thinking and consequently, different languages may probably express their unique ways of understanding the world. For two different speech communities, the greater their structural differentiations are, the more diverse their conceptualization of the world will be. For example, western culture is more direct and eastern culture more euphemistic. What’s more, English culture tends to be individualism, focusing on detail, through which it reflects the whole, while Chinese culture tends to be collective. Different thinking patterns will add difficulty for machine to translate texts.<br />
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====5.3 Limitation of computer====<br />
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Recently, there are some breakthroughs and innovation in machine translation. However, due to its own limitation, online translation has limitation in some ways. Firstly, compared with machine, human brain is much more complicated, consisting of ten billions of neuron, each of which has different function to affect human’s daily activities and help humans avoid some errors. However, computer can only function according to preset programming has no intention or consciousness. Until now, countless related scholars have invested much time in machine translation. They upload massive language database, which include almost all linguistic rules. But computers still fail to precisely reflect the meaning of source language for many times due to the complexity and flexibility of language. On the other hand, computers can’t take context into consideration. During translation, it is often the case that machine chooses the most-frequently used meaning of one word. So without the correct and exact meaning, readers are easier to feel confused and even misunderstand the meaning of source language. (Qiu 2021:4)#<br />
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===6.Conclusion===<br />
From the analysis above, we can draw a conclusion that machine deals with informative text best, followed by non-literary translation of expressive text. What’s more, machine can be a useful tool to get to know the gist and main idea of a specific topic, for the simple sentence structure and numerous terms. And it can improve translating efficiency with high speed. But machine has difficulty in translating literary works, especially proses and poems.<br />
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Machine translation has mixed future. From the perspective of commercial, machine translation boasts a bright future. With the process of globalization, the demand for translation is increasing accordingly. On one hand, if we only depend on human translator to deal with translating works, the quality and accuracy of translation can be greatly affected. On the other hand, if machine is used properly to do some basic work, human translators only need to make preparation before translating, progress, polish and other advanced work, contributing to highly-qualified translation and high working efficiency.<br />
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However, compared with manual translation, machine translation has a bleak future. It is still impossible for machine to replace interpreter or translator in a short term. With intelligence and initiative, humans are able to learn new knowledge constantly, which machine will never accomplish. Besides, machine is not used to replace translators but to assist them in work. In other words, translators and machine carry out their own duties and they are not incompatible.(He 2021:5)#<br />
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To draw a conclusion, although there are certain limitations of machine translation, it can serve as a catalyst for translating works. Therefore, with the rapid development of artificial intelligence and related technology, there are still many opportunities for machine translation.<br />
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===Reference ===<br />
<br />
Chen Cheng陈诚.机器翻译技术的综述[J][Overview of Machine Translation Technology].Electronic Techonology 电子技术,2021,50(11):290-291.<br />
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Cui Zihan 崔子涵.机器翻译译文质量对比——以谷歌翻译和DeepL为例[J] [Comparison among Machine Translation--Taking Google Translation and Deepl for Example].Overseas English 海外英语,2021(15):182-183.<br />
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He Xinyu何馨宇.机器翻译的发展及其对翻译职业化的影响研究[J] [The Development of Machine Translation and its Effect on Professional Transltors].Overseas English 海外英语,2021(20):48-49.<br />
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He Wen 何雯, Wang Xiufeng 王秀峰.信息型文本的在线机器翻译错误研究[J][Research on Errors in Online Machine Translation of Informative text ].Overseas English海外英语,2021(15):188-189.<br />
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Li Deyi 李德毅. (2018). 人工智能导论 [Introduction to Artificial Intelligence]. Beijing: China Science and Technology Press 中国科学技术出版社.<br />
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Liu Qin刘琴.功能目的论对于不同文本类型的翻译解读[J][Analysis of Translations in Different Types of Text based on Functionalist Approaches].Overseas Engliosh 海外英语,2021(17):8-9.<br />
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Li Hanji 李晗佶. (2021). 人工智能时代翻译技术与译者关系演变与重构 [Evolution and reconstruction of the relationship between translation technology and translators in the era of artificial intelligence]. 西华师范大学学报(哲学社会科学版) Journal of West China Normal University (PHILOSOPHY AND SOCIAL SCIENCES EDITION) (2021-12-04) 1-6.<br />
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(英) Peter Newmark A Textbook of Translation[M] Shanghai Foreign Education Press, 2002<br />
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Qiu Quanju 仇全菊.大数据时代背景下机器翻译及其发展趋势[J][Machine Translation and its Development Trend under the Background of Big Data Era]. English Teachers 英语教师,2021,21(16):60-62.<br />
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Wang Hongqi 王红旗. (2008). 语言学概论 [Introduction to Linguistics]. Beijing: Peking University Press 北京大学出版社.<br />
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Wei Guang魏光. 人工翻译与机器翻译译文编辑比较研究[J][Comparative Study of Translation Editing between Manual Translation and Machine Translation]. Overseas English 海外英语,2021(19):18-19+21.<br />
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Zhuo Jianbin 卓键滨,Liu Wenxian 刘文娴,Peng Zili 彭子莉.机器翻译对各类型文本的德汉翻译能力探究[J][Research on the German Chinese Translation Ability of Machine Translation for Various Types of Texts]. Comparative Study of Cultural innovation 文化创新比较研究,2021,5(28):122-125.<br />
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Zhang Peiji 张培基.英译中国现代散文选[M][Selected Modern Chinese Prose Writings]. Shanghai Foreign Languages Education Press 上海外语教育出版社, 2002.<br />
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--[[User:Xiong Min|Xiong Min]] ([[User talk:Xiong Min|talk]]) 01:36, 15 December 2021 (UTC)<br />
<br />
=Chapter 11 陈惠妮=Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts=<br />
<br />
机器翻译的译前编辑研究——以医学类文摘为例<br />
<br />
陈惠妮 Chen Huini, Hunan Normal University<br />
<br />
[[Machine_Trans_EN_11]]<br />
<br />
===Abstract===<br />
At present, globalization is accelerating and the market demand for language services is rapidly increasing . Machine translation, as an important translation method, can greatly improve translation efficiency due to its low cost and high speed. However, because of the limitations of machine translation and the differences between Chinese and English language, machine translation is not accurate enough. In order to balance translation efficiency and translation quality, a great number of manual revisions in translation are required for the machine translating texts. Medical papers are specialized, special and purposeful, so it requires accurate,qualified and professional translation. However, the quality of translations by machine is inefficient to meet the high-quality requirements of medical papers translation. Therefore, the introduction of pre-editing can greatly improve the efficiency and quality of machine translation.<br />
<br />
===Key words===<br />
Pre-editing, Machine translation, Medical texts<br />
<br />
===题目===<br />
Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts<br />
<br />
===摘要===<br />
在全球化加速发展的今天，市场对语言服务的需求迅速增加。机器翻译作为一种重要的翻译途径，由于其成本低、速度快，可以大大提高翻译效率。然而，由于机器翻译的局限性以及中英文语言的差异，机器翻译的准确性不高。为了平衡翻译效率和翻译质量，机器翻译文本需要大量的手工修改。<br />
医学论文具有专业性、特殊性和目的性，要求其译文准确、合格、专业。然而，机器翻译的质量较低，无法满足医学论文对翻译的高质量要求。因此，译前编辑的引入可以大大提高机器翻译的效率和质量。<br />
<br />
===关键词===<br />
译前编辑；机器翻译；医学文本<br />
<br />
===1. Introduction===<br />
===1.1 Definition of Machine Translation===<br />
As Cronin(2013) revealed: "Translation is undergoing a revolutionary upheaval. The influence of digital technology and the Internet on translation is continuous, extensive and profound. From the popularity of automatic online translation applications, translation revolution is everywhere (Cronin,2013) However,the concept of machine translation was firstly proposed in the 1930s. Since 1940s, the machine translation technology has been evolving from rule-based machine translation (RBMT) to statistical machine translation (SMT), and to neural machine translation (NMT). Machine translation refers to the automatic translation of source language into target language by using a computer system. That is, machine translation refers to the automatic translation of text from one language into another natural language by computer software or other online translation webs. Machine translation is also defined as the process of “using a computer system to automatically translate text or speech from one natural language to another” according to the definition by ISO (Cui 2014:68-73).O 'Brien (2002) defines it as "the behavior of modifying errors in machine translation to ensure that the target translation meets certain quality requirements". On the basic level, machine translation performs mechanical substitution of words in one language for words in another language, but that rarely produces a good translation, therefore, recognition of the whole phrases and their closest counterparts in the target language is needed. Not all words in one language have equivalents in another language, and many words have more than one meaning. A huge demand for translation is greatly needed in today’s global world, which creates new opportunities for the development of machine translation, attracts more and more attention and becomes one of the current research focuses.<br />
<br />
As Cronin(2013) revealed: "Translation is undergoing a revolutionary upheaval. The influence of digital technology and the Internet on translation is continuous, extensive and profound. From the popularity of automatic online translation applications, translation revolution is everywhere (Cronin,2013) However,the concept of machine translation was firstly proposed in the 1930s. Since 1940s, the machine translation technology has been evolving from rule-based machine translation (RBMT) to statistical machine translation (SMT), and to neural machine translation (NMT). Machine translation refers to the automatic translation of source language into target language by using a computer system. That is, machine translation refers to the automatic translation of text from one language into another natural language by computer software or other online translation webs. Machine translation is also defined as the process of “using a computer system to automatically translate text or speech from one natural language to another” according to the definition by ISO (Cui 2014:68-73).O 'Brien (2002) defines it as "the behavior of modifying errors in machine translation to ensure that the target translation meets certain quality requirements". On the basic level, machine translation performs mechanical substitution of words in one language for words in another language, but that rarely produces a good translation, therefore, recognition of the whole phrases and their closest counterparts in the target language is needed. Not all words in one language have equivalents in another language, and many words have more than one meaning. A huge demand for translation is greatly needed in today’s global world, which creates new opportunities for the development of machine translation, attracts more and more attention and becomes one of the current research focuses.<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:34, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===1.2 Definition of Pre-editing===<br />
Pre-editing means to adjust and modify the source language to make it fit more with the characteristics of the machine translation software before putting the source language before the into machine translation, so as to improve the quality of the translation machine translation (Wei Changhong 2008:93-94). Pre-editing is to modify the original text before putting it into machine translation software, in order to improve the recognition rate of machine translation, optimize the output quality of translated text and reduce the workload of post-editing. Because pre-translation editing only needs to be modified in one language, the operation is simpler than post-translation editing, which can realize the double improvement of quality and efficiency. A good pre-editing translation can help machine translation more smoothly, thus improving the machine readability and quality of the output translation. Pre-editing is mostly applied in the following situations: one is when the original text is of poor quality and the machine is difficult to recognize the meaning of the sentence, such as user generated content with poor readability and translatability (Gerlach et al 2003:45-53); Documents that need to be published in multiple languages; Next is when the original text contains a lot of jargon; The last is the original text has a corresponding translation memory bank. If the original text is edited, it can better match the content of the translation memory bank. Pre-editing is a process of identifying problems. It requires to pre-edit the source texts before putting it into machine translation according to the requirements, listing the expressions or sentences that may have trouble in machine translation and then pre-edit it by human. The purpose is to enable the computer to translate better, improve the translatability of machine translation. (Slype G V & Guinet J F & Seitz F 1984:115)<br />
<br />
Pre-editing means to adjust and modify the source language to make it fit more with the characteristics of the machine translation software before putting the source language before the into machine translation, so as to improve the quality of the translation machine translation (Wei Changhong 2008:93-94). Pre-editing is to modify the original text before putting it into machine translation software, in order to improve the recognition rate of machine translation, optimize the output quality of translated text and reduce the workload of post-editing. Because pre-translation editing only needs to be modified in one language, the operation is simpler than post-translation editing, which can realize the double improvement of quality and efficiency. A good pre-editing translation can help machine translation more smoothly, thus improving the machine readability and quality of the output translation. Pre-editing is mostly applied in the following situations: one is when the original text is of poor quality and the machine is difficult to recognize the meaning of the sentence, such as user generated content with poor readability and translatability (Gerlach et al 2003:45-53); Documents that need to be published in multiple languages; Next is when the original text contains a lot of jargon; The last is the original text has a corresponding translation memory bank. If the original text is edited, it can better match the content of the translation memory bank. Pre-editing is a process of identifying problems. It requires to pre-edit the source texts before putting it into machine translation according to the requirements, listing the expressions or sentences that may have trouble in machine translation and then pre-edit it by human. The purpose is to enable the computer to translate better, improve the translatability of machine translation. (Slype G V & Guinet J F & Seitz F 1984:115)<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:36, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===1.3 Machine Translation Mode===<br />
According to the different knowledge acquisition methods, machine translation modes can be classified as follows: one is rule-based machine translation, which is based on bilingual dictionaries and a library of language rules for each language. The quality of translation depends on whether the source language conforms to the existing rules, but the inexhaustible rules are the hinders of this model. The second mode is machine translation based on statistics. This translation model relies on the principles of mathematics and statistics to find various existing translations corresponding to the translation tasks through the employment of corpus, analyzing the frequency of their occurrence and selecting the translation with the highest frequency for output. The disadvantage of this translation model is that it ignores the flexibility of language and the importance of context. The last one is neural network language model. This model is different from previous translation models in that it uses end-to-end neural network to realize automatic translation between natural languages. At present, the quality of its translation is much higher than that of the previous translation models.<br />
<br />
According to the different knowledge acquisition methods, machine translation modes can be classified as follows: one is rule-based machine translation, which is based on bilingual dictionaries and a library of language rules for each language. The quality of translation depends on whether the source language conforms to the existing rules, but the inexhaustible rules are the hinders of this model. The second mode is machine translation based on statistics. This translation model relies on the principles of mathematics and statistics to find various existing translations corresponding to the translation tasks through the employment of corpus, analyzing the frequency of their occurrence and selecting the translation with the highest frequency for output. The disadvantage of this translation model is that it ignores the flexibility of language and the importance of context. The last one is neural network language model. This model is different from previous translation models in that it uses end-to-end neural network to realize automatic translation between natural languages. At present, the quality of its translation is much higher than that of the previous translation models.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===1.4 Source of Study Abstracts===<br />
The core medical journals from domestic are selected in order to make the paper more representative and authoritative, such as National Medical Journal of China, Journal of Peking University (Health Science), and Journal of Third Military Medical University. All of them include basic medical science, biomedical technology, laboratory medical science and other fields. In this way, the pre-editing approaches included are applicable enough to machine translation of medical abstracts.<br />
<br />
The core medical journals from domestic are selected in order to make the paper more representative and authoritative, such as National Medical Journal of China, Journal of Peking University (Health Science), and Journal of Third Military Medical University. All of them include basic medical science, biomedical technology, laboratory medical science and other fields. In this way, the pre-editing approaches included are applicable enough to machine translation of medical abstracts.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===1.5 Selection of Translation Software===<br />
Generally, machine translation software includes Google Translation, Youdao Translator and Niu Translation, which has their own special use in translation. Take Google as an example, it is more like neurons in human brain, enabling to learn and collect information to establish connections with its neural machine translator’s neurons. However, it also causes many errors because of the lack of enough information. In this paper, contrastive analysis will be carried on by using Google translation. On the one hand, being a pioneer in the translation of NET, it is inevitably to sue Google as the translation software to translate medical texts in this paper. Comparing the program called “Google brain”, other NMT of translation software are relatively disadvantages. On the other hand, the Google translation enjoys the largest users in the world, with its downloads of more than one billion. The output quality of Google translation is more correct and complete than other machine translation software. With years of development and improvement, Google Translation has been greatly promoted. In this paper, Chinese medical abstract will be automatically translated by Google translation, and then the translation output will be compared with the translation by human and the publication of English abstracts. The main purpose is to prove that the improvement and promotion of quality and accuracy in the medical abstracts will be obtained through the pre-editing approaches.<br />
<br />
Generally, machine translation software includes Google Translation, Youdao Translator and Niu Translation, which has their own special use in translation. Take Google as an example, it is more like neurons in human brain, enabling to learn and collect information to establish connections with its neural machine translator’s neurons. However, it also causes many errors because of the lack of enough information. In this paper, contrastive analysis will be carried on by using Google translation. On the one hand, being a pioneer in the translation of NET, it is inevitably to sue Google as the translation software to translate medical texts in this paper. Comparing the program called “Google brain”, other NMT of translation software are relatively disadvantages. On the other hand, the Google translation enjoys the largest users in the world, with its downloads of more than one billion. The output quality of Google translation is more correct and complete than other machine translation software. With years of development and improvement, Google Translation has been greatly promoted. In this paper, Chinese medical abstract will be automatically translated by Google translation, and then the translation output will be compared with the translation by human and the publication of English abstracts. The main purpose is to prove that the improvement and promotion of quality and accuracy in the medical abstracts will be obtained through the pre-editing approaches.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===2.Language Characteristics and Error Division===<br />
Since Chinese and English are two different languages, it is quite neccessary to identify their own characteristics so as to better analyze and understand the two languages. Tytler (1978: 118-119) argued in his Essay on the Principles of Translation that there are three principles of translation that in all the translation should give readers the same feelings as the source text, except for complete transcript of ideas. There actually also exit some mistakes of these two languages. So the following will make some clarrifications of these two languages to ensure more accurate translation.<br />
<br />
Since Chinese and English are two different languages, it is quite neccessary to identify their own characteristics so as to better analyze and understand the two languages. Tytler (1978: 118-119) argued in his Essay on the Principles of Translation that there are three principles of translation that in all the translation should give readers the same feelings as the source text, except for complete transcript of ideas. There actually also exit some mistakes of these two languages. So the following will make some clarrifications of these two languages to ensure more accurate translation.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===2.1 Language Characteristics of Medical Abstracts===<br />
Chinese and English belong to different language systems, so there are differences in their language structure and the way users think of the languages. When using machine translation from Chinese to English, due to the unequal language levels, there will be many mistakes in the translation process, especially for ESP texts, such as medical papers.Here, these differences mainly refer to the linguistic characteristics of medical abstracts. In medical abstracts, it usually includes structured and unstructured abstracts. Although in different forms, they both describe the purpose, methods and conclusions of the research.<br />
<br />
In the method section of medial abstract, several Chinese sentences can be connected with commas, but each sentence may convey different information. In contrast, English sentences contain a great deal of information, but in order to ensure clarity, some modifiers need to be isolated and then reconstructed. However, in Chinese-English machine translation, a lot of information is put into the sentence because the machine segments the sentence on the basis of the full comma.<br />
<br />
In addition, subjectless sentence is used in the objective and method parts of Chinese medical abstracts. The subjectless sentence means the sentence without or free from subject and is usually employed in two contexts. The first is "needless to say". In Chinese sentences, it is common to omit the subject of the sentence. Chinese sentences can convey meanings by using incomplete sentence structures, so Chinese speakers can understand the meanings of the sentences even though the sentence subject is omitted. The second is "emphasis of action". In this context, subjectless sentences are used to describe behaviors, especially in the study of traditional Chinese medicine abstracts. Comparatively speaking, it should be avoided in English medical abstracts. Abstract sentences are more subjective when describing the learning process, while the essential requirement of English medical abstract is objectivity. From this point, sentences without subject should be avoided in English medical abstracts. Another feature is voice. Few words with passive meanings appear in Chinese abstracts. English sentences are more favoured in using passive voice. When translating from Chinese to English, passive voice should be used to make the contents objective, which is also the basic requirement of medical papers.<br />
<br />
These are the linguistic features of Chinese medical abstracts. There are great differences in sentence structure and expression between Chinese and English medical abstracts. These differences may reduce the accuracy of machine translation, so it is necessary to introduce pre-editing to edit the source text to ensure that the source text can be accurately recognized by the machine and fully translated into English.<br />
<br />
Chinese and English belong to different language systems, so there are differences in their language structure and the way users think of the languages. When using machine translation from Chinese to English, due to the unequal language levels, there will be many mistakes in the translation process, especially for ESP texts, such as medical papers.Here, these differences mainly refer to the linguistic characteristics of medical abstracts. In medical abstracts, it usually includes structured and unstructured abstracts. Although in different forms, they both describe the purpose, methods and conclusions of the research.<br />
<br />
In the method section of medial abstract, several Chinese sentences can be connected with commas, but each sentence may convey different information. In contrast, English sentences contain a great deal of information, but in order to ensure clarity, some modifiers need to be isolated and then reconstructed. However, in Chinese-English machine translation, a lot of information is put into the sentence because the machine segments the sentence on the basis of the full comma.<br />
<br />
In addition, subjectless sentence is used in the objective and method parts of Chinese medical abstracts. The subjectless sentence means the sentence without or free from subject and is usually employed in two contexts. The first is "needless to say". In Chinese sentences, it is common to omit the subject of the sentence. Chinese sentences can convey meanings by using incomplete sentence structures, so Chinese speakers can understand the meanings of the sentences even though the sentence subject is omitted. The second is "emphasis of action". In this context, subjectless sentences are used to describe behaviors, especially in the study of traditional Chinese medicine abstracts. Comparatively speaking, it should be avoided in English medical abstracts. Abstract sentences are more subjective when describing the learning process, while the essential requirement of English medical abstract is objectivity. From this point, sentences without subject should be avoided in English medical abstracts. Another feature is voice. Few words with passive meanings appear in Chinese abstracts. English sentences are more favoured in using passive voice. When translating from Chinese to English, passive voice should be used to make the contents objective, which is also the basic requirement of medical papers.<br />
<br />
These are the linguistic features of Chinese medical abstracts. There are great differences in sentence structure and expression between Chinese and English medical abstracts. These differences may reduce the accuracy of machine translation, so it is necessary to introduce pre-editing to edit the source text to ensure that the source text can be accurately recognized by the machine and fully translated into English.<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===2.2 Error Division of Machine Translation in Translating Abstracts of Medical Papers from Chinese to English===<br />
In this paper, errors in machine translation are listed out after analyzing. Some are due to the principles, such as statistical-based MT and NMT, employed by machine translation. Based on the original errors, they can be studied from two levels. One is from the macro-level, referring to mistakes caused by objective factors. These are not human factors, but the disadvantage of machine translation and the limitations of language. The limitations of machine translation are derived from the principles and models adopted and manifests itself as a reliance on reference sources. In this case, semantic ambiguity and textual incoherence may occur in the absence of reference sources.<br />
<br />
However, for ESP texts such as medical papers, the requirements for terms and sentence patterns are far beyond the existing corpora. For unfamiliar texts (that is, the corresponding texts cannot be found in the corpora). The quality of output translation will be relatively low, which is determined by the principles of machine translation. As mentioned above, machine translation has evolved over the past few decades from rules-based to corp-based statistics to NMT. However, there still exist some limitations in machine translation.<br />
<br />
Mistakes on micro-level are mainly caused by the variations and differences of linguistic structure between Chinese and English. Chinese is an implicit language, while English is an explicit one (Lian, 2010) To put it in another way, Chinese expression does not depend on language structures, while English does the opposite. This may result in mismatches between the original and machine translated sentences. This kind of error is mainly divided into two types: component fragment and component missing. For a medical paper, such errors are very serious. As a kind of ESP discourse, medical paper has the characteristics of fixed, objective and accurate language structure. In order to reproduce the characteristics of medical abstracts in translation, it is necessary to avoid errors at the level of words and sentences and pay attention to logic and consistency.<br />
<br />
Lexical errors here refer to the inconsistency of fixed expressions in the translation of terms. Although these terms are machine translation based on the corpus, the corpus may not be perfect for ESP texts such as medical papers. Therefore, fixed expressions are not used in term translation. This is also the most common mistake in machine translation (the term here includes but is not limited to nouns). For ESP texts, medical text in particular, the fixed expressions and the accuracy of terms are of great importance.<br />
<br />
In this paper, errors in machine translation are listed out after analyzing. Some are due to the principles, such as statistical-based MT and NMT, employed by machine translation. Based on the original errors, they can be studied from two levels. One is from the macro-level, referring to mistakes caused by objective factors. These are not human factors, but the disadvantage of machine translation and the limitations of language. The limitations of machine translation are derived from the principles and models adopted and manifests itself as a reliance on reference sources. In this case, semantic ambiguity and textual incoherence may occur in the absence of reference sources.<br />
<br />
However, for ESP texts such as medical papers, the requirements for terms and sentence patterns are far beyond the existing corpora. For unfamiliar texts (that is, the corresponding texts cannot be found in the corpora). The quality of output translation will be relatively low, which is determined by the principles of machine translation. As mentioned above, machine translation has evolved over the past few decades from rules-based to corp-based statistics to NMT. However, there still exist some limitations in machine translation.<br />
<br />
Mistakes on micro-level are mainly caused by the variations and differences of linguistic structure between Chinese and English. Chinese is an implicit language, while English is an explicit one (Lian, 2010) To put it in another way, Chinese expression does not depend on language structures, while English does the opposite. This may result in mismatches between the original and machine translated sentences. This kind of error is mainly divided into two types: component fragment and component missing. For a medical paper, such errors are very serious. As a kind of ESP discourse, medical paper has the characteristics of fixed, objective and accurate language structure. In order to reproduce the characteristics of medical abstracts in translation, it is necessary to avoid errors at the level of words and sentences and pay attention to logic and consistency.<br />
<br />
Lexical errors here refer to the inconsistency of fixed expressions in the translation of terms. Although these terms are machine translation based on the corpus, the corpus may not be perfect for ESP texts such as medical papers. Therefore, fixed expressions are not used in term translation. This is also the most common mistake in machine translation (the term here includes but is not limited to nouns). For ESP texts, medical text in particular, the fixed expressions and the accuracy of terms are of great importance.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===3.Approaches Proposed for Pre-editing ===<br />
Generally speaking, there is a close relationship between pre-editing and post-editing, both of which aim to convey information and ensure high or publishable translation quality. Proper pre-editing can improve the quality of machine translation in terms of adequacy and consistency. <br />
Complexity of natural language and people use language arbitrarily, bringing many difficulties to Chinese-English machine translation, Hu Qingping (2005:24) has proposed "the research of translation software and the development of the controlled language are the two directions to improve the quality of machine translation : the former aims at the difficulty in the natural language processing, the latter overcomes the arbitrariness of natural language". Feng Quangong and Gao Lin (2017: 63-68) put forward: "The writing principles of controlled language can be applied to pre-editing of machine translation. Pre-editing based on controlled language can effectively reduce the complexity and ambiguity of source text, improve the identifiability of machine translation (the translatability of source text itself), and thus reduce (fully) the workload of post-editing".<br />
The central task of pre-editing is to transform human-friendly content into machine-friendly content, so words and sentences need to be repositioned or even changed. Based on the analysis of the language characteristics of Chinese and English, the Chinese ideographic group should be split before the Chinese source text is input into the machine software to translate so that the sentence structure is complete which can be easily recognized by the machine translation software.<br />
<br />
Generally speaking, there is a close relationship between pre-editing and post-editing, both of which aim to convey information and ensure high or publishable translation quality. Proper pre-editing can improve the quality of machine translation in terms of adequacy and consistency. <br />
<br />
Complexity of natural language and people use language arbitrarily, bringing many difficulties to Chinese-English machine translation, Hu Qingping (2005:24) has proposed "the research of translation software and the development of the controlled language are the two directions to improve the quality of machine translation : the former aims at the difficulty in the natural language processing, the latter overcomes the arbitrariness of natural language". Feng Quangong and Gao Lin (2017: 63-68) put forward: "The writing principles of controlled language can be applied to pre-editing of machine translation. Pre-editing based on controlled language can effectively reduce the complexity and ambiguity of source text, improve the identifiability of machine translation (the translatability of source text itself), and thus reduce (fully) the workload of post-editing".<br />
<br />
The central task of pre-editing is to transform human-friendly content into machine-friendly content, so words and sentences need to be repositioned or even changed. Based on the analysis of the language characteristics of Chinese and English, the Chinese ideographic group should be split before the Chinese source text is input into the machine software to translate so that the sentence structure is complete which can be easily recognized by the machine translation software.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufefng<br />
<br />
===3.1 Extraction and Replacement of Terms in the Source Text===<br />
As a branch of applied English, medical English is the product of the combination of English language knowledge and medical knowledge. The terms in medical papers have the characteristics of fixed and complex language structure. Although Google Translation is based on a corpus, the language structure is not fixed due to the limited size of the corpus. Therefore, the following two steps should be followed before machine translation. The first is to extract terms and create a glossary, and then replace the Chinese terms in the source text with the corresponding English expressions in the glossary. Of course, the terms of extraction should be searched and verified according to the actual situation. All of these words are verified before they can be replaced. This method can not only ensure the accuracy of term translation, but also maintain the consistency of expression, especially when dealing with long texts.<br />
Example1<br />
Source abstract: 口腔白斑病癌变相关缺氧应答基因和微小RNA的芯片及表达验证。<br />
Google translation: Microarray detection/Chip detection and expression verification of hypoxia response genes and microRNAs related to oral leukoplakia canceration.<br />
Published translation:Transcriptome array screening and verification of oral leukoplakia carcinogenesis-related hypoxia-responsive gene and microRNA. <br />
Analysis: The expression “ Affymetrix GeneChip” empolyed in this thesis is a human transcription array for transcriptome array. In the source abstract, “芯片检测“ can be meant “screening” but not detection, so the proper and appropriate translation of these expression should be “transcription array screening”, but the Google translates it into “microarry detection of chip detection”. Therefore, term extraction is essential and inevitable before putting the source abstracts into the machine translation software.<br />
After pre-editing source abstracts: 对 oral leukoplakia carcinogenesis 相关 hypoxia-responsive gene 和微小RNA进行 transcriptome array screening 及表达验证。<br />
After pre-editing translation: Transcriptome array screening and expression- verification of hypoxia-responsive genes and microRNAs related to oral leukoplakia carcinogenesis.<br />
<br />
As a branch of applied English, medical English is the product of the combination of English language knowledge and medical knowledge. The terms in medical papers have the characteristics of fixed and complex language structure. Although Google Translation is based on a corpus, the language structure is not fixed due to the limited size of the corpus. Therefore, the following two steps should be followed before machine translation. The first is to extract terms and create a glossary, and then replace the Chinese terms in the source text with the corresponding English expressions in the glossary. Of course, the terms of extraction should be searched and verified according to the actual situation. All of these words are verified before they can be replaced. This method can not only ensure the accuracy of term translation, but also maintain the consistency of expression, especially when dealing with long texts.<br />
<br />
Example1<br />
Source abstract: 口腔白斑病癌变相关缺氧应答基因和微小RNA的芯片及表达验证。<br />
Google translation: Microarray detection/Chip detection and expression verification of hypoxia response genes and microRNAs related to oral leukoplakia canceration.<br />
Published translation:Transcriptome array screening and verification of oral leukoplakia carcinogenesis-related hypoxia-responsive gene and microRNA. <br />
Analysis: The expression “ Affymetrix GeneChip” empolyed in this thesis is a human transcription array for transcriptome array. In the source abstract, “芯片检测“ can be meant “screening” but not detection, so the proper and appropriate translation of these expression should be “transcription array screening”, but the Google translates it into “microarry detection of chip detection”. Therefore, term extraction is essential and inevitable before putting the source abstracts into the machine translation software.<br />
After pre-editing source abstracts: 对 oral leukoplakia carcinogenesis 相关 hypoxia-responsive gene 和微小RNA进行 transcriptome array screening 及表达验证。<br />
After pre-editing translation: Transcriptome array screening and expression- verification of hypoxia-responsive genes and microRNAs related to oral leukoplakia carcinogenesis.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===3.2 Explication of Subordination===<br />
As mentioned above, the subordination of Chinese sentences is judged by certain specific words in machine translation . Chinese is a paratactic language, and sentences are often connected by internal logical relationships; while English depends on sentence structure, so sentences are often closely linked by various language forms. In order to improve the accuracy of machine translation, we can adjust the sentence structure and adjust the position of adjectives and their modifiers. <br />
<br />
Example 2<br />
Source abstracts:回顾性分析南京医科大学附属儿童医院中重度HIE患儿49例及同期就诊的无神经系统症状体征的足月新生儿为对照组31例的头颅磁共振成像（MRI）资料。<br />
Google translation: A retrospective analysis that the brain magnetic resonance imaging (MRI) data of 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University and full-term neonates with no neurological symptoms and signs during the same period were included in the control group.<br />
Published translation: A total of 49 children with moderate to severe HIE admitted to the children’s Hospital Affiliated to Nanjing Medical University were retrospectively analyzed. Cranial magnetic resonance imaging (MRI) date of 31 full-term neonates without neurological symptoms and signs who visited the hospital during the same period were recruited as the control group. <br />
<br />
Analysis: As the above example shows that “中重度HIE患儿” and “足月新生儿” in the source abstracts are from the Children’s Hospital Affiliated Nanjing Medical University. The Google translation shows that 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University. There is an error due to the misuse of subordination. There are some advice in order to solve the problem. That is, first to segment the sentence and then reconstruct the sentence. For instance, the Chinese expression “南京医科大学附属儿童医院” carries many modifiers, which requires to cut the modifiers and reconstruct the sentence. <br />
Therefore, the Chinese expression “南京医科大学附属儿童医院’can be divided into abstractions, leaving two sentences instead of one long sentence with modifiers..<br />
After pre-editing source abstracts: 对49例中重度HIE患儿以及31例无神经系统症状体征的足月新生儿的MRI资料进行回顾性分析。这些患者收治于南京医科大学儿童附属医院。<br />
After pre-editing translation: The MRI data of 49 children with moderate to severe HIE and 31 full-term newborns without neurological symptoms and signs were retrospectively analyzed. These patients were admitted to the Children’s Hospital of Nanjing Medical University.<br />
<br />
As mentioned above, the subordination of Chinese sentences is judged by certain specific words in machine translation . Chinese is a paratactic language, and sentences are often connected by internal logical relationships; while English depends on sentence structure, so sentences are often closely linked by various language forms. In order to improve the accuracy of machine translation, we can adjust the sentence structure and adjust the position of adjectives and their modifiers. <br />
<br />
Example 2<br />
Source abstracts:回顾性分析南京医科大学附属儿童医院中重度HIE患儿49例及同期就诊的无神经系统症状体征的足月新生儿为对照组31例的头颅磁共振成像（MRI）资料。<br />
Google translation: A retrospective analysis that the brain magnetic resonance imaging (MRI) data of 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University and full-term neonates with no neurological symptoms and signs during the same period were included in the control group.<br />
Published translation: A total of 49 children with moderate to severe HIE admitted to the children’s Hospital Affiliated to Nanjing Medical University were retrospectively analyzed. Cranial magnetic resonance imaging (MRI) date of 31 full-term neonates without neurological symptoms and signs who visited the hospital during the same period were recruited as the control group. <br />
<br />
Analysis: As the above example shows that “中重度HIE患儿” and “足月新生儿” in the source abstracts are from the Children’s Hospital Affiliated Nanjing Medical University. The Google translation shows that 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University. There is an error due to the misuse of subordination. There are some advice in order to solve the problem. That is, first to segment the sentence and then reconstruct the sentence. For instance, the Chinese expression “南京医科大学附属儿童医院” carries many modifiers, which requires to cut the modifiers and reconstruct the sentence. <br />
<br />
Therefore, the Chinese expression “南京医科大学附属儿童医院’can be divided into abstractions, leaving two sentences instead of one long sentence with modifiers..<br />
After pre-editing source abstracts: 对49例中重度HIE患儿以及31例无神经系统症状体征的足月新生儿的MRI资料进行回顾性分析。这些患者收治于南京医科大学儿童附属医院。<br />
After pre-editing translation: The MRI data of 49 children with moderate to severe HIE and 31 full-term newborns without neurological symptoms and signs were retrospectively analyzed. These patients were admitted to the Children’s Hospital of Nanjing Medical University.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===3.3 Explication of Subject through Voice Changing===<br />
The extensive use of subjectless sentences are quite common in the Chinese abstracts, while English prefers to employ passive voice in the sentences so as to make them object and accurate. In order to take the characteristics of English sentences into great and careful consideration, the sentences written in passive voice in particular, it will be helpful for machine translation to find out the subject and reconstruct a sentence in passive voice (Wang Yan: 2008). The first is to discover the “doee” in a sentence and then is to reconstruct the sentence structure and put the “doee” before the verb. The last is to explicate the passive relation between the noun and the verb.<br />
Example 3<br />
Source abstract: 回顾性分析2018年1月至2020年12月解放军总医院第七医学中心收治的500例老年髋部骨折患者的资料。<br />
Google translation: A retrospective analysis of the data of 500 elderly patients with hip fractures admitted to the Seventh Medical Center of the PLA General Hospital from January 2018 to December 2020.<br />
Published translation: From January 2018 to December 2020, the data of 500 elderly patients with hip fracture treated in the Seventh Medical Center of PLA General Hospital were analyzed retrospectively.<br />
Analysis: The above example indicates that the “回顾性分析”in the Google Translate is a noun phrase, but the source abstracts actually describes an action or a behavior. The reason for such a mistake is that the machine can’t recognize the Chinese subjetless sentences. To find the subject of the sentence, the source abstracts can be revised and adjusted. Finding the “doee” is the first step, which refers to “500例老年髋部骨折患者的资料”in the source abstracts. And next is to put it in front of the verb, that is to place it in the beginning of the sentence. Last but not least, the passive voice should be explicated in the sentence. <br />
After pre-editing source abstract: 500例老年髋部骨折患者的资料被回顾性分析，他们在2018年1月之2020年12月期间收治于解放军总医院第七医学中心。<br />
After pre-editing translation: The data of 500 elderly hip fracture patients were retrospectively analyzed. They were admitted to the Seventh Medical Center of the PLA General Hospital between January 2018 and December 2020.<br />
<br />
The extensive use of subjectless sentences are quite common in the Chinese abstracts, while English prefers to employ passive voice in the sentences so as to make them object and accurate. In order to take the characteristics of English sentences into great and careful consideration, the sentences written in passive voice in particular, it will be helpful for machine translation to find out the subject and reconstruct a sentence in passive voice (Wang Yan: 2008). The first is to discover the “doee” in a sentence and then is to reconstruct the sentence structure and put the “doee” before the verb. The last is to explicate the passive relation between the noun and the verb.<br />
<br />
Example 3<br />
Source abstract: 回顾性分析2018年1月至2020年12月解放军总医院第七医学中心收治的500例老年髋部骨折患者的资料。<br />
Google translation: A retrospective analysis of the data of 500 elderly patients with hip fractures admitted to the Seventh Medical Center of the PLA General Hospital from January 2018 to December 2020.<br />
Published translation: From January 2018 to December 2020, the data of 500 elderly patients with hip fracture treated in the Seventh Medical Center of PLA General Hospital were analyzed retrospectively.<br />
<br />
Analysis: The above example indicates that the “回顾性分析”in the Google Translate is a noun phrase, but the source abstracts actually describes an action or a behavior. The reason for such a mistake is that the machine can’t recognize the Chinese subjetless sentences. To find the subject of the sentence, the source abstracts can be revised and adjusted. Finding the “doee” is the first step, which refers to “500例老年髋部骨折患者的资料”in the source abstracts. And next is to put it in front of the verb, that is to place it in the beginning of the sentence. Last but not least, the passive voice should be explicated in the sentence. <br />
After pre-editing source abstract: 500例老年髋部骨折患者的资料被回顾性分析，他们在2018年1月之2020年12月期间收治于解放军总医院第七医学中心。<br />
After pre-editing translation: The data of 500 elderly hip fracture patients were retrospectively analyzed. They were admitted to the Seventh Medical Center of the PLA General Hospital between January 2018 and December 2020.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===3.4 Relocation of Modifiers===<br />
Modifiers, including noun, adverbial and attributive are constantly employed in the Chinese medical papers in order to add information to subject and object in the sentence. By doing this, complicated sentences can be structured, thus causing obstacles to machine translation for recognizing this complex sentence structures when translating Chinese-English sentences. To make the machine translation successfully recognize the sentence structure, simplifying the sentence structure is quite necessary. The key is to moving the location of the modifiers and thus making the modifiers an independent sentence. In other words, the modifiers ought to be put before or behind the main part of the sentence to satisfy the common use of English. <br />
Usually, the modifiers in Chinese sentence can only be placed in front of the core word, while in English, modifiers are very flexible. It is all right to place the modifiers in front of or behind the major part of the sentences with adjective or a connective noun.<br />
<br />
Example 4<br />
Source abstracts: 利用DNA重组技术以pET-28a表达系统在E.coli BL21(DE3) 中重组表达Hepl。<br />
Google Translation: Hepl is recombined in E.coli BL21 (DE3) using DNA recombination technology with pET-28a expression system.<br />
Published translation: The recombinant Hcpl protein was expressed by using DNA recombination technology through pET-28a expression system in E. coli BL21 (De3).<br />
Analysis: The Chinese medical text includes two modifiers, “利用DNA”and “以pET-28a)表达系统”. These two modifiers will be translated by machine, which catches more attention to the two constituents. From the Google translation above, one failure is obvious that it misplaces the location of the two modifiers and presents it in not accurate form. To make it translate correctly by machine translation, dividing the sentences is very important so that the source abstracts can be correctly recognized by machine translation.<br />
<br />
After pre-editing source abstract: 利用DNA重组技术，Hcp1重组表达在E.coli BL21 (DE3)中， 通过pET-28a表达系统在E. coli BL21 (DE3)中。<br />
Google translation: Using DNA recombination technology, Hcp1 recombination is expressed in E.coli BL21 (DE3), via pET-28a expression system in E. coli BL21 (DE3).<br />
The second is the independence of modifiers, that is, modifiers can also be reconstructed into clauses to modify core words. Compared with English, There is no subordinate clause in Chinese, so redundant Chinese modifiers need to be reconstructed into subordinate clauses in Chinese-English translation to meet the characteristics of English. English, especially sentences with multiple modifiers. Otherwise, sentence structure may be confused, such as scattered modifiers of core words. To avoid this mistake, it is necessary to separate the modifier from the main part of the sentence. These modifiers should be reconstituted into clauses. Also, keep your sentences simple and easy to understand.<br />
<br />
Modifiers, including noun, adverbial and attributive are constantly employed in the Chinese medical papers in order to add information to subject and object in the sentence. By doing this, complicated sentences can be structured, thus causing obstacles to machine translation for recognizing this complex sentence structures when translating Chinese-English sentences. To make the machine translation successfully recognize the sentence structure, simplifying the sentence structure is quite necessary. The key is to moving the location of the modifiers and thus making the modifiers an independent sentence. In other words, the modifiers ought to be put before or behind the main part of the sentence to satisfy the common use of English. <br />
Usually, the modifiers in Chinese sentence can only be placed in front of the core word, while in English, modifiers are very flexible. It is all right to place the modifiers in front of or behind the major part of the sentences with adjective or a connective noun.<br />
<br />
Example 4<br />
Source abstracts: 利用DNA重组技术以pET-28a表达系统在E.coli BL21(DE3) 中重组表达Hepl。<br />
Google Translation: Hepl is recombined in E.coli BL21 (DE3) using DNA recombination technology with pET-28a expression system.<br />
Published translation: The recombinant Hcpl protein was expressed by using DNA recombination technology through pET-28a expression system in E. coli BL21 (De3).<br />
Analysis: The Chinese medical text includes two modifiers, “利用DNA”and “以pET-28a)表达系统”. These two modifiers will be translated by machine, which catches more attention to the two constituents. From the Google translation above, one failure is obvious that it misplaces the location of the two modifiers and presents it in not accurate form. To make it translate correctly by machine translation, dividing the sentences is very important so that the source abstracts can be correctly recognized by machine translation.<br />
<br />
After pre-editing source abstract: 利用DNA重组技术，Hcp1重组表达在E.coli BL21 (DE3)中， 通过pET-28a表达系统在E. coli BL21 (DE3)中。<br />
Google translation: Using DNA recombination technology, Hcp1 recombination is expressed in E.coli BL21 (DE3), via pET-28a expression system in E. coli BL21 (DE3).<br />
The second is the independence of modifiers, that is, modifiers can also be reconstructed into clauses to modify core words. Compared with English, There is no subordinate clause in Chinese, so redundant Chinese modifiers need to be reconstructed into subordinate clauses in Chinese-English translation to meet the characteristics of English. English, especially sentences with multiple modifiers. Otherwise, sentence structure may be confused, such as scattered modifiers of core words. To avoid this mistake, it is necessary to separate the modifier from the main part of the sentence. These modifiers should be reconstituted into clauses. Also, keep your sentences simple and easy to understand.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===3.5 Proper Omission and Deletion of Category Words===<br />
Category words are commonly used in Chinese. Category words complement the meaning of words, including problems, positions, situations and jobs. Adding this supplementary word is more in line with Chinese custom. In many cases, it has no real meaning, so it can be omitted in translation. In Chinese, category words are frequently used. However, it is rarely used in English, which is one of the differences between Chinese and English. There are many kinds of category words. Considering objectivity and the fixed structure of language, redundant category words should be deleted in Chinese-English translation. In the general, the most commonly used category of words in the Chinese medical abstracts are "process", "behavior", and "situation". These categories of words hinder the language conversion of Chinese and English, resulting in redundancy. <br />
<br />
Example 5<br />
Source abstract: 探讨口腔白斑病癌变进程中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia response genes and associated microRNAs in the process of oral leukoplakia cancer was discussed.<br />
Published translation: To study the hypoxia response gene and microRNA (miRNA)expression profiles in the pathogenesis and progression of oral leukoplakia (OLK).<br />
Analysis: As the above example shows, the Chinese words “进程” belongs to a category word. However, the Chinese expression “癌变”contains the process, so the “进程” expression can be deleted before placing it into the machine translation, because the meaning of it has been overlapping between the expression “癌变”. Therefore, its place can be replaced with preposition “during”.<br />
<br />
After pre-editing source abstract: 探讨口腔白斑病癌变中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia responsive genes and miRNA in oral leukoplakia cancer was investigated.<br />
<br />
Category words are commonly used in Chinese. Category words complement the meaning of words, including problems, positions, situations and jobs. Adding this supplementary word is more in line with Chinese custom. In many cases, it has no real meaning, so it can be omitted in translation. In Chinese, category words are frequently used. However, it is rarely used in English, which is one of the differences between Chinese and English. There are many kinds of category words. Considering objectivity and the fixed structure of language, redundant category words should be deleted in Chinese-English translation. In the general, the most commonly used category of words in the Chinese medical abstracts are "process", "behavior", and "situation". These categories of words hinder the language conversion of Chinese and English, resulting in redundancy. <br />
<br />
Example 5<br />
Source abstract: 探讨口腔白斑病癌变进程中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia response genes and associated microRNAs in the process of oral leukoplakia cancer was discussed.<br />
Published translation: To study the hypoxia response gene and microRNA (miRNA)expression profiles in the pathogenesis and progression of oral leukoplakia (OLK).<br />
Analysis: As the above example shows, the Chinese words “进程” belongs to a category word. However, the Chinese expression “癌变”contains the process, so the “进程” expression can be deleted before placing it into the machine translation, because the meaning of it has been overlapping between the expression “癌变”. Therefore, its place can be replaced with preposition “during”.<br />
<br />
After pre-editing source abstract: 探讨口腔白斑病癌变中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia responsive genes and miRNA in oral leukoplakia cancer was investigated.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===Conclusion===<br />
After years of development, machine translation has made great progress. The accuracy of machine translation has been greatly improved in both text recognition and sentence pattern conversion. However, machine translation has its own limitations. In other words, it needs to rely on the parallel corpus as a reference source for improving its accuracy. ESP text, in particular, is harder to get the high quality by machine translation. <br />
<br />
As one of the research papers, the characteristics of medical abstracts are fixed language structures, objectivity and accuracy (Qin Yi 2004:421-423). Therefore, medical translation must be accurate, object and understandable to follow the specific demands of the medical paper. Being an important field in the human society, medical paper translation is on a great demand, which means that it needs a huge demand for human labor. However, with the machine translation promoting, it will be more efficient to translate medical papers combining the effort by human and machine. The improvement and development of machine translation requires the joint efforts of computer science, information science, statistics, linguistics and other academic circles to achieve more mature human-computer mutual assistance translation (Li Yafei, Zhang Ruihua 2019:38-45).<br />
<br />
However, errors can occur during the process of machine translation of Chinese- English, because of the differences of the Chinese and English and the processing of the machine. Errors from the perspective of linguistic or grammar can affect the machine translation a lot. After division and recognition of errors, some pre-editing approaches are put forward to help the machine translation more accurate and readable, that are, extraction and replacement of terms in the source text, relocation of modifiers, explication of subordination, proper omission, deletion of category words and explication of subject through voice changing. <br />
<br />
<br />
The paper mainly focuses on the pre-editing machine translation by using medical papers as a case study. The errors of machine translation occurring in the translation of medical abstracts and pre-editing approaches for machine translation. The quality of machine translation of medical papers is greatly improved after employing the pre-editing methods. However, machine translation is not as flexible and accurate as human brain, so it is of importance to combine pre-editing and post-editing approaches with machine translation in order to produce more accurate, more object machine translation of medical papers.<br />
<br />
After years of development, machine translation has made great progress. The accuracy of machine translation has been greatly improved in both text recognition and sentence pattern conversion. However, machine translation has its own limitations. In other words, it needs to rely on the parallel corpus as a reference source for improving its accuracy. ESP text, in particular, is harder to get the high quality by machine translation. <br />
<br />
As one of the research papers, the characteristics of medical abstracts are fixed language structures, objectivity and accuracy (Qin Yi 2004:421-423). Therefore, medical translation must be accurate, object and understandable to follow the specific demands of the medical paper. Being an important field in the human society, medical paper translation is on a great demand, which means that it needs a huge demand for human labor. However, with the machine translation promoting, it will be more efficient to translate medical papers combining the effort by human and machine. The improvement and development of machine translation requires the joint efforts of computer science, information science, statistics, linguistics and other academic circles to achieve more mature human-computer mutual assistance translation (Li Yafei, Zhang Ruihua 2019:38-45).<br />
<br />
However, errors can occur during the process of machine translation of Chinese- English, because of the differences of the Chinese and English and the processing of the machine. Errors from the perspective of linguistic or grammar can affect the machine translation a lot. After division and recognition of errors, some pre-editing approaches are put forward to help the machine translation more accurate and readable, that are, extraction and replacement of terms in the source text, relocation of modifiers, explication of subordination, proper omission, deletion of category words and explication of subject through voice changing. <br />
<br />
The paper mainly focuses on the pre-editing machine translation by using medical papers as a case study. The errors of machine translation occurring in the translation of medical abstracts and pre-editing approaches for machine translation. The quality of machine translation of medical papers is greatly improved after employing the pre-editing methods. However, machine translation is not as flexible and accurate as human brain, so it is of importance to combine pre-editing and post-editing approaches with machine translation in order to produce more accurate, more object machine translation of medical papers.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
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Slype G V & Guinet J F & Seitz F (1984). ''Better Translation for Better Communication'' [M] . Oxford: Pergamon Press Ltd (U.K.). 90-93<br />
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O'Brien S (2002). Teaching Post-editing: A Proposal for Course Con-tent [EB/OL]. http://mt-archive. Info/EAMT-2002-0brien. Pdf.<br />
<br />
Tytler, A. F. (1978). ''Essay On The Principles of Translation''[M]. Amsterdam: JohnBenjamins Publishing. 118-119<br />
<br />
Wang Yan王燕 (2008). 医学英语翻译与写作教程[M] ''Medical English Translation and Writing Course''. 重庆:重庆大学出版社 Chongqing: Chongqing University Press. 60-61<br />
<br />
=12 蔡珠凤=(The Mistranslation of C-J Machine Translation of Political Statements)=<br />
<br />
机器翻译中政治发言中译日的误译<br />
<br />
蔡珠凤 Cai Zhufeng, Hunan Normal University<br />
<br />
[[Machine_Trans_EN_12]]<br />
===Abstract===<br />
Language is the main way of communication between people. With the continuous development of globalization, the scale of cross-border exchanges is also expanding. However, due to cultural differences and diversity, the languages of different countries and regions are very different, which seriously hinders people's communication. The demand for efficient and convenient translation tools is increasing. At the same time, with the development of network technology and artificial intelligence, recognition technology based on deep learning is more and more widely used in English, Japanese and other fields.<br />
<br />
===Key words===<br />
machine translation; political statements; mistranslation of C-J machine translation<br />
===题目===<br />
The Mistranslation of C-J Machine Translation of Political Statements<br />
===摘要===<br />
语言是人与人之间交流的主要方式。随着全球化的不断发展，跨境交流的规模也在不断扩大。然而，由于文化的差异和多样性，不同国家和地区的语言差异很大，这严重阻碍了人们的交流。对高效便捷的翻译工具的需求正在增加。同时，随着网络技术和人工智能的发展，基于深度学习的识别技术在英语、日语等领域的应用越来越广泛。<br />
<br />
===关键词===<br />
机器翻译；政治发言；政治发言中译日的误译<br />
===1. Introduction===<br />
===Introduction to machine translation===<br />
Machine translation, also known as automatic translation, is a process of using computers to convert one natural language (source language) into another natural language (target language). It is a branch of computational linguistics, one of the ultimate goals of artificial intelligence, and has important scientific research value.At the same time, machine translation has important practical value. With the rapid development of economic globalization and the Internet, machine translation technology plays a more and more important role in promoting political, economic and cultural exchanges.The development of machine translation technology has been closely accompanied by the development of computer technology, information theory, linguistics and other disciplines. From the early dictionary matching, to the rule translation of dictionaries combined with linguistic expert knowledge, and then to the statistical machine translation based on corpus, with the improvement of computer computing power and the explosive growth of multilingual information, machine translation technology gradually stepped out of the ivory tower and began to provide real-time and convenient translation services for general users.（Zhang 2019:5-6)<br />
<br />
=== C-J machine translation software===<br />
Today's online machine translation software includes Baidu translation, Tencent translation, Google translation, Youdao translation, Bing translation and so on. Google was the first company to launch the machine translation system, and Baidu was the first company to import the machine translation system in China. In addition, Tencent and Youdao have attracted much attention.Machine translation is the process of using computers to convert one natural language into another. It usually refers to sentence and full-text translation between natural languages. In order to continuously improve the translation quality, R & D personnel have added artificial intelligence technologies such as speech recognition, image processing and deep neural network to machine translation on the basis of traditional machine translation based on rules, statistics and examples.With the increase of using machine translation, the joint cooperation between manual translation and machine translation will also increase significantly in the future. What criteria should be used to evaluate the quality of machine translation? In the evolving field of machine translation, there is an urgent need to clarify the unsolvable questions and solved problems.(Lv 1996:3)<br />
<br />
===The history of machine translation===<br />
The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. alchuni put forward the idea of using machines for translation. In 1933, Soviet inventor П.П. Trojansky designed a machine to translate one language into another, and registered his invention on September 5 of the same year; However, due to the low technical level in the 1930s, his translation machine was not made. In 1946, the first modern electronic computer ENIAC was born. Shortly after that, W. weaver, an American scientist and A. D. booth, a British engineer, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947 when discussing the application scope of electronic computer. In 1949, W. Weaver published the translation memorandum, which formally put forward the idea of machine translation. After 60 years of ups and downs, machine translation has experienced a tortuous and long development path. The academic community generally divides it into the following four stages:<br />
<br />
Pioneering period（1947-1964）<br />
<br />
In 1954, with the cooperation of IBM, Georgetown University completed the English Russian machine translation experiment with ibm-701 computer for the first time, showing the feasibility of machine translation to the public and the scientific community, thus opening the prelude to the study of machine translation.<br />
It is not too late for China to start this research. As early as 1956, the state included this research in the national scientific work development plan. The topic name is "machine translation, the construction of natural language translation rules and the mathematical theory of natural language". In 1957, the Institute of language and the Institute of computing technology of the Chinese Academy of Sciences cooperated in the Russian Chinese machine translation experiment, translating 9 different types of more complex sentences.From the 1950s to the first half of the 1960s, machine translation research has been on the rise. The United States and the former Soviet Union, two superpowers, have provided a lot of financial support for machine translation projects for military, political and economic purposes, while European countries have also paid considerable attention to machine translation research due to geopolitical and economic needs, and machine translation has become an upsurge for a time. In this period, although machine translation is just in the pioneering stage, it has entered an optimistic period of prosperity.<br />
<br />
Frustrated period（1964-1975）<br />
<br />
In 1964, in order to evaluate the research progress of machine translation, the American Academy of Sciences established the automatic language processing Advisory Committee (Alpac Committee) and began a two-year comprehensive investigation, analysis and test.In November 1966, the committee published a report entitled "language and machine" (Alpac report for short), which comprehensively denied the feasibility of machine translation and suggested stopping the financial support for machine translation projects. The publication of this report has dealt a blow to the booming machine translation, and the research of machine translation has fallen into a standstill. Coincidentally, during this period, China broke out the "ten-year Cultural Revolution", and basically these studies also stagnated. Machine translation has entered a depression.<br />
<br />
convalescence（1975-1989）<br />
<br />
Since the 1970s, with the development of science and technology and the increasingly frequent exchange of scientific and technological information among countries, the language barriers between countries have become more serious. The traditional manual operation mode has been far from meeting the needs, and there is an urgent need for computers to engage in translation. At the same time, the development of computer science and linguistics, especially the substantial improvement of computer hardware technology and the application of artificial intelligence in natural language processing, have promoted the recovery of machine translation research from the technical level. Machine translation projects have begun to develop again, and various practical and experimental systems have been launched successively, such as weinder system Eurpotra multilingual translation system, taum-meteo system, etc.However, after the end of the "ten-year holocaust", China has perked up again, and machine translation research has been put on the agenda again. The "784" project has paid enough attention to machine translation research. After the mid-1980s, the development of machine translation research in China has further accelerated. Firstly, two English Chinese machine translation systems, ky-1 and MT / ec863, have been successfully developed, indicating that China has made great progress in machine translation technology.(Chen 2016:5)<br />
<br />
New period(1990 present)<br />
<br />
With the universal application of the Internet, the acceleration of the process of world economic integration and the increasingly frequent exchanges in the international community, the traditional way of manual operation is far from meeting the rapidly growing needs of translation. People's demand for machine translation has increased unprecedentedly, and machine translation has ushered in a new development opportunity. International conferences on machine translation research have been held frequently, and China has made unprecedented achievements. A series of machine translation software have been launched, such as "Yixing", "Yaxin", "Tongyi", "Huajian", etc. Driven by the market demand, the commercial machine translation system has entered the practical stage, entered the market and came to the users.Since the new century, with the emergence and popularization of the Internet, the amount of data has increased sharply, and statistical methods have been fully applied. Internet companies have set up machine translation research groups and developed machine translation systems based on Internet big data, so as to make machine translation really practical, such as "Baidu translation", "Google translation", etc. In recent years, with the progress of in-depth learning, machine translation technology has further developed, which has promoted the rapid improvement of translation quality, and the translation in oral and other fields is more authentic and fluent.(Liu 2014:6)<br />
<br />
===The problem of machine translation at present===<br />
Error is inevitable<br />
Many people have misunderstandings about machine translation. They think that machine translation has great deviation and can't help people solve any problems. In fact, the error is inevitable. The reason is that machine translation uses linguistic principles. The machine automatically recognizes grammar, calls the stored thesaurus and automatically performs corresponding translation. However, errors are inevitable due to changes or irregularities in grammar, morphology and syntax, such as sentences with adverbials after "give me a reason to kill you first" in Dahua journey to the West. After all, a machine is a machine. No one has special feelings for language. How can it feel the lasting charm of "the tenderness of lowering its head, like the shame of a water lotus? After all, the meaning of Chinese is very different due to the changes of morphology, grammar and syntax and the change of context. Even many Chinese people are zhanger monks - they can't touch their heads, let alone machines.(Liu 2014：3）<br />
<br />
Bottleneck<br />
In fact, no matter which method, the biggest factor affecting the development of machine translation lies in the quality of translation. Judging from the achievements, the quality of machine translation is still far from the ultimate goal.<br />
Chinese mathematician and linguist Zhou Haizhong once pointed out in his paper "fifty years of machine translation": to improve the quality of machine translation, the first thing to solve is the problem of language itself rather than programming; It is certainly impossible to improve the quality of machine translation by relying on several programs alone. At the same time, he also pointed out that it is impossible for machine translation to achieve the degree of "faithfulness, expressiveness and elegance" when human beings have not yet understood how the brain performs fuzzy recognition and logical judgment of language. This view may reveal the bottleneck restricting the quality of translation. <br />
It is worth mentioning that American inventor and futurist ray cozwell predicted in an interview with Huffington Post that the quality of machine translation will reach the level of human translation by 2029. There are still many disputes about this thesis in the academic circles.（Cui 2019：4）<br />
<br />
===2.Mistranslation of Chinese Japanese machine translation===<br />
===2.1Vocabulary mistranslation in Chinese Japanese machine translation===<br />
===2.1.1Mistranslation of proper nouns===<br />
In political materials, there are often political dignitaries' names, place names or a large number of proper nouns in the political field. The morphemes of such words are definite and inseparable. Mistranslation will make the source language lose its specific meaning. <br />
<br />
Japanese translation into Chinese Chinese translation into Japanese<br />
<br />
original text translation by Youdao reference translation original text translation by Youdao reference translation<br />
<br />
朱鎔基 朱基 朱镕基 栗战书 栗戰史書 栗戰書<br />
<br />
労安 劳安 劳安 李克强 李克強 李克強 <br />
<br />
筑紫哲也 筑紫哲也 筑紫哲也 习近平 習近平 習近平<br />
<br />
山口百惠 山口百惠 山口百惠 韩正 韓中 韓正<br />
<br />
田中角栄 田中角荣 田中角荣 王沪宁 王上海氏 王滬寧<br />
<br />
東条英機 东条英社 东条英机 汪洋 汪洋 汪洋<br />
<br />
毛沢东 毛泽东 毛泽东 赵乐际 趙樂南 趙樂際<br />
<br />
トウ・ショウヘイ 大酱 邓小平 江泽民 江沢民 江沢民<br />
<br />
周恩来 周恩来 周恩来<br />
<br />
クリントン 克林顿 克林顿<br />
<br />
The above table counts 18 special names in the two texts, and 7 machine translation errors. In terms of mistranslation, there are not only "战书" but also "戰史" and "史書" in Chinese. "沪" is the abbreviation of Shanghai. In other words, since "战书" and "沪" are originally common nouns, this disrupts the choice of target language in machine translation. Except Katakana interference (except for トウシゃウヘイ, most of the mistranslations appear in the new Standing Committee. It can be seen that the machine translation system does not update the thesaurus in time. Different from other words, people's names have particularity. Especially as members of the Standing Committee of the Political Bureau of the CPC Central Committee, the translation of their names is officially unified. In this regard, public figures such as political dignitaries, stars, famous hosts and important. In addition, the machine also translates Japanese surnames such as "タ二モト" (谷本), "アンドウ" (安藤) into "塔尼莫特" and "龙胆". It can be found that the language feature that Japanese will be written together with Chinese characters and Hiragana also directly affects the translation quality of Japanese Chinese machine translation. Japanese people often use Katakana pronunciation. For example, when Japanese people talk with Premier Zhu, they often use "二ーハウ" (Hello). However, the machine only recognizes the two pseudonyms "二" and "ハ" and transliterates them into "尼哈" , ignoring the long sound after "二" and "ハ".(Guan 2018:10-12)<br />
<br />
original text Translation by Youdao reference translation<br />
<br />
日美安全体制 日米の安全体制 日米安保体制<br />
<br />
中国共产党第十九次全国代表大会 中国共産党第19回全国代表大会 中国共産党第19回全国代表大会（第19回党大会）<br />
<br />
十八大 十八大 第18回党大会中国特色社会主义<br />
<br />
中国特色社会主義 中国の特色ある社会主義 第18回党大会<br />
<br />
中国共产党中央委员会 中国共産党中央委員会 中国共産党中央委員会<br />
<br />
中国共産党中央委員会十八届中共中央政治局常委 第18代中国共產党中央政治局常務委員 第18期中共中央政治局常務委員<br />
<br />
十八届中共中央政治局委员 18期の中国共產党中央政治局委員 第18期中共中央政治局委員<br />
<br />
十九届中共中央政治局常委 十九回中国共產党中央政治局常務委員 第19期中央政治局常務委員<br />
<br />
中共十九届一中全会 中国共產党第十九回一中央委員会 第19期中央委員会第1回全体会議<br />
<br />
The above table is a comparison of the original and translated versions of some proper nouns. As shown in the table, the mistranslation problems are mainly reflected in the mismatch of numerals + quantifiers, the wrong addition of case auxiliary word "の", the lack of connectors, the Mistranslation of abbreviations, dead translation, and the writing errors of Chinese characters. The following is a specific analysis one by one.<br />
<br />
"十八届中共中央政治局常委", "十八届中共中央政治局委员", "十九届中共中央政治局常委" and "中共十九届一中全会" all have quantifiers "届", which are translated into "代", "期" and "回" respectively. The meaning is vague and should be uniformly translated into "期"; Among them, the translation of the last three proper nouns lacks the Chinese conjunction "第". The case auxiliary word "の" was added by mistake in the translation of "十八届中共中央政治局委员" and "日美安全体制". The "中国共产党中央委员会" did not write in the form of Japanese Ming Dynasty characters, but directly used simplified Chinese characters.<br />
<br />
The full name of "中共十九届一中全会" is "中国共产党第十九届中央委员会第一次全体会议", in which "一" stands for "第一次", which is an ordinal word rather than a cardinal word. Machine translation does not produce a correct translation. The fundamental reason is that there are no "规则" for translating such words in machine translation. If we can formulate corresponding rules for such words (for example, 中共m'1届m2 中全会→第m1期中央委员会第m2回全体会议), the translation system must be able to translate well no matter how many plenary sessions of the CPC Central Committee.(Guan 2018:6-7)<br />
<br />
===2.1.2Mistranslation of Polysemy===<br />
original text Translation by Youdao reference translation<br />
<br />
スタジオ 摄影棚/工作室 直播现场/演播厅<br />
<br />
日中関係の話 中日关系的故事 就中日关系(话题)<br />
<br />
溝 水沟 鸿沟<br />
<br />
それでは日中の問題について質問のある方。 那么对白天的问题有提问的人。 关于中日问题的话题，举手提问。<br />
<br />
私たちのクラスは20人ちょっとですが、 我们班有20人左右， 我们班二十多人的意见统一很难，<br />
<br />
いろいろな意見が出て、まとめるのは大変です。 但是又各种各样的意见，总结起来很困难。 <br />
<br />
一体どうやって、13億人もの人をまとめているんですか。 到底是怎么处理13亿的人的呢？ 中国是怎样把13亿人凝聚在一起的？<br />
<br />
In the original text, the word "スタジオ" appeared four times and was translated into "摄影棚" or "工作室" respectively, but the context is on-site interview, not the production site of photography or film. "話" has many semantics, such as "说话", "事情", "道理", etc. In accordance with the practice of the interview program, Premier Zhu Rongji was invited to answer five questions on the topic of China Japan relations. Obviously, the meaning of the word "故事" is very abrupt. The inherent Japanese word "日中" means "晌午、白天". At the same time, it is also the abbreviation of the names of the two countries. The machine failed to deal with it correctly according to the context. "溝" refers to the gap between China and Japan, not a "水沟". The former "まとめる" appears in the original text with the word "意见", which is intended to describe that it is difficult to unify everyone's opinions. The latter "まとめている" also refers to unifying the thoughts of 1.3 billion people, not "处理". Therefore, it is more appropriate to use "统一" and "处理" in human translation, rather than "总结意见" or "处理意见".(Zhang 2019:5)<br />
<br />
Mistranslation of polysemy has always been a difficult problem in machine translation research. The translation of each word is correct, but it is often very different from the original expression in the context. Zhang Zhengzeng said that ambiguity is a common phenomenon in natural language. Its essence is that the same language form may have different meanings, which is also one of the differences between natural language and artificial language. Therefore, one of the difficulties faced by machine translation is language disambiguation (Zhang Zheng, 2005:60). In this regard, we can mark all the meanings of polysemous words and judge which meaning to choose by common collocation with other words. At the same time, strengthen the text recognition ability of the machine to avoid the translation inconsistent with the current context. In this way, we can avoid the mistake of "大酱" in the place where famous figures such as Deng Xiaoping should have appeared in the previous political articles.(Wang 2020:7-9)<br />
<br />
===2.1.3Mistranslation of compound words===<br />
Multi class words refer to a word with two or more parts of speech, also known as the same word and different classes.<br />
<br />
original text Translation by Youdao reference translation<br />
<br />
1998年江泽民主席曾经访问日本, 1998年の江沢民国家主席の日本訪問し、 1998年、江沢民総書記が日本を訪問し、かつ<br />
<br />
同已故小渊首相签署了联合宣言。 かつて同じ故小渕首相が署名した共同宣言 亡くなられた小渕総理と宣言に調印されました<br />
<br />
Chinese "同" has two parts of speech: prepositions and conjunctions: "故" has many parts of speech, such as nouns, verbs, adjectives and conjunctions. This directly affects the judgment of the machine at the source language level. The word "同" in the above table is used as a conjunction to indicate the other party of a common act. "故" is used as a verb with the semantic meaning of "死亡", which is a modifier of "小渊首相". The machine regards "同" as an adjective and "故" as a noun "原因", which leads to confusion in the structure and unclear semantics of the translation. Different from the polysemy problem, multi category words have at least two parts of speech, and there is often not only one meaning under each part of speech. In this regard, software R & D personnel should fully consider the existence of multi category words, so that the translation machine can distinguish the meaning of words on the basis of marking the part of speech, so as to select the translation through the context and the components of the word in the sentence. Of course, the realization of this function is difficult, and we need to give full play to the wisdom of R & D personnel.(Guan 2018:9-12)<br />
<br />
===2.2 Syntactic mistranslation in Chinese Japanese machine translation===<br />
===2.2.1Mistranslation of tenses===<br />
original text：History is written by the people, and all achievements are attributed to the people. <br />
<br />
Translation by Youdao：歴史は人民が書いたものであり、すべての成果は人民のためである。<br />
<br />
reference translation：歴史は人民が綴っていくものであり、すべての成果は人民に帰することとなります。<br />
<br />
The original sentence meaning is "历史是人民书写的历史" or "历史是人民书写的东西". When translated into Japanese, due to the influence of Japanese language habits, the formal noun "もの" should be supplemented accordingly. In this sentence, both the machine and the interpreter have translated correctly. However, the machine's recognition of "的" is biased, resulting in tense translation errors. The past, present and future will become "history", and this is a continuous action. The form translated as "~ ていく" not only reflects that the action is a continuous action, but also conforms to the tense and semantic information of the original text. In addition, the machine's treatment of the preposition "于" is also inappropriate. In the original text, "人民" is the recipient of action, not the target language. As the most obvious feature of isolated language, function words in Chinese play an important role in semantic expression, and their translation should be regarded as a focus of machine translation research.(Zuo 2021:8)<br />
<br />
original text：李克强同志是十六届中共中央政治局常委,其他五位同志都是十六届中共中央政治局委员。<br />
<br />
Translation by Youdao：李克強総理は第16代中国共産党中央政治局常務委員であり、他の５人の同志はいずれも16期の中国共産党中央政治局委員である。<br />
<br />
reference translation：李克強同志は第16期中国共産党中央政治局常務委員を務め、他の５人は第16期中共中央政治局委員を務めました。<br />
<br />
Judging the verb "是" in the original text plays its most basic positive role, but due to the complexity of Chinese language, "是" often can not be completely transformed into the form of "だ / である" in the process of translation. This sentence is a judgment of what has happened. Compared with the 19th session, the 18th session has become history. This is an implicit temporal information. It is very difficult for machines without human brain to recognize this implicit information. "中共中央政治局常委" is the abbreviation of "中共中央政治局常务委员会委员" and it is a kind of position. In Japanese, often use it with verbs such as "務める","担当する",etc. The translator adopts the past tense of "務める", which conforms to the expression habit of Japanese and deals with the problem of tense at the same time. However, machine translation only mechanically translates this sentence into judgment sentence, and fails to correctly deal with the past information implied by the word "十八届".(Guan 2018:4)<br />
<br />
===2.2.2Mistranslation of honorifics===<br />
Honorific language is a language means to show respect to the listener. Different from Japanese, there is no grammatical category of honorifics in Chinese. There is no specific fixed grammatical form to express honorifics, self modesty and politeness. Instead, specific words such as "您", "请" and "劳驾" are used to express all kinds of respect or self modesty. (Yang 2020:5-9)<br />
<br />
Original text translation by Youdao reference translation<br />
<br />
女士们，先生们，同志们，朋友们 さんたち、先生たち、同志たち、お友达さん ご列席の皆さん<br />
<br />
谢谢大家！ ありがとうございます！ ご清聴ありがとうございました。<br />
<br />
これはどうされますか。 这是怎么回事呢? 您将如何解决这一问题？<br />
<br />
こうした問題をどうお考えでしょうか。 我们会如何考虑这些问题呢？ 您如何看待这一问题？<br />
<br />
For example, for the processing of "女士们，先生们，同志们，朋友们", the machine makes the translation correspond to the original one by one based on the principle of unchanged format, but there are errors in semantic communication and pragmatic habits. When speaking on formal occasions, Chinese expression tends to be comprehensive and detailed, as well as the address of the audience. In contrast, Japanese usually uses general terms such as "皆様", "ご臨席の皆さん" or "代表団の方々" as the opening remarks. In terms of Japanese Chinese translation, the machine also failed to recognize the usage of Japanese honorifics such as "さ れ る" and "お考え". It can be seen that Chinese Japanese machine translation has a low ability to deal with honorific expressions. The occasion is more formal. A good translation should not only be fluent in meaning, but also conform to the expression habits of the target language and match the current translation environment.(Che 2021:3-7)<br />
<br />
===Conclusion===<br />
In the process of discussing the Mistranslation of machine translation, this paper mainly cites the Mistranslation of Youdao translator. When I studied the problem of machine translation mistranslation, I also used a large number of translation software such as Google and Baidu for parallel comparison, and found that these translation software also had similar problems with Youdao translator. For example, the "新世界新未来" in Chinese ABAC structural phrases is translated by Baidu as "新し世界の新しい未来", which, like Youdao, is not translated in the form of parallel phrases; Google online translation translates the word "“全心全意" into a completely wrong "全面的に"; The original sentence "我吃了很多亏" in the Mistranslation of the unique expression in the language is translated by Google online as "私はたくさんの損失を食べました". Because the "吃" and "亏" in the original text are not closely adjacent, the machine can not recognize this echo and mistakenly treats "亏" as a kind of food, so the machine translates the predicate as "食べました"; Japanese "こうした問題をどう考えでしょうか", Google's online translation is "你如何看待这些问题?", "你" tone can not reflect the tone of Japanese honorific, while Baidu translates as "你怎么想这样的问题呢?" Although the meaning can be understood, it is an irregular spoken language. Google and Baidu also made the same mistakes as Youdao in the translation of proper nouns. For example, they translated "十七届(中共中央政治局常委)”" into "第17回..." .In short, similar mistranslations of Youdao are also common in Baidu and Google. Due to space constraints, they will not be listed one by one here.(Cui 2019:7)<br />
<br />
Mr. Liu Yongquan, Institute of language, Chinese Academy of Social Sciences (1997) It has been pointed out that machine translation is a linguistic problem in the final analysis. Although corpus based machine translation does not require a lot of linguistic knowledge, language expression is ever-changing. Machine translation based solely on statistical ideas can not avoid the translation quality problems caused by the lack of language rules. The following is based on the analysis of lexical, syntactic and other mistranslations From the perspective of the characteristics of the source language, this paper summarizes some difficulties of Chinese and Japanese in machine translation.(Liu 2014:8)<br />
<br />
(1) The difficulties of Chinese in machine translation <br />
<br />
Chinese is a typical isolated language. The relationship between words needs to be reflected by word order and function words. We should pay attention to the transformation of function words such as "的", "在", "向" and "了". Some special verbs, such as "是", "做" and "作", are widely used. How to translate on the basis of conforming to Japanese pragmatic habits and expressions is a difficulty in machine translation research. In terms of word order, Chinese is basically "subject→predicate→object". At the same time, Chinese pays attention to parataxis, and there is no need to be clear in expression with meaningful cohesion, which increases the difficulty of Chinese Japanese machine translation. When there are multiple verbs or modifier components in complex long sentences, machine translation usually can not accurately divide the components of the sentence, resulting in the result that the translation is completely unreadable.(Guan 2018:6-12) <br />
<br />
(2) Difficulties of Japanese in machine translation <br />
<br />
Both Chinese and Japanese languages use Chinese characters, and most machines produce the target language through the corresponding translation of Chinese characters. However, pseudonyms in Japanese play an important role in judging the part of speech and meaning, and can not only recognize Chinese characters and judge the structure and semantics of sentences. Japanese vocabulary is composed of Chinese vocabulary, inherent vocabulary and foreign vocabulary. Among them, the first two have a great impact on Chinese Japanese machine translation. For example "提出" is translated as "提出する" or "打ち出す"; and "重视" is translated as "重視する" or "大切にする". Japanese is an adhesive language, which contains a lot of "けど", "が" and "けれども" Some of them are transitional and progressive structures, but there are also some sequential expressions that do not need translation, and these translation software often can not accurately grasp them.(Che 2021:10)<br />
<br />
===References===<br />
[1] Navroz Kaur Kahlon,(2021(prepublish));Williamjeet Singh.Machine translation from text to sign language: a systematic review[J].Universal Access in the Information Society,1-35.<br />
<br />
[2] Cao Qianyu;Hao Hanmei,(2021);Ahmed Syed Hassan.A Chaotic Neural Network Model for English Machine Translation Based on Big Data Analysis[J].Computational Intelligence and Neuroscience,3274326-3274326.<br />
<br />
[3]Hwang Yongkeun;Kim Yanghoon;Jung Kyomin.(2021)Context-Aware Neural Machine Translation for Korean Honorific Expressions[J].Electronics,10(13):1589-1589.<br />
<br />
[4]Zakaryia Almahasees.(2021)Analysing English-Arabic Machine Translation:Google Translate, Microsoft Translator and Sakhr.<br />
<br />
[5](2021)Machine learning in translation[J].Nature Biomedical Engineering,5(6):485-486.<br />
<br />
[6]Shaimaa Marzouk.(2021(prepublish))An in-depth analysis of the individual impact of controlled language rules on machine translation output: a mixed-methods approach[J].Machine Translation,1-37.<br />
<br />
[7]Welnitzová Katarína;Munková Daša.(2021)Sentence-structure errors of machine translation into Slovak[J].Topics in Linguistics,22(1):78-92.<br />
<br />
[8]Xu Xueyuan.(2021).Machine learning-based prediction of urban soil environment and corpus translation teaching[J].Arabian Journal of Geosciences,14(11). <br />
<br />
[9]Chen Bingchang 陈丙昌(2016).機械翻訳の誤訳分析【D】.Error analysis of mechanical translation.贵州大学.2016(05) <br />
<br />
[10]Lv Yinqiu 呂寅秋(1996).機械翻訳の言語規則と伝統文法との相違点.【D】The language rules of mechanical translation, the traditional grammar, and the points of contradiction.日本学研究.Japanese Studies.1996(00):21-22 <br />
<br />
[11]Liu Jun 刘君(2014).基于语料库的中日同形词词义用法对比及其日中机器翻译研究【D】.A Corpus-based Comparison of the Meanings of Chinese and Japanese Homographs and Research on Japanese-Chinese Machine Translation.广西大学.(03) <br />
<br />
[12]Cun Qianqian 崔倩倩(2019).机器翻译错误与译后编辑策略研究【D】.Research on Machine Translation Errors and Post-Editing Strategies.北京外国语大学.(09) <br />
<br />
[13]Zhang Yi 张义(2019).机器翻译的译文分析【D】.Translation analysis of machine translation.西安外国语大学.(10) <br />
<br />
[14]Zhang Linqian 张琳婧(2019).在线机器翻译中日翻译错误原因及对策【D】.Causes and countermeasures of online machine translation errors in Chinese-Japanese translation.山西大学.(02)<br />
<br />
[15]Wang Dan 王丹(2020).基于机器翻译的专利文本译后编辑对策研究【D】.Research on countermeasures for post-translational editing of patent texts based on machine translation.大连理工大学.(06)<br />
<br />
[16]Yang Xiaokun 杨晓琨(2020).日中机器翻译中的前编辑规则与效果验证【D】.Pre-editing rules and effect verification in Japanese-Chinese machine translation.大连理工大学.(06)<br />
<br />
[17]Zuo Jia 左嘉(2021). 机器翻译日译汉误译研究【D】. Research on Mistranslation of Machine Translation from Japanese to Chinese.北京第二外国语学院.<br />
<br />
[18]Guan Biying 关碧莹(2018).关于政治类发言的汉日机器翻译误译分析【D】.Analysis of Chinese-Japanese Machine Translation Mistranslations of Political Speeches.哈尔滨理工大学.<br />
<br />
[19]Che Tong 车彤(2021).汉译日机器翻译质量评估及译后编辑策略研究【D】.Research on Quality Evaluation of Chinese-Japanese Machine Translation and Post-translation Editing Strategies.北京外国语大学.(09)<br />
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Networking Linking<br />
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http://www.elecfans.com/rengongzhineng/692245.html<br />
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https://baike.baidu.com/item/%E6%9C%BA%E5%99%A8%E7%BF%BB%E8%AF%91/411793<br />
<br />
=13 陈湘琼Chen Xiangqiong（Study on Post-editing from the Perspective of Functional Equivalence Theory ）=<br />
[[Machine_Trans_EN_13]]<br />
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=14 Bi bi Nadia（Machine Translation a Challenge for Human Translators)=<br />
[[Machine_Trans_EN_14]]<br />
<br />
===Abstract===<br />
Machine translation is a big obstacle in the way of Human translators or interpreters although it is quick and less time consuming.People are trying to get translation of their target language using through source language.For this purpose they are using digital apps like Google translation Google translation does not give accurate and exact interpretation.Google translator is translating word to word but it doesn't clarify it's true and actual meaning.On the other hand human translators can give exact and accurate translation ,they take care of grammatical mistakes , diction and sentence structure.They clarify the purpose of target language through using source language.<br />
<br />
===Keywords===<br />
Descriptive translation, academic, interdiscipline, comparative literature, localization,Translatology,school of thought,translation , studies, linguistics, corresponding.<br />
<br />
===Body of article===<br />
Translation is the process of reworking text from one language into another to maintain the original message and communication. But, like anything else, there are different methods of translation, and they vary in form and function. What is translation? The term has become widely used among knowledge transferring researchers and practitioners, especially in the fields of health and health care. In a landmark review, Jonathan Lomas began to argue that 'The tasks… may be defined as to establish and maintain links between researchers and their audience, via the appropriate translation of research findings' (Lomas 1997, p 4). In 2004, World Health Organization's World Report on Knowledge for Better Health suggested that 'One of the key contributions of research to health systems is the translation of knowledge into actions' (WHO 2004, p 33 and p 100). By 2006, special issues of WHO's Bulletin as well as the journals Evaluation and the Health Professions and the Journal of Continuing Education in the Health Professions were dedicated to translation.But what does 'translation' mean? It may be a new word for an old problem, meaning nothing more than 'transfer'. The rapidly emerging field of 'translational medicine' seems to take translation to mean generally what transfer might have meant, that is the transmission of knowledge and evidence 'from bench to bedside'. As one commentator has observed, "Translational medicine" as a fashionable term is being increasingly used to describe the wish of biomedical researchers to ultimately help patients' (Wehling 2008, abstract). <br />
Semantic uncertainty persists, not least because of the different interests of scientists, clinicians, patients and commercial firms (Littman et al 2007): 'Translational research means different things to different people, but it seems important to almost everyone' (Woolf 2008, p 211).<br />
In related fields, such as public health (Armstrong et al 2006), 'translation' seems to signify dissatisfaction with 'transfer'. It wants to move away from thinking of knowledge transfer as a form of technology transfer or dissemination, rejecting if only by implication its mechanistic assumptions and its model of linear messaging from A to B. But still, what does it signify?<br />
<br />
===Why translation?===<br />
'Translation' indicates a closer attention to the problem of shared meaning and how it might be developed. It seems to represent some new epistemological lubricant, facilitating the dissemination of texts and the application and use of the knowledge and information they in. Simply, translation might be the key to transfer. And yet, when we stop to think, we are more ambivalent. What is translated often seems somehow inferior, not real or original. Note how readily commentators reach for the idea that things might be 'lost in translation'. Knowing at a distance – made in and mediated by translation - makes for incomplete renditions, blurred images, partial truths. So what might 'translation' really mean? The purpose of this paper is to set out, for policy makers and practitioners, the theoretical and conceptual resources translation holds and seems to represent. In doing so, it explores understandings of translation in the fields of literature and linguistics and in the sociology of science and technology. It begins by setting out just why this idea of translation should make immediate, intuitive sense in relation to research, policy and practice.<br />
1translation in research, policy and practice research as translation<br />
Research often entails translation from one language to another: where data is collected from more than one ethnic group, for example, or where the language of the researcher is other than that of the research subject. It may draw on a secondary literature or source documents written in different languages, and may be published and disseminated in languages other than the one in which it is first written up.<br />
2In a different way, to conduct an interview is to ask for an account of experience and its meanings, but it is also to construct and translate that experience in terms defined at least in part by the researcher. In representing what is said, transcripts then select data, usually excluding significant gesture and eye-contact, for example. Often, certain characteristics of speech-acts (such as hesitations) will be edited out. In turn, the format of the transcript shapes the analytic use the researcher may make of it. The basis of research 'findings', then, is an artefact, a transcript or translation, not an original interaction (Ochs 1979, Barnes, Bloor and Henry 1996, Ross 2009).<br />
3In this way, the researcher recasts aspects of his or her problem or topic in new, scientific form: 'All researchers "translate" the experiences of others' (Temple 1997, p 609). Research is invariably conducted in a sort of 'metalanguage' (Hantrais and Ager 1985): the research process can be conceived as one of successive translations (from theoretical formulation to operationalization, transcription, interpretation and dissemination). Theorization is a process of reciprocal back and forth between theory and fact, in which conceptions of each are revised in order that one fit the other (Baldamus 1974).<br />
4 It is a kind of translation: a rereading, re-use, re-application or re-representation of what we know in new terms (Turner 1980). Referencing, too, is an act of translation, a form of appropriation and incorporation of one text by another (Gilbert 1977).policy as translation Each of the fields covered by the paper is diverse and ill-defined, and there is no intention here to provide a comprehensive account of any them. Sources have been chosen for their relevance: main references are cited in the text, and additional sources listed in footnotes.<br />
<br />
2For a brief introduction to the technical issues involved in social research in more than one language, see Birbili (2000). For translation issues in survey research and question design in general, see Ervin and Bower (1952) and Deutscher (1968); on translating survey research instruments (in this instance health-related quality of life measures), Bowden and Fox-Rushby (2003). On the use of translators and interpreters, see Temple (1997) and Jentsch (1998).<br />
3For an interesting discussion of this problem, see Bourdieu (1999), esp pp 621-626, 'The risks of writing'. <br />
===Difference between machine translation and human translators===<br />
Few people disagree on the differences between the two, but many argue over the quality of the translations. How accurate are machine translations? How reliable are human translations? Some say machine translation produces near-perfect translations while others are adamant that translations are incomprehensible and cause more problems than they solve. Results will, of course, vary depending on the source and target languages, the machine translation service used (e.g. Google Translate), and the complexity of the original text.<br />
Machine translation, love it or hate it, is here to stay. In fact, the machine translation market is growing at such a fast pace that it is predicted to reach $980 million by 2022.<br />
===Machine translation: the pros and cons===<br />
The advantages of machine translation generally come down to two factors: it’s faster and cheaper. The downside to this is the standard of translation can be anywhere from inaccurate, to incomprehensible, and potentially dangerous (more on that shortly).<br />
==The advantages of machine translation==<br />
Many free tools are readily available (Google Translate, Skype Translator, etc.)<br />
Quick turnaround time <br />
You can translate between multiple languages using one tool<br />
Translation technology is constantly improving<br />
The disadvantages of machine translation<br />
Level of accuracy can be very low <br />
Accuracy is also very inconsistent across different languages<br />
==Machines can't translate context ==<br />
Mistakes are sometimes costly <br />
Sometimes translation simply doesn’t work<br />
The most important thing to consider with any kind of translation is the cost of potential mistakes. Translating instructions for medical equipment, aviation manuals, legal documents and many other kinds of content require 100% accuracy. In such cases, mistakes can cost lives, huge amounts of money and irreparable damage to your company’s image. So choose carefully!<br />
Human translation: the pros and cons<br />
Human translation essentially switches the table in terms of pros and cons. A higher standard of accuracy comes at the price of longer turnaround times and higher costs. What you have to decide is whether that initial investment outweighs the potential cost of mistakes. Alternatively, whether mistakes simply aren’t an option, like the cases we looked at in the previous section.<br />
<br />
==The advantages of human translation ==<br />
It’s a translator’s job to ensure the highest accuracy<br />
Humans can interpret context and capture the same meaning, rather than simply translating words<br />
Human translators can review their work and provide a quality process<br />
Humans can interpret the creative use of language, e.g. puns, metaphors, slogans, etc.<br />
Professional translators understand the idiomatic differences between their languages<br />
Humans can spot pieces of content where literal translation isn’t possible and find the most suitable alternative<br />
The disadvantages of human translation<br />
Turnaround time is longer <br />
Translators rarely work for free <br />
Unless you use a translation agency, with access to thousands of translators, you’re limited to the languages any one translator can work with<br />
Simply put, human translation is your best option when accuracy is even remotely important. Other considerations to make are the complexity of your source material and the two languages you’re translating between – both of which can render machines pretty useless.<br />
<br />
When to use machine and human translation<br />
<br />
The truth is, the debate over machine vs human translation is an unnecessary distraction. What we should really be talking about is when to use these two different types of translation services, because they both serve a very valid purpose.<br />
<br />
Examples of when to use machine translation<br />
<br />
When you have a large bulk of content to translate and the general meaning is enough<br />
When your translation never reaches the final audience, e.g. you’re translating a resource as research for another piece of content<br />
Translating documents for internal use within a company, provided 100% accuracy isn’t needed<br />
To partially translate large chunks of content for a human translator to improve upon<br />
Examples of when to use human translation<br />
When accuracy is important<br />
Most cases where your translated content is received by a consumer audience<br />
When you have a duty of care to provide accurate translations (e.g. legal documents, product instructions, medical guidelines or health and safety content)<br />
When translating marketing material or other texts for creative language uses.<br />
<br />
types of machine translation.<br />
<br />
What is Machine Translation? Rule Based Machine Translation vs. Statistical Machine Translation. Machine translation (MT) is automated translation. It is the process by which computer software is used to translate a text from one natural language (such as English) to another (such as Spanish).<br />
<br />
To process any translation, human or automated, the meaning of a text in the original (source) language must be fully restored in the target language, i.e. the translation. While on the surface this seems straightforward, it is far more complex. Translation is not a mere word-for-word substitution. A translator must interpret and analyze all of the elements in the text and know how each word may influence another. This requires extensive expertise in grammar, syntax (sentence structure), semantics (meanings), etc., in the source and target languages, as well as familiarity with each local region.<br />
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Human and machine translation each have their share of challenges. For example, no two individual translators can produce identical translations of the same text in the same language pair, and it may take several rounds of revisions to meet customer satisfaction. But the greater challenge lies in how machine translation can produce publishable quality translations.<br />
<br />
Rule-Based Machine Translation Technology<br />
Rule-based machine translation relies on countless built-in linguistic rules and millions of bilingual dictionaries for each language pair.<br />
The software parses text and creates a transitional representation from which the text in the target language is generated. This process requires extensive lexicons with morphological, syntactic, and semantic information, and large sets of rules. The software uses these complex rule sets and then transfers the grammatical structure of the source language into the target language.<br />
Translations are built on gigantic dictionaries and sophisticated linguistic rules. Users can improve the out-of-the-box translation quality by adding their terminology into the translation process. They create user-defined dictionaries which override the system’s default settings.<br />
In most cases, there are two steps: an initial investment that significantly increases the quality at a limited cost, and an ongoing investment to increase quality incrementally. While rule-based MT brings companies to the quality threshold and beyond, the quality improvement process may be long and expensive.<br />
<br />
Statistical Machine Translation Technology<br />
Statistical machine translation utilizes statistical translation models whose parameters stem from the analysis of monolingual and bilingual corpora. Building statistical translation models is a quick process, but the technology relies heavily on existing multilingual corpora. A minimum of 2 million words for a specific domain and even more for general language are required. Theoretically it is possible to reach the quality threshold but most companies do not have such large amounts of existing multilingual corpora to build the necessary translation models. Additionally, statistical machine translation is CPU intensive and requires an extensive hardware configuration to run translation models for average performance levels.<br />
<br />
Rule-Based MT vs. Statistical MT<br />
Rule-based MT provides good out-of-domain quality and is by nature predictable. Dictionary-based customization guarantees improved quality and compliance with corporate terminology. But translation results may lack the fluency readers expect. In terms of investment, the customization cycle needed to reach the quality threshold can be long and costly. The performance is high even on standard hardware.<br />
<br />
Statistical MT provides good quality when large and qualified corpora are available. The translation is fluent, meaning it reads well and therefore meets user expectations. However, the translation is neither predictable nor consistent. Training from good corpora is automated and cheaper. But training on general language corpora, meaning text other than the specified domain, is poor. Furthermore, statistical MT requires significant hardware to build and manage large translation models.<br />
<br />
Rule-Based MT Statistical MT<br />
+ Consistent and predictable quality – Unpredictable translation quality<br />
+ Out-of-domain translation quality – Poor out-of-domain quality<br />
+ Knows grammatical rules – Does not know grammar <br />
+ High performance and robustness – High CPU and disk space requirements<br />
+ Consistency between versions – Inconsistency between versions <br />
– Lack of fluency + Good fluency<br />
– Hard to handle exceptions to rules + Good for catching exceptions to rules <br />
– High development and customization costs + Rapid and cost-effective development costs provided the required corpus exists<br />
Given the overall requirements, there is a clear need for a third approach through which users would reach better translation quality and high performance (similar to rule-based MT), with less investment (similar to statistical MT).<br />
Post-Edited Machine Translation (PEMT)<br />
Often, PEMT is used to bridge the gap between the speed of machine translation and the quality of human translation, as translators review, edit and improve machine-translated texts. PEMT services cost more than plain machine translations but less than 100% human translation, especially since the post-editors don’t have to be fluently bilingual—they just have to be skilled proofreaders with some experience in the language and target region.<br />
Successful translation is about more than just the words, which is why we advocate for not just human translation by skilled linguists, but for translation by people deeply familiar with the cultures they’re writing for. Life experience, study and the knowledge that only comes from living in a geographic region can make the difference between words that are understandable and language that is capable of having real, positive impact. <br />
<br />
PacTranz<br />
The HUGE list of 51 translation types, methods and techniques<br />
Upper section of infographic of 51 common types of translation classified in 4 broad categoriesThere are a bewildering number of different types of translation.<br />
So we’ve identified the 51 types you’re most likely to come across, and explain exactly what each one means.<br />
This includes all the main translation methods, techniques, strategies, procedures and areas of specialisation.<br />
It’s our way of helping you make sense of the many different kinds of translation – and deciding which ones are right for you.<br />
Don’t miss our free summary pdf download later in the article!<br />
The 51 types of translation we’ve identified fall neatly into four distinct categories.<br />
Translation Category A: 15 types of translation based on the technical field or subject area of the text<br />
Icons representing 15 types of translation categorised by the technical field or subject area of the textTranslation companies often define the various kinds of translation they provide according to the subject area of the text.<br />
This is a useful way of classifying translation types because specialist texts normally require translators with specialist knowledge.<br />
Here are the most common types you’re like to come across in this category.<br />
<br />
1. General Translation<br />
What is it?<br />
The translation of non-specialised text. That is, text that we can all understand without needing specialist knowledge in some area.<br />
The text may still contain some technical terms and jargon, but these will either be widely understood, or easily researched.<br />
What this means<br />
The implication is that you don’t need someone with specialist knowledge for this type of translation – any professional translator can handle them.<br />
Translators who only do this kind of translation (don’t have a specialist field) are sometimes referred to as ‘generalist’ or ‘general purpose’ translators.<br />
Examples<br />
Most business correspondence, website content, company and product/service info, non-technical reports.<br />
Most of the rest of the translation types in this Category do require specialist translators.<br />
Check out our video on 13 types of translation requiring special translator expertise:<br />
<br />
2. Technical Translation<br />
What is it?<br />
We use the term “technical translation” in two different ways:<br />
Broad meaning: any translation where the translator needs specialist knowledge in some domain or area.<br />
This definition would include almost all the translation types described in this section.<br />
Narrow meaning: limited to the translation of engineering (in all its forms), IT and industrial texts.<br />
This narrower meaning would exclude legal, financial and medical translations for example, where these would be included in the broader definition.<br />
What this means<br />
Technical translations require knowledge of the specialist field or domain of the text.<br />
That’s because without it translators won’t completely understand the text and its implications. And this is essential if we want a fully accurate and appropriate translation.Good to know Many technical translation projects also have a typesetting/dtp requirement. Be sure your translation provider can handle this component, and that you’ve allowed for it in your project costings and time frames.<br />
Examples<br />
Manuals, specialist reports, product brochures<br />
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3. Scientific Translation<br />
What is it?<br />
The translation of scientific research or documents relating to it.<br />
What this means<br />
These texts invariably contain domain-specific terminology, and often involve cutting edge research.<br />
So it’s imperative the translator has the necessary knowledge of the field to fully understand the text. That’s why scientific translators are typically either experts in the field who have turned to translation, or professionally qualified translators who also have qualifications and/or experience in that domain.<br />
On occasion the translator may have to consult either with the author or other domain experts to fully comprehend the material and so translate it appropriately.<br />
Examples<br />
Research papers, journal articles, experiment/trial results<br />
<br />
4. Medical Translation<br />
What is it?<br />
The translation of healthcare, medical product, pharmaceutical and biotechnology materials.<br />
Medical translation is a very broad term covering a wide variety of specialist areas and materials – everything from patient information to regulatory, marketing and technical documents.<br />
As a result, this translation type has numerous potential sub-categories – ‘medical device translations’ and ‘clinical trial translations’, for example.<br />
What this means<br />
As with any text, the translators need to fully understand the materials they’re translating. That means sound knowledge of medical terminology and they’ll often also need specific subject-matter expertise.<br />
Good to know<br />
Many countries have specific requirements governing the translation of medical device and pharmaceutical documentation. This includes both your client-facing and product-related materials.<br />
Examples<br />
Medical reports, product instructions, labeling, clinical trial documentation<br />
<br />
5. Financial Translation<br />
What is it?<br />
In broad terms, the translation of banking, stock exchange, forex, financing and financial reporting documents.<br />
However, the term is generally used only for the more technical of these documents that require translators with knowledge of the field.<br />
Any competent translator could translate a bank statement, for example, so that wouldn’t typically be considered a financial translation.<br />
What this means<br />
You need translators with domain expertise to correctly understand and translate the financial terminology in these texts.<br />
Examples<br />
Company accounts, annual reports, fund or product prospectuses, audit reports, IPO documentation<br />
<br />
6. Economic Translations<br />
What is it?<br />
1. Sometimes used as a synonym for financial translations.<br />
2. Other times used somewhat loosely to refer to any area of economic activity – so combining business/commercial, financial and some types of technical translations.<br />
3. More narrowly, the translation of documents relating specifically to the economy and the field of economics.<br />
What this means<br />
As always, you need translators with the relevant expertise and knowledge for this type of translation.<br />
<br />
7. Legal Translation<br />
What is it?<br />
The translation of documents relating to the law and legal process.<br />
What this means<br />
Legal texts require translators with a legal background.<br />
That’s because without it, a translator may not:<br />
– fully understand the legal concepts<br />
– write in legal style<br />
– understand the differences between legal systems, and how best to translate concepts that don’t correspond.<br />
And we need all that to produce professional quality legal translations – translations that are accurate, terminologically correct and stylistically appropriate.<br />
Examples<br />
Contracts, legal reports, court judgments, expert opinions, legislation<br />
<br />
8. Juridical Translation<br />
What is it?<br />
1. Generally used as a synonym for legal translations.<br />
2. Alternatively, can refer to translations requiring some form of legal verification, certification or notarization that is common in many jurisdictions.<br />
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9. Judicial Translation<br />
What is it?<br />
1. Most commonly a synonym for legal translations.<br />
2. Rarely, used to refer specifically to the translation of court proceeding documentation – so judgments, minutes, testimonies, etc. <br />
<br />
10. Patent Translation<br />
What is it?<br />
The translation of intellectual property and patent-related documents.<br />
Key features<br />
Patents have a specific structure, established terminology and a requirement for complete consistency throughout – read more on this here. These are key aspects to patent translations that translators need to get right.<br />
In addition, subject matter can be highly technical.<br />
What this means<br />
You need translators who have been trained in the specific requirements for translating patent documents. And with the domain expertise needed to handle any technical content.<br />
Examples<br />
Patent specifications, prior art documents, oppositions, opinions<br />
<br />
11. Literary Translation<br />
What is it?<br />
The translation of literary works – novels, short stories, plays, essays, poems.<br />
Key features<br />
Literary translation is widely regarded as the most difficult form of translation.<br />
That’s because it involves much more than simply conveying all meaning in an appropriate style. The translator’s challenge is to also reproduce the character, subtlety and impact of the original – the essence of what makes that work unique.<br />
This is a monumental task, and why it’s often said that the translation of a literary work should be a literary work in its own right.<br />
What this means<br />
Literary translators must be talented wordsmiths with exceptional creative writing skills.<br />
Because few translators have this skillset, you should only consider dedicated literary translators for this type of translation.<br />
<br />
<br />
12. Commercial Translation<br />
What is it?<br />
The translation of documents relating to the world of business.<br />
This is a very generic, wide-reaching translation type. It includes other more specialised forms of translation – legal, financial and technical, for example. And all types of more general business documentation.<br />
Also, some documents will require familiarity with business jargon and an ability to write in that style.<br />
What this means<br />
Different translators will be required for different document types – specialists should handle materials involving technical and specialist fields, whereas generalist translators can translate non-specialist materials.<br />
Examples<br />
Business correspondence, reports, marketing and promotional materials, sales proposals<br />
<br />
13. Business Translations<br />
What is it?<br />
A synonym for Commercial Translations.<br />
<br />
14. Administrative Translations<br />
What is it?<br />
The translation of business management and administration documents.<br />
So it’s a subset of business / commercial translations.<br />
What this means<br />
The implication is these documents will include business jargon and ‘management speak’, so require a translator familiar with, and practised at, writing in that style.<br />
Examples<br />
Management reports and proposals<br />
<br />
15. Marketing Translations<br />
What is it?<br />
The translation of advertising, marketing and promotional materials.<br />
This is a subset of business or commercial translations.<br />
Key features<br />
Marketing copy is designed to have a specific impact on the audience – to appeal and persuade.<br />
So the translated copy must do this too.<br />
But a direct translation will seldom achieve this – so translators need to adapt their wording to produce the impact the text is seeking.<br />
And sometimes a completely new message might be needed – see transcreation in our next category of translation types.<br />
What this means<br />
Marketing translations require translators who are skilled writers with a flair for producing persuasive, impactful copy.<br />
As relatively few translators have these skills, engaging the right translator is key.<br />
Good to know<br />
This type of translation often comes with a typesetting or dtp requirement – particularly for adverts, posters, brochures, etc.<br />
Its best for your translation provider to handle this component. That’s because multilingual typesetters understand the design and aesthetic conventions in other languages/cultures. And these are essential to ensure your materials have the desired impact and appeal in your target markets.<br />
Examples<br />
Advertising, brochures, some website/social media text.<br />
Translation Category B: 14 types of translation based on the end product or use of the translation<br />
This category is all about how the translation is going to be used or the end product that’s produced.<br />
Most of these types involve either adapting or processing a completed translation in some way, or converting or incorporating it into another program or format.<br />
You’ll see that some are very specialised, and complex.<br />
It’s another way translation providers refer to the range of services they provide.<br />
Check out our video of the most specialised of these types of translation:<br />
<br />
16. Document Translations<br />
What is it?<br />
The translation of documents of all sorts.<br />
Here the translation itself is the end product and needs no further processing beyond standard formatting and layout.<br />
<br />
17. Text Translations<br />
What is it?<br />
A synonym for document translation.<br />
<br />
18. Certified Translations<br />
What is it?<br />
A translation with some form of certification.<br />
Key features<br />
The certification can take many forms. It can be a statement by the translation company, signed and dated, and optionally with their company seal. Or a similar certification by the translator.<br />
The exact format and wording will depend on what clients and authorities require – here’s an example.<br />
<br />
19. Official Translations<br />
What is it?<br />
1. Generally used as a synonym for certified translations.<br />
2. Can also refer to the translation of ‘official’ documents issued by the authorities in a foreign country. These will almost always need to be certified.<br />
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20. Software Localisation<br />
What is it?<br />
Adapting software for another language/culture.<br />
Key features<br />
The goal of software localisation is not just to make the program or product available in other languages. It’s also about ensuring the user experience in those languages is as natural and effective as possible.<br />
Translating the user interface, messaging, documentation, etc is a major part of the process.<br />
Also key is a customisation process to ensure everything matches the conventions, norms and expectations of the target cultures.<br />
Adjusting time, date and currency formats are examples of simple customisations. Others might involve adapting symbols, graphics, colours and even concepts and ideas.<br />
Localisation is often preceded by internationalisation – a review process to ensure the software is optimally designed to handle other languages.<br />
And it’s almost always followed by thorough testing – to ensure all text is in the correct place and fits the space, and that everything makes sense, functions as intended and is culturally appropriate.<br />
Localisation is often abbreviated to L10N, internationalisation to i18n.<br />
What this means<br />
Software localisation is a specialised kind of translation, and you should always engage a company that specialises in it.<br />
They’ll have the systems, tools, personnel and experience needed to achieve top quality outcomes for your product.<br />
<br />
21. Game Localisation<br />
What is it?<br />
Adapting games for other languages and markets.<br />
<br />
It’s a subset of software localisation.<br />
Key features<br />
The goal of game localisation is to provide an engaging and fun gaming experience for speakers of other languages.<br />
<br />
It involves translating all text and recording any required foreign language audio.<br />
<br />
But also adapting anything that would clash with the target culture’s customs, sensibilities and regulations.<br />
<br />
For example, content involving alcohol, violence or gambling may either be censored or inappropriate in the target market.<br />
<br />
And at a more basic level, anything that makes users feel uncomfortable or awkward will detract from their experience and thus the success of the game in that market.<br />
<br />
So portions of the game may have to be removed, added to or re-worked.<br />
<br />
Game localisation involves at least the steps of translation, adaptation, integrating the translations and adaptations into the game, and testing.<br />
<br />
What this means<br />
Game localisation is a very specialised type of translation best left to those with specific expertise and experience in this area.<br />
<br />
22. Multimedia Localisation<br />
What is it?<br />
Adapting multimedia for other languages and cultures.<br />
<br />
Multimedia refers to any material that combines visual, audio and/or interactive elements. So videos and movies, on-line presentations, e-Learning courses, etc.<br />
Key features<br />
Anything a user can see or hear may need localising.<br />
<br />
That means the audio and any text appearing on screen or in images and animations.<br />
<br />
Plus it can mean reviewing and adapting the visuals and/or script if these aren’t suitable for the target culture.<br />
<br />
The localisation process will typical involve:<br />
– Translation<br />
– Modifying the translation for cultural reasons and/or to meet technical requirements<br />
– Producing the other language versions<br />
<br />
Audio output may be voice-overs, dubbing or subtitling.<br />
<br />
And output for visuals can involve re-creating elements, or supplying the translated text for the designers/engineers to incorporate.<br />
<br />
What this means<br />
Multimedia localisation projects vary hugely, and it’s essential your translation providers have the specific expertise needed for your materials.<br />
<br />
<br />
<br />
23. Script Translations<br />
What is it?<br />
Preparing the text of recorded material for recording in other languages.<br />
Key features<br />
There are several issues with script translation.<br />
<br />
One is that translations typically end up longer than the original script. So voicing the translation would take up more space/time on the video than the original language.<br />
<br />
Sometimes that space will be available and this will be OK.<br />
<br />
But generally it won’t be. So the translation has to be edited back until it can be comfortably voiced within the time available on the video.<br />
<br />
Another challenge is the translation may have to synchronise with specific actions, animations or text on screen.<br />
<br />
Also, some scripts also deal with technical subject areas involving specialist technical terminology.<br />
<br />
Finally, some scripts may be very culture-specific – featuring humour, customs or activities that won’t work well in another language. Here the script, and sometimes also the associated visuals, may need to be adjusted before beginning the translation process.<br />
<br />
It goes without saying that a script translation must be done well. If it’s not, there’ll be problems producing a good foreign language audio, which will compromise the effectiveness of the video.<br />
<br />
Translators typically work from a time-coded transcript. This is the original script marked to show the time available for each section of the translation.<br />
<br />
What this means<br />
There are several potential pitfalls in script translations. So it’s vital your translation provider is practiced at this type of translation and able to handle any technical content.<br />
<br />
<br />
<br />
24. Voice-over and Dubbing Projects<br />
What is it?<br />
Translation and recording of scripts in other languages.<br />
<br />
Voice-overs vs dubbing<br />
There is a technical difference.<br />
A voice-over adds a new track to the production, dubbing replaces an existing one.<br />
<br />
<br />
Key features<br />
These projects involve two parts:<br />
– a script translation (as described above), and<br />
– producing the audio<br />
<br />
So they involve the combined efforts of translators and voice artists.<br />
The task for the voice artist is to produce a high quality read. That’s one that matches the style, tone and richness of the original.<br />
<br />
Often each section of the new audio will need to be the same length as the original.<br />
<br />
But sometimes the segments will need to be shorter – for example where the voice-over lags the original by a second or two. This is common in interviews etc, where the original voice is heard initially then drops out.<br />
<br />
The most difficult form of dubbing is lip-syncing – where the new audio needs to synchronise with the original speaker’s lip movements, gestures and actions.<br />
<br />
Lip-syncing requires an exceptionally skilled voice talent and considerable time spent rehearsing and fine tuning the translation.<br />
<br />
What this means<br />
You need to use experienced professionals every step of the way in this type of project.<br />
<br />
That’s to ensure firstly that your foreign-language scripts are first class, then that the voicing is of high professional standard.<br />
<br />
Anything less will mean your foreign language versions will be way less effective and appealing to your target audience.<br />
<br />
<br />
<br />
25. Subtitle Translations<br />
What is it?<br />
Producing foreign language captions for sub or surtitles.<br />
Key features<br />
The goal with subtitling is to produce captions that viewers can comfortably read in the time available and still follow what’s happening on the video.<br />
<br />
To achieve this, languages have “rules” governing the number of characters per line and the minimum time each subtitle should display.<br />
<br />
Sticking to these guidelines is essential if your subtitles are to be effective.<br />
<br />
But this is no easy task – it requires simple language, short words, and a very succinct style. Translators will spend considerable time mulling over and re-working their translation to get it just right.<br />
<br />
Most subtitle translators use specialised software that will output the captions in the format sound engineers need for incorporation into the video.<br />
<br />
What this means<br />
As with other specialised types of translation, you should only use translators with specific expertise and experience in subtitling.<br />
<br />
<br />
<br />
26. Website Localisation<br />
What is it?<br />
The translation and adapting of relevant content on a website to best suit the target language and culture.<br />
<br />
Note: Many providers use the term website translation as a synonym for localisation. Strictly speaking though, translation is just one part of localisation.<br />
Key features<br />
<br />
Not all pages on a website may need to be localised – clients should review their content to identify what’s relevant for the other language versions.<br />
Some content may need specialist translators – legal and technical pages for example.<br />
There may also be videos, linked documents, and text or captions in graphics to translate.<br />
Adaptation can mean changing date, time, currency and number formats, units of measure, etc.<br />
But also images, colours and even the overall site design and style if these won’t have the desired impact in the target culture.<br />
Translated files can be supplied in a wide range of formats – translators usually coordinate output with the site webmasters.<br />
New language versions are normally thoroughly reviewed and tested before going live to confirm everything is displaying correctly, works as intended and is cultural appropriate.<br />
What this means<br />
The first step should be to review your content and identify what needs to be translated. This might lead you to modify some pages for the foreign language versions.<br />
<br />
In choosing your translation providers be sure they can:<br />
– handle any technical or legal content,<br />
– provide your webmaster with the file types they want.<br />
<br />
And you should always get your translators to systematically review the foreign language versions before going live.<br />
<br />
<br />
<br />
27. Transcreation<br />
What is it?<br />
Adapting a message to elicit the same emotional response in another language and culture.<br />
Translation is all about conveying the message or meaning of a text in another language. But sometimes that message or meaning won’t have the desired effect in the target culture.<br />
<br />
This is where transcreation comes in. Transcreation creates a new message that will get the desired emotional response in that culture, while preserving the style and tone of the original.<br />
<br />
So it’s a sort of creative translation – which is where the word comes from, a combination of ‘translation’ and ‘creation’.<br />
<br />
At one level transcreation may be as simple as choosing an appropriate idiom to convey the same intent in the target language – something translators do all the time.<br />
<br />
But mostly the term is used to refer to adapting key advertising and marketing messaging. Which requires copywriting skills, cultural awareness and an excellent knowledge of the target market.<br />
<br />
Who does it?<br />
Some translation companies have suitably skilled personnel and offer transcreation services.<br />
<br />
Often though it’s done in the target country by specialist copywriters or an advertising or marketing agency – particularly for significant campaigns and to establish a brand in the target marketplace.<br />
<br />
What this means<br />
Most general marketing and promotional texts won’t need transcreation – they can be handled by a translator with excellent creative writing skills.<br />
<br />
But slogans, by-lines, advertising copy and branding statements often do.<br />
<br />
Whether you should opt for a translation company or an in-market agency will depend on the nature and importance of the material, and of course your budget.<br />
<br />
<br />
<br />
28. Audio Translations<br />
What is it?<br />
Broad meaning: the translation of any type of recorded material into another language.<br />
<br />
More commonly: the translation of a foreign language video or audio recording into your own language. So this is where you want to know and document what a recording says.<br />
Key features<br />
The first challenge with audio translations is it’s often impossible to pick up every word that’s said. That’s because audio quality, speech clarity and speaking speed can all vary enormously.<br />
<br />
It’s also a mentally challenging task to listen to an audio and translate it directly into another language. It’s easy to miss a word or an aspect of meaning.<br />
<br />
So best practice is to first transcribe the audio (type up exactly what is said in the language it is spoken in), then translate that transcription.<br />
<br />
However, this is time consuming and therefore costly, and there are other options if lesser precision is acceptable.<br />
<br />
What this means<br />
It’s best to discuss your requirements for this kind of translation with your translation provider. They’ll be able to suggest the best translation process for your needs.<br />
<br />
Examples<br />
Interviews, product videos, police recordings, social media videos.<br />
<br />
<br />
<br />
29. Translations with DTP<br />
What is it?<br />
Translation incorporated into graphic design files.multilingual dtp example in the form of a Rubik's Cube with foreign text on each square<br />
Key features<br />
Graphic design programs are used by professional designers and graphic artists to combine text and images to create brochures, books, posters, packaging, etc.<br />
<br />
Translation plus dtp projects involve 3 steps – translation, typesetting, output.<br />
<br />
The typesetting component requires specific expertise and resources – software and fonts, typesetting know-how, an appreciation of foreign language display conventions and aesthetics.<br />
<br />
What this means<br />
Make sure your translation company has the required multilingual typesetting/desktop publishing expertise whenever you’re translating a document created in a graphic design program.<br />
<br />
Translation Category C: 13 types of translation based on the translation method employed<br />
This category has two sub-groups:<br />
– the practical methods translation providers use to produce their translations, and<br />
– the translation strategies/methods identified and discussed within academia.<br />
<br />
The translation methods translation providers use<br />
There are 4 main methods used in the translation industry today. We have an overview of each below, but for more detail, including when to use each one, see our comprehensive blog article.<br />
<br />
Or watch our video.<br />
<br />
Important: If you’re a client you need to understand these 4 methods – choose the wrong one and the translation you end up with may not meet your needs!<br />
<br />
<br />
30. Machine Translation (MT)<br />
What is it?<br />
A translation produced entirely by a software program with no human intervention.<br />
<br />
A widely used, and free, example is Google Translate. And there are also commercial MT engines, generally tailored to specific domains, languages and/or clients.<br />
<br />
Pros and cons<br />
There are two limitations to MT:<br />
– they make mistakes (incorrect translations), and<br />
– quality of wording is patchy (some parts good, others unnatural or even nonsensical)<br />
<br />
On they positive side they are virtually instantaneous and many are free.<br />
<br />
Best suited for:<br />
Getting the general idea of what a text says.<br />
<br />
This method should never be relied on when high accuracy and/or good quality wording is needed.<br />
<br />
<br />
<br />
31. Machine Translation plus Human Editing (PEMT)<br />
What is it?<br />
A machine translation subsequently edited by a human translator or editor (often called Post-editing Machine Translation = PEMT).<br />
<br />
The editing process is designed to rectify some of the deficiencies of a machine translation.<br />
<br />
This process can take different forms, with different desired outcomes. Probably most common is a ‘light editing’ process where the editor ensures the text is understandable, without trying to fix quality of expression.<br />
<br />
Pros and cons<br />
This method won’t necessarily eliminate all translation mistakes. That’s because the program may have chosen a wrong word (meaning) that wasn’t obvious to the editor.<br />
<br />
And wording won’t generally be as good as a professional human translator would produce.<br />
<br />
Its advantage is it’s generally quicker and a little cheaper than a full translation by a professional translator.<br />
<br />
Best suited for:<br />
Translations for information purposes only.<br />
<br />
Again, this method shouldn’t be used when full accuracy and/or consistent, natural wording is needed.<br />
<br />
<br />
<br />
32. Human Translation<br />
What is it?<br />
Translation by a professional human translator.<br />
Pros and cons<br />
Professional translators should produce translations that are fully accurate and well-worded.<br />
<br />
That said, there is always the possibility of ‘human error’, which is why translation companies like us typically offer an additional review process – see next method.<br />
<br />
This method will take a little longer and likely cost more than the PEMT method.<br />
<br />
Best suited for:<br />
Most if not all translation purposes.<br />
<br />
<br />
<br />
33. Human Translation + Revision<br />
What is it?<br />
A human translation with an additional review by a second translator.<br />
<br />
The review is essentially a safety check – designed to pick up any translation errors and refine wording if need be.<br />
<br />
Pros and cons<br />
This produces the highest level of translation quality.<br />
<br />
It’s also the most expensive of the 4 methods, and takes the longest.<br />
<br />
Best suited for:<br />
All translation purposes.<br />
<br />
Gearwheel with 5 practical translation methods written on the teeth <br />
There’s also one other common term used by practitioners and academics alike to describe a type (method) of translation:<br />
<br />
34. Computer-Assisted Translation (CAT)<br />
What is it?<br />
A human translator using computer tools to aid the translation process.<br />
<br />
Key features<br />
Virtually all translators use such tools these days.<br />
<br />
The most prevalent tool is Translation Memory (TM) software. This creates a database of previous translations that can be accessed for future work.<br />
<br />
TM software is particularly useful when dealing with repeated and closely-matching text, and for ensuring consistency of terminology. For certain projects it can speed up the translation process.<br />
<br />
<br />
<br />
The translation methods described by academia<br />
A great deal has been written within academia analysing how human translators go about their craft.<br />
<br />
Seminal has been the work of Newmark, and the following methods of translation attributed to him are widely discussed in the literature.Gearwheel with Newmark's 8 translation methods written on the teeth <br />
These methods are approaches and strategies for translating the text as a whole, not techniques for handling smaller text units, which we discuss in our final translation category.<br />
<br />
<br />
<br />
35. Word-for-word Translation<br />
This method translates each word into the other language using its most common meaning and keeping the word order of the original language.<br />
<br />
So the translator deliberately ignores context and target language grammar and syntax.<br />
<br />
Its main purpose is to help understand the source language structure and word use.<br />
<br />
Often the translation will be placed below the original text to aid comparison.<br />
<br />
<br />
<br />
36. Literal Translation<br />
Words are again translated independently using their most common meanings and out of context, but word order changed to the closest acceptable target language grammatical structure to the original.<br />
<br />
Its main suggested purpose is to help someone read the original text.<br />
<br />
<br />
<br />
37. Faithful Translation<br />
Faithful translation focuses on the intention of the author and seeks to convey the precise meaning of the original text.<br />
<br />
It uses correct target language structures, but structure is less important than meaning.<br />
<br />
<br />
<br />
38. Semantic Translation<br />
Semantic translation is also author-focused and seeks to convey the exact meaning.<br />
<br />
Where it differs from faithful translation is that it places equal emphasis on aesthetics, ie the ‘sounds’ of the text – repetition, word play, assonance, etc.<br />
<br />
In this method form is as important as meaning as it seeks to “recreate the precise flavour and tone of the original” (Newmark).slide showing definition of semantic translation as a translation method<br />
<br />
39. Communicative Translation<br />
Seeks to communicate the message and meaning of the text in a natural and easily understood way.<br />
<br />
It’s described as reader-focused, seeking to produce the same effect on the reader as the original text.<br />
<br />
A good comparison of Communicative and Semantic translation can be found here.<br />
<br />
40. Free Translation<br />
Here conveying the meaning and effect of the original are all important.<br />
<br />
There are no constraints on grammatical form or word choice to achieve this.<br />
<br />
Often the translation will paraphrase, so may be of markedly different length to the original.<br />
<br />
41. Adaptation<br />
Mainly used for poetry and plays, this method involves re-writing the text where the translation would otherwise lack the same resonance and impact on the audience.<br />
<br />
Themes, storylines and characters will generally be retained, but cultural references, acts and situations adapted to relevant target culture ones.<br />
<br />
So this is effectively a re-creation of the work for the target culture.<br />
<br />
42. Idiomatic Translation<br />
Reproduces the meaning or message of the text using idioms and colloquial expressions and language wherever possible.<br />
<br />
The goal is to produce a translation with language that is as natural as possible.<br />
<br />
Translation Category D: 9 types of translation based on the translation technique used<br />
These translation types are specific strategies, techniques and procedures for dealing with short chunks of text – generally words or phrases.<br />
<br />
They’re often thought of as techniques for solving translation problems.<br />
<br />
They differ from the translation methods of the previous category which deal with the text as a whole.<br />
9 translation techniques as titles of books in a bookcase<br />
<br />
43. Borrowing<br />
What is it?<br />
Using a word or phrase from the original text unchanged in the translation.<br />
<br />
Key features<br />
With this procedure we don’t translate the word or phrase at all – we simply ‘borrow’ it from the source language.<br />
<br />
Borrowing is a very common strategy across languages. Initially, borrowed words seem clearly ‘foreign’, but as they become more familiar, they can lose that ‘foreignness’.<br />
<br />
Translators use this technique:<br />
– when it’s the best word to use – either because it has become the standard, or it’s the most precise term, or<br />
– for stylist effect – borrowings can add a prestigious or scholarly flavour.<br />
<br />
Borrowed words or phrases are often italicised in English.<br />
<br />
Examples of borrowings in English<br />
grand prix, kindergarten, tango, perestroika, barista, sampan, karaoke, tofu<br />
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<br />
44. Transliteration<br />
What is it?<br />
Reproducing the approximate sounds of a name or term from a language with a different writing system.<br />
<br />
Key features<br />
In English we use the Roman (Latin) alphabet in common with many other languages including almost all European languages.<br />
<br />
Other writing systems include Arabic, Cyrillic, Chinese, Japanese, Korean, Thai, and the Indian languages.<br />
<br />
Transliteration from such systems into the Roman alphabet is also called romanisation.<br />
<br />
There are accepted systems for how individual letters/sounds should be romanised from most other languages – there are three common systems for Chinese, for example.<br />
<br />
English borrowings from languages using non-Roman writing systems also require transliteration – perestroika, sampan, karaoke, tofu are examples from the above list.<br />
<br />
Translators mostly use transliteration as a procedure for translating proper names.<br />
<br />
Examples<br />
毛泽东 Mao Tse-tung or Mao Zedong<br />
Владимир Путин Vladimir Putin<br />
서울 Seoul<br />
ភ្នំពេញ Phnom Penh<br />
<br />
45. Calque or Loan Translation<br />
What is it?<br />
A literal translation of a foreign word or phrase to create a new term with the same meaning in the target language.<br />
<br />
So a calque is a borrowing with translation if you like. The new term may be changed slightly to reflect target language structures.<br />
<br />
Examples<br />
German ‘Kindergarten’ has been calqued as детский сад in Russian, literally ‘children garden’ in both languages.<br />
<br />
Chinese 洗腦 ‘wash’ + ‘brain’ is the origin of ‘brainwash’ in English.<br />
<br />
English skyscraper is calqued as gratte-ciel in French and rascacielos in Spanish, literally ‘scratches sky’ in both languages.<br />
<br />
46. Word-for-word translation<br />
What is it?<br />
A literal translation that is natural and correct in the target language.<br />
<br />
Alternative names are ‘literal translation’ or ‘metaphrase’.<br />
<br />
Note: this technique is different to the translation method of the same name, which does not produce correct and natural text and has a different purpose.<br />
<br />
Key features<br />
This translation strategy will only work between languages that have very similar grammatical structures.<br />
<br />
And even then, only sometimes.<br />
<br />
For example, standard word order in Turkish is Subject-Object-Verb whereas in English it’s Subject-Verb-Object. So a literal translation between these two will seldom work:<br />
– Yusuf elmayı yedi is literally ‘Joseph the apple ate’.<br />
<br />
When word-for-word translations don’t produce natural and correct text, translators resort to some of the other techniques described below.<br />
Examples<br />
French ‘Quelle heure est-il?’ works into English as ‘What time is it?’.<br />
<br />
Russian ‘Oн хочет что-нибудь поесть’ is ‘He wants something to eat’.<br />
<br />
47. Transposition<br />
What is it?<br />
Translation with a change of grammatical structure.<br />
<br />
This technique gives the translation more natural wording and/or makes it grammatically correct.<br />
<br />
Examples<br />
A change in word order:<br />
Our Turkish example Yusuf elmayı yedi (literally ‘Joseph the apple ate’) –> Joseph ate the apple.<br />
<br />
Spanish La Casa Blanca (literally ‘The House White’) –> The White House<br />
<br />
A change in grammatical category:<br />
German Er hört gerne Musik (literally ‘he listens gladly [to] music’)<br />
= subject pronoun + verb + adverb + noun<br />
becomes Spanish Le gusta escuchar música (literally ‘[to] him [it] pleases to listen [to] music’)<br />
= indirect object pronoun + verb + infinitive + noun<br />
and English He likes listening to music<br />
= subject pronoun + verb + gerund + noun.<br />
<br />
48. Modulation<br />
What is it?<br />
Translation with a change of focus or point of view in the target language.<br />
<br />
This technique makes the translation more idiomatic – how people would normally say it in the language.<br />
<br />
Examples<br />
English talks of the ‘top floor’ of a building, French the dernier étage = last floor. ‘Last floor’ would be unnatural in English, so too ‘top floor’ in French.<br />
<br />
German uses the term Lebensgefahr (literally ‘danger to life’) where in English we’d be more likely to say ‘risk of death’.<br />
In English we’d say ‘I dropped the key’, in Spanish se me cayó la llave, literally ‘the key fell from me’. The English perspective is that I did something (dropped the key), whereas in Spanish something happened to me – I’m the recipient of the action.<br />
<br />
49. Equivalence or Reformulation<br />
What is it?<br />
Translating the underlying concept or meaning using a totally different expression.<br />
<br />
This technique is widely used when translating idioms and proverbs.<br />
<br />
And it’s common in titles and advertising slogans.<br />
<br />
It’s a common strategy where a direct translation either wouldn’t make sense or wouldn’t resonate in the same way.<br />
<br />
Examples<br />
Here are some equivalents of the English saying “Pigs may fly”, meaning something will never happen, or “you’re being unrealistic” (Source):<br />
– Thai: ชาติหน้าตอนบ่าย ๆ – literally, ‘One afternoon in your next reincarnation’<br />
– French: Quand les poules auront des dents – literally, ‘When hens have teeth’<br />
– Russian, Когда рак на горе свистнет – literally, ‘When a lobster whistles on top of a mountain’<br />
– Dutch, Als de koeien op het ijs dansen – literally, ‘When the cows dance on the ice’<br />
– Chinese: 除非太陽從西邊出來！– literally, ‘Only if the sun rises in the west’<br />
<br />
50. Adaptation<br />
What is it?<br />
A translation that substitutes a culturally-specific reference with something that’s more relevant or meaningful in the target language.<br />
<br />
It’s also known as cultural substitution or cultural equivalence.<br />
<br />
It’s a useful technique when a reference wouldn’t be understood at all, or the associated nuances or connotations would be lost in the target language.<br />
<br />
Note: the translation method of the same name is a similar concept but applied to the text as a whole.<br />
<br />
Examples<br />
Different cultures celebrate different coming of age birthdays – 21 in many cultures, 20, 15 or 16 in others. A translator might consider changing the age to the target culture custom where the coming of age implications were important in the original text.<br />
Animals have different connotations across languages and cultures. Owls for example are associated with wisdom in English, but are a bad omen to Vietnamese. A translator might want to remove or amend an animal reference where this would create a different image in the target language.<br />
<br />
51. Compensation<br />
What is it?<br />
A meaning or nuance that can’t be directly translated is expressed in another way in the text.<br />
Example<br />
Many languages have ways of expressing social status (honorifics) encoded into their grammatical structures.<br />
<br />
So you can convey different levels of respect, politeness, humility, etc simply by choosing different forms of words or grammatical elements.<br />
But these nuances will be lost when translating into languages that don’t have these structures.<br />
Then translating into languages that don’t have these structures<br />
Then translating into languages that don’t have these structures.<br />
<br />
===Conclusion ===<br />
<br />
In the light of above mentioned facts and figures here by we would say that machine translation is a challenge for human translators because it can reduce the workload of translation but can't give accurate and exact translation of the target language.It can be less reliable than human translation..</div>Chen Huinihttps://bou.de/u/index.php?title=Machine_translation&diff=133227Machine translation2021-12-15T04:56:43Z<p>Chen Huini: /* Conclusion */</p>
<hr />
<div>'''Machine Translation - A challenge or a chance for human translators?'''<br />
<br />
[[Machine_translation|Overview Page of Machine Translation]]<br />
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30 Chapters（0/30)<br />
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[[Machine_Trans_EN_1]] [[Machine_Trans_EN_2]] [[Machine_Trans_EN_3]] [[Machine_Trans_EN_4]] [[Machine_Trans_EN_5]] [[Machine_Trans_EN_6]] [[Machine_Trans_EN_7]] [[Machine_Trans_EN_8]] [[Machine_Trans_EN_9]] [[Machine_Trans_EN_10]] [[Machine_Trans_EN_11]] [[Machine_Trans_EN_12]] [[Machine_Trans_EN_13]] [[Machine_Trans_EN_14]] [[Machine_Trans_EN_15]] [[Machine_Trans_EN_16]] [[Machine_Trans_EN_17]] [[Machine_Trans_EN_18]] [[Machine_Trans_EN_19]] [[Machine_Trans_EN_20]] [[Machine_Trans_EN_21]] [[Machine_Trans_EN_22]] [[Machine_Trans_EN_23]] [[Machine_Trans_EN_24]] [[Machine_Trans_EN_25]] [[Machine_Trans_EN_26]] [[Machine_Trans_EN_27]] [[Machine_Trans_EN_28]] [[Machine_Trans_EN_29]] [[Machine_Trans_EN_30]] ...<br />
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[[Book_projects|Back to translation project overview]]<br />
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[[DCG-To-Do|To the To Do list]]<br />
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=Chapter 1:A Comparison Between the Quality of Machine Translation and Human Translation——A Case Study of the Application of artificial intelligence in Sports Events=<br />
'''论机器翻译与人工翻译的质量对比——以人工智能在体育赛事领域的应用为例'''<br />
<br />
卫怡雯 Wei Yiwen, Hunan Normal University, China<br />
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[[Machine_Trans_EN_1]]<br />
<br />
=Chapter 3：On the Realm Advantages And Mutual Development of Machine Translation And Huamn Translation)=<br />
"'论机器翻译与人工翻译的领域优势及共同发展'"<br />
<br />
肖毅瑶 Xiao Yiyao, Hunan Normal University, China<br />
[[Machine_Trans_EN_3]]<br />
<br />
=Chapter 4 : A Comparison Between Machine Translation of Netease and Traditional Human Translation—A Case Study of The Economist Articles)= <br />
'''网易有道机器翻译与人工翻译的译文对比——以经济学人语料为例'''<br />
<br />
王李菲 Wang Lifei, Hunan Normal University<br />
<br />
[[Machine_Trans_EN_4]]<br />
<br />
=Chapter 5: Muhammad Saqib Mehran - Problems in translation studies=<br />
[[Machine_Trans_EN_5]]<br />
<br />
=6 徐敏赟(Machine Translation Based on Neural Network --Challenge or Chance?)=<br />
[[Machine_Trans_EN_6]]<br />
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=Chapter 7: The Cultivation of artificial intelligence and translation talents in the Belt and Road Initiative=<br />
[[Machine_Trans_EN_7]]<br />
<br />
一带一路背景下人工智能与翻译人才的培养<br />
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颜莉莉 Yan Lili, Hunan Normal University, China<br />
<br />
=Chapter 8: On Machine Translation Under Lanuguage Intelligence——An Option and Opportunity for Human Translators=<br />
'''论语言智能下的机器翻译——译者的选择与机遇'''<br />
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颜静 Yan Jing, Hunan Normal University, China<br />
<br />
[[Machine_Trans_EN_8]]<br />
<br />
=9 谢佳芬（ Machine Translation and Artificial Translation in the Era of Artificial Intelligence）=<br />
[[Machine_Trans_EN_9]]<br />
<br />
=10 熊敏(Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts)=<br />
机器翻译对各类型文本的英汉翻译能力探究<br />
<br />
熊敏, Xiong Min, Hunan Normal University<br />
<br />
[[Machine_Trans_EN_10]]<br />
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<br />
===Abstract===<br />
The history of machine translation can be traced to 1940s, undergoing germination, recession, revival and flourish. Nowadays, with the rapid development of information technology, machine translation technology emerged and is gradually becoming mature. Whether manual translation can be replaced by machine translation becomes a widely-disputed topic. So in order to explore the ability of machine translation, I adopts two versions of translation, which are manual translation and machine translation(this paper uses Youdao translation) for different types of texts(according to Peter Newmark's types of text：informative text, expressive text and vocative text). The results are quite different in terms of quality and accuracy. The results show that machine translation is more suitable for informative text for its brevity, while the expressive texts especially literary works and vocative texts are difficult to be translated, because there are too many loaded words and cultural images in expressive text and dependence on the readers. To draw a conclusion, the relationship between machine translation and manual translation should be complementary rather than competitive.<br />
<br />
===Key words===<br />
machine translation; manual translation; Newmark's type of texts<br />
<br />
===题目===<br />
Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts<br />
<br />
===摘要===<br />
机器翻译的历史可以追溯到上个世纪40年代，经历了萌芽期、萧条期、复兴期和繁荣期四个阶段。如今，随着信息技术的高速发展，机器翻译技术日益成熟，于是机器翻译能不能替代人工翻译这个话题引起了广泛热议。为了探究机器翻译的能力水平是否足以替代人工翻译，本人根据皮特纽马克的文本类型分类理论（信息类文本，表达文本，呼吁文本），选择了相应的译文类型，并且将其机器翻译的版本以及人工翻译的版本进行对比。结果发现，就质量和准确度而言，译文的水平大相径庭。因为其简洁的特性，机器比较适合翻译信息文本。而就表达文本，尤其是文学作品，因其存在文化负载词及相应的文化现象，所以机器翻译这类文本存在难度。而呼唤文本因其目的性以及对读者的依赖性较强，翻译难度较大。由此得知，机器翻译和人工翻译的关系应该是互补的，而不是竞争的。<br />
<br />
===关键词===<br />
机器翻译；人工翻译；纽马克文本类型<br />
<br />
===1. Introduction===<br />
====1.1.Introduction to machine translation====<br />
Machine translation, also known as computer translation is a technique to translating through machine translator, such as Google translation and Youdao translation. Machine translation is one of the branches of computational linguistics, ranging from computer science, statistics, information science and so on. Machine translation plays an important role in all aspects.<br />
Machine translation can be traced back to 1940s, when British engineer Booth and American engineer Weaver proposed using computer to translate and started to study machines used for translation. And the first machine translating system launched in 1954, used in English and Russian translation. And this means machine translation becomes a reality. However, the arrival of new things always accompanies barriers and setbacks. In the 1960s, reports from ALPAC (Automated Language Processing Advisory Committee) showed studies on machine translation had stagnated for a decade. At that time, due to the high cost of machine, choosing human translators to finish translating works was more economical for most companies. What’s worse, machine had difficulty in understanding semantic and pragmatic meanings. Fortunately, in the 1970s, with the advance and popularity of computer, machine translation was gradually back on track. With the development of science and technology, machine translation has better technological guarantee, such as artificial intelligence and computer technology. In the last decades, from the late 90s till now, machine translation is becoming more and more mature. The initial rule-oriented machine translation has been updated and Corpus-aided machine translation appears. And nowadays, technology in voice recognition has been further developed. This technique is frequently applied in speech translation products. Users only need to speak source language to the online translator and instantly it will speak out the target version. But machine can’t fully provide precise and accurate translation without any grammatical or semantic problems. Machine can understand the literal meaning of texts, but not the meaning in context. For example, there is polysemy in English, which refers to words having multiple meanings. So when translating these words, translators should take contexts into consideration. But machine has troubles in this way. In addition, machine has no feeling or intention. Hence, it is also difficult to convey the feelings from the source language.(Wei 2021:5)#<br />
<br />
====1.2.Process of machine translation====<br />
The process of manual translation is different from that of machine translation. Here is the process of the former. (1) Understand source language. (2) Use target language to organize language. (3) Generate translation. Unlike manual translation, machine translation tends to analyze and code source language first, then look for related codes in corpus, and work out the code that represents target language, generating translation. But they share a common feature, which is that Lexicon, grammatical rules and syntactic structure are taken into consideration. This is one of the biggest challenges for machine translation.<br />
<br />
===2.Theorectical framework===<br />
====2.1Newmark’s text typology====<br />
Text typology is a theory from linguistics. It undergoes several phrases. Karl Buhler, a German psychologist and linguist defines communicative functions into expressive, information and vocative. Then Roman Jakobson proposes categorization of texts, which are intralingual translation, interlingual translation and intersemiotic translation. Accordingly, he defines six main functions of language, including the referential function, conative function, emotive function, poetic function, metalingual function and the phatic function. Based on the two theories, Peter Newmark, a translation theorist, summarizes the language function into six parts: the informative function, the vocative function, the expressive function, the phatic function, the aesthetic function, and the metalingual function. Later, he further divided texts into informative type, expressive text and vocative type according to the linguistic functions of various texts. (Newmark 2002:2)#<br />
The core of the informative texts is the truth. It is to convey facts, information, knowledge of certain topics, reality and theories. The language style of the text is objective, logical and standardized. Reports, papers, scientific and technological textbooks are all attributed to informative texts. Translators are required to take format into account for different styles.<br />
The core of the expressive text is the sender’s emotions and attitudes. It is to express sender’s preferences, feelings, views and so on. The language style of it is subjective. Imaginative literature, including fictions, poems and dramas, autobiography and authoritative statements belong to expressive text. It requires translators to be faithful to the source language and maintain the style.<br />
The core of the vocative text is readership. It is to call upon readers to act, think, feel and react in the way intended by the text. So it is reader-oriented. Such texts as advertisement, propaganda and notices are of vocative text. Newmark noted that translators need to consider cultural background of the source language and the semantic and pragmatic effect of target language. If readers can comprehend the meaning at once and do as the text says, then the goal of information communication is achieved. (Liu 2021:3)#<br />
To draw a conclusion, informative texts focus on the information and facts. Expressive texts center on the mind of addressers, including feelings, prejudices and so on. And vocative texts are oriented on readers and call on them to practice as the texts say.<br />
<br />
====2.2Study method====<br />
Manual translations of the three texts are selected from authoritative versions and universally acknowledged. And machine translations of those come from Youdao Translator. And in this thesis I will compare and evaluate the two methods in word diction, sentence structure, word order and redundancy.<br />
<br />
===3.Comparison and analysis of machine translation and manual translation ===<br />
====3.1Informative text ====<br />
（1）English into Chinese<br />
<br />
①Source language:<br />
<br />
Keep the tip of Apple Pencil clean, as dirt and other small particles may cause excessive wear to the tip or damage the screen of i-pad.<br />
<br />
Target language:<br />
<br />
Machine translation: Apple Pencil笔尖应保持清洁，灰尘等小颗粒可能会导致笔尖过度磨损或损坏ipad屏幕。<br />
<br />
Manual translation: 保持Apple Pencil铅笔的笔尖干净，因为灰尘和其他微粒可能会导致笔尖的过度磨损或损坏iPad屏幕。<br />
<br />
Analysis: Here is the instruction of Apple Pencil. And the manual translation is the Chinese version on the instruction.Product instruction tends to be professional, since there are many terms for some concepts. Machine can easily identify these terms and provide related words to translate. The machine version is faithful and expressive to the source language. So it is well-qualified and readable for readers to understand the instruction. So we can use machine to translate informative text.<br />
<br />
②Source language:<br />
<br />
China on Saturday launched a rocket carrying three astronauts-two men and one woman - to the core module of a future space station where they will live and work for six months, the longest orbit for Chinese astronauts.<br />
<br />
Target language:<br />
<br />
Machine translation: 周六，中国发射了一枚运载三名宇航员(两男一女)的火箭，进入未来空间站的核心舱，他们将在那里生活和工作6个月，这是中国宇航员最长的轨道。<br />
<br />
Manual translation: 周六，中国发射了一枚搭载三名宇航员(两男一女)的火箭，进入未来空间站的核心舱，他们将在那里生活和工作6个月，这是中国宇航员最漫长的一次轨道飞行。<br />
<br />
Analysis: This is a news from Reuters, reporting that China has launched a rocket.The meaning of the two translations is almost the same, except for some word diction. But there are some details dealt with different choice. For example, the last sentence of the machine translation is a bit of obscure and direct. There are some ambiguous words and expressions.<br />
<br />
(2)Chinese into English<br />
<br />
Source language:湖南省博物馆是湖南省最大的历史艺术类博物馆，占地面积4.9万平方米，总建筑面积为9.1万平方米，是首批国家一级博物馆，中央地方共建的八个国家级重点博物馆之一、全国文化系统先进集体、文化强省建设有突出贡献先进集体。<br />
<br />
Target language:<br />
Manual translation: As the largest history and art museum in Hunan province, the Hunan Museum covers an area of 49,000㎡, with the building area reaching 91,000㎡. It is one of the first batch of national first-level museums and one of the first eight national museums co-funded by central and local governments.<br />
<br />
Machine translation: Museum in hunan province is one of the largest historical art museum in hunan province, covers an area of 49000 square meters, a total construction area of 91000 square meters, is the first national museum, the central place to build one of the eight national key museum, national cultural system advanced collectives, strong culture began with outstanding contribution of advanced collective.<br />
<br />
Analysis: Machine translation is not faithful enough in content. For instance, “首批国家一级博物馆” is translated into “first national museum”, which is not the meaning of the source language. And there are some obvious grammar mistakes in the machine translation. For example, machine translates it into just one sentence but there are multiple predicates in it. So it is not grammatically permissible. What’s more, the sentence structure of machine translation is confusing and the focus is not specific enough.<br />
<br />
====3.2Expressive text ====<br />
(1)English into Chinese<br />
<br />
Source language:<br />
<br />
An individual human existence should be like a river- small at first, narrowly contained within its banks, and rushing passionately past rocks and over waterfalls. Gradually the river grows wider, the banks recede, the waters flow more quietly, and in the end, without any visible breaks, they become merged in the sea, and painlessly lose their individual being.()<br />
<br />
Target language:<br />
<br />
Machine translation: 一个人的存在应该像一条河流——开始很小，被紧紧地夹在两岸中间，然后热情奔放地冲过岩石，飞下瀑布。渐渐地，河面变宽，两岸后退，水流更加平缓，最后，没有任何明显的停顿，它们汇入大海，毫无痛苦地失去了自己的存在。<br />
<br />
Manual translation:人生在世，如若河流；河口初始狭窄，河岸虬曲，而后狂涛击石，飞泻成瀑。河道渐趋开阔，峡岸退去，水流潺缓，终了，一马平川，汇于大海，消逝无影。<br />
<br />
Analysis: Here is a well-known metaphor in the prose How to Grow Old written by Bertrand Russell. The manual translation is written by Tian Rongchang.This is a philosophical prose with graceful language. Literary translation is a most important and difficult branch of translation. Translator should focus on the literal meaning, culture, writing style and so on. It is a combination of beauty and elegance. Therefore, translators find it in a dilemma of beauty and faithfulness, let alone translating machine. Compared with manual translation, machine translation has difficulty in word choice. It is faithful and expressive, but not elegant enough.<br />
<br />
(2)Chinese into English<br />
<br />
Source language:没有一个人将小草叫做“大力士”，但是它的力量之大，的确是世界无比。这种力，是一般人看不见的生命力，只要生命存在，这种力就要显现，上面的石块，丝毫不足以阻挡。因为它是一种“长期抗战”的力，有弹性，能屈能伸的力，有韧性，不达目的不止的力。(Zhang, 2007:186)#<br />
<br />
Target language:<br />
<br />
Machine translation: No one calls the little grass "hercules", but its power is truly matchless in the world. This force is invisible life force. As long as there is life, this force will show itself. The stone above is not strong enough to stop it. Because it is a "long-term resistance" of the force, elastic, can bend and extend force, tenacity, not to achieve the purpose of the force.<br />
<br />
Manual translation: Though nobody describes the little grass as a “husky”, yet its herculean strength is unrivalled. It is the force of life invisible to naked eye. It will display itself so long as there is life. The rock is utterly helpless before this force- a force that will forever remain militant, a force that is resilient and can take temporary setbacks calmly, a force that is tenacity itself and will never give up until the goal is reached. (by Zhang Peiji)<br />
<br />
Analysis:This is the excerpt of a well-known Chinese prose written by Xia Yan. It is written during the war of Resistance Against Japan. So the prose holds symbolic meaning, eulogizing the invisible tenacious vitality so as to encourage Chinese to have confidence in the anti-aggression war. Compared with manual translation, machine translation is much more abstract and confusing, especially for the word diction. For example, “大力士” is translated into “hercules” which is a man of exceptional strength and size in Greek and Roman Mythology, making it difficult to understand if readers of target language have no idea of the allusion. What’s worse, the machine version doesn’t reveal the symbolic meaning of the text, which is the core of this prose.<br />
<br />
====3.3Vocative text ====<br />
<br />
(1)English into Chinese<br />
<br />
①Source language:<br />
<br />
iPhone went to film school, so you don’t have to. (Advertisement of iPhone13)<br />
<br />
Target language:<br />
<br />
Machine translation: iPhone上的是电影学院，所以你不用去。<br />
<br />
Manual translation:电影专业课，iPhone同学替你上完了。<br />
<br />
Analysis：Here are advertisements of iPhone on Apple official website. There is a personification in the source language. It is used to stress the advancement and proficiency in camera, which is an appealing selling point to potential buyers. Compared with manual translation, machine translation is plain and not eye-catching enough for customers.<br />
<br />
②Source language: <br />
<br />
5G speed OMGGGGG<br />
<br />
Machine language: 5克的速度 OMGGGGG<br />
<br />
Manual translation:<br />
<br />
iPhone的5G 巨巨巨巨巨5G<br />
<br />
Analysis: The “G” in the source language is the unit of speed, standing for generation. However, it is mistaken as a unit of weight, representing gram in the machine translation. So the meaning is not faithful to the source language at all. As for manual translation, it complies with the source in form. Specifically speaking, five “G”s in the former complies with five characters “巨”in the latter. And the pronunciation of the two is similar. There are two layers of meaning for the 5 “G”s. One exclaims the fast speed of 5 generation network and the other new technology. In the manual version, “巨”can be used to show degree, meaning “quite” or “very”. <br />
<br />
③Source language: <br />
<br />
History, faith and reason show the way, the way of unity. We can see each other not as adversaries but as neighbors. We can treat each other with dignity and respect, we can join forces, stop the shouting and lower the temperature. For without unity, there is no peace, only bitterness and fury."<br />
<br />
Target language:<br />
<br />
Machine translation: 历史、信仰和理性指明了团结的道路。我们可以把彼此视为邻居，而不是对手。我们可以尊严地对待彼此，我们可以联合起来，停止大喊大叫，降低温度。因为没有团结，就没有和平，只有痛苦和愤怒。<br />
<br />
Manual translation:历史、信仰和理性为我们指明道路。那是团结之路。我们可以把彼此视为邻居，而不是对手。我们可以有尊严地相互尊重。我们可以联合起来，停止喊叫，减少愤怒。因为没有团结就没有和平，只有痛苦和愤怒<br />
<br />
Analysis: Speech is a way to propagate some activity in public. It is an art to inspire emotion of the audience. The source language is the excerpt of Joe Biden’s inaugural speech. The speech should be inspiring and logic. The machine translation has some misunderstanding. Taking the translation of “lower the temperature” for example, machine only translates its literal meaning, relating to the temperature itself, without considering the context. What’s more, it is less logic than the manual one. Therefore, it adds difficulty to inspire the audience and infect their emotion.<br />
<br />
===4.Common mistakes in machine translation ===<br />
<br />
====4.1 lexical mistakes ====<br />
<br />
Common lexical mistakes include misunderstandings in word category, lexical meaning and emotive and evaluative meaning. Misunderstanding in word category shows in the classification of word in the source language. As for misunderstanding in lexical meaning, machine has difficulty in precisely reflecting the meaning of the original texts, due to different cultural background and different language system. And for misunderstanding in emotive meaning, machine has no intention and emotion like human-beings. Therefore, it’s impossible for it to know writers’ feelings and their writing purposes. So sometimes, it may translate something negative into something positive. (Wang 2008:45)#<br />
<br />
====4.2 grammatical mistakes====<br />
<br />
Grammatical analysis plays an important part in translation. Normally speaking, every language has its own unique grammatical rules. So in the process of translation, if translators don’t know the formation rule well, the sentence meaning will be affected. Even though all the lexical meanings are well-known by translators, the lack of consciousness of grammaticality makes it harder to arrange words according to sequential rule. English tends to be hypotactic, while Chinese tends to be paratactic. English sentences are connected through syntactic devices and lexical devices. While Chinese sentences are semantically connected, which means there are limited logical words and connection words in Chinese. So when translating English sentence, we should first analyze its grammaticality and logical structure and then rearrange its sequence. However, online translating machine has troubles in grammatical analysis, which makes its improvement more difficult.<br />
<br />
====4.3 other mistakes====<br />
<br />
The two mistakes above are the internal ones. Apart from mistakes in linguistic system, there are some mistakes in other aspects, such as cultural background.<br />
<br />
===5.Reasons for its common mistakes ===<br />
<br />
====5.1 Difference in two linguistic system====<br />
<br />
With different history, English and Chinese have different ways of expression. Commonly speaking, English is synthetic language which expresses grammatical meaning through inflection such as tense and Chinese is analytic language which expresses grammatical meaning through word order and function word. In addition, English is more compact with full sentences. Subordinate sentence is one of the most important features in modern English. Chinese, on the other hand, is more diffusive with minor sentences.<br />
<br />
====5.2 Difference in thinking patterns and cultural background====<br />
<br />
According to Sapir-Whorf’s Hypothesis, our language helps mould our way of thinking and consequently, different languages may probably express their unique ways of understanding the world. For two different speech communities, the greater their structural differentiations are, the more diverse their conceptualization of the world will be. For example, western culture is more direct and eastern culture more euphemistic. What’s more, English culture tends to be individualism, focusing on detail, through which it reflects the whole, while Chinese culture tends to be collective. Different thinking patterns will add difficulty for machine to translate texts.<br />
<br />
====5.3 Limitation of computer====<br />
<br />
Recently, there are some breakthroughs and innovation in machine translation. However, due to its own limitation, online translation has limitation in some ways. Firstly, compared with machine, human brain is much more complicated, consisting of ten billions of neuron, each of which has different function to affect human’s daily activities and help humans avoid some errors. However, computer can only function according to preset programming has no intention or consciousness. Until now, countless related scholars have invested much time in machine translation. They upload massive language database, which include almost all linguistic rules. But computers still fail to precisely reflect the meaning of source language for many times due to the complexity and flexibility of language. On the other hand, computers can’t take context into consideration. During translation, it is often the case that machine chooses the most-frequently used meaning of one word. So without the correct and exact meaning, readers are easier to feel confused and even misunderstand the meaning of source language. (Qiu 2021:4)#<br />
<br />
===6.Conclusion===<br />
From the analysis above, we can draw a conclusion that machine deals with informative text best, followed by non-literary translation of expressive text. What’s more, machine can be a useful tool to get to know the gist and main idea of a specific topic, for the simple sentence structure and numerous terms. And it can improve translating efficiency with high speed. But machine has difficulty in translating literary works, especially proses and poems.<br />
<br />
Machine translation has mixed future. From the perspective of commercial, machine translation boasts a bright future. With the process of globalization, the demand for translation is increasing accordingly. On one hand, if we only depend on human translator to deal with translating works, the quality and accuracy of translation can be greatly affected. On the other hand, if machine is used properly to do some basic work, human translators only need to make preparation before translating, progress, polish and other advanced work, contributing to highly-qualified translation and high working efficiency.<br />
<br />
However, compared with manual translation, machine translation has a bleak future. It is still impossible for machine to replace interpreter or translator in a short term. With intelligence and initiative, humans are able to learn new knowledge constantly, which machine will never accomplish. Besides, machine is not used to replace translators but to assist them in work. In other words, translators and machine carry out their own duties and they are not incompatible.(He 2021:5)#<br />
<br />
To draw a conclusion, although there are certain limitations of machine translation, it can serve as a catalyst for translating works. Therefore, with the rapid development of artificial intelligence and related technology, there are still many opportunities for machine translation.<br />
<br />
===Reference ===<br />
<br />
Chen Cheng陈诚.机器翻译技术的综述[J][Overview of Machine Translation Technology].Electronic Techonology 电子技术,2021,50(11):290-291.<br />
<br />
Cui Zihan 崔子涵.机器翻译译文质量对比——以谷歌翻译和DeepL为例[J] [Comparison among Machine Translation--Taking Google Translation and Deepl for Example].Overseas English 海外英语,2021(15):182-183.<br />
<br />
He Xinyu何馨宇.机器翻译的发展及其对翻译职业化的影响研究[J] [The Development of Machine Translation and its Effect on Professional Transltors].Overseas English 海外英语,2021(20):48-49.<br />
<br />
He Wen 何雯, Wang Xiufeng 王秀峰.信息型文本的在线机器翻译错误研究[J][Research on Errors in Online Machine Translation of Informative text ].Overseas English海外英语,2021(15):188-189.<br />
<br />
Li Deyi 李德毅. (2018). 人工智能导论 [Introduction to Artificial Intelligence]. Beijing: China Science and Technology Press 中国科学技术出版社.<br />
<br />
Liu Qin刘琴.功能目的论对于不同文本类型的翻译解读[J][Analysis of Translations in Different Types of Text based on Functionalist Approaches].Overseas Engliosh 海外英语,2021(17):8-9.<br />
<br />
Li Hanji 李晗佶. (2021). 人工智能时代翻译技术与译者关系演变与重构 [Evolution and reconstruction of the relationship between translation technology and translators in the era of artificial intelligence]. 西华师范大学学报(哲学社会科学版) Journal of West China Normal University (PHILOSOPHY AND SOCIAL SCIENCES EDITION) (2021-12-04) 1-6.<br />
<br />
(英) Peter Newmark A Textbook of Translation[M] Shanghai Foreign Education Press, 2002<br />
<br />
Qiu Quanju 仇全菊.大数据时代背景下机器翻译及其发展趋势[J][Machine Translation and its Development Trend under the Background of Big Data Era]. English Teachers 英语教师,2021,21(16):60-62.<br />
<br />
Wang Hongqi 王红旗. (2008). 语言学概论 [Introduction to Linguistics]. Beijing: Peking University Press 北京大学出版社.<br />
<br />
Wei Guang魏光. 人工翻译与机器翻译译文编辑比较研究[J][Comparative Study of Translation Editing between Manual Translation and Machine Translation]. Overseas English 海外英语,2021(19):18-19+21.<br />
<br />
Zhuo Jianbin 卓键滨,Liu Wenxian 刘文娴,Peng Zili 彭子莉.机器翻译对各类型文本的德汉翻译能力探究[J][Research on the German Chinese Translation Ability of Machine Translation for Various Types of Texts]. Comparative Study of Cultural innovation 文化创新比较研究,2021,5(28):122-125.<br />
<br />
Zhang Peiji 张培基.英译中国现代散文选[M][Selected Modern Chinese Prose Writings]. Shanghai Foreign Languages Education Press 上海外语教育出版社, 2002.<br />
<br />
--[[User:Xiong Min|Xiong Min]] ([[User talk:Xiong Min|talk]]) 01:36, 15 December 2021 (UTC)<br />
<br />
=Chapter 11 陈惠妮=Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts=<br />
<br />
机器翻译的译前编辑研究——以医学类文摘为例<br />
<br />
陈惠妮 Chen Huini, Hunan Normal University<br />
<br />
[[Machine_Trans_EN_11]]<br />
<br />
===Abstract===<br />
At present, globalization is accelerating and the market demand for language services is rapidly increasing . Machine translation, as an important translation method, can greatly improve translation efficiency due to its low cost and high speed. However, because of the limitations of machine translation and the differences between Chinese and English language, machine translation is not accurate enough. In order to balance translation efficiency and translation quality, a great number of manual revisions in translation are required for the machine translating texts. Medical papers are specialized, special and purposeful, so it requires accurate,qualified and professional translation. However, the quality of translations by machine is inefficient to meet the high-quality requirements of medical papers translation. Therefore, the introduction of pre-editing can greatly improve the efficiency and quality of machine translation.<br />
<br />
===Key words===<br />
Pre-editing, Machine translation, Medical texts<br />
<br />
===题目===<br />
Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts<br />
<br />
===摘要===<br />
在全球化加速发展的今天，市场对语言服务的需求迅速增加。机器翻译作为一种重要的翻译途径，由于其成本低、速度快，可以大大提高翻译效率。然而，由于机器翻译的局限性以及中英文语言的差异，机器翻译的准确性不高。为了平衡翻译效率和翻译质量，机器翻译文本需要大量的手工修改。<br />
医学论文具有专业性、特殊性和目的性，要求其译文准确、合格、专业。然而，机器翻译的质量较低，无法满足医学论文对翻译的高质量要求。因此，译前编辑的引入可以大大提高机器翻译的效率和质量。<br />
<br />
===关键词===<br />
译前编辑；机器翻译；医学文本<br />
<br />
===1. Introduction===<br />
===1.1 Definition of Machine Translation===<br />
As Cronin(2013) revealed: "Translation is undergoing a revolutionary upheaval. The influence of digital technology and the Internet on translation is continuous, extensive and profound. From the popularity of automatic online translation applications, translation revolution is everywhere (Cronin,2013) However,the concept of machine translation was firstly proposed in the 1930s. Since 1940s, the machine translation technology has been evolving from rule-based machine translation (RBMT) to statistical machine translation (SMT), and to neural machine translation (NMT). Machine translation refers to the automatic translation of source language into target language by using a computer system. That is, machine translation refers to the automatic translation of text from one language into another natural language by computer software or other online translation webs. Machine translation is also defined as the process of “using a computer system to automatically translate text or speech from one natural language to another” according to the definition by ISO (Cui 2014:68-73).O 'Brien (2002) defines it as "the behavior of modifying errors in machine translation to ensure that the target translation meets certain quality requirements". On the basic level, machine translation performs mechanical substitution of words in one language for words in another language, but that rarely produces a good translation, therefore, recognition of the whole phrases and their closest counterparts in the target language is needed. Not all words in one language have equivalents in another language, and many words have more than one meaning. A huge demand for translation is greatly needed in today’s global world, which creates new opportunities for the development of machine translation, attracts more and more attention and becomes one of the current research focuses.<br />
<br />
As Cronin(2013) revealed: "Translation is undergoing a revolutionary upheaval. The influence of digital technology and the Internet on translation is continuous, extensive and profound. From the popularity of automatic online translation applications, translation revolution is everywhere (Cronin,2013) However,the concept of machine translation was firstly proposed in the 1930s. Since 1940s, the machine translation technology has been evolving from rule-based machine translation (RBMT) to statistical machine translation (SMT), and to neural machine translation (NMT). Machine translation refers to the automatic translation of source language into target language by using a computer system. That is, machine translation refers to the automatic translation of text from one language into another natural language by computer software or other online translation webs. Machine translation is also defined as the process of “using a computer system to automatically translate text or speech from one natural language to another” according to the definition by ISO (Cui 2014:68-73).O 'Brien (2002) defines it as "the behavior of modifying errors in machine translation to ensure that the target translation meets certain quality requirements". On the basic level, machine translation performs mechanical substitution of words in one language for words in another language, but that rarely produces a good translation, therefore, recognition of the whole phrases and their closest counterparts in the target language is needed. Not all words in one language have equivalents in another language, and many words have more than one meaning. A huge demand for translation is greatly needed in today’s global world, which creates new opportunities for the development of machine translation, attracts more and more attention and becomes one of the current research focuses.<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:34, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===1.2 Definition of Pre-editing===<br />
Pre-editing means to adjust and modify the source language to make it fit more with the characteristics of the machine translation software before putting the source language before the into machine translation, so as to improve the quality of the translation machine translation (Wei Changhong 2008:93-94). Pre-editing is to modify the original text before putting it into machine translation software, in order to improve the recognition rate of machine translation, optimize the output quality of translated text and reduce the workload of post-editing. Because pre-translation editing only needs to be modified in one language, the operation is simpler than post-translation editing, which can realize the double improvement of quality and efficiency. A good pre-editing translation can help machine translation more smoothly, thus improving the machine readability and quality of the output translation. Pre-editing is mostly applied in the following situations: one is when the original text is of poor quality and the machine is difficult to recognize the meaning of the sentence, such as user generated content with poor readability and translatability (Gerlach et al 2003:45-53); Documents that need to be published in multiple languages; Next is when the original text contains a lot of jargon; The last is the original text has a corresponding translation memory bank. If the original text is edited, it can better match the content of the translation memory bank. Pre-editing is a process of identifying problems. It requires to pre-edit the source texts before putting it into machine translation according to the requirements, listing the expressions or sentences that may have trouble in machine translation and then pre-edit it by human. The purpose is to enable the computer to translate better, improve the translatability of machine translation. (Slype G V & Guinet J F & Seitz F 1984:115)<br />
<br />
Pre-editing means to adjust and modify the source language to make it fit more with the characteristics of the machine translation software before putting the source language before the into machine translation, so as to improve the quality of the translation machine translation (Wei Changhong 2008:93-94). Pre-editing is to modify the original text before putting it into machine translation software, in order to improve the recognition rate of machine translation, optimize the output quality of translated text and reduce the workload of post-editing. Because pre-translation editing only needs to be modified in one language, the operation is simpler than post-translation editing, which can realize the double improvement of quality and efficiency. A good pre-editing translation can help machine translation more smoothly, thus improving the machine readability and quality of the output translation. Pre-editing is mostly applied in the following situations: one is when the original text is of poor quality and the machine is difficult to recognize the meaning of the sentence, such as user generated content with poor readability and translatability (Gerlach et al 2003:45-53); Documents that need to be published in multiple languages; Next is when the original text contains a lot of jargon; The last is the original text has a corresponding translation memory bank. If the original text is edited, it can better match the content of the translation memory bank. Pre-editing is a process of identifying problems. It requires to pre-edit the source texts before putting it into machine translation according to the requirements, listing the expressions or sentences that may have trouble in machine translation and then pre-edit it by human. The purpose is to enable the computer to translate better, improve the translatability of machine translation. (Slype G V & Guinet J F & Seitz F 1984:115)<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:36, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===1.3 Machine Translation Mode===<br />
According to the different knowledge acquisition methods, machine translation modes can be classified as follows: one is rule-based machine translation, which is based on bilingual dictionaries and a library of language rules for each language. The quality of translation depends on whether the source language conforms to the existing rules, but the inexhaustible rules are the hinders of this model. The second mode is machine translation based on statistics. This translation model relies on the principles of mathematics and statistics to find various existing translations corresponding to the translation tasks through the employment of corpus, analyzing the frequency of their occurrence and selecting the translation with the highest frequency for output. The disadvantage of this translation model is that it ignores the flexibility of language and the importance of context. The last one is neural network language model. This model is different from previous translation models in that it uses end-to-end neural network to realize automatic translation between natural languages. At present, the quality of its translation is much higher than that of the previous translation models.<br />
<br />
According to the different knowledge acquisition methods, machine translation modes can be classified as follows: one is rule-based machine translation, which is based on bilingual dictionaries and a library of language rules for each language. The quality of translation depends on whether the source language conforms to the existing rules, but the inexhaustible rules are the hinders of this model. The second mode is machine translation based on statistics. This translation model relies on the principles of mathematics and statistics to find various existing translations corresponding to the translation tasks through the employment of corpus, analyzing the frequency of their occurrence and selecting the translation with the highest frequency for output. The disadvantage of this translation model is that it ignores the flexibility of language and the importance of context. The last one is neural network language model. This model is different from previous translation models in that it uses end-to-end neural network to realize automatic translation between natural languages. At present, the quality of its translation is much higher than that of the previous translation models.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===1.4 Source of Study Abstracts===<br />
The core medical journals from domestic are selected in order to make the paper more representative and authoritative, such as National Medical Journal of China, Journal of Peking University (Health Science), and Journal of Third Military Medical University. All of them include basic medical science, biomedical technology, laboratory medical science and other fields. In this way, the pre-editing approaches included are applicable enough to machine translation of medical abstracts.<br />
<br />
The core medical journals from domestic are selected in order to make the paper more representative and authoritative, such as National Medical Journal of China, Journal of Peking University (Health Science), and Journal of Third Military Medical University. All of them include basic medical science, biomedical technology, laboratory medical science and other fields. In this way, the pre-editing approaches included are applicable enough to machine translation of medical abstracts.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===1.5 Selection of Translation Software===<br />
Generally, machine translation software includes Google Translation, Youdao Translator and Niu Translation, which has their own special use in translation. Take Google as an example, it is more like neurons in human brain, enabling to learn and collect information to establish connections with its neural machine translator’s neurons. However, it also causes many errors because of the lack of enough information. In this paper, contrastive analysis will be carried on by using Google translation. On the one hand, being a pioneer in the translation of NET, it is inevitably to sue Google as the translation software to translate medical texts in this paper. Comparing the program called “Google brain”, other NMT of translation software are relatively disadvantages. On the other hand, the Google translation enjoys the largest users in the world, with its downloads of more than one billion. The output quality of Google translation is more correct and complete than other machine translation software. With years of development and improvement, Google Translation has been greatly promoted. In this paper, Chinese medical abstract will be automatically translated by Google translation, and then the translation output will be compared with the translation by human and the publication of English abstracts. The main purpose is to prove that the improvement and promotion of quality and accuracy in the medical abstracts will be obtained through the pre-editing approaches.<br />
<br />
Generally, machine translation software includes Google Translation, Youdao Translator and Niu Translation, which has their own special use in translation. Take Google as an example, it is more like neurons in human brain, enabling to learn and collect information to establish connections with its neural machine translator’s neurons. However, it also causes many errors because of the lack of enough information. In this paper, contrastive analysis will be carried on by using Google translation. On the one hand, being a pioneer in the translation of NET, it is inevitably to sue Google as the translation software to translate medical texts in this paper. Comparing the program called “Google brain”, other NMT of translation software are relatively disadvantages. On the other hand, the Google translation enjoys the largest users in the world, with its downloads of more than one billion. The output quality of Google translation is more correct and complete than other machine translation software. With years of development and improvement, Google Translation has been greatly promoted. In this paper, Chinese medical abstract will be automatically translated by Google translation, and then the translation output will be compared with the translation by human and the publication of English abstracts. The main purpose is to prove that the improvement and promotion of quality and accuracy in the medical abstracts will be obtained through the pre-editing approaches.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===2.Language Characteristics and Error Division===<br />
Since Chinese and English are two different languages, it is quite neccessary to identify their own characteristics so as to better analyze and understand the two languages. Tytler (1978: 118-119) argued in his Essay on the Principles of Translation that there are three principles of translation that in all the translation should give readers the same feelings as the source text, except for complete transcript of ideas. There actually also exit some mistakes of these two languages. So the following will make some clarrifications of these two languages to ensure more accurate translation.<br />
<br />
Since Chinese and English are two different languages, it is quite neccessary to identify their own characteristics so as to better analyze and understand the two languages. Tytler (1978: 118-119) argued in his Essay on the Principles of Translation that there are three principles of translation that in all the translation should give readers the same feelings as the source text, except for complete transcript of ideas. There actually also exit some mistakes of these two languages. So the following will make some clarrifications of these two languages to ensure more accurate translation.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===2.1 Language Characteristics of Medical Abstracts===<br />
Chinese and English belong to different language systems, so there are differences in their language structure and the way users think of the languages. When using machine translation from Chinese to English, due to the unequal language levels, there will be many mistakes in the translation process, especially for ESP texts, such as medical papers.Here, these differences mainly refer to the linguistic characteristics of medical abstracts. In medical abstracts, it usually includes structured and unstructured abstracts. Although in different forms, they both describe the purpose, methods and conclusions of the research.<br />
<br />
In the method section of medial abstract, several Chinese sentences can be connected with commas, but each sentence may convey different information. In contrast, English sentences contain a great deal of information, but in order to ensure clarity, some modifiers need to be isolated and then reconstructed. However, in Chinese-English machine translation, a lot of information is put into the sentence because the machine segments the sentence on the basis of the full comma.<br />
<br />
In addition, subjectless sentence is used in the objective and method parts of Chinese medical abstracts. The subjectless sentence means the sentence without or free from subject and is usually employed in two contexts. The first is "needless to say". In Chinese sentences, it is common to omit the subject of the sentence. Chinese sentences can convey meanings by using incomplete sentence structures, so Chinese speakers can understand the meanings of the sentences even though the sentence subject is omitted. The second is "emphasis of action". In this context, subjectless sentences are used to describe behaviors, especially in the study of traditional Chinese medicine abstracts. Comparatively speaking, it should be avoided in English medical abstracts. Abstract sentences are more subjective when describing the learning process, while the essential requirement of English medical abstract is objectivity. From this point, sentences without subject should be avoided in English medical abstracts. Another feature is voice. Few words with passive meanings appear in Chinese abstracts. English sentences are more favoured in using passive voice. When translating from Chinese to English, passive voice should be used to make the contents objective, which is also the basic requirement of medical papers.<br />
<br />
These are the linguistic features of Chinese medical abstracts. There are great differences in sentence structure and expression between Chinese and English medical abstracts. These differences may reduce the accuracy of machine translation, so it is necessary to introduce pre-editing to edit the source text to ensure that the source text can be accurately recognized by the machine and fully translated into English.<br />
<br />
Chinese and English belong to different language systems, so there are differences in their language structure and the way users think of the languages. When using machine translation from Chinese to English, due to the unequal language levels, there will be many mistakes in the translation process, especially for ESP texts, such as medical papers.Here, these differences mainly refer to the linguistic characteristics of medical abstracts. In medical abstracts, it usually includes structured and unstructured abstracts. Although in different forms, they both describe the purpose, methods and conclusions of the research.<br />
<br />
In the method section of medial abstract, several Chinese sentences can be connected with commas, but each sentence may convey different information. In contrast, English sentences contain a great deal of information, but in order to ensure clarity, some modifiers need to be isolated and then reconstructed. However, in Chinese-English machine translation, a lot of information is put into the sentence because the machine segments the sentence on the basis of the full comma.<br />
<br />
In addition, subjectless sentence is used in the objective and method parts of Chinese medical abstracts. The subjectless sentence means the sentence without or free from subject and is usually employed in two contexts. The first is "needless to say". In Chinese sentences, it is common to omit the subject of the sentence. Chinese sentences can convey meanings by using incomplete sentence structures, so Chinese speakers can understand the meanings of the sentences even though the sentence subject is omitted. The second is "emphasis of action". In this context, subjectless sentences are used to describe behaviors, especially in the study of traditional Chinese medicine abstracts. Comparatively speaking, it should be avoided in English medical abstracts. Abstract sentences are more subjective when describing the learning process, while the essential requirement of English medical abstract is objectivity. From this point, sentences without subject should be avoided in English medical abstracts. Another feature is voice. Few words with passive meanings appear in Chinese abstracts. English sentences are more favoured in using passive voice. When translating from Chinese to English, passive voice should be used to make the contents objective, which is also the basic requirement of medical papers.<br />
<br />
These are the linguistic features of Chinese medical abstracts. There are great differences in sentence structure and expression between Chinese and English medical abstracts. These differences may reduce the accuracy of machine translation, so it is necessary to introduce pre-editing to edit the source text to ensure that the source text can be accurately recognized by the machine and fully translated into English.<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===2.2 Error Division of Machine Translation in Translating Abstracts of Medical Papers from Chinese to English===<br />
In this paper, errors in machine translation are listed out after analyzing. Some are due to the principles, such as statistical-based MT and NMT, employed by machine translation. Based on the original errors, they can be studied from two levels. One is from the macro-level, referring to mistakes caused by objective factors. These are not human factors, but the disadvantage of machine translation and the limitations of language. The limitations of machine translation are derived from the principles and models adopted and manifests itself as a reliance on reference sources. In this case, semantic ambiguity and textual incoherence may occur in the absence of reference sources.<br />
<br />
However, for ESP texts such as medical papers, the requirements for terms and sentence patterns are far beyond the existing corpora. For unfamiliar texts (that is, the corresponding texts cannot be found in the corpora). The quality of output translation will be relatively low, which is determined by the principles of machine translation. As mentioned above, machine translation has evolved over the past few decades from rules-based to corp-based statistics to NMT. However, there still exist some limitations in machine translation.<br />
<br />
Mistakes on micro-level are mainly caused by the variations and differences of linguistic structure between Chinese and English. Chinese is an implicit language, while English is an explicit one (Lian, 2010) To put it in another way, Chinese expression does not depend on language structures, while English does the opposite. This may result in mismatches between the original and machine translated sentences. This kind of error is mainly divided into two types: component fragment and component missing. For a medical paper, such errors are very serious. As a kind of ESP discourse, medical paper has the characteristics of fixed, objective and accurate language structure. In order to reproduce the characteristics of medical abstracts in translation, it is necessary to avoid errors at the level of words and sentences and pay attention to logic and consistency.<br />
<br />
Lexical errors here refer to the inconsistency of fixed expressions in the translation of terms. Although these terms are machine translation based on the corpus, the corpus may not be perfect for ESP texts such as medical papers. Therefore, fixed expressions are not used in term translation. This is also the most common mistake in machine translation (the term here includes but is not limited to nouns). For ESP texts, medical text in particular, the fixed expressions and the accuracy of terms are of great importance.<br />
<br />
In this paper, errors in machine translation are listed out after analyzing. Some are due to the principles, such as statistical-based MT and NMT, employed by machine translation. Based on the original errors, they can be studied from two levels. One is from the macro-level, referring to mistakes caused by objective factors. These are not human factors, but the disadvantage of machine translation and the limitations of language. The limitations of machine translation are derived from the principles and models adopted and manifests itself as a reliance on reference sources. In this case, semantic ambiguity and textual incoherence may occur in the absence of reference sources.<br />
<br />
However, for ESP texts such as medical papers, the requirements for terms and sentence patterns are far beyond the existing corpora. For unfamiliar texts (that is, the corresponding texts cannot be found in the corpora). The quality of output translation will be relatively low, which is determined by the principles of machine translation. As mentioned above, machine translation has evolved over the past few decades from rules-based to corp-based statistics to NMT. However, there still exist some limitations in machine translation.<br />
<br />
Mistakes on micro-level are mainly caused by the variations and differences of linguistic structure between Chinese and English. Chinese is an implicit language, while English is an explicit one (Lian, 2010) To put it in another way, Chinese expression does not depend on language structures, while English does the opposite. This may result in mismatches between the original and machine translated sentences. This kind of error is mainly divided into two types: component fragment and component missing. For a medical paper, such errors are very serious. As a kind of ESP discourse, medical paper has the characteristics of fixed, objective and accurate language structure. In order to reproduce the characteristics of medical abstracts in translation, it is necessary to avoid errors at the level of words and sentences and pay attention to logic and consistency.<br />
<br />
Lexical errors here refer to the inconsistency of fixed expressions in the translation of terms. Although these terms are machine translation based on the corpus, the corpus may not be perfect for ESP texts such as medical papers. Therefore, fixed expressions are not used in term translation. This is also the most common mistake in machine translation (the term here includes but is not limited to nouns). For ESP texts, medical text in particular, the fixed expressions and the accuracy of terms are of great importance.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===3.Approaches Proposed for Pre-editing ===<br />
Generally speaking, there is a close relationship between pre-editing and post-editing, both of which aim to convey information and ensure high or publishable translation quality. Proper pre-editing can improve the quality of machine translation in terms of adequacy and consistency. <br />
Complexity of natural language and people use language arbitrarily, bringing many difficulties to Chinese-English machine translation, Hu Qingping (2005:24) has proposed "the research of translation software and the development of the controlled language are the two directions to improve the quality of machine translation : the former aims at the difficulty in the natural language processing, the latter overcomes the arbitrariness of natural language". Feng Quangong and Gao Lin (2017: 63-68) put forward: "The writing principles of controlled language can be applied to pre-editing of machine translation. Pre-editing based on controlled language can effectively reduce the complexity and ambiguity of source text, improve the identifiability of machine translation (the translatability of source text itself), and thus reduce (fully) the workload of post-editing".<br />
The central task of pre-editing is to transform human-friendly content into machine-friendly content, so words and sentences need to be repositioned or even changed. Based on the analysis of the language characteristics of Chinese and English, the Chinese ideographic group should be split before the Chinese source text is input into the machine software to translate so that the sentence structure is complete which can be easily recognized by the machine translation software.<br />
<br />
Generally speaking, there is a close relationship between pre-editing and post-editing, both of which aim to convey information and ensure high or publishable translation quality. Proper pre-editing can improve the quality of machine translation in terms of adequacy and consistency. <br />
<br />
Complexity of natural language and people use language arbitrarily, bringing many difficulties to Chinese-English machine translation, Hu Qingping (2005:24) has proposed "the research of translation software and the development of the controlled language are the two directions to improve the quality of machine translation : the former aims at the difficulty in the natural language processing, the latter overcomes the arbitrariness of natural language". Feng Quangong and Gao Lin (2017: 63-68) put forward: "The writing principles of controlled language can be applied to pre-editing of machine translation. Pre-editing based on controlled language can effectively reduce the complexity and ambiguity of source text, improve the identifiability of machine translation (the translatability of source text itself), and thus reduce (fully) the workload of post-editing".<br />
<br />
The central task of pre-editing is to transform human-friendly content into machine-friendly content, so words and sentences need to be repositioned or even changed. Based on the analysis of the language characteristics of Chinese and English, the Chinese ideographic group should be split before the Chinese source text is input into the machine software to translate so that the sentence structure is complete which can be easily recognized by the machine translation software.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufefng<br />
<br />
===3.1 Extraction and Replacement of Terms in the Source Text===<br />
As a branch of applied English, medical English is the product of the combination of English language knowledge and medical knowledge. The terms in medical papers have the characteristics of fixed and complex language structure. Although Google Translation is based on a corpus, the language structure is not fixed due to the limited size of the corpus. Therefore, the following two steps should be followed before machine translation. The first is to extract terms and create a glossary, and then replace the Chinese terms in the source text with the corresponding English expressions in the glossary. Of course, the terms of extraction should be searched and verified according to the actual situation. All of these words are verified before they can be replaced. This method can not only ensure the accuracy of term translation, but also maintain the consistency of expression, especially when dealing with long texts.<br />
Example1<br />
Source abstract: 口腔白斑病癌变相关缺氧应答基因和微小RNA的芯片及表达验证。<br />
Google translation: Microarray detection/Chip detection and expression verification of hypoxia response genes and microRNAs related to oral leukoplakia canceration.<br />
Published translation:Transcriptome array screening and verification of oral leukoplakia carcinogenesis-related hypoxia-responsive gene and microRNA. <br />
Analysis: The expression “ Affymetrix GeneChip” empolyed in this thesis is a human transcription array for transcriptome array. In the source abstract, “芯片检测“ can be meant “screening” but not detection, so the proper and appropriate translation of these expression should be “transcription array screening”, but the Google translates it into “microarry detection of chip detection”. Therefore, term extraction is essential and inevitable before putting the source abstracts into the machine translation software.<br />
After pre-editing source abstracts: 对 oral leukoplakia carcinogenesis 相关 hypoxia-responsive gene 和微小RNA进行 transcriptome array screening 及表达验证。<br />
After pre-editing translation: Transcriptome array screening and expression- verification of hypoxia-responsive genes and microRNAs related to oral leukoplakia carcinogenesis.<br />
<br />
As a branch of applied English, medical English is the product of the combination of English language knowledge and medical knowledge. The terms in medical papers have the characteristics of fixed and complex language structure. Although Google Translation is based on a corpus, the language structure is not fixed due to the limited size of the corpus. Therefore, the following two steps should be followed before machine translation. The first is to extract terms and create a glossary, and then replace the Chinese terms in the source text with the corresponding English expressions in the glossary. Of course, the terms of extraction should be searched and verified according to the actual situation. All of these words are verified before they can be replaced. This method can not only ensure the accuracy of term translation, but also maintain the consistency of expression, especially when dealing with long texts.<br />
<br />
Example1<br />
Source abstract: 口腔白斑病癌变相关缺氧应答基因和微小RNA的芯片及表达验证。<br />
Google translation: Microarray detection/Chip detection and expression verification of hypoxia response genes and microRNAs related to oral leukoplakia canceration.<br />
Published translation:Transcriptome array screening and verification of oral leukoplakia carcinogenesis-related hypoxia-responsive gene and microRNA. <br />
Analysis: The expression “ Affymetrix GeneChip” empolyed in this thesis is a human transcription array for transcriptome array. In the source abstract, “芯片检测“ can be meant “screening” but not detection, so the proper and appropriate translation of these expression should be “transcription array screening”, but the Google translates it into “microarry detection of chip detection”. Therefore, term extraction is essential and inevitable before putting the source abstracts into the machine translation software.<br />
After pre-editing source abstracts: 对 oral leukoplakia carcinogenesis 相关 hypoxia-responsive gene 和微小RNA进行 transcriptome array screening 及表达验证。<br />
After pre-editing translation: Transcriptome array screening and expression- verification of hypoxia-responsive genes and microRNAs related to oral leukoplakia carcinogenesis.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===3.2 Explication of Subordination===<br />
As mentioned above, the subordination of Chinese sentences is judged by certain specific words in machine translation . Chinese is a paratactic language, and sentences are often connected by internal logical relationships; while English depends on sentence structure, so sentences are often closely linked by various language forms. In order to improve the accuracy of machine translation, we can adjust the sentence structure and adjust the position of adjectives and their modifiers. <br />
<br />
Example 2<br />
Source abstracts:回顾性分析南京医科大学附属儿童医院中重度HIE患儿49例及同期就诊的无神经系统症状体征的足月新生儿为对照组31例的头颅磁共振成像（MRI）资料。<br />
Google translation: A retrospective analysis that the brain magnetic resonance imaging (MRI) data of 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University and full-term neonates with no neurological symptoms and signs during the same period were included in the control group.<br />
Published translation: A total of 49 children with moderate to severe HIE admitted to the children’s Hospital Affiliated to Nanjing Medical University were retrospectively analyzed. Cranial magnetic resonance imaging (MRI) date of 31 full-term neonates without neurological symptoms and signs who visited the hospital during the same period were recruited as the control group. <br />
<br />
Analysis: As the above example shows that “中重度HIE患儿” and “足月新生儿” in the source abstracts are from the Children’s Hospital Affiliated Nanjing Medical University. The Google translation shows that 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University. There is an error due to the misuse of subordination. There are some advice in order to solve the problem. That is, first to segment the sentence and then reconstruct the sentence. For instance, the Chinese expression “南京医科大学附属儿童医院” carries many modifiers, which requires to cut the modifiers and reconstruct the sentence. <br />
Therefore, the Chinese expression “南京医科大学附属儿童医院’can be divided into abstractions, leaving two sentences instead of one long sentence with modifiers..<br />
After pre-editing source abstracts: 对49例中重度HIE患儿以及31例无神经系统症状体征的足月新生儿的MRI资料进行回顾性分析。这些患者收治于南京医科大学儿童附属医院。<br />
After pre-editing translation: The MRI data of 49 children with moderate to severe HIE and 31 full-term newborns without neurological symptoms and signs were retrospectively analyzed. These patients were admitted to the Children’s Hospital of Nanjing Medical University.<br />
<br />
As mentioned above, the subordination of Chinese sentences is judged by certain specific words in machine translation . Chinese is a paratactic language, and sentences are often connected by internal logical relationships; while English depends on sentence structure, so sentences are often closely linked by various language forms. In order to improve the accuracy of machine translation, we can adjust the sentence structure and adjust the position of adjectives and their modifiers. <br />
<br />
Example 2<br />
Source abstracts:回顾性分析南京医科大学附属儿童医院中重度HIE患儿49例及同期就诊的无神经系统症状体征的足月新生儿为对照组31例的头颅磁共振成像（MRI）资料。<br />
Google translation: A retrospective analysis that the brain magnetic resonance imaging (MRI) data of 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University and full-term neonates with no neurological symptoms and signs during the same period were included in the control group.<br />
Published translation: A total of 49 children with moderate to severe HIE admitted to the children’s Hospital Affiliated to Nanjing Medical University were retrospectively analyzed. Cranial magnetic resonance imaging (MRI) date of 31 full-term neonates without neurological symptoms and signs who visited the hospital during the same period were recruited as the control group. <br />
<br />
Analysis: As the above example shows that “中重度HIE患儿” and “足月新生儿” in the source abstracts are from the Children’s Hospital Affiliated Nanjing Medical University. The Google translation shows that 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University. There is an error due to the misuse of subordination. There are some advice in order to solve the problem. That is, first to segment the sentence and then reconstruct the sentence. For instance, the Chinese expression “南京医科大学附属儿童医院” carries many modifiers, which requires to cut the modifiers and reconstruct the sentence. <br />
<br />
Therefore, the Chinese expression “南京医科大学附属儿童医院’can be divided into abstractions, leaving two sentences instead of one long sentence with modifiers..<br />
After pre-editing source abstracts: 对49例中重度HIE患儿以及31例无神经系统症状体征的足月新生儿的MRI资料进行回顾性分析。这些患者收治于南京医科大学儿童附属医院。<br />
After pre-editing translation: The MRI data of 49 children with moderate to severe HIE and 31 full-term newborns without neurological symptoms and signs were retrospectively analyzed. These patients were admitted to the Children’s Hospital of Nanjing Medical University.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===3.3 Explication of Subject through Voice Changing===<br />
The extensive use of subjectless sentences are quite common in the Chinese abstracts, while English prefers to employ passive voice in the sentences so as to make them object and accurate. In order to take the characteristics of English sentences into great and careful consideration, the sentences written in passive voice in particular, it will be helpful for machine translation to find out the subject and reconstruct a sentence in passive voice (Wang Yan: 2008). The first is to discover the “doee” in a sentence and then is to reconstruct the sentence structure and put the “doee” before the verb. The last is to explicate the passive relation between the noun and the verb.<br />
Example 3<br />
Source abstract: 回顾性分析2018年1月至2020年12月解放军总医院第七医学中心收治的500例老年髋部骨折患者的资料。<br />
Google translation: A retrospective analysis of the data of 500 elderly patients with hip fractures admitted to the Seventh Medical Center of the PLA General Hospital from January 2018 to December 2020.<br />
Published translation: From January 2018 to December 2020, the data of 500 elderly patients with hip fracture treated in the Seventh Medical Center of PLA General Hospital were analyzed retrospectively.<br />
Analysis: The above example indicates that the “回顾性分析”in the Google Translate is a noun phrase, but the source abstracts actually describes an action or a behavior. The reason for such a mistake is that the machine can’t recognize the Chinese subjetless sentences. To find the subject of the sentence, the source abstracts can be revised and adjusted. Finding the “doee” is the first step, which refers to “500例老年髋部骨折患者的资料”in the source abstracts. And next is to put it in front of the verb, that is to place it in the beginning of the sentence. Last but not least, the passive voice should be explicated in the sentence. <br />
After pre-editing source abstract: 500例老年髋部骨折患者的资料被回顾性分析，他们在2018年1月之2020年12月期间收治于解放军总医院第七医学中心。<br />
After pre-editing translation: The data of 500 elderly hip fracture patients were retrospectively analyzed. They were admitted to the Seventh Medical Center of the PLA General Hospital between January 2018 and December 2020.<br />
<br />
The extensive use of subjectless sentences are quite common in the Chinese abstracts, while English prefers to employ passive voice in the sentences so as to make them object and accurate. In order to take the characteristics of English sentences into great and careful consideration, the sentences written in passive voice in particular, it will be helpful for machine translation to find out the subject and reconstruct a sentence in passive voice (Wang Yan: 2008). The first is to discover the “doee” in a sentence and then is to reconstruct the sentence structure and put the “doee” before the verb. The last is to explicate the passive relation between the noun and the verb.<br />
<br />
Example 3<br />
Source abstract: 回顾性分析2018年1月至2020年12月解放军总医院第七医学中心收治的500例老年髋部骨折患者的资料。<br />
Google translation: A retrospective analysis of the data of 500 elderly patients with hip fractures admitted to the Seventh Medical Center of the PLA General Hospital from January 2018 to December 2020.<br />
Published translation: From January 2018 to December 2020, the data of 500 elderly patients with hip fracture treated in the Seventh Medical Center of PLA General Hospital were analyzed retrospectively.<br />
<br />
Analysis: The above example indicates that the “回顾性分析”in the Google Translate is a noun phrase, but the source abstracts actually describes an action or a behavior. The reason for such a mistake is that the machine can’t recognize the Chinese subjetless sentences. To find the subject of the sentence, the source abstracts can be revised and adjusted. Finding the “doee” is the first step, which refers to “500例老年髋部骨折患者的资料”in the source abstracts. And next is to put it in front of the verb, that is to place it in the beginning of the sentence. Last but not least, the passive voice should be explicated in the sentence. <br />
After pre-editing source abstract: 500例老年髋部骨折患者的资料被回顾性分析，他们在2018年1月之2020年12月期间收治于解放军总医院第七医学中心。<br />
After pre-editing translation: The data of 500 elderly hip fracture patients were retrospectively analyzed. They were admitted to the Seventh Medical Center of the PLA General Hospital between January 2018 and December 2020.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===3.4 Relocation of Modifiers===<br />
Modifiers, including noun, adverbial and attributive are constantly employed in the Chinese medical papers in order to add information to subject and object in the sentence. By doing this, complicated sentences can be structured, thus causing obstacles to machine translation for recognizing this complex sentence structures when translating Chinese-English sentences. To make the machine translation successfully recognize the sentence structure, simplifying the sentence structure is quite necessary. The key is to moving the location of the modifiers and thus making the modifiers an independent sentence. In other words, the modifiers ought to be put before or behind the main part of the sentence to satisfy the common use of English. <br />
Usually, the modifiers in Chinese sentence can only be placed in front of the core word, while in English, modifiers are very flexible. It is all right to place the modifiers in front of or behind the major part of the sentences with adjective or a connective noun.<br />
<br />
Example 4<br />
Source abstracts: 利用DNA重组技术以pET-28a表达系统在E.coli BL21(DE3) 中重组表达Hepl。<br />
Google Translation: Hepl is recombined in E.coli BL21 (DE3) using DNA recombination technology with pET-28a expression system.<br />
Published translation: The recombinant Hcpl protein was expressed by using DNA recombination technology through pET-28a expression system in E. coli BL21 (De3).<br />
Analysis: The Chinese medical text includes two modifiers, “利用DNA”and “以pET-28a)表达系统”. These two modifiers will be translated by machine, which catches more attention to the two constituents. From the Google translation above, one failure is obvious that it misplaces the location of the two modifiers and presents it in not accurate form. To make it translate correctly by machine translation, dividing the sentences is very important so that the source abstracts can be correctly recognized by machine translation.<br />
<br />
After pre-editing source abstract: 利用DNA重组技术，Hcp1重组表达在E.coli BL21 (DE3)中， 通过pET-28a表达系统在E. coli BL21 (DE3)中。<br />
Google translation: Using DNA recombination technology, Hcp1 recombination is expressed in E.coli BL21 (DE3), via pET-28a expression system in E. coli BL21 (DE3).<br />
The second is the independence of modifiers, that is, modifiers can also be reconstructed into clauses to modify core words. Compared with English, There is no subordinate clause in Chinese, so redundant Chinese modifiers need to be reconstructed into subordinate clauses in Chinese-English translation to meet the characteristics of English. English, especially sentences with multiple modifiers. Otherwise, sentence structure may be confused, such as scattered modifiers of core words. To avoid this mistake, it is necessary to separate the modifier from the main part of the sentence. These modifiers should be reconstituted into clauses. Also, keep your sentences simple and easy to understand.<br />
<br />
Modifiers, including noun, adverbial and attributive are constantly employed in the Chinese medical papers in order to add information to subject and object in the sentence. By doing this, complicated sentences can be structured, thus causing obstacles to machine translation for recognizing this complex sentence structures when translating Chinese-English sentences. To make the machine translation successfully recognize the sentence structure, simplifying the sentence structure is quite necessary. The key is to moving the location of the modifiers and thus making the modifiers an independent sentence. In other words, the modifiers ought to be put before or behind the main part of the sentence to satisfy the common use of English. <br />
Usually, the modifiers in Chinese sentence can only be placed in front of the core word, while in English, modifiers are very flexible. It is all right to place the modifiers in front of or behind the major part of the sentences with adjective or a connective noun.<br />
<br />
Example 4<br />
Source abstracts: 利用DNA重组技术以pET-28a表达系统在E.coli BL21(DE3) 中重组表达Hepl。<br />
Google Translation: Hepl is recombined in E.coli BL21 (DE3) using DNA recombination technology with pET-28a expression system.<br />
Published translation: The recombinant Hcpl protein was expressed by using DNA recombination technology through pET-28a expression system in E. coli BL21 (De3).<br />
Analysis: The Chinese medical text includes two modifiers, “利用DNA”and “以pET-28a)表达系统”. These two modifiers will be translated by machine, which catches more attention to the two constituents. From the Google translation above, one failure is obvious that it misplaces the location of the two modifiers and presents it in not accurate form. To make it translate correctly by machine translation, dividing the sentences is very important so that the source abstracts can be correctly recognized by machine translation.<br />
<br />
After pre-editing source abstract: 利用DNA重组技术，Hcp1重组表达在E.coli BL21 (DE3)中， 通过pET-28a表达系统在E. coli BL21 (DE3)中。<br />
Google translation: Using DNA recombination technology, Hcp1 recombination is expressed in E.coli BL21 (DE3), via pET-28a expression system in E. coli BL21 (DE3).<br />
The second is the independence of modifiers, that is, modifiers can also be reconstructed into clauses to modify core words. Compared with English, There is no subordinate clause in Chinese, so redundant Chinese modifiers need to be reconstructed into subordinate clauses in Chinese-English translation to meet the characteristics of English. English, especially sentences with multiple modifiers. Otherwise, sentence structure may be confused, such as scattered modifiers of core words. To avoid this mistake, it is necessary to separate the modifier from the main part of the sentence. These modifiers should be reconstituted into clauses. Also, keep your sentences simple and easy to understand.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===3.5 Proper Omission and Deletion of Category Words===<br />
Category words are commonly used in Chinese. Category words complement the meaning of words, including problems, positions, situations and jobs. Adding this supplementary word is more in line with Chinese custom. In many cases, it has no real meaning, so it can be omitted in translation. In Chinese, category words are frequently used. However, it is rarely used in English, which is one of the differences between Chinese and English. There are many kinds of category words. Considering objectivity and the fixed structure of language, redundant category words should be deleted in Chinese-English translation. In the general, the most commonly used category of words in the Chinese medical abstracts are "process", "behavior", and "situation". These categories of words hinder the language conversion of Chinese and English, resulting in redundancy. <br />
<br />
Example 5<br />
Source abstract: 探讨口腔白斑病癌变进程中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia response genes and associated microRNAs in the process of oral leukoplakia cancer was discussed.<br />
Published translation: To study the hypoxia response gene and microRNA (miRNA)expression profiles in the pathogenesis and progression of oral leukoplakia (OLK).<br />
Analysis: As the above example shows, the Chinese words “进程” belongs to a category word. However, the Chinese expression “癌变”contains the process, so the “进程” expression can be deleted before placing it into the machine translation, because the meaning of it has been overlapping between the expression “癌变”. Therefore, its place can be replaced with preposition “during”.<br />
<br />
After pre-editing source abstract: 探讨口腔白斑病癌变中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia responsive genes and miRNA in oral leukoplakia cancer was investigated.<br />
<br />
Category words are commonly used in Chinese. Category words complement the meaning of words, including problems, positions, situations and jobs. Adding this supplementary word is more in line with Chinese custom. In many cases, it has no real meaning, so it can be omitted in translation. In Chinese, category words are frequently used. However, it is rarely used in English, which is one of the differences between Chinese and English. There are many kinds of category words. Considering objectivity and the fixed structure of language, redundant category words should be deleted in Chinese-English translation. In the general, the most commonly used category of words in the Chinese medical abstracts are "process", "behavior", and "situation". These categories of words hinder the language conversion of Chinese and English, resulting in redundancy. <br />
<br />
Example 5<br />
Source abstract: 探讨口腔白斑病癌变进程中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia response genes and associated microRNAs in the process of oral leukoplakia cancer was discussed.<br />
Published translation: To study the hypoxia response gene and microRNA (miRNA)expression profiles in the pathogenesis and progression of oral leukoplakia (OLK).<br />
Analysis: As the above example shows, the Chinese words “进程” belongs to a category word. However, the Chinese expression “癌变”contains the process, so the “进程” expression can be deleted before placing it into the machine translation, because the meaning of it has been overlapping between the expression “癌变”. Therefore, its place can be replaced with preposition “during”.<br />
<br />
After pre-editing source abstract: 探讨口腔白斑病癌变中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia responsive genes and miRNA in oral leukoplakia cancer was investigated.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===Conclusion===<br />
After years of development, machine translation has made great progress. The accuracy of machine translation has been greatly improved in both text recognition and sentence pattern conversion. However, machine translation has its own limitations. In other words, it needs to rely on the parallel corpus as a reference source for improving its accuracy. ESP text, in particular, is harder to get the high quality by machine translation. <br />
<br />
As one of the research papers, the characteristics of medical abstracts are fixed language structures, objectivity and accuracy (Qin Yi 2004:421-423). Therefore, medical translation must be accurate, object and understandable to follow the specific demands of the medical paper. Being an important field in the human society, medical paper translation is on a great demand, which means that it needs a huge demand for human labor. However, with the machine translation promoting, it will be more efficient to translate medical papers combining the effort by human and machine. The improvement and development of machine translation requires the joint efforts of computer science, information science, statistics, linguistics and other academic circles to achieve more mature human-computer mutual assistance translation (Li Yafei, Zhang Ruihua 2019:38-45).<br />
<br />
However, errors can occur during the process of machine translation of Chinese- English, because of the differences of the Chinese and English and the processing of the machine. Errors from the perspective of linguistic or grammar can affect the machine translation a lot. After division and recognition of errors, some pre-editing approaches are put forward to help the machine translation more accurate and readable, that are, extraction and replacement of terms in the source text, relocation of modifiers, explication of subordination, proper omission, deletion of category words and explication of subject through voice changing. <br />
<br />
--[[User:Chen Huini|Chen Huini]] ([[User talk:Chen Huini|talk]]) 04:56, 15 December 2021 (UTC)written by Chen Huini<br />
<br />
The paper mainly focuses on the pre-editing machine translation by using medical papers as a case study. The errors of machine translation occurring in the translation of medical abstracts and pre-editing approaches for machine translation. The quality of machine translation of medical papers is greatly improved after employing the pre-editing methods. However, machine translation is not as flexible and accurate as human brain, so it is of importance to combine pre-editing and post-editing approaches with machine translation in order to produce more accurate, more object machine translation of medical papers.<br />
<br />
After years of development, machine translation has made great progress. The accuracy of machine translation has been greatly improved in both text recognition and sentence pattern conversion. However, machine translation has its own limitations. In other words, it needs to rely on the parallel corpus as a reference source for improving its accuracy. ESP text, in particular, is harder to get the high quality by machine translation. <br />
<br />
As one of the research papers, the characteristics of medical abstracts are fixed language structures, objectivity and accuracy (Qin Yi 2004:421-423). Therefore, medical translation must be accurate, object and understandable to follow the specific demands of the medical paper. Being an important field in the human society, medical paper translation is on a great demand, which means that it needs a huge demand for human labor. However, with the machine translation promoting, it will be more efficient to translate medical papers combining the effort by human and machine. The improvement and development of machine translation requires the joint efforts of computer science, information science, statistics, linguistics and other academic circles to achieve more mature human-computer mutual assistance translation (Li Yafei, Zhang Ruihua 2019:38-45).<br />
<br />
However, errors can occur during the process of machine translation of Chinese- English, because of the differences of the Chinese and English and the processing of the machine. Errors from the perspective of linguistic or grammar can affect the machine translation a lot. After division and recognition of errors, some pre-editing approaches are put forward to help the machine translation more accurate and readable, that are, extraction and replacement of terms in the source text, relocation of modifiers, explication of subordination, proper omission, deletion of category words and explication of subject through voice changing. <br />
<br />
The paper mainly focuses on the pre-editing machine translation by using medical papers as a case study. The errors of machine translation occurring in the translation of medical abstracts and pre-editing approaches for machine translation. The quality of machine translation of medical papers is greatly improved after employing the pre-editing methods. However, machine translation is not as flexible and accurate as human brain, so it is of importance to combine pre-editing and post-editing approaches with machine translation in order to produce more accurate, more object machine translation of medical papers.<br />
<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng<br />
<br />
===References===<br />
<br />
Cui Qiliang崔启亮(2014).论机器翻译的译后编辑[J] ''On Post-Editing of Machine Translatio''. 中国翻译 Chinese Translators Journal, 035(006):68-73<br />
<br />
Feng Quangong, Gao Lin冯全功,高琳 (2017). 基于受控语言的译前编辑对机器翻译的影响[J] ''Influence of Pre-editing Based on Controlled Language on Machine Translation''. 当代外语研究Contemporary Foreign Language Research,(2): 63-68+87+110.<br />
<br />
GERLACH J, et al ( 2013). ''Combining Pre-editing and Post-editing to Improve SMT of User-generated Content''[M]// Proceedings of MT Summit XIV Workshop on Post-editing Technology and Practice. 45-53<br />
<br />
Hu Qingping胡清平(2005). 机器翻译中的受控语言[J] ''Controlled Language in Machine Translation''. 中国科技翻译 Chinese Science and Technology Translation, (03): 24-27. <br />
<br />
Lian Shuneng连淑能 (2010). 英汉对比研究增订本[M]''An Updated Version of English-Chinese Contrastive Studies'' . 北京:高等教育出版社Beijing: Higher Education Publishing House. 35-36.<br />
<br />
Li Yafei, Zhang Ruihua黎亚飞,张瑞华 (2019). 机器翻译发展与现状[J]''The Development and Current Situation of Machine Translation''. 中国轻工教育 China Light Industry Education, (5):38-45. <br />
<br />
Qin Yi秦毅(2004),从翻译基本标准议医学英语的翻译[J] ''On the Translation of Medical English from the Basic Standard of Translation''. 遵义医学院学报 Journal of Zunyi Medical College,27 (4): 421-423. <br />
<br />
Slype G V & Guinet J F & Seitz F (1984). ''Better Translation for Better Communication'' [M] . Oxford: Pergamon Press Ltd (U.K.). 90-93<br />
<br />
O'Brien S (2002). Teaching Post-editing: A Proposal for Course Con-tent [EB/OL]. http://mt-archive. Info/EAMT-2002-0brien. Pdf.<br />
<br />
Tytler, A. F. (1978). ''Essay On The Principles of Translation''[M]. Amsterdam: JohnBenjamins Publishing. 118-119<br />
<br />
Wang Yan王燕 (2008). 医学英语翻译与写作教程[M] ''Medical English Translation and Writing Course''. 重庆:重庆大学出版社 Chongqing: Chongqing University Press. 60-61<br />
<br />
=12 蔡珠凤=(The Mistranslation of C-J Machine Translation of Political Statements)=<br />
<br />
机器翻译中政治发言中译日的误译<br />
<br />
蔡珠凤 Cai Zhufeng, Hunan Normal University<br />
<br />
[[Machine_Trans_EN_12]]<br />
===Abstract===<br />
Language is the main way of communication between people. With the continuous development of globalization, the scale of cross-border exchanges is also expanding. However, due to cultural differences and diversity, the languages of different countries and regions are very different, which seriously hinders people's communication. The demand for efficient and convenient translation tools is increasing. At the same time, with the development of network technology and artificial intelligence, recognition technology based on deep learning is more and more widely used in English, Japanese and other fields.<br />
<br />
===Key words===<br />
machine translation; political statements; mistranslation of C-J machine translation<br />
===题目===<br />
The Mistranslation of C-J Machine Translation of Political Statements<br />
===摘要===<br />
语言是人与人之间交流的主要方式。随着全球化的不断发展，跨境交流的规模也在不断扩大。然而，由于文化的差异和多样性，不同国家和地区的语言差异很大，这严重阻碍了人们的交流。对高效便捷的翻译工具的需求正在增加。同时，随着网络技术和人工智能的发展，基于深度学习的识别技术在英语、日语等领域的应用越来越广泛。<br />
<br />
===关键词===<br />
机器翻译；政治发言；政治发言中译日的误译<br />
===1. Introduction===<br />
===Introduction to machine translation===<br />
Machine translation, also known as automatic translation, is a process of using computers to convert one natural language (source language) into another natural language (target language). It is a branch of computational linguistics, one of the ultimate goals of artificial intelligence, and has important scientific research value.At the same time, machine translation has important practical value. With the rapid development of economic globalization and the Internet, machine translation technology plays a more and more important role in promoting political, economic and cultural exchanges.The development of machine translation technology has been closely accompanied by the development of computer technology, information theory, linguistics and other disciplines. From the early dictionary matching, to the rule translation of dictionaries combined with linguistic expert knowledge, and then to the statistical machine translation based on corpus, with the improvement of computer computing power and the explosive growth of multilingual information, machine translation technology gradually stepped out of the ivory tower and began to provide real-time and convenient translation services for general users.（Zhang 2019:5-6)<br />
<br />
=== C-J machine translation software===<br />
Today's online machine translation software includes Baidu translation, Tencent translation, Google translation, Youdao translation, Bing translation and so on. Google was the first company to launch the machine translation system, and Baidu was the first company to import the machine translation system in China. In addition, Tencent and Youdao have attracted much attention.Machine translation is the process of using computers to convert one natural language into another. It usually refers to sentence and full-text translation between natural languages. In order to continuously improve the translation quality, R & D personnel have added artificial intelligence technologies such as speech recognition, image processing and deep neural network to machine translation on the basis of traditional machine translation based on rules, statistics and examples.With the increase of using machine translation, the joint cooperation between manual translation and machine translation will also increase significantly in the future. What criteria should be used to evaluate the quality of machine translation? In the evolving field of machine translation, there is an urgent need to clarify the unsolvable questions and solved problems.(Lv 1996:3)<br />
<br />
===The history of machine translation===<br />
The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. alchuni put forward the idea of using machines for translation. In 1933, Soviet inventor П.П. Trojansky designed a machine to translate one language into another, and registered his invention on September 5 of the same year; However, due to the low technical level in the 1930s, his translation machine was not made. In 1946, the first modern electronic computer ENIAC was born. Shortly after that, W. weaver, an American scientist and A. D. booth, a British engineer, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947 when discussing the application scope of electronic computer. In 1949, W. Weaver published the translation memorandum, which formally put forward the idea of machine translation. After 60 years of ups and downs, machine translation has experienced a tortuous and long development path. The academic community generally divides it into the following four stages:<br />
<br />
Pioneering period（1947-1964）<br />
<br />
In 1954, with the cooperation of IBM, Georgetown University completed the English Russian machine translation experiment with ibm-701 computer for the first time, showing the feasibility of machine translation to the public and the scientific community, thus opening the prelude to the study of machine translation.<br />
It is not too late for China to start this research. As early as 1956, the state included this research in the national scientific work development plan. The topic name is "machine translation, the construction of natural language translation rules and the mathematical theory of natural language". In 1957, the Institute of language and the Institute of computing technology of the Chinese Academy of Sciences cooperated in the Russian Chinese machine translation experiment, translating 9 different types of more complex sentences.From the 1950s to the first half of the 1960s, machine translation research has been on the rise. The United States and the former Soviet Union, two superpowers, have provided a lot of financial support for machine translation projects for military, political and economic purposes, while European countries have also paid considerable attention to machine translation research due to geopolitical and economic needs, and machine translation has become an upsurge for a time. In this period, although machine translation is just in the pioneering stage, it has entered an optimistic period of prosperity.<br />
<br />
Frustrated period（1964-1975）<br />
<br />
In 1964, in order to evaluate the research progress of machine translation, the American Academy of Sciences established the automatic language processing Advisory Committee (Alpac Committee) and began a two-year comprehensive investigation, analysis and test.In November 1966, the committee published a report entitled "language and machine" (Alpac report for short), which comprehensively denied the feasibility of machine translation and suggested stopping the financial support for machine translation projects. The publication of this report has dealt a blow to the booming machine translation, and the research of machine translation has fallen into a standstill. Coincidentally, during this period, China broke out the "ten-year Cultural Revolution", and basically these studies also stagnated. Machine translation has entered a depression.<br />
<br />
convalescence（1975-1989）<br />
<br />
Since the 1970s, with the development of science and technology and the increasingly frequent exchange of scientific and technological information among countries, the language barriers between countries have become more serious. The traditional manual operation mode has been far from meeting the needs, and there is an urgent need for computers to engage in translation. At the same time, the development of computer science and linguistics, especially the substantial improvement of computer hardware technology and the application of artificial intelligence in natural language processing, have promoted the recovery of machine translation research from the technical level. Machine translation projects have begun to develop again, and various practical and experimental systems have been launched successively, such as weinder system Eurpotra multilingual translation system, taum-meteo system, etc.However, after the end of the "ten-year holocaust", China has perked up again, and machine translation research has been put on the agenda again. The "784" project has paid enough attention to machine translation research. After the mid-1980s, the development of machine translation research in China has further accelerated. Firstly, two English Chinese machine translation systems, ky-1 and MT / ec863, have been successfully developed, indicating that China has made great progress in machine translation technology.(Chen 2016:5)<br />
<br />
New period(1990 present)<br />
<br />
With the universal application of the Internet, the acceleration of the process of world economic integration and the increasingly frequent exchanges in the international community, the traditional way of manual operation is far from meeting the rapidly growing needs of translation. People's demand for machine translation has increased unprecedentedly, and machine translation has ushered in a new development opportunity. International conferences on machine translation research have been held frequently, and China has made unprecedented achievements. A series of machine translation software have been launched, such as "Yixing", "Yaxin", "Tongyi", "Huajian", etc. Driven by the market demand, the commercial machine translation system has entered the practical stage, entered the market and came to the users.Since the new century, with the emergence and popularization of the Internet, the amount of data has increased sharply, and statistical methods have been fully applied. Internet companies have set up machine translation research groups and developed machine translation systems based on Internet big data, so as to make machine translation really practical, such as "Baidu translation", "Google translation", etc. In recent years, with the progress of in-depth learning, machine translation technology has further developed, which has promoted the rapid improvement of translation quality, and the translation in oral and other fields is more authentic and fluent.(Liu 2014:6)<br />
<br />
===The problem of machine translation at present===<br />
Error is inevitable<br />
Many people have misunderstandings about machine translation. They think that machine translation has great deviation and can't help people solve any problems. In fact, the error is inevitable. The reason is that machine translation uses linguistic principles. The machine automatically recognizes grammar, calls the stored thesaurus and automatically performs corresponding translation. However, errors are inevitable due to changes or irregularities in grammar, morphology and syntax, such as sentences with adverbials after "give me a reason to kill you first" in Dahua journey to the West. After all, a machine is a machine. No one has special feelings for language. How can it feel the lasting charm of "the tenderness of lowering its head, like the shame of a water lotus? After all, the meaning of Chinese is very different due to the changes of morphology, grammar and syntax and the change of context. Even many Chinese people are zhanger monks - they can't touch their heads, let alone machines.(Liu 2014：3）<br />
<br />
Bottleneck<br />
In fact, no matter which method, the biggest factor affecting the development of machine translation lies in the quality of translation. Judging from the achievements, the quality of machine translation is still far from the ultimate goal.<br />
Chinese mathematician and linguist Zhou Haizhong once pointed out in his paper "fifty years of machine translation": to improve the quality of machine translation, the first thing to solve is the problem of language itself rather than programming; It is certainly impossible to improve the quality of machine translation by relying on several programs alone. At the same time, he also pointed out that it is impossible for machine translation to achieve the degree of "faithfulness, expressiveness and elegance" when human beings have not yet understood how the brain performs fuzzy recognition and logical judgment of language. This view may reveal the bottleneck restricting the quality of translation. <br />
It is worth mentioning that American inventor and futurist ray cozwell predicted in an interview with Huffington Post that the quality of machine translation will reach the level of human translation by 2029. There are still many disputes about this thesis in the academic circles.（Cui 2019：4）<br />
<br />
===2.Mistranslation of Chinese Japanese machine translation===<br />
===2.1Vocabulary mistranslation in Chinese Japanese machine translation===<br />
===2.1.1Mistranslation of proper nouns===<br />
In political materials, there are often political dignitaries' names, place names or a large number of proper nouns in the political field. The morphemes of such words are definite and inseparable. Mistranslation will make the source language lose its specific meaning. <br />
<br />
Japanese translation into Chinese Chinese translation into Japanese<br />
<br />
original text translation by Youdao reference translation original text translation by Youdao reference translation<br />
<br />
朱鎔基 朱基 朱镕基 栗战书 栗戰史書 栗戰書<br />
<br />
労安 劳安 劳安 李克强 李克強 李克強 <br />
<br />
筑紫哲也 筑紫哲也 筑紫哲也 习近平 習近平 習近平<br />
<br />
山口百惠 山口百惠 山口百惠 韩正 韓中 韓正<br />
<br />
田中角栄 田中角荣 田中角荣 王沪宁 王上海氏 王滬寧<br />
<br />
東条英機 东条英社 东条英机 汪洋 汪洋 汪洋<br />
<br />
毛沢东 毛泽东 毛泽东 赵乐际 趙樂南 趙樂際<br />
<br />
トウ・ショウヘイ 大酱 邓小平 江泽民 江沢民 江沢民<br />
<br />
周恩来 周恩来 周恩来<br />
<br />
クリントン 克林顿 克林顿<br />
<br />
The above table counts 18 special names in the two texts, and 7 machine translation errors. In terms of mistranslation, there are not only "战书" but also "戰史" and "史書" in Chinese. "沪" is the abbreviation of Shanghai. In other words, since "战书" and "沪" are originally common nouns, this disrupts the choice of target language in machine translation. Except Katakana interference (except for トウシゃウヘイ, most of the mistranslations appear in the new Standing Committee. It can be seen that the machine translation system does not update the thesaurus in time. Different from other words, people's names have particularity. Especially as members of the Standing Committee of the Political Bureau of the CPC Central Committee, the translation of their names is officially unified. In this regard, public figures such as political dignitaries, stars, famous hosts and important. In addition, the machine also translates Japanese surnames such as "タ二モト" (谷本), "アンドウ" (安藤) into "塔尼莫特" and "龙胆". It can be found that the language feature that Japanese will be written together with Chinese characters and Hiragana also directly affects the translation quality of Japanese Chinese machine translation. Japanese people often use Katakana pronunciation. For example, when Japanese people talk with Premier Zhu, they often use "二ーハウ" (Hello). However, the machine only recognizes the two pseudonyms "二" and "ハ" and transliterates them into "尼哈" , ignoring the long sound after "二" and "ハ".(Guan 2018:10-12)<br />
<br />
original text Translation by Youdao reference translation<br />
<br />
日美安全体制 日米の安全体制 日米安保体制<br />
<br />
中国共产党第十九次全国代表大会 中国共産党第19回全国代表大会 中国共産党第19回全国代表大会（第19回党大会）<br />
<br />
十八大 十八大 第18回党大会中国特色社会主义<br />
<br />
中国特色社会主義 中国の特色ある社会主義 第18回党大会<br />
<br />
中国共产党中央委员会 中国共産党中央委員会 中国共産党中央委員会<br />
<br />
中国共産党中央委員会十八届中共中央政治局常委 第18代中国共產党中央政治局常務委員 第18期中共中央政治局常務委員<br />
<br />
十八届中共中央政治局委员 18期の中国共產党中央政治局委員 第18期中共中央政治局委員<br />
<br />
十九届中共中央政治局常委 十九回中国共產党中央政治局常務委員 第19期中央政治局常務委員<br />
<br />
中共十九届一中全会 中国共產党第十九回一中央委員会 第19期中央委員会第1回全体会議<br />
<br />
The above table is a comparison of the original and translated versions of some proper nouns. As shown in the table, the mistranslation problems are mainly reflected in the mismatch of numerals + quantifiers, the wrong addition of case auxiliary word "の", the lack of connectors, the Mistranslation of abbreviations, dead translation, and the writing errors of Chinese characters. The following is a specific analysis one by one.<br />
<br />
"十八届中共中央政治局常委", "十八届中共中央政治局委员", "十九届中共中央政治局常委" and "中共十九届一中全会" all have quantifiers "届", which are translated into "代", "期" and "回" respectively. The meaning is vague and should be uniformly translated into "期"; Among them, the translation of the last three proper nouns lacks the Chinese conjunction "第". The case auxiliary word "の" was added by mistake in the translation of "十八届中共中央政治局委员" and "日美安全体制". The "中国共产党中央委员会" did not write in the form of Japanese Ming Dynasty characters, but directly used simplified Chinese characters.<br />
<br />
The full name of "中共十九届一中全会" is "中国共产党第十九届中央委员会第一次全体会议", in which "一" stands for "第一次", which is an ordinal word rather than a cardinal word. Machine translation does not produce a correct translation. The fundamental reason is that there are no "规则" for translating such words in machine translation. If we can formulate corresponding rules for such words (for example, 中共m'1届m2 中全会→第m1期中央委员会第m2回全体会议), the translation system must be able to translate well no matter how many plenary sessions of the CPC Central Committee.(Guan 2018:6-7)<br />
<br />
===2.1.2Mistranslation of Polysemy===<br />
original text Translation by Youdao reference translation<br />
<br />
スタジオ 摄影棚/工作室 直播现场/演播厅<br />
<br />
日中関係の話 中日关系的故事 就中日关系(话题)<br />
<br />
溝 水沟 鸿沟<br />
<br />
それでは日中の問題について質問のある方。 那么对白天的问题有提问的人。 关于中日问题的话题，举手提问。<br />
<br />
私たちのクラスは20人ちょっとですが、 我们班有20人左右， 我们班二十多人的意见统一很难，<br />
<br />
いろいろな意見が出て、まとめるのは大変です。 但是又各种各样的意见，总结起来很困难。 <br />
<br />
一体どうやって、13億人もの人をまとめているんですか。 到底是怎么处理13亿的人的呢？ 中国是怎样把13亿人凝聚在一起的？<br />
<br />
In the original text, the word "スタジオ" appeared four times and was translated into "摄影棚" or "工作室" respectively, but the context is on-site interview, not the production site of photography or film. "話" has many semantics, such as "说话", "事情", "道理", etc. In accordance with the practice of the interview program, Premier Zhu Rongji was invited to answer five questions on the topic of China Japan relations. Obviously, the meaning of the word "故事" is very abrupt. The inherent Japanese word "日中" means "晌午、白天". At the same time, it is also the abbreviation of the names of the two countries. The machine failed to deal with it correctly according to the context. "溝" refers to the gap between China and Japan, not a "水沟". The former "まとめる" appears in the original text with the word "意见", which is intended to describe that it is difficult to unify everyone's opinions. The latter "まとめている" also refers to unifying the thoughts of 1.3 billion people, not "处理". Therefore, it is more appropriate to use "统一" and "处理" in human translation, rather than "总结意见" or "处理意见".(Zhang 2019:5)<br />
<br />
Mistranslation of polysemy has always been a difficult problem in machine translation research. The translation of each word is correct, but it is often very different from the original expression in the context. Zhang Zhengzeng said that ambiguity is a common phenomenon in natural language. Its essence is that the same language form may have different meanings, which is also one of the differences between natural language and artificial language. Therefore, one of the difficulties faced by machine translation is language disambiguation (Zhang Zheng, 2005:60). In this regard, we can mark all the meanings of polysemous words and judge which meaning to choose by common collocation with other words. At the same time, strengthen the text recognition ability of the machine to avoid the translation inconsistent with the current context. In this way, we can avoid the mistake of "大酱" in the place where famous figures such as Deng Xiaoping should have appeared in the previous political articles.(Wang 2020:7-9)<br />
<br />
===2.1.3Mistranslation of compound words===<br />
Multi class words refer to a word with two or more parts of speech, also known as the same word and different classes.<br />
<br />
original text Translation by Youdao reference translation<br />
<br />
1998年江泽民主席曾经访问日本, 1998年の江沢民国家主席の日本訪問し、 1998年、江沢民総書記が日本を訪問し、かつ<br />
<br />
同已故小渊首相签署了联合宣言。 かつて同じ故小渕首相が署名した共同宣言 亡くなられた小渕総理と宣言に調印されました<br />
<br />
Chinese "同" has two parts of speech: prepositions and conjunctions: "故" has many parts of speech, such as nouns, verbs, adjectives and conjunctions. This directly affects the judgment of the machine at the source language level. The word "同" in the above table is used as a conjunction to indicate the other party of a common act. "故" is used as a verb with the semantic meaning of "死亡", which is a modifier of "小渊首相". The machine regards "同" as an adjective and "故" as a noun "原因", which leads to confusion in the structure and unclear semantics of the translation. Different from the polysemy problem, multi category words have at least two parts of speech, and there is often not only one meaning under each part of speech. In this regard, software R & D personnel should fully consider the existence of multi category words, so that the translation machine can distinguish the meaning of words on the basis of marking the part of speech, so as to select the translation through the context and the components of the word in the sentence. Of course, the realization of this function is difficult, and we need to give full play to the wisdom of R & D personnel.(Guan 2018:9-12)<br />
<br />
===2.2 Syntactic mistranslation in Chinese Japanese machine translation===<br />
===2.2.1Mistranslation of tenses===<br />
original text：History is written by the people, and all achievements are attributed to the people. <br />
<br />
Translation by Youdao：歴史は人民が書いたものであり、すべての成果は人民のためである。<br />
<br />
reference translation：歴史は人民が綴っていくものであり、すべての成果は人民に帰することとなります。<br />
<br />
The original sentence meaning is "历史是人民书写的历史" or "历史是人民书写的东西". When translated into Japanese, due to the influence of Japanese language habits, the formal noun "もの" should be supplemented accordingly. In this sentence, both the machine and the interpreter have translated correctly. However, the machine's recognition of "的" is biased, resulting in tense translation errors. The past, present and future will become "history", and this is a continuous action. The form translated as "~ ていく" not only reflects that the action is a continuous action, but also conforms to the tense and semantic information of the original text. In addition, the machine's treatment of the preposition "于" is also inappropriate. In the original text, "人民" is the recipient of action, not the target language. As the most obvious feature of isolated language, function words in Chinese play an important role in semantic expression, and their translation should be regarded as a focus of machine translation research.(Zuo 2021:8)<br />
<br />
original text：李克强同志是十六届中共中央政治局常委,其他五位同志都是十六届中共中央政治局委员。<br />
<br />
Translation by Youdao：李克強総理は第16代中国共産党中央政治局常務委員であり、他の５人の同志はいずれも16期の中国共産党中央政治局委員である。<br />
<br />
reference translation：李克強同志は第16期中国共産党中央政治局常務委員を務め、他の５人は第16期中共中央政治局委員を務めました。<br />
<br />
Judging the verb "是" in the original text plays its most basic positive role, but due to the complexity of Chinese language, "是" often can not be completely transformed into the form of "だ / である" in the process of translation. This sentence is a judgment of what has happened. Compared with the 19th session, the 18th session has become history. This is an implicit temporal information. It is very difficult for machines without human brain to recognize this implicit information. "中共中央政治局常委" is the abbreviation of "中共中央政治局常务委员会委员" and it is a kind of position. In Japanese, often use it with verbs such as "務める","担当する",etc. The translator adopts the past tense of "務める", which conforms to the expression habit of Japanese and deals with the problem of tense at the same time. However, machine translation only mechanically translates this sentence into judgment sentence, and fails to correctly deal with the past information implied by the word "十八届".(Guan 2018:4)<br />
<br />
===2.2.2Mistranslation of honorifics===<br />
Honorific language is a language means to show respect to the listener. Different from Japanese, there is no grammatical category of honorifics in Chinese. There is no specific fixed grammatical form to express honorifics, self modesty and politeness. Instead, specific words such as "您", "请" and "劳驾" are used to express all kinds of respect or self modesty. (Yang 2020:5-9)<br />
<br />
Original text translation by Youdao reference translation<br />
<br />
女士们，先生们，同志们，朋友们 さんたち、先生たち、同志たち、お友达さん ご列席の皆さん<br />
<br />
谢谢大家！ ありがとうございます！ ご清聴ありがとうございました。<br />
<br />
これはどうされますか。 这是怎么回事呢? 您将如何解决这一问题？<br />
<br />
こうした問題をどうお考えでしょうか。 我们会如何考虑这些问题呢？ 您如何看待这一问题？<br />
<br />
For example, for the processing of "女士们，先生们，同志们，朋友们", the machine makes the translation correspond to the original one by one based on the principle of unchanged format, but there are errors in semantic communication and pragmatic habits. When speaking on formal occasions, Chinese expression tends to be comprehensive and detailed, as well as the address of the audience. In contrast, Japanese usually uses general terms such as "皆様", "ご臨席の皆さん" or "代表団の方々" as the opening remarks. In terms of Japanese Chinese translation, the machine also failed to recognize the usage of Japanese honorifics such as "さ れ る" and "お考え". It can be seen that Chinese Japanese machine translation has a low ability to deal with honorific expressions. The occasion is more formal. A good translation should not only be fluent in meaning, but also conform to the expression habits of the target language and match the current translation environment.(Che 2021:3-7)<br />
<br />
===Conclusion===<br />
In the process of discussing the Mistranslation of machine translation, this paper mainly cites the Mistranslation of Youdao translator. When I studied the problem of machine translation mistranslation, I also used a large number of translation software such as Google and Baidu for parallel comparison, and found that these translation software also had similar problems with Youdao translator. For example, the "新世界新未来" in Chinese ABAC structural phrases is translated by Baidu as "新し世界の新しい未来", which, like Youdao, is not translated in the form of parallel phrases; Google online translation translates the word "“全心全意" into a completely wrong "全面的に"; The original sentence "我吃了很多亏" in the Mistranslation of the unique expression in the language is translated by Google online as "私はたくさんの損失を食べました". Because the "吃" and "亏" in the original text are not closely adjacent, the machine can not recognize this echo and mistakenly treats "亏" as a kind of food, so the machine translates the predicate as "食べました"; Japanese "こうした問題をどう考えでしょうか", Google's online translation is "你如何看待这些问题?", "你" tone can not reflect the tone of Japanese honorific, while Baidu translates as "你怎么想这样的问题呢?" Although the meaning can be understood, it is an irregular spoken language. Google and Baidu also made the same mistakes as Youdao in the translation of proper nouns. For example, they translated "十七届(中共中央政治局常委)”" into "第17回..." .In short, similar mistranslations of Youdao are also common in Baidu and Google. Due to space constraints, they will not be listed one by one here.(Cui 2019:7)<br />
<br />
Mr. Liu Yongquan, Institute of language, Chinese Academy of Social Sciences (1997) It has been pointed out that machine translation is a linguistic problem in the final analysis. Although corpus based machine translation does not require a lot of linguistic knowledge, language expression is ever-changing. Machine translation based solely on statistical ideas can not avoid the translation quality problems caused by the lack of language rules. The following is based on the analysis of lexical, syntactic and other mistranslations From the perspective of the characteristics of the source language, this paper summarizes some difficulties of Chinese and Japanese in machine translation.(Liu 2014:8)<br />
<br />
(1) The difficulties of Chinese in machine translation <br />
<br />
Chinese is a typical isolated language. The relationship between words needs to be reflected by word order and function words. We should pay attention to the transformation of function words such as "的", "在", "向" and "了". Some special verbs, such as "是", "做" and "作", are widely used. How to translate on the basis of conforming to Japanese pragmatic habits and expressions is a difficulty in machine translation research. In terms of word order, Chinese is basically "subject→predicate→object". At the same time, Chinese pays attention to parataxis, and there is no need to be clear in expression with meaningful cohesion, which increases the difficulty of Chinese Japanese machine translation. When there are multiple verbs or modifier components in complex long sentences, machine translation usually can not accurately divide the components of the sentence, resulting in the result that the translation is completely unreadable.(Guan 2018:6-12) <br />
<br />
(2) Difficulties of Japanese in machine translation <br />
<br />
Both Chinese and Japanese languages use Chinese characters, and most machines produce the target language through the corresponding translation of Chinese characters. However, pseudonyms in Japanese play an important role in judging the part of speech and meaning, and can not only recognize Chinese characters and judge the structure and semantics of sentences. Japanese vocabulary is composed of Chinese vocabulary, inherent vocabulary and foreign vocabulary. Among them, the first two have a great impact on Chinese Japanese machine translation. For example "提出" is translated as "提出する" or "打ち出す"; and "重视" is translated as "重視する" or "大切にする". Japanese is an adhesive language, which contains a lot of "けど", "が" and "けれども" Some of them are transitional and progressive structures, but there are also some sequential expressions that do not need translation, and these translation software often can not accurately grasp them.(Che 2021:10)<br />
<br />
===References===<br />
[1] Navroz Kaur Kahlon,(2021(prepublish));Williamjeet Singh.Machine translation from text to sign language: a systematic review[J].Universal Access in the Information Society,1-35.<br />
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[2] Cao Qianyu;Hao Hanmei,(2021);Ahmed Syed Hassan.A Chaotic Neural Network Model for English Machine Translation Based on Big Data Analysis[J].Computational Intelligence and Neuroscience,3274326-3274326.<br />
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[3]Hwang Yongkeun;Kim Yanghoon;Jung Kyomin.(2021)Context-Aware Neural Machine Translation for Korean Honorific Expressions[J].Electronics,10(13):1589-1589.<br />
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[4]Zakaryia Almahasees.(2021)Analysing English-Arabic Machine Translation:Google Translate, Microsoft Translator and Sakhr.<br />
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[5](2021)Machine learning in translation[J].Nature Biomedical Engineering,5(6):485-486.<br />
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[6]Shaimaa Marzouk.(2021(prepublish))An in-depth analysis of the individual impact of controlled language rules on machine translation output: a mixed-methods approach[J].Machine Translation,1-37.<br />
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[7]Welnitzová Katarína;Munková Daša.(2021)Sentence-structure errors of machine translation into Slovak[J].Topics in Linguistics,22(1):78-92.<br />
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[8]Xu Xueyuan.(2021).Machine learning-based prediction of urban soil environment and corpus translation teaching[J].Arabian Journal of Geosciences,14(11). <br />
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[9]Chen Bingchang 陈丙昌(2016).機械翻訳の誤訳分析【D】.Error analysis of mechanical translation.贵州大学.2016(05) <br />
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[10]Lv Yinqiu 呂寅秋(1996).機械翻訳の言語規則と伝統文法との相違点.【D】The language rules of mechanical translation, the traditional grammar, and the points of contradiction.日本学研究.Japanese Studies.1996(00):21-22 <br />
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[11]Liu Jun 刘君(2014).基于语料库的中日同形词词义用法对比及其日中机器翻译研究【D】.A Corpus-based Comparison of the Meanings of Chinese and Japanese Homographs and Research on Japanese-Chinese Machine Translation.广西大学.(03) <br />
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[12]Cun Qianqian 崔倩倩(2019).机器翻译错误与译后编辑策略研究【D】.Research on Machine Translation Errors and Post-Editing Strategies.北京外国语大学.(09) <br />
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[13]Zhang Yi 张义(2019).机器翻译的译文分析【D】.Translation analysis of machine translation.西安外国语大学.(10) <br />
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[14]Zhang Linqian 张琳婧(2019).在线机器翻译中日翻译错误原因及对策【D】.Causes and countermeasures of online machine translation errors in Chinese-Japanese translation.山西大学.(02)<br />
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[15]Wang Dan 王丹(2020).基于机器翻译的专利文本译后编辑对策研究【D】.Research on countermeasures for post-translational editing of patent texts based on machine translation.大连理工大学.(06)<br />
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[16]Yang Xiaokun 杨晓琨(2020).日中机器翻译中的前编辑规则与效果验证【D】.Pre-editing rules and effect verification in Japanese-Chinese machine translation.大连理工大学.(06)<br />
<br />
[17]Zuo Jia 左嘉(2021). 机器翻译日译汉误译研究【D】. Research on Mistranslation of Machine Translation from Japanese to Chinese.北京第二外国语学院.<br />
<br />
[18]Guan Biying 关碧莹(2018).关于政治类发言的汉日机器翻译误译分析【D】.Analysis of Chinese-Japanese Machine Translation Mistranslations of Political Speeches.哈尔滨理工大学.<br />
<br />
[19]Che Tong 车彤(2021).汉译日机器翻译质量评估及译后编辑策略研究【D】.Research on Quality Evaluation of Chinese-Japanese Machine Translation and Post-translation Editing Strategies.北京外国语大学.(09)<br />
<br />
Networking Linking<br />
<br />
http://www.elecfans.com/rengongzhineng/692245.html<br />
<br />
https://baike.baidu.com/item/%E6%9C%BA%E5%99%A8%E7%BF%BB%E8%AF%91/411793<br />
<br />
=13 陈湘琼Chen Xiangqiong（Study on Post-editing from the Perspective of Functional Equivalence Theory ）=<br />
[[Machine_Trans_EN_13]]<br />
<br />
=14 Bi bi Nadia（Machine Translation a Challenge for Human Translators)=<br />
[[Machine_Trans_EN_14]]<br />
<br />
===Abstract===<br />
Machine translation is a big obstacle in the way of Human translators or interpreters although it is quick and less time consuming.People are trying to get translation of their target language using through source language.For this purpose they are using digital apps like Google translation Google translation does not give accurate and exact interpretation.Google translator is translating word to word but it doesn't clarify it's true and actual meaning.On the other hand human translators can give exact and accurate translation ,they take care of grammatical mistakes , diction and sentence structure.They clarify the purpose of target language through using source language.<br />
<br />
===Keywords===<br />
Descriptive translation, academic, interdiscipline, comparative literature, localization,Translatology,school of thought,translation , studies, linguistics, corresponding.<br />
<br />
===Body of article===<br />
Translation is the process of reworking text from one language into another to maintain the original message and communication. But, like anything else, there are different methods of translation, and they vary in form and function. What is translation? The term has become widely used among knowledge transferring researchers and practitioners, especially in the fields of health and health care. In a landmark review, Jonathan Lomas began to argue that 'The tasks… may be defined as to establish and maintain links between researchers and their audience, via the appropriate translation of research findings' (Lomas 1997, p 4). In 2004, World Health Organization's World Report on Knowledge for Better Health suggested that 'One of the key contributions of research to health systems is the translation of knowledge into actions' (WHO 2004, p 33 and p 100). By 2006, special issues of WHO's Bulletin as well as the journals Evaluation and the Health Professions and the Journal of Continuing Education in the Health Professions were dedicated to translation.But what does 'translation' mean? It may be a new word for an old problem, meaning nothing more than 'transfer'. The rapidly emerging field of 'translational medicine' seems to take translation to mean generally what transfer might have meant, that is the transmission of knowledge and evidence 'from bench to bedside'. As one commentator has observed, "Translational medicine" as a fashionable term is being increasingly used to describe the wish of biomedical researchers to ultimately help patients' (Wehling 2008, abstract). <br />
Semantic uncertainty persists, not least because of the different interests of scientists, clinicians, patients and commercial firms (Littman et al 2007): 'Translational research means different things to different people, but it seems important to almost everyone' (Woolf 2008, p 211).<br />
In related fields, such as public health (Armstrong et al 2006), 'translation' seems to signify dissatisfaction with 'transfer'. It wants to move away from thinking of knowledge transfer as a form of technology transfer or dissemination, rejecting if only by implication its mechanistic assumptions and its model of linear messaging from A to B. But still, what does it signify?<br />
<br />
===Why translation?===<br />
'Translation' indicates a closer attention to the problem of shared meaning and how it might be developed. It seems to represent some new epistemological lubricant, facilitating the dissemination of texts and the application and use of the knowledge and information they in. Simply, translation might be the key to transfer. And yet, when we stop to think, we are more ambivalent. What is translated often seems somehow inferior, not real or original. Note how readily commentators reach for the idea that things might be 'lost in translation'. Knowing at a distance – made in and mediated by translation - makes for incomplete renditions, blurred images, partial truths. So what might 'translation' really mean? The purpose of this paper is to set out, for policy makers and practitioners, the theoretical and conceptual resources translation holds and seems to represent. In doing so, it explores understandings of translation in the fields of literature and linguistics and in the sociology of science and technology. It begins by setting out just why this idea of translation should make immediate, intuitive sense in relation to research, policy and practice.<br />
1translation in research, policy and practice research as translation<br />
Research often entails translation from one language to another: where data is collected from more than one ethnic group, for example, or where the language of the researcher is other than that of the research subject. It may draw on a secondary literature or source documents written in different languages, and may be published and disseminated in languages other than the one in which it is first written up.<br />
2In a different way, to conduct an interview is to ask for an account of experience and its meanings, but it is also to construct and translate that experience in terms defined at least in part by the researcher. In representing what is said, transcripts then select data, usually excluding significant gesture and eye-contact, for example. Often, certain characteristics of speech-acts (such as hesitations) will be edited out. In turn, the format of the transcript shapes the analytic use the researcher may make of it. The basis of research 'findings', then, is an artefact, a transcript or translation, not an original interaction (Ochs 1979, Barnes, Bloor and Henry 1996, Ross 2009).<br />
3In this way, the researcher recasts aspects of his or her problem or topic in new, scientific form: 'All researchers "translate" the experiences of others' (Temple 1997, p 609). Research is invariably conducted in a sort of 'metalanguage' (Hantrais and Ager 1985): the research process can be conceived as one of successive translations (from theoretical formulation to operationalization, transcription, interpretation and dissemination). Theorization is a process of reciprocal back and forth between theory and fact, in which conceptions of each are revised in order that one fit the other (Baldamus 1974).<br />
4 It is a kind of translation: a rereading, re-use, re-application or re-representation of what we know in new terms (Turner 1980). Referencing, too, is an act of translation, a form of appropriation and incorporation of one text by another (Gilbert 1977).policy as translation Each of the fields covered by the paper is diverse and ill-defined, and there is no intention here to provide a comprehensive account of any them. Sources have been chosen for their relevance: main references are cited in the text, and additional sources listed in footnotes.<br />
<br />
2For a brief introduction to the technical issues involved in social research in more than one language, see Birbili (2000). For translation issues in survey research and question design in general, see Ervin and Bower (1952) and Deutscher (1968); on translating survey research instruments (in this instance health-related quality of life measures), Bowden and Fox-Rushby (2003). On the use of translators and interpreters, see Temple (1997) and Jentsch (1998).<br />
3For an interesting discussion of this problem, see Bourdieu (1999), esp pp 621-626, 'The risks of writing'. <br />
===Difference between machine translation and human translators===<br />
Few people disagree on the differences between the two, but many argue over the quality of the translations. How accurate are machine translations? How reliable are human translations? Some say machine translation produces near-perfect translations while others are adamant that translations are incomprehensible and cause more problems than they solve. Results will, of course, vary depending on the source and target languages, the machine translation service used (e.g. Google Translate), and the complexity of the original text.<br />
Machine translation, love it or hate it, is here to stay. In fact, the machine translation market is growing at such a fast pace that it is predicted to reach $980 million by 2022.<br />
===Machine translation: the pros and cons===<br />
The advantages of machine translation generally come down to two factors: it’s faster and cheaper. The downside to this is the standard of translation can be anywhere from inaccurate, to incomprehensible, and potentially dangerous (more on that shortly).<br />
==The advantages of machine translation==<br />
Many free tools are readily available (Google Translate, Skype Translator, etc.)<br />
Quick turnaround time <br />
You can translate between multiple languages using one tool<br />
Translation technology is constantly improving<br />
The disadvantages of machine translation<br />
Level of accuracy can be very low <br />
Accuracy is also very inconsistent across different languages<br />
==Machines can't translate context ==<br />
Mistakes are sometimes costly <br />
Sometimes translation simply doesn’t work<br />
The most important thing to consider with any kind of translation is the cost of potential mistakes. Translating instructions for medical equipment, aviation manuals, legal documents and many other kinds of content require 100% accuracy. In such cases, mistakes can cost lives, huge amounts of money and irreparable damage to your company’s image. So choose carefully!<br />
Human translation: the pros and cons<br />
Human translation essentially switches the table in terms of pros and cons. A higher standard of accuracy comes at the price of longer turnaround times and higher costs. What you have to decide is whether that initial investment outweighs the potential cost of mistakes. Alternatively, whether mistakes simply aren’t an option, like the cases we looked at in the previous section.<br />
<br />
==The advantages of human translation ==<br />
It’s a translator’s job to ensure the highest accuracy<br />
Humans can interpret context and capture the same meaning, rather than simply translating words<br />
Human translators can review their work and provide a quality process<br />
Humans can interpret the creative use of language, e.g. puns, metaphors, slogans, etc.<br />
Professional translators understand the idiomatic differences between their languages<br />
Humans can spot pieces of content where literal translation isn’t possible and find the most suitable alternative<br />
The disadvantages of human translation<br />
Turnaround time is longer <br />
Translators rarely work for free <br />
Unless you use a translation agency, with access to thousands of translators, you’re limited to the languages any one translator can work with<br />
Simply put, human translation is your best option when accuracy is even remotely important. Other considerations to make are the complexity of your source material and the two languages you’re translating between – both of which can render machines pretty useless.<br />
<br />
When to use machine and human translation<br />
<br />
The truth is, the debate over machine vs human translation is an unnecessary distraction. What we should really be talking about is when to use these two different types of translation services, because they both serve a very valid purpose.<br />
<br />
Examples of when to use machine translation<br />
<br />
When you have a large bulk of content to translate and the general meaning is enough<br />
When your translation never reaches the final audience, e.g. you’re translating a resource as research for another piece of content<br />
Translating documents for internal use within a company, provided 100% accuracy isn’t needed<br />
To partially translate large chunks of content for a human translator to improve upon<br />
Examples of when to use human translation<br />
When accuracy is important<br />
Most cases where your translated content is received by a consumer audience<br />
When you have a duty of care to provide accurate translations (e.g. legal documents, product instructions, medical guidelines or health and safety content)<br />
When translating marketing material or other texts for creative language uses.<br />
<br />
types of machine translation.<br />
<br />
What is Machine Translation? Rule Based Machine Translation vs. Statistical Machine Translation. Machine translation (MT) is automated translation. It is the process by which computer software is used to translate a text from one natural language (such as English) to another (such as Spanish).<br />
<br />
To process any translation, human or automated, the meaning of a text in the original (source) language must be fully restored in the target language, i.e. the translation. While on the surface this seems straightforward, it is far more complex. Translation is not a mere word-for-word substitution. A translator must interpret and analyze all of the elements in the text and know how each word may influence another. This requires extensive expertise in grammar, syntax (sentence structure), semantics (meanings), etc., in the source and target languages, as well as familiarity with each local region.<br />
<br />
Human and machine translation each have their share of challenges. For example, no two individual translators can produce identical translations of the same text in the same language pair, and it may take several rounds of revisions to meet customer satisfaction. But the greater challenge lies in how machine translation can produce publishable quality translations.<br />
<br />
Rule-Based Machine Translation Technology<br />
Rule-based machine translation relies on countless built-in linguistic rules and millions of bilingual dictionaries for each language pair.<br />
The software parses text and creates a transitional representation from which the text in the target language is generated. This process requires extensive lexicons with morphological, syntactic, and semantic information, and large sets of rules. The software uses these complex rule sets and then transfers the grammatical structure of the source language into the target language.<br />
Translations are built on gigantic dictionaries and sophisticated linguistic rules. Users can improve the out-of-the-box translation quality by adding their terminology into the translation process. They create user-defined dictionaries which override the system’s default settings.<br />
In most cases, there are two steps: an initial investment that significantly increases the quality at a limited cost, and an ongoing investment to increase quality incrementally. While rule-based MT brings companies to the quality threshold and beyond, the quality improvement process may be long and expensive.<br />
<br />
Statistical Machine Translation Technology<br />
Statistical machine translation utilizes statistical translation models whose parameters stem from the analysis of monolingual and bilingual corpora. Building statistical translation models is a quick process, but the technology relies heavily on existing multilingual corpora. A minimum of 2 million words for a specific domain and even more for general language are required. Theoretically it is possible to reach the quality threshold but most companies do not have such large amounts of existing multilingual corpora to build the necessary translation models. Additionally, statistical machine translation is CPU intensive and requires an extensive hardware configuration to run translation models for average performance levels.<br />
<br />
Rule-Based MT vs. Statistical MT<br />
Rule-based MT provides good out-of-domain quality and is by nature predictable. Dictionary-based customization guarantees improved quality and compliance with corporate terminology. But translation results may lack the fluency readers expect. In terms of investment, the customization cycle needed to reach the quality threshold can be long and costly. The performance is high even on standard hardware.<br />
<br />
Statistical MT provides good quality when large and qualified corpora are available. The translation is fluent, meaning it reads well and therefore meets user expectations. However, the translation is neither predictable nor consistent. Training from good corpora is automated and cheaper. But training on general language corpora, meaning text other than the specified domain, is poor. Furthermore, statistical MT requires significant hardware to build and manage large translation models.<br />
<br />
Rule-Based MT Statistical MT<br />
+ Consistent and predictable quality – Unpredictable translation quality<br />
+ Out-of-domain translation quality – Poor out-of-domain quality<br />
+ Knows grammatical rules – Does not know grammar <br />
+ High performance and robustness – High CPU and disk space requirements<br />
+ Consistency between versions – Inconsistency between versions <br />
– Lack of fluency + Good fluency<br />
– Hard to handle exceptions to rules + Good for catching exceptions to rules <br />
– High development and customization costs + Rapid and cost-effective development costs provided the required corpus exists<br />
Given the overall requirements, there is a clear need for a third approach through which users would reach better translation quality and high performance (similar to rule-based MT), with less investment (similar to statistical MT).<br />
Post-Edited Machine Translation (PEMT)<br />
Often, PEMT is used to bridge the gap between the speed of machine translation and the quality of human translation, as translators review, edit and improve machine-translated texts. PEMT services cost more than plain machine translations but less than 100% human translation, especially since the post-editors don’t have to be fluently bilingual—they just have to be skilled proofreaders with some experience in the language and target region.<br />
Successful translation is about more than just the words, which is why we advocate for not just human translation by skilled linguists, but for translation by people deeply familiar with the cultures they’re writing for. Life experience, study and the knowledge that only comes from living in a geographic region can make the difference between words that are understandable and language that is capable of having real, positive impact. <br />
<br />
PacTranz<br />
The HUGE list of 51 translation types, methods and techniques<br />
Upper section of infographic of 51 common types of translation classified in 4 broad categoriesThere are a bewildering number of different types of translation.<br />
So we’ve identified the 51 types you’re most likely to come across, and explain exactly what each one means.<br />
This includes all the main translation methods, techniques, strategies, procedures and areas of specialisation.<br />
It’s our way of helping you make sense of the many different kinds of translation – and deciding which ones are right for you.<br />
Don’t miss our free summary pdf download later in the article!<br />
The 51 types of translation we’ve identified fall neatly into four distinct categories.<br />
Translation Category A: 15 types of translation based on the technical field or subject area of the text<br />
Icons representing 15 types of translation categorised by the technical field or subject area of the textTranslation companies often define the various kinds of translation they provide according to the subject area of the text.<br />
This is a useful way of classifying translation types because specialist texts normally require translators with specialist knowledge.<br />
Here are the most common types you’re like to come across in this category.<br />
<br />
1. General Translation<br />
What is it?<br />
The translation of non-specialised text. That is, text that we can all understand without needing specialist knowledge in some area.<br />
The text may still contain some technical terms and jargon, but these will either be widely understood, or easily researched.<br />
What this means<br />
The implication is that you don’t need someone with specialist knowledge for this type of translation – any professional translator can handle them.<br />
Translators who only do this kind of translation (don’t have a specialist field) are sometimes referred to as ‘generalist’ or ‘general purpose’ translators.<br />
Examples<br />
Most business correspondence, website content, company and product/service info, non-technical reports.<br />
Most of the rest of the translation types in this Category do require specialist translators.<br />
Check out our video on 13 types of translation requiring special translator expertise:<br />
<br />
2. Technical Translation<br />
What is it?<br />
We use the term “technical translation” in two different ways:<br />
Broad meaning: any translation where the translator needs specialist knowledge in some domain or area.<br />
This definition would include almost all the translation types described in this section.<br />
Narrow meaning: limited to the translation of engineering (in all its forms), IT and industrial texts.<br />
This narrower meaning would exclude legal, financial and medical translations for example, where these would be included in the broader definition.<br />
What this means<br />
Technical translations require knowledge of the specialist field or domain of the text.<br />
That’s because without it translators won’t completely understand the text and its implications. And this is essential if we want a fully accurate and appropriate translation.Good to know Many technical translation projects also have a typesetting/dtp requirement. Be sure your translation provider can handle this component, and that you’ve allowed for it in your project costings and time frames.<br />
Examples<br />
Manuals, specialist reports, product brochures<br />
<br />
3. Scientific Translation<br />
What is it?<br />
The translation of scientific research or documents relating to it.<br />
What this means<br />
These texts invariably contain domain-specific terminology, and often involve cutting edge research.<br />
So it’s imperative the translator has the necessary knowledge of the field to fully understand the text. That’s why scientific translators are typically either experts in the field who have turned to translation, or professionally qualified translators who also have qualifications and/or experience in that domain.<br />
On occasion the translator may have to consult either with the author or other domain experts to fully comprehend the material and so translate it appropriately.<br />
Examples<br />
Research papers, journal articles, experiment/trial results<br />
<br />
4. Medical Translation<br />
What is it?<br />
The translation of healthcare, medical product, pharmaceutical and biotechnology materials.<br />
Medical translation is a very broad term covering a wide variety of specialist areas and materials – everything from patient information to regulatory, marketing and technical documents.<br />
As a result, this translation type has numerous potential sub-categories – ‘medical device translations’ and ‘clinical trial translations’, for example.<br />
What this means<br />
As with any text, the translators need to fully understand the materials they’re translating. That means sound knowledge of medical terminology and they’ll often also need specific subject-matter expertise.<br />
Good to know<br />
Many countries have specific requirements governing the translation of medical device and pharmaceutical documentation. This includes both your client-facing and product-related materials.<br />
Examples<br />
Medical reports, product instructions, labeling, clinical trial documentation<br />
<br />
5. Financial Translation<br />
What is it?<br />
In broad terms, the translation of banking, stock exchange, forex, financing and financial reporting documents.<br />
However, the term is generally used only for the more technical of these documents that require translators with knowledge of the field.<br />
Any competent translator could translate a bank statement, for example, so that wouldn’t typically be considered a financial translation.<br />
What this means<br />
You need translators with domain expertise to correctly understand and translate the financial terminology in these texts.<br />
Examples<br />
Company accounts, annual reports, fund or product prospectuses, audit reports, IPO documentation<br />
<br />
6. Economic Translations<br />
What is it?<br />
1. Sometimes used as a synonym for financial translations.<br />
2. Other times used somewhat loosely to refer to any area of economic activity – so combining business/commercial, financial and some types of technical translations.<br />
3. More narrowly, the translation of documents relating specifically to the economy and the field of economics.<br />
What this means<br />
As always, you need translators with the relevant expertise and knowledge for this type of translation.<br />
<br />
7. Legal Translation<br />
What is it?<br />
The translation of documents relating to the law and legal process.<br />
What this means<br />
Legal texts require translators with a legal background.<br />
That’s because without it, a translator may not:<br />
– fully understand the legal concepts<br />
– write in legal style<br />
– understand the differences between legal systems, and how best to translate concepts that don’t correspond.<br />
And we need all that to produce professional quality legal translations – translations that are accurate, terminologically correct and stylistically appropriate.<br />
Examples<br />
Contracts, legal reports, court judgments, expert opinions, legislation<br />
<br />
8. Juridical Translation<br />
What is it?<br />
1. Generally used as a synonym for legal translations.<br />
2. Alternatively, can refer to translations requiring some form of legal verification, certification or notarization that is common in many jurisdictions.<br />
<br />
9. Judicial Translation<br />
What is it?<br />
1. Most commonly a synonym for legal translations.<br />
2. Rarely, used to refer specifically to the translation of court proceeding documentation – so judgments, minutes, testimonies, etc. <br />
<br />
10. Patent Translation<br />
What is it?<br />
The translation of intellectual property and patent-related documents.<br />
Key features<br />
Patents have a specific structure, established terminology and a requirement for complete consistency throughout – read more on this here. These are key aspects to patent translations that translators need to get right.<br />
In addition, subject matter can be highly technical.<br />
What this means<br />
You need translators who have been trained in the specific requirements for translating patent documents. And with the domain expertise needed to handle any technical content.<br />
Examples<br />
Patent specifications, prior art documents, oppositions, opinions<br />
<br />
11. Literary Translation<br />
What is it?<br />
The translation of literary works – novels, short stories, plays, essays, poems.<br />
Key features<br />
Literary translation is widely regarded as the most difficult form of translation.<br />
That’s because it involves much more than simply conveying all meaning in an appropriate style. The translator’s challenge is to also reproduce the character, subtlety and impact of the original – the essence of what makes that work unique.<br />
This is a monumental task, and why it’s often said that the translation of a literary work should be a literary work in its own right.<br />
What this means<br />
Literary translators must be talented wordsmiths with exceptional creative writing skills.<br />
Because few translators have this skillset, you should only consider dedicated literary translators for this type of translation.<br />
<br />
<br />
12. Commercial Translation<br />
What is it?<br />
The translation of documents relating to the world of business.<br />
This is a very generic, wide-reaching translation type. It includes other more specialised forms of translation – legal, financial and technical, for example. And all types of more general business documentation.<br />
Also, some documents will require familiarity with business jargon and an ability to write in that style.<br />
What this means<br />
Different translators will be required for different document types – specialists should handle materials involving technical and specialist fields, whereas generalist translators can translate non-specialist materials.<br />
Examples<br />
Business correspondence, reports, marketing and promotional materials, sales proposals<br />
<br />
13. Business Translations<br />
What is it?<br />
A synonym for Commercial Translations.<br />
<br />
14. Administrative Translations<br />
What is it?<br />
The translation of business management and administration documents.<br />
So it’s a subset of business / commercial translations.<br />
What this means<br />
The implication is these documents will include business jargon and ‘management speak’, so require a translator familiar with, and practised at, writing in that style.<br />
Examples<br />
Management reports and proposals<br />
<br />
15. Marketing Translations<br />
What is it?<br />
The translation of advertising, marketing and promotional materials.<br />
This is a subset of business or commercial translations.<br />
Key features<br />
Marketing copy is designed to have a specific impact on the audience – to appeal and persuade.<br />
So the translated copy must do this too.<br />
But a direct translation will seldom achieve this – so translators need to adapt their wording to produce the impact the text is seeking.<br />
And sometimes a completely new message might be needed – see transcreation in our next category of translation types.<br />
What this means<br />
Marketing translations require translators who are skilled writers with a flair for producing persuasive, impactful copy.<br />
As relatively few translators have these skills, engaging the right translator is key.<br />
Good to know<br />
This type of translation often comes with a typesetting or dtp requirement – particularly for adverts, posters, brochures, etc.<br />
Its best for your translation provider to handle this component. That’s because multilingual typesetters understand the design and aesthetic conventions in other languages/cultures. And these are essential to ensure your materials have the desired impact and appeal in your target markets.<br />
Examples<br />
Advertising, brochures, some website/social media text.<br />
Translation Category B: 14 types of translation based on the end product or use of the translation<br />
This category is all about how the translation is going to be used or the end product that’s produced.<br />
Most of these types involve either adapting or processing a completed translation in some way, or converting or incorporating it into another program or format.<br />
You’ll see that some are very specialised, and complex.<br />
It’s another way translation providers refer to the range of services they provide.<br />
Check out our video of the most specialised of these types of translation:<br />
<br />
16. Document Translations<br />
What is it?<br />
The translation of documents of all sorts.<br />
Here the translation itself is the end product and needs no further processing beyond standard formatting and layout.<br />
<br />
17. Text Translations<br />
What is it?<br />
A synonym for document translation.<br />
<br />
18. Certified Translations<br />
What is it?<br />
A translation with some form of certification.<br />
Key features<br />
The certification can take many forms. It can be a statement by the translation company, signed and dated, and optionally with their company seal. Or a similar certification by the translator.<br />
The exact format and wording will depend on what clients and authorities require – here’s an example.<br />
<br />
19. Official Translations<br />
What is it?<br />
1. Generally used as a synonym for certified translations.<br />
2. Can also refer to the translation of ‘official’ documents issued by the authorities in a foreign country. These will almost always need to be certified.<br />
<br />
20. Software Localisation<br />
What is it?<br />
Adapting software for another language/culture.<br />
Key features<br />
The goal of software localisation is not just to make the program or product available in other languages. It’s also about ensuring the user experience in those languages is as natural and effective as possible.<br />
Translating the user interface, messaging, documentation, etc is a major part of the process.<br />
Also key is a customisation process to ensure everything matches the conventions, norms and expectations of the target cultures.<br />
Adjusting time, date and currency formats are examples of simple customisations. Others might involve adapting symbols, graphics, colours and even concepts and ideas.<br />
Localisation is often preceded by internationalisation – a review process to ensure the software is optimally designed to handle other languages.<br />
And it’s almost always followed by thorough testing – to ensure all text is in the correct place and fits the space, and that everything makes sense, functions as intended and is culturally appropriate.<br />
Localisation is often abbreviated to L10N, internationalisation to i18n.<br />
What this means<br />
Software localisation is a specialised kind of translation, and you should always engage a company that specialises in it.<br />
They’ll have the systems, tools, personnel and experience needed to achieve top quality outcomes for your product.<br />
<br />
21. Game Localisation<br />
What is it?<br />
Adapting games for other languages and markets.<br />
<br />
It’s a subset of software localisation.<br />
Key features<br />
The goal of game localisation is to provide an engaging and fun gaming experience for speakers of other languages.<br />
<br />
It involves translating all text and recording any required foreign language audio.<br />
<br />
But also adapting anything that would clash with the target culture’s customs, sensibilities and regulations.<br />
<br />
For example, content involving alcohol, violence or gambling may either be censored or inappropriate in the target market.<br />
<br />
And at a more basic level, anything that makes users feel uncomfortable or awkward will detract from their experience and thus the success of the game in that market.<br />
<br />
So portions of the game may have to be removed, added to or re-worked.<br />
<br />
Game localisation involves at least the steps of translation, adaptation, integrating the translations and adaptations into the game, and testing.<br />
<br />
What this means<br />
Game localisation is a very specialised type of translation best left to those with specific expertise and experience in this area.<br />
<br />
22. Multimedia Localisation<br />
What is it?<br />
Adapting multimedia for other languages and cultures.<br />
<br />
Multimedia refers to any material that combines visual, audio and/or interactive elements. So videos and movies, on-line presentations, e-Learning courses, etc.<br />
Key features<br />
Anything a user can see or hear may need localising.<br />
<br />
That means the audio and any text appearing on screen or in images and animations.<br />
<br />
Plus it can mean reviewing and adapting the visuals and/or script if these aren’t suitable for the target culture.<br />
<br />
The localisation process will typical involve:<br />
– Translation<br />
– Modifying the translation for cultural reasons and/or to meet technical requirements<br />
– Producing the other language versions<br />
<br />
Audio output may be voice-overs, dubbing or subtitling.<br />
<br />
And output for visuals can involve re-creating elements, or supplying the translated text for the designers/engineers to incorporate.<br />
<br />
What this means<br />
Multimedia localisation projects vary hugely, and it’s essential your translation providers have the specific expertise needed for your materials.<br />
<br />
<br />
<br />
23. Script Translations<br />
What is it?<br />
Preparing the text of recorded material for recording in other languages.<br />
Key features<br />
There are several issues with script translation.<br />
<br />
One is that translations typically end up longer than the original script. So voicing the translation would take up more space/time on the video than the original language.<br />
<br />
Sometimes that space will be available and this will be OK.<br />
<br />
But generally it won’t be. So the translation has to be edited back until it can be comfortably voiced within the time available on the video.<br />
<br />
Another challenge is the translation may have to synchronise with specific actions, animations or text on screen.<br />
<br />
Also, some scripts also deal with technical subject areas involving specialist technical terminology.<br />
<br />
Finally, some scripts may be very culture-specific – featuring humour, customs or activities that won’t work well in another language. Here the script, and sometimes also the associated visuals, may need to be adjusted before beginning the translation process.<br />
<br />
It goes without saying that a script translation must be done well. If it’s not, there’ll be problems producing a good foreign language audio, which will compromise the effectiveness of the video.<br />
<br />
Translators typically work from a time-coded transcript. This is the original script marked to show the time available for each section of the translation.<br />
<br />
What this means<br />
There are several potential pitfalls in script translations. So it’s vital your translation provider is practiced at this type of translation and able to handle any technical content.<br />
<br />
<br />
<br />
24. Voice-over and Dubbing Projects<br />
What is it?<br />
Translation and recording of scripts in other languages.<br />
<br />
Voice-overs vs dubbing<br />
There is a technical difference.<br />
A voice-over adds a new track to the production, dubbing replaces an existing one.<br />
<br />
<br />
Key features<br />
These projects involve two parts:<br />
– a script translation (as described above), and<br />
– producing the audio<br />
<br />
So they involve the combined efforts of translators and voice artists.<br />
The task for the voice artist is to produce a high quality read. That’s one that matches the style, tone and richness of the original.<br />
<br />
Often each section of the new audio will need to be the same length as the original.<br />
<br />
But sometimes the segments will need to be shorter – for example where the voice-over lags the original by a second or two. This is common in interviews etc, where the original voice is heard initially then drops out.<br />
<br />
The most difficult form of dubbing is lip-syncing – where the new audio needs to synchronise with the original speaker’s lip movements, gestures and actions.<br />
<br />
Lip-syncing requires an exceptionally skilled voice talent and considerable time spent rehearsing and fine tuning the translation.<br />
<br />
What this means<br />
You need to use experienced professionals every step of the way in this type of project.<br />
<br />
That’s to ensure firstly that your foreign-language scripts are first class, then that the voicing is of high professional standard.<br />
<br />
Anything less will mean your foreign language versions will be way less effective and appealing to your target audience.<br />
<br />
<br />
<br />
25. Subtitle Translations<br />
What is it?<br />
Producing foreign language captions for sub or surtitles.<br />
Key features<br />
The goal with subtitling is to produce captions that viewers can comfortably read in the time available and still follow what’s happening on the video.<br />
<br />
To achieve this, languages have “rules” governing the number of characters per line and the minimum time each subtitle should display.<br />
<br />
Sticking to these guidelines is essential if your subtitles are to be effective.<br />
<br />
But this is no easy task – it requires simple language, short words, and a very succinct style. Translators will spend considerable time mulling over and re-working their translation to get it just right.<br />
<br />
Most subtitle translators use specialised software that will output the captions in the format sound engineers need for incorporation into the video.<br />
<br />
What this means<br />
As with other specialised types of translation, you should only use translators with specific expertise and experience in subtitling.<br />
<br />
<br />
<br />
26. Website Localisation<br />
What is it?<br />
The translation and adapting of relevant content on a website to best suit the target language and culture.<br />
<br />
Note: Many providers use the term website translation as a synonym for localisation. Strictly speaking though, translation is just one part of localisation.<br />
Key features<br />
<br />
Not all pages on a website may need to be localised – clients should review their content to identify what’s relevant for the other language versions.<br />
Some content may need specialist translators – legal and technical pages for example.<br />
There may also be videos, linked documents, and text or captions in graphics to translate.<br />
Adaptation can mean changing date, time, currency and number formats, units of measure, etc.<br />
But also images, colours and even the overall site design and style if these won’t have the desired impact in the target culture.<br />
Translated files can be supplied in a wide range of formats – translators usually coordinate output with the site webmasters.<br />
New language versions are normally thoroughly reviewed and tested before going live to confirm everything is displaying correctly, works as intended and is cultural appropriate.<br />
What this means<br />
The first step should be to review your content and identify what needs to be translated. This might lead you to modify some pages for the foreign language versions.<br />
<br />
In choosing your translation providers be sure they can:<br />
– handle any technical or legal content,<br />
– provide your webmaster with the file types they want.<br />
<br />
And you should always get your translators to systematically review the foreign language versions before going live.<br />
<br />
<br />
<br />
27. Transcreation<br />
What is it?<br />
Adapting a message to elicit the same emotional response in another language and culture.<br />
Translation is all about conveying the message or meaning of a text in another language. But sometimes that message or meaning won’t have the desired effect in the target culture.<br />
<br />
This is where transcreation comes in. Transcreation creates a new message that will get the desired emotional response in that culture, while preserving the style and tone of the original.<br />
<br />
So it’s a sort of creative translation – which is where the word comes from, a combination of ‘translation’ and ‘creation’.<br />
<br />
At one level transcreation may be as simple as choosing an appropriate idiom to convey the same intent in the target language – something translators do all the time.<br />
<br />
But mostly the term is used to refer to adapting key advertising and marketing messaging. Which requires copywriting skills, cultural awareness and an excellent knowledge of the target market.<br />
<br />
Who does it?<br />
Some translation companies have suitably skilled personnel and offer transcreation services.<br />
<br />
Often though it’s done in the target country by specialist copywriters or an advertising or marketing agency – particularly for significant campaigns and to establish a brand in the target marketplace.<br />
<br />
What this means<br />
Most general marketing and promotional texts won’t need transcreation – they can be handled by a translator with excellent creative writing skills.<br />
<br />
But slogans, by-lines, advertising copy and branding statements often do.<br />
<br />
Whether you should opt for a translation company or an in-market agency will depend on the nature and importance of the material, and of course your budget.<br />
<br />
<br />
<br />
28. Audio Translations<br />
What is it?<br />
Broad meaning: the translation of any type of recorded material into another language.<br />
<br />
More commonly: the translation of a foreign language video or audio recording into your own language. So this is where you want to know and document what a recording says.<br />
Key features<br />
The first challenge with audio translations is it’s often impossible to pick up every word that’s said. That’s because audio quality, speech clarity and speaking speed can all vary enormously.<br />
<br />
It’s also a mentally challenging task to listen to an audio and translate it directly into another language. It’s easy to miss a word or an aspect of meaning.<br />
<br />
So best practice is to first transcribe the audio (type up exactly what is said in the language it is spoken in), then translate that transcription.<br />
<br />
However, this is time consuming and therefore costly, and there are other options if lesser precision is acceptable.<br />
<br />
What this means<br />
It’s best to discuss your requirements for this kind of translation with your translation provider. They’ll be able to suggest the best translation process for your needs.<br />
<br />
Examples<br />
Interviews, product videos, police recordings, social media videos.<br />
<br />
<br />
<br />
29. Translations with DTP<br />
What is it?<br />
Translation incorporated into graphic design files.multilingual dtp example in the form of a Rubik's Cube with foreign text on each square<br />
Key features<br />
Graphic design programs are used by professional designers and graphic artists to combine text and images to create brochures, books, posters, packaging, etc.<br />
<br />
Translation plus dtp projects involve 3 steps – translation, typesetting, output.<br />
<br />
The typesetting component requires specific expertise and resources – software and fonts, typesetting know-how, an appreciation of foreign language display conventions and aesthetics.<br />
<br />
What this means<br />
Make sure your translation company has the required multilingual typesetting/desktop publishing expertise whenever you’re translating a document created in a graphic design program.<br />
<br />
Translation Category C: 13 types of translation based on the translation method employed<br />
This category has two sub-groups:<br />
– the practical methods translation providers use to produce their translations, and<br />
– the translation strategies/methods identified and discussed within academia.<br />
<br />
The translation methods translation providers use<br />
There are 4 main methods used in the translation industry today. We have an overview of each below, but for more detail, including when to use each one, see our comprehensive blog article.<br />
<br />
Or watch our video.<br />
<br />
Important: If you’re a client you need to understand these 4 methods – choose the wrong one and the translation you end up with may not meet your needs!<br />
<br />
<br />
30. Machine Translation (MT)<br />
What is it?<br />
A translation produced entirely by a software program with no human intervention.<br />
<br />
A widely used, and free, example is Google Translate. And there are also commercial MT engines, generally tailored to specific domains, languages and/or clients.<br />
<br />
Pros and cons<br />
There are two limitations to MT:<br />
– they make mistakes (incorrect translations), and<br />
– quality of wording is patchy (some parts good, others unnatural or even nonsensical)<br />
<br />
On they positive side they are virtually instantaneous and many are free.<br />
<br />
Best suited for:<br />
Getting the general idea of what a text says.<br />
<br />
This method should never be relied on when high accuracy and/or good quality wording is needed.<br />
<br />
<br />
<br />
31. Machine Translation plus Human Editing (PEMT)<br />
What is it?<br />
A machine translation subsequently edited by a human translator or editor (often called Post-editing Machine Translation = PEMT).<br />
<br />
The editing process is designed to rectify some of the deficiencies of a machine translation.<br />
<br />
This process can take different forms, with different desired outcomes. Probably most common is a ‘light editing’ process where the editor ensures the text is understandable, without trying to fix quality of expression.<br />
<br />
Pros and cons<br />
This method won’t necessarily eliminate all translation mistakes. That’s because the program may have chosen a wrong word (meaning) that wasn’t obvious to the editor.<br />
<br />
And wording won’t generally be as good as a professional human translator would produce.<br />
<br />
Its advantage is it’s generally quicker and a little cheaper than a full translation by a professional translator.<br />
<br />
Best suited for:<br />
Translations for information purposes only.<br />
<br />
Again, this method shouldn’t be used when full accuracy and/or consistent, natural wording is needed.<br />
<br />
<br />
<br />
32. Human Translation<br />
What is it?<br />
Translation by a professional human translator.<br />
Pros and cons<br />
Professional translators should produce translations that are fully accurate and well-worded.<br />
<br />
That said, there is always the possibility of ‘human error’, which is why translation companies like us typically offer an additional review process – see next method.<br />
<br />
This method will take a little longer and likely cost more than the PEMT method.<br />
<br />
Best suited for:<br />
Most if not all translation purposes.<br />
<br />
<br />
<br />
33. Human Translation + Revision<br />
What is it?<br />
A human translation with an additional review by a second translator.<br />
<br />
The review is essentially a safety check – designed to pick up any translation errors and refine wording if need be.<br />
<br />
Pros and cons<br />
This produces the highest level of translation quality.<br />
<br />
It’s also the most expensive of the 4 methods, and takes the longest.<br />
<br />
Best suited for:<br />
All translation purposes.<br />
<br />
Gearwheel with 5 practical translation methods written on the teeth <br />
There’s also one other common term used by practitioners and academics alike to describe a type (method) of translation:<br />
<br />
34. Computer-Assisted Translation (CAT)<br />
What is it?<br />
A human translator using computer tools to aid the translation process.<br />
<br />
Key features<br />
Virtually all translators use such tools these days.<br />
<br />
The most prevalent tool is Translation Memory (TM) software. This creates a database of previous translations that can be accessed for future work.<br />
<br />
TM software is particularly useful when dealing with repeated and closely-matching text, and for ensuring consistency of terminology. For certain projects it can speed up the translation process.<br />
<br />
<br />
<br />
The translation methods described by academia<br />
A great deal has been written within academia analysing how human translators go about their craft.<br />
<br />
Seminal has been the work of Newmark, and the following methods of translation attributed to him are widely discussed in the literature.Gearwheel with Newmark's 8 translation methods written on the teeth <br />
These methods are approaches and strategies for translating the text as a whole, not techniques for handling smaller text units, which we discuss in our final translation category.<br />
<br />
<br />
<br />
35. Word-for-word Translation<br />
This method translates each word into the other language using its most common meaning and keeping the word order of the original language.<br />
<br />
So the translator deliberately ignores context and target language grammar and syntax.<br />
<br />
Its main purpose is to help understand the source language structure and word use.<br />
<br />
Often the translation will be placed below the original text to aid comparison.<br />
<br />
<br />
<br />
36. Literal Translation<br />
Words are again translated independently using their most common meanings and out of context, but word order changed to the closest acceptable target language grammatical structure to the original.<br />
<br />
Its main suggested purpose is to help someone read the original text.<br />
<br />
<br />
<br />
37. Faithful Translation<br />
Faithful translation focuses on the intention of the author and seeks to convey the precise meaning of the original text.<br />
<br />
It uses correct target language structures, but structure is less important than meaning.<br />
<br />
<br />
<br />
38. Semantic Translation<br />
Semantic translation is also author-focused and seeks to convey the exact meaning.<br />
<br />
Where it differs from faithful translation is that it places equal emphasis on aesthetics, ie the ‘sounds’ of the text – repetition, word play, assonance, etc.<br />
<br />
In this method form is as important as meaning as it seeks to “recreate the precise flavour and tone of the original” (Newmark).slide showing definition of semantic translation as a translation method<br />
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39. Communicative Translation<br />
Seeks to communicate the message and meaning of the text in a natural and easily understood way.<br />
<br />
It’s described as reader-focused, seeking to produce the same effect on the reader as the original text.<br />
<br />
A good comparison of Communicative and Semantic translation can be found here.<br />
<br />
40. Free Translation<br />
Here conveying the meaning and effect of the original are all important.<br />
<br />
There are no constraints on grammatical form or word choice to achieve this.<br />
<br />
Often the translation will paraphrase, so may be of markedly different length to the original.<br />
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41. Adaptation<br />
Mainly used for poetry and plays, this method involves re-writing the text where the translation would otherwise lack the same resonance and impact on the audience.<br />
<br />
Themes, storylines and characters will generally be retained, but cultural references, acts and situations adapted to relevant target culture ones.<br />
<br />
So this is effectively a re-creation of the work for the target culture.<br />
<br />
42. Idiomatic Translation<br />
Reproduces the meaning or message of the text using idioms and colloquial expressions and language wherever possible.<br />
<br />
The goal is to produce a translation with language that is as natural as possible.<br />
<br />
Translation Category D: 9 types of translation based on the translation technique used<br />
These translation types are specific strategies, techniques and procedures for dealing with short chunks of text – generally words or phrases.<br />
<br />
They’re often thought of as techniques for solving translation problems.<br />
<br />
They differ from the translation methods of the previous category which deal with the text as a whole.<br />
9 translation techniques as titles of books in a bookcase<br />
<br />
43. Borrowing<br />
What is it?<br />
Using a word or phrase from the original text unchanged in the translation.<br />
<br />
Key features<br />
With this procedure we don’t translate the word or phrase at all – we simply ‘borrow’ it from the source language.<br />
<br />
Borrowing is a very common strategy across languages. Initially, borrowed words seem clearly ‘foreign’, but as they become more familiar, they can lose that ‘foreignness’.<br />
<br />
Translators use this technique:<br />
– when it’s the best word to use – either because it has become the standard, or it’s the most precise term, or<br />
– for stylist effect – borrowings can add a prestigious or scholarly flavour.<br />
<br />
Borrowed words or phrases are often italicised in English.<br />
<br />
Examples of borrowings in English<br />
grand prix, kindergarten, tango, perestroika, barista, sampan, karaoke, tofu<br />
<br />
<br />
44. Transliteration<br />
What is it?<br />
Reproducing the approximate sounds of a name or term from a language with a different writing system.<br />
<br />
Key features<br />
In English we use the Roman (Latin) alphabet in common with many other languages including almost all European languages.<br />
<br />
Other writing systems include Arabic, Cyrillic, Chinese, Japanese, Korean, Thai, and the Indian languages.<br />
<br />
Transliteration from such systems into the Roman alphabet is also called romanisation.<br />
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There are accepted systems for how individual letters/sounds should be romanised from most other languages – there are three common systems for Chinese, for example.<br />
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English borrowings from languages using non-Roman writing systems also require transliteration – perestroika, sampan, karaoke, tofu are examples from the above list.<br />
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Translators mostly use transliteration as a procedure for translating proper names.<br />
<br />
Examples<br />
毛泽东 Mao Tse-tung or Mao Zedong<br />
Владимир Путин Vladimir Putin<br />
서울 Seoul<br />
ភ្នំពេញ Phnom Penh<br />
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45. Calque or Loan Translation<br />
What is it?<br />
A literal translation of a foreign word or phrase to create a new term with the same meaning in the target language.<br />
<br />
So a calque is a borrowing with translation if you like. The new term may be changed slightly to reflect target language structures.<br />
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Examples<br />
German ‘Kindergarten’ has been calqued as детский сад in Russian, literally ‘children garden’ in both languages.<br />
<br />
Chinese 洗腦 ‘wash’ + ‘brain’ is the origin of ‘brainwash’ in English.<br />
<br />
English skyscraper is calqued as gratte-ciel in French and rascacielos in Spanish, literally ‘scratches sky’ in both languages.<br />
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46. Word-for-word translation<br />
What is it?<br />
A literal translation that is natural and correct in the target language.<br />
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Alternative names are ‘literal translation’ or ‘metaphrase’.<br />
<br />
Note: this technique is different to the translation method of the same name, which does not produce correct and natural text and has a different purpose.<br />
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Key features<br />
This translation strategy will only work between languages that have very similar grammatical structures.<br />
<br />
And even then, only sometimes.<br />
<br />
For example, standard word order in Turkish is Subject-Object-Verb whereas in English it’s Subject-Verb-Object. So a literal translation between these two will seldom work:<br />
– Yusuf elmayı yedi is literally ‘Joseph the apple ate’.<br />
<br />
When word-for-word translations don’t produce natural and correct text, translators resort to some of the other techniques described below.<br />
Examples<br />
French ‘Quelle heure est-il?’ works into English as ‘What time is it?’.<br />
<br />
Russian ‘Oн хочет что-нибудь поесть’ is ‘He wants something to eat’.<br />
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47. Transposition<br />
What is it?<br />
Translation with a change of grammatical structure.<br />
<br />
This technique gives the translation more natural wording and/or makes it grammatically correct.<br />
<br />
Examples<br />
A change in word order:<br />
Our Turkish example Yusuf elmayı yedi (literally ‘Joseph the apple ate’) –> Joseph ate the apple.<br />
<br />
Spanish La Casa Blanca (literally ‘The House White’) –> The White House<br />
<br />
A change in grammatical category:<br />
German Er hört gerne Musik (literally ‘he listens gladly [to] music’)<br />
= subject pronoun + verb + adverb + noun<br />
becomes Spanish Le gusta escuchar música (literally ‘[to] him [it] pleases to listen [to] music’)<br />
= indirect object pronoun + verb + infinitive + noun<br />
and English He likes listening to music<br />
= subject pronoun + verb + gerund + noun.<br />
<br />
48. Modulation<br />
What is it?<br />
Translation with a change of focus or point of view in the target language.<br />
<br />
This technique makes the translation more idiomatic – how people would normally say it in the language.<br />
<br />
Examples<br />
English talks of the ‘top floor’ of a building, French the dernier étage = last floor. ‘Last floor’ would be unnatural in English, so too ‘top floor’ in French.<br />
<br />
German uses the term Lebensgefahr (literally ‘danger to life’) where in English we’d be more likely to say ‘risk of death’.<br />
In English we’d say ‘I dropped the key’, in Spanish se me cayó la llave, literally ‘the key fell from me’. The English perspective is that I did something (dropped the key), whereas in Spanish something happened to me – I’m the recipient of the action.<br />
<br />
49. Equivalence or Reformulation<br />
What is it?<br />
Translating the underlying concept or meaning using a totally different expression.<br />
<br />
This technique is widely used when translating idioms and proverbs.<br />
<br />
And it’s common in titles and advertising slogans.<br />
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It’s a common strategy where a direct translation either wouldn’t make sense or wouldn’t resonate in the same way.<br />
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Examples<br />
Here are some equivalents of the English saying “Pigs may fly”, meaning something will never happen, or “you’re being unrealistic” (Source):<br />
– Thai: ชาติหน้าตอนบ่าย ๆ – literally, ‘One afternoon in your next reincarnation’<br />
– French: Quand les poules auront des dents – literally, ‘When hens have teeth’<br />
– Russian, Когда рак на горе свистнет – literally, ‘When a lobster whistles on top of a mountain’<br />
– Dutch, Als de koeien op het ijs dansen – literally, ‘When the cows dance on the ice’<br />
– Chinese: 除非太陽從西邊出來！– literally, ‘Only if the sun rises in the west’<br />
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50. Adaptation<br />
What is it?<br />
A translation that substitutes a culturally-specific reference with something that’s more relevant or meaningful in the target language.<br />
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It’s also known as cultural substitution or cultural equivalence.<br />
<br />
It’s a useful technique when a reference wouldn’t be understood at all, or the associated nuances or connotations would be lost in the target language.<br />
<br />
Note: the translation method of the same name is a similar concept but applied to the text as a whole.<br />
<br />
Examples<br />
Different cultures celebrate different coming of age birthdays – 21 in many cultures, 20, 15 or 16 in others. A translator might consider changing the age to the target culture custom where the coming of age implications were important in the original text.<br />
Animals have different connotations across languages and cultures. Owls for example are associated with wisdom in English, but are a bad omen to Vietnamese. A translator might want to remove or amend an animal reference where this would create a different image in the target language.<br />
<br />
51. Compensation<br />
What is it?<br />
A meaning or nuance that can’t be directly translated is expressed in another way in the text.<br />
Example<br />
Many languages have ways of expressing social status (honorifics) encoded into their grammatical structures.<br />
<br />
So you can convey different levels of respect, politeness, humility, etc simply by choosing different forms of words or grammatical elements.<br />
But these nuances will be lost when translating into languages that don’t have these structures.<br />
Then translating into languages that don’t have these structures<br />
Then translating into languages that don’t have these structures.<br />
<br />
===Conclusion ===<br />
<br />
In the light of above mentioned facts and figures here by we would say that machine translation is a challenge for human translators because it can reduce the workload of translation but can't give accurate and exact translation of the target language.It can be less reliable than human translation..</div>Chen Huinihttps://bou.de/u/index.php?title=Machine_translation&diff=131973Machine translation2021-12-13T13:10:38Z<p>Chen Huini: /* References */</p>
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<div>'''Machine Translation - A challenge or a chance for human translators?'''<br />
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[[Machine_translation|Overview Page of Machine Translation]]<br />
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30 Chapters（0/30)<br />
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[[Machine_Trans_EN_1]] [[Machine_Trans_EN_2]] [[Machine_Trans_EN_3]] [[Machine_Trans_EN_4]] [[Machine_Trans_EN_5]] [[Machine_Trans_EN_6]] [[Machine_Trans_EN_7]] [[Machine_Trans_EN_8]] [[Machine_Trans_EN_9]] [[Machine_Trans_EN_10]] [[Machine_Trans_EN_11]] [[Machine_Trans_EN_12]] [[Machine_Trans_EN_13]] [[Machine_Trans_EN_14]] [[Machine_Trans_EN_15]] [[Machine_Trans_EN_16]] [[Machine_Trans_EN_17]] [[Machine_Trans_EN_18]] [[Machine_Trans_EN_19]] [[Machine_Trans_EN_20]] [[Machine_Trans_EN_21]] [[Machine_Trans_EN_22]] [[Machine_Trans_EN_23]] [[Machine_Trans_EN_24]] [[Machine_Trans_EN_25]] [[Machine_Trans_EN_26]] [[Machine_Trans_EN_27]] [[Machine_Trans_EN_28]] [[Machine_Trans_EN_29]] [[Machine_Trans_EN_30]] ...<br />
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[[Book_projects|Back to translation project overview]]<br />
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[[DCG-To-Do|To the To Do list]]<br />
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=Chapter 1:A Comparison Between the Quality of Machine Translation and Human Translation——A Case Study of the Application of artificial intelligence in Sports Events=<br />
'''论机器翻译与人工翻译的质量对比——以人工智能在体育赛事领域的应用为例'''<br />
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卫怡雯 Wei Yiwen, Hunan Normal University, China<br />
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[[Machine_Trans_EN_1]]<br />
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=Chapter 3：On the Realm Advantages And Mutual Development of Machine Translation And Huamn Translation)=<br />
"'论机器翻译与人工翻译的领域优势及共同发展'"<br />
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肖毅瑶 Xiao Yiyao, Hunan Normal University, China<br />
[[Machine_Trans_EN_3]]<br />
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=Chapter 4 : A Comparison Between Machine Translation of Netease and Traditional Human Translation—A Case Study of The Economist Articles)= <br />
'''网易有道机器翻译与人工翻译的译文对比——以经济学人语料为例'''<br />
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王李菲 Wang Lifei, Hunan Normal University<br />
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[[Machine_Trans_EN_4]]<br />
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=Chapter 5: Muhammad Saqib Mehran - Problems in translation studies=<br />
[[Machine_Trans_EN_5]]<br />
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=6 徐敏赟(Machine Translation Based on Neural Network --Challenge or Chance?)=<br />
[[Machine_Trans_EN_6]]<br />
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=Chapter 7: The Cultivation of artificial intelligence and translation talents in the Belt and Road Initiative=<br />
[[Machine_Trans_EN_7]]<br />
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一带一路背景下人工智能与翻译人才的培养<br />
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颜莉莉 Yan Lili, Hunan Normal University, China<br />
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=Chapter 8: On Machine Translation Under Lanuguage Intelligence——An Option and Opportunity for Human Translators=<br />
'''论语言智能下的机器翻译——译者的选择与机遇'''<br />
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颜静 Yan Jing, Hunan Normal University, China<br />
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[[Machine_Trans_EN_8]]<br />
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=9 谢佳芬（ Machine Translation and Artificial Translation in the Era of Artificial Intelligence）=<br />
[[Machine_Trans_EN_9]]<br />
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=10 熊敏(Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts)=<br />
[[Machine_Trans_EN_10]]<br />
===Abstract===<br />
The history of machine translation can be traced to 1940s, undergoing germination, recession, revival and flourish. Nowadays, with the rapid development of information technology, machine translation technology emerged and is gradually becoming mature. Whether manual translation can be replaced by machine translation becomes a widely-disputed topic. So in order to explore the ability of machine translation, I adopts two versions of translation, which are manual translation and machine translation(this paper uses Youdao translation) for different types of texts(according to Peter Newmark's types of text：informative text, expressive text and vocative text). The results are quite different in terms of quality and accuracy. The results show that machine translation is more suitable for informative text for its brevity, while the expressive texts especially literary works and vocative texts are difficult to be translated, because there are too many loaded words and cultural images in expressive text and dependence on the readers. To draw a conclusion, the relationship between machine translation and manual translation should be complementary rather than competitive.<br />
<br />
===Key words===<br />
machine translation; manual translation; Newmark's type of texts<br />
<br />
===题目===<br />
Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts<br />
<br />
===摘要===<br />
机器翻译的历史可以追溯到上个世纪40年代，经历了萌芽期、萧条期、复兴期和繁荣期四个阶段。如今，随着信息技术的高速发展，机器翻译技术日益成熟，于是机器翻译能不能替代人工翻译这个话题引起了广泛热议。为了探究机器翻译的能力水平是否足以替代人工翻译，本人根据皮特纽马克的文本类型分类理论（信息类文本，表达文本，呼吁文本），选择了相应的译文类型，并且将其机器翻译的版本以及人工翻译的版本进行对比。结果发现，就质量和准确度而言，译文的水平大相径庭。因为其简洁的特性，机器比较适合翻译信息文本。而就表达文本，尤其是文学作品，因其存在文化负载词及相应的文化现象，所以机器翻译这类文本存在难度。而呼唤文本因其目的性以及对读者的依赖性较强，翻译难度较大。由此得知，机器翻译和人工翻译的关系应该是互补的，而不是竞争的。<br />
<br />
===关键词===<br />
机器翻译；人工翻译；纽马克文本类型<br />
<br />
===1. Introduction===<br />
====1.1.Introduction to machine translation====<br />
Machine translation, also known as computer translation is a technique to translating through machine translator, such as Google translation and Youdao translation. Machine translation is one of the branches of computational linguistics, ranging from computer science, statistics, information science and so on. Machine translation plays an important role in all aspects.<br />
Machine translation can be traced back to 1940s, when British engineer Booth and American engineer Weaver proposed using computer to translate and started to study machines used for translation. And the first machine translating system launched in 1954, used in English and Russian translation. And this means machine translation becomes a reality. However, the arrival of new things always accompanies barriers and setbacks. In the 1960s, reports from ALPAC (Automated Language Processing Advisory Committee) showed studies on machine translation had stagnated for a decade. At that time, due to the high cost of machine, choosing human translators to finish translating works was more economical for most companies. What’s worse, machine had difficulty in understanding semantic and pragmatic meanings. Fortunately, in the 1970s, with the advance and popularity of computer, machine translation was gradually back on track. With the development of science and technology, machine translation has better technological guarantee, such as artificial intelligence and computer technology. In the last decades, from the late 90s till now, machine translation is becoming more and more mature. The initial rule-oriented machine translation has been updated and Corpus-aided machine translation appears. And nowadays, technology in voice recognition has been further developed. This technique is frequently applied in speech translation products. Users only need to speak source language to the online translator and instantly it will speak out the target version. But machine can’t fully provide precise and accurate translation without any grammatical or semantic problems. Machine can understand the literal meaning of texts, but not the meaning in context. For example, there is polysemy in English, which refers to words having multiple meanings. So when translating these words, translators should take contexts into consideration. But machine has troubles in this way. In addition, machine has no feeling or intention. Hence, it is also difficult to convey the feelings from the source language.<br />
<br />
====1.2.Process of machine translation====<br />
The process of manual translation is different from that of machine translation. Here is the process of the former. (1) Understand source language. (2) Use target language to organize language. (3) Generate translation. Unlike manual translation, machine translation tends to analyze and code source language first, then look for related codes in corpus, and work out the code that represents target language, generating translation. But they share a common feature, which is that Lexicon, grammatical rules and syntactic structure are taken into consideration. This is one of the biggest challenges for machine translation.<br />
<br />
===2.Theorectical framework===<br />
====2.1Newmark’s text typology====<br />
Text typology is a theory from linguistics. It undergoes several phrases. Karl Buhler, a German psychologist and linguist defines communicative functions into expressive, information and vocative. Then Roman Jakobson proposes categorization of texts, which are intralingual translation, interlingual translation and intersemiotic translation. Accordingly, he defines six main functions of language, including the referential function, conative function, emotive function, poetic function, metalingual function and the phatic function. Based on the two theories, Peter Newmark, a translation theorist, summarizes the language function into six parts: the informative function, the vocative function, the expressive function, the phatic function, the aesthetic function, and the metalingual function. Later, he further divided texts into informative type, expressive text and vocative type according to the linguistic functions of various texts. <br />
The core of the informative texts is the truth. It is to convey facts, information, knowledge of certain topics, reality and theories. The language style of the text is objective, logical and standardized. Reports, papers, scientific and technological textbooks are all attributed to informative texts. Translators are required to take format into account for different styles.<br />
The core of the expressive text is the sender’s emotions and attitudes. It is to express sender’s preferences, feelings, views and so on. The language style of it is subjective. Imaginative literature, including fictions, poems and dramas, autobiography and authoritative statements belong to expressive text. It requires translators to be faithful to the source language and maintain the style.<br />
The core of the vocative text is readership. It is to call upon readers to act, think, feel and react in the way intended by the text. So it is reader-oriented. Such texts as advertisement, propaganda and notices are of vocative text. Newmark noted that translators need to consider cultural background of the source language and the semantic and pragmatic effect of target language. If readers can comprehend the meaning at once and do as the text says, then the goal of information communication is achieved.<br />
To draw a conclusion, informative texts focus on the information and facts. Expressive texts center on the mind of addressers, including feelings, prejudices and so on. And vocative texts are oriented on readers and call on them to practice as the texts say.<br />
<br />
====2.2Study method====<br />
Manual translations of the three texts are selected from authoritative versions and universally acknowledged. And machine translations of those come from Youdao Translator. And in this thesis I will compare and evaluate the two methods in word diction, sentence structure, word order and redundancy.<br />
<br />
===3.Comparison and analysis of machine translation and manual translation ===<br />
====3.1Informative text ====<br />
（1）English into Chinese<br />
<br />
①Source language:<br />
<br />
Keep the tip of Apple Pencil clean, as dirt and other small particles may cause excessive wear to the tip or damage the screen of i-pad.<br />
<br />
Target language:<br />
<br />
Machine translation: Apple Pencil笔尖应保持清洁，灰尘等小颗粒可能会导致笔尖过度磨损或损坏ipad屏幕。<br />
<br />
Manual translation: 保持Apple Pencil铅笔的笔尖干净，因为灰尘和其他微粒可能会导致笔尖的过度磨损或损坏iPad屏幕。<br />
<br />
Analysis: Here is the instruction of Apple Pencil. And the manual translation is the Chinese version on the instruction.Product instruction tends to be professional, since there are many terms for some concepts. Machine can easily identify these terms and provide related words to translate. The machine version is faithful and expressive to the source language. So it is well-qualified and readable for readers to understand the instruction. So we can use machine to translate informative text.<br />
<br />
②Source language:<br />
<br />
China on Saturday launched a rocket carrying three astronauts-two men and one woman - to the core module of a future space station where they will live and work for six months, the longest orbit for Chinese astronauts.<br />
<br />
Target language:<br />
<br />
Machine translation: 周六，中国发射了一枚运载三名宇航员(两男一女)的火箭，进入未来空间站的核心舱，他们将在那里生活和工作6个月，这是中国宇航员最长的轨道。<br />
<br />
Manual translation: 周六，中国发射了一枚搭载三名宇航员(两男一女)的火箭，进入未来空间站的核心舱，他们将在那里生活和工作6个月，这是中国宇航员最漫长的一次轨道飞行。<br />
<br />
Analysis: This is a news from Reuters, reporting that China has launched a rocket.The meaning of the two translations is almost the same, except for some word diction. But there are some details dealt with different choice. For example, the last sentence of the machine translation is a bit of obscure and direct. There are some ambiguous words and expressions.<br />
<br />
(2)Chinese into English<br />
<br />
Source language:湖南省博物馆是湖南省最大的历史艺术类博物馆，占地面积4.9万平方米，总建筑面积为9.1万平方米，是首批国家一级博物馆，中央地方共建的八个国家级重点博物馆之一、全国文化系统先进集体、文化强省建设有突出贡献先进集体。<br />
<br />
Target language:<br />
Manual translation: As the largest history and art museum in Hunan province, the Hunan Museum covers an area of 49,000㎡, with the building area reaching 91,000㎡. It is one of the first batch of national first-level museums and one of the first eight national museums co-funded by central and local governments.<br />
<br />
Machine translation: Museum in hunan province is one of the largest historical art museum in hunan province, covers an area of 49000 square meters, a total construction area of 91000 square meters, is the first national museum, the central place to build one of the eight national key museum, national cultural system advanced collectives, strong culture began with outstanding contribution of advanced collective.<br />
<br />
Analysis: Machine translation is not faithful enough in content. For instance, “首批国家一级博物馆” is translated into “first national museum”, which is not the meaning of the source language. And there are some obvious grammar mistakes in the machine translation. For example, machine translates it into just one sentence but there are multiple predicates in it. So it is not grammatically permissible. What’s more, the sentence structure of machine translation is confusing and the focus is not specific enough.<br />
<br />
====3.2Expressive text ====<br />
(1)English into Chinese<br />
<br />
Source language:<br />
<br />
An individual human existence should be like a river- small at first, narrowly contained within its banks, and rushing passionately past rocks and over waterfalls. Gradually the river grows wider, the banks recede, the waters flow more quietly, and in the end, without any visible breaks, they become merged in the sea, and painlessly lose their individual being.<br />
<br />
Target language:<br />
<br />
Machine translation: 一个人的存在应该像一条河流——开始很小，被紧紧地夹在两岸中间，然后热情奔放地冲过岩石，飞下瀑布。渐渐地，河面变宽，两岸后退，水流更加平缓，最后，没有任何明显的停顿，它们汇入大海，毫无痛苦地失去了自己的存在。<br />
<br />
Manual translation:人生在世，如若河流；河口初始狭窄，河岸虬曲，而后狂涛击石，飞泻成瀑。河道渐趋开阔，峡岸退去，水流潺缓，终了，一马平川，汇于大海，消逝无影。<br />
<br />
Analysis: Here is a well-known metaphor in the prose How to Grow Old written by Bertrand Russell. The manual translation is written by Tian Rongchang.This is a philosophical prose with graceful language. Literary translation is a most important and difficult branch of translation. Translator should focus on the literal meaning, culture, writing style and so on. It is a combination of beauty and elegance. Therefore, translators find it in a dilemma of beauty and faithfulness, let alone translating machine. Compared with manual translation, machine translation has difficulty in word choice. It is faithful and expressive, but not elegant enough.<br />
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(2)Chinese into English<br />
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Source language:没有一个人将小草叫做“大力士”，但是它的力量之大，的确是世界无比。这种力，是一般人看不见的生命力，只要生命存在，这种力就要显现，上面的石块，丝毫不足以阻挡。因为它是一种“长期抗战”的力，有弹性，能屈能伸的力，有韧性，不达目的不止的力。<br />
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Target language:<br />
<br />
Machine translation: No one calls the little grass "hercules", but its power is truly matchless in the world. This force is invisible life force. As long as there is life, this force will show itself. The stone above is not strong enough to stop it. Because it is a "long-term resistance" of the force, elastic, can bend and extend force, tenacity, not to achieve the purpose of the force.<br />
<br />
Manual translation: Though nobody describes the little grass as a “husky”, yet its herculean strength is unrivalled. It is the force of life invisible to naked eye. It will display itself so long as there is life. The rock is utterly helpless before this force- a force that will forever remain militant, a force that is resilient and can take temporary setbacks calmly, a force that is tenacity itself and will never give up until the goal is reached. (by Zhang Peiji)<br />
<br />
Analysis:This is the excerpt of a well-known Chinese prose written by Xia Yan. It is written during the war of Resistance Against Japan. So the prose holds symbolic meaning, eulogizing the invisible tenacious vitality so as to encourage Chinese to have confidence in the anti-aggression war. Compared with manual translation, machine translation is much more abstract and confusing, especially for the word diction. For example, “大力士” is translated into “hercules” which is a man of exceptional strength and size in Greek and Roman Mythology, making it difficult to understand if readers of target language have no idea of the allusion. What’s worse, the machine version doesn’t reveal the symbolic meaning of the text, which is the core of this prose.<br />
<br />
====3.3Vocative text ====<br />
<br />
(1)English into Chinese<br />
<br />
①Source language:<br />
<br />
iPhone went to film school, so you don’t have to. (Advertisement of iPhone13)<br />
<br />
Target language:<br />
<br />
Machine translation: iPhone上的是电影学院，所以你不用去。<br />
<br />
Manual translation:电影专业课，iPhone同学替你上完了。<br />
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Analysis：Here are advertisements of iPhone on Apple official website. There is a personification in the source language. It is used to stress the advancement and proficiency in camera, which is an appealing selling point to potential buyers. Compared with manual translation, machine translation is plain and not eye-catching enough for customers.<br />
<br />
②Source language: <br />
<br />
5G speed OMGGGGG<br />
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Machine language: 5克的速度 OMGGGGG<br />
<br />
Manual translation:<br />
<br />
iPhone的5G 巨巨巨巨巨5G<br />
<br />
Analysis: The “G” in the source language is the unit of speed, standing for generation. However, it is mistaken as a unit of weight, representing gram in the machine translation. So the meaning is not faithful to the source language at all. As for manual translation, it complies with the source in form. Specifically speaking, five “G”s in the former complies with five characters “巨”in the latter. And the pronunciation of the two is similar. There are two layers of meaning for the 5 “G”s. One exclaims the fast speed of 5 generation network and the other new technology. In the manual version, “巨”can be used to show degree, meaning “quite” or “very”. <br />
<br />
③Source language: <br />
<br />
History, faith and reason show the way, the way of unity. We can see each other not as adversaries but as neighbors. We can treat each other with dignity and respect, we can join forces, stop the shouting and lower the temperature. For without unity, there is no peace, only bitterness and fury."<br />
<br />
Target language:<br />
<br />
Machine translation: 历史、信仰和理性指明了团结的道路。我们可以把彼此视为邻居，而不是对手。我们可以尊严地对待彼此，我们可以联合起来，停止大喊大叫，降低温度。因为没有团结，就没有和平，只有痛苦和愤怒。<br />
<br />
Manual translation:历史、信仰和理性为我们指明道路。那是团结之路。我们可以把彼此视为邻居，而不是对手。我们可以有尊严地相互尊重。我们可以联合起来，停止喊叫，减少愤怒。因为没有团结就没有和平，只有痛苦和愤怒<br />
<br />
Analysis: Speech is a way to propagate some activity in public. It is an art to inspire emotion of the audience. The source language is the excerpt of Joe Biden’s inaugural speech. The speech should be inspiring and logic. The machine translation has some misunderstanding. Taking the translation of “lower the temperature” for example, machine only translates its literal meaning, relating to the temperature itself, without considering the context. What’s more, it is less logic than the manual one. Therefore, it adds difficulty to inspire the audience and infect their emotion.<br />
<br />
===4.Common mistakes in machine translation ===<br />
<br />
====4.1 lexical mistakes ====<br />
<br />
Common lexical mistakes include misunderstandings in word category, lexical meaning and emotive and evaluative meaning. Misunderstanding in word category shows in the classification of word in the source language. As for misunderstanding in lexical meaning, machine has difficulty in precisely reflecting the meaning of the original texts, due to different cultural background and different language system. And for misunderstanding in emotive meaning, machine has no intention and emotion like human-beings. Therefore, it’s impossible for it to know writers’ feelings and their writing purposes. So sometimes, it may translate something negative into something positive.<br />
<br />
====4.2 grammatical mistakes====<br />
<br />
Grammatical analysis plays an important part in translation. Normally speaking, every language has its own unique grammatical rules. So in the process of translation, if translators don’t know the formation rule well, the sentence meaning will be affected. Even though all the lexical meanings are well-known by translators, the lack of consciousness of grammaticality makes it harder to arrange words according to sequential rule. English tends to be hypotactic, while Chinese tends to be paratactic. English sentences are connected through syntactic devices and lexical devices. While Chinese sentences are semantically connected, which means there are limited logical words and connection words in Chinese. So when translating English sentence, we should first analyze its grammaticality and logical structure and then rearrange its sequence. However, online translating machine has troubles in grammatical analysis, which makes its improvement more difficult.<br />
<br />
====4.3 other mistakes====<br />
<br />
The two mistakes above are the internal ones. Apart from mistakes in linguistic system, there are some mistakes in other aspects, such as cultural background.<br />
<br />
===5.Reasons for its common mistakes ===<br />
<br />
====5.1 Difference in two linguistic system====<br />
<br />
With different history, English and Chinese have different ways of expression. Commonly speaking, English is synthetic language which expresses grammatical meaning through inflection such as tense and Chinese is analytic language which expresses grammatical meaning through word order and function word. In addition, English is more compact with full sentences. Subordinate sentence is one of the most important features in modern English. Chinese, on the other hand, is more diffusive with minor sentences.<br />
<br />
====5.2 Difference in thinking patterns and cultural background====<br />
<br />
According to Sapir-Whorf’s Hypothesis, our language helps mould our way of thinking and consequently, different languages may probably express their unique ways of understanding the world. For two different speech communities, the greater their structural differentiations are, the more diverse their conceptualization of the world will be. For example, western culture is more direct and eastern culture more euphemistic. What’s more, English culture tends to be individualism, focusing on detail, through which it reflects the whole, while Chinese culture tends to be collective. Different thinking patterns will add difficulty for machine to translate texts.<br />
<br />
====5.3 Limitation of computer====<br />
<br />
Recently, there are some breakthroughs and innovation in machine translation. However, due to its own limitation, online translation has limitation in some ways. Firstly, compared with machine, human brain is much more complicated, consisting of ten billions of neuron, each of which has different function to affect human’s daily activities and help humans avoid some errors. However, computer can only function according to preset programming has no intention or consciousness. Until now, countless related scholars have invested much time in machine translation. They upload massive language database, which include almost all linguistic rules. But computers still fail to precisely reflect the meaning of source language for many times due to the complexity and flexibility of language. On the other hand, computers can’t take context into consideration. During translation, it is often the case that machine chooses the most-frequently used meaning of one word. So without the correct and exact meaning, readers are easier to feel confused and even misunderstand the meaning of source language.<br />
<br />
===6.Conclusion===<br />
From the analysis above, we can draw a conclusion that machine deals with informative text best, followed by non-literary translation of expressive text. What’s more, machine can be a useful tool to get to know the gist and main idea of a specific topic, for the simple sentence structure and numerous terms. And it can improve translating efficiency with high speed. But machine has difficulty in translating literary works, especially proses and poems.<br />
<br />
Machine translation has mixed future. From the perspective of commercial, machine translation boasts a bright future. With the process of globalization, the demand for translation is increasing accordingly. On one hand, if we only depend on human translator to deal with translating works, the quality and accuracy of translation can be greatly affected. On the other hand, if machine is used properly to do some basic work, human translators only need to make preparation before translating, progress, polish and other advanced work, contributing to highly-qualified translation and high working efficiency.<br />
<br />
However, compared with manual translation, machine translation has a bleak future. It is still impossible for machine to replace interpreter or translator in a short term. With intelligence and initiative, humans are able to learn new knowledge constantly, which machine will never accomplish. Besides, machine is not used to replace translators but to assist them in work. In other words, translators and machine carry out their own duties and they are not incompatible.<br />
<br />
To draw a conclusion, although there are certain limitations of machine translation, it can serve as a catalyst for translating works. Therefore, with the rapid development of artificial intelligence and related technology, there are still many opportunities for machine translation.<br />
<br />
===Reference ===<br />
<br />
Cui Zihan 崔子涵.机器翻译译文质量对比——以谷歌翻译和DeepL为例[J] [Comparison among Machine Translation--Taking Google Translation and Deepl for Example].Overseas English 海外英语,2021(15):182-183.<br />
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Li Deyi 李德毅. (2018). 人工智能导论 [Introduction to Artificial Intelligence]. Beijing: China Science and Technology Press 中国科学技术出版社.<br />
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Qiu Quanju 仇全菊.大数据时代背景下机器翻译及其发展趋势[J][Machine Translation and its Development Trend under the Background of Big Data Era]. English Teachers 英语教师,2021,21(16):60-62.<br />
<br />
Zhuo Jianbin 卓键滨,Liu Wenxian 刘文娴,Peng Zili 彭子莉.机器翻译对各类型文本的德汉翻译能力探究[J][Research on the German Chinese Translation Ability of Machine Translation for Various Types of Texts]. Comparative Study of Cultural innovation 文化创新比较研究,2021,5(28):122-125.<br />
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(英) Peter Newmark A Textbook of Translation[M] Shanghai Foreign Education Press, 2002<br />
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Wang Hongqi 王红旗. (2008). 语言学概论 [Introduction to Linguistics]. Beijing: Peking University Press 北京大学出版社.<br />
<br />
Liu Qin刘琴.功能目的论对于不同文本类型的翻译解读[J][Analysis of Translations in Different Types of Text based on Functionalist Approaches].Overseas Engliosh 海外英语,2021(17):8-9.<br />
<br />
Zhang Peiji 张培基.英译中国现代散文选[M][Selected Modern Chinese Prose Writings]. Shanghai Foreign Languages Education Press 上海外语教育出版社, 2002.<br />
<br />
Chen Cheng陈诚.机器翻译技术的综述[J][Overview of Machine Translation Technology].Electronic Techonology 电子技术,2021,50(11):290-291.<br />
<br />
He Xinyu何馨宇.机器翻译的发展及其对翻译职业化的影响研究[J] [The Development of Machine Translation and its Effect on Professional Transltors].Overseas English 海外英语,2021(20):48-49.<br />
<br />
He Wen 何雯, Wang Xiufeng 王秀峰.信息型文本的在线机器翻译错误研究[J][Research on Errors in Online Machine Translation of Informative text ].Overseas English海外英语,2021(15):188-189.<br />
<br />
Li Hanji 李晗佶. (2021). 人工智能时代翻译技术与译者关系演变与重构 [Evolution and reconstruction of the relationship between translation technology and translators in the era of artificial intelligence]. 西华师范大学学报(哲学社会科学版) Journal of West China Normal University (PHILOSOPHY AND SOCIAL SCIENCES EDITION) (2021-12-04) 1-6.<br />
<br />
Wei Guang魏光. 人工翻译与机器翻译译文编辑比较研究[J][Comparative Study of Translation Editing between Manual Translation and Machine Translation]. Overseas English 海外英语,2021(19):18-19+21.<br />
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=Chapter 11 陈惠妮=Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts=<br />
<br />
机器翻译的译前编辑研究——以医学类文摘为例<br />
<br />
陈惠妮 Chen Huini, Hunan Normal University<br />
<br />
[[Machine_Trans_EN_11]]<br />
<br />
===Abstract===<br />
At present, globalization is accelerating and the market demand for language services is rapidly increasing . Machine translation, as an important translation method, can greatly improve translation efficiency due to its low cost and high speed. However, because of the limitations of machine translation and the differences between Chinese and English language, machine translation is not accurate enough. In order to balance translation efficiency and translation quality, a great number of manual revisions in translation are required for the machine translating texts. Medical papers are specialized, special and purposeful, so it requires accurate,qualified and professional translation. However, the quality of translations by machine is inefficient to meet the high-quality requirements of medical papers translation. Therefore, the introduction of pre-editing can greatly improve the efficiency and quality of machine translation.<br />
<br />
===Key words===<br />
Pre-editing, Machine translation, Medical texts<br />
<br />
===题目===<br />
Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts<br />
<br />
===摘要===<br />
在全球化加速发展的今天，市场对语言服务的需求迅速增加。机器翻译作为一种重要的翻译途径，由于其成本低、速度快，可以大大提高翻译效率。然而，由于机器翻译的局限性以及中英文语言的差异，机器翻译的准确性不高。为了平衡翻译效率和翻译质量，机器翻译文本需要大量的手工修改。<br />
医学论文具有专业性、特殊性和目的性，要求其译文准确、合格、专业。然而，机器翻译的质量较低，无法满足医学论文对翻译的高质量要求。因此，译前编辑的引入可以大大提高机器翻译的效率和质量。<br />
<br />
===关键词===<br />
译前编辑；机器翻译；医学文本<br />
<br />
===1. Introduction===<br />
===1.1 Definition of Machine Translation===<br />
As Cronin(2013) revealed: "Translation is undergoing a revolutionary upheaval. The influence of digital technology and the Internet on translation is continuous, extensive and profound. From the popularity of automatic online translation applications, translation revolution is everywhere (Cronin,2013) However,the concept of machine translation was firstly proposed in the 1930s. Since 1940s, the machine translation technology has been evolving from rule-based machine translation (RBMT) to statistical machine translation (SMT), and to neural machine translation (NMT). Machine translation refers to the automatic translation of source language into target language by using a computer system. That is, machine translation refers to the automatic translation of text from one language into another natural language by computer software or other online translation webs. Machine translation is also defined as the process of “using a computer system to automatically translate text or speech from one natural language to another” according to the definition by ISO (Cui 2014:68-73).O 'Brien (2002) defines it as "the behavior of modifying errors in machine translation to ensure that the target translation meets certain quality requirements". On the basic level, machine translation performs mechanical substitution of words in one language for words in another language, but that rarely produces a good translation, therefore, recognition of the whole phrases and their closest counterparts in the target language is needed. Not all words in one language have equivalents in another language, and many words have more than one meaning. A huge demand for translation is greatly needed in today’s global world, which creates new opportunities for the development of machine translation, attracts more and more attention and becomes one of the current research focuses.<br />
<br />
===1.2 Definition of Pre-editing===<br />
Pre-editing means to adjust and modify the source language to make it fit more with the characteristics of the machine translation software before putting the source language before the into machine translation, so as to improve the quality of the translation machine translation (Wei Changhong 2008:93-94). Pre-editing is to modify the original text before putting it into machine translation software, in order to improve the recognition rate of machine translation, optimize the output quality of translated text and reduce the workload of post-editing. Because pre-translation editing only needs to be modified in one language, the operation is simpler than post-translation editing, which can realize the double improvement of quality and efficiency. A good pre-editing translation can help machine translation more smoothly, thus improving the machine readability and quality of the output translation. Pre-editing is mostly applied in the following situations: one is when the original text is of poor quality and the machine is difficult to recognize the meaning of the sentence, such as user generated content with poor readability and translatability (Gerlach et al 2003:45-53); Documents that need to be published in multiple languages; Next is when the original text contains a lot of jargon; The last is the original text has a corresponding translation memory bank. If the original text is edited, it can better match the content of the translation memory bank. Pre-editing is a process of identifying problems. It requires to pre-edit the source texts before putting it into machine translation according to the requirements, listing the expressions or sentences that may have trouble in machine translation and then pre-edit it by human. The purpose is to enable the computer to translate better, improve the translatability of machine translation. (Slype G V & Guinet J F & Seitz F 1984:115)<br />
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===1.3 Machine Translation Mode===<br />
According to the different knowledge acquisition methods, machine translation modes can be classified as follows: one is rule-based machine translation, which is based on bilingual dictionaries and a library of language rules for each language. The quality of translation depends on whether the source language conforms to the existing rules, but the inexhaustible rules are the hinders of this model. The second mode is machine translation based on statistics. This translation model relies on the principles of mathematics and statistics to find various existing translations corresponding to the translation tasks through the employment of corpus, analyzing the frequency of their occurrence and selecting the translation with the highest frequency for output. The disadvantage of this translation model is that it ignores the flexibility of language and the importance of context. The last one is neural network language model. This model is different from previous translation models in that it uses end-to-end neural network to realize automatic translation between natural languages. At present, the quality of its translation is much higher than that of the previous translation models.<br />
===1.4 Source of Study Abstracts===<br />
The core medical journals from domestic are selected in order to make the paper more representative and authoritative, such as National Medical Journal of China, Journal of Peking University (Health Science), and Journal of Third Military Medical University. All of them include basic medical science, biomedical technology, laboratory medical science and other fields. In this way, the pre-editing approaches included are applicable enough to machine translation of medical abstracts.<br />
===1.5 Selection of Translation Software===<br />
Generally, machine translation software includes Google Translation, Youdao Translator and Niu Translation, which has their own special use in translation. Take Google as an example, it is more like neurons in human brain, enabling to learn and collect information to establish connections with its neural machine translator’s neurons. However, it also causes many errors because of the lack of enough information. In this paper, contrastive analysis will be carried on by using Google translation. On the one hand, being a pioneer in the translation of NET, it is inevitably to sue Google as the translation software to translate medical texts in this paper. Comparing the program called “Google brain”, other NMT of translation software are relatively disadvantages. On the other hand, the Google translation enjoys the largest users in the world, with its downloads of more than one billion. The output quality of Google translation is more correct and complete than other machine translation software. With years of development and improvement, Google Translation has been greatly promoted. In this paper, Chinese medical abstract will be automatically translated by Google translation, and then the translation output will be compared with the translation by human and the publication of English abstracts. The main purpose is to prove that the improvement and promotion of quality and accuracy in the medical abstracts will be obtained through the pre-editing approaches.<br />
<br />
===2.Language Characteristics and Error Division===<br />
Since Chinese and English are two different languages, it is quite neccessary to identify their own characteristics so as to better analyze and understand the two languages. Tytler (1978: 118-119) argued in his Essay on the Principles of Translation that there are three principles of translation that in all the translation should give readers the same feelings as the source text, except for complete transcript of ideas. There actually also exit some mistakes of these two languages. So the following will make some clarrifications of these two languages to ensure more accurate translation.<br />
<br />
===2.1 Language Characteristics of Medical Abstracts===<br />
Chinese and English belong to different language systems, so there are differences in their language structure and the way users think of the languages. When using machine translation from Chinese to English, due to the unequal language levels, there will be many mistakes in the translation process, especially for ESP texts, such as medical papers.Here, these differences mainly refer to the linguistic characteristics of medical abstracts. In medical abstracts, it usually includes structured and unstructured abstracts. Although in different forms, they both describe the purpose, methods and conclusions of the research.<br />
<br />
In the method section of medial abstract, several Chinese sentences can be connected with commas, but each sentence may convey different information. In contrast, English sentences contain a great deal of information, but in order to ensure clarity, some modifiers need to be isolated and then reconstructed. However, in Chinese-English machine translation, a lot of information is put into the sentence because the machine segments the sentence on the basis of the full comma.<br />
<br />
In addition, subjectless sentence is used in the objective and method parts of Chinese medical abstracts. The subjectless sentence means the sentence without or free from subject and is usually employed in two contexts. The first is "needless to say". In Chinese sentences, it is common to omit the subject of the sentence. Chinese sentences can convey meanings by using incomplete sentence structures, so Chinese speakers can understand the meanings of the sentences even though the sentence subject is omitted. The second is "emphasis of action". In this context, subjectless sentences are used to describe behaviors, especially in the study of traditional Chinese medicine abstracts. Comparatively speaking, it should be avoided in English medical abstracts. Abstract sentences are more subjective when describing the learning process, while the essential requirement of English medical abstract is objectivity. From this point, sentences without subject should be avoided in English medical abstracts. Another feature is voice. Few words with passive meanings appear in Chinese abstracts. English sentences are more favoured in using passive voice. When translating from Chinese to English, passive voice should be used to make the contents objective, which is also the basic requirement of medical papers.<br />
<br />
These are the linguistic features of Chinese medical abstracts. There are great differences in sentence structure and expression between Chinese and English medical abstracts. These differences may reduce the accuracy of machine translation, so it is necessary to introduce pre-editing to edit the source text to ensure that the source text can be accurately recognized by the machine and fully translated into English.<br />
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===2.2 Error Division of Machine Translation in Translating Abstracts of Medical Papers from Chinese to English===<br />
In this paper, errors in machine translation are listed out after analyzing. Some are due to the principles, such as statistical-based MT and NMT, employed by machine translation. Based on the original errors, they can be studied from two levels. One is from the macro-level, referring to mistakes caused by objective factors. These are not human factors, but the disadvantage of machine translation and the limitations of language. The limitations of machine translation are derived from the principles and models adopted and manifests itself as a reliance on reference sources. In this case, semantic ambiguity and textual incoherence may occur in the absence of reference sources.<br />
<br />
However, for ESP texts such as medical papers, the requirements for terms and sentence patterns are far beyond the existing corpora. For unfamiliar texts (that is, the corresponding texts cannot be found in the corpora). The quality of output translation will be relatively low, which is determined by the principles of machine translation. As mentioned above, machine translation has evolved over the past few decades from rules-based to corp-based statistics to NMT. However, there still exist some limitations in machine translation.<br />
<br />
Mistakes on micro-level are mainly caused by the variations and differences of linguistic structure between Chinese and English. Chinese is an implicit language, while English is an explicit one (Lian, 2010) To put it in another way, Chinese expression does not depend on language structures, while English does the opposite. This may result in mismatches between the original and machine translated sentences. This kind of error is mainly divided into two types: component fragment and component missing. For a medical paper, such errors are very serious. As a kind of ESP discourse, medical paper has the characteristics of fixed, objective and accurate language structure. In order to reproduce the characteristics of medical abstracts in translation, it is necessary to avoid errors at the level of words and sentences and pay attention to logic and consistency.<br />
<br />
Lexical errors here refer to the inconsistency of fixed expressions in the translation of terms. Although these terms are machine translation based on the corpus, the corpus may not be perfect for ESP texts such as medical papers. Therefore, fixed expressions are not used in term translation. This is also the most common mistake in machine translation (the term here includes but is not limited to nouns). For ESP texts, medical text in particular, the fixed expressions and the accuracy of terms are of great importance.<br />
<br />
===3.Approaches Proposed for Pre-editing ===<br />
Generally speaking, there is a close relationship between pre-editing and post-editing, both of which aim to convey information and ensure high or publishable translation quality. Proper pre-editing can improve the quality of machine translation in terms of adequacy and consistency. <br />
Complexity of natural language and people use language arbitrarily, bringing many difficulties to Chinese-English machine translation, Hu Qingping (2005:24) has proposed "the research of translation software and the development of the controlled language are the two directions to improve the quality of machine translation : the former aims at the difficulty in the natural language processing, the latter overcomes the arbitrariness of natural language". Feng Quangong and Gao Lin (2017: 63-68) put forward: "The writing principles of controlled language can be applied to pre-editing of machine translation. Pre-editing based on controlled language can effectively reduce the complexity and ambiguity of source text, improve the identifiability of machine translation (the translatability of source text itself), and thus reduce (fully) the workload of post-editing".<br />
The central task of pre-editing is to transform human-friendly content into machine-friendly content, so words and sentences need to be repositioned or even changed. Based on the analysis of the language characteristics of Chinese and English, the Chinese ideographic group should be split before the Chinese source text is input into the machine software to translate so that the sentence structure is complete which can be easily recognized by the machine translation software.<br />
===3.1 Extraction and Replacement of Terms in the Source Text===<br />
As a branch of applied English, medical English is the product of the combination of English language knowledge and medical knowledge. The terms in medical papers have the characteristics of fixed and complex language structure. Although Google Translation is based on a corpus, the language structure is not fixed due to the limited size of the corpus. Therefore, the following two steps should be followed before machine translation. The first is to extract terms and create a glossary, and then replace the Chinese terms in the source text with the corresponding English expressions in the glossary. Of course, the terms of extraction should be searched and verified according to the actual situation. All of these words are verified before they can be replaced. This method can not only ensure the accuracy of term translation, but also maintain the consistency of expression, especially when dealing with long texts.<br />
Example1<br />
Source abstract: 口腔白斑病癌变相关缺氧应答基因和微小RNA的芯片及表达验证。<br />
Google translation: Microarray detection/Chip detection and expression verification of hypoxia response genes and microRNAs related to oral leukoplakia canceration.<br />
Published translation:Transcriptome array screening and verification of oral leukoplakia carcinogenesis-related hypoxia-responsive gene and microRNA. <br />
Analysis: The expression “ Affymetrix GeneChip” empolyed in this thesis is a human transcription array for transcriptome array. In the source abstract, “芯片检测“ can be meant “screening” but not detection, so the proper and appropriate translation of these expression should be “transcription array screening”, but the Google translates it into “microarry detection of chip detection”. Therefore, term extraction is essential and inevitable before putting the source abstracts into the machine translation software.<br />
After pre-editing source abstracts: 对 oral leukoplakia carcinogenesis 相关 hypoxia-responsive gene 和微小RNA进行 transcriptome array screening 及表达验证。<br />
After pre-editing translation: Transcriptome array screening and expression- verification of hypoxia-responsive genes and microRNAs related to oral leukoplakia carcinogenesis.<br />
===3.2 Explication of Subordination===<br />
As mentioned above, the subordination of Chinese sentences is judged by certain specific words in machine translation . Chinese is a paratactic language, and sentences are often connected by internal logical relationships; while English depends on sentence structure, so sentences are often closely linked by various language forms. In order to improve the accuracy of machine translation, we can adjust the sentence structure and adjust the position of adjectives and their modifiers. <br />
Example 2<br />
Source abstracts:回顾性分析南京医科大学附属儿童医院中重度HIE患儿49例及同期就诊的无神经系统症状体征的足月新生儿为对照组31例的头颅磁共振成像（MRI）资料。<br />
Google translation: A retrospective analysis that the brain magnetic resonance imaging (MRI) data of 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University and full-term neonates with no neurological symptoms and signs during the same period were included in the control group.<br />
Published translation: A total of 49 children with moderate to severe HIE admitted to the children’s Hospital Affiliated to Nanjing Medical University were retrospectively analyzed. Cranial magnetic resonance imaging (MRI) date of 31 full-term neonates without neurological symptoms and signs who visited the hospital during the same period were recruited as the control group. <br />
Analysis: As the above example shows that “中重度HIE患儿” and “足月新生儿” in the source abstracts are from the Children’s Hospital Affiliated Nanjing Medical University. The Google translation shows that 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University. There is an error due to the misuse of subordination. There are some advice in order to solve the problem. That is, first to segment the sentence and then reconstruct the sentence. For instance, the Chinese expression “南京医科大学附属儿童医院” carries many modifiers, which requires to cut the modifiers and reconstruct the sentence. Therefore, the Chinese expression “南京医科大学附属儿童医院’can be divided into abstractions, leaving two sentences instead of one long sentence with modifiers..<br />
After pre-editing source abstracts: 对49例中重度HIE患儿以及31例无神经系统症状体征的足月新生儿的MRI资料进行回顾性分析。这些患者收治于南京医科大学儿童附属医院。<br />
After pre-editing translation: The MRI data of 49 children with moderate to severe HIE and 31 full-term newborns without neurological symptoms and signs were retrospectively analyzed. These patients were admitted to the Children’s Hospital of Nanjing Medical University.<br />
===3.3 Explication of Subject through Voice Changing===<br />
The extensive use of subjectless sentences are quite common in the Chinese abstracts, while English prefers to employ passive voice in the sentences so as to make them object and accurate. In order to take the characteristics of English sentences into great and careful consideration, the sentences written in passive voice in particular, it will be helpful for machine translation to find out the subject and reconstruct a sentence in passive voice (Wang Yan: 2008). The first is to discover the “doee” in a sentence and then is to reconstruct the sentence structure and put the “doee” before the verb. The last is to explicate the passive relation between the noun and the verb.<br />
Example 3<br />
Source abstract: 回顾性分析2018年1月至2020年12月解放军总医院第七医学中心收治的500例老年髋部骨折患者的资料。<br />
Google translation: A retrospective analysis of the data of 500 elderly patients with hip fractures admitted to the Seventh Medical Center of the PLA General Hospital from January 2018 to December 2020.<br />
Published translation: From January 2018 to December 2020, the data of 500 elderly patients with hip fracture treated in the Seventh Medical Center of PLA General Hospital were analyzed retrospectively.<br />
Analysis: The above example indicates that the “回顾性分析”in the Google Translate is a noun phrase, but the source abstracts actually describes an action or a behavior. The reason for such a mistake is that the machine can’t recognize the Chinese subjetless sentences. To find the subject of the sentence, the source abstracts can be revised and adjusted. Finding the “doee” is the first step, which refers to “500例老年髋部骨折患者的资料”in the source abstracts. And next is to put it in front of the verb, that is to place it in the beginning of the sentence. Last but not least, the passive voice should be explicated in the sentence. <br />
After pre-editing source abstract: 500例老年髋部骨折患者的资料被回顾性分析，他们在2018年1月之2020年12月期间收治于解放军总医院第七医学中心。<br />
After pre-editing translation: The data of 500 elderly hip fracture patients were retrospectively analyzed. They were admitted to the Seventh Medical Center of the PLA General Hospital between January 2018 and December 2020.<br />
===3.4 Relocation of Modifiers===<br />
Modifiers, including noun, adverbial and attributive are constantly employed in the Chinese medical papers in order to add information to subject and object in the sentence. By doing this, complicated sentences can be structured, thus causing obstacles to machine translation for recognizing this complex sentence structures when translating Chinese-English sentences. To make the machine translation successfully recognize the sentence structure, simplifying the sentence structure is quite necessary. The key is to moving the location of the modifiers and thus making the modifiers an independent sentence. In other words, the modifiers ought to be put before or behind the main part of the sentence to satisfy the common use of English. <br />
Usually, the modifiers in Chinese sentence can only be placed in front of the core word, while in English, modifiers are very flexible. It is all right to place the modifiers in front of or behind the major part of the sentences with adjective or a connective noun.<br />
Example 4<br />
Source abstracts: 利用DNA重组技术以pET-28a表达系统在E.coli BL21(DE3) 中重组表达Hepl。<br />
Google Translation: Hepl is recombined in E.coli BL21 (DE3) using DNA recombination technology with pET-28a expression system.<br />
Published translation: The recombinant Hcpl protein was expressed by using DNA recombination technology through pET-28a expression system in E. coli BL21 (De3).<br />
Analysis: The Chinese medical text includes two modifiers, “利用DNA”and “以pET-28a)表达系统”. These two modifiers will be translated by machine, which catches more attention to the two constituents. From the Google translation above, one failure is obvious that it misplaces the location of the two modifiers and presents it in not accurate form. To make it translate correctly by machine translation, dividing the sentences is very important so that the source abstracts can be correctly recognized by machine translation.<br />
After pre-editing source abstract: 利用DNA重组技术，Hcp1重组表达在E.coli BL21 (DE3)中， 通过pET-28a表达系统在E. coli BL21 (DE3)中。<br />
Google translation: Using DNA recombination technology, Hcp1 recombination is expressed in E.coli BL21 (DE3), via pET-28a expression system in E. coli BL21 (DE3).<br />
The second is the independence of modifiers, that is, modifiers can also be reconstructed into clauses to modify core words. Compared with English, There is no subordinate clause in Chinese, so redundant Chinese modifiers need to be reconstructed into subordinate clauses in Chinese-English translation to meet the characteristics of English. English, especially sentences with multiple modifiers. Otherwise, sentence structure may be confused, such as scattered modifiers of core words. To avoid this mistake, it is necessary to separate the modifier from the main part of the sentence. These modifiers should be reconstituted into clauses. Also, keep your sentences simple and easy to understand.<br />
===3.5 Proper Omission and Deletion of Category Words===<br />
Category words are commonly used in Chinese. Category words complement the meaning of words, including problems, positions, situations and jobs. Adding this supplementary word is more in line with Chinese custom. In many cases, it has no real meaning, so it can be omitted in translation. In Chinese, category words are frequently used. However, it is rarely used in English, which is one of the differences between Chinese and English. There are many kinds of category words. Considering objectivity and the fixed structure of language, redundant category words should be deleted in Chinese-English translation. In the general, the most commonly used category of words in the Chinese medical abstracts are "process", "behavior", and "situation". These categories of words hinder the language conversion of Chinese and English, resulting in redundancy. <br />
Example 5<br />
Source abstract: 探讨口腔白斑病癌变进程中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia response genes and associated microRNAs in the process of oral leukoplakia cancer was discussed.<br />
Published translation: To study the hypoxia response gene and microRNA (miRNA)expression profiles in the pathogenesis and progression of oral leukoplakia (OLK).<br />
Analysis: As the above example shows, the Chinese words “进程” belongs to a category word. However, the Chinese expression “癌变”contains the process, so the “进程” expression can be deleted before placing it into the machine translation, because the meaning of it has been overlapping between the expression “癌变”. Therefore, its place can be replaced with preposition “during”.<br />
After pre-editing source abstract: 探讨口腔白斑病癌变中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia responsive genes and miRNA in oral leukoplakia cancer was investigated.<br />
<br />
===Conclusion===<br />
After years of development, machine translation has made great progress. The accuracy of machine translation has been greatly improved in both text recognition and sentence pattern conversion. However, machine translation has its own limitations. In other words, it needs to rely on the parallel corpus as a reference source for improving its accuracy. ESP text, in particular, is harder to get the high quality by machine translation. <br />
<br />
As one of the research papers, the characteristics of medical abstracts are fixed language structures, objectivity and accuracy (Qin Yi 2004:421-423). Therefore, medical translation must be accurate, object and understandable to follow the specific demands of the medical paper. Being an important field in the human society, medical paper translation is on a great demand, which means that it needs a huge demand for human labor. However, with the machine translation promoting, it will be more efficient to translate medical papers combining the effort by human and machine. The improvement and development of machine translation requires the joint efforts of computer science, information science, statistics, linguistics and other academic circles to achieve more mature human-computer mutual assistance translation (Li Yafei, Zhang Ruihua 2019:38-45).<br />
<br />
However, errors can occur during the process of machine translation of Chinese- English, because of the differences of the Chinese and English and the processing of the machine. Errors from the perspective of linguistic or grammar can affect the machine translation a lot. After division and recognition of errors, some pre-editing approaches are put forward to help the machine translation more accurate and readable, that are, extraction and replacement of terms in the source text, relocation of modifiers, explication of subordination, proper omission, deletion of category words and explication of subject through voice changing. <br />
<br />
The paper mainly focuses on the pre-editing machine translation by using medical papers as a case study. The errors of machine translation occurring in the translation of medical abstracts and pre-editing approaches for machine translation. The quality of machine translation of medical papers is greatly improved after employing the pre-editing methods. However, machine translation is not as flexible and accurate as human brain, so it is of importance to combine pre-editing and post-editing approaches with machine translation in order to produce more accurate, more object machine translation of medical papers.<br />
<br />
===References===<br />
<br />
Cui Qiliang崔启亮(2014).论机器翻译的译后编辑[J] ''On Post-Editing of Machine Translatio''. 中国翻译 Chinese Translators Journal, 035(006):68-73<br />
<br />
Feng Quangong, Gao Lin冯全功,高琳 (2017). 基于受控语言的译前编辑对机器翻译的影响[J] ''Influence of Pre-editing Based on Controlled Language on Machine Translation''. 当代外语研究Contemporary Foreign Language Research,(2): 63-68+87+110.<br />
<br />
GERLACH J, et al ( 2013). ''Combining Pre-editing and Post-editing to Improve SMT of User-generated Content''[M]// Proceedings of MT Summit XIV Workshop on Post-editing Technology and Practice. 45-53<br />
<br />
Hu Qingping胡清平(2005). 机器翻译中的受控语言[J] ''Controlled Language in Machine Translation''. 中国科技翻译 Chinese Science and Technology Translation, (03): 24-27. <br />
<br />
Lian Shuneng连淑能 (2010). 英汉对比研究增订本[M]''An Updated Version of English-Chinese Contrastive Studies'' . 北京:高等教育出版社Beijing: Higher Education Publishing House. 35-36.<br />
<br />
Li Yafei, Zhang Ruihua黎亚飞,张瑞华 (2019). 机器翻译发展与现状[J]''The Development and Current Situation of Machine Translation''. 中国轻工教育 China Light Industry Education, (5):38-45. <br />
<br />
Qin Yi秦毅(2004),从翻译基本标准议医学英语的翻译[J] ''On the Translation of Medical English from the Basic Standard of Translation''. 遵义医学院学报 Journal of Zunyi Medical College,27 (4): 421-423. <br />
<br />
Slype G V & Guinet J F & Seitz F (1984). ''Better Translation for Better Communication'' [M] . Oxford: Pergamon Press Ltd (U.K.). 90-93<br />
<br />
O'Brien S (2002). Teaching Post-editing: A Proposal for Course Con-tent [EB/OL]. http://mt-archive. Info/EAMT-2002-0brien. Pdf.<br />
<br />
Tytler, A. F. (1978). ''Essay On The Principles of Translation''[M]. Amsterdam: JohnBenjamins Publishing. 118-119<br />
<br />
Wang Yan王燕 (2008). 医学英语翻译与写作教程[M] ''Medical English Translation and Writing Course''. 重庆:重庆大学出版社 Chongqing: Chongqing University Press. 60-61<br />
<br />
=12 蔡珠凤=(The Mistranslation of C-J Machine Translation of Political Statements)=<br />
[[Machine_Trans_EN_12]]<br />
===Abstract===<br />
Language is the main way of communication between people. With the continuous development of globalization, the scale of cross-border exchanges is also expanding. However, due to cultural differences and diversity, the languages of different countries and regions are very different, which seriously hinders people's communication. The demand for efficient and convenient translation tools is increasing. At the same time, with the development of network technology and artificial intelligence, recognition technology based on deep learning is more and more widely used in English, Japanese and other fields.<br />
<br />
===Key words===<br />
machine translation; political statements; mistranslation of C-J machine translation<br />
===题目===<br />
The Mistranslation of C-J Machine Translation of Political Statements<br />
===摘要===<br />
语言是人与人之间交流的主要方式。随着全球化的不断发展，跨境交流的规模也在不断扩大。然而，由于文化的差异和多样性，不同国家和地区的语言差异很大，这严重阻碍了人们的交流。对高效便捷的翻译工具的需求正在增加。同时，随着网络技术和人工智能的发展，基于深度学习的识别技术在英语、日语等领域的应用越来越广泛。<br />
<br />
===关键词===<br />
机器翻译；政治发言；政治发言中译日的误译<br />
===1. Introduction===<br />
===Introduction to machine translation===<br />
Machine translation, also known as automatic translation, is a process of using computers to convert one natural language (source language) into another natural language (target language). It is a branch of computational linguistics, one of the ultimate goals of artificial intelligence, and has important scientific research value.At the same time, machine translation has important practical value. With the rapid development of economic globalization and the Internet, machine translation technology plays a more and more important role in promoting political, economic and cultural exchanges.The development of machine translation technology has been closely accompanied by the development of computer technology, information theory, linguistics and other disciplines. From the early dictionary matching, to the rule translation of dictionaries combined with linguistic expert knowledge, and then to the statistical machine translation based on corpus, with the improvement of computer computing power and the explosive growth of multilingual information, machine translation technology gradually stepped out of the ivory tower and began to provide real-time and convenient translation services for general users.（Zhang 2019:5-6)<br />
<br />
=== C-J machine translation software===<br />
Today's online machine translation software includes Baidu translation, Tencent translation, Google translation, Youdao translation, Bing translation and so on. Google was the first company to launch the machine translation system, and Baidu was the first company to import the machine translation system in China. In addition, Tencent and Youdao have attracted much attention.Machine translation is the process of using computers to convert one natural language into another. It usually refers to sentence and full-text translation between natural languages. In order to continuously improve the translation quality, R & D personnel have added artificial intelligence technologies such as speech recognition, image processing and deep neural network to machine translation on the basis of traditional machine translation based on rules, statistics and examples.With the increase of using machine translation, the joint cooperation between manual translation and machine translation will also increase significantly in the future. What criteria should be used to evaluate the quality of machine translation? In the evolving field of machine translation, there is an urgent need to clarify the unsolvable questions and solved problems.<br />
<br />
===The history of machine translation===<br />
The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. alchuni put forward the idea of using machines for translation. In 1933, Soviet inventor П.П. Trojansky designed a machine to translate one language into another, and registered his invention on September 5 of the same year; However, due to the low technical level in the 1930s, his translation machine was not made. In 1946, the first modern electronic computer ENIAC was born. Shortly after that, W. weaver, an American scientist and A. D. booth, a British engineer, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947 when discussing the application scope of electronic computer. In 1949, W. Weaver published the translation memorandum, which formally put forward the idea of machine translation. After 60 years of ups and downs, machine translation has experienced a tortuous and long development path. The academic community generally divides it into the following four stages:<br />
<br />
Pioneering period（1947-1964）<br />
<br />
In 1954, with the cooperation of IBM, Georgetown University completed the English Russian machine translation experiment with ibm-701 computer for the first time, showing the feasibility of machine translation to the public and the scientific community, thus opening the prelude to the study of machine translation.<br />
It is not too late for China to start this research. As early as 1956, the state included this research in the national scientific work development plan. The topic name is "machine translation, the construction of natural language translation rules and the mathematical theory of natural language". In 1957, the Institute of language and the Institute of computing technology of the Chinese Academy of Sciences cooperated in the Russian Chinese machine translation experiment, translating 9 different types of more complex sentences.From the 1950s to the first half of the 1960s, machine translation research has been on the rise. The United States and the former Soviet Union, two superpowers, have provided a lot of financial support for machine translation projects for military, political and economic purposes, while European countries have also paid considerable attention to machine translation research due to geopolitical and economic needs, and machine translation has become an upsurge for a time. In this period, although machine translation is just in the pioneering stage, it has entered an optimistic period of prosperity.<br />
<br />
Frustrated period（1964-1975）<br />
<br />
In 1964, in order to evaluate the research progress of machine translation, the American Academy of Sciences established the automatic language processing Advisory Committee (Alpac Committee) and began a two-year comprehensive investigation, analysis and test.In November 1966, the committee published a report entitled "language and machine" (Alpac report for short), which comprehensively denied the feasibility of machine translation and suggested stopping the financial support for machine translation projects. The publication of this report has dealt a blow to the booming machine translation, and the research of machine translation has fallen into a standstill. Coincidentally, during this period, China broke out the "ten-year Cultural Revolution", and basically these studies also stagnated. Machine translation has entered a depression.<br />
<br />
convalescence（1975-1989）<br />
<br />
Since the 1970s, with the development of science and technology and the increasingly frequent exchange of scientific and technological information among countries, the language barriers between countries have become more serious. The traditional manual operation mode has been far from meeting the needs, and there is an urgent need for computers to engage in translation. At the same time, the development of computer science and linguistics, especially the substantial improvement of computer hardware technology and the application of artificial intelligence in natural language processing, have promoted the recovery of machine translation research from the technical level. Machine translation projects have begun to develop again, and various practical and experimental systems have been launched successively, such as weinder system Eurpotra multilingual translation system, taum-meteo system, etc.However, after the end of the "ten-year holocaust", China has perked up again, and machine translation research has been put on the agenda again. The "784" project has paid enough attention to machine translation research. After the mid-1980s, the development of machine translation research in China has further accelerated. Firstly, two English Chinese machine translation systems, ky-1 and MT / ec863, have been successfully developed, indicating that China has made great progress in machine translation technology.<br />
<br />
New period(1990 present)<br />
<br />
With the universal application of the Internet, the acceleration of the process of world economic integration and the increasingly frequent exchanges in the international community, the traditional way of manual operation is far from meeting the rapidly growing needs of translation. People's demand for machine translation has increased unprecedentedly, and machine translation has ushered in a new development opportunity. International conferences on machine translation research have been held frequently, and China has made unprecedented achievements. A series of machine translation software have been launched, such as "Yixing", "Yaxin", "Tongyi", "Huajian", etc. Driven by the market demand, the commercial machine translation system has entered the practical stage, entered the market and came to the users.Since the new century, with the emergence and popularization of the Internet, the amount of data has increased sharply, and statistical methods have been fully applied. Internet companies have set up machine translation research groups and developed machine translation systems based on Internet big data, so as to make machine translation really practical, such as "Baidu translation", "Google translation", etc. In recent years, with the progress of in-depth learning, machine translation technology has further developed, which has promoted the rapid improvement of translation quality, and the translation in oral and other fields is more authentic and fluent.<br />
<br />
===The problem of machine translation at present===<br />
Error is inevitable<br />
Many people have misunderstandings about machine translation. They think that machine translation has great deviation and can't help people solve any problems. In fact, the error is inevitable. The reason is that machine translation uses linguistic principles. The machine automatically recognizes grammar, calls the stored thesaurus and automatically performs corresponding translation. However, errors are inevitable due to changes or irregularities in grammar, morphology and syntax, such as sentences with adverbials after "give me a reason to kill you first" in Dahua journey to the West. After all, a machine is a machine. No one has special feelings for language. How can it feel the lasting charm of "the tenderness of lowering its head, like the shame of a water lotus? After all, the meaning of Chinese is very different due to the changes of morphology, grammar and syntax and the change of context. Even many Chinese people are zhanger monks - they can't touch their heads, let alone machines.<br />
Bottleneck<br />
In fact, no matter which method, the biggest factor affecting the development of machine translation lies in the quality of translation. Judging from the achievements, the quality of machine translation is still far from the ultimate goal.<br />
Chinese mathematician and linguist Zhou Haizhong once pointed out in his paper "fifty years of machine translation": to improve the quality of machine translation, the first thing to solve is the problem of language itself rather than programming; It is certainly impossible to improve the quality of machine translation by relying on several programs alone. At the same time, he also pointed out that it is impossible for machine translation to achieve the degree of "faithfulness, expressiveness and elegance" when human beings have not yet understood how the brain performs fuzzy recognition and logical judgment of language. This view may reveal the bottleneck restricting the quality of translation. <br />
It is worth mentioning that American inventor and futurist ray cozwell predicted in an interview with Huffington Post that the quality of machine translation will reach the level of human translation by 2029. There are still many disputes about this thesis in the academic circles.<br />
<br />
===2.Mistranslation of Chinese Japanese machine translation===<br />
===2.1Vocabulary mistranslation in Chinese Japanese machine translation===<br />
===2.1.1Mistranslation of proper nouns===<br />
In political materials, there are often political dignitaries' names, place names or a large number of proper nouns in the political field. The morphemes of such words are definite and inseparable. Mistranslation will make the source language lose its specific meaning. <br />
<br />
Japanese translation into Chinese Chinese translation into Japanese<br />
<br />
original text translation by Youdao reference translation original text translation by Youdao reference translation<br />
<br />
朱鎔基 朱基 朱镕基 栗战书 栗戰史書 栗戰書<br />
<br />
労安 劳安 劳安 李克强 李克強 李克強 <br />
<br />
筑紫哲也 筑紫哲也 筑紫哲也 习近平 習近平 習近平<br />
<br />
山口百惠 山口百惠 山口百惠 韩正 韓中 韓正<br />
<br />
田中角栄 田中角荣 田中角荣 王沪宁 王上海氏 王滬寧<br />
<br />
東条英機 东条英社 东条英机 汪洋 汪洋 汪洋<br />
<br />
毛沢东 毛泽东 毛泽东 赵乐际 趙樂南 趙樂際<br />
<br />
トウ・ショウヘイ 大酱 邓小平 江泽民 江沢民 江沢民<br />
<br />
周恩来 周恩来 周恩来<br />
<br />
クリントン 克林顿 克林顿<br />
<br />
The above table counts 18 special names in the two texts, and 7 machine translation errors. In terms of mistranslation, there are not only "战书" but also "戰史" and "史書" in Chinese. "沪" is the abbreviation of Shanghai. In other words, since "战书" and "沪" are originally common nouns, this disrupts the choice of target language in machine translation. Except Katakana interference (except for トウシゃウヘイ, most of the mistranslations appear in the new Standing Committee. It can be seen that the machine translation system does not update the thesaurus in time. Different from other words, people's names have particularity. Especially as members of the Standing Committee of the Political Bureau of the CPC Central Committee, the translation of their names is officially unified. In this regard, public figures such as political dignitaries, stars, famous hosts and important. In addition, the machine also translates Japanese surnames such as "タ二モト" (谷本), "アンドウ" (安藤) into "塔尼莫特" and "龙胆". It can be found that the language feature that Japanese will be written together with Chinese characters and Hiragana also directly affects the translation quality of Japanese Chinese machine translation. Japanese people often use Katakana pronunciation. For example, when Japanese people talk with Premier Zhu, they often use "二ーハウ" (Hello). However, the machine only recognizes the two pseudonyms "二" and "ハ" and transliterates them into "尼哈" , ignoring the long sound after "二" and "ハ".<br />
<br />
original text Translation by Youdao reference translation<br />
<br />
日美安全体制 日米の安全体制 日米安保体制<br />
<br />
中国共产党第十九次全国代表大会 中国共産党第19回全国代表大会 中国共産党第19回全国代表大会（第19回党大会）<br />
<br />
十八大 十八大 第18回党大会中国特色社会主义<br />
<br />
中国特色社会主義 中国の特色ある社会主義 第18回党大会<br />
<br />
中国共产党中央委员会 中国共産党中央委員会 中国共産党中央委員会<br />
<br />
中国共産党中央委員会十八届中共中央政治局常委 第18代中国共產党中央政治局常務委員 第18期中共中央政治局常務委員<br />
<br />
十八届中共中央政治局委员 18期の中国共產党中央政治局委員 第18期中共中央政治局委員<br />
<br />
十九届中共中央政治局常委 十九回中国共產党中央政治局常務委員 第19期中央政治局常務委員<br />
<br />
中共十九届一中全会 中国共產党第十九回一中央委員会 第19期中央委員会第1回全体会議<br />
<br />
The above table is a comparison of the original and translated versions of some proper nouns. As shown in the table, the mistranslation problems are mainly reflected in the mismatch of numerals + quantifiers, the wrong addition of case auxiliary word "の", the lack of connectors, the Mistranslation of abbreviations, dead translation, and the writing errors of Chinese characters. The following is a specific analysis one by one.<br />
<br />
"十八届中共中央政治局常委", "十八届中共中央政治局委员", "十九届中共中央政治局常委" and "中共十九届一中全会" all have quantifiers "届", which are translated into "代", "期" and "回" respectively. The meaning is vague and should be uniformly translated into "期"; Among them, the translation of the last three proper nouns lacks the Chinese conjunction "第". The case auxiliary word "の" was added by mistake in the translation of "十八届中共中央政治局委员" and "日美安全体制". The "中国共产党中央委员会" did not write in the form of Japanese Ming Dynasty characters, but directly used simplified Chinese characters.<br />
<br />
The full name of "中共十九届一中全会" is "中国共产党第十九届中央委员会第一次全体会议", in which "一" stands for "第一次", which is an ordinal word rather than a cardinal word. Machine translation does not produce a correct translation. The fundamental reason is that there are no "规则" for translating such words in machine translation. If we can formulate corresponding rules for such words (for example, 中共m'1届m2 中全会→第m1期中央委员会第m2回全体会议), the translation system must be able to translate well no matter how many plenary sessions of the CPC Central Committee.<br />
<br />
===2.1.2Mistranslation of Polysemy===<br />
original text Translation by Youdao reference translation<br />
<br />
スタジオ 摄影棚/工作室 直播现场/演播厅<br />
<br />
日中関係の話 中日关系的故事 就中日关系(话题)<br />
<br />
溝 水沟 鸿沟<br />
<br />
それでは日中の問題について質問のある方。 那么对白天的问题有提问的人。 关于中日问题的话题，举手提问。<br />
<br />
私たちのクラスは20人ちょっとですが、 我们班有20人左右， 我们班二十多人的意见统一很难，<br />
<br />
いろいろな意見が出て、まとめるのは大変です。 但是又各种各样的意见，总结起来很困难。 <br />
<br />
一体どうやって、13億人もの人をまとめているんですか。 到底是怎么处理13亿的人的呢？ 中国是怎样把13亿人凝聚在一起的？<br />
<br />
In the original text, the word "スタジオ" appeared four times and was translated into "摄影棚" or "工作室" respectively, but the context is on-site interview, not the production site of photography or film. "話" has many semantics, such as "说话", "事情", "道理", etc. In accordance with the practice of the interview program, Premier Zhu Rongji was invited to answer five questions on the topic of China Japan relations. Obviously, the meaning of the word "故事" is very abrupt. The inherent Japanese word "日中" means "晌午、白天". At the same time, it is also the abbreviation of the names of the two countries. The machine failed to deal with it correctly according to the context. "溝" refers to the gap between China and Japan, not a "水沟". The former "まとめる" appears in the original text with the word "意见", which is intended to describe that it is difficult to unify everyone's opinions. The latter "まとめている" also refers to unifying the thoughts of 1.3 billion people, not "处理". Therefore, it is more appropriate to use "统一" and "处理" in human translation, rather than "总结意见" or "处理意见".<br />
<br />
Mistranslation of polysemy has always been a difficult problem in machine translation research. The translation of each word is correct, but it is often very different from the original expression in the context. Zhang Zhengzeng said that ambiguity is a common phenomenon in natural language. Its essence is that the same language form may have different meanings, which is also one of the differences between natural language and artificial language. Therefore, one of the difficulties faced by machine translation is language disambiguation (Zhang Zheng, 2005:60). In this regard, we can mark all the meanings of polysemous words and judge which meaning to choose by common collocation with other words. At the same time, strengthen the text recognition ability of the machine to avoid the translation inconsistent with the current context. In this way, we can avoid the mistake of "大酱" in the place where famous figures such as Deng Xiaoping should have appeared in the previous political articles.<br />
<br />
===2.1.3Mistranslation of compound words===<br />
Multi class words refer to a word with two or more parts of speech, also known as the same word and different classes.<br />
<br />
original text Translation by Youdao reference translation<br />
<br />
1998年江泽民主席曾经访问日本, 1998年の江沢民国家主席の日本訪問し、 1998年、江沢民総書記が日本を訪問し、かつ<br />
<br />
同已故小渊首相签署了联合宣言。 かつて同じ故小渕首相が署名した共同宣言 亡くなられた小渕総理と宣言に調印されました<br />
<br />
Chinese "同" has two parts of speech: prepositions and conjunctions: "故" has many parts of speech, such as nouns, verbs, adjectives and conjunctions. This directly affects the judgment of the machine at the source language level. The word "同" in the above table is used as a conjunction to indicate the other party of a common act. "故" is used as a verb with the semantic meaning of "死亡", which is a modifier of "小渊首相". The machine regards "同" as an adjective and "故" as a noun "原因", which leads to confusion in the structure and unclear semantics of the translation. Different from the polysemy problem, multi category words have at least two parts of speech, and there is often not only one meaning under each part of speech. In this regard, software R & D personnel should fully consider the existence of multi category words, so that the translation machine can distinguish the meaning of words on the basis of marking the part of speech, so as to select the translation through the context and the components of the word in the sentence. Of course, the realization of this function is difficult, and we need to give full play to the wisdom of R & D personnel.<br />
<br />
===2.2 Syntactic mistranslation in Chinese Japanese machine translation===<br />
===2.2.1Mistranslation of tenses===<br />
original text：History is written by the people, and all achievements are attributed to the people. <br />
<br />
Translation by Youdao：歴史は人民が書いたものであり、すべての成果は人民のためである。<br />
<br />
reference translation：歴史は人民が綴っていくものであり、すべての成果は人民に帰することとなります。<br />
<br />
The original sentence meaning is "历史是人民书写的历史" or "历史是人民书写的东西". When translated into Japanese, due to the influence of Japanese language habits, the formal noun "もの" should be supplemented accordingly. In this sentence, both the machine and the interpreter have translated correctly. However, the machine's recognition of "的" is biased, resulting in tense translation errors. The past, present and future will become "history", and this is a continuous action. The form translated as "~ ていく" not only reflects that the action is a continuous action, but also conforms to the tense and semantic information of the original text. In addition, the machine's treatment of the preposition "于" is also inappropriate. In the original text, "人民" is the recipient of action, not the target language. As the most obvious feature of isolated language, function words in Chinese play an important role in semantic expression, and their translation should be regarded as a focus of machine translation research.<br />
<br />
original text：李克强同志是十六届中共中央政治局常委,其他五位同志都是十六届中共中央政治局委员。<br />
<br />
Translation by Youdao：李克強総理は第16代中国共産党中央政治局常務委員であり、他の５人の同志はいずれも16期の中国共産党中央政治局委員である。<br />
<br />
reference translation：李克強同志は第16期中国共産党中央政治局常務委員を務め、他の５人は第16期中共中央政治局委員を務めました。<br />
<br />
Judging the verb "是" in the original text plays its most basic positive role, but due to the complexity of Chinese language, "是" often can not be completely transformed into the form of "だ / である" in the process of translation. This sentence is a judgment of what has happened. Compared with the 19th session, the 18th session has become history. This is an implicit temporal information. It is very difficult for machines without human brain to recognize this implicit information. "中共中央政治局常委" is the abbreviation of "中共中央政治局常务委员会委员" and it is a kind of position. In Japanese, often use it with verbs such as "務める","担当する",etc. The translator adopts the past tense of "務める", which conforms to the expression habit of Japanese and deals with the problem of tense at the same time. However, machine translation only mechanically translates this sentence into judgment sentence, and fails to correctly deal with the past information implied by the word "十八届".<br />
<br />
===2.2.2Mistranslation of honorifics===<br />
Honorific language is a language means to show respect to the listener. Different from Japanese, there is no grammatical category of honorifics in Chinese. There is no specific fixed grammatical form to express honorifics, self modesty and politeness. Instead, specific words such as "您", "请" and "劳驾" are used to express all kinds of respect or self modesty. <br />
<br />
Original text translation by Youdao reference translation<br />
<br />
女士们，先生们，同志们，朋友们 さんたち、先生たち、同志たち、お友达さん ご列席の皆さん<br />
<br />
谢谢大家！ ありがとうございます！ ご清聴ありがとうございました。<br />
<br />
これはどうされますか。 这是怎么回事呢? 您将如何解决这一问题？<br />
<br />
こうした問題をどうお考えでしょうか。 我们会如何考虑这些问题呢？ 您如何看待这一问题？<br />
<br />
For example, for the processing of "女士们，先生们，同志们，朋友们", the machine makes the translation correspond to the original one by one based on the principle of unchanged format, but there are errors in semantic communication and pragmatic habits. When speaking on formal occasions, Chinese expression tends to be comprehensive and detailed, as well as the address of the audience. In contrast, Japanese usually uses general terms such as "皆様", "ご臨席の皆さん" or "代表団の方々" as the opening remarks. In terms of Japanese Chinese translation, the machine also failed to recognize the usage of Japanese honorifics such as "さ れ る" and "お考え". It can be seen that Chinese Japanese machine translation has a low ability to deal with honorific expressions. The occasion is more formal. A good translation should not only be fluent in meaning, but also conform to the expression habits of the target language and match the current translation environment.<br />
<br />
===Conclusion===<br />
In the process of discussing the Mistranslation of machine translation, this paper mainly cites the Mistranslation of Youdao translator. When I studied the problem of machine translation mistranslation, I also used a large number of translation software such as Google and Baidu for parallel comparison, and found that these translation software also had similar problems with Youdao translator. For example, the "新世界新未来" in Chinese ABAC structural phrases is translated by Baidu as "新し世界の新しい未来", which, like Youdao, is not translated in the form of parallel phrases; Google online translation translates the word "“全心全意" into a completely wrong "全面的に"; The original sentence "我吃了很多亏" in the Mistranslation of the unique expression in the language is translated by Google online as "私はたくさんの損失を食べました". Because the "吃" and "亏" in the original text are not closely adjacent, the machine can not recognize this echo and mistakenly treats "亏" as a kind of food, so the machine translates the predicate as "食べました"; Japanese "こうした問題をどう考えでしょうか", Google's online translation is "你如何看待这些问题?", "你" tone can not reflect the tone of Japanese honorific, while Baidu translates as "你怎么想这样的问题呢?" Although the meaning can be understood, it is an irregular spoken language. Google and Baidu also made the same mistakes as Youdao in the translation of proper nouns. For example, they translated "十七届(中共中央政治局常委)”" into "第17回..." .In short, similar mistranslations of Youdao are also common in Baidu and Google. Due to space constraints, they will not be listed one by one here.<br />
<br />
Mr. Liu Yongquan, Institute of language, Chinese Academy of Social Sciences (1997) It has been pointed out that machine translation is a linguistic problem in the final analysis. Although corpus based machine translation does not require a lot of linguistic knowledge, language expression is ever-changing. Machine translation based solely on statistical ideas can not avoid the translation quality problems caused by the lack of language rules. The following is based on the analysis of lexical, syntactic and other mistranslations From the perspective of the characteristics of the source language, this paper summarizes some difficulties of Chinese and Japanese in machine translation.<br />
<br />
(1) The difficulties of Chinese in machine translation <br />
<br />
Chinese is a typical isolated language. The relationship between words needs to be reflected by word order and function words. We should pay attention to the transformation of function words such as "的", "在", "向" and "了". Some special verbs, such as "是", "做" and "作", are widely used. How to translate on the basis of conforming to Japanese pragmatic habits and expressions is a difficulty in machine translation research. In terms of word order, Chinese is basically "subject→predicate→object". At the same time, Chinese pays attention to parataxis, and there is no need to be clear in expression with meaningful cohesion, which increases the difficulty of Chinese Japanese machine translation. When there are multiple verbs or modifier components in complex long sentences, machine translation usually can not accurately divide the components of the sentence, resulting in the result that the translation is completely unreadable. <br />
<br />
(2) Difficulties of Japanese in machine translation <br />
<br />
Both Chinese and Japanese languages use Chinese characters, and most machines produce the target language through the corresponding translation of Chinese characters. However, pseudonyms in Japanese play an important role in judging the part of speech and meaning, and can not only recognize Chinese characters and judge the structure and semantics of sentences. Japanese vocabulary is composed of Chinese vocabulary, inherent vocabulary and foreign vocabulary. Among them, the first two have a great impact on Chinese Japanese machine translation. For example "提出" is translated as "提出する" or "打ち出す"; and "重视" is translated as "重視する" or "大切にする". Japanese is an adhesive language, which contains a lot of "けど", "が" and "けれども" Some of them are transitional and progressive structures, but there are also some sequential expressions that do not need translation, and these translation software often can not accurately grasp them.<br />
<br />
===References===<br />
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[10]Lv Yinqiu 呂寅秋(1996).機械翻訳の言語規則と伝統文法との相違点.【D】The language rules of mechanical translation, the traditional grammar, and the points of contradiction.日本学研究.Japanese Studies.1996(00):21-22 <br />
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[11]Liu Jun 刘君(2014).基于语料库的中日同形词词义用法对比及其日中机器翻译研究【D】.A Corpus-based Comparison of the Meanings of Chinese and Japanese Homographs and Research on Japanese-Chinese Machine Translation.广西大学.(03) <br />
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[13]Zhang Yi 张义(2019).机器翻译的译文分析【D】.Translation analysis of machine translation.西安外国语大学.(10) <br />
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[14]Zhang Linqian 张琳婧(2019).在线机器翻译中日翻译错误原因及对策【D】.Causes and countermeasures of online machine translation errors in Chinese-Japanese translation.山西大学.(02)<br />
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[15]Wang Dan 王丹(2020).基于机器翻译的专利文本译后编辑对策研究【D】.Research on countermeasures for post-translational editing of patent texts based on machine translation.大连理工大学.(06)<br />
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[16]Yang Xiaokun 杨晓琨(2020).日中机器翻译中的前编辑规则与效果验证【D】.Pre-editing rules and effect verification in Japanese-Chinese machine translation.大连理工大学.(06)<br />
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[17]Zuo Jia 左嘉(2021). 机器翻译日译汉误译研究【D】. Research on Mistranslation of Machine Translation from Japanese to Chinese.北京第二外国语学院.<br />
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[19]Che Tong 车彤(2021).汉译日机器翻译质量评估及译后编辑策略研究【D】.Research on Quality Evaluation of Chinese-Japanese Machine Translation and Post-translation Editing Strategies.北京外国语大学.(09)<br />
<br />
Networking Linking<br />
<br />
http://www.elecfans.com/rengongzhineng/692245.html<br />
<br />
https://baike.baidu.com/item/%E6%9C%BA%E5%99%A8%E7%BF%BB%E8%AF%91/411793<br />
<br />
=13 陈湘琼Chen Xiangqiong（Study on Post-editing from the Perspective of Functional Equivalence Theory ）=<br />
[[Machine_Trans_EN_13]]<br />
<br />
=14 Bi bi Nadia（Machine Translation a Challenge for Human Translators)=<br />
[[Machine_Trans_EN_14]]<br />
<br />
===Abstract===<br />
Machine translation is a big obstacle in the way of Human translators or interpretors although it is quick and less time consuming.people are trying to get translation of their target language using through source language.For this purpose they are using digital apps like Google translation Google translation does not give accurate and exact interpretation.Google translator is translates word to word translate that doesn't clarifies it's true and actual meaning.On the other hand human translators can give exact and accurate translation ,they take care of grammatical errors , diction and sentence structure.They clarify the purpose of target language through using source language.<br />
<br />
===Keywords===<br />
Descriptive translation, academic, interdiscipline, comparative literature, localization,Translatology,school of thought,translation , studies, linguistics, corresponding.<br />
<br />
===Body of article===<br />
Translation is the process of reworking text from one language into another to maintain the original message and communication. But, like everything else, there are different methods of translation, and they vary in form and function. What is translation? The term has become widely used among knowledge transfer researchers and practitioners, especially in the fields of health and health care. In a landmark review, Jonathan Lomas began to argue that 'The tasks… may be defined as to establish and maintain links between researchers and their audience, via the appropriate translation of research findings' (Lomas 1997, p 4). In 2004, the World Health Organization's World Report on Knowledge for Better Health suggested that 'One of the key contributions of research to health systems is the translation of knowledge into actions' (WHO 2004, p 33 and p 100). By 2006, special issues of WHO's Bulletin as well as the journals Evaluation and the Health Professions and the Journal of Continuing Education in the Health Professions were dedicated to translation.But what does 'translation' mean? It may be a new word for an old problem, meaning nothing more than 'transfer'. The rapidly emerging field of 'translational medicine' seems to take translation to mean generally what transfer might have meant, that is the transmission of knowledge and evidence 'from bench to bedside'. As one commentator has observed, "Translational medicine" as a fashionable term is being increasingly used to describe the wish of biomedical researchers to ultimately help patients' (Wehling 2008, abstract). <br />
Semantic uncertainty persists, not least because of the different interests of scientists, clinicians, patients and commercial firms (Littman et al 2007): 'Translational research means different things to different people, but it seems important to almost everyone' (Woolf 2008, p 211).<br />
In related fields, such as public health (Armstrong et al 2006), 'translation' seems to signify dissatisfaction with 'transfer'. It wants to move away from thinking of knowledge transfer as a form of technology transfer or dissemination, rejecting if only by implication its mechanistic assumptions and its model of linear messaging from A to B. But still, what does it signify?<br />
<br />
===Why translation?===<br />
'Translation' indicates a closer attention to the problem of shared meaning and how it might be developed. It seems to represent some new epistemological lubricant, facilitating the dissemination of texts and the application and use of the knowledge and information they in. Simply, translation might be the key to transfer. And yet, when we stop to think, we are more ambivalent. What is translated often seems somehow inferior, not real or original. Note how readily commentators reach for the idea that things might be 'lost in translation'. Knowing at a distance – made in and mediated by translation - makes for incomplete renditions, blurred images, partial truths. So what might 'translation' really mean? The purpose of this paper is to set out, for policy makers and practitioners, the theoretical and conceptual resources translation holds and seems to represent. In doing so, it explores understandings of translation in the fields of literature and linguistics and in the sociology of science and technology. It begins by setting out just why this idea of translation should make immediate, intuitive sense in relation to research, policy and practice.<br />
1translation in research, policy and practice research as translation<br />
Research often entails translation from one language to another: where data is collected from more than one ethnic group, for example, or where the language of the researcher is other than that of the research subject. It may draw on a secondary literature or source documents written in different languages, and may be published and disseminated in languages other than the one in which it is first written up.<br />
2In a different way, to conduct an interview is to ask for an account of experience and its meanings, but it is also to construct and translate that experience in terms defined at least in part by the researcher. In representing what is said, transcripts then select data, usually excluding significant gesture and eye-contact, for example. Often, certain characteristics of speech-acts (such as hesitations) will be edited out. In turn, the format of the transcript shapes the analytic use the researcher may make of it. The basis of research 'findings', then, is an artefact, a transcript or translation, not an original interaction (Ochs 1979, Barnes, Bloor and Henry 1996, Ross 2009).<br />
3In this way, the researcher recasts aspects of his or her problem or topic in new, scientific form: 'All researchers "translate" the experiences of others' (Temple 1997, p 609). Research is invariably conducted in a sort of 'metalanguage' (Hantrais and Ager 1985): the research process can be conceived as one of successive translations (from theoretical formulation to operationalization, transcription, interpretation and dissemination). Theorization is a process of reciprocal back and forth between theory and fact, in which conceptions of each are revised in order that one fit the other (Baldamus 1974).<br />
4 It is a kind of translation: a rereading, re-use, re-application or re-representation of what we know in new terms (Turner 1980). Referencing, too, is an act of translation, a form of appropriation and incorporation of one text by another (Gilbert 1977).policy as translation Each of the fields covered by the paper is diverse and ill-defined, and there is no intention here to provide a comprehensive account of any them. Sources have been chosen for their relevance: main references are cited in the text, and additional sources listed in footnotes.<br />
<br />
2For a brief introduction to the technical issues involved in social research in more than one language, see Birbili (2000). For translation issues in survey research and question design in general, see Ervin and Bower (1952) and Deutscher (1968); on translating survey research instruments (in this instance health-related quality of life measures), Bowden and Fox-Rushby (2003). On the use of translators and interpreters, see Temple (1997) and Jentsch (1998).<br />
3For an interesting discussion of this problem, see Bourdieu (1999), esp pp 621-626, 'The risks of writing'. <br />
===Difference between machine translation and human translators===<br />
Few people disagree on the differences between the two, but many argue over the quality of the translations. How accurate are machine translations? How reliable are human translations? Some say machine translation produces near-perfect translations while others are adamant that translations are incomprehensible and cause more problems than they solve. Results will, of course, vary depending on the source and target languages, the machine translation service used (e.g. Google Translate), and the complexity of the original text.<br />
Machine translation, love it or hate it, is here to stay. In fact, the machine translation market is growing at such a fast pace that it is predicted to reach $980 million by 2022.<br />
===Machine translation: the pros and cons===<br />
The advantages of machine translation generally come down to two factors: it’s faster and cheaper. The downside to this is the standard of translation can be anywhere from inaccurate, to incomprehensible, and potentially dangerous (more on that shortly).<br />
==The advantages of machine translation==<br />
Many free tools are readily available (Google Translate, Skype Translator, etc.)<br />
Quick turnaround time <br />
You can translate between multiple languages using one tool<br />
Translation technology is constantly improving<br />
The disadvantages of machine translation<br />
Level of accuracy can be very low <br />
Accuracy is also very inconsistent across different languages<br />
==Machines can't translate context ==<br />
Mistakes are sometimes costly <br />
Sometimes translation simply doesn’t work<br />
The most important thing to consider with any kind of translation is the cost of potential mistakes. Translating instructions for medical equipment, aviation manuals, legal documents and many other kinds of content require 100% accuracy. In such cases, mistakes can cost lives, huge amounts of money and irreparable damage to your company’s image. So choose carefully!<br />
Human translation: the pros and cons<br />
Human translation essentially switches the table in terms of pros and cons. A higher standard of accuracy comes at the price of longer turnaround times and higher costs. What you have to decide is whether that initial investment outweighs the potential cost of mistakes. Alternatively, whether mistakes simply aren’t an option, like the cases we looked at in the previous section.<br />
<br />
==The advantages of human translation ==<br />
It’s a translator’s job to ensure the highest accuracy<br />
Humans can interpret context and capture the same meaning, rather than simply translating words<br />
Human translators can review their work and provide a quality process<br />
Humans can interpret the creative use of language, e.g. puns, metaphors, slogans, etc.<br />
Professional translators understand the idiomatic differences between their languages<br />
Humans can spot pieces of content where literal translation isn’t possible and find the most suitable alternative<br />
The disadvantages of human translation<br />
Turnaround time is longer <br />
Translators rarely work for free <br />
Unless you use a translation agency, with access to thousands of translators, you’re limited to the languages any one translator can work with<br />
Simply put, human translation is your best option when accuracy is even remotely important. Other considerations to make are the complexity of your source material and the two languages you’re translating between – both of which can render machines pretty useless.<br />
<br />
When to use machine and human translation<br />
The truth is, the debate over machine vs human translation is an unnecessary distraction. What we should really be talking about is when to use these two different types of translation services, because they both serve a very valid purpose.<br />
<br />
Examples of when to use machine translation<br />
When you have a large bulk of content to translate and the general meaning is enough<br />
When your translation never reaches the final audience, e.g. you’re translating a resource as research for another piece of content<br />
Translating documents for internal use within a company, provided 100% accuracy isn’t needed<br />
To partially translate large chunks of content for a human translator to improve upon<br />
Examples of when to use human translation<br />
When accuracy is important<br />
Most cases where your translated content is received by a consumer audience<br />
When you have a duty of care to provide accurate translations (e.g. legal documents, product instructions, medical guidelines or health and safety content)<br />
When translating marketing material or other texts for creative language uses.<br />
<br />
types of machine translation.<br />
<br />
What is Machine Translation? Rule Based Machine Translation vs. Statistical Machine Translation. Machine translation (MT) is automated translation. It is the process by which computer software is used to translate a text from one natural language (such as English) to another (such as Spanish).<br />
<br />
To process any translation, human or automated, the meaning of a text in the original (source) language must be fully restored in the target language, i.e. the translation. While on the surface this seems straightforward, it is far more complex. Translation is not a mere word-for-word substitution. A translator must interpret and analyze all of the elements in the text and know how each word may influence another. This requires extensive expertise in grammar, syntax (sentence structure), semantics (meanings), etc., in the source and target languages, as well as familiarity with each local region.<br />
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Human and machine translation each have their share of challenges. For example, no two individual translators can produce identical translations of the same text in the same language pair, and it may take several rounds of revisions to meet customer satisfaction. But the greater challenge lies in how machine translation can produce publishable quality translations.<br />
<br />
Rule-Based Machine Translation Technology<br />
Rule-based machine translation relies on countless built-in linguistic rules and millions of bilingual dictionaries for each language pair.<br />
The software parses text and creates a transitional representation from which the text in the target language is generated. This process requires extensive lexicons with morphological, syntactic, and semantic information, and large sets of rules. The software uses these complex rule sets and then transfers the grammatical structure of the source language into the target language.<br />
Translations are built on gigantic dictionaries and sophisticated linguistic rules. Users can improve the out-of-the-box translation quality by adding their terminology into the translation process. They create user-defined dictionaries which override the system’s default settings.<br />
In most cases, there are two steps: an initial investment that significantly increases the quality at a limited cost, and an ongoing investment to increase quality incrementally. While rule-based MT brings companies to the quality threshold and beyond, the quality improvement process may be long and expensive.<br />
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Statistical Machine Translation Technology<br />
Statistical machine translation utilizes statistical translation models whose parameters stem from the analysis of monolingual and bilingual corpora. Building statistical translation models is a quick process, but the technology relies heavily on existing multilingual corpora. A minimum of 2 million words for a specific domain and even more for general language are required. Theoretically it is possible to reach the quality threshold but most companies do not have such large amounts of existing multilingual corpora to build the necessary translation models. Additionally, statistical machine translation is CPU intensive and requires an extensive hardware configuration to run translation models for average performance levels.<br />
<br />
Rule-Based MT vs. Statistical MT<br />
Rule-based MT provides good out-of-domain quality and is by nature predictable. Dictionary-based customization guarantees improved quality and compliance with corporate terminology. But translation results may lack the fluency readers expect. In terms of investment, the customization cycle needed to reach the quality threshold can be long and costly. The performance is high even on standard hardware.<br />
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Statistical MT provides good quality when large and qualified corpora are available. The translation is fluent, meaning it reads well and therefore meets user expectations. However, the translation is neither predictable nor consistent. Training from good corpora is automated and cheaper. But training on general language corpora, meaning text other than the specified domain, is poor. Furthermore, statistical MT requires significant hardware to build and manage large translation models.<br />
<br />
Rule-Based MT Statistical MT<br />
+ Consistent and predictable quality – Unpredictable translation quality<br />
+ Out-of-domain translation quality – Poor out-of-domain quality<br />
+ Knows grammatical rules – Does not know grammar <br />
+ High performance and robustness – High CPU and disk space requirements<br />
+ Consistency between versions – Inconsistency between versions <br />
– Lack of fluency + Good fluency<br />
– Hard to handle exceptions to rules + Good for catching exceptions to rules <br />
– High development and customization costs + Rapid and cost-effective development costs provided the required corpus exists<br />
Given the overall requirements, there is a clear need for a third approach through which users would reach better translation quality and high performance (similar to rule-based MT), with less investment (similar to statistical MT).<br />
Post-Edited Machine Translation (PEMT)<br />
Often, PEMT is used to bridge the gap between the speed of machine translation and the quality of human translation, as translators review, edit and improve machine-translated texts. PEMT services cost more than plain machine translations but less than 100% human translation, especially since the post-editors don’t have to be fluently bilingual—they just have to be skilled proofreaders with some experience in the language and target region.<br />
Successful translation is about more than just the words, which is why we advocate for not just human translation by skilled linguists, but for translation by people deeply familiar with the cultures they’re writing for. Life experience, study and the knowledge that only comes from living in a geographic region can make the difference between words that are understandable and language that is capable of having real, positive impact. <br />
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PacTranz<br />
The HUGE list of 51 translation types, methods and techniques<br />
Upper section of infographic of 51 common types of translation classified in 4 broad categoriesThere are a bewildering number of different types of translation.<br />
So we’ve identified the 51 types you’re most likely to come across, and explain exactly what each one means.<br />
This includes all the main translation methods, techniques, strategies, procedures and areas of specialisation.<br />
It’s our way of helping you make sense of the many different kinds of translation – and deciding which ones are right for you.<br />
Don’t miss our free summary pdf download later in the article!<br />
The 51 types of translation we’ve identified fall neatly into four distinct categories.<br />
Translation Category A: 15 types of translation based on the technical field or subject area of the text<br />
Icons representing 15 types of translation categorised by the technical field or subject area of the textTranslation companies often define the various kinds of translation they provide according to the subject area of the text.<br />
This is a useful way of classifying translation types because specialist texts normally require translators with specialist knowledge.<br />
Here are the most common types you’re like to come across in this category.<br />
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1. General Translation<br />
What is it?<br />
The translation of non-specialised text. That is, text that we can all understand without needing specialist knowledge in some area.<br />
The text may still contain some technical terms and jargon, but these will either be widely understood, or easily researched.<br />
What this means<br />
The implication is that you don’t need someone with specialist knowledge for this type of translation – any professional translator can handle them.<br />
Translators who only do this kind of translation (don’t have a specialist field) are sometimes referred to as ‘generalist’ or ‘general purpose’ translators.<br />
Examples<br />
Most business correspondence, website content, company and product/service info, non-technical reports.<br />
Most of the rest of the translation types in this Category do require specialist translators.<br />
Check out our video on 13 types of translation requiring special translator expertise:<br />
<br />
2. Technical Translation<br />
What is it?<br />
We use the term “technical translation” in two different ways:<br />
Broad meaning: any translation where the translator needs specialist knowledge in some domain or area.<br />
This definition would include almost all the translation types described in this section.<br />
Narrow meaning: limited to the translation of engineering (in all its forms), IT and industrial texts.<br />
This narrower meaning would exclude legal, financial and medical translations for example, where these would be included in the broader definition.<br />
What this means<br />
Technical translations require knowledge of the specialist field or domain of the text.<br />
That’s because without it translators won’t completely understand the text and its implications. And this is essential if we want a fully accurate and appropriate translation.Good to know Many technical translation projects also have a typesetting/dtp requirement. Be sure your translation provider can handle this component, and that you’ve allowed for it in your project costings and time frames.<br />
Examples<br />
Manuals, specialist reports, product brochures<br />
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3. Scientific Translation<br />
What is it?<br />
The translation of scientific research or documents relating to it.<br />
What this means<br />
These texts invariably contain domain-specific terminology, and often involve cutting edge research.<br />
So it’s imperative the translator has the necessary knowledge of the field to fully understand the text. That’s why scientific translators are typically either experts in the field who have turned to translation, or professionally qualified translators who also have qualifications and/or experience in that domain.<br />
On occasion the translator may have to consult either with the author or other domain experts to fully comprehend the material and so translate it appropriately.<br />
Examples<br />
Research papers, journal articles, experiment/trial results<br />
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4. Medical Translation<br />
What is it?<br />
The translation of healthcare, medical product, pharmaceutical and biotechnology materials.<br />
Medical translation is a very broad term covering a wide variety of specialist areas and materials – everything from patient information to regulatory, marketing and technical documents.<br />
As a result, this translation type has numerous potential sub-categories – ‘medical device translations’ and ‘clinical trial translations’, for example.<br />
What this means<br />
As with any text, the translators need to fully understand the materials they’re translating. That means sound knowledge of medical terminology and they’ll often also need specific subject-matter expertise.<br />
Good to know<br />
Many countries have specific requirements governing the translation of medical device and pharmaceutical documentation. This includes both your client-facing and product-related materials.<br />
Examples<br />
Medical reports, product instructions, labeling, clinical trial documentation<br />
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5. Financial Translation<br />
What is it?<br />
In broad terms, the translation of banking, stock exchange, forex, financing and financial reporting documents.<br />
However, the term is generally used only for the more technical of these documents that require translators with knowledge of the field.<br />
Any competent translator could translate a bank statement, for example, so that wouldn’t typically be considered a financial translation.<br />
What this means<br />
You need translators with domain expertise to correctly understand and translate the financial terminology in these texts.<br />
Examples<br />
Company accounts, annual reports, fund or product prospectuses, audit reports, IPO documentation<br />
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6. Economic Translations<br />
What is it?<br />
1. Sometimes used as a synonym for financial translations.<br />
2. Other times used somewhat loosely to refer to any area of economic activity – so combining business/commercial, financial and some types of technical translations.<br />
3. More narrowly, the translation of documents relating specifically to the economy and the field of economics.<br />
What this means<br />
As always, you need translators with the relevant expertise and knowledge for this type of translation.<br />
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7. Legal Translation<br />
What is it?<br />
The translation of documents relating to the law and legal process.<br />
What this means<br />
Legal texts require translators with a legal background.<br />
That’s because without it, a translator may not:<br />
– fully understand the legal concepts<br />
– write in legal style<br />
– understand the differences between legal systems, and how best to translate concepts that don’t correspond.<br />
And we need all that to produce professional quality legal translations – translations that are accurate, terminologically correct and stylistically appropriate.<br />
Examples<br />
Contracts, legal reports, court judgments, expert opinions, legislation<br />
<br />
8. Juridical Translation<br />
What is it?<br />
1. Generally used as a synonym for legal translations.<br />
2. Alternatively, can refer to translations requiring some form of legal verification, certification or notarization that is common in many jurisdictions.<br />
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9. Judicial Translation<br />
What is it?<br />
1. Most commonly a synonym for legal translations.<br />
2. Rarely, used to refer specifically to the translation of court proceeding documentation – so judgments, minutes, testimonies, etc. <br />
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10. Patent Translation<br />
What is it?<br />
The translation of intellectual property and patent-related documents.<br />
Key features<br />
Patents have a specific structure, established terminology and a requirement for complete consistency throughout – read more on this here. These are key aspects to patent translations that translators need to get right.<br />
In addition, subject matter can be highly technical.<br />
What this means<br />
You need translators who have been trained in the specific requirements for translating patent documents. And with the domain expertise needed to handle any technical content.<br />
Examples<br />
Patent specifications, prior art documents, oppositions, opinions<br />
<br />
11. Literary Translation<br />
What is it?<br />
The translation of literary works – novels, short stories, plays, essays, poems.<br />
Key features<br />
Literary translation is widely regarded as the most difficult form of translation.<br />
That’s because it involves much more than simply conveying all meaning in an appropriate style. The translator’s challenge is to also reproduce the character, subtlety and impact of the original – the essence of what makes that work unique.<br />
This is a monumental task, and why it’s often said that the translation of a literary work should be a literary work in its own right.<br />
What this means<br />
Literary translators must be talented wordsmiths with exceptional creative writing skills.<br />
Because few translators have this skillset, you should only consider dedicated literary translators for this type of translation.<br />
<br />
<br />
12. Commercial Translation<br />
What is it?<br />
The translation of documents relating to the world of business.<br />
This is a very generic, wide-reaching translation type. It includes other more specialised forms of translation – legal, financial and technical, for example. And all types of more general business documentation.<br />
Also, some documents will require familiarity with business jargon and an ability to write in that style.<br />
What this means<br />
Different translators will be required for different document types – specialists should handle materials involving technical and specialist fields, whereas generalist translators can translate non-specialist materials.<br />
Examples<br />
Business correspondence, reports, marketing and promotional materials, sales proposals<br />
<br />
13. Business Translations<br />
What is it?<br />
A synonym for Commercial Translations.<br />
<br />
14. Administrative Translations<br />
What is it?<br />
The translation of business management and administration documents.<br />
So it’s a subset of business / commercial translations.<br />
What this means<br />
The implication is these documents will include business jargon and ‘management speak’, so require a translator familiar with, and practised at, writing in that style.<br />
Examples<br />
Management reports and proposals<br />
<br />
15. Marketing Translations<br />
What is it?<br />
The translation of advertising, marketing and promotional materials.<br />
This is a subset of business or commercial translations.<br />
Key features<br />
Marketing copy is designed to have a specific impact on the audience – to appeal and persuade.<br />
So the translated copy must do this too.<br />
But a direct translation will seldom achieve this – so translators need to adapt their wording to produce the impact the text is seeking.<br />
And sometimes a completely new message might be needed – see transcreation in our next category of translation types.<br />
What this means<br />
Marketing translations require translators who are skilled writers with a flair for producing persuasive, impactful copy.<br />
As relatively few translators have these skills, engaging the right translator is key.<br />
Good to know<br />
This type of translation often comes with a typesetting or dtp requirement – particularly for adverts, posters, brochures, etc.<br />
Its best for your translation provider to handle this component. That’s because multilingual typesetters understand the design and aesthetic conventions in other languages/cultures. And these are essential to ensure your materials have the desired impact and appeal in your target markets.<br />
Examples<br />
Advertising, brochures, some website/social media text.<br />
Translation Category B: 14 types of translation based on the end product or use of the translation<br />
This category is all about how the translation is going to be used or the end product that’s produced.<br />
Most of these types involve either adapting or processing a completed translation in some way, or converting or incorporating it into another program or format.<br />
You’ll see that some are very specialised, and complex.<br />
It’s another way translation providers refer to the range of services they provide.<br />
Check out our video of the most specialised of these types of translation:<br />
<br />
16. Document Translations<br />
What is it?<br />
The translation of documents of all sorts.<br />
Here the translation itself is the end product and needs no further processing beyond standard formatting and layout.<br />
<br />
17. Text Translations<br />
What is it?<br />
A synonym for document translation.<br />
<br />
18. Certified Translations<br />
What is it?<br />
A translation with some form of certification.<br />
Key features<br />
The certification can take many forms. It can be a statement by the translation company, signed and dated, and optionally with their company seal. Or a similar certification by the translator.<br />
The exact format and wording will depend on what clients and authorities require – here’s an example.<br />
<br />
19. Official Translations<br />
What is it?<br />
1. Generally used as a synonym for certified translations.<br />
2. Can also refer to the translation of ‘official’ documents issued by the authorities in a foreign country. These will almost always need to be certified.<br />
<br />
20. Software Localisation<br />
What is it?<br />
Adapting software for another language/culture.<br />
Key features<br />
The goal of software localisation is not just to make the program or product available in other languages. It’s also about ensuring the user experience in those languages is as natural and effective as possible.<br />
Translating the user interface, messaging, documentation, etc is a major part of the process.<br />
Also key is a customisation process to ensure everything matches the conventions, norms and expectations of the target cultures.<br />
Adjusting time, date and currency formats are examples of simple customisations. Others might involve adapting symbols, graphics, colours and even concepts and ideas.<br />
Localisation is often preceded by internationalisation – a review process to ensure the software is optimally designed to handle other languages.<br />
And it’s almost always followed by thorough testing – to ensure all text is in the correct place and fits the space, and that everything makes sense, functions as intended and is culturally appropriate.<br />
Localisation is often abbreviated to L10N, internationalisation to i18n.<br />
What this means<br />
Software localisation is a specialised kind of translation, and you should always engage a company that specialises in it.<br />
They’ll have the systems, tools, personnel and experience needed to achieve top quality outcomes for your product.<br />
<br />
21. Game Localisation<br />
What is it?<br />
Adapting games for other languages and markets.<br />
<br />
It’s a subset of software localisation.<br />
Key features<br />
The goal of game localisation is to provide an engaging and fun gaming experience for speakers of other languages.<br />
<br />
It involves translating all text and recording any required foreign language audio.<br />
<br />
But also adapting anything that would clash with the target culture’s customs, sensibilities and regulations.<br />
<br />
For example, content involving alcohol, violence or gambling may either be censored or inappropriate in the target market.<br />
<br />
And at a more basic level, anything that makes users feel uncomfortable or awkward will detract from their experience and thus the success of the game in that market.<br />
<br />
So portions of the game may have to be removed, added to or re-worked.<br />
<br />
Game localisation involves at least the steps of translation, adaptation, integrating the translations and adaptations into the game, and testing.<br />
<br />
What this means<br />
Game localisation is a very specialised type of translation best left to those with specific expertise and experience in this area.<br />
<br />
22. Multimedia Localisation<br />
What is it?<br />
Adapting multimedia for other languages and cultures.<br />
<br />
Multimedia refers to any material that combines visual, audio and/or interactive elements. So videos and movies, on-line presentations, e-Learning courses, etc.<br />
Key features<br />
Anything a user can see or hear may need localising.<br />
<br />
That means the audio and any text appearing on screen or in images and animations.<br />
<br />
Plus it can mean reviewing and adapting the visuals and/or script if these aren’t suitable for the target culture.<br />
<br />
The localisation process will typical involve:<br />
– Translation<br />
– Modifying the translation for cultural reasons and/or to meet technical requirements<br />
– Producing the other language versions<br />
<br />
Audio output may be voice-overs, dubbing or subtitling.<br />
<br />
And output for visuals can involve re-creating elements, or supplying the translated text for the designers/engineers to incorporate.<br />
<br />
What this means<br />
Multimedia localisation projects vary hugely, and it’s essential your translation providers have the specific expertise needed for your materials.<br />
<br />
<br />
<br />
23. Script Translations<br />
What is it?<br />
Preparing the text of recorded material for recording in other languages.<br />
Key features<br />
There are several issues with script translation.<br />
<br />
One is that translations typically end up longer than the original script. So voicing the translation would take up more space/time on the video than the original language.<br />
<br />
Sometimes that space will be available and this will be OK.<br />
<br />
But generally it won’t be. So the translation has to be edited back until it can be comfortably voiced within the time available on the video.<br />
<br />
Another challenge is the translation may have to synchronise with specific actions, animations or text on screen.<br />
<br />
Also, some scripts also deal with technical subject areas involving specialist technical terminology.<br />
<br />
Finally, some scripts may be very culture-specific – featuring humour, customs or activities that won’t work well in another language. Here the script, and sometimes also the associated visuals, may need to be adjusted before beginning the translation process.<br />
<br />
It goes without saying that a script translation must be done well. If it’s not, there’ll be problems producing a good foreign language audio, which will compromise the effectiveness of the video.<br />
<br />
Translators typically work from a time-coded transcript. This is the original script marked to show the time available for each section of the translation.<br />
<br />
What this means<br />
There are several potential pitfalls in script translations. So it’s vital your translation provider is practiced at this type of translation and able to handle any technical content.<br />
<br />
<br />
<br />
24. Voice-over and Dubbing Projects<br />
What is it?<br />
Translation and recording of scripts in other languages.<br />
<br />
Voice-overs vs dubbing<br />
There is a technical difference.<br />
A voice-over adds a new track to the production, dubbing replaces an existing one.<br />
<br />
<br />
Key features<br />
These projects involve two parts:<br />
– a script translation (as described above), and<br />
– producing the audio<br />
<br />
So they involve the combined efforts of translators and voice artists.<br />
The task for the voice artist is to produce a high quality read. That’s one that matches the style, tone and richness of the original.<br />
<br />
Often each section of the new audio will need to be the same length as the original.<br />
<br />
But sometimes the segments will need to be shorter – for example where the voice-over lags the original by a second or two. This is common in interviews etc, where the original voice is heard initially then drops out.<br />
<br />
The most difficult form of dubbing is lip-syncing – where the new audio needs to synchronise with the original speaker’s lip movements, gestures and actions.<br />
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Lip-syncing requires an exceptionally skilled voice talent and considerable time spent rehearsing and fine tuning the translation.<br />
<br />
What this means<br />
You need to use experienced professionals every step of the way in this type of project.<br />
<br />
That’s to ensure firstly that your foreign-language scripts are first class, then that the voicing is of high professional standard.<br />
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Anything less will mean your foreign language versions will be way less effective and appealing to your target audience.<br />
<br />
<br />
<br />
25. Subtitle Translations<br />
What is it?<br />
Producing foreign language captions for sub or surtitles.<br />
Key features<br />
The goal with subtitling is to produce captions that viewers can comfortably read in the time available and still follow what’s happening on the video.<br />
<br />
To achieve this, languages have “rules” governing the number of characters per line and the minimum time each subtitle should display.<br />
<br />
Sticking to these guidelines is essential if your subtitles are to be effective.<br />
<br />
But this is no easy task – it requires simple language, short words, and a very succinct style. Translators will spend considerable time mulling over and re-working their translation to get it just right.<br />
<br />
Most subtitle translators use specialised software that will output the captions in the format sound engineers need for incorporation into the video.<br />
<br />
What this means<br />
As with other specialised types of translation, you should only use translators with specific expertise and experience in subtitling.<br />
<br />
<br />
<br />
26. Website Localisation<br />
What is it?<br />
The translation and adapting of relevant content on a website to best suit the target language and culture.<br />
<br />
Note: Many providers use the term website translation as a synonym for localisation. Strictly speaking though, translation is just one part of localisation.<br />
Key features<br />
<br />
Not all pages on a website may need to be localised – clients should review their content to identify what’s relevant for the other language versions.<br />
Some content may need specialist translators – legal and technical pages for example.<br />
There may also be videos, linked documents, and text or captions in graphics to translate.<br />
Adaptation can mean changing date, time, currency and number formats, units of measure, etc.<br />
But also images, colours and even the overall site design and style if these won’t have the desired impact in the target culture.<br />
Translated files can be supplied in a wide range of formats – translators usually coordinate output with the site webmasters.<br />
New language versions are normally thoroughly reviewed and tested before going live to confirm everything is displaying correctly, works as intended and is cultural appropriate.<br />
What this means<br />
The first step should be to review your content and identify what needs to be translated. This might lead you to modify some pages for the foreign language versions.<br />
<br />
In choosing your translation providers be sure they can:<br />
– handle any technical or legal content,<br />
– provide your webmaster with the file types they want.<br />
<br />
And you should always get your translators to systematically review the foreign language versions before going live.<br />
<br />
<br />
<br />
27. Transcreation<br />
What is it?<br />
Adapting a message to elicit the same emotional response in another language and culture.<br />
Translation is all about conveying the message or meaning of a text in another language. But sometimes that message or meaning won’t have the desired effect in the target culture.<br />
<br />
This is where transcreation comes in. Transcreation creates a new message that will get the desired emotional response in that culture, while preserving the style and tone of the original.<br />
<br />
So it’s a sort of creative translation – which is where the word comes from, a combination of ‘translation’ and ‘creation’.<br />
<br />
At one level transcreation may be as simple as choosing an appropriate idiom to convey the same intent in the target language – something translators do all the time.<br />
<br />
But mostly the term is used to refer to adapting key advertising and marketing messaging. Which requires copywriting skills, cultural awareness and an excellent knowledge of the target market.<br />
<br />
Who does it?<br />
Some translation companies have suitably skilled personnel and offer transcreation services.<br />
<br />
Often though it’s done in the target country by specialist copywriters or an advertising or marketing agency – particularly for significant campaigns and to establish a brand in the target marketplace.<br />
<br />
What this means<br />
Most general marketing and promotional texts won’t need transcreation – they can be handled by a translator with excellent creative writing skills.<br />
<br />
But slogans, by-lines, advertising copy and branding statements often do.<br />
<br />
Whether you should opt for a translation company or an in-market agency will depend on the nature and importance of the material, and of course your budget.<br />
<br />
<br />
<br />
28. Audio Translations<br />
What is it?<br />
Broad meaning: the translation of any type of recorded material into another language.<br />
<br />
More commonly: the translation of a foreign language video or audio recording into your own language. So this is where you want to know and document what a recording says.<br />
Key features<br />
The first challenge with audio translations is it’s often impossible to pick up every word that’s said. That’s because audio quality, speech clarity and speaking speed can all vary enormously.<br />
<br />
It’s also a mentally challenging task to listen to an audio and translate it directly into another language. It’s easy to miss a word or an aspect of meaning.<br />
<br />
So best practice is to first transcribe the audio (type up exactly what is said in the language it is spoken in), then translate that transcription.<br />
<br />
However, this is time consuming and therefore costly, and there are other options if lesser precision is acceptable.<br />
<br />
What this means<br />
It’s best to discuss your requirements for this kind of translation with your translation provider. They’ll be able to suggest the best translation process for your needs.<br />
<br />
Examples<br />
Interviews, product videos, police recordings, social media videos.<br />
<br />
<br />
<br />
29. Translations with DTP<br />
What is it?<br />
Translation incorporated into graphic design files.multilingual dtp example in the form of a Rubik's Cube with foreign text on each square<br />
Key features<br />
Graphic design programs are used by professional designers and graphic artists to combine text and images to create brochures, books, posters, packaging, etc.<br />
<br />
Translation plus dtp projects involve 3 steps – translation, typesetting, output.<br />
<br />
The typesetting component requires specific expertise and resources – software and fonts, typesetting know-how, an appreciation of foreign language display conventions and aesthetics.<br />
<br />
What this means<br />
Make sure your translation company has the required multilingual typesetting/desktop publishing expertise whenever you’re translating a document created in a graphic design program.<br />
<br />
Translation Category C: 13 types of translation based on the translation method employed<br />
This category has two sub-groups:<br />
– the practical methods translation providers use to produce their translations, and<br />
– the translation strategies/methods identified and discussed within academia.<br />
<br />
The translation methods translation providers use<br />
There are 4 main methods used in the translation industry today. We have an overview of each below, but for more detail, including when to use each one, see our comprehensive blog article.<br />
<br />
Or watch our video.<br />
<br />
Important: If you’re a client you need to understand these 4 methods – choose the wrong one and the translation you end up with may not meet your needs!<br />
<br />
<br />
30. Machine Translation (MT)<br />
What is it?<br />
A translation produced entirely by a software program with no human intervention.<br />
<br />
A widely used, and free, example is Google Translate. And there are also commercial MT engines, generally tailored to specific domains, languages and/or clients.<br />
<br />
Pros and cons<br />
There are two limitations to MT:<br />
– they make mistakes (incorrect translations), and<br />
– quality of wording is patchy (some parts good, others unnatural or even nonsensical)<br />
<br />
On they positive side they are virtually instantaneous and many are free.<br />
<br />
Best suited for:<br />
Getting the general idea of what a text says.<br />
<br />
This method should never be relied on when high accuracy and/or good quality wording is needed.<br />
<br />
<br />
<br />
31. Machine Translation plus Human Editing (PEMT)<br />
What is it?<br />
A machine translation subsequently edited by a human translator or editor (often called Post-editing Machine Translation = PEMT).<br />
<br />
The editing process is designed to rectify some of the deficiencies of a machine translation.<br />
<br />
This process can take different forms, with different desired outcomes. Probably most common is a ‘light editing’ process where the editor ensures the text is understandable, without trying to fix quality of expression.<br />
<br />
Pros and cons<br />
This method won’t necessarily eliminate all translation mistakes. That’s because the program may have chosen a wrong word (meaning) that wasn’t obvious to the editor.<br />
<br />
And wording won’t generally be as good as a professional human translator would produce.<br />
<br />
Its advantage is it’s generally quicker and a little cheaper than a full translation by a professional translator.<br />
<br />
Best suited for:<br />
Translations for information purposes only.<br />
<br />
Again, this method shouldn’t be used when full accuracy and/or consistent, natural wording is needed.<br />
<br />
<br />
<br />
32. Human Translation<br />
What is it?<br />
Translation by a professional human translator.<br />
Pros and cons<br />
Professional translators should produce translations that are fully accurate and well-worded.<br />
<br />
That said, there is always the possibility of ‘human error’, which is why translation companies like us typically offer an additional review process – see next method.<br />
<br />
This method will take a little longer and likely cost more than the PEMT method.<br />
<br />
Best suited for:<br />
Most if not all translation purposes.<br />
<br />
<br />
<br />
33. Human Translation + Revision<br />
What is it?<br />
A human translation with an additional review by a second translator.<br />
<br />
The review is essentially a safety check – designed to pick up any translation errors and refine wording if need be.<br />
<br />
Pros and cons<br />
This produces the highest level of translation quality.<br />
<br />
It’s also the most expensive of the 4 methods, and takes the longest.<br />
<br />
Best suited for:<br />
All translation purposes.<br />
<br />
Gearwheel with 5 practical translation methods written on the teeth <br />
There’s also one other common term used by practitioners and academics alike to describe a type (method) of translation:<br />
<br />
34. Computer-Assisted Translation (CAT)<br />
What is it?<br />
A human translator using computer tools to aid the translation process.<br />
<br />
Key features<br />
Virtually all translators use such tools these days.<br />
<br />
The most prevalent tool is Translation Memory (TM) software. This creates a database of previous translations that can be accessed for future work.<br />
<br />
TM software is particularly useful when dealing with repeated and closely-matching text, and for ensuring consistency of terminology. For certain projects it can speed up the translation process.<br />
<br />
<br />
<br />
The translation methods described by academia<br />
A great deal has been written within academia analysing how human translators go about their craft.<br />
<br />
Seminal has been the work of Newmark, and the following methods of translation attributed to him are widely discussed in the literature.Gearwheel with Newmark's 8 translation methods written on the teeth <br />
These methods are approaches and strategies for translating the text as a whole, not techniques for handling smaller text units, which we discuss in our final translation category.<br />
<br />
<br />
<br />
35. Word-for-word Translation<br />
This method translates each word into the other language using its most common meaning and keeping the word order of the original language.<br />
<br />
So the translator deliberately ignores context and target language grammar and syntax.<br />
<br />
Its main purpose is to help understand the source language structure and word use.<br />
<br />
Often the translation will be placed below the original text to aid comparison.<br />
<br />
<br />
<br />
36. Literal Translation<br />
Words are again translated independently using their most common meanings and out of context, but word order changed to the closest acceptable target language grammatical structure to the original.<br />
<br />
Its main suggested purpose is to help someone read the original text.<br />
<br />
<br />
<br />
37. Faithful Translation<br />
Faithful translation focuses on the intention of the author and seeks to convey the precise meaning of the original text.<br />
<br />
It uses correct target language structures, but structure is less important than meaning.<br />
<br />
<br />
<br />
38. Semantic Translation<br />
Semantic translation is also author-focused and seeks to convey the exact meaning.<br />
<br />
Where it differs from faithful translation is that it places equal emphasis on aesthetics, ie the ‘sounds’ of the text – repetition, word play, assonance, etc.<br />
<br />
In this method form is as important as meaning as it seeks to “recreate the precise flavour and tone of the original” (Newmark).slide showing definition of semantic translation as a translation method<br />
<br />
39. Communicative Translation<br />
Seeks to communicate the message and meaning of the text in a natural and easily understood way.<br />
<br />
It’s described as reader-focused, seeking to produce the same effect on the reader as the original text.<br />
<br />
A good comparison of Communicative and Semantic translation can be found here.<br />
<br />
40. Free Translation<br />
Here conveying the meaning and effect of the original are all important.<br />
<br />
There are no constraints on grammatical form or word choice to achieve this.<br />
<br />
Often the translation will paraphrase, so may be of markedly different length to the original.<br />
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41. Adaptation<br />
Mainly used for poetry and plays, this method involves re-writing the text where the translation would otherwise lack the same resonance and impact on the audience.<br />
<br />
Themes, storylines and characters will generally be retained, but cultural references, acts and situations adapted to relevant target culture ones.<br />
<br />
So this is effectively a re-creation of the work for the target culture.<br />
<br />
42. Idiomatic Translation<br />
Reproduces the meaning or message of the text using idioms and colloquial expressions and language wherever possible.<br />
<br />
The goal is to produce a translation with language that is as natural as possible.<br />
<br />
Translation Category D: 9 types of translation based on the translation technique used<br />
These translation types are specific strategies, techniques and procedures for dealing with short chunks of text – generally words or phrases.<br />
<br />
They’re often thought of as techniques for solving translation problems.<br />
<br />
They differ from the translation methods of the previous category which deal with the text as a whole.<br />
9 translation techniques as titles of books in a bookcase<br />
<br />
43. Borrowing<br />
What is it?<br />
Using a word or phrase from the original text unchanged in the translation.<br />
<br />
Key features<br />
With this procedure we don’t translate the word or phrase at all – we simply ‘borrow’ it from the source language.<br />
<br />
Borrowing is a very common strategy across languages. Initially, borrowed words seem clearly ‘foreign’, but as they become more familiar, they can lose that ‘foreignness’.<br />
<br />
Translators use this technique:<br />
– when it’s the best word to use – either because it has become the standard, or it’s the most precise term, or<br />
– for stylist effect – borrowings can add a prestigious or scholarly flavour.<br />
<br />
Borrowed words or phrases are often italicised in English.<br />
<br />
Examples of borrowings in English<br />
grand prix, kindergarten, tango, perestroika, barista, sampan, karaoke, tofu<br />
<br />
<br />
44. Transliteration<br />
What is it?<br />
Reproducing the approximate sounds of a name or term from a language with a different writing system.<br />
<br />
Key features<br />
In English we use the Roman (Latin) alphabet in common with many other languages including almost all European languages.<br />
<br />
Other writing systems include Arabic, Cyrillic, Chinese, Japanese, Korean, Thai, and the Indian languages.<br />
<br />
Transliteration from such systems into the Roman alphabet is also called romanisation.<br />
<br />
There are accepted systems for how individual letters/sounds should be romanised from most other languages – there are three common systems for Chinese, for example.<br />
<br />
English borrowings from languages using non-Roman writing systems also require transliteration – perestroika, sampan, karaoke, tofu are examples from the above list.<br />
<br />
Translators mostly use transliteration as a procedure for translating proper names.<br />
<br />
Examples<br />
毛泽东 Mao Tse-tung or Mao Zedong<br />
Владимир Путин Vladimir Putin<br />
서울 Seoul<br />
ភ្នំពេញ Phnom Penh<br />
<br />
45. Calque or Loan Translation<br />
What is it?<br />
A literal translation of a foreign word or phrase to create a new term with the same meaning in the target language.<br />
<br />
So a calque is a borrowing with translation if you like. The new term may be changed slightly to reflect target language structures.<br />
<br />
Examples<br />
German ‘Kindergarten’ has been calqued as детский сад in Russian, literally ‘children garden’ in both languages.<br />
<br />
Chinese 洗腦 ‘wash’ + ‘brain’ is the origin of ‘brainwash’ in English.<br />
<br />
English skyscraper is calqued as gratte-ciel in French and rascacielos in Spanish, literally ‘scratches sky’ in both languages.<br />
<br />
46. Word-for-word translation<br />
What is it?<br />
A literal translation that is natural and correct in the target language.<br />
<br />
Alternative names are ‘literal translation’ or ‘metaphrase’.<br />
<br />
Note: this technique is different to the translation method of the same name, which does not produce correct and natural text and has a different purpose.<br />
<br />
Key features<br />
This translation strategy will only work between languages that have very similar grammatical structures.<br />
<br />
And even then, only sometimes.<br />
<br />
For example, standard word order in Turkish is Subject-Object-Verb whereas in English it’s Subject-Verb-Object. So a literal translation between these two will seldom work:<br />
– Yusuf elmayı yedi is literally ‘Joseph the apple ate’.<br />
<br />
When word-for-word translations don’t produce natural and correct text, translators resort to some of the other techniques described below.<br />
Examples<br />
French ‘Quelle heure est-il?’ works into English as ‘What time is it?’.<br />
<br />
Russian ‘Oн хочет что-нибудь поесть’ is ‘He wants something to eat’.<br />
<br />
47. Transposition<br />
What is it?<br />
Translation with a change of grammatical structure.<br />
<br />
This technique gives the translation more natural wording and/or makes it grammatically correct.<br />
<br />
Examples<br />
A change in word order:<br />
Our Turkish example Yusuf elmayı yedi (literally ‘Joseph the apple ate’) –> Joseph ate the apple.<br />
<br />
Spanish La Casa Blanca (literally ‘The House White’) –> The White House<br />
<br />
A change in grammatical category:<br />
German Er hört gerne Musik (literally ‘he listens gladly [to] music’)<br />
= subject pronoun + verb + adverb + noun<br />
becomes Spanish Le gusta escuchar música (literally ‘[to] him [it] pleases to listen [to] music’)<br />
= indirect object pronoun + verb + infinitive + noun<br />
and English He likes listening to music<br />
= subject pronoun + verb + gerund + noun.<br />
<br />
48. Modulation<br />
What is it?<br />
Translation with a change of focus or point of view in the target language.<br />
<br />
This technique makes the translation more idiomatic – how people would normally say it in the language.<br />
<br />
Examples<br />
English talks of the ‘top floor’ of a building, French the dernier étage = last floor. ‘Last floor’ would be unnatural in English, so too ‘top floor’ in French.<br />
<br />
German uses the term Lebensgefahr (literally ‘danger to life’) where in English we’d be more likely to say ‘risk of death’.<br />
In English we’d say ‘I dropped the key’, in Spanish se me cayó la llave, literally ‘the key fell from me’. The English perspective is that I did something (dropped the key), whereas in Spanish something happened to me – I’m the recipient of the action.<br />
<br />
49. Equivalence or Reformulation<br />
What is it?<br />
Translating the underlying concept or meaning using a totally different expression.<br />
<br />
This technique is widely used when translating idioms and proverbs.<br />
<br />
And it’s common in titles and advertising slogans.<br />
<br />
It’s a common strategy where a direct translation either wouldn’t make sense or wouldn’t resonate in the same way.<br />
<br />
Examples<br />
Here are some equivalents of the English saying “Pigs may fly”, meaning something will never happen, or “you’re being unrealistic” (Source):<br />
– Thai: ชาติหน้าตอนบ่าย ๆ – literally, ‘One afternoon in your next reincarnation’<br />
– French: Quand les poules auront des dents – literally, ‘When hens have teeth’<br />
– Russian, Когда рак на горе свистнет – literally, ‘When a lobster whistles on top of a mountain’<br />
– Dutch, Als de koeien op het ijs dansen – literally, ‘When the cows dance on the ice’<br />
– Chinese: 除非太陽從西邊出來！– literally, ‘Only if the sun rises in the west’<br />
<br />
50. Adaptation<br />
What is it?<br />
A translation that substitutes a culturally-specific reference with something that’s more relevant or meaningful in the target language.<br />
<br />
It’s also known as cultural substitution or cultural equivalence.<br />
<br />
It’s a useful technique when a reference wouldn’t be understood at all, or the associated nuances or connotations would be lost in the target language.<br />
<br />
Note: the translation method of the same name is a similar concept but applied to the text as a whole.<br />
<br />
Examples<br />
Different cultures celebrate different coming of age birthdays – 21 in many cultures, 20, 15 or 16 in others. A translator might consider changing the age to the target culture custom where the coming of age implications were important in the original text.<br />
Animals have different connotations across languages and cultures. Owls for example are associated with wisdom in English, but are a bad omen to Vietnamese. A translator might want to remove or amend an animal reference where this would create a different image in the target language.<br />
<br />
51. Compensation<br />
What is it?<br />
A meaning or nuance that can’t be directly translated is expressed in another way in the text.<br />
Example<br />
Many languages have ways of expressing social status (honorifics) encoded into their grammatical structures.<br />
<br />
So you can convey different levels of respect, politeness, humility, etc simply by choosing different forms of words or grammatical elements.<br />
But these nuances will be lost when translating into languages that don’t have these structures.<br />
Then translating into languages that don’t have these structures<br />
Then translating into languages that don’t have these structures.<br />
<br />
===Conclusion ===<br />
<br />
In the light of above mentioned facts and figures here by we would say that machine translation is challenge for human translators because it can reduces the wokload of translation but can't give accurate and exact translation of the traget language.It can be less reliable than human translation..</div>Chen Huinihttps://bou.de/u/index.php?title=Machine_translation&diff=131961Machine translation2021-12-13T13:06:15Z<p>Chen Huini: /* 2.2 Error Division of Machine Translation in Translating Abstracts of Medical Papers from Chinese to English */</p>
<hr />
<div>'''Machine Translation - A challenge or a chance for human translators?'''<br />
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[[Machine_translation|Overview Page of Machine Translation]]<br />
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30 Chapters（0/30)<br />
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[[Machine_Trans_EN_1]] [[Machine_Trans_EN_2]] [[Machine_Trans_EN_3]] [[Machine_Trans_EN_4]] [[Machine_Trans_EN_5]] [[Machine_Trans_EN_6]] [[Machine_Trans_EN_7]] [[Machine_Trans_EN_8]] [[Machine_Trans_EN_9]] [[Machine_Trans_EN_10]] [[Machine_Trans_EN_11]] [[Machine_Trans_EN_12]] [[Machine_Trans_EN_13]] [[Machine_Trans_EN_14]] [[Machine_Trans_EN_15]] [[Machine_Trans_EN_16]] [[Machine_Trans_EN_17]] [[Machine_Trans_EN_18]] [[Machine_Trans_EN_19]] [[Machine_Trans_EN_20]] [[Machine_Trans_EN_21]] [[Machine_Trans_EN_22]] [[Machine_Trans_EN_23]] [[Machine_Trans_EN_24]] [[Machine_Trans_EN_25]] [[Machine_Trans_EN_26]] [[Machine_Trans_EN_27]] [[Machine_Trans_EN_28]] [[Machine_Trans_EN_29]] [[Machine_Trans_EN_30]] ...<br />
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[[Book_projects|Back to translation project overview]]<br />
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[[DCG-To-Do|To the To Do list]]<br />
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=Chapter 1:A Comparison Between the Quality of Machine Translation and Human Translation——A Case Study of the Application of artificial intelligence in Sports Events=<br />
'''论机器翻译与人工翻译的质量对比——以人工智能在体育赛事领域的应用为例'''<br />
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卫怡雯 Wei Yiwen, Hunan Normal University, China<br />
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[[Machine_Trans_EN_1]]<br />
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=Chapter 3：On the Realm Advantages And Mutual Development of Machine Translation And Huamn Translation)=<br />
"'论机器翻译与人工翻译的领域优势及共同发展'"<br />
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肖毅瑶 Xiao Yiyao, Hunan Normal University, China<br />
[[Machine_Trans_EN_3]]<br />
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=Chapter 4 : A Comparison Between Machine Translation of Netease and Traditional Human Translation—A Case Study of The Economist Articles)= <br />
'''网易有道机器翻译与人工翻译的译文对比——以经济学人语料为例'''<br />
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王李菲 Wang Lifei, Hunan Normal University<br />
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[[Machine_Trans_EN_4]]<br />
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=Chapter 5: Muhammad Saqib Mehran - Problems in translation studies=<br />
[[Machine_Trans_EN_5]]<br />
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=6 徐敏赟(Machine Translation Based on Neural Network --Challenge or Chance?)=<br />
[[Machine_Trans_EN_6]]<br />
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=Chapter 7: The Cultivation of artificial intelligence and translation talents in the Belt and Road Initiative=<br />
[[Machine_Trans_EN_7]]<br />
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一带一路背景下人工智能与翻译人才的培养<br />
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颜莉莉 Yan Lili, Hunan Normal University, China<br />
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=Chapter 8: On Machine Translation Under Lanuguage Intelligence——An Option and Opportunity for Human Translators=<br />
'''论语言智能下的机器翻译——译者的选择与机遇'''<br />
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颜静 Yan Jing, Hunan Normal University, China<br />
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[[Machine_Trans_EN_8]]<br />
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=9 谢佳芬（ Machine Translation and Artificial Translation in the Era of Artificial Intelligence）=<br />
[[Machine_Trans_EN_9]]<br />
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=10 熊敏(Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts)=<br />
[[Machine_Trans_EN_10]]<br />
===Abstract===<br />
The history of machine translation can be traced to 1940s, undergoing germination, recession, revival and flourish. Nowadays, with the rapid development of information technology, machine translation technology emerged and is gradually becoming mature. Whether manual translation can be replaced by machine translation becomes a widely-disputed topic. So in order to explore the ability of machine translation, I adopts two versions of translation, which are manual translation and machine translation(this paper uses Youdao translation) for different types of texts(according to Peter Newmark's types of text：informative text, expressive text and vocative text). The results are quite different in terms of quality and accuracy. The results show that machine translation is more suitable for informative text for its brevity, while the expressive texts especially literary works and vocative texts are difficult to be translated, because there are too many loaded words and cultural images in expressive text and dependence on the readers. To draw a conclusion, the relationship between machine translation and manual translation should be complementary rather than competitive.<br />
<br />
===Key words===<br />
machine translation; manual translation; Newmark's type of texts<br />
<br />
===题目===<br />
Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts<br />
<br />
===摘要===<br />
机器翻译的历史可以追溯到上个世纪40年代，经历了萌芽期、萧条期、复兴期和繁荣期四个阶段。如今，随着信息技术的高速发展，机器翻译技术日益成熟，于是机器翻译能不能替代人工翻译这个话题引起了广泛热议。为了探究机器翻译的能力水平是否足以替代人工翻译，本人根据皮特纽马克的文本类型分类理论（信息类文本，表达文本，呼吁文本），选择了相应的译文类型，并且将其机器翻译的版本以及人工翻译的版本进行对比。结果发现，就质量和准确度而言，译文的水平大相径庭。因为其简洁的特性，机器比较适合翻译信息文本。而就表达文本，尤其是文学作品，因其存在文化负载词及相应的文化现象，所以机器翻译这类文本存在难度。而呼唤文本因其目的性以及对读者的依赖性较强，翻译难度较大。由此得知，机器翻译和人工翻译的关系应该是互补的，而不是竞争的。<br />
<br />
===关键词===<br />
机器翻译；人工翻译；纽马克文本类型<br />
<br />
===1. Introduction===<br />
====1.1.Introduction to machine translation====<br />
Machine translation, also known as computer translation is a technique to translating through machine translator, such as Google translation and Youdao translation. Machine translation is one of the branches of computational linguistics, ranging from computer science, statistics, information science and so on. Machine translation plays an important role in all aspects.<br />
Machine translation can be traced back to 1940s, when British engineer Booth and American engineer Weaver proposed using computer to translate and started to study machines used for translation. And the first machine translating system launched in 1954, used in English and Russian translation. And this means machine translation becomes a reality. However, the arrival of new things always accompanies barriers and setbacks. In the 1960s, reports from ALPAC (Automated Language Processing Advisory Committee) showed studies on machine translation had stagnated for a decade. At that time, due to the high cost of machine, choosing human translators to finish translating works was more economical for most companies. What’s worse, machine had difficulty in understanding semantic and pragmatic meanings. Fortunately, in the 1970s, with the advance and popularity of computer, machine translation was gradually back on track. With the development of science and technology, machine translation has better technological guarantee, such as artificial intelligence and computer technology. In the last decades, from the late 90s till now, machine translation is becoming more and more mature. The initial rule-oriented machine translation has been updated and Corpus-aided machine translation appears. And nowadays, technology in voice recognition has been further developed. This technique is frequently applied in speech translation products. Users only need to speak source language to the online translator and instantly it will speak out the target version. But machine can’t fully provide precise and accurate translation without any grammatical or semantic problems. Machine can understand the literal meaning of texts, but not the meaning in context. For example, there is polysemy in English, which refers to words having multiple meanings. So when translating these words, translators should take contexts into consideration. But machine has troubles in this way. In addition, machine has no feeling or intention. Hence, it is also difficult to convey the feelings from the source language.<br />
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====1.2.Process of machine translation====<br />
The process of manual translation is different from that of machine translation. Here is the process of the former. (1) Understand source language. (2) Use target language to organize language. (3) Generate translation. Unlike manual translation, machine translation tends to analyze and code source language first, then look for related codes in corpus, and work out the code that represents target language, generating translation. But they share a common feature, which is that Lexicon, grammatical rules and syntactic structure are taken into consideration. This is one of the biggest challenges for machine translation.<br />
<br />
===2.Theorectical framework===<br />
====2.1Newmark’s text typology====<br />
Text typology is a theory from linguistics. It undergoes several phrases. Karl Buhler, a German psychologist and linguist defines communicative functions into expressive, information and vocative. Then Roman Jakobson proposes categorization of texts, which are intralingual translation, interlingual translation and intersemiotic translation. Accordingly, he defines six main functions of language, including the referential function, conative function, emotive function, poetic function, metalingual function and the phatic function. Based on the two theories, Peter Newmark, a translation theorist, summarizes the language function into six parts: the informative function, the vocative function, the expressive function, the phatic function, the aesthetic function, and the metalingual function. Later, he further divided texts into informative type, expressive text and vocative type according to the linguistic functions of various texts. <br />
The core of the informative texts is the truth. It is to convey facts, information, knowledge of certain topics, reality and theories. The language style of the text is objective, logical and standardized. Reports, papers, scientific and technological textbooks are all attributed to informative texts. Translators are required to take format into account for different styles.<br />
The core of the expressive text is the sender’s emotions and attitudes. It is to express sender’s preferences, feelings, views and so on. The language style of it is subjective. Imaginative literature, including fictions, poems and dramas, autobiography and authoritative statements belong to expressive text. It requires translators to be faithful to the source language and maintain the style.<br />
The core of the vocative text is readership. It is to call upon readers to act, think, feel and react in the way intended by the text. So it is reader-oriented. Such texts as advertisement, propaganda and notices are of vocative text. Newmark noted that translators need to consider cultural background of the source language and the semantic and pragmatic effect of target language. If readers can comprehend the meaning at once and do as the text says, then the goal of information communication is achieved.<br />
To draw a conclusion, informative texts focus on the information and facts. Expressive texts center on the mind of addressers, including feelings, prejudices and so on. And vocative texts are oriented on readers and call on them to practice as the texts say.<br />
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====2.2Study method====<br />
Manual translations of the three texts are selected from authoritative versions and universally acknowledged. And machine translations of those come from Youdao Translator. And in this thesis I will compare and evaluate the two methods in word diction, sentence structure, word order and redundancy.<br />
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===3.Comparison and analysis of machine translation and manual translation ===<br />
====3.1Informative text ====<br />
（1）English into Chinese<br />
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①Source language:<br />
<br />
Keep the tip of Apple Pencil clean, as dirt and other small particles may cause excessive wear to the tip or damage the screen of i-pad.<br />
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Target language:<br />
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Machine translation: Apple Pencil笔尖应保持清洁，灰尘等小颗粒可能会导致笔尖过度磨损或损坏ipad屏幕。<br />
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Manual translation: 保持Apple Pencil铅笔的笔尖干净，因为灰尘和其他微粒可能会导致笔尖的过度磨损或损坏iPad屏幕。<br />
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Analysis: Here is the instruction of Apple Pencil. And the manual translation is the Chinese version on the instruction.Product instruction tends to be professional, since there are many terms for some concepts. Machine can easily identify these terms and provide related words to translate. The machine version is faithful and expressive to the source language. So it is well-qualified and readable for readers to understand the instruction. So we can use machine to translate informative text.<br />
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②Source language:<br />
<br />
China on Saturday launched a rocket carrying three astronauts-two men and one woman - to the core module of a future space station where they will live and work for six months, the longest orbit for Chinese astronauts.<br />
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Target language:<br />
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Machine translation: 周六，中国发射了一枚运载三名宇航员(两男一女)的火箭，进入未来空间站的核心舱，他们将在那里生活和工作6个月，这是中国宇航员最长的轨道。<br />
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Manual translation: 周六，中国发射了一枚搭载三名宇航员(两男一女)的火箭，进入未来空间站的核心舱，他们将在那里生活和工作6个月，这是中国宇航员最漫长的一次轨道飞行。<br />
<br />
Analysis: This is a news from Reuters, reporting that China has launched a rocket.The meaning of the two translations is almost the same, except for some word diction. But there are some details dealt with different choice. For example, the last sentence of the machine translation is a bit of obscure and direct. There are some ambiguous words and expressions.<br />
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(2)Chinese into English<br />
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Source language:湖南省博物馆是湖南省最大的历史艺术类博物馆，占地面积4.9万平方米，总建筑面积为9.1万平方米，是首批国家一级博物馆，中央地方共建的八个国家级重点博物馆之一、全国文化系统先进集体、文化强省建设有突出贡献先进集体。<br />
<br />
Target language:<br />
Manual translation: As the largest history and art museum in Hunan province, the Hunan Museum covers an area of 49,000㎡, with the building area reaching 91,000㎡. It is one of the first batch of national first-level museums and one of the first eight national museums co-funded by central and local governments.<br />
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Machine translation: Museum in hunan province is one of the largest historical art museum in hunan province, covers an area of 49000 square meters, a total construction area of 91000 square meters, is the first national museum, the central place to build one of the eight national key museum, national cultural system advanced collectives, strong culture began with outstanding contribution of advanced collective.<br />
<br />
Analysis: Machine translation is not faithful enough in content. For instance, “首批国家一级博物馆” is translated into “first national museum”, which is not the meaning of the source language. And there are some obvious grammar mistakes in the machine translation. For example, machine translates it into just one sentence but there are multiple predicates in it. So it is not grammatically permissible. What’s more, the sentence structure of machine translation is confusing and the focus is not specific enough.<br />
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====3.2Expressive text ====<br />
(1)English into Chinese<br />
<br />
Source language:<br />
<br />
An individual human existence should be like a river- small at first, narrowly contained within its banks, and rushing passionately past rocks and over waterfalls. Gradually the river grows wider, the banks recede, the waters flow more quietly, and in the end, without any visible breaks, they become merged in the sea, and painlessly lose their individual being.<br />
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Target language:<br />
<br />
Machine translation: 一个人的存在应该像一条河流——开始很小，被紧紧地夹在两岸中间，然后热情奔放地冲过岩石，飞下瀑布。渐渐地，河面变宽，两岸后退，水流更加平缓，最后，没有任何明显的停顿，它们汇入大海，毫无痛苦地失去了自己的存在。<br />
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Manual translation:人生在世，如若河流；河口初始狭窄，河岸虬曲，而后狂涛击石，飞泻成瀑。河道渐趋开阔，峡岸退去，水流潺缓，终了，一马平川，汇于大海，消逝无影。<br />
<br />
Analysis: Here is a well-known metaphor in the prose How to Grow Old written by Bertrand Russell. The manual translation is written by Tian Rongchang.This is a philosophical prose with graceful language. Literary translation is a most important and difficult branch of translation. Translator should focus on the literal meaning, culture, writing style and so on. It is a combination of beauty and elegance. Therefore, translators find it in a dilemma of beauty and faithfulness, let alone translating machine. Compared with manual translation, machine translation has difficulty in word choice. It is faithful and expressive, but not elegant enough.<br />
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(2)Chinese into English<br />
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Source language:没有一个人将小草叫做“大力士”，但是它的力量之大，的确是世界无比。这种力，是一般人看不见的生命力，只要生命存在，这种力就要显现，上面的石块，丝毫不足以阻挡。因为它是一种“长期抗战”的力，有弹性，能屈能伸的力，有韧性，不达目的不止的力。<br />
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Target language:<br />
<br />
Machine translation: No one calls the little grass "hercules", but its power is truly matchless in the world. This force is invisible life force. As long as there is life, this force will show itself. The stone above is not strong enough to stop it. Because it is a "long-term resistance" of the force, elastic, can bend and extend force, tenacity, not to achieve the purpose of the force.<br />
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Manual translation: Though nobody describes the little grass as a “husky”, yet its herculean strength is unrivalled. It is the force of life invisible to naked eye. It will display itself so long as there is life. The rock is utterly helpless before this force- a force that will forever remain militant, a force that is resilient and can take temporary setbacks calmly, a force that is tenacity itself and will never give up until the goal is reached. (by Zhang Peiji)<br />
<br />
Analysis:This is the excerpt of a well-known Chinese prose written by Xia Yan. It is written during the war of Resistance Against Japan. So the prose holds symbolic meaning, eulogizing the invisible tenacious vitality so as to encourage Chinese to have confidence in the anti-aggression war. Compared with manual translation, machine translation is much more abstract and confusing, especially for the word diction. For example, “大力士” is translated into “hercules” which is a man of exceptional strength and size in Greek and Roman Mythology, making it difficult to understand if readers of target language have no idea of the allusion. What’s worse, the machine version doesn’t reveal the symbolic meaning of the text, which is the core of this prose.<br />
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====3.3Vocative text ====<br />
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(1)English into Chinese<br />
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①Source language:<br />
<br />
iPhone went to film school, so you don’t have to. (Advertisement of iPhone13)<br />
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Target language:<br />
<br />
Machine translation: iPhone上的是电影学院，所以你不用去。<br />
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Manual translation:电影专业课，iPhone同学替你上完了。<br />
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Analysis：Here are advertisements of iPhone on Apple official website. There is a personification in the source language. It is used to stress the advancement and proficiency in camera, which is an appealing selling point to potential buyers. Compared with manual translation, machine translation is plain and not eye-catching enough for customers.<br />
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②Source language: <br />
<br />
5G speed OMGGGGG<br />
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Machine language: 5克的速度 OMGGGGG<br />
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Manual translation:<br />
<br />
iPhone的5G 巨巨巨巨巨5G<br />
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Analysis: The “G” in the source language is the unit of speed, standing for generation. However, it is mistaken as a unit of weight, representing gram in the machine translation. So the meaning is not faithful to the source language at all. As for manual translation, it complies with the source in form. Specifically speaking, five “G”s in the former complies with five characters “巨”in the latter. And the pronunciation of the two is similar. There are two layers of meaning for the 5 “G”s. One exclaims the fast speed of 5 generation network and the other new technology. In the manual version, “巨”can be used to show degree, meaning “quite” or “very”. <br />
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③Source language: <br />
<br />
History, faith and reason show the way, the way of unity. We can see each other not as adversaries but as neighbors. We can treat each other with dignity and respect, we can join forces, stop the shouting and lower the temperature. For without unity, there is no peace, only bitterness and fury."<br />
<br />
Target language:<br />
<br />
Machine translation: 历史、信仰和理性指明了团结的道路。我们可以把彼此视为邻居，而不是对手。我们可以尊严地对待彼此，我们可以联合起来，停止大喊大叫，降低温度。因为没有团结，就没有和平，只有痛苦和愤怒。<br />
<br />
Manual translation:历史、信仰和理性为我们指明道路。那是团结之路。我们可以把彼此视为邻居，而不是对手。我们可以有尊严地相互尊重。我们可以联合起来，停止喊叫，减少愤怒。因为没有团结就没有和平，只有痛苦和愤怒<br />
<br />
Analysis: Speech is a way to propagate some activity in public. It is an art to inspire emotion of the audience. The source language is the excerpt of Joe Biden’s inaugural speech. The speech should be inspiring and logic. The machine translation has some misunderstanding. Taking the translation of “lower the temperature” for example, machine only translates its literal meaning, relating to the temperature itself, without considering the context. What’s more, it is less logic than the manual one. Therefore, it adds difficulty to inspire the audience and infect their emotion.<br />
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===4.Common mistakes in machine translation ===<br />
<br />
====4.1 lexical mistakes ====<br />
<br />
Common lexical mistakes include misunderstandings in word category, lexical meaning and emotive and evaluative meaning. Misunderstanding in word category shows in the classification of word in the source language. As for misunderstanding in lexical meaning, machine has difficulty in precisely reflecting the meaning of the original texts, due to different cultural background and different language system. And for misunderstanding in emotive meaning, machine has no intention and emotion like human-beings. Therefore, it’s impossible for it to know writers’ feelings and their writing purposes. So sometimes, it may translate something negative into something positive.<br />
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====4.2 grammatical mistakes====<br />
<br />
Grammatical analysis plays an important part in translation. Normally speaking, every language has its own unique grammatical rules. So in the process of translation, if translators don’t know the formation rule well, the sentence meaning will be affected. Even though all the lexical meanings are well-known by translators, the lack of consciousness of grammaticality makes it harder to arrange words according to sequential rule. English tends to be hypotactic, while Chinese tends to be paratactic. English sentences are connected through syntactic devices and lexical devices. While Chinese sentences are semantically connected, which means there are limited logical words and connection words in Chinese. So when translating English sentence, we should first analyze its grammaticality and logical structure and then rearrange its sequence. However, online translating machine has troubles in grammatical analysis, which makes its improvement more difficult.<br />
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====4.3 other mistakes====<br />
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The two mistakes above are the internal ones. Apart from mistakes in linguistic system, there are some mistakes in other aspects, such as cultural background.<br />
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===5.Reasons for its common mistakes ===<br />
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====5.1 Difference in two linguistic system====<br />
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With different history, English and Chinese have different ways of expression. Commonly speaking, English is synthetic language which expresses grammatical meaning through inflection such as tense and Chinese is analytic language which expresses grammatical meaning through word order and function word. In addition, English is more compact with full sentences. Subordinate sentence is one of the most important features in modern English. Chinese, on the other hand, is more diffusive with minor sentences.<br />
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====5.2 Difference in thinking patterns and cultural background====<br />
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According to Sapir-Whorf’s Hypothesis, our language helps mould our way of thinking and consequently, different languages may probably express their unique ways of understanding the world. For two different speech communities, the greater their structural differentiations are, the more diverse their conceptualization of the world will be. For example, western culture is more direct and eastern culture more euphemistic. What’s more, English culture tends to be individualism, focusing on detail, through which it reflects the whole, while Chinese culture tends to be collective. Different thinking patterns will add difficulty for machine to translate texts.<br />
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====5.3 Limitation of computer====<br />
<br />
Recently, there are some breakthroughs and innovation in machine translation. However, due to its own limitation, online translation has limitation in some ways. Firstly, compared with machine, human brain is much more complicated, consisting of ten billions of neuron, each of which has different function to affect human’s daily activities and help humans avoid some errors. However, computer can only function according to preset programming has no intention or consciousness. Until now, countless related scholars have invested much time in machine translation. They upload massive language database, which include almost all linguistic rules. But computers still fail to precisely reflect the meaning of source language for many times due to the complexity and flexibility of language. On the other hand, computers can’t take context into consideration. During translation, it is often the case that machine chooses the most-frequently used meaning of one word. So without the correct and exact meaning, readers are easier to feel confused and even misunderstand the meaning of source language.<br />
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===6.Conclusion===<br />
From the analysis above, we can draw a conclusion that machine deals with informative text best, followed by non-literary translation of expressive text. What’s more, machine can be a useful tool to get to know the gist and main idea of a specific topic, for the simple sentence structure and numerous terms. And it can improve translating efficiency with high speed. But machine has difficulty in translating literary works, especially proses and poems.<br />
<br />
Machine translation has mixed future. From the perspective of commercial, machine translation boasts a bright future. With the process of globalization, the demand for translation is increasing accordingly. On one hand, if we only depend on human translator to deal with translating works, the quality and accuracy of translation can be greatly affected. On the other hand, if machine is used properly to do some basic work, human translators only need to make preparation before translating, progress, polish and other advanced work, contributing to highly-qualified translation and high working efficiency.<br />
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However, compared with manual translation, machine translation has a bleak future. It is still impossible for machine to replace interpreter or translator in a short term. With intelligence and initiative, humans are able to learn new knowledge constantly, which machine will never accomplish. Besides, machine is not used to replace translators but to assist them in work. In other words, translators and machine carry out their own duties and they are not incompatible.<br />
<br />
To draw a conclusion, although there are certain limitations of machine translation, it can serve as a catalyst for translating works. Therefore, with the rapid development of artificial intelligence and related technology, there are still many opportunities for machine translation.<br />
<br />
===Reference ===<br />
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Cui Zihan 崔子涵.机器翻译译文质量对比——以谷歌翻译和DeepL为例[J] [Comparison among Machine Translation--Taking Google Translation and Deepl for Example].Overseas English 海外英语,2021(15):182-183.<br />
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Li Deyi 李德毅. (2018). 人工智能导论 [Introduction to Artificial Intelligence]. Beijing: China Science and Technology Press 中国科学技术出版社.<br />
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Qiu Quanju 仇全菊.大数据时代背景下机器翻译及其发展趋势[J][Machine Translation and its Development Trend under the Background of Big Data Era]. English Teachers 英语教师,2021,21(16):60-62.<br />
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Zhuo Jianbin 卓键滨,Liu Wenxian 刘文娴,Peng Zili 彭子莉.机器翻译对各类型文本的德汉翻译能力探究[J][Research on the German Chinese Translation Ability of Machine Translation for Various Types of Texts]. Comparative Study of Cultural innovation 文化创新比较研究,2021,5(28):122-125.<br />
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Liu Qin刘琴.功能目的论对于不同文本类型的翻译解读[J][Analysis of Translations in Different Types of Text based on Functionalist Approaches].Overseas Engliosh 海外英语,2021(17):8-9.<br />
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Zhang Peiji 张培基.英译中国现代散文选[M][Selected Modern Chinese Prose Writings]. Shanghai Foreign Languages Education Press 上海外语教育出版社, 2002.<br />
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Chen Cheng陈诚.机器翻译技术的综述[J][Overview of Machine Translation Technology].Electronic Techonology 电子技术,2021,50(11):290-291.<br />
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He Xinyu何馨宇.机器翻译的发展及其对翻译职业化的影响研究[J] [The Development of Machine Translation and its Effect on Professional Transltors].Overseas English 海外英语,2021(20):48-49.<br />
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He Wen 何雯, Wang Xiufeng 王秀峰.信息型文本的在线机器翻译错误研究[J][Research on Errors in Online Machine Translation of Informative text ].Overseas English海外英语,2021(15):188-189.<br />
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Li Hanji 李晗佶. (2021). 人工智能时代翻译技术与译者关系演变与重构 [Evolution and reconstruction of the relationship between translation technology and translators in the era of artificial intelligence]. 西华师范大学学报(哲学社会科学版) Journal of West China Normal University (PHILOSOPHY AND SOCIAL SCIENCES EDITION) (2021-12-04) 1-6.<br />
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Wei Guang魏光. 人工翻译与机器翻译译文编辑比较研究[J][Comparative Study of Translation Editing between Manual Translation and Machine Translation]. Overseas English 海外英语,2021(19):18-19+21.<br />
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=Chapter 11 陈惠妮=Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts=<br />
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机器翻译的译前编辑研究——以医学类文摘为例<br />
<br />
陈惠妮 Chen Huini, Hunan Normal University<br />
<br />
[[Machine_Trans_EN_11]]<br />
<br />
===Abstract===<br />
At present, globalization is accelerating and the market demand for language services is rapidly increasing . Machine translation, as an important translation method, can greatly improve translation efficiency due to its low cost and high speed. However, because of the limitations of machine translation and the differences between Chinese and English language, machine translation is not accurate enough. In order to balance translation efficiency and translation quality, a great number of manual revisions in translation are required for the machine translating texts. Medical papers are specialized, special and purposeful, so it requires accurate,qualified and professional translation. However, the quality of translations by machine is inefficient to meet the high-quality requirements of medical papers translation. Therefore, the introduction of pre-editing can greatly improve the efficiency and quality of machine translation.<br />
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===Key words===<br />
Pre-editing, Machine translation, Medical texts<br />
<br />
===题目===<br />
Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts<br />
<br />
===摘要===<br />
在全球化加速发展的今天，市场对语言服务的需求迅速增加。机器翻译作为一种重要的翻译途径，由于其成本低、速度快，可以大大提高翻译效率。然而，由于机器翻译的局限性以及中英文语言的差异，机器翻译的准确性不高。为了平衡翻译效率和翻译质量，机器翻译文本需要大量的手工修改。<br />
医学论文具有专业性、特殊性和目的性，要求其译文准确、合格、专业。然而，机器翻译的质量较低，无法满足医学论文对翻译的高质量要求。因此，译前编辑的引入可以大大提高机器翻译的效率和质量。<br />
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===关键词===<br />
译前编辑；机器翻译；医学文本<br />
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===1. Introduction===<br />
===1.1 Definition of Machine Translation===<br />
As Cronin(2013) revealed: "Translation is undergoing a revolutionary upheaval. The influence of digital technology and the Internet on translation is continuous, extensive and profound. From the popularity of automatic online translation applications, translation revolution is everywhere (Cronin,2013) However,the concept of machine translation was firstly proposed in the 1930s. Since 1940s, the machine translation technology has been evolving from rule-based machine translation (RBMT) to statistical machine translation (SMT), and to neural machine translation (NMT). Machine translation refers to the automatic translation of source language into target language by using a computer system. That is, machine translation refers to the automatic translation of text from one language into another natural language by computer software or other online translation webs. Machine translation is also defined as the process of “using a computer system to automatically translate text or speech from one natural language to another” according to the definition by ISO (Cui 2014:68-73).O 'Brien (2002) defines it as "the behavior of modifying errors in machine translation to ensure that the target translation meets certain quality requirements". On the basic level, machine translation performs mechanical substitution of words in one language for words in another language, but that rarely produces a good translation, therefore, recognition of the whole phrases and their closest counterparts in the target language is needed. Not all words in one language have equivalents in another language, and many words have more than one meaning. A huge demand for translation is greatly needed in today’s global world, which creates new opportunities for the development of machine translation, attracts more and more attention and becomes one of the current research focuses.<br />
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===1.2 Definition of Pre-editing===<br />
Pre-editing means to adjust and modify the source language to make it fit more with the characteristics of the machine translation software before putting the source language before the into machine translation, so as to improve the quality of the translation machine translation (Wei Changhong 2008:93-94). Pre-editing is to modify the original text before putting it into machine translation software, in order to improve the recognition rate of machine translation, optimize the output quality of translated text and reduce the workload of post-editing. Because pre-translation editing only needs to be modified in one language, the operation is simpler than post-translation editing, which can realize the double improvement of quality and efficiency. A good pre-editing translation can help machine translation more smoothly, thus improving the machine readability and quality of the output translation. Pre-editing is mostly applied in the following situations: one is when the original text is of poor quality and the machine is difficult to recognize the meaning of the sentence, such as user generated content with poor readability and translatability (Gerlach et al 2003:45-53); Documents that need to be published in multiple languages; Next is when the original text contains a lot of jargon; The last is the original text has a corresponding translation memory bank. If the original text is edited, it can better match the content of the translation memory bank. Pre-editing is a process of identifying problems. It requires to pre-edit the source texts before putting it into machine translation according to the requirements, listing the expressions or sentences that may have trouble in machine translation and then pre-edit it by human. The purpose is to enable the computer to translate better, improve the translatability of machine translation. (Slype G V & Guinet J F & Seitz F 1984:115)<br />
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===1.3 Machine Translation Mode===<br />
According to the different knowledge acquisition methods, machine translation modes can be classified as follows: one is rule-based machine translation, which is based on bilingual dictionaries and a library of language rules for each language. The quality of translation depends on whether the source language conforms to the existing rules, but the inexhaustible rules are the hinders of this model. The second mode is machine translation based on statistics. This translation model relies on the principles of mathematics and statistics to find various existing translations corresponding to the translation tasks through the employment of corpus, analyzing the frequency of their occurrence and selecting the translation with the highest frequency for output. The disadvantage of this translation model is that it ignores the flexibility of language and the importance of context. The last one is neural network language model. This model is different from previous translation models in that it uses end-to-end neural network to realize automatic translation between natural languages. At present, the quality of its translation is much higher than that of the previous translation models.<br />
===1.4 Source of Study Abstracts===<br />
The core medical journals from domestic are selected in order to make the paper more representative and authoritative, such as National Medical Journal of China, Journal of Peking University (Health Science), and Journal of Third Military Medical University. All of them include basic medical science, biomedical technology, laboratory medical science and other fields. In this way, the pre-editing approaches included are applicable enough to machine translation of medical abstracts.<br />
===1.5 Selection of Translation Software===<br />
Generally, machine translation software includes Google Translation, Youdao Translator and Niu Translation, which has their own special use in translation. Take Google as an example, it is more like neurons in human brain, enabling to learn and collect information to establish connections with its neural machine translator’s neurons. However, it also causes many errors because of the lack of enough information. In this paper, contrastive analysis will be carried on by using Google translation. On the one hand, being a pioneer in the translation of NET, it is inevitably to sue Google as the translation software to translate medical texts in this paper. Comparing the program called “Google brain”, other NMT of translation software are relatively disadvantages. On the other hand, the Google translation enjoys the largest users in the world, with its downloads of more than one billion. The output quality of Google translation is more correct and complete than other machine translation software. With years of development and improvement, Google Translation has been greatly promoted. In this paper, Chinese medical abstract will be automatically translated by Google translation, and then the translation output will be compared with the translation by human and the publication of English abstracts. The main purpose is to prove that the improvement and promotion of quality and accuracy in the medical abstracts will be obtained through the pre-editing approaches.<br />
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===2.Language Characteristics and Error Division===<br />
Since Chinese and English are two different languages, it is quite neccessary to identify their own characteristics so as to better analyze and understand the two languages. Tytler (1978: 118-119) argued in his Essay on the Principles of Translation that there are three principles of translation that in all the translation should give readers the same feelings as the source text, except for complete transcript of ideas. There actually also exit some mistakes of these two languages. So the following will make some clarrifications of these two languages to ensure more accurate translation.<br />
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===2.1 Language Characteristics of Medical Abstracts===<br />
Chinese and English belong to different language systems, so there are differences in their language structure and the way users think of the languages. When using machine translation from Chinese to English, due to the unequal language levels, there will be many mistakes in the translation process, especially for ESP texts, such as medical papers.Here, these differences mainly refer to the linguistic characteristics of medical abstracts. In medical abstracts, it usually includes structured and unstructured abstracts. Although in different forms, they both describe the purpose, methods and conclusions of the research.<br />
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In the method section of medial abstract, several Chinese sentences can be connected with commas, but each sentence may convey different information. In contrast, English sentences contain a great deal of information, but in order to ensure clarity, some modifiers need to be isolated and then reconstructed. However, in Chinese-English machine translation, a lot of information is put into the sentence because the machine segments the sentence on the basis of the full comma.<br />
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In addition, subjectless sentence is used in the objective and method parts of Chinese medical abstracts. The subjectless sentence means the sentence without or free from subject and is usually employed in two contexts. The first is "needless to say". In Chinese sentences, it is common to omit the subject of the sentence. Chinese sentences can convey meanings by using incomplete sentence structures, so Chinese speakers can understand the meanings of the sentences even though the sentence subject is omitted. The second is "emphasis of action". In this context, subjectless sentences are used to describe behaviors, especially in the study of traditional Chinese medicine abstracts. Comparatively speaking, it should be avoided in English medical abstracts. Abstract sentences are more subjective when describing the learning process, while the essential requirement of English medical abstract is objectivity. From this point, sentences without subject should be avoided in English medical abstracts. Another feature is voice. Few words with passive meanings appear in Chinese abstracts. English sentences are more favoured in using passive voice. When translating from Chinese to English, passive voice should be used to make the contents objective, which is also the basic requirement of medical papers.<br />
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These are the linguistic features of Chinese medical abstracts. There are great differences in sentence structure and expression between Chinese and English medical abstracts. These differences may reduce the accuracy of machine translation, so it is necessary to introduce pre-editing to edit the source text to ensure that the source text can be accurately recognized by the machine and fully translated into English.<br />
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===2.2 Error Division of Machine Translation in Translating Abstracts of Medical Papers from Chinese to English===<br />
In this paper, errors in machine translation are listed out after analyzing. Some are due to the principles, such as statistical-based MT and NMT, employed by machine translation. Based on the original errors, they can be studied from two levels. One is from the macro-level, referring to mistakes caused by objective factors. These are not human factors, but the disadvantage of machine translation and the limitations of language. The limitations of machine translation are derived from the principles and models adopted and manifests itself as a reliance on reference sources. In this case, semantic ambiguity and textual incoherence may occur in the absence of reference sources.<br />
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However, for ESP texts such as medical papers, the requirements for terms and sentence patterns are far beyond the existing corpora. For unfamiliar texts (that is, the corresponding texts cannot be found in the corpora). The quality of output translation will be relatively low, which is determined by the principles of machine translation. As mentioned above, machine translation has evolved over the past few decades from rules-based to corp-based statistics to NMT. However, there still exist some limitations in machine translation.<br />
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Mistakes on micro-level are mainly caused by the variations and differences of linguistic structure between Chinese and English. Chinese is an implicit language, while English is an explicit one (Lian, 2010) To put it in another way, Chinese expression does not depend on language structures, while English does the opposite. This may result in mismatches between the original and machine translated sentences. This kind of error is mainly divided into two types: component fragment and component missing. For a medical paper, such errors are very serious. As a kind of ESP discourse, medical paper has the characteristics of fixed, objective and accurate language structure. In order to reproduce the characteristics of medical abstracts in translation, it is necessary to avoid errors at the level of words and sentences and pay attention to logic and consistency.<br />
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Lexical errors here refer to the inconsistency of fixed expressions in the translation of terms. Although these terms are machine translation based on the corpus, the corpus may not be perfect for ESP texts such as medical papers. Therefore, fixed expressions are not used in term translation. This is also the most common mistake in machine translation (the term here includes but is not limited to nouns). For ESP texts, medical text in particular, the fixed expressions and the accuracy of terms are of great importance.<br />
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===3.Approaches Proposed for Pre-editing ===<br />
Generally speaking, there is a close relationship between pre-editing and post-editing, both of which aim to convey information and ensure high or publishable translation quality. Proper pre-editing can improve the quality of machine translation in terms of adequacy and consistency. <br />
Complexity of natural language and people use language arbitrarily, bringing many difficulties to Chinese-English machine translation, Hu Qingping (2005:24) has proposed "the research of translation software and the development of the controlled language are the two directions to improve the quality of machine translation : the former aims at the difficulty in the natural language processing, the latter overcomes the arbitrariness of natural language". Feng Quangong and Gao Lin (2017: 63-68) put forward: "The writing principles of controlled language can be applied to pre-editing of machine translation. Pre-editing based on controlled language can effectively reduce the complexity and ambiguity of source text, improve the identifiability of machine translation (the translatability of source text itself), and thus reduce (fully) the workload of post-editing".<br />
The central task of pre-editing is to transform human-friendly content into machine-friendly content, so words and sentences need to be repositioned or even changed. Based on the analysis of the language characteristics of Chinese and English, the Chinese ideographic group should be split before the Chinese source text is input into the machine software to translate so that the sentence structure is complete which can be easily recognized by the machine translation software.<br />
===3.1 Extraction and Replacement of Terms in the Source Text===<br />
As a branch of applied English, medical English is the product of the combination of English language knowledge and medical knowledge. The terms in medical papers have the characteristics of fixed and complex language structure. Although Google Translation is based on a corpus, the language structure is not fixed due to the limited size of the corpus. Therefore, the following two steps should be followed before machine translation. The first is to extract terms and create a glossary, and then replace the Chinese terms in the source text with the corresponding English expressions in the glossary. Of course, the terms of extraction should be searched and verified according to the actual situation. All of these words are verified before they can be replaced. This method can not only ensure the accuracy of term translation, but also maintain the consistency of expression, especially when dealing with long texts.<br />
Example1<br />
Source abstract: 口腔白斑病癌变相关缺氧应答基因和微小RNA的芯片及表达验证。<br />
Google translation: Microarray detection/Chip detection and expression verification of hypoxia response genes and microRNAs related to oral leukoplakia canceration.<br />
Published translation:Transcriptome array screening and verification of oral leukoplakia carcinogenesis-related hypoxia-responsive gene and microRNA. <br />
Analysis: The expression “ Affymetrix GeneChip” empolyed in this thesis is a human transcription array for transcriptome array. In the source abstract, “芯片检测“ can be meant “screening” but not detection, so the proper and appropriate translation of these expression should be “transcription array screening”, but the Google translates it into “microarry detection of chip detection”. Therefore, term extraction is essential and inevitable before putting the source abstracts into the machine translation software.<br />
After pre-editing source abstracts: 对 oral leukoplakia carcinogenesis 相关 hypoxia-responsive gene 和微小RNA进行 transcriptome array screening 及表达验证。<br />
After pre-editing translation: Transcriptome array screening and expression- verification of hypoxia-responsive genes and microRNAs related to oral leukoplakia carcinogenesis.<br />
===3.2 Explication of Subordination===<br />
As mentioned above, the subordination of Chinese sentences is judged by certain specific words in machine translation . Chinese is a paratactic language, and sentences are often connected by internal logical relationships; while English depends on sentence structure, so sentences are often closely linked by various language forms. In order to improve the accuracy of machine translation, we can adjust the sentence structure and adjust the position of adjectives and their modifiers. <br />
Example 2<br />
Source abstracts:回顾性分析南京医科大学附属儿童医院中重度HIE患儿49例及同期就诊的无神经系统症状体征的足月新生儿为对照组31例的头颅磁共振成像（MRI）资料。<br />
Google translation: A retrospective analysis that the brain magnetic resonance imaging (MRI) data of 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University and full-term neonates with no neurological symptoms and signs during the same period were included in the control group.<br />
Published translation: A total of 49 children with moderate to severe HIE admitted to the children’s Hospital Affiliated to Nanjing Medical University were retrospectively analyzed. Cranial magnetic resonance imaging (MRI) date of 31 full-term neonates without neurological symptoms and signs who visited the hospital during the same period were recruited as the control group. <br />
Analysis: As the above example shows that “中重度HIE患儿” and “足月新生儿” in the source abstracts are from the Children’s Hospital Affiliated Nanjing Medical University. The Google translation shows that 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University. There is an error due to the misuse of subordination. There are some advice in order to solve the problem. That is, first to segment the sentence and then reconstruct the sentence. For instance, the Chinese expression “南京医科大学附属儿童医院” carries many modifiers, which requires to cut the modifiers and reconstruct the sentence. Therefore, the Chinese expression “南京医科大学附属儿童医院’can be divided into abstractions, leaving two sentences instead of one long sentence with modifiers..<br />
After pre-editing source abstracts: 对49例中重度HIE患儿以及31例无神经系统症状体征的足月新生儿的MRI资料进行回顾性分析。这些患者收治于南京医科大学儿童附属医院。<br />
After pre-editing translation: The MRI data of 49 children with moderate to severe HIE and 31 full-term newborns without neurological symptoms and signs were retrospectively analyzed. These patients were admitted to the Children’s Hospital of Nanjing Medical University.<br />
===3.3 Explication of Subject through Voice Changing===<br />
The extensive use of subjectless sentences are quite common in the Chinese abstracts, while English prefers to employ passive voice in the sentences so as to make them object and accurate. In order to take the characteristics of English sentences into great and careful consideration, the sentences written in passive voice in particular, it will be helpful for machine translation to find out the subject and reconstruct a sentence in passive voice (Wang Yan: 2008). The first is to discover the “doee” in a sentence and then is to reconstruct the sentence structure and put the “doee” before the verb. The last is to explicate the passive relation between the noun and the verb.<br />
Example 3<br />
Source abstract: 回顾性分析2018年1月至2020年12月解放军总医院第七医学中心收治的500例老年髋部骨折患者的资料。<br />
Google translation: A retrospective analysis of the data of 500 elderly patients with hip fractures admitted to the Seventh Medical Center of the PLA General Hospital from January 2018 to December 2020.<br />
Published translation: From January 2018 to December 2020, the data of 500 elderly patients with hip fracture treated in the Seventh Medical Center of PLA General Hospital were analyzed retrospectively.<br />
Analysis: The above example indicates that the “回顾性分析”in the Google Translate is a noun phrase, but the source abstracts actually describes an action or a behavior. The reason for such a mistake is that the machine can’t recognize the Chinese subjetless sentences. To find the subject of the sentence, the source abstracts can be revised and adjusted. Finding the “doee” is the first step, which refers to “500例老年髋部骨折患者的资料”in the source abstracts. And next is to put it in front of the verb, that is to place it in the beginning of the sentence. Last but not least, the passive voice should be explicated in the sentence. <br />
After pre-editing source abstract: 500例老年髋部骨折患者的资料被回顾性分析，他们在2018年1月之2020年12月期间收治于解放军总医院第七医学中心。<br />
After pre-editing translation: The data of 500 elderly hip fracture patients were retrospectively analyzed. They were admitted to the Seventh Medical Center of the PLA General Hospital between January 2018 and December 2020.<br />
===3.4 Relocation of Modifiers===<br />
Modifiers, including noun, adverbial and attributive are constantly employed in the Chinese medical papers in order to add information to subject and object in the sentence. By doing this, complicated sentences can be structured, thus causing obstacles to machine translation for recognizing this complex sentence structures when translating Chinese-English sentences. To make the machine translation successfully recognize the sentence structure, simplifying the sentence structure is quite necessary. The key is to moving the location of the modifiers and thus making the modifiers an independent sentence. In other words, the modifiers ought to be put before or behind the main part of the sentence to satisfy the common use of English. <br />
Usually, the modifiers in Chinese sentence can only be placed in front of the core word, while in English, modifiers are very flexible. It is all right to place the modifiers in front of or behind the major part of the sentences with adjective or a connective noun.<br />
Example 4<br />
Source abstracts: 利用DNA重组技术以pET-28a表达系统在E.coli BL21(DE3) 中重组表达Hepl。<br />
Google Translation: Hepl is recombined in E.coli BL21 (DE3) using DNA recombination technology with pET-28a expression system.<br />
Published translation: The recombinant Hcpl protein was expressed by using DNA recombination technology through pET-28a expression system in E. coli BL21 (De3).<br />
Analysis: The Chinese medical text includes two modifiers, “利用DNA”and “以pET-28a)表达系统”. These two modifiers will be translated by machine, which catches more attention to the two constituents. From the Google translation above, one failure is obvious that it misplaces the location of the two modifiers and presents it in not accurate form. To make it translate correctly by machine translation, dividing the sentences is very important so that the source abstracts can be correctly recognized by machine translation.<br />
After pre-editing source abstract: 利用DNA重组技术，Hcp1重组表达在E.coli BL21 (DE3)中， 通过pET-28a表达系统在E. coli BL21 (DE3)中。<br />
Google translation: Using DNA recombination technology, Hcp1 recombination is expressed in E.coli BL21 (DE3), via pET-28a expression system in E. coli BL21 (DE3).<br />
The second is the independence of modifiers, that is, modifiers can also be reconstructed into clauses to modify core words. Compared with English, There is no subordinate clause in Chinese, so redundant Chinese modifiers need to be reconstructed into subordinate clauses in Chinese-English translation to meet the characteristics of English. English, especially sentences with multiple modifiers. Otherwise, sentence structure may be confused, such as scattered modifiers of core words. To avoid this mistake, it is necessary to separate the modifier from the main part of the sentence. These modifiers should be reconstituted into clauses. Also, keep your sentences simple and easy to understand.<br />
===3.5 Proper Omission and Deletion of Category Words===<br />
Category words are commonly used in Chinese. Category words complement the meaning of words, including problems, positions, situations and jobs. Adding this supplementary word is more in line with Chinese custom. In many cases, it has no real meaning, so it can be omitted in translation. In Chinese, category words are frequently used. However, it is rarely used in English, which is one of the differences between Chinese and English. There are many kinds of category words. Considering objectivity and the fixed structure of language, redundant category words should be deleted in Chinese-English translation. In the general, the most commonly used category of words in the Chinese medical abstracts are "process", "behavior", and "situation". These categories of words hinder the language conversion of Chinese and English, resulting in redundancy. <br />
Example 5<br />
Source abstract: 探讨口腔白斑病癌变进程中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia response genes and associated microRNAs in the process of oral leukoplakia cancer was discussed.<br />
Published translation: To study the hypoxia response gene and microRNA (miRNA)expression profiles in the pathogenesis and progression of oral leukoplakia (OLK).<br />
Analysis: As the above example shows, the Chinese words “进程” belongs to a category word. However, the Chinese expression “癌变”contains the process, so the “进程” expression can be deleted before placing it into the machine translation, because the meaning of it has been overlapping between the expression “癌变”. Therefore, its place can be replaced with preposition “during”.<br />
After pre-editing source abstract: 探讨口腔白斑病癌变中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia responsive genes and miRNA in oral leukoplakia cancer was investigated.<br />
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===Conclusion===<br />
After years of development, machine translation has made great progress. The accuracy of machine translation has been greatly improved in both text recognition and sentence pattern conversion. However, machine translation has its own limitations. In other words, it needs to rely on the parallel corpus as a reference source for improving its accuracy. ESP text, in particular, is harder to get the high quality by machine translation. <br />
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As one of the research papers, the characteristics of medical abstracts are fixed language structures, objectivity and accuracy (Qin Yi 2004:421-423). Therefore, medical translation must be accurate, object and understandable to follow the specific demands of the medical paper. Being an important field in the human society, medical paper translation is on a great demand, which means that it needs a huge demand for human labor. However, with the machine translation promoting, it will be more efficient to translate medical papers combining the effort by human and machine. The improvement and development of machine translation requires the joint efforts of computer science, information science, statistics, linguistics and other academic circles to achieve more mature human-computer mutual assistance translation (Li Yafei, Zhang Ruihua 2019:38-45).<br />
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However, errors can occur during the process of machine translation of Chinese- English, because of the differences of the Chinese and English and the processing of the machine. Errors from the perspective of linguistic or grammar can affect the machine translation a lot. After division and recognition of errors, some pre-editing approaches are put forward to help the machine translation more accurate and readable, that are, extraction and replacement of terms in the source text, relocation of modifiers, explication of subordination, proper omission, deletion of category words and explication of subject through voice changing. <br />
<br />
The paper mainly focuses on the pre-editing machine translation by using medical papers as a case study. The errors of machine translation occurring in the translation of medical abstracts and pre-editing approaches for machine translation. The quality of machine translation of medical papers is greatly improved after employing the pre-editing methods. However, machine translation is not as flexible and accurate as human brain, so it is of importance to combine pre-editing and post-editing approaches with machine translation in order to produce more accurate, more object machine translation of medical papers.<br />
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===References===<br />
Cronin, Michael (2013). ''Translation in the Digital Age''[M]. New York & London: Routledge. 86<br />
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Cui Qiliang崔启亮(2014).论机器翻译的译后编辑[J] ''On Post-Editing of Machine Translatio''. 中国翻译 Chinese Translators Journal, 035(006):68-73<br />
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Feng Quangong, Gao Lin冯全功,高琳 (2017). 基于受控语言的译前编辑对机器翻译的影响[J] ''Influence of Pre-editing Based on Controlled Language on Machine Translation''. 当代外语研究Contemporary Foreign Language Research,(2): 63-68+87+110.<br />
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GERLACH J, et al ( 2013). ''Combining Pre-editing and Post-editing to Improve SMT of User-generated Content''[M]// Proceedings of MT Summit XIV Workshop on Post-editing Technology and Practice. 45-53<br />
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Hu Qingping胡清平(2005). 机器翻译中的受控语言[J] ''Controlled Language in Machine Translation''. 中国科技翻译 Chinese Science and Technology Translation, (03): 24-27. <br />
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Lian Shuneng连淑能 (2010). 英汉对比研究增订本[M]''An Updated Version of English-Chinese Contrastive Studies'' . 北京:高等教育出版社Beijing: Higher Education Publishing House. 35-36.<br />
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Li Yafei, Zhang Ruihua黎亚飞,张瑞华 (2019). 机器翻译发展与现状[J]''The Development and Current Situation of Machine Translation''. 中国轻工教育 China Light Industry Education, (5):38-45. <br />
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Slype G V & Guinet J F & Seitz F (1984). ''Better Translation for Better Communication'' [M] . Oxford: Pergamon Press Ltd (U.K.). 90-93<br />
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O'Brien S (2002). Teaching Post-editing: A Proposal for Course Con-tent [EB/OL]. http://mt-archive. Info/EAMT-2002-0brien. Pdf.<br />
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Tytler, A. F. (1978). ''Essay On The Principles of Translation''[M]. Amsterdam: JohnBenjamins Publishing. 118-119<br />
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=12 蔡珠凤=(The Mistranslation of C-J Machine Translation of Political Statements)=<br />
[[Machine_Trans_EN_12]]<br />
===Abstract===<br />
Language is the main way of communication between people. With the continuous development of globalization, the scale of cross-border exchanges is also expanding. However, due to cultural differences and diversity, the languages of different countries and regions are very different, which seriously hinders people's communication. The demand for efficient and convenient translation tools is increasing. At the same time, with the development of network technology and artificial intelligence, recognition technology based on deep learning is more and more widely used in English, Japanese and other fields.<br />
<br />
===Key words===<br />
machine translation; political statements; mistranslation of C-J machine translation<br />
===题目===<br />
The Mistranslation of C-J Machine Translation of Political Statements<br />
===摘要===<br />
语言是人与人之间交流的主要方式。随着全球化的不断发展，跨境交流的规模也在不断扩大。然而，由于文化的差异和多样性，不同国家和地区的语言差异很大，这严重阻碍了人们的交流。对高效便捷的翻译工具的需求正在增加。同时，随着网络技术和人工智能的发展，基于深度学习的识别技术在英语、日语等领域的应用越来越广泛。<br />
<br />
===关键词===<br />
机器翻译；政治发言；政治发言中译日的误译<br />
===1. Introduction===<br />
===Introduction to machine translation===<br />
Machine translation, also known as automatic translation, is a process of using computers to convert one natural language (source language) into another natural language (target language). It is a branch of computational linguistics, one of the ultimate goals of artificial intelligence, and has important scientific research value.At the same time, machine translation has important practical value. With the rapid development of economic globalization and the Internet, machine translation technology plays a more and more important role in promoting political, economic and cultural exchanges.The development of machine translation technology has been closely accompanied by the development of computer technology, information theory, linguistics and other disciplines. From the early dictionary matching, to the rule translation of dictionaries combined with linguistic expert knowledge, and then to the statistical machine translation based on corpus, with the improvement of computer computing power and the explosive growth of multilingual information, machine translation technology gradually stepped out of the ivory tower and began to provide real-time and convenient translation services for general users.（Zhang 2019:5-6)<br />
<br />
=== C-J machine translation software===<br />
Today's online machine translation software includes Baidu translation, Tencent translation, Google translation, Youdao translation, Bing translation and so on. Google was the first company to launch the machine translation system, and Baidu was the first company to import the machine translation system in China. In addition, Tencent and Youdao have attracted much attention.Machine translation is the process of using computers to convert one natural language into another. It usually refers to sentence and full-text translation between natural languages. In order to continuously improve the translation quality, R & D personnel have added artificial intelligence technologies such as speech recognition, image processing and deep neural network to machine translation on the basis of traditional machine translation based on rules, statistics and examples.With the increase of using machine translation, the joint cooperation between manual translation and machine translation will also increase significantly in the future. What criteria should be used to evaluate the quality of machine translation? In the evolving field of machine translation, there is an urgent need to clarify the unsolvable questions and solved problems.<br />
<br />
===The history of machine translation===<br />
The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. alchuni put forward the idea of using machines for translation. In 1933, Soviet inventor П.П. Trojansky designed a machine to translate one language into another, and registered his invention on September 5 of the same year; However, due to the low technical level in the 1930s, his translation machine was not made. In 1946, the first modern electronic computer ENIAC was born. Shortly after that, W. weaver, an American scientist and A. D. booth, a British engineer, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947 when discussing the application scope of electronic computer. In 1949, W. Weaver published the translation memorandum, which formally put forward the idea of machine translation. After 60 years of ups and downs, machine translation has experienced a tortuous and long development path. The academic community generally divides it into the following four stages:<br />
<br />
Pioneering period（1947-1964）<br />
<br />
In 1954, with the cooperation of IBM, Georgetown University completed the English Russian machine translation experiment with ibm-701 computer for the first time, showing the feasibility of machine translation to the public and the scientific community, thus opening the prelude to the study of machine translation.<br />
It is not too late for China to start this research. As early as 1956, the state included this research in the national scientific work development plan. The topic name is "machine translation, the construction of natural language translation rules and the mathematical theory of natural language". In 1957, the Institute of language and the Institute of computing technology of the Chinese Academy of Sciences cooperated in the Russian Chinese machine translation experiment, translating 9 different types of more complex sentences.From the 1950s to the first half of the 1960s, machine translation research has been on the rise. The United States and the former Soviet Union, two superpowers, have provided a lot of financial support for machine translation projects for military, political and economic purposes, while European countries have also paid considerable attention to machine translation research due to geopolitical and economic needs, and machine translation has become an upsurge for a time. In this period, although machine translation is just in the pioneering stage, it has entered an optimistic period of prosperity.<br />
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Frustrated period（1964-1975）<br />
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In 1964, in order to evaluate the research progress of machine translation, the American Academy of Sciences established the automatic language processing Advisory Committee (Alpac Committee) and began a two-year comprehensive investigation, analysis and test.In November 1966, the committee published a report entitled "language and machine" (Alpac report for short), which comprehensively denied the feasibility of machine translation and suggested stopping the financial support for machine translation projects. The publication of this report has dealt a blow to the booming machine translation, and the research of machine translation has fallen into a standstill. Coincidentally, during this period, China broke out the "ten-year Cultural Revolution", and basically these studies also stagnated. Machine translation has entered a depression.<br />
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convalescence（1975-1989）<br />
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Since the 1970s, with the development of science and technology and the increasingly frequent exchange of scientific and technological information among countries, the language barriers between countries have become more serious. The traditional manual operation mode has been far from meeting the needs, and there is an urgent need for computers to engage in translation. At the same time, the development of computer science and linguistics, especially the substantial improvement of computer hardware technology and the application of artificial intelligence in natural language processing, have promoted the recovery of machine translation research from the technical level. Machine translation projects have begun to develop again, and various practical and experimental systems have been launched successively, such as weinder system Eurpotra multilingual translation system, taum-meteo system, etc.However, after the end of the "ten-year holocaust", China has perked up again, and machine translation research has been put on the agenda again. The "784" project has paid enough attention to machine translation research. After the mid-1980s, the development of machine translation research in China has further accelerated. Firstly, two English Chinese machine translation systems, ky-1 and MT / ec863, have been successfully developed, indicating that China has made great progress in machine translation technology.<br />
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New period(1990 present)<br />
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With the universal application of the Internet, the acceleration of the process of world economic integration and the increasingly frequent exchanges in the international community, the traditional way of manual operation is far from meeting the rapidly growing needs of translation. People's demand for machine translation has increased unprecedentedly, and machine translation has ushered in a new development opportunity. International conferences on machine translation research have been held frequently, and China has made unprecedented achievements. A series of machine translation software have been launched, such as "Yixing", "Yaxin", "Tongyi", "Huajian", etc. Driven by the market demand, the commercial machine translation system has entered the practical stage, entered the market and came to the users.Since the new century, with the emergence and popularization of the Internet, the amount of data has increased sharply, and statistical methods have been fully applied. Internet companies have set up machine translation research groups and developed machine translation systems based on Internet big data, so as to make machine translation really practical, such as "Baidu translation", "Google translation", etc. In recent years, with the progress of in-depth learning, machine translation technology has further developed, which has promoted the rapid improvement of translation quality, and the translation in oral and other fields is more authentic and fluent.<br />
<br />
===The problem of machine translation at present===<br />
Error is inevitable<br />
Many people have misunderstandings about machine translation. They think that machine translation has great deviation and can't help people solve any problems. In fact, the error is inevitable. The reason is that machine translation uses linguistic principles. The machine automatically recognizes grammar, calls the stored thesaurus and automatically performs corresponding translation. However, errors are inevitable due to changes or irregularities in grammar, morphology and syntax, such as sentences with adverbials after "give me a reason to kill you first" in Dahua journey to the West. After all, a machine is a machine. No one has special feelings for language. How can it feel the lasting charm of "the tenderness of lowering its head, like the shame of a water lotus? After all, the meaning of Chinese is very different due to the changes of morphology, grammar and syntax and the change of context. Even many Chinese people are zhanger monks - they can't touch their heads, let alone machines.<br />
Bottleneck<br />
In fact, no matter which method, the biggest factor affecting the development of machine translation lies in the quality of translation. Judging from the achievements, the quality of machine translation is still far from the ultimate goal.<br />
Chinese mathematician and linguist Zhou Haizhong once pointed out in his paper "fifty years of machine translation": to improve the quality of machine translation, the first thing to solve is the problem of language itself rather than programming; It is certainly impossible to improve the quality of machine translation by relying on several programs alone. At the same time, he also pointed out that it is impossible for machine translation to achieve the degree of "faithfulness, expressiveness and elegance" when human beings have not yet understood how the brain performs fuzzy recognition and logical judgment of language. This view may reveal the bottleneck restricting the quality of translation. <br />
It is worth mentioning that American inventor and futurist ray cozwell predicted in an interview with Huffington Post that the quality of machine translation will reach the level of human translation by 2029. There are still many disputes about this thesis in the academic circles.<br />
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===2.Mistranslation of Chinese Japanese machine translation===<br />
===2.1Vocabulary mistranslation in Chinese Japanese machine translation===<br />
===2.1.1Mistranslation of proper nouns===<br />
In political materials, there are often political dignitaries' names, place names or a large number of proper nouns in the political field. The morphemes of such words are definite and inseparable. Mistranslation will make the source language lose its specific meaning. <br />
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Japanese translation into Chinese Chinese translation into Japanese<br />
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original text translation by Youdao reference translation original text translation by Youdao reference translation<br />
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朱鎔基 朱基 朱镕基 栗战书 栗戰史書 栗戰書<br />
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労安 劳安 劳安 李克强 李克強 李克強 <br />
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筑紫哲也 筑紫哲也 筑紫哲也 习近平 習近平 習近平<br />
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山口百惠 山口百惠 山口百惠 韩正 韓中 韓正<br />
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田中角栄 田中角荣 田中角荣 王沪宁 王上海氏 王滬寧<br />
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東条英機 东条英社 东条英机 汪洋 汪洋 汪洋<br />
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毛沢东 毛泽东 毛泽东 赵乐际 趙樂南 趙樂際<br />
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トウ・ショウヘイ 大酱 邓小平 江泽民 江沢民 江沢民<br />
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周恩来 周恩来 周恩来<br />
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クリントン 克林顿 克林顿<br />
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The above table counts 18 special names in the two texts, and 7 machine translation errors. In terms of mistranslation, there are not only "战书" but also "戰史" and "史書" in Chinese. "沪" is the abbreviation of Shanghai. In other words, since "战书" and "沪" are originally common nouns, this disrupts the choice of target language in machine translation. Except Katakana interference (except for トウシゃウヘイ, most of the mistranslations appear in the new Standing Committee. It can be seen that the machine translation system does not update the thesaurus in time. Different from other words, people's names have particularity. Especially as members of the Standing Committee of the Political Bureau of the CPC Central Committee, the translation of their names is officially unified. In this regard, public figures such as political dignitaries, stars, famous hosts and important. In addition, the machine also translates Japanese surnames such as "タ二モト" (谷本), "アンドウ" (安藤) into "塔尼莫特" and "龙胆". It can be found that the language feature that Japanese will be written together with Chinese characters and Hiragana also directly affects the translation quality of Japanese Chinese machine translation. Japanese people often use Katakana pronunciation. For example, when Japanese people talk with Premier Zhu, they often use "二ーハウ" (Hello). However, the machine only recognizes the two pseudonyms "二" and "ハ" and transliterates them into "尼哈" , ignoring the long sound after "二" and "ハ".<br />
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original text Translation by Youdao reference translation<br />
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日美安全体制 日米の安全体制 日米安保体制<br />
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中国共产党第十九次全国代表大会 中国共産党第19回全国代表大会 中国共産党第19回全国代表大会（第19回党大会）<br />
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十八大 十八大 第18回党大会中国特色社会主义<br />
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中国特色社会主義 中国の特色ある社会主義 第18回党大会<br />
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中国共产党中央委员会 中国共産党中央委員会 中国共産党中央委員会<br />
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中国共産党中央委員会十八届中共中央政治局常委 第18代中国共產党中央政治局常務委員 第18期中共中央政治局常務委員<br />
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十八届中共中央政治局委员 18期の中国共產党中央政治局委員 第18期中共中央政治局委員<br />
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十九届中共中央政治局常委 十九回中国共產党中央政治局常務委員 第19期中央政治局常務委員<br />
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中共十九届一中全会 中国共產党第十九回一中央委員会 第19期中央委員会第1回全体会議<br />
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The above table is a comparison of the original and translated versions of some proper nouns. As shown in the table, the mistranslation problems are mainly reflected in the mismatch of numerals + quantifiers, the wrong addition of case auxiliary word "の", the lack of connectors, the Mistranslation of abbreviations, dead translation, and the writing errors of Chinese characters. The following is a specific analysis one by one.<br />
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"十八届中共中央政治局常委", "十八届中共中央政治局委员", "十九届中共中央政治局常委" and "中共十九届一中全会" all have quantifiers "届", which are translated into "代", "期" and "回" respectively. The meaning is vague and should be uniformly translated into "期"; Among them, the translation of the last three proper nouns lacks the Chinese conjunction "第". The case auxiliary word "の" was added by mistake in the translation of "十八届中共中央政治局委员" and "日美安全体制". The "中国共产党中央委员会" did not write in the form of Japanese Ming Dynasty characters, but directly used simplified Chinese characters.<br />
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The full name of "中共十九届一中全会" is "中国共产党第十九届中央委员会第一次全体会议", in which "一" stands for "第一次", which is an ordinal word rather than a cardinal word. Machine translation does not produce a correct translation. The fundamental reason is that there are no "规则" for translating such words in machine translation. If we can formulate corresponding rules for such words (for example, 中共m'1届m2 中全会→第m1期中央委员会第m2回全体会议), the translation system must be able to translate well no matter how many plenary sessions of the CPC Central Committee.<br />
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===2.1.2Mistranslation of Polysemy===<br />
original text Translation by Youdao reference translation<br />
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スタジオ 摄影棚/工作室 直播现场/演播厅<br />
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日中関係の話 中日关系的故事 就中日关系(话题)<br />
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溝 水沟 鸿沟<br />
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それでは日中の問題について質問のある方。 那么对白天的问题有提问的人。 关于中日问题的话题，举手提问。<br />
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私たちのクラスは20人ちょっとですが、 我们班有20人左右， 我们班二十多人的意见统一很难，<br />
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いろいろな意見が出て、まとめるのは大変です。 但是又各种各样的意见，总结起来很困难。 <br />
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一体どうやって、13億人もの人をまとめているんですか。 到底是怎么处理13亿的人的呢？ 中国是怎样把13亿人凝聚在一起的？<br />
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In the original text, the word "スタジオ" appeared four times and was translated into "摄影棚" or "工作室" respectively, but the context is on-site interview, not the production site of photography or film. "話" has many semantics, such as "说话", "事情", "道理", etc. In accordance with the practice of the interview program, Premier Zhu Rongji was invited to answer five questions on the topic of China Japan relations. Obviously, the meaning of the word "故事" is very abrupt. The inherent Japanese word "日中" means "晌午、白天". At the same time, it is also the abbreviation of the names of the two countries. The machine failed to deal with it correctly according to the context. "溝" refers to the gap between China and Japan, not a "水沟". The former "まとめる" appears in the original text with the word "意见", which is intended to describe that it is difficult to unify everyone's opinions. The latter "まとめている" also refers to unifying the thoughts of 1.3 billion people, not "处理". Therefore, it is more appropriate to use "统一" and "处理" in human translation, rather than "总结意见" or "处理意见".<br />
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Mistranslation of polysemy has always been a difficult problem in machine translation research. The translation of each word is correct, but it is often very different from the original expression in the context. Zhang Zhengzeng said that ambiguity is a common phenomenon in natural language. Its essence is that the same language form may have different meanings, which is also one of the differences between natural language and artificial language. Therefore, one of the difficulties faced by machine translation is language disambiguation (Zhang Zheng, 2005:60). In this regard, we can mark all the meanings of polysemous words and judge which meaning to choose by common collocation with other words. At the same time, strengthen the text recognition ability of the machine to avoid the translation inconsistent with the current context. In this way, we can avoid the mistake of "大酱" in the place where famous figures such as Deng Xiaoping should have appeared in the previous political articles.<br />
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===2.1.3Mistranslation of compound words===<br />
Multi class words refer to a word with two or more parts of speech, also known as the same word and different classes.<br />
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original text Translation by Youdao reference translation<br />
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1998年江泽民主席曾经访问日本, 1998年の江沢民国家主席の日本訪問し、 1998年、江沢民総書記が日本を訪問し、かつ<br />
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同已故小渊首相签署了联合宣言。 かつて同じ故小渕首相が署名した共同宣言 亡くなられた小渕総理と宣言に調印されました<br />
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Chinese "同" has two parts of speech: prepositions and conjunctions: "故" has many parts of speech, such as nouns, verbs, adjectives and conjunctions. This directly affects the judgment of the machine at the source language level. The word "同" in the above table is used as a conjunction to indicate the other party of a common act. "故" is used as a verb with the semantic meaning of "死亡", which is a modifier of "小渊首相". The machine regards "同" as an adjective and "故" as a noun "原因", which leads to confusion in the structure and unclear semantics of the translation. Different from the polysemy problem, multi category words have at least two parts of speech, and there is often not only one meaning under each part of speech. In this regard, software R & D personnel should fully consider the existence of multi category words, so that the translation machine can distinguish the meaning of words on the basis of marking the part of speech, so as to select the translation through the context and the components of the word in the sentence. Of course, the realization of this function is difficult, and we need to give full play to the wisdom of R & D personnel.<br />
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===2.2 Syntactic mistranslation in Chinese Japanese machine translation===<br />
===2.2.1Mistranslation of tenses===<br />
original text：History is written by the people, and all achievements are attributed to the people. <br />
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Translation by Youdao：歴史は人民が書いたものであり、すべての成果は人民のためである。<br />
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reference translation：歴史は人民が綴っていくものであり、すべての成果は人民に帰することとなります。<br />
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The original sentence meaning is "历史是人民书写的历史" or "历史是人民书写的东西". When translated into Japanese, due to the influence of Japanese language habits, the formal noun "もの" should be supplemented accordingly. In this sentence, both the machine and the interpreter have translated correctly. However, the machine's recognition of "的" is biased, resulting in tense translation errors. The past, present and future will become "history", and this is a continuous action. The form translated as "~ ていく" not only reflects that the action is a continuous action, but also conforms to the tense and semantic information of the original text. In addition, the machine's treatment of the preposition "于" is also inappropriate. In the original text, "人民" is the recipient of action, not the target language. As the most obvious feature of isolated language, function words in Chinese play an important role in semantic expression, and their translation should be regarded as a focus of machine translation research.<br />
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original text：李克强同志是十六届中共中央政治局常委,其他五位同志都是十六届中共中央政治局委员。<br />
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Translation by Youdao：李克強総理は第16代中国共産党中央政治局常務委員であり、他の５人の同志はいずれも16期の中国共産党中央政治局委員である。<br />
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reference translation：李克強同志は第16期中国共産党中央政治局常務委員を務め、他の５人は第16期中共中央政治局委員を務めました。<br />
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Judging the verb "是" in the original text plays its most basic positive role, but due to the complexity of Chinese language, "是" often can not be completely transformed into the form of "だ / である" in the process of translation. This sentence is a judgment of what has happened. Compared with the 19th session, the 18th session has become history. This is an implicit temporal information. It is very difficult for machines without human brain to recognize this implicit information. "中共中央政治局常委" is the abbreviation of "中共中央政治局常务委员会委员" and it is a kind of position. In Japanese, often use it with verbs such as "務める","担当する",etc. The translator adopts the past tense of "務める", which conforms to the expression habit of Japanese and deals with the problem of tense at the same time. However, machine translation only mechanically translates this sentence into judgment sentence, and fails to correctly deal with the past information implied by the word "十八届".<br />
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===2.2.2Mistranslation of honorifics===<br />
Honorific language is a language means to show respect to the listener. Different from Japanese, there is no grammatical category of honorifics in Chinese. There is no specific fixed grammatical form to express honorifics, self modesty and politeness. Instead, specific words such as "您", "请" and "劳驾" are used to express all kinds of respect or self modesty. <br />
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Original text translation by Youdao reference translation<br />
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女士们，先生们，同志们，朋友们 さんたち、先生たち、同志たち、お友达さん ご列席の皆さん<br />
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谢谢大家！ ありがとうございます！ ご清聴ありがとうございました。<br />
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これはどうされますか。 这是怎么回事呢? 您将如何解决这一问题？<br />
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こうした問題をどうお考えでしょうか。 我们会如何考虑这些问题呢？ 您如何看待这一问题？<br />
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For example, for the processing of "女士们，先生们，同志们，朋友们", the machine makes the translation correspond to the original one by one based on the principle of unchanged format, but there are errors in semantic communication and pragmatic habits. When speaking on formal occasions, Chinese expression tends to be comprehensive and detailed, as well as the address of the audience. In contrast, Japanese usually uses general terms such as "皆様", "ご臨席の皆さん" or "代表団の方々" as the opening remarks. In terms of Japanese Chinese translation, the machine also failed to recognize the usage of Japanese honorifics such as "さ れ る" and "お考え". It can be seen that Chinese Japanese machine translation has a low ability to deal with honorific expressions. The occasion is more formal. A good translation should not only be fluent in meaning, but also conform to the expression habits of the target language and match the current translation environment.<br />
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===Conclusion===<br />
In the process of discussing the Mistranslation of machine translation, this paper mainly cites the Mistranslation of Youdao translator. When I studied the problem of machine translation mistranslation, I also used a large number of translation software such as Google and Baidu for parallel comparison, and found that these translation software also had similar problems with Youdao translator. For example, the "新世界新未来" in Chinese ABAC structural phrases is translated by Baidu as "新し世界の新しい未来", which, like Youdao, is not translated in the form of parallel phrases; Google online translation translates the word "“全心全意" into a completely wrong "全面的に"; The original sentence "我吃了很多亏" in the Mistranslation of the unique expression in the language is translated by Google online as "私はたくさんの損失を食べました". Because the "吃" and "亏" in the original text are not closely adjacent, the machine can not recognize this echo and mistakenly treats "亏" as a kind of food, so the machine translates the predicate as "食べました"; Japanese "こうした問題をどう考えでしょうか", Google's online translation is "你如何看待这些问题?", "你" tone can not reflect the tone of Japanese honorific, while Baidu translates as "你怎么想这样的问题呢?" Although the meaning can be understood, it is an irregular spoken language. Google and Baidu also made the same mistakes as Youdao in the translation of proper nouns. For example, they translated "十七届(中共中央政治局常委)”" into "第17回..." .In short, similar mistranslations of Youdao are also common in Baidu and Google. Due to space constraints, they will not be listed one by one here.<br />
<br />
Mr. Liu Yongquan, Institute of language, Chinese Academy of Social Sciences (1997) It has been pointed out that machine translation is a linguistic problem in the final analysis. Although corpus based machine translation does not require a lot of linguistic knowledge, language expression is ever-changing. Machine translation based solely on statistical ideas can not avoid the translation quality problems caused by the lack of language rules. The following is based on the analysis of lexical, syntactic and other mistranslations From the perspective of the characteristics of the source language, this paper summarizes some difficulties of Chinese and Japanese in machine translation.<br />
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(1) The difficulties of Chinese in machine translation <br />
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Chinese is a typical isolated language. The relationship between words needs to be reflected by word order and function words. We should pay attention to the transformation of function words such as "的", "在", "向" and "了". Some special verbs, such as "是", "做" and "作", are widely used. How to translate on the basis of conforming to Japanese pragmatic habits and expressions is a difficulty in machine translation research. In terms of word order, Chinese is basically "subject→predicate→object". At the same time, Chinese pays attention to parataxis, and there is no need to be clear in expression with meaningful cohesion, which increases the difficulty of Chinese Japanese machine translation. When there are multiple verbs or modifier components in complex long sentences, machine translation usually can not accurately divide the components of the sentence, resulting in the result that the translation is completely unreadable. <br />
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(2) Difficulties of Japanese in machine translation <br />
<br />
Both Chinese and Japanese languages use Chinese characters, and most machines produce the target language through the corresponding translation of Chinese characters. However, pseudonyms in Japanese play an important role in judging the part of speech and meaning, and can not only recognize Chinese characters and judge the structure and semantics of sentences. Japanese vocabulary is composed of Chinese vocabulary, inherent vocabulary and foreign vocabulary. Among them, the first two have a great impact on Chinese Japanese machine translation. For example "提出" is translated as "提出する" or "打ち出す"; and "重视" is translated as "重視する" or "大切にする". Japanese is an adhesive language, which contains a lot of "けど", "が" and "けれども" Some of them are transitional and progressive structures, but there are also some sequential expressions that do not need translation, and these translation software often can not accurately grasp them.<br />
<br />
===References===<br />
[1] Navroz Kaur Kahlon,(2021(prepublish));Williamjeet Singh.Machine translation from text to sign language: a systematic review[J].Universal Access in the Information Society,1-35.<br />
<br />
[2] Cao Qianyu;Hao Hanmei,(2021);Ahmed Syed Hassan.A Chaotic Neural Network Model for English Machine Translation Based on Big Data Analysis[J].Computational Intelligence and Neuroscience,3274326-3274326.<br />
<br />
[3]Hwang Yongkeun;Kim Yanghoon;Jung Kyomin.(2021)Context-Aware Neural Machine Translation for Korean Honorific Expressions[J].Electronics,10(13):1589-1589.<br />
<br />
[4]Zakaryia Almahasees.(2021)Analysing English-Arabic Machine Translation:Google Translate, Microsoft Translator and Sakhr.<br />
<br />
[5](2021)Machine learning in translation[J].Nature Biomedical Engineering,5(6):485-486.<br />
<br />
[6]Shaimaa Marzouk.(2021(prepublish))An in-depth analysis of the individual impact of controlled language rules on machine translation output: a mixed-methods approach[J].Machine Translation,1-37.<br />
<br />
[7]Welnitzová Katarína;Munková Daša.(2021)Sentence-structure errors of machine translation into Slovak[J].Topics in Linguistics,22(1):78-92.<br />
<br />
[8]Xu Xueyuan.(2021).Machine learning-based prediction of urban soil environment and corpus translation teaching[J].Arabian Journal of Geosciences,14(11). <br />
<br />
[9]Chen Bingchang 陈丙昌(2016).機械翻訳の誤訳分析【D】.Error analysis of mechanical translation.贵州大学.2016(05) <br />
<br />
[10]Lv Yinqiu 呂寅秋(1996).機械翻訳の言語規則と伝統文法との相違点.【D】The language rules of mechanical translation, the traditional grammar, and the points of contradiction.日本学研究.Japanese Studies.1996(00):21-22 <br />
<br />
[11]Liu Jun 刘君(2014).基于语料库的中日同形词词义用法对比及其日中机器翻译研究【D】.A Corpus-based Comparison of the Meanings of Chinese and Japanese Homographs and Research on Japanese-Chinese Machine Translation.广西大学.(03) <br />
<br />
[12]Cun Qianqian 崔倩倩(2019).机器翻译错误与译后编辑策略研究【D】.Research on Machine Translation Errors and Post-Editing Strategies.北京外国语大学.(09) <br />
<br />
[13]Zhang Yi 张义(2019).机器翻译的译文分析【D】.Translation analysis of machine translation.西安外国语大学.(10) <br />
<br />
[14]Zhang Linqian 张琳婧(2019).在线机器翻译中日翻译错误原因及对策【D】.Causes and countermeasures of online machine translation errors in Chinese-Japanese translation.山西大学.(02)<br />
<br />
[15]Wang Dan 王丹(2020).基于机器翻译的专利文本译后编辑对策研究【D】.Research on countermeasures for post-translational editing of patent texts based on machine translation.大连理工大学.(06)<br />
<br />
[16]Yang Xiaokun 杨晓琨(2020).日中机器翻译中的前编辑规则与效果验证【D】.Pre-editing rules and effect verification in Japanese-Chinese machine translation.大连理工大学.(06)<br />
<br />
[17]Zuo Jia 左嘉(2021). 机器翻译日译汉误译研究【D】. Research on Mistranslation of Machine Translation from Japanese to Chinese.北京第二外国语学院.<br />
<br />
[18]Guan Biying 关碧莹(2018).关于政治类发言的汉日机器翻译误译分析【D】.Analysis of Chinese-Japanese Machine Translation Mistranslations of Political Speeches.哈尔滨理工大学.<br />
<br />
[19]Che Tong 车彤(2021).汉译日机器翻译质量评估及译后编辑策略研究【D】.Research on Quality Evaluation of Chinese-Japanese Machine Translation and Post-translation Editing Strategies.北京外国语大学.(09)<br />
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Networking Linking<br />
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http://www.elecfans.com/rengongzhineng/692245.html<br />
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https://baike.baidu.com/item/%E6%9C%BA%E5%99%A8%E7%BF%BB%E8%AF%91/411793<br />
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=13 陈湘琼Chen Xiangqiong（Study on Post-editing from the Perspective of Functional Equivalence Theory ）=<br />
[[Machine_Trans_EN_13]]<br />
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=14 Bi bi Nadia（Machine Translation a Challenge for Human Translators)=<br />
[[Machine_Trans_EN_14]]<br />
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===Abstract===<br />
Machine translation is a big obstacle in the way of Human translators or interpretors although it is quick and less time consuming.people are trying to get translation of their target language using through source language.For this purpose they are using digital apps like Google translation Google translation does not give accurate and exact interpretation.Google translator is translates word to word translate that doesn't clarifies it's true and actual meaning.On the other hand human translators can give exact and accurate translation ,they take care of grammatical errors , diction and sentence structure.They clarify the purpose of target language through using source language.<br />
<br />
===Keywords===<br />
Descriptive translation, academic, interdiscipline, comparative literature, localization,Translatology,school of thought,translation , studies, linguistics, corresponding.<br />
<br />
===Body of article===<br />
Translation is the process of reworking text from one language into another to maintain the original message and communication. But, like everything else, there are different methods of translation, and they vary in form and function. What is translation? The term has become widely used among knowledge transfer researchers and practitioners, especially in the fields of health and health care. In a landmark review, Jonathan Lomas began to argue that 'The tasks… may be defined as to establish and maintain links between researchers and their audience, via the appropriate translation of research findings' (Lomas 1997, p 4). In 2004, the World Health Organization's World Report on Knowledge for Better Health suggested that 'One of the key contributions of research to health systems is the translation of knowledge into actions' (WHO 2004, p 33 and p 100). By 2006, special issues of WHO's Bulletin as well as the journals Evaluation and the Health Professions and the Journal of Continuing Education in the Health Professions were dedicated to translation.But what does 'translation' mean? It may be a new word for an old problem, meaning nothing more than 'transfer'. The rapidly emerging field of 'translational medicine' seems to take translation to mean generally what transfer might have meant, that is the transmission of knowledge and evidence 'from bench to bedside'. As one commentator has observed, "Translational medicine" as a fashionable term is being increasingly used to describe the wish of biomedical researchers to ultimately help patients' (Wehling 2008, abstract). <br />
Semantic uncertainty persists, not least because of the different interests of scientists, clinicians, patients and commercial firms (Littman et al 2007): 'Translational research means different things to different people, but it seems important to almost everyone' (Woolf 2008, p 211).<br />
In related fields, such as public health (Armstrong et al 2006), 'translation' seems to signify dissatisfaction with 'transfer'. It wants to move away from thinking of knowledge transfer as a form of technology transfer or dissemination, rejecting if only by implication its mechanistic assumptions and its model of linear messaging from A to B. But still, what does it signify?<br />
<br />
===Why translation?===<br />
'Translation' indicates a closer attention to the problem of shared meaning and how it might be developed. It seems to represent some new epistemological lubricant, facilitating the dissemination of texts and the application and use of the knowledge and information they in. Simply, translation might be the key to transfer. And yet, when we stop to think, we are more ambivalent. What is translated often seems somehow inferior, not real or original. Note how readily commentators reach for the idea that things might be 'lost in translation'. Knowing at a distance – made in and mediated by translation - makes for incomplete renditions, blurred images, partial truths. So what might 'translation' really mean? The purpose of this paper is to set out, for policy makers and practitioners, the theoretical and conceptual resources translation holds and seems to represent. In doing so, it explores understandings of translation in the fields of literature and linguistics and in the sociology of science and technology. It begins by setting out just why this idea of translation should make immediate, intuitive sense in relation to research, policy and practice.<br />
1translation in research, policy and practice research as translation<br />
Research often entails translation from one language to another: where data is collected from more than one ethnic group, for example, or where the language of the researcher is other than that of the research subject. It may draw on a secondary literature or source documents written in different languages, and may be published and disseminated in languages other than the one in which it is first written up.<br />
2In a different way, to conduct an interview is to ask for an account of experience and its meanings, but it is also to construct and translate that experience in terms defined at least in part by the researcher. In representing what is said, transcripts then select data, usually excluding significant gesture and eye-contact, for example. Often, certain characteristics of speech-acts (such as hesitations) will be edited out. In turn, the format of the transcript shapes the analytic use the researcher may make of it. The basis of research 'findings', then, is an artefact, a transcript or translation, not an original interaction (Ochs 1979, Barnes, Bloor and Henry 1996, Ross 2009).<br />
3In this way, the researcher recasts aspects of his or her problem or topic in new, scientific form: 'All researchers "translate" the experiences of others' (Temple 1997, p 609). Research is invariably conducted in a sort of 'metalanguage' (Hantrais and Ager 1985): the research process can be conceived as one of successive translations (from theoretical formulation to operationalization, transcription, interpretation and dissemination). Theorization is a process of reciprocal back and forth between theory and fact, in which conceptions of each are revised in order that one fit the other (Baldamus 1974).<br />
4 It is a kind of translation: a rereading, re-use, re-application or re-representation of what we know in new terms (Turner 1980). Referencing, too, is an act of translation, a form of appropriation and incorporation of one text by another (Gilbert 1977).policy as translation Each of the fields covered by the paper is diverse and ill-defined, and there is no intention here to provide a comprehensive account of any them. Sources have been chosen for their relevance: main references are cited in the text, and additional sources listed in footnotes.<br />
<br />
2For a brief introduction to the technical issues involved in social research in more than one language, see Birbili (2000). For translation issues in survey research and question design in general, see Ervin and Bower (1952) and Deutscher (1968); on translating survey research instruments (in this instance health-related quality of life measures), Bowden and Fox-Rushby (2003). On the use of translators and interpreters, see Temple (1997) and Jentsch (1998).<br />
3For an interesting discussion of this problem, see Bourdieu (1999), esp pp 621-626, 'The risks of writing'. <br />
===Difference between machine translation and human translators===<br />
Few people disagree on the differences between the two, but many argue over the quality of the translations. How accurate are machine translations? How reliable are human translations? Some say machine translation produces near-perfect translations while others are adamant that translations are incomprehensible and cause more problems than they solve. Results will, of course, vary depending on the source and target languages, the machine translation service used (e.g. Google Translate), and the complexity of the original text.<br />
Machine translation, love it or hate it, is here to stay. In fact, the machine translation market is growing at such a fast pace that it is predicted to reach $980 million by 2022.<br />
===Machine translation: the pros and cons===<br />
The advantages of machine translation generally come down to two factors: it’s faster and cheaper. The downside to this is the standard of translation can be anywhere from inaccurate, to incomprehensible, and potentially dangerous (more on that shortly).<br />
==The advantages of machine translation==<br />
Many free tools are readily available (Google Translate, Skype Translator, etc.)<br />
Quick turnaround time <br />
You can translate between multiple languages using one tool<br />
Translation technology is constantly improving<br />
The disadvantages of machine translation<br />
Level of accuracy can be very low <br />
Accuracy is also very inconsistent across different languages<br />
==Machines can't translate context ==<br />
Mistakes are sometimes costly <br />
Sometimes translation simply doesn’t work<br />
The most important thing to consider with any kind of translation is the cost of potential mistakes. Translating instructions for medical equipment, aviation manuals, legal documents and many other kinds of content require 100% accuracy. In such cases, mistakes can cost lives, huge amounts of money and irreparable damage to your company’s image. So choose carefully!<br />
Human translation: the pros and cons<br />
Human translation essentially switches the table in terms of pros and cons. A higher standard of accuracy comes at the price of longer turnaround times and higher costs. What you have to decide is whether that initial investment outweighs the potential cost of mistakes. Alternatively, whether mistakes simply aren’t an option, like the cases we looked at in the previous section.<br />
<br />
==The advantages of human translation ==<br />
It’s a translator’s job to ensure the highest accuracy<br />
Humans can interpret context and capture the same meaning, rather than simply translating words<br />
Human translators can review their work and provide a quality process<br />
Humans can interpret the creative use of language, e.g. puns, metaphors, slogans, etc.<br />
Professional translators understand the idiomatic differences between their languages<br />
Humans can spot pieces of content where literal translation isn’t possible and find the most suitable alternative<br />
The disadvantages of human translation<br />
Turnaround time is longer <br />
Translators rarely work for free <br />
Unless you use a translation agency, with access to thousands of translators, you’re limited to the languages any one translator can work with<br />
Simply put, human translation is your best option when accuracy is even remotely important. Other considerations to make are the complexity of your source material and the two languages you’re translating between – both of which can render machines pretty useless.<br />
<br />
When to use machine and human translation<br />
The truth is, the debate over machine vs human translation is an unnecessary distraction. What we should really be talking about is when to use these two different types of translation services, because they both serve a very valid purpose.<br />
<br />
Examples of when to use machine translation<br />
When you have a large bulk of content to translate and the general meaning is enough<br />
When your translation never reaches the final audience, e.g. you’re translating a resource as research for another piece of content<br />
Translating documents for internal use within a company, provided 100% accuracy isn’t needed<br />
To partially translate large chunks of content for a human translator to improve upon<br />
Examples of when to use human translation<br />
When accuracy is important<br />
Most cases where your translated content is received by a consumer audience<br />
When you have a duty of care to provide accurate translations (e.g. legal documents, product instructions, medical guidelines or health and safety content)<br />
When translating marketing material or other texts for creative language uses.<br />
<br />
types of machine translation.<br />
<br />
What is Machine Translation? Rule Based Machine Translation vs. Statistical Machine Translation. Machine translation (MT) is automated translation. It is the process by which computer software is used to translate a text from one natural language (such as English) to another (such as Spanish).<br />
<br />
To process any translation, human or automated, the meaning of a text in the original (source) language must be fully restored in the target language, i.e. the translation. While on the surface this seems straightforward, it is far more complex. Translation is not a mere word-for-word substitution. A translator must interpret and analyze all of the elements in the text and know how each word may influence another. This requires extensive expertise in grammar, syntax (sentence structure), semantics (meanings), etc., in the source and target languages, as well as familiarity with each local region.<br />
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Human and machine translation each have their share of challenges. For example, no two individual translators can produce identical translations of the same text in the same language pair, and it may take several rounds of revisions to meet customer satisfaction. But the greater challenge lies in how machine translation can produce publishable quality translations.<br />
<br />
Rule-Based Machine Translation Technology<br />
Rule-based machine translation relies on countless built-in linguistic rules and millions of bilingual dictionaries for each language pair.<br />
The software parses text and creates a transitional representation from which the text in the target language is generated. This process requires extensive lexicons with morphological, syntactic, and semantic information, and large sets of rules. The software uses these complex rule sets and then transfers the grammatical structure of the source language into the target language.<br />
Translations are built on gigantic dictionaries and sophisticated linguistic rules. Users can improve the out-of-the-box translation quality by adding their terminology into the translation process. They create user-defined dictionaries which override the system’s default settings.<br />
In most cases, there are two steps: an initial investment that significantly increases the quality at a limited cost, and an ongoing investment to increase quality incrementally. While rule-based MT brings companies to the quality threshold and beyond, the quality improvement process may be long and expensive.<br />
<br />
Statistical Machine Translation Technology<br />
Statistical machine translation utilizes statistical translation models whose parameters stem from the analysis of monolingual and bilingual corpora. Building statistical translation models is a quick process, but the technology relies heavily on existing multilingual corpora. A minimum of 2 million words for a specific domain and even more for general language are required. Theoretically it is possible to reach the quality threshold but most companies do not have such large amounts of existing multilingual corpora to build the necessary translation models. Additionally, statistical machine translation is CPU intensive and requires an extensive hardware configuration to run translation models for average performance levels.<br />
<br />
Rule-Based MT vs. Statistical MT<br />
Rule-based MT provides good out-of-domain quality and is by nature predictable. Dictionary-based customization guarantees improved quality and compliance with corporate terminology. But translation results may lack the fluency readers expect. In terms of investment, the customization cycle needed to reach the quality threshold can be long and costly. The performance is high even on standard hardware.<br />
<br />
Statistical MT provides good quality when large and qualified corpora are available. The translation is fluent, meaning it reads well and therefore meets user expectations. However, the translation is neither predictable nor consistent. Training from good corpora is automated and cheaper. But training on general language corpora, meaning text other than the specified domain, is poor. Furthermore, statistical MT requires significant hardware to build and manage large translation models.<br />
<br />
Rule-Based MT Statistical MT<br />
+ Consistent and predictable quality – Unpredictable translation quality<br />
+ Out-of-domain translation quality – Poor out-of-domain quality<br />
+ Knows grammatical rules – Does not know grammar <br />
+ High performance and robustness – High CPU and disk space requirements<br />
+ Consistency between versions – Inconsistency between versions <br />
– Lack of fluency + Good fluency<br />
– Hard to handle exceptions to rules + Good for catching exceptions to rules <br />
– High development and customization costs + Rapid and cost-effective development costs provided the required corpus exists<br />
Given the overall requirements, there is a clear need for a third approach through which users would reach better translation quality and high performance (similar to rule-based MT), with less investment (similar to statistical MT).<br />
Post-Edited Machine Translation (PEMT)<br />
Often, PEMT is used to bridge the gap between the speed of machine translation and the quality of human translation, as translators review, edit and improve machine-translated texts. PEMT services cost more than plain machine translations but less than 100% human translation, especially since the post-editors don’t have to be fluently bilingual—they just have to be skilled proofreaders with some experience in the language and target region.<br />
Successful translation is about more than just the words, which is why we advocate for not just human translation by skilled linguists, but for translation by people deeply familiar with the cultures they’re writing for. Life experience, study and the knowledge that only comes from living in a geographic region can make the difference between words that are understandable and language that is capable of having real, positive impact. <br />
<br />
PacTranz<br />
The HUGE list of 51 translation types, methods and techniques<br />
Upper section of infographic of 51 common types of translation classified in 4 broad categoriesThere are a bewildering number of different types of translation.<br />
So we’ve identified the 51 types you’re most likely to come across, and explain exactly what each one means.<br />
This includes all the main translation methods, techniques, strategies, procedures and areas of specialisation.<br />
It’s our way of helping you make sense of the many different kinds of translation – and deciding which ones are right for you.<br />
Don’t miss our free summary pdf download later in the article!<br />
The 51 types of translation we’ve identified fall neatly into four distinct categories.<br />
Translation Category A: 15 types of translation based on the technical field or subject area of the text<br />
Icons representing 15 types of translation categorised by the technical field or subject area of the textTranslation companies often define the various kinds of translation they provide according to the subject area of the text.<br />
This is a useful way of classifying translation types because specialist texts normally require translators with specialist knowledge.<br />
Here are the most common types you’re like to come across in this category.<br />
<br />
1. General Translation<br />
What is it?<br />
The translation of non-specialised text. That is, text that we can all understand without needing specialist knowledge in some area.<br />
The text may still contain some technical terms and jargon, but these will either be widely understood, or easily researched.<br />
What this means<br />
The implication is that you don’t need someone with specialist knowledge for this type of translation – any professional translator can handle them.<br />
Translators who only do this kind of translation (don’t have a specialist field) are sometimes referred to as ‘generalist’ or ‘general purpose’ translators.<br />
Examples<br />
Most business correspondence, website content, company and product/service info, non-technical reports.<br />
Most of the rest of the translation types in this Category do require specialist translators.<br />
Check out our video on 13 types of translation requiring special translator expertise:<br />
<br />
2. Technical Translation<br />
What is it?<br />
We use the term “technical translation” in two different ways:<br />
Broad meaning: any translation where the translator needs specialist knowledge in some domain or area.<br />
This definition would include almost all the translation types described in this section.<br />
Narrow meaning: limited to the translation of engineering (in all its forms), IT and industrial texts.<br />
This narrower meaning would exclude legal, financial and medical translations for example, where these would be included in the broader definition.<br />
What this means<br />
Technical translations require knowledge of the specialist field or domain of the text.<br />
That’s because without it translators won’t completely understand the text and its implications. And this is essential if we want a fully accurate and appropriate translation.Good to know Many technical translation projects also have a typesetting/dtp requirement. Be sure your translation provider can handle this component, and that you’ve allowed for it in your project costings and time frames.<br />
Examples<br />
Manuals, specialist reports, product brochures<br />
<br />
3. Scientific Translation<br />
What is it?<br />
The translation of scientific research or documents relating to it.<br />
What this means<br />
These texts invariably contain domain-specific terminology, and often involve cutting edge research.<br />
So it’s imperative the translator has the necessary knowledge of the field to fully understand the text. That’s why scientific translators are typically either experts in the field who have turned to translation, or professionally qualified translators who also have qualifications and/or experience in that domain.<br />
On occasion the translator may have to consult either with the author or other domain experts to fully comprehend the material and so translate it appropriately.<br />
Examples<br />
Research papers, journal articles, experiment/trial results<br />
<br />
4. Medical Translation<br />
What is it?<br />
The translation of healthcare, medical product, pharmaceutical and biotechnology materials.<br />
Medical translation is a very broad term covering a wide variety of specialist areas and materials – everything from patient information to regulatory, marketing and technical documents.<br />
As a result, this translation type has numerous potential sub-categories – ‘medical device translations’ and ‘clinical trial translations’, for example.<br />
What this means<br />
As with any text, the translators need to fully understand the materials they’re translating. That means sound knowledge of medical terminology and they’ll often also need specific subject-matter expertise.<br />
Good to know<br />
Many countries have specific requirements governing the translation of medical device and pharmaceutical documentation. This includes both your client-facing and product-related materials.<br />
Examples<br />
Medical reports, product instructions, labeling, clinical trial documentation<br />
<br />
5. Financial Translation<br />
What is it?<br />
In broad terms, the translation of banking, stock exchange, forex, financing and financial reporting documents.<br />
However, the term is generally used only for the more technical of these documents that require translators with knowledge of the field.<br />
Any competent translator could translate a bank statement, for example, so that wouldn’t typically be considered a financial translation.<br />
What this means<br />
You need translators with domain expertise to correctly understand and translate the financial terminology in these texts.<br />
Examples<br />
Company accounts, annual reports, fund or product prospectuses, audit reports, IPO documentation<br />
<br />
6. Economic Translations<br />
What is it?<br />
1. Sometimes used as a synonym for financial translations.<br />
2. Other times used somewhat loosely to refer to any area of economic activity – so combining business/commercial, financial and some types of technical translations.<br />
3. More narrowly, the translation of documents relating specifically to the economy and the field of economics.<br />
What this means<br />
As always, you need translators with the relevant expertise and knowledge for this type of translation.<br />
<br />
7. Legal Translation<br />
What is it?<br />
The translation of documents relating to the law and legal process.<br />
What this means<br />
Legal texts require translators with a legal background.<br />
That’s because without it, a translator may not:<br />
– fully understand the legal concepts<br />
– write in legal style<br />
– understand the differences between legal systems, and how best to translate concepts that don’t correspond.<br />
And we need all that to produce professional quality legal translations – translations that are accurate, terminologically correct and stylistically appropriate.<br />
Examples<br />
Contracts, legal reports, court judgments, expert opinions, legislation<br />
<br />
8. Juridical Translation<br />
What is it?<br />
1. Generally used as a synonym for legal translations.<br />
2. Alternatively, can refer to translations requiring some form of legal verification, certification or notarization that is common in many jurisdictions.<br />
<br />
9. Judicial Translation<br />
What is it?<br />
1. Most commonly a synonym for legal translations.<br />
2. Rarely, used to refer specifically to the translation of court proceeding documentation – so judgments, minutes, testimonies, etc. <br />
<br />
10. Patent Translation<br />
What is it?<br />
The translation of intellectual property and patent-related documents.<br />
Key features<br />
Patents have a specific structure, established terminology and a requirement for complete consistency throughout – read more on this here. These are key aspects to patent translations that translators need to get right.<br />
In addition, subject matter can be highly technical.<br />
What this means<br />
You need translators who have been trained in the specific requirements for translating patent documents. And with the domain expertise needed to handle any technical content.<br />
Examples<br />
Patent specifications, prior art documents, oppositions, opinions<br />
<br />
11. Literary Translation<br />
What is it?<br />
The translation of literary works – novels, short stories, plays, essays, poems.<br />
Key features<br />
Literary translation is widely regarded as the most difficult form of translation.<br />
That’s because it involves much more than simply conveying all meaning in an appropriate style. The translator’s challenge is to also reproduce the character, subtlety and impact of the original – the essence of what makes that work unique.<br />
This is a monumental task, and why it’s often said that the translation of a literary work should be a literary work in its own right.<br />
What this means<br />
Literary translators must be talented wordsmiths with exceptional creative writing skills.<br />
Because few translators have this skillset, you should only consider dedicated literary translators for this type of translation.<br />
<br />
<br />
12. Commercial Translation<br />
What is it?<br />
The translation of documents relating to the world of business.<br />
This is a very generic, wide-reaching translation type. It includes other more specialised forms of translation – legal, financial and technical, for example. And all types of more general business documentation.<br />
Also, some documents will require familiarity with business jargon and an ability to write in that style.<br />
What this means<br />
Different translators will be required for different document types – specialists should handle materials involving technical and specialist fields, whereas generalist translators can translate non-specialist materials.<br />
Examples<br />
Business correspondence, reports, marketing and promotional materials, sales proposals<br />
<br />
13. Business Translations<br />
What is it?<br />
A synonym for Commercial Translations.<br />
<br />
14. Administrative Translations<br />
What is it?<br />
The translation of business management and administration documents.<br />
So it’s a subset of business / commercial translations.<br />
What this means<br />
The implication is these documents will include business jargon and ‘management speak’, so require a translator familiar with, and practised at, writing in that style.<br />
Examples<br />
Management reports and proposals<br />
<br />
15. Marketing Translations<br />
What is it?<br />
The translation of advertising, marketing and promotional materials.<br />
This is a subset of business or commercial translations.<br />
Key features<br />
Marketing copy is designed to have a specific impact on the audience – to appeal and persuade.<br />
So the translated copy must do this too.<br />
But a direct translation will seldom achieve this – so translators need to adapt their wording to produce the impact the text is seeking.<br />
And sometimes a completely new message might be needed – see transcreation in our next category of translation types.<br />
What this means<br />
Marketing translations require translators who are skilled writers with a flair for producing persuasive, impactful copy.<br />
As relatively few translators have these skills, engaging the right translator is key.<br />
Good to know<br />
This type of translation often comes with a typesetting or dtp requirement – particularly for adverts, posters, brochures, etc.<br />
Its best for your translation provider to handle this component. That’s because multilingual typesetters understand the design and aesthetic conventions in other languages/cultures. And these are essential to ensure your materials have the desired impact and appeal in your target markets.<br />
Examples<br />
Advertising, brochures, some website/social media text.<br />
Translation Category B: 14 types of translation based on the end product or use of the translation<br />
This category is all about how the translation is going to be used or the end product that’s produced.<br />
Most of these types involve either adapting or processing a completed translation in some way, or converting or incorporating it into another program or format.<br />
You’ll see that some are very specialised, and complex.<br />
It’s another way translation providers refer to the range of services they provide.<br />
Check out our video of the most specialised of these types of translation:<br />
<br />
16. Document Translations<br />
What is it?<br />
The translation of documents of all sorts.<br />
Here the translation itself is the end product and needs no further processing beyond standard formatting and layout.<br />
<br />
17. Text Translations<br />
What is it?<br />
A synonym for document translation.<br />
<br />
18. Certified Translations<br />
What is it?<br />
A translation with some form of certification.<br />
Key features<br />
The certification can take many forms. It can be a statement by the translation company, signed and dated, and optionally with their company seal. Or a similar certification by the translator.<br />
The exact format and wording will depend on what clients and authorities require – here’s an example.<br />
<br />
19. Official Translations<br />
What is it?<br />
1. Generally used as a synonym for certified translations.<br />
2. Can also refer to the translation of ‘official’ documents issued by the authorities in a foreign country. These will almost always need to be certified.<br />
<br />
20. Software Localisation<br />
What is it?<br />
Adapting software for another language/culture.<br />
Key features<br />
The goal of software localisation is not just to make the program or product available in other languages. It’s also about ensuring the user experience in those languages is as natural and effective as possible.<br />
Translating the user interface, messaging, documentation, etc is a major part of the process.<br />
Also key is a customisation process to ensure everything matches the conventions, norms and expectations of the target cultures.<br />
Adjusting time, date and currency formats are examples of simple customisations. Others might involve adapting symbols, graphics, colours and even concepts and ideas.<br />
Localisation is often preceded by internationalisation – a review process to ensure the software is optimally designed to handle other languages.<br />
And it’s almost always followed by thorough testing – to ensure all text is in the correct place and fits the space, and that everything makes sense, functions as intended and is culturally appropriate.<br />
Localisation is often abbreviated to L10N, internationalisation to i18n.<br />
What this means<br />
Software localisation is a specialised kind of translation, and you should always engage a company that specialises in it.<br />
They’ll have the systems, tools, personnel and experience needed to achieve top quality outcomes for your product.<br />
<br />
21. Game Localisation<br />
What is it?<br />
Adapting games for other languages and markets.<br />
<br />
It’s a subset of software localisation.<br />
Key features<br />
The goal of game localisation is to provide an engaging and fun gaming experience for speakers of other languages.<br />
<br />
It involves translating all text and recording any required foreign language audio.<br />
<br />
But also adapting anything that would clash with the target culture’s customs, sensibilities and regulations.<br />
<br />
For example, content involving alcohol, violence or gambling may either be censored or inappropriate in the target market.<br />
<br />
And at a more basic level, anything that makes users feel uncomfortable or awkward will detract from their experience and thus the success of the game in that market.<br />
<br />
So portions of the game may have to be removed, added to or re-worked.<br />
<br />
Game localisation involves at least the steps of translation, adaptation, integrating the translations and adaptations into the game, and testing.<br />
<br />
What this means<br />
Game localisation is a very specialised type of translation best left to those with specific expertise and experience in this area.<br />
<br />
22. Multimedia Localisation<br />
What is it?<br />
Adapting multimedia for other languages and cultures.<br />
<br />
Multimedia refers to any material that combines visual, audio and/or interactive elements. So videos and movies, on-line presentations, e-Learning courses, etc.<br />
Key features<br />
Anything a user can see or hear may need localising.<br />
<br />
That means the audio and any text appearing on screen or in images and animations.<br />
<br />
Plus it can mean reviewing and adapting the visuals and/or script if these aren’t suitable for the target culture.<br />
<br />
The localisation process will typical involve:<br />
– Translation<br />
– Modifying the translation for cultural reasons and/or to meet technical requirements<br />
– Producing the other language versions<br />
<br />
Audio output may be voice-overs, dubbing or subtitling.<br />
<br />
And output for visuals can involve re-creating elements, or supplying the translated text for the designers/engineers to incorporate.<br />
<br />
What this means<br />
Multimedia localisation projects vary hugely, and it’s essential your translation providers have the specific expertise needed for your materials.<br />
<br />
<br />
<br />
23. Script Translations<br />
What is it?<br />
Preparing the text of recorded material for recording in other languages.<br />
Key features<br />
There are several issues with script translation.<br />
<br />
One is that translations typically end up longer than the original script. So voicing the translation would take up more space/time on the video than the original language.<br />
<br />
Sometimes that space will be available and this will be OK.<br />
<br />
But generally it won’t be. So the translation has to be edited back until it can be comfortably voiced within the time available on the video.<br />
<br />
Another challenge is the translation may have to synchronise with specific actions, animations or text on screen.<br />
<br />
Also, some scripts also deal with technical subject areas involving specialist technical terminology.<br />
<br />
Finally, some scripts may be very culture-specific – featuring humour, customs or activities that won’t work well in another language. Here the script, and sometimes also the associated visuals, may need to be adjusted before beginning the translation process.<br />
<br />
It goes without saying that a script translation must be done well. If it’s not, there’ll be problems producing a good foreign language audio, which will compromise the effectiveness of the video.<br />
<br />
Translators typically work from a time-coded transcript. This is the original script marked to show the time available for each section of the translation.<br />
<br />
What this means<br />
There are several potential pitfalls in script translations. So it’s vital your translation provider is practiced at this type of translation and able to handle any technical content.<br />
<br />
<br />
<br />
24. Voice-over and Dubbing Projects<br />
What is it?<br />
Translation and recording of scripts in other languages.<br />
<br />
Voice-overs vs dubbing<br />
There is a technical difference.<br />
A voice-over adds a new track to the production, dubbing replaces an existing one.<br />
<br />
<br />
Key features<br />
These projects involve two parts:<br />
– a script translation (as described above), and<br />
– producing the audio<br />
<br />
So they involve the combined efforts of translators and voice artists.<br />
The task for the voice artist is to produce a high quality read. That’s one that matches the style, tone and richness of the original.<br />
<br />
Often each section of the new audio will need to be the same length as the original.<br />
<br />
But sometimes the segments will need to be shorter – for example where the voice-over lags the original by a second or two. This is common in interviews etc, where the original voice is heard initially then drops out.<br />
<br />
The most difficult form of dubbing is lip-syncing – where the new audio needs to synchronise with the original speaker’s lip movements, gestures and actions.<br />
<br />
Lip-syncing requires an exceptionally skilled voice talent and considerable time spent rehearsing and fine tuning the translation.<br />
<br />
What this means<br />
You need to use experienced professionals every step of the way in this type of project.<br />
<br />
That’s to ensure firstly that your foreign-language scripts are first class, then that the voicing is of high professional standard.<br />
<br />
Anything less will mean your foreign language versions will be way less effective and appealing to your target audience.<br />
<br />
<br />
<br />
25. Subtitle Translations<br />
What is it?<br />
Producing foreign language captions for sub or surtitles.<br />
Key features<br />
The goal with subtitling is to produce captions that viewers can comfortably read in the time available and still follow what’s happening on the video.<br />
<br />
To achieve this, languages have “rules” governing the number of characters per line and the minimum time each subtitle should display.<br />
<br />
Sticking to these guidelines is essential if your subtitles are to be effective.<br />
<br />
But this is no easy task – it requires simple language, short words, and a very succinct style. Translators will spend considerable time mulling over and re-working their translation to get it just right.<br />
<br />
Most subtitle translators use specialised software that will output the captions in the format sound engineers need for incorporation into the video.<br />
<br />
What this means<br />
As with other specialised types of translation, you should only use translators with specific expertise and experience in subtitling.<br />
<br />
<br />
<br />
26. Website Localisation<br />
What is it?<br />
The translation and adapting of relevant content on a website to best suit the target language and culture.<br />
<br />
Note: Many providers use the term website translation as a synonym for localisation. Strictly speaking though, translation is just one part of localisation.<br />
Key features<br />
<br />
Not all pages on a website may need to be localised – clients should review their content to identify what’s relevant for the other language versions.<br />
Some content may need specialist translators – legal and technical pages for example.<br />
There may also be videos, linked documents, and text or captions in graphics to translate.<br />
Adaptation can mean changing date, time, currency and number formats, units of measure, etc.<br />
But also images, colours and even the overall site design and style if these won’t have the desired impact in the target culture.<br />
Translated files can be supplied in a wide range of formats – translators usually coordinate output with the site webmasters.<br />
New language versions are normally thoroughly reviewed and tested before going live to confirm everything is displaying correctly, works as intended and is cultural appropriate.<br />
What this means<br />
The first step should be to review your content and identify what needs to be translated. This might lead you to modify some pages for the foreign language versions.<br />
<br />
In choosing your translation providers be sure they can:<br />
– handle any technical or legal content,<br />
– provide your webmaster with the file types they want.<br />
<br />
And you should always get your translators to systematically review the foreign language versions before going live.<br />
<br />
<br />
<br />
27. Transcreation<br />
What is it?<br />
Adapting a message to elicit the same emotional response in another language and culture.<br />
Translation is all about conveying the message or meaning of a text in another language. But sometimes that message or meaning won’t have the desired effect in the target culture.<br />
<br />
This is where transcreation comes in. Transcreation creates a new message that will get the desired emotional response in that culture, while preserving the style and tone of the original.<br />
<br />
So it’s a sort of creative translation – which is where the word comes from, a combination of ‘translation’ and ‘creation’.<br />
<br />
At one level transcreation may be as simple as choosing an appropriate idiom to convey the same intent in the target language – something translators do all the time.<br />
<br />
But mostly the term is used to refer to adapting key advertising and marketing messaging. Which requires copywriting skills, cultural awareness and an excellent knowledge of the target market.<br />
<br />
Who does it?<br />
Some translation companies have suitably skilled personnel and offer transcreation services.<br />
<br />
Often though it’s done in the target country by specialist copywriters or an advertising or marketing agency – particularly for significant campaigns and to establish a brand in the target marketplace.<br />
<br />
What this means<br />
Most general marketing and promotional texts won’t need transcreation – they can be handled by a translator with excellent creative writing skills.<br />
<br />
But slogans, by-lines, advertising copy and branding statements often do.<br />
<br />
Whether you should opt for a translation company or an in-market agency will depend on the nature and importance of the material, and of course your budget.<br />
<br />
<br />
<br />
28. Audio Translations<br />
What is it?<br />
Broad meaning: the translation of any type of recorded material into another language.<br />
<br />
More commonly: the translation of a foreign language video or audio recording into your own language. So this is where you want to know and document what a recording says.<br />
Key features<br />
The first challenge with audio translations is it’s often impossible to pick up every word that’s said. That’s because audio quality, speech clarity and speaking speed can all vary enormously.<br />
<br />
It’s also a mentally challenging task to listen to an audio and translate it directly into another language. It’s easy to miss a word or an aspect of meaning.<br />
<br />
So best practice is to first transcribe the audio (type up exactly what is said in the language it is spoken in), then translate that transcription.<br />
<br />
However, this is time consuming and therefore costly, and there are other options if lesser precision is acceptable.<br />
<br />
What this means<br />
It’s best to discuss your requirements for this kind of translation with your translation provider. They’ll be able to suggest the best translation process for your needs.<br />
<br />
Examples<br />
Interviews, product videos, police recordings, social media videos.<br />
<br />
<br />
<br />
29. Translations with DTP<br />
What is it?<br />
Translation incorporated into graphic design files.multilingual dtp example in the form of a Rubik's Cube with foreign text on each square<br />
Key features<br />
Graphic design programs are used by professional designers and graphic artists to combine text and images to create brochures, books, posters, packaging, etc.<br />
<br />
Translation plus dtp projects involve 3 steps – translation, typesetting, output.<br />
<br />
The typesetting component requires specific expertise and resources – software and fonts, typesetting know-how, an appreciation of foreign language display conventions and aesthetics.<br />
<br />
What this means<br />
Make sure your translation company has the required multilingual typesetting/desktop publishing expertise whenever you’re translating a document created in a graphic design program.<br />
<br />
Translation Category C: 13 types of translation based on the translation method employed<br />
This category has two sub-groups:<br />
– the practical methods translation providers use to produce their translations, and<br />
– the translation strategies/methods identified and discussed within academia.<br />
<br />
The translation methods translation providers use<br />
There are 4 main methods used in the translation industry today. We have an overview of each below, but for more detail, including when to use each one, see our comprehensive blog article.<br />
<br />
Or watch our video.<br />
<br />
Important: If you’re a client you need to understand these 4 methods – choose the wrong one and the translation you end up with may not meet your needs!<br />
<br />
<br />
30. Machine Translation (MT)<br />
What is it?<br />
A translation produced entirely by a software program with no human intervention.<br />
<br />
A widely used, and free, example is Google Translate. And there are also commercial MT engines, generally tailored to specific domains, languages and/or clients.<br />
<br />
Pros and cons<br />
There are two limitations to MT:<br />
– they make mistakes (incorrect translations), and<br />
– quality of wording is patchy (some parts good, others unnatural or even nonsensical)<br />
<br />
On they positive side they are virtually instantaneous and many are free.<br />
<br />
Best suited for:<br />
Getting the general idea of what a text says.<br />
<br />
This method should never be relied on when high accuracy and/or good quality wording is needed.<br />
<br />
<br />
<br />
31. Machine Translation plus Human Editing (PEMT)<br />
What is it?<br />
A machine translation subsequently edited by a human translator or editor (often called Post-editing Machine Translation = PEMT).<br />
<br />
The editing process is designed to rectify some of the deficiencies of a machine translation.<br />
<br />
This process can take different forms, with different desired outcomes. Probably most common is a ‘light editing’ process where the editor ensures the text is understandable, without trying to fix quality of expression.<br />
<br />
Pros and cons<br />
This method won’t necessarily eliminate all translation mistakes. That’s because the program may have chosen a wrong word (meaning) that wasn’t obvious to the editor.<br />
<br />
And wording won’t generally be as good as a professional human translator would produce.<br />
<br />
Its advantage is it’s generally quicker and a little cheaper than a full translation by a professional translator.<br />
<br />
Best suited for:<br />
Translations for information purposes only.<br />
<br />
Again, this method shouldn’t be used when full accuracy and/or consistent, natural wording is needed.<br />
<br />
<br />
<br />
32. Human Translation<br />
What is it?<br />
Translation by a professional human translator.<br />
Pros and cons<br />
Professional translators should produce translations that are fully accurate and well-worded.<br />
<br />
That said, there is always the possibility of ‘human error’, which is why translation companies like us typically offer an additional review process – see next method.<br />
<br />
This method will take a little longer and likely cost more than the PEMT method.<br />
<br />
Best suited for:<br />
Most if not all translation purposes.<br />
<br />
<br />
<br />
33. Human Translation + Revision<br />
What is it?<br />
A human translation with an additional review by a second translator.<br />
<br />
The review is essentially a safety check – designed to pick up any translation errors and refine wording if need be.<br />
<br />
Pros and cons<br />
This produces the highest level of translation quality.<br />
<br />
It’s also the most expensive of the 4 methods, and takes the longest.<br />
<br />
Best suited for:<br />
All translation purposes.<br />
<br />
Gearwheel with 5 practical translation methods written on the teeth <br />
There’s also one other common term used by practitioners and academics alike to describe a type (method) of translation:<br />
<br />
34. Computer-Assisted Translation (CAT)<br />
What is it?<br />
A human translator using computer tools to aid the translation process.<br />
<br />
Key features<br />
Virtually all translators use such tools these days.<br />
<br />
The most prevalent tool is Translation Memory (TM) software. This creates a database of previous translations that can be accessed for future work.<br />
<br />
TM software is particularly useful when dealing with repeated and closely-matching text, and for ensuring consistency of terminology. For certain projects it can speed up the translation process.<br />
<br />
<br />
<br />
The translation methods described by academia<br />
A great deal has been written within academia analysing how human translators go about their craft.<br />
<br />
Seminal has been the work of Newmark, and the following methods of translation attributed to him are widely discussed in the literature.Gearwheel with Newmark's 8 translation methods written on the teeth <br />
These methods are approaches and strategies for translating the text as a whole, not techniques for handling smaller text units, which we discuss in our final translation category.<br />
<br />
<br />
<br />
35. Word-for-word Translation<br />
This method translates each word into the other language using its most common meaning and keeping the word order of the original language.<br />
<br />
So the translator deliberately ignores context and target language grammar and syntax.<br />
<br />
Its main purpose is to help understand the source language structure and word use.<br />
<br />
Often the translation will be placed below the original text to aid comparison.<br />
<br />
<br />
<br />
36. Literal Translation<br />
Words are again translated independently using their most common meanings and out of context, but word order changed to the closest acceptable target language grammatical structure to the original.<br />
<br />
Its main suggested purpose is to help someone read the original text.<br />
<br />
<br />
<br />
37. Faithful Translation<br />
Faithful translation focuses on the intention of the author and seeks to convey the precise meaning of the original text.<br />
<br />
It uses correct target language structures, but structure is less important than meaning.<br />
<br />
<br />
<br />
38. Semantic Translation<br />
Semantic translation is also author-focused and seeks to convey the exact meaning.<br />
<br />
Where it differs from faithful translation is that it places equal emphasis on aesthetics, ie the ‘sounds’ of the text – repetition, word play, assonance, etc.<br />
<br />
In this method form is as important as meaning as it seeks to “recreate the precise flavour and tone of the original” (Newmark).slide showing definition of semantic translation as a translation method<br />
<br />
39. Communicative Translation<br />
Seeks to communicate the message and meaning of the text in a natural and easily understood way.<br />
<br />
It’s described as reader-focused, seeking to produce the same effect on the reader as the original text.<br />
<br />
A good comparison of Communicative and Semantic translation can be found here.<br />
<br />
40. Free Translation<br />
Here conveying the meaning and effect of the original are all important.<br />
<br />
There are no constraints on grammatical form or word choice to achieve this.<br />
<br />
Often the translation will paraphrase, so may be of markedly different length to the original.<br />
<br />
41. Adaptation<br />
Mainly used for poetry and plays, this method involves re-writing the text where the translation would otherwise lack the same resonance and impact on the audience.<br />
<br />
Themes, storylines and characters will generally be retained, but cultural references, acts and situations adapted to relevant target culture ones.<br />
<br />
So this is effectively a re-creation of the work for the target culture.<br />
<br />
42. Idiomatic Translation<br />
Reproduces the meaning or message of the text using idioms and colloquial expressions and language wherever possible.<br />
<br />
The goal is to produce a translation with language that is as natural as possible.<br />
<br />
Translation Category D: 9 types of translation based on the translation technique used<br />
These translation types are specific strategies, techniques and procedures for dealing with short chunks of text – generally words or phrases.<br />
<br />
They’re often thought of as techniques for solving translation problems.<br />
<br />
They differ from the translation methods of the previous category which deal with the text as a whole.<br />
9 translation techniques as titles of books in a bookcase<br />
<br />
43. Borrowing<br />
What is it?<br />
Using a word or phrase from the original text unchanged in the translation.<br />
<br />
Key features<br />
With this procedure we don’t translate the word or phrase at all – we simply ‘borrow’ it from the source language.<br />
<br />
Borrowing is a very common strategy across languages. Initially, borrowed words seem clearly ‘foreign’, but as they become more familiar, they can lose that ‘foreignness’.<br />
<br />
Translators use this technique:<br />
– when it’s the best word to use – either because it has become the standard, or it’s the most precise term, or<br />
– for stylist effect – borrowings can add a prestigious or scholarly flavour.<br />
<br />
Borrowed words or phrases are often italicised in English.<br />
<br />
Examples of borrowings in English<br />
grand prix, kindergarten, tango, perestroika, barista, sampan, karaoke, tofu<br />
<br />
<br />
44. Transliteration<br />
What is it?<br />
Reproducing the approximate sounds of a name or term from a language with a different writing system.<br />
<br />
Key features<br />
In English we use the Roman (Latin) alphabet in common with many other languages including almost all European languages.<br />
<br />
Other writing systems include Arabic, Cyrillic, Chinese, Japanese, Korean, Thai, and the Indian languages.<br />
<br />
Transliteration from such systems into the Roman alphabet is also called romanisation.<br />
<br />
There are accepted systems for how individual letters/sounds should be romanised from most other languages – there are three common systems for Chinese, for example.<br />
<br />
English borrowings from languages using non-Roman writing systems also require transliteration – perestroika, sampan, karaoke, tofu are examples from the above list.<br />
<br />
Translators mostly use transliteration as a procedure for translating proper names.<br />
<br />
Examples<br />
毛泽东 Mao Tse-tung or Mao Zedong<br />
Владимир Путин Vladimir Putin<br />
서울 Seoul<br />
ភ្នំពេញ Phnom Penh<br />
<br />
45. Calque or Loan Translation<br />
What is it?<br />
A literal translation of a foreign word or phrase to create a new term with the same meaning in the target language.<br />
<br />
So a calque is a borrowing with translation if you like. The new term may be changed slightly to reflect target language structures.<br />
<br />
Examples<br />
German ‘Kindergarten’ has been calqued as детский сад in Russian, literally ‘children garden’ in both languages.<br />
<br />
Chinese 洗腦 ‘wash’ + ‘brain’ is the origin of ‘brainwash’ in English.<br />
<br />
English skyscraper is calqued as gratte-ciel in French and rascacielos in Spanish, literally ‘scratches sky’ in both languages.<br />
<br />
46. Word-for-word translation<br />
What is it?<br />
A literal translation that is natural and correct in the target language.<br />
<br />
Alternative names are ‘literal translation’ or ‘metaphrase’.<br />
<br />
Note: this technique is different to the translation method of the same name, which does not produce correct and natural text and has a different purpose.<br />
<br />
Key features<br />
This translation strategy will only work between languages that have very similar grammatical structures.<br />
<br />
And even then, only sometimes.<br />
<br />
For example, standard word order in Turkish is Subject-Object-Verb whereas in English it’s Subject-Verb-Object. So a literal translation between these two will seldom work:<br />
– Yusuf elmayı yedi is literally ‘Joseph the apple ate’.<br />
<br />
When word-for-word translations don’t produce natural and correct text, translators resort to some of the other techniques described below.<br />
Examples<br />
French ‘Quelle heure est-il?’ works into English as ‘What time is it?’.<br />
<br />
Russian ‘Oн хочет что-нибудь поесть’ is ‘He wants something to eat’.<br />
<br />
47. Transposition<br />
What is it?<br />
Translation with a change of grammatical structure.<br />
<br />
This technique gives the translation more natural wording and/or makes it grammatically correct.<br />
<br />
Examples<br />
A change in word order:<br />
Our Turkish example Yusuf elmayı yedi (literally ‘Joseph the apple ate’) –> Joseph ate the apple.<br />
<br />
Spanish La Casa Blanca (literally ‘The House White’) –> The White House<br />
<br />
A change in grammatical category:<br />
German Er hört gerne Musik (literally ‘he listens gladly [to] music’)<br />
= subject pronoun + verb + adverb + noun<br />
becomes Spanish Le gusta escuchar música (literally ‘[to] him [it] pleases to listen [to] music’)<br />
= indirect object pronoun + verb + infinitive + noun<br />
and English He likes listening to music<br />
= subject pronoun + verb + gerund + noun.<br />
<br />
48. Modulation<br />
What is it?<br />
Translation with a change of focus or point of view in the target language.<br />
<br />
This technique makes the translation more idiomatic – how people would normally say it in the language.<br />
<br />
Examples<br />
English talks of the ‘top floor’ of a building, French the dernier étage = last floor. ‘Last floor’ would be unnatural in English, so too ‘top floor’ in French.<br />
<br />
German uses the term Lebensgefahr (literally ‘danger to life’) where in English we’d be more likely to say ‘risk of death’.<br />
In English we’d say ‘I dropped the key’, in Spanish se me cayó la llave, literally ‘the key fell from me’. The English perspective is that I did something (dropped the key), whereas in Spanish something happened to me – I’m the recipient of the action.<br />
<br />
49. Equivalence or Reformulation<br />
What is it?<br />
Translating the underlying concept or meaning using a totally different expression.<br />
<br />
This technique is widely used when translating idioms and proverbs.<br />
<br />
And it’s common in titles and advertising slogans.<br />
<br />
It’s a common strategy where a direct translation either wouldn’t make sense or wouldn’t resonate in the same way.<br />
<br />
Examples<br />
Here are some equivalents of the English saying “Pigs may fly”, meaning something will never happen, or “you’re being unrealistic” (Source):<br />
– Thai: ชาติหน้าตอนบ่าย ๆ – literally, ‘One afternoon in your next reincarnation’<br />
– French: Quand les poules auront des dents – literally, ‘When hens have teeth’<br />
– Russian, Когда рак на горе свистнет – literally, ‘When a lobster whistles on top of a mountain’<br />
– Dutch, Als de koeien op het ijs dansen – literally, ‘When the cows dance on the ice’<br />
– Chinese: 除非太陽從西邊出來！– literally, ‘Only if the sun rises in the west’<br />
<br />
50. Adaptation<br />
What is it?<br />
A translation that substitutes a culturally-specific reference with something that’s more relevant or meaningful in the target language.<br />
<br />
It’s also known as cultural substitution or cultural equivalence.<br />
<br />
It’s a useful technique when a reference wouldn’t be understood at all, or the associated nuances or connotations would be lost in the target language.<br />
<br />
Note: the translation method of the same name is a similar concept but applied to the text as a whole.<br />
<br />
Examples<br />
Different cultures celebrate different coming of age birthdays – 21 in many cultures, 20, 15 or 16 in others. A translator might consider changing the age to the target culture custom where the coming of age implications were important in the original text.<br />
Animals have different connotations across languages and cultures. Owls for example are associated with wisdom in English, but are a bad omen to Vietnamese. A translator might want to remove or amend an animal reference where this would create a different image in the target language.<br />
<br />
51. Compensation<br />
What is it?<br />
A meaning or nuance that can’t be directly translated is expressed in another way in the text.<br />
Example<br />
Many languages have ways of expressing social status (honorifics) encoded into their grammatical structures.<br />
<br />
So you can convey different levels of respect, politeness, humility, etc simply by choosing different forms of words or grammatical elements.<br />
But these nuances will be lost when translating into languages that don’t have these structures.<br />
Then translating into languages that don’t have these structures<br />
Then translating into languages that don’t have these structures.<br />
<br />
===Conclusion ===<br />
<br />
In the light of above mentioned facts and figures here by we would say that machine translation is challenge for human translators because it can reduces the wokload of translation but can't give accurate and exact translation of the traget language.It can be less reliable than human translation..</div>Chen Huinihttps://bou.de/u/index.php?title=Machine_translation&diff=131957Machine translation2021-12-13T13:05:15Z<p>Chen Huini: /* 2.1 Language Characteristics of Medical Abstracts */</p>
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<div>'''Machine Translation - A challenge or a chance for human translators?'''<br />
<br />
[[Machine_translation|Overview Page of Machine Translation]]<br />
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30 Chapters（0/30)<br />
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[[Machine_Trans_EN_1]] [[Machine_Trans_EN_2]] [[Machine_Trans_EN_3]] [[Machine_Trans_EN_4]] [[Machine_Trans_EN_5]] [[Machine_Trans_EN_6]] [[Machine_Trans_EN_7]] [[Machine_Trans_EN_8]] [[Machine_Trans_EN_9]] [[Machine_Trans_EN_10]] [[Machine_Trans_EN_11]] [[Machine_Trans_EN_12]] [[Machine_Trans_EN_13]] [[Machine_Trans_EN_14]] [[Machine_Trans_EN_15]] [[Machine_Trans_EN_16]] [[Machine_Trans_EN_17]] [[Machine_Trans_EN_18]] [[Machine_Trans_EN_19]] [[Machine_Trans_EN_20]] [[Machine_Trans_EN_21]] [[Machine_Trans_EN_22]] [[Machine_Trans_EN_23]] [[Machine_Trans_EN_24]] [[Machine_Trans_EN_25]] [[Machine_Trans_EN_26]] [[Machine_Trans_EN_27]] [[Machine_Trans_EN_28]] [[Machine_Trans_EN_29]] [[Machine_Trans_EN_30]] ...<br />
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[[Book_projects|Back to translation project overview]]<br />
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[[DCG-To-Do|To the To Do list]]<br />
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=Chapter 1:A Comparison Between the Quality of Machine Translation and Human Translation——A Case Study of the Application of artificial intelligence in Sports Events=<br />
'''论机器翻译与人工翻译的质量对比——以人工智能在体育赛事领域的应用为例'''<br />
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卫怡雯 Wei Yiwen, Hunan Normal University, China<br />
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[[Machine_Trans_EN_1]]<br />
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=Chapter 3：On the Realm Advantages And Mutual Development of Machine Translation And Huamn Translation)=<br />
"'论机器翻译与人工翻译的领域优势及共同发展'"<br />
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肖毅瑶 Xiao Yiyao, Hunan Normal University, China<br />
[[Machine_Trans_EN_3]]<br />
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=Chapter 4 : A Comparison Between Machine Translation of Netease and Traditional Human Translation—A Case Study of The Economist Articles)= <br />
'''网易有道机器翻译与人工翻译的译文对比——以经济学人语料为例'''<br />
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王李菲 Wang Lifei, Hunan Normal University<br />
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[[Machine_Trans_EN_4]]<br />
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=Chapter 5: Muhammad Saqib Mehran - Problems in translation studies=<br />
[[Machine_Trans_EN_5]]<br />
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=6 徐敏赟(Machine Translation Based on Neural Network --Challenge or Chance?)=<br />
[[Machine_Trans_EN_6]]<br />
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=Chapter 7: The Cultivation of artificial intelligence and translation talents in the Belt and Road Initiative=<br />
[[Machine_Trans_EN_7]]<br />
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一带一路背景下人工智能与翻译人才的培养<br />
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颜莉莉 Yan Lili, Hunan Normal University, China<br />
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=Chapter 8: On Machine Translation Under Lanuguage Intelligence——An Option and Opportunity for Human Translators=<br />
'''论语言智能下的机器翻译——译者的选择与机遇'''<br />
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颜静 Yan Jing, Hunan Normal University, China<br />
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[[Machine_Trans_EN_8]]<br />
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=9 谢佳芬（ Machine Translation and Artificial Translation in the Era of Artificial Intelligence）=<br />
[[Machine_Trans_EN_9]]<br />
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=10 熊敏(Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts)=<br />
[[Machine_Trans_EN_10]]<br />
===Abstract===<br />
The history of machine translation can be traced to 1940s, undergoing germination, recession, revival and flourish. Nowadays, with the rapid development of information technology, machine translation technology emerged and is gradually becoming mature. Whether manual translation can be replaced by machine translation becomes a widely-disputed topic. So in order to explore the ability of machine translation, I adopts two versions of translation, which are manual translation and machine translation(this paper uses Youdao translation) for different types of texts(according to Peter Newmark's types of text：informative text, expressive text and vocative text). The results are quite different in terms of quality and accuracy. The results show that machine translation is more suitable for informative text for its brevity, while the expressive texts especially literary works and vocative texts are difficult to be translated, because there are too many loaded words and cultural images in expressive text and dependence on the readers. To draw a conclusion, the relationship between machine translation and manual translation should be complementary rather than competitive.<br />
<br />
===Key words===<br />
machine translation; manual translation; Newmark's type of texts<br />
<br />
===题目===<br />
Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts<br />
<br />
===摘要===<br />
机器翻译的历史可以追溯到上个世纪40年代，经历了萌芽期、萧条期、复兴期和繁荣期四个阶段。如今，随着信息技术的高速发展，机器翻译技术日益成熟，于是机器翻译能不能替代人工翻译这个话题引起了广泛热议。为了探究机器翻译的能力水平是否足以替代人工翻译，本人根据皮特纽马克的文本类型分类理论（信息类文本，表达文本，呼吁文本），选择了相应的译文类型，并且将其机器翻译的版本以及人工翻译的版本进行对比。结果发现，就质量和准确度而言，译文的水平大相径庭。因为其简洁的特性，机器比较适合翻译信息文本。而就表达文本，尤其是文学作品，因其存在文化负载词及相应的文化现象，所以机器翻译这类文本存在难度。而呼唤文本因其目的性以及对读者的依赖性较强，翻译难度较大。由此得知，机器翻译和人工翻译的关系应该是互补的，而不是竞争的。<br />
<br />
===关键词===<br />
机器翻译；人工翻译；纽马克文本类型<br />
<br />
===1. Introduction===<br />
====1.1.Introduction to machine translation====<br />
Machine translation, also known as computer translation is a technique to translating through machine translator, such as Google translation and Youdao translation. Machine translation is one of the branches of computational linguistics, ranging from computer science, statistics, information science and so on. Machine translation plays an important role in all aspects.<br />
Machine translation can be traced back to 1940s, when British engineer Booth and American engineer Weaver proposed using computer to translate and started to study machines used for translation. And the first machine translating system launched in 1954, used in English and Russian translation. And this means machine translation becomes a reality. However, the arrival of new things always accompanies barriers and setbacks. In the 1960s, reports from ALPAC (Automated Language Processing Advisory Committee) showed studies on machine translation had stagnated for a decade. At that time, due to the high cost of machine, choosing human translators to finish translating works was more economical for most companies. What’s worse, machine had difficulty in understanding semantic and pragmatic meanings. Fortunately, in the 1970s, with the advance and popularity of computer, machine translation was gradually back on track. With the development of science and technology, machine translation has better technological guarantee, such as artificial intelligence and computer technology. In the last decades, from the late 90s till now, machine translation is becoming more and more mature. The initial rule-oriented machine translation has been updated and Corpus-aided machine translation appears. And nowadays, technology in voice recognition has been further developed. This technique is frequently applied in speech translation products. Users only need to speak source language to the online translator and instantly it will speak out the target version. But machine can’t fully provide precise and accurate translation without any grammatical or semantic problems. Machine can understand the literal meaning of texts, but not the meaning in context. For example, there is polysemy in English, which refers to words having multiple meanings. So when translating these words, translators should take contexts into consideration. But machine has troubles in this way. In addition, machine has no feeling or intention. Hence, it is also difficult to convey the feelings from the source language.<br />
<br />
====1.2.Process of machine translation====<br />
The process of manual translation is different from that of machine translation. Here is the process of the former. (1) Understand source language. (2) Use target language to organize language. (3) Generate translation. Unlike manual translation, machine translation tends to analyze and code source language first, then look for related codes in corpus, and work out the code that represents target language, generating translation. But they share a common feature, which is that Lexicon, grammatical rules and syntactic structure are taken into consideration. This is one of the biggest challenges for machine translation.<br />
<br />
===2.Theorectical framework===<br />
====2.1Newmark’s text typology====<br />
Text typology is a theory from linguistics. It undergoes several phrases. Karl Buhler, a German psychologist and linguist defines communicative functions into expressive, information and vocative. Then Roman Jakobson proposes categorization of texts, which are intralingual translation, interlingual translation and intersemiotic translation. Accordingly, he defines six main functions of language, including the referential function, conative function, emotive function, poetic function, metalingual function and the phatic function. Based on the two theories, Peter Newmark, a translation theorist, summarizes the language function into six parts: the informative function, the vocative function, the expressive function, the phatic function, the aesthetic function, and the metalingual function. Later, he further divided texts into informative type, expressive text and vocative type according to the linguistic functions of various texts. <br />
The core of the informative texts is the truth. It is to convey facts, information, knowledge of certain topics, reality and theories. The language style of the text is objective, logical and standardized. Reports, papers, scientific and technological textbooks are all attributed to informative texts. Translators are required to take format into account for different styles.<br />
The core of the expressive text is the sender’s emotions and attitudes. It is to express sender’s preferences, feelings, views and so on. The language style of it is subjective. Imaginative literature, including fictions, poems and dramas, autobiography and authoritative statements belong to expressive text. It requires translators to be faithful to the source language and maintain the style.<br />
The core of the vocative text is readership. It is to call upon readers to act, think, feel and react in the way intended by the text. So it is reader-oriented. Such texts as advertisement, propaganda and notices are of vocative text. Newmark noted that translators need to consider cultural background of the source language and the semantic and pragmatic effect of target language. If readers can comprehend the meaning at once and do as the text says, then the goal of information communication is achieved.<br />
To draw a conclusion, informative texts focus on the information and facts. Expressive texts center on the mind of addressers, including feelings, prejudices and so on. And vocative texts are oriented on readers and call on them to practice as the texts say.<br />
<br />
====2.2Study method====<br />
Manual translations of the three texts are selected from authoritative versions and universally acknowledged. And machine translations of those come from Youdao Translator. And in this thesis I will compare and evaluate the two methods in word diction, sentence structure, word order and redundancy.<br />
<br />
===3.Comparison and analysis of machine translation and manual translation ===<br />
====3.1Informative text ====<br />
（1）English into Chinese<br />
<br />
①Source language:<br />
<br />
Keep the tip of Apple Pencil clean, as dirt and other small particles may cause excessive wear to the tip or damage the screen of i-pad.<br />
<br />
Target language:<br />
<br />
Machine translation: Apple Pencil笔尖应保持清洁，灰尘等小颗粒可能会导致笔尖过度磨损或损坏ipad屏幕。<br />
<br />
Manual translation: 保持Apple Pencil铅笔的笔尖干净，因为灰尘和其他微粒可能会导致笔尖的过度磨损或损坏iPad屏幕。<br />
<br />
Analysis: Here is the instruction of Apple Pencil. And the manual translation is the Chinese version on the instruction.Product instruction tends to be professional, since there are many terms for some concepts. Machine can easily identify these terms and provide related words to translate. The machine version is faithful and expressive to the source language. So it is well-qualified and readable for readers to understand the instruction. So we can use machine to translate informative text.<br />
<br />
②Source language:<br />
<br />
China on Saturday launched a rocket carrying three astronauts-two men and one woman - to the core module of a future space station where they will live and work for six months, the longest orbit for Chinese astronauts.<br />
<br />
Target language:<br />
<br />
Machine translation: 周六，中国发射了一枚运载三名宇航员(两男一女)的火箭，进入未来空间站的核心舱，他们将在那里生活和工作6个月，这是中国宇航员最长的轨道。<br />
<br />
Manual translation: 周六，中国发射了一枚搭载三名宇航员(两男一女)的火箭，进入未来空间站的核心舱，他们将在那里生活和工作6个月，这是中国宇航员最漫长的一次轨道飞行。<br />
<br />
Analysis: This is a news from Reuters, reporting that China has launched a rocket.The meaning of the two translations is almost the same, except for some word diction. But there are some details dealt with different choice. For example, the last sentence of the machine translation is a bit of obscure and direct. There are some ambiguous words and expressions.<br />
<br />
(2)Chinese into English<br />
<br />
Source language:湖南省博物馆是湖南省最大的历史艺术类博物馆，占地面积4.9万平方米，总建筑面积为9.1万平方米，是首批国家一级博物馆，中央地方共建的八个国家级重点博物馆之一、全国文化系统先进集体、文化强省建设有突出贡献先进集体。<br />
<br />
Target language:<br />
Manual translation: As the largest history and art museum in Hunan province, the Hunan Museum covers an area of 49,000㎡, with the building area reaching 91,000㎡. It is one of the first batch of national first-level museums and one of the first eight national museums co-funded by central and local governments.<br />
<br />
Machine translation: Museum in hunan province is one of the largest historical art museum in hunan province, covers an area of 49000 square meters, a total construction area of 91000 square meters, is the first national museum, the central place to build one of the eight national key museum, national cultural system advanced collectives, strong culture began with outstanding contribution of advanced collective.<br />
<br />
Analysis: Machine translation is not faithful enough in content. For instance, “首批国家一级博物馆” is translated into “first national museum”, which is not the meaning of the source language. And there are some obvious grammar mistakes in the machine translation. For example, machine translates it into just one sentence but there are multiple predicates in it. So it is not grammatically permissible. What’s more, the sentence structure of machine translation is confusing and the focus is not specific enough.<br />
<br />
====3.2Expressive text ====<br />
(1)English into Chinese<br />
<br />
Source language:<br />
<br />
An individual human existence should be like a river- small at first, narrowly contained within its banks, and rushing passionately past rocks and over waterfalls. Gradually the river grows wider, the banks recede, the waters flow more quietly, and in the end, without any visible breaks, they become merged in the sea, and painlessly lose their individual being.<br />
<br />
Target language:<br />
<br />
Machine translation: 一个人的存在应该像一条河流——开始很小，被紧紧地夹在两岸中间，然后热情奔放地冲过岩石，飞下瀑布。渐渐地，河面变宽，两岸后退，水流更加平缓，最后，没有任何明显的停顿，它们汇入大海，毫无痛苦地失去了自己的存在。<br />
<br />
Manual translation:人生在世，如若河流；河口初始狭窄，河岸虬曲，而后狂涛击石，飞泻成瀑。河道渐趋开阔，峡岸退去，水流潺缓，终了，一马平川，汇于大海，消逝无影。<br />
<br />
Analysis: Here is a well-known metaphor in the prose How to Grow Old written by Bertrand Russell. The manual translation is written by Tian Rongchang.This is a philosophical prose with graceful language. Literary translation is a most important and difficult branch of translation. Translator should focus on the literal meaning, culture, writing style and so on. It is a combination of beauty and elegance. Therefore, translators find it in a dilemma of beauty and faithfulness, let alone translating machine. Compared with manual translation, machine translation has difficulty in word choice. It is faithful and expressive, but not elegant enough.<br />
<br />
(2)Chinese into English<br />
<br />
Source language:没有一个人将小草叫做“大力士”，但是它的力量之大，的确是世界无比。这种力，是一般人看不见的生命力，只要生命存在，这种力就要显现，上面的石块，丝毫不足以阻挡。因为它是一种“长期抗战”的力，有弹性，能屈能伸的力，有韧性，不达目的不止的力。<br />
<br />
Target language:<br />
<br />
Machine translation: No one calls the little grass "hercules", but its power is truly matchless in the world. This force is invisible life force. As long as there is life, this force will show itself. The stone above is not strong enough to stop it. Because it is a "long-term resistance" of the force, elastic, can bend and extend force, tenacity, not to achieve the purpose of the force.<br />
<br />
Manual translation: Though nobody describes the little grass as a “husky”, yet its herculean strength is unrivalled. It is the force of life invisible to naked eye. It will display itself so long as there is life. The rock is utterly helpless before this force- a force that will forever remain militant, a force that is resilient and can take temporary setbacks calmly, a force that is tenacity itself and will never give up until the goal is reached. (by Zhang Peiji)<br />
<br />
Analysis:This is the excerpt of a well-known Chinese prose written by Xia Yan. It is written during the war of Resistance Against Japan. So the prose holds symbolic meaning, eulogizing the invisible tenacious vitality so as to encourage Chinese to have confidence in the anti-aggression war. Compared with manual translation, machine translation is much more abstract and confusing, especially for the word diction. For example, “大力士” is translated into “hercules” which is a man of exceptional strength and size in Greek and Roman Mythology, making it difficult to understand if readers of target language have no idea of the allusion. What’s worse, the machine version doesn’t reveal the symbolic meaning of the text, which is the core of this prose.<br />
<br />
====3.3Vocative text ====<br />
<br />
(1)English into Chinese<br />
<br />
①Source language:<br />
<br />
iPhone went to film school, so you don’t have to. (Advertisement of iPhone13)<br />
<br />
Target language:<br />
<br />
Machine translation: iPhone上的是电影学院，所以你不用去。<br />
<br />
Manual translation:电影专业课，iPhone同学替你上完了。<br />
<br />
Analysis：Here are advertisements of iPhone on Apple official website. There is a personification in the source language. It is used to stress the advancement and proficiency in camera, which is an appealing selling point to potential buyers. Compared with manual translation, machine translation is plain and not eye-catching enough for customers.<br />
<br />
②Source language: <br />
<br />
5G speed OMGGGGG<br />
<br />
Machine language: 5克的速度 OMGGGGG<br />
<br />
Manual translation:<br />
<br />
iPhone的5G 巨巨巨巨巨5G<br />
<br />
Analysis: The “G” in the source language is the unit of speed, standing for generation. However, it is mistaken as a unit of weight, representing gram in the machine translation. So the meaning is not faithful to the source language at all. As for manual translation, it complies with the source in form. Specifically speaking, five “G”s in the former complies with five characters “巨”in the latter. And the pronunciation of the two is similar. There are two layers of meaning for the 5 “G”s. One exclaims the fast speed of 5 generation network and the other new technology. In the manual version, “巨”can be used to show degree, meaning “quite” or “very”. <br />
<br />
③Source language: <br />
<br />
History, faith and reason show the way, the way of unity. We can see each other not as adversaries but as neighbors. We can treat each other with dignity and respect, we can join forces, stop the shouting and lower the temperature. For without unity, there is no peace, only bitterness and fury."<br />
<br />
Target language:<br />
<br />
Machine translation: 历史、信仰和理性指明了团结的道路。我们可以把彼此视为邻居，而不是对手。我们可以尊严地对待彼此，我们可以联合起来，停止大喊大叫，降低温度。因为没有团结，就没有和平，只有痛苦和愤怒。<br />
<br />
Manual translation:历史、信仰和理性为我们指明道路。那是团结之路。我们可以把彼此视为邻居，而不是对手。我们可以有尊严地相互尊重。我们可以联合起来，停止喊叫，减少愤怒。因为没有团结就没有和平，只有痛苦和愤怒<br />
<br />
Analysis: Speech is a way to propagate some activity in public. It is an art to inspire emotion of the audience. The source language is the excerpt of Joe Biden’s inaugural speech. The speech should be inspiring and logic. The machine translation has some misunderstanding. Taking the translation of “lower the temperature” for example, machine only translates its literal meaning, relating to the temperature itself, without considering the context. What’s more, it is less logic than the manual one. Therefore, it adds difficulty to inspire the audience and infect their emotion.<br />
<br />
===4.Common mistakes in machine translation ===<br />
<br />
====4.1 lexical mistakes ====<br />
<br />
Common lexical mistakes include misunderstandings in word category, lexical meaning and emotive and evaluative meaning. Misunderstanding in word category shows in the classification of word in the source language. As for misunderstanding in lexical meaning, machine has difficulty in precisely reflecting the meaning of the original texts, due to different cultural background and different language system. And for misunderstanding in emotive meaning, machine has no intention and emotion like human-beings. Therefore, it’s impossible for it to know writers’ feelings and their writing purposes. So sometimes, it may translate something negative into something positive.<br />
<br />
====4.2 grammatical mistakes====<br />
<br />
Grammatical analysis plays an important part in translation. Normally speaking, every language has its own unique grammatical rules. So in the process of translation, if translators don’t know the formation rule well, the sentence meaning will be affected. Even though all the lexical meanings are well-known by translators, the lack of consciousness of grammaticality makes it harder to arrange words according to sequential rule. English tends to be hypotactic, while Chinese tends to be paratactic. English sentences are connected through syntactic devices and lexical devices. While Chinese sentences are semantically connected, which means there are limited logical words and connection words in Chinese. So when translating English sentence, we should first analyze its grammaticality and logical structure and then rearrange its sequence. However, online translating machine has troubles in grammatical analysis, which makes its improvement more difficult.<br />
<br />
====4.3 other mistakes====<br />
<br />
The two mistakes above are the internal ones. Apart from mistakes in linguistic system, there are some mistakes in other aspects, such as cultural background.<br />
<br />
===5.Reasons for its common mistakes ===<br />
<br />
====5.1 Difference in two linguistic system====<br />
<br />
With different history, English and Chinese have different ways of expression. Commonly speaking, English is synthetic language which expresses grammatical meaning through inflection such as tense and Chinese is analytic language which expresses grammatical meaning through word order and function word. In addition, English is more compact with full sentences. Subordinate sentence is one of the most important features in modern English. Chinese, on the other hand, is more diffusive with minor sentences.<br />
<br />
====5.2 Difference in thinking patterns and cultural background====<br />
<br />
According to Sapir-Whorf’s Hypothesis, our language helps mould our way of thinking and consequently, different languages may probably express their unique ways of understanding the world. For two different speech communities, the greater their structural differentiations are, the more diverse their conceptualization of the world will be. For example, western culture is more direct and eastern culture more euphemistic. What’s more, English culture tends to be individualism, focusing on detail, through which it reflects the whole, while Chinese culture tends to be collective. Different thinking patterns will add difficulty for machine to translate texts.<br />
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====5.3 Limitation of computer====<br />
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Recently, there are some breakthroughs and innovation in machine translation. However, due to its own limitation, online translation has limitation in some ways. Firstly, compared with machine, human brain is much more complicated, consisting of ten billions of neuron, each of which has different function to affect human’s daily activities and help humans avoid some errors. However, computer can only function according to preset programming has no intention or consciousness. Until now, countless related scholars have invested much time in machine translation. They upload massive language database, which include almost all linguistic rules. But computers still fail to precisely reflect the meaning of source language for many times due to the complexity and flexibility of language. On the other hand, computers can’t take context into consideration. During translation, it is often the case that machine chooses the most-frequently used meaning of one word. So without the correct and exact meaning, readers are easier to feel confused and even misunderstand the meaning of source language.<br />
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===6.Conclusion===<br />
From the analysis above, we can draw a conclusion that machine deals with informative text best, followed by non-literary translation of expressive text. What’s more, machine can be a useful tool to get to know the gist and main idea of a specific topic, for the simple sentence structure and numerous terms. And it can improve translating efficiency with high speed. But machine has difficulty in translating literary works, especially proses and poems.<br />
<br />
Machine translation has mixed future. From the perspective of commercial, machine translation boasts a bright future. With the process of globalization, the demand for translation is increasing accordingly. On one hand, if we only depend on human translator to deal with translating works, the quality and accuracy of translation can be greatly affected. On the other hand, if machine is used properly to do some basic work, human translators only need to make preparation before translating, progress, polish and other advanced work, contributing to highly-qualified translation and high working efficiency.<br />
<br />
However, compared with manual translation, machine translation has a bleak future. It is still impossible for machine to replace interpreter or translator in a short term. With intelligence and initiative, humans are able to learn new knowledge constantly, which machine will never accomplish. Besides, machine is not used to replace translators but to assist them in work. In other words, translators and machine carry out their own duties and they are not incompatible.<br />
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To draw a conclusion, although there are certain limitations of machine translation, it can serve as a catalyst for translating works. Therefore, with the rapid development of artificial intelligence and related technology, there are still many opportunities for machine translation.<br />
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===Reference ===<br />
<br />
Cui Zihan 崔子涵.机器翻译译文质量对比——以谷歌翻译和DeepL为例[J] [Comparison among Machine Translation--Taking Google Translation and Deepl for Example].Overseas English 海外英语,2021(15):182-183.<br />
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Li Deyi 李德毅. (2018). 人工智能导论 [Introduction to Artificial Intelligence]. Beijing: China Science and Technology Press 中国科学技术出版社.<br />
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Qiu Quanju 仇全菊.大数据时代背景下机器翻译及其发展趋势[J][Machine Translation and its Development Trend under the Background of Big Data Era]. English Teachers 英语教师,2021,21(16):60-62.<br />
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Zhuo Jianbin 卓键滨,Liu Wenxian 刘文娴,Peng Zili 彭子莉.机器翻译对各类型文本的德汉翻译能力探究[J][Research on the German Chinese Translation Ability of Machine Translation for Various Types of Texts]. Comparative Study of Cultural innovation 文化创新比较研究,2021,5(28):122-125.<br />
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(英) Peter Newmark A Textbook of Translation[M] Shanghai Foreign Education Press, 2002<br />
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Wang Hongqi 王红旗. (2008). 语言学概论 [Introduction to Linguistics]. Beijing: Peking University Press 北京大学出版社.<br />
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Liu Qin刘琴.功能目的论对于不同文本类型的翻译解读[J][Analysis of Translations in Different Types of Text based on Functionalist Approaches].Overseas Engliosh 海外英语,2021(17):8-9.<br />
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Zhang Peiji 张培基.英译中国现代散文选[M][Selected Modern Chinese Prose Writings]. Shanghai Foreign Languages Education Press 上海外语教育出版社, 2002.<br />
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Chen Cheng陈诚.机器翻译技术的综述[J][Overview of Machine Translation Technology].Electronic Techonology 电子技术,2021,50(11):290-291.<br />
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He Xinyu何馨宇.机器翻译的发展及其对翻译职业化的影响研究[J] [The Development of Machine Translation and its Effect on Professional Transltors].Overseas English 海外英语,2021(20):48-49.<br />
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He Wen 何雯, Wang Xiufeng 王秀峰.信息型文本的在线机器翻译错误研究[J][Research on Errors in Online Machine Translation of Informative text ].Overseas English海外英语,2021(15):188-189.<br />
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Li Hanji 李晗佶. (2021). 人工智能时代翻译技术与译者关系演变与重构 [Evolution and reconstruction of the relationship between translation technology and translators in the era of artificial intelligence]. 西华师范大学学报(哲学社会科学版) Journal of West China Normal University (PHILOSOPHY AND SOCIAL SCIENCES EDITION) (2021-12-04) 1-6.<br />
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Wei Guang魏光. 人工翻译与机器翻译译文编辑比较研究[J][Comparative Study of Translation Editing between Manual Translation and Machine Translation]. Overseas English 海外英语,2021(19):18-19+21.<br />
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=Chapter 11 陈惠妮=Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts=<br />
<br />
机器翻译的译前编辑研究——以医学类文摘为例<br />
<br />
陈惠妮 Chen Huini, Hunan Normal University<br />
<br />
[[Machine_Trans_EN_11]]<br />
<br />
===Abstract===<br />
At present, globalization is accelerating and the market demand for language services is rapidly increasing . Machine translation, as an important translation method, can greatly improve translation efficiency due to its low cost and high speed. However, because of the limitations of machine translation and the differences between Chinese and English language, machine translation is not accurate enough. In order to balance translation efficiency and translation quality, a great number of manual revisions in translation are required for the machine translating texts. Medical papers are specialized, special and purposeful, so it requires accurate,qualified and professional translation. However, the quality of translations by machine is inefficient to meet the high-quality requirements of medical papers translation. Therefore, the introduction of pre-editing can greatly improve the efficiency and quality of machine translation.<br />
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===Key words===<br />
Pre-editing, Machine translation, Medical texts<br />
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===题目===<br />
Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts<br />
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===摘要===<br />
在全球化加速发展的今天，市场对语言服务的需求迅速增加。机器翻译作为一种重要的翻译途径，由于其成本低、速度快，可以大大提高翻译效率。然而，由于机器翻译的局限性以及中英文语言的差异，机器翻译的准确性不高。为了平衡翻译效率和翻译质量，机器翻译文本需要大量的手工修改。<br />
医学论文具有专业性、特殊性和目的性，要求其译文准确、合格、专业。然而，机器翻译的质量较低，无法满足医学论文对翻译的高质量要求。因此，译前编辑的引入可以大大提高机器翻译的效率和质量。<br />
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===关键词===<br />
译前编辑；机器翻译；医学文本<br />
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===1. Introduction===<br />
===1.1 Definition of Machine Translation===<br />
As Cronin(2013) revealed: "Translation is undergoing a revolutionary upheaval. The influence of digital technology and the Internet on translation is continuous, extensive and profound. From the popularity of automatic online translation applications, translation revolution is everywhere (Cronin,2013) However,the concept of machine translation was firstly proposed in the 1930s. Since 1940s, the machine translation technology has been evolving from rule-based machine translation (RBMT) to statistical machine translation (SMT), and to neural machine translation (NMT). Machine translation refers to the automatic translation of source language into target language by using a computer system. That is, machine translation refers to the automatic translation of text from one language into another natural language by computer software or other online translation webs. Machine translation is also defined as the process of “using a computer system to automatically translate text or speech from one natural language to another” according to the definition by ISO (Cui 2014:68-73).O 'Brien (2002) defines it as "the behavior of modifying errors in machine translation to ensure that the target translation meets certain quality requirements". On the basic level, machine translation performs mechanical substitution of words in one language for words in another language, but that rarely produces a good translation, therefore, recognition of the whole phrases and their closest counterparts in the target language is needed. Not all words in one language have equivalents in another language, and many words have more than one meaning. A huge demand for translation is greatly needed in today’s global world, which creates new opportunities for the development of machine translation, attracts more and more attention and becomes one of the current research focuses.<br />
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===1.2 Definition of Pre-editing===<br />
Pre-editing means to adjust and modify the source language to make it fit more with the characteristics of the machine translation software before putting the source language before the into machine translation, so as to improve the quality of the translation machine translation (Wei Changhong 2008:93-94). Pre-editing is to modify the original text before putting it into machine translation software, in order to improve the recognition rate of machine translation, optimize the output quality of translated text and reduce the workload of post-editing. Because pre-translation editing only needs to be modified in one language, the operation is simpler than post-translation editing, which can realize the double improvement of quality and efficiency. A good pre-editing translation can help machine translation more smoothly, thus improving the machine readability and quality of the output translation. Pre-editing is mostly applied in the following situations: one is when the original text is of poor quality and the machine is difficult to recognize the meaning of the sentence, such as user generated content with poor readability and translatability (Gerlach et al 2003:45-53); Documents that need to be published in multiple languages; Next is when the original text contains a lot of jargon; The last is the original text has a corresponding translation memory bank. If the original text is edited, it can better match the content of the translation memory bank. Pre-editing is a process of identifying problems. It requires to pre-edit the source texts before putting it into machine translation according to the requirements, listing the expressions or sentences that may have trouble in machine translation and then pre-edit it by human. The purpose is to enable the computer to translate better, improve the translatability of machine translation. (Slype G V & Guinet J F & Seitz F 1984:115)<br />
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===1.3 Machine Translation Mode===<br />
According to the different knowledge acquisition methods, machine translation modes can be classified as follows: one is rule-based machine translation, which is based on bilingual dictionaries and a library of language rules for each language. The quality of translation depends on whether the source language conforms to the existing rules, but the inexhaustible rules are the hinders of this model. The second mode is machine translation based on statistics. This translation model relies on the principles of mathematics and statistics to find various existing translations corresponding to the translation tasks through the employment of corpus, analyzing the frequency of their occurrence and selecting the translation with the highest frequency for output. The disadvantage of this translation model is that it ignores the flexibility of language and the importance of context. The last one is neural network language model. This model is different from previous translation models in that it uses end-to-end neural network to realize automatic translation between natural languages. At present, the quality of its translation is much higher than that of the previous translation models.<br />
===1.4 Source of Study Abstracts===<br />
The core medical journals from domestic are selected in order to make the paper more representative and authoritative, such as National Medical Journal of China, Journal of Peking University (Health Science), and Journal of Third Military Medical University. All of them include basic medical science, biomedical technology, laboratory medical science and other fields. In this way, the pre-editing approaches included are applicable enough to machine translation of medical abstracts.<br />
===1.5 Selection of Translation Software===<br />
Generally, machine translation software includes Google Translation, Youdao Translator and Niu Translation, which has their own special use in translation. Take Google as an example, it is more like neurons in human brain, enabling to learn and collect information to establish connections with its neural machine translator’s neurons. However, it also causes many errors because of the lack of enough information. In this paper, contrastive analysis will be carried on by using Google translation. On the one hand, being a pioneer in the translation of NET, it is inevitably to sue Google as the translation software to translate medical texts in this paper. Comparing the program called “Google brain”, other NMT of translation software are relatively disadvantages. On the other hand, the Google translation enjoys the largest users in the world, with its downloads of more than one billion. The output quality of Google translation is more correct and complete than other machine translation software. With years of development and improvement, Google Translation has been greatly promoted. In this paper, Chinese medical abstract will be automatically translated by Google translation, and then the translation output will be compared with the translation by human and the publication of English abstracts. The main purpose is to prove that the improvement and promotion of quality and accuracy in the medical abstracts will be obtained through the pre-editing approaches.<br />
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===2.Language Characteristics and Error Division===<br />
Since Chinese and English are two different languages, it is quite neccessary to identify their own characteristics so as to better analyze and understand the two languages. Tytler (1978: 118-119) argued in his Essay on the Principles of Translation that there are three principles of translation that in all the translation should give readers the same feelings as the source text, except for complete transcript of ideas. There actually also exit some mistakes of these two languages. So the following will make some clarrifications of these two languages to ensure more accurate translation.<br />
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===2.1 Language Characteristics of Medical Abstracts===<br />
Chinese and English belong to different language systems, so there are differences in their language structure and the way users think of the languages. When using machine translation from Chinese to English, due to the unequal language levels, there will be many mistakes in the translation process, especially for ESP texts, such as medical papers.Here, these differences mainly refer to the linguistic characteristics of medical abstracts. In medical abstracts, it usually includes structured and unstructured abstracts. Although in different forms, they both describe the purpose, methods and conclusions of the research.<br />
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In the method section of medial abstract, several Chinese sentences can be connected with commas, but each sentence may convey different information. In contrast, English sentences contain a great deal of information, but in order to ensure clarity, some modifiers need to be isolated and then reconstructed. However, in Chinese-English machine translation, a lot of information is put into the sentence because the machine segments the sentence on the basis of the full comma.<br />
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In addition, subjectless sentence is used in the objective and method parts of Chinese medical abstracts. The subjectless sentence means the sentence without or free from subject and is usually employed in two contexts. The first is "needless to say". In Chinese sentences, it is common to omit the subject of the sentence. Chinese sentences can convey meanings by using incomplete sentence structures, so Chinese speakers can understand the meanings of the sentences even though the sentence subject is omitted. The second is "emphasis of action". In this context, subjectless sentences are used to describe behaviors, especially in the study of traditional Chinese medicine abstracts. Comparatively speaking, it should be avoided in English medical abstracts. Abstract sentences are more subjective when describing the learning process, while the essential requirement of English medical abstract is objectivity. From this point, sentences without subject should be avoided in English medical abstracts. Another feature is voice. Few words with passive meanings appear in Chinese abstracts. English sentences are more favoured in using passive voice. When translating from Chinese to English, passive voice should be used to make the contents objective, which is also the basic requirement of medical papers.<br />
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These are the linguistic features of Chinese medical abstracts. There are great differences in sentence structure and expression between Chinese and English medical abstracts. These differences may reduce the accuracy of machine translation, so it is necessary to introduce pre-editing to edit the source text to ensure that the source text can be accurately recognized by the machine and fully translated into English.<br />
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===2.2 Error Division of Machine Translation in Translating Abstracts of Medical Papers from Chinese to English===<br />
In this paper, errors in machine translation are listed out after analyzing. Some are due to the principles, such as statistical-based MT and NMT, employed by machine translation. Based on the original errors, they can be studied from two levels. One is from the macro-level, referring to mistakes caused by objective factors. These are not human factors, but the disadvantage of machine translation and the limitations of language. The limitations of machine translation are derived from the principles and models adopted and manifests itself as a reliance on reference sources. In this case, semantic ambiguity and textual incoherence may occur in the absence of reference sources.<br />
However, for ESP texts such as medical papers, the requirements for terms and sentence patterns are far beyond the existing corpora. For unfamiliar texts (that is, the corresponding texts cannot be found in the corpora). The quality of output translation will be relatively low, which is determined by the principles of machine translation. As mentioned above, machine translation has evolved over the past few decades from rules-based to corp-based statistics to NMT. However, there still exist some limitations in machine translation.<br />
Mistakes on micro-level are mainly caused by the variations and differences of linguistic structure between Chinese and English. Chinese is an implicit language, while English is an explicit one (Lian, 2010) To put it in another way, Chinese expression does not depend on language structures, while English does the opposite. This may result in mismatches between the original and machine translated sentences. This kind of error is mainly divided into two types: component fragment and component missing. For a medical paper, such errors are very serious. As a kind of ESP discourse, medical paper has the characteristics of fixed, objective and accurate language structure. In order to reproduce the characteristics of medical abstracts in translation, it is necessary to avoid errors at the level of words and sentences and pay attention to logic and consistency.<br />
Lexical errors here refer to the inconsistency of fixed expressions in the translation of terms. Although these terms are machine translation based on the corpus, the corpus may not be perfect for ESP texts such as medical papers. Therefore, fixed expressions are not used in term translation. This is also the most common mistake in machine translation (the term here includes but is not limited to nouns). For ESP texts, medical text in particular, the fixed expressions and the accuracy of terms are of great importance.<br />
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===3.Approaches Proposed for Pre-editing ===<br />
Generally speaking, there is a close relationship between pre-editing and post-editing, both of which aim to convey information and ensure high or publishable translation quality. Proper pre-editing can improve the quality of machine translation in terms of adequacy and consistency. <br />
Complexity of natural language and people use language arbitrarily, bringing many difficulties to Chinese-English machine translation, Hu Qingping (2005:24) has proposed "the research of translation software and the development of the controlled language are the two directions to improve the quality of machine translation : the former aims at the difficulty in the natural language processing, the latter overcomes the arbitrariness of natural language". Feng Quangong and Gao Lin (2017: 63-68) put forward: "The writing principles of controlled language can be applied to pre-editing of machine translation. Pre-editing based on controlled language can effectively reduce the complexity and ambiguity of source text, improve the identifiability of machine translation (the translatability of source text itself), and thus reduce (fully) the workload of post-editing".<br />
The central task of pre-editing is to transform human-friendly content into machine-friendly content, so words and sentences need to be repositioned or even changed. Based on the analysis of the language characteristics of Chinese and English, the Chinese ideographic group should be split before the Chinese source text is input into the machine software to translate so that the sentence structure is complete which can be easily recognized by the machine translation software.<br />
===3.1 Extraction and Replacement of Terms in the Source Text===<br />
As a branch of applied English, medical English is the product of the combination of English language knowledge and medical knowledge. The terms in medical papers have the characteristics of fixed and complex language structure. Although Google Translation is based on a corpus, the language structure is not fixed due to the limited size of the corpus. Therefore, the following two steps should be followed before machine translation. The first is to extract terms and create a glossary, and then replace the Chinese terms in the source text with the corresponding English expressions in the glossary. Of course, the terms of extraction should be searched and verified according to the actual situation. All of these words are verified before they can be replaced. This method can not only ensure the accuracy of term translation, but also maintain the consistency of expression, especially when dealing with long texts.<br />
Example1<br />
Source abstract: 口腔白斑病癌变相关缺氧应答基因和微小RNA的芯片及表达验证。<br />
Google translation: Microarray detection/Chip detection and expression verification of hypoxia response genes and microRNAs related to oral leukoplakia canceration.<br />
Published translation:Transcriptome array screening and verification of oral leukoplakia carcinogenesis-related hypoxia-responsive gene and microRNA. <br />
Analysis: The expression “ Affymetrix GeneChip” empolyed in this thesis is a human transcription array for transcriptome array. In the source abstract, “芯片检测“ can be meant “screening” but not detection, so the proper and appropriate translation of these expression should be “transcription array screening”, but the Google translates it into “microarry detection of chip detection”. Therefore, term extraction is essential and inevitable before putting the source abstracts into the machine translation software.<br />
After pre-editing source abstracts: 对 oral leukoplakia carcinogenesis 相关 hypoxia-responsive gene 和微小RNA进行 transcriptome array screening 及表达验证。<br />
After pre-editing translation: Transcriptome array screening and expression- verification of hypoxia-responsive genes and microRNAs related to oral leukoplakia carcinogenesis.<br />
===3.2 Explication of Subordination===<br />
As mentioned above, the subordination of Chinese sentences is judged by certain specific words in machine translation . Chinese is a paratactic language, and sentences are often connected by internal logical relationships; while English depends on sentence structure, so sentences are often closely linked by various language forms. In order to improve the accuracy of machine translation, we can adjust the sentence structure and adjust the position of adjectives and their modifiers. <br />
Example 2<br />
Source abstracts:回顾性分析南京医科大学附属儿童医院中重度HIE患儿49例及同期就诊的无神经系统症状体征的足月新生儿为对照组31例的头颅磁共振成像（MRI）资料。<br />
Google translation: A retrospective analysis that the brain magnetic resonance imaging (MRI) data of 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University and full-term neonates with no neurological symptoms and signs during the same period were included in the control group.<br />
Published translation: A total of 49 children with moderate to severe HIE admitted to the children’s Hospital Affiliated to Nanjing Medical University were retrospectively analyzed. Cranial magnetic resonance imaging (MRI) date of 31 full-term neonates without neurological symptoms and signs who visited the hospital during the same period were recruited as the control group. <br />
Analysis: As the above example shows that “中重度HIE患儿” and “足月新生儿” in the source abstracts are from the Children’s Hospital Affiliated Nanjing Medical University. The Google translation shows that 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University. There is an error due to the misuse of subordination. There are some advice in order to solve the problem. That is, first to segment the sentence and then reconstruct the sentence. For instance, the Chinese expression “南京医科大学附属儿童医院” carries many modifiers, which requires to cut the modifiers and reconstruct the sentence. Therefore, the Chinese expression “南京医科大学附属儿童医院’can be divided into abstractions, leaving two sentences instead of one long sentence with modifiers..<br />
After pre-editing source abstracts: 对49例中重度HIE患儿以及31例无神经系统症状体征的足月新生儿的MRI资料进行回顾性分析。这些患者收治于南京医科大学儿童附属医院。<br />
After pre-editing translation: The MRI data of 49 children with moderate to severe HIE and 31 full-term newborns without neurological symptoms and signs were retrospectively analyzed. These patients were admitted to the Children’s Hospital of Nanjing Medical University.<br />
===3.3 Explication of Subject through Voice Changing===<br />
The extensive use of subjectless sentences are quite common in the Chinese abstracts, while English prefers to employ passive voice in the sentences so as to make them object and accurate. In order to take the characteristics of English sentences into great and careful consideration, the sentences written in passive voice in particular, it will be helpful for machine translation to find out the subject and reconstruct a sentence in passive voice (Wang Yan: 2008). The first is to discover the “doee” in a sentence and then is to reconstruct the sentence structure and put the “doee” before the verb. The last is to explicate the passive relation between the noun and the verb.<br />
Example 3<br />
Source abstract: 回顾性分析2018年1月至2020年12月解放军总医院第七医学中心收治的500例老年髋部骨折患者的资料。<br />
Google translation: A retrospective analysis of the data of 500 elderly patients with hip fractures admitted to the Seventh Medical Center of the PLA General Hospital from January 2018 to December 2020.<br />
Published translation: From January 2018 to December 2020, the data of 500 elderly patients with hip fracture treated in the Seventh Medical Center of PLA General Hospital were analyzed retrospectively.<br />
Analysis: The above example indicates that the “回顾性分析”in the Google Translate is a noun phrase, but the source abstracts actually describes an action or a behavior. The reason for such a mistake is that the machine can’t recognize the Chinese subjetless sentences. To find the subject of the sentence, the source abstracts can be revised and adjusted. Finding the “doee” is the first step, which refers to “500例老年髋部骨折患者的资料”in the source abstracts. And next is to put it in front of the verb, that is to place it in the beginning of the sentence. Last but not least, the passive voice should be explicated in the sentence. <br />
After pre-editing source abstract: 500例老年髋部骨折患者的资料被回顾性分析，他们在2018年1月之2020年12月期间收治于解放军总医院第七医学中心。<br />
After pre-editing translation: The data of 500 elderly hip fracture patients were retrospectively analyzed. They were admitted to the Seventh Medical Center of the PLA General Hospital between January 2018 and December 2020.<br />
===3.4 Relocation of Modifiers===<br />
Modifiers, including noun, adverbial and attributive are constantly employed in the Chinese medical papers in order to add information to subject and object in the sentence. By doing this, complicated sentences can be structured, thus causing obstacles to machine translation for recognizing this complex sentence structures when translating Chinese-English sentences. To make the machine translation successfully recognize the sentence structure, simplifying the sentence structure is quite necessary. The key is to moving the location of the modifiers and thus making the modifiers an independent sentence. In other words, the modifiers ought to be put before or behind the main part of the sentence to satisfy the common use of English. <br />
Usually, the modifiers in Chinese sentence can only be placed in front of the core word, while in English, modifiers are very flexible. It is all right to place the modifiers in front of or behind the major part of the sentences with adjective or a connective noun.<br />
Example 4<br />
Source abstracts: 利用DNA重组技术以pET-28a表达系统在E.coli BL21(DE3) 中重组表达Hepl。<br />
Google Translation: Hepl is recombined in E.coli BL21 (DE3) using DNA recombination technology with pET-28a expression system.<br />
Published translation: The recombinant Hcpl protein was expressed by using DNA recombination technology through pET-28a expression system in E. coli BL21 (De3).<br />
Analysis: The Chinese medical text includes two modifiers, “利用DNA”and “以pET-28a)表达系统”. These two modifiers will be translated by machine, which catches more attention to the two constituents. From the Google translation above, one failure is obvious that it misplaces the location of the two modifiers and presents it in not accurate form. To make it translate correctly by machine translation, dividing the sentences is very important so that the source abstracts can be correctly recognized by machine translation.<br />
After pre-editing source abstract: 利用DNA重组技术，Hcp1重组表达在E.coli BL21 (DE3)中， 通过pET-28a表达系统在E. coli BL21 (DE3)中。<br />
Google translation: Using DNA recombination technology, Hcp1 recombination is expressed in E.coli BL21 (DE3), via pET-28a expression system in E. coli BL21 (DE3).<br />
The second is the independence of modifiers, that is, modifiers can also be reconstructed into clauses to modify core words. Compared with English, There is no subordinate clause in Chinese, so redundant Chinese modifiers need to be reconstructed into subordinate clauses in Chinese-English translation to meet the characteristics of English. English, especially sentences with multiple modifiers. Otherwise, sentence structure may be confused, such as scattered modifiers of core words. To avoid this mistake, it is necessary to separate the modifier from the main part of the sentence. These modifiers should be reconstituted into clauses. Also, keep your sentences simple and easy to understand.<br />
===3.5 Proper Omission and Deletion of Category Words===<br />
Category words are commonly used in Chinese. Category words complement the meaning of words, including problems, positions, situations and jobs. Adding this supplementary word is more in line with Chinese custom. In many cases, it has no real meaning, so it can be omitted in translation. In Chinese, category words are frequently used. However, it is rarely used in English, which is one of the differences between Chinese and English. There are many kinds of category words. Considering objectivity and the fixed structure of language, redundant category words should be deleted in Chinese-English translation. In the general, the most commonly used category of words in the Chinese medical abstracts are "process", "behavior", and "situation". These categories of words hinder the language conversion of Chinese and English, resulting in redundancy. <br />
Example 5<br />
Source abstract: 探讨口腔白斑病癌变进程中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia response genes and associated microRNAs in the process of oral leukoplakia cancer was discussed.<br />
Published translation: To study the hypoxia response gene and microRNA (miRNA)expression profiles in the pathogenesis and progression of oral leukoplakia (OLK).<br />
Analysis: As the above example shows, the Chinese words “进程” belongs to a category word. However, the Chinese expression “癌变”contains the process, so the “进程” expression can be deleted before placing it into the machine translation, because the meaning of it has been overlapping between the expression “癌变”. Therefore, its place can be replaced with preposition “during”.<br />
After pre-editing source abstract: 探讨口腔白斑病癌变中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia responsive genes and miRNA in oral leukoplakia cancer was investigated.<br />
<br />
===Conclusion===<br />
After years of development, machine translation has made great progress. The accuracy of machine translation has been greatly improved in both text recognition and sentence pattern conversion. However, machine translation has its own limitations. In other words, it needs to rely on the parallel corpus as a reference source for improving its accuracy. ESP text, in particular, is harder to get the high quality by machine translation. <br />
<br />
As one of the research papers, the characteristics of medical abstracts are fixed language structures, objectivity and accuracy (Qin Yi 2004:421-423). Therefore, medical translation must be accurate, object and understandable to follow the specific demands of the medical paper. Being an important field in the human society, medical paper translation is on a great demand, which means that it needs a huge demand for human labor. However, with the machine translation promoting, it will be more efficient to translate medical papers combining the effort by human and machine. The improvement and development of machine translation requires the joint efforts of computer science, information science, statistics, linguistics and other academic circles to achieve more mature human-computer mutual assistance translation (Li Yafei, Zhang Ruihua 2019:38-45).<br />
<br />
However, errors can occur during the process of machine translation of Chinese- English, because of the differences of the Chinese and English and the processing of the machine. Errors from the perspective of linguistic or grammar can affect the machine translation a lot. After division and recognition of errors, some pre-editing approaches are put forward to help the machine translation more accurate and readable, that are, extraction and replacement of terms in the source text, relocation of modifiers, explication of subordination, proper omission, deletion of category words and explication of subject through voice changing. <br />
<br />
The paper mainly focuses on the pre-editing machine translation by using medical papers as a case study. The errors of machine translation occurring in the translation of medical abstracts and pre-editing approaches for machine translation. The quality of machine translation of medical papers is greatly improved after employing the pre-editing methods. However, machine translation is not as flexible and accurate as human brain, so it is of importance to combine pre-editing and post-editing approaches with machine translation in order to produce more accurate, more object machine translation of medical papers.<br />
<br />
===References===<br />
Cronin, Michael (2013). ''Translation in the Digital Age''[M]. New York & London: Routledge. 86<br />
<br />
Cui Qiliang崔启亮(2014).论机器翻译的译后编辑[J] ''On Post-Editing of Machine Translatio''. 中国翻译 Chinese Translators Journal, 035(006):68-73<br />
<br />
Feng Quangong, Gao Lin冯全功,高琳 (2017). 基于受控语言的译前编辑对机器翻译的影响[J] ''Influence of Pre-editing Based on Controlled Language on Machine Translation''. 当代外语研究Contemporary Foreign Language Research,(2): 63-68+87+110.<br />
<br />
GERLACH J, et al ( 2013). ''Combining Pre-editing and Post-editing to Improve SMT of User-generated Content''[M]// Proceedings of MT Summit XIV Workshop on Post-editing Technology and Practice. 45-53<br />
<br />
Hu Qingping胡清平(2005). 机器翻译中的受控语言[J] ''Controlled Language in Machine Translation''. 中国科技翻译 Chinese Science and Technology Translation, (03): 24-27. <br />
<br />
Lian Shuneng连淑能 (2010). 英汉对比研究增订本[M]''An Updated Version of English-Chinese Contrastive Studies'' . 北京:高等教育出版社Beijing: Higher Education Publishing House. 35-36.<br />
<br />
Li Yafei, Zhang Ruihua黎亚飞,张瑞华 (2019). 机器翻译发展与现状[J]''The Development and Current Situation of Machine Translation''. 中国轻工教育 China Light Industry Education, (5):38-45. <br />
<br />
Qin Yi秦毅(2004),从翻译基本标准议医学英语的翻译[J] ''On the Translation of Medical English from the Basic Standard of Translation''. 遵义医学院学报 Journal of Zunyi Medical College,27 (4): 421-423. <br />
<br />
Slype G V & Guinet J F & Seitz F (1984). ''Better Translation for Better Communication'' [M] . Oxford: Pergamon Press Ltd (U.K.). 90-93<br />
<br />
O'Brien S (2002). Teaching Post-editing: A Proposal for Course Con-tent [EB/OL]. http://mt-archive. Info/EAMT-2002-0brien. Pdf.<br />
<br />
Tytler, A. F. (1978). ''Essay On The Principles of Translation''[M]. Amsterdam: JohnBenjamins Publishing. 118-119<br />
<br />
Wang Yan王燕 (2008). 医学英语翻译与写作教程[M] ''Medical English Translation and Writing Course''. 重庆:重庆大学出版社 Chongqing: Chongqing University Press. 60-61<br />
<br />
=12 蔡珠凤=(The Mistranslation of C-J Machine Translation of Political Statements)=<br />
[[Machine_Trans_EN_12]]<br />
===Abstract===<br />
Language is the main way of communication between people. With the continuous development of globalization, the scale of cross-border exchanges is also expanding. However, due to cultural differences and diversity, the languages of different countries and regions are very different, which seriously hinders people's communication. The demand for efficient and convenient translation tools is increasing. At the same time, with the development of network technology and artificial intelligence, recognition technology based on deep learning is more and more widely used in English, Japanese and other fields.<br />
<br />
===Key words===<br />
machine translation; political statements; mistranslation of C-J machine translation<br />
===题目===<br />
The Mistranslation of C-J Machine Translation of Political Statements<br />
===摘要===<br />
语言是人与人之间交流的主要方式。随着全球化的不断发展，跨境交流的规模也在不断扩大。然而，由于文化的差异和多样性，不同国家和地区的语言差异很大，这严重阻碍了人们的交流。对高效便捷的翻译工具的需求正在增加。同时，随着网络技术和人工智能的发展，基于深度学习的识别技术在英语、日语等领域的应用越来越广泛。<br />
<br />
===关键词===<br />
机器翻译；政治发言；政治发言中译日的误译<br />
===1. Introduction===<br />
===Introduction to machine translation===<br />
Machine translation, also known as automatic translation, is a process of using computers to convert one natural language (source language) into another natural language (target language). It is a branch of computational linguistics, one of the ultimate goals of artificial intelligence, and has important scientific research value.At the same time, machine translation has important practical value. With the rapid development of economic globalization and the Internet, machine translation technology plays a more and more important role in promoting political, economic and cultural exchanges.The development of machine translation technology has been closely accompanied by the development of computer technology, information theory, linguistics and other disciplines. From the early dictionary matching, to the rule translation of dictionaries combined with linguistic expert knowledge, and then to the statistical machine translation based on corpus, with the improvement of computer computing power and the explosive growth of multilingual information, machine translation technology gradually stepped out of the ivory tower and began to provide real-time and convenient translation services for general users.（Zhang 2019:5-6)<br />
<br />
=== C-J machine translation software===<br />
Today's online machine translation software includes Baidu translation, Tencent translation, Google translation, Youdao translation, Bing translation and so on. Google was the first company to launch the machine translation system, and Baidu was the first company to import the machine translation system in China. In addition, Tencent and Youdao have attracted much attention.Machine translation is the process of using computers to convert one natural language into another. It usually refers to sentence and full-text translation between natural languages. In order to continuously improve the translation quality, R & D personnel have added artificial intelligence technologies such as speech recognition, image processing and deep neural network to machine translation on the basis of traditional machine translation based on rules, statistics and examples.With the increase of using machine translation, the joint cooperation between manual translation and machine translation will also increase significantly in the future. What criteria should be used to evaluate the quality of machine translation? In the evolving field of machine translation, there is an urgent need to clarify the unsolvable questions and solved problems.<br />
<br />
===The history of machine translation===<br />
The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. alchuni put forward the idea of using machines for translation. In 1933, Soviet inventor П.П. Trojansky designed a machine to translate one language into another, and registered his invention on September 5 of the same year; However, due to the low technical level in the 1930s, his translation machine was not made. In 1946, the first modern electronic computer ENIAC was born. Shortly after that, W. weaver, an American scientist and A. D. booth, a British engineer, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947 when discussing the application scope of electronic computer. In 1949, W. Weaver published the translation memorandum, which formally put forward the idea of machine translation. After 60 years of ups and downs, machine translation has experienced a tortuous and long development path. The academic community generally divides it into the following four stages:<br />
<br />
Pioneering period（1947-1964）<br />
<br />
In 1954, with the cooperation of IBM, Georgetown University completed the English Russian machine translation experiment with ibm-701 computer for the first time, showing the feasibility of machine translation to the public and the scientific community, thus opening the prelude to the study of machine translation.<br />
It is not too late for China to start this research. As early as 1956, the state included this research in the national scientific work development plan. The topic name is "machine translation, the construction of natural language translation rules and the mathematical theory of natural language". In 1957, the Institute of language and the Institute of computing technology of the Chinese Academy of Sciences cooperated in the Russian Chinese machine translation experiment, translating 9 different types of more complex sentences.From the 1950s to the first half of the 1960s, machine translation research has been on the rise. The United States and the former Soviet Union, two superpowers, have provided a lot of financial support for machine translation projects for military, political and economic purposes, while European countries have also paid considerable attention to machine translation research due to geopolitical and economic needs, and machine translation has become an upsurge for a time. In this period, although machine translation is just in the pioneering stage, it has entered an optimistic period of prosperity.<br />
<br />
Frustrated period（1964-1975）<br />
<br />
In 1964, in order to evaluate the research progress of machine translation, the American Academy of Sciences established the automatic language processing Advisory Committee (Alpac Committee) and began a two-year comprehensive investigation, analysis and test.In November 1966, the committee published a report entitled "language and machine" (Alpac report for short), which comprehensively denied the feasibility of machine translation and suggested stopping the financial support for machine translation projects. The publication of this report has dealt a blow to the booming machine translation, and the research of machine translation has fallen into a standstill. Coincidentally, during this period, China broke out the "ten-year Cultural Revolution", and basically these studies also stagnated. Machine translation has entered a depression.<br />
<br />
convalescence（1975-1989）<br />
<br />
Since the 1970s, with the development of science and technology and the increasingly frequent exchange of scientific and technological information among countries, the language barriers between countries have become more serious. The traditional manual operation mode has been far from meeting the needs, and there is an urgent need for computers to engage in translation. At the same time, the development of computer science and linguistics, especially the substantial improvement of computer hardware technology and the application of artificial intelligence in natural language processing, have promoted the recovery of machine translation research from the technical level. Machine translation projects have begun to develop again, and various practical and experimental systems have been launched successively, such as weinder system Eurpotra multilingual translation system, taum-meteo system, etc.However, after the end of the "ten-year holocaust", China has perked up again, and machine translation research has been put on the agenda again. The "784" project has paid enough attention to machine translation research. After the mid-1980s, the development of machine translation research in China has further accelerated. Firstly, two English Chinese machine translation systems, ky-1 and MT / ec863, have been successfully developed, indicating that China has made great progress in machine translation technology.<br />
<br />
New period(1990 present)<br />
<br />
With the universal application of the Internet, the acceleration of the process of world economic integration and the increasingly frequent exchanges in the international community, the traditional way of manual operation is far from meeting the rapidly growing needs of translation. People's demand for machine translation has increased unprecedentedly, and machine translation has ushered in a new development opportunity. International conferences on machine translation research have been held frequently, and China has made unprecedented achievements. A series of machine translation software have been launched, such as "Yixing", "Yaxin", "Tongyi", "Huajian", etc. Driven by the market demand, the commercial machine translation system has entered the practical stage, entered the market and came to the users.Since the new century, with the emergence and popularization of the Internet, the amount of data has increased sharply, and statistical methods have been fully applied. Internet companies have set up machine translation research groups and developed machine translation systems based on Internet big data, so as to make machine translation really practical, such as "Baidu translation", "Google translation", etc. In recent years, with the progress of in-depth learning, machine translation technology has further developed, which has promoted the rapid improvement of translation quality, and the translation in oral and other fields is more authentic and fluent.<br />
<br />
===The problem of machine translation at present===<br />
Error is inevitable<br />
Many people have misunderstandings about machine translation. They think that machine translation has great deviation and can't help people solve any problems. In fact, the error is inevitable. The reason is that machine translation uses linguistic principles. The machine automatically recognizes grammar, calls the stored thesaurus and automatically performs corresponding translation. However, errors are inevitable due to changes or irregularities in grammar, morphology and syntax, such as sentences with adverbials after "give me a reason to kill you first" in Dahua journey to the West. After all, a machine is a machine. No one has special feelings for language. How can it feel the lasting charm of "the tenderness of lowering its head, like the shame of a water lotus? After all, the meaning of Chinese is very different due to the changes of morphology, grammar and syntax and the change of context. Even many Chinese people are zhanger monks - they can't touch their heads, let alone machines.<br />
Bottleneck<br />
In fact, no matter which method, the biggest factor affecting the development of machine translation lies in the quality of translation. Judging from the achievements, the quality of machine translation is still far from the ultimate goal.<br />
Chinese mathematician and linguist Zhou Haizhong once pointed out in his paper "fifty years of machine translation": to improve the quality of machine translation, the first thing to solve is the problem of language itself rather than programming; It is certainly impossible to improve the quality of machine translation by relying on several programs alone. At the same time, he also pointed out that it is impossible for machine translation to achieve the degree of "faithfulness, expressiveness and elegance" when human beings have not yet understood how the brain performs fuzzy recognition and logical judgment of language. This view may reveal the bottleneck restricting the quality of translation. <br />
It is worth mentioning that American inventor and futurist ray cozwell predicted in an interview with Huffington Post that the quality of machine translation will reach the level of human translation by 2029. There are still many disputes about this thesis in the academic circles.<br />
<br />
===2.Mistranslation of Chinese Japanese machine translation===<br />
===2.1Vocabulary mistranslation in Chinese Japanese machine translation===<br />
===2.1.1Mistranslation of proper nouns===<br />
In political materials, there are often political dignitaries' names, place names or a large number of proper nouns in the political field. The morphemes of such words are definite and inseparable. Mistranslation will make the source language lose its specific meaning. <br />
<br />
Japanese translation into Chinese Chinese translation into Japanese<br />
<br />
original text translation by Youdao reference translation original text translation by Youdao reference translation<br />
<br />
朱鎔基 朱基 朱镕基 栗战书 栗戰史書 栗戰書<br />
<br />
労安 劳安 劳安 李克强 李克強 李克強 <br />
<br />
筑紫哲也 筑紫哲也 筑紫哲也 习近平 習近平 習近平<br />
<br />
山口百惠 山口百惠 山口百惠 韩正 韓中 韓正<br />
<br />
田中角栄 田中角荣 田中角荣 王沪宁 王上海氏 王滬寧<br />
<br />
東条英機 东条英社 东条英机 汪洋 汪洋 汪洋<br />
<br />
毛沢东 毛泽东 毛泽东 赵乐际 趙樂南 趙樂際<br />
<br />
トウ・ショウヘイ 大酱 邓小平 江泽民 江沢民 江沢民<br />
<br />
周恩来 周恩来 周恩来<br />
<br />
クリントン 克林顿 克林顿<br />
<br />
The above table counts 18 special names in the two texts, and 7 machine translation errors. In terms of mistranslation, there are not only "战书" but also "戰史" and "史書" in Chinese. "沪" is the abbreviation of Shanghai. In other words, since "战书" and "沪" are originally common nouns, this disrupts the choice of target language in machine translation. Except Katakana interference (except for トウシゃウヘイ, most of the mistranslations appear in the new Standing Committee. It can be seen that the machine translation system does not update the thesaurus in time. Different from other words, people's names have particularity. Especially as members of the Standing Committee of the Political Bureau of the CPC Central Committee, the translation of their names is officially unified. In this regard, public figures such as political dignitaries, stars, famous hosts and important. In addition, the machine also translates Japanese surnames such as "タ二モト" (谷本), "アンドウ" (安藤) into "塔尼莫特" and "龙胆". It can be found that the language feature that Japanese will be written together with Chinese characters and Hiragana also directly affects the translation quality of Japanese Chinese machine translation. Japanese people often use Katakana pronunciation. For example, when Japanese people talk with Premier Zhu, they often use "二ーハウ" (Hello). However, the machine only recognizes the two pseudonyms "二" and "ハ" and transliterates them into "尼哈" , ignoring the long sound after "二" and "ハ".<br />
<br />
original text Translation by Youdao reference translation<br />
<br />
日美安全体制 日米の安全体制 日米安保体制<br />
<br />
中国共产党第十九次全国代表大会 中国共産党第19回全国代表大会 中国共産党第19回全国代表大会（第19回党大会）<br />
<br />
十八大 十八大 第18回党大会中国特色社会主义<br />
<br />
中国特色社会主義 中国の特色ある社会主義 第18回党大会<br />
<br />
中国共产党中央委员会 中国共産党中央委員会 中国共産党中央委員会<br />
<br />
中国共産党中央委員会十八届中共中央政治局常委 第18代中国共產党中央政治局常務委員 第18期中共中央政治局常務委員<br />
<br />
十八届中共中央政治局委员 18期の中国共產党中央政治局委員 第18期中共中央政治局委員<br />
<br />
十九届中共中央政治局常委 十九回中国共產党中央政治局常務委員 第19期中央政治局常務委員<br />
<br />
中共十九届一中全会 中国共產党第十九回一中央委員会 第19期中央委員会第1回全体会議<br />
<br />
The above table is a comparison of the original and translated versions of some proper nouns. As shown in the table, the mistranslation problems are mainly reflected in the mismatch of numerals + quantifiers, the wrong addition of case auxiliary word "の", the lack of connectors, the Mistranslation of abbreviations, dead translation, and the writing errors of Chinese characters. The following is a specific analysis one by one.<br />
<br />
"十八届中共中央政治局常委", "十八届中共中央政治局委员", "十九届中共中央政治局常委" and "中共十九届一中全会" all have quantifiers "届", which are translated into "代", "期" and "回" respectively. The meaning is vague and should be uniformly translated into "期"; Among them, the translation of the last three proper nouns lacks the Chinese conjunction "第". The case auxiliary word "の" was added by mistake in the translation of "十八届中共中央政治局委员" and "日美安全体制". The "中国共产党中央委员会" did not write in the form of Japanese Ming Dynasty characters, but directly used simplified Chinese characters.<br />
<br />
The full name of "中共十九届一中全会" is "中国共产党第十九届中央委员会第一次全体会议", in which "一" stands for "第一次", which is an ordinal word rather than a cardinal word. Machine translation does not produce a correct translation. The fundamental reason is that there are no "规则" for translating such words in machine translation. If we can formulate corresponding rules for such words (for example, 中共m'1届m2 中全会→第m1期中央委员会第m2回全体会议), the translation system must be able to translate well no matter how many plenary sessions of the CPC Central Committee.<br />
<br />
===2.1.2Mistranslation of Polysemy===<br />
original text Translation by Youdao reference translation<br />
<br />
スタジオ 摄影棚/工作室 直播现场/演播厅<br />
<br />
日中関係の話 中日关系的故事 就中日关系(话题)<br />
<br />
溝 水沟 鸿沟<br />
<br />
それでは日中の問題について質問のある方。 那么对白天的问题有提问的人。 关于中日问题的话题，举手提问。<br />
<br />
私たちのクラスは20人ちょっとですが、 我们班有20人左右， 我们班二十多人的意见统一很难，<br />
<br />
いろいろな意見が出て、まとめるのは大変です。 但是又各种各样的意见，总结起来很困难。 <br />
<br />
一体どうやって、13億人もの人をまとめているんですか。 到底是怎么处理13亿的人的呢？ 中国是怎样把13亿人凝聚在一起的？<br />
<br />
In the original text, the word "スタジオ" appeared four times and was translated into "摄影棚" or "工作室" respectively, but the context is on-site interview, not the production site of photography or film. "話" has many semantics, such as "说话", "事情", "道理", etc. In accordance with the practice of the interview program, Premier Zhu Rongji was invited to answer five questions on the topic of China Japan relations. Obviously, the meaning of the word "故事" is very abrupt. The inherent Japanese word "日中" means "晌午、白天". At the same time, it is also the abbreviation of the names of the two countries. The machine failed to deal with it correctly according to the context. "溝" refers to the gap between China and Japan, not a "水沟". The former "まとめる" appears in the original text with the word "意见", which is intended to describe that it is difficult to unify everyone's opinions. The latter "まとめている" also refers to unifying the thoughts of 1.3 billion people, not "处理". Therefore, it is more appropriate to use "统一" and "处理" in human translation, rather than "总结意见" or "处理意见".<br />
<br />
Mistranslation of polysemy has always been a difficult problem in machine translation research. The translation of each word is correct, but it is often very different from the original expression in the context. Zhang Zhengzeng said that ambiguity is a common phenomenon in natural language. Its essence is that the same language form may have different meanings, which is also one of the differences between natural language and artificial language. Therefore, one of the difficulties faced by machine translation is language disambiguation (Zhang Zheng, 2005:60). In this regard, we can mark all the meanings of polysemous words and judge which meaning to choose by common collocation with other words. At the same time, strengthen the text recognition ability of the machine to avoid the translation inconsistent with the current context. In this way, we can avoid the mistake of "大酱" in the place where famous figures such as Deng Xiaoping should have appeared in the previous political articles.<br />
<br />
===2.1.3Mistranslation of compound words===<br />
Multi class words refer to a word with two or more parts of speech, also known as the same word and different classes.<br />
<br />
original text Translation by Youdao reference translation<br />
<br />
1998年江泽民主席曾经访问日本, 1998年の江沢民国家主席の日本訪問し、 1998年、江沢民総書記が日本を訪問し、かつ<br />
<br />
同已故小渊首相签署了联合宣言。 かつて同じ故小渕首相が署名した共同宣言 亡くなられた小渕総理と宣言に調印されました<br />
<br />
Chinese "同" has two parts of speech: prepositions and conjunctions: "故" has many parts of speech, such as nouns, verbs, adjectives and conjunctions. This directly affects the judgment of the machine at the source language level. The word "同" in the above table is used as a conjunction to indicate the other party of a common act. "故" is used as a verb with the semantic meaning of "死亡", which is a modifier of "小渊首相". The machine regards "同" as an adjective and "故" as a noun "原因", which leads to confusion in the structure and unclear semantics of the translation. Different from the polysemy problem, multi category words have at least two parts of speech, and there is often not only one meaning under each part of speech. In this regard, software R & D personnel should fully consider the existence of multi category words, so that the translation machine can distinguish the meaning of words on the basis of marking the part of speech, so as to select the translation through the context and the components of the word in the sentence. Of course, the realization of this function is difficult, and we need to give full play to the wisdom of R & D personnel.<br />
<br />
===2.2 Syntactic mistranslation in Chinese Japanese machine translation===<br />
===2.2.1Mistranslation of tenses===<br />
original text：History is written by the people, and all achievements are attributed to the people. <br />
<br />
Translation by Youdao：歴史は人民が書いたものであり、すべての成果は人民のためである。<br />
<br />
reference translation：歴史は人民が綴っていくものであり、すべての成果は人民に帰することとなります。<br />
<br />
The original sentence meaning is "历史是人民书写的历史" or "历史是人民书写的东西". When translated into Japanese, due to the influence of Japanese language habits, the formal noun "もの" should be supplemented accordingly. In this sentence, both the machine and the interpreter have translated correctly. However, the machine's recognition of "的" is biased, resulting in tense translation errors. The past, present and future will become "history", and this is a continuous action. The form translated as "~ ていく" not only reflects that the action is a continuous action, but also conforms to the tense and semantic information of the original text. In addition, the machine's treatment of the preposition "于" is also inappropriate. In the original text, "人民" is the recipient of action, not the target language. As the most obvious feature of isolated language, function words in Chinese play an important role in semantic expression, and their translation should be regarded as a focus of machine translation research.<br />
<br />
original text：李克强同志是十六届中共中央政治局常委,其他五位同志都是十六届中共中央政治局委员。<br />
<br />
Translation by Youdao：李克強総理は第16代中国共産党中央政治局常務委員であり、他の５人の同志はいずれも16期の中国共産党中央政治局委員である。<br />
<br />
reference translation：李克強同志は第16期中国共産党中央政治局常務委員を務め、他の５人は第16期中共中央政治局委員を務めました。<br />
<br />
Judging the verb "是" in the original text plays its most basic positive role, but due to the complexity of Chinese language, "是" often can not be completely transformed into the form of "だ / である" in the process of translation. This sentence is a judgment of what has happened. Compared with the 19th session, the 18th session has become history. This is an implicit temporal information. It is very difficult for machines without human brain to recognize this implicit information. "中共中央政治局常委" is the abbreviation of "中共中央政治局常务委员会委员" and it is a kind of position. In Japanese, often use it with verbs such as "務める","担当する",etc. The translator adopts the past tense of "務める", which conforms to the expression habit of Japanese and deals with the problem of tense at the same time. However, machine translation only mechanically translates this sentence into judgment sentence, and fails to correctly deal with the past information implied by the word "十八届".<br />
<br />
===2.2.2Mistranslation of honorifics===<br />
Honorific language is a language means to show respect to the listener. Different from Japanese, there is no grammatical category of honorifics in Chinese. There is no specific fixed grammatical form to express honorifics, self modesty and politeness. Instead, specific words such as "您", "请" and "劳驾" are used to express all kinds of respect or self modesty. <br />
<br />
Original text translation by Youdao reference translation<br />
<br />
女士们，先生们，同志们，朋友们 さんたち、先生たち、同志たち、お友达さん ご列席の皆さん<br />
<br />
谢谢大家！ ありがとうございます！ ご清聴ありがとうございました。<br />
<br />
これはどうされますか。 这是怎么回事呢? 您将如何解决这一问题？<br />
<br />
こうした問題をどうお考えでしょうか。 我们会如何考虑这些问题呢？ 您如何看待这一问题？<br />
<br />
For example, for the processing of "女士们，先生们，同志们，朋友们", the machine makes the translation correspond to the original one by one based on the principle of unchanged format, but there are errors in semantic communication and pragmatic habits. When speaking on formal occasions, Chinese expression tends to be comprehensive and detailed, as well as the address of the audience. In contrast, Japanese usually uses general terms such as "皆様", "ご臨席の皆さん" or "代表団の方々" as the opening remarks. In terms of Japanese Chinese translation, the machine also failed to recognize the usage of Japanese honorifics such as "さ れ る" and "お考え". It can be seen that Chinese Japanese machine translation has a low ability to deal with honorific expressions. The occasion is more formal. A good translation should not only be fluent in meaning, but also conform to the expression habits of the target language and match the current translation environment.<br />
<br />
===Conclusion===<br />
In the process of discussing the Mistranslation of machine translation, this paper mainly cites the Mistranslation of Youdao translator. When I studied the problem of machine translation mistranslation, I also used a large number of translation software such as Google and Baidu for parallel comparison, and found that these translation software also had similar problems with Youdao translator. For example, the "新世界新未来" in Chinese ABAC structural phrases is translated by Baidu as "新し世界の新しい未来", which, like Youdao, is not translated in the form of parallel phrases; Google online translation translates the word "“全心全意" into a completely wrong "全面的に"; The original sentence "我吃了很多亏" in the Mistranslation of the unique expression in the language is translated by Google online as "私はたくさんの損失を食べました". Because the "吃" and "亏" in the original text are not closely adjacent, the machine can not recognize this echo and mistakenly treats "亏" as a kind of food, so the machine translates the predicate as "食べました"; Japanese "こうした問題をどう考えでしょうか", Google's online translation is "你如何看待这些问题?", "你" tone can not reflect the tone of Japanese honorific, while Baidu translates as "你怎么想这样的问题呢?" Although the meaning can be understood, it is an irregular spoken language. Google and Baidu also made the same mistakes as Youdao in the translation of proper nouns. For example, they translated "十七届(中共中央政治局常委)”" into "第17回..." .In short, similar mistranslations of Youdao are also common in Baidu and Google. Due to space constraints, they will not be listed one by one here.<br />
<br />
Mr. Liu Yongquan, Institute of language, Chinese Academy of Social Sciences (1997) It has been pointed out that machine translation is a linguistic problem in the final analysis. Although corpus based machine translation does not require a lot of linguistic knowledge, language expression is ever-changing. Machine translation based solely on statistical ideas can not avoid the translation quality problems caused by the lack of language rules. The following is based on the analysis of lexical, syntactic and other mistranslations From the perspective of the characteristics of the source language, this paper summarizes some difficulties of Chinese and Japanese in machine translation.<br />
<br />
(1) The difficulties of Chinese in machine translation <br />
<br />
Chinese is a typical isolated language. The relationship between words needs to be reflected by word order and function words. We should pay attention to the transformation of function words such as "的", "在", "向" and "了". Some special verbs, such as "是", "做" and "作", are widely used. How to translate on the basis of conforming to Japanese pragmatic habits and expressions is a difficulty in machine translation research. In terms of word order, Chinese is basically "subject→predicate→object". At the same time, Chinese pays attention to parataxis, and there is no need to be clear in expression with meaningful cohesion, which increases the difficulty of Chinese Japanese machine translation. When there are multiple verbs or modifier components in complex long sentences, machine translation usually can not accurately divide the components of the sentence, resulting in the result that the translation is completely unreadable. <br />
<br />
(2) Difficulties of Japanese in machine translation <br />
<br />
Both Chinese and Japanese languages use Chinese characters, and most machines produce the target language through the corresponding translation of Chinese characters. However, pseudonyms in Japanese play an important role in judging the part of speech and meaning, and can not only recognize Chinese characters and judge the structure and semantics of sentences. Japanese vocabulary is composed of Chinese vocabulary, inherent vocabulary and foreign vocabulary. Among them, the first two have a great impact on Chinese Japanese machine translation. For example "提出" is translated as "提出する" or "打ち出す"; and "重视" is translated as "重視する" or "大切にする". Japanese is an adhesive language, which contains a lot of "けど", "が" and "けれども" Some of them are transitional and progressive structures, but there are also some sequential expressions that do not need translation, and these translation software often can not accurately grasp them.<br />
<br />
===References===<br />
[1] Navroz Kaur Kahlon,(2021(prepublish));Williamjeet Singh.Machine translation from text to sign language: a systematic review[J].Universal Access in the Information Society,1-35.<br />
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[2] Cao Qianyu;Hao Hanmei,(2021);Ahmed Syed Hassan.A Chaotic Neural Network Model for English Machine Translation Based on Big Data Analysis[J].Computational Intelligence and Neuroscience,3274326-3274326.<br />
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[3]Hwang Yongkeun;Kim Yanghoon;Jung Kyomin.(2021)Context-Aware Neural Machine Translation for Korean Honorific Expressions[J].Electronics,10(13):1589-1589.<br />
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[4]Zakaryia Almahasees.(2021)Analysing English-Arabic Machine Translation:Google Translate, Microsoft Translator and Sakhr.<br />
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[5](2021)Machine learning in translation[J].Nature Biomedical Engineering,5(6):485-486.<br />
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[6]Shaimaa Marzouk.(2021(prepublish))An in-depth analysis of the individual impact of controlled language rules on machine translation output: a mixed-methods approach[J].Machine Translation,1-37.<br />
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[7]Welnitzová Katarína;Munková Daša.(2021)Sentence-structure errors of machine translation into Slovak[J].Topics in Linguistics,22(1):78-92.<br />
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[8]Xu Xueyuan.(2021).Machine learning-based prediction of urban soil environment and corpus translation teaching[J].Arabian Journal of Geosciences,14(11). <br />
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[9]Chen Bingchang 陈丙昌(2016).機械翻訳の誤訳分析【D】.Error analysis of mechanical translation.贵州大学.2016(05) <br />
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[10]Lv Yinqiu 呂寅秋(1996).機械翻訳の言語規則と伝統文法との相違点.【D】The language rules of mechanical translation, the traditional grammar, and the points of contradiction.日本学研究.Japanese Studies.1996(00):21-22 <br />
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[11]Liu Jun 刘君(2014).基于语料库的中日同形词词义用法对比及其日中机器翻译研究【D】.A Corpus-based Comparison of the Meanings of Chinese and Japanese Homographs and Research on Japanese-Chinese Machine Translation.广西大学.(03) <br />
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[12]Cun Qianqian 崔倩倩(2019).机器翻译错误与译后编辑策略研究【D】.Research on Machine Translation Errors and Post-Editing Strategies.北京外国语大学.(09) <br />
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[13]Zhang Yi 张义(2019).机器翻译的译文分析【D】.Translation analysis of machine translation.西安外国语大学.(10) <br />
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[14]Zhang Linqian 张琳婧(2019).在线机器翻译中日翻译错误原因及对策【D】.Causes and countermeasures of online machine translation errors in Chinese-Japanese translation.山西大学.(02)<br />
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[15]Wang Dan 王丹(2020).基于机器翻译的专利文本译后编辑对策研究【D】.Research on countermeasures for post-translational editing of patent texts based on machine translation.大连理工大学.(06)<br />
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[16]Yang Xiaokun 杨晓琨(2020).日中机器翻译中的前编辑规则与效果验证【D】.Pre-editing rules and effect verification in Japanese-Chinese machine translation.大连理工大学.(06)<br />
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[17]Zuo Jia 左嘉(2021). 机器翻译日译汉误译研究【D】. Research on Mistranslation of Machine Translation from Japanese to Chinese.北京第二外国语学院.<br />
<br />
[18]Guan Biying 关碧莹(2018).关于政治类发言的汉日机器翻译误译分析【D】.Analysis of Chinese-Japanese Machine Translation Mistranslations of Political Speeches.哈尔滨理工大学.<br />
<br />
[19]Che Tong 车彤(2021).汉译日机器翻译质量评估及译后编辑策略研究【D】.Research on Quality Evaluation of Chinese-Japanese Machine Translation and Post-translation Editing Strategies.北京外国语大学.(09)<br />
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Networking Linking<br />
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http://www.elecfans.com/rengongzhineng/692245.html<br />
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https://baike.baidu.com/item/%E6%9C%BA%E5%99%A8%E7%BF%BB%E8%AF%91/411793<br />
<br />
=13 陈湘琼Chen Xiangqiong（Study on Post-editing from the Perspective of Functional Equivalence Theory ）=<br />
[[Machine_Trans_EN_13]]<br />
<br />
=14 Bi bi Nadia（Machine Translation a Challenge for Human Translators)=<br />
[[Machine_Trans_EN_14]]<br />
<br />
===Abstract===<br />
Machine translation is a big obstacle in the way of Human translators or interpretors although it is quick and less time consuming.people are trying to get translation of their target language using through source language.For this purpose they are using digital apps like Google translation Google translation does not give accurate and exact interpretation.Google translator is translates word to word translate that doesn't clarifies it's true and actual meaning.On the other hand human translators can give exact and accurate translation ,they take care of grammatical errors , diction and sentence structure.They clarify the purpose of target language through using source language.<br />
<br />
===Keywords===<br />
Descriptive translation, academic, interdiscipline, comparative literature, localization,Translatology,school of thought,translation , studies, linguistics, corresponding.<br />
<br />
===Body of article===<br />
Translation is the process of reworking text from one language into another to maintain the original message and communication. But, like everything else, there are different methods of translation, and they vary in form and function. What is translation? The term has become widely used among knowledge transfer researchers and practitioners, especially in the fields of health and health care. In a landmark review, Jonathan Lomas began to argue that 'The tasks… may be defined as to establish and maintain links between researchers and their audience, via the appropriate translation of research findings' (Lomas 1997, p 4). In 2004, the World Health Organization's World Report on Knowledge for Better Health suggested that 'One of the key contributions of research to health systems is the translation of knowledge into actions' (WHO 2004, p 33 and p 100). By 2006, special issues of WHO's Bulletin as well as the journals Evaluation and the Health Professions and the Journal of Continuing Education in the Health Professions were dedicated to translation.But what does 'translation' mean? It may be a new word for an old problem, meaning nothing more than 'transfer'. The rapidly emerging field of 'translational medicine' seems to take translation to mean generally what transfer might have meant, that is the transmission of knowledge and evidence 'from bench to bedside'. As one commentator has observed, "Translational medicine" as a fashionable term is being increasingly used to describe the wish of biomedical researchers to ultimately help patients' (Wehling 2008, abstract). <br />
Semantic uncertainty persists, not least because of the different interests of scientists, clinicians, patients and commercial firms (Littman et al 2007): 'Translational research means different things to different people, but it seems important to almost everyone' (Woolf 2008, p 211).<br />
In related fields, such as public health (Armstrong et al 2006), 'translation' seems to signify dissatisfaction with 'transfer'. It wants to move away from thinking of knowledge transfer as a form of technology transfer or dissemination, rejecting if only by implication its mechanistic assumptions and its model of linear messaging from A to B. But still, what does it signify?<br />
<br />
===Why translation?===<br />
'Translation' indicates a closer attention to the problem of shared meaning and how it might be developed. It seems to represent some new epistemological lubricant, facilitating the dissemination of texts and the application and use of the knowledge and information they in. Simply, translation might be the key to transfer. And yet, when we stop to think, we are more ambivalent. What is translated often seems somehow inferior, not real or original. Note how readily commentators reach for the idea that things might be 'lost in translation'. Knowing at a distance – made in and mediated by translation - makes for incomplete renditions, blurred images, partial truths. So what might 'translation' really mean? The purpose of this paper is to set out, for policy makers and practitioners, the theoretical and conceptual resources translation holds and seems to represent. In doing so, it explores understandings of translation in the fields of literature and linguistics and in the sociology of science and technology. It begins by setting out just why this idea of translation should make immediate, intuitive sense in relation to research, policy and practice.<br />
1translation in research, policy and practice research as translation<br />
Research often entails translation from one language to another: where data is collected from more than one ethnic group, for example, or where the language of the researcher is other than that of the research subject. It may draw on a secondary literature or source documents written in different languages, and may be published and disseminated in languages other than the one in which it is first written up.<br />
2In a different way, to conduct an interview is to ask for an account of experience and its meanings, but it is also to construct and translate that experience in terms defined at least in part by the researcher. In representing what is said, transcripts then select data, usually excluding significant gesture and eye-contact, for example. Often, certain characteristics of speech-acts (such as hesitations) will be edited out. In turn, the format of the transcript shapes the analytic use the researcher may make of it. The basis of research 'findings', then, is an artefact, a transcript or translation, not an original interaction (Ochs 1979, Barnes, Bloor and Henry 1996, Ross 2009).<br />
3In this way, the researcher recasts aspects of his or her problem or topic in new, scientific form: 'All researchers "translate" the experiences of others' (Temple 1997, p 609). Research is invariably conducted in a sort of 'metalanguage' (Hantrais and Ager 1985): the research process can be conceived as one of successive translations (from theoretical formulation to operationalization, transcription, interpretation and dissemination). Theorization is a process of reciprocal back and forth between theory and fact, in which conceptions of each are revised in order that one fit the other (Baldamus 1974).<br />
4 It is a kind of translation: a rereading, re-use, re-application or re-representation of what we know in new terms (Turner 1980). Referencing, too, is an act of translation, a form of appropriation and incorporation of one text by another (Gilbert 1977).policy as translation Each of the fields covered by the paper is diverse and ill-defined, and there is no intention here to provide a comprehensive account of any them. Sources have been chosen for their relevance: main references are cited in the text, and additional sources listed in footnotes.<br />
<br />
2For a brief introduction to the technical issues involved in social research in more than one language, see Birbili (2000). For translation issues in survey research and question design in general, see Ervin and Bower (1952) and Deutscher (1968); on translating survey research instruments (in this instance health-related quality of life measures), Bowden and Fox-Rushby (2003). On the use of translators and interpreters, see Temple (1997) and Jentsch (1998).<br />
3For an interesting discussion of this problem, see Bourdieu (1999), esp pp 621-626, 'The risks of writing'. <br />
===Difference between machine translation and human translators===<br />
Few people disagree on the differences between the two, but many argue over the quality of the translations. How accurate are machine translations? How reliable are human translations? Some say machine translation produces near-perfect translations while others are adamant that translations are incomprehensible and cause more problems than they solve. Results will, of course, vary depending on the source and target languages, the machine translation service used (e.g. Google Translate), and the complexity of the original text.<br />
Machine translation, love it or hate it, is here to stay. In fact, the machine translation market is growing at such a fast pace that it is predicted to reach $980 million by 2022.<br />
===Machine translation: the pros and cons===<br />
The advantages of machine translation generally come down to two factors: it’s faster and cheaper. The downside to this is the standard of translation can be anywhere from inaccurate, to incomprehensible, and potentially dangerous (more on that shortly).<br />
==The advantages of machine translation==<br />
Many free tools are readily available (Google Translate, Skype Translator, etc.)<br />
Quick turnaround time <br />
You can translate between multiple languages using one tool<br />
Translation technology is constantly improving<br />
The disadvantages of machine translation<br />
Level of accuracy can be very low <br />
Accuracy is also very inconsistent across different languages<br />
==Machines can't translate context ==<br />
Mistakes are sometimes costly <br />
Sometimes translation simply doesn’t work<br />
The most important thing to consider with any kind of translation is the cost of potential mistakes. Translating instructions for medical equipment, aviation manuals, legal documents and many other kinds of content require 100% accuracy. In such cases, mistakes can cost lives, huge amounts of money and irreparable damage to your company’s image. So choose carefully!<br />
Human translation: the pros and cons<br />
Human translation essentially switches the table in terms of pros and cons. A higher standard of accuracy comes at the price of longer turnaround times and higher costs. What you have to decide is whether that initial investment outweighs the potential cost of mistakes. Alternatively, whether mistakes simply aren’t an option, like the cases we looked at in the previous section.<br />
<br />
==The advantages of human translation ==<br />
It’s a translator’s job to ensure the highest accuracy<br />
Humans can interpret context and capture the same meaning, rather than simply translating words<br />
Human translators can review their work and provide a quality process<br />
Humans can interpret the creative use of language, e.g. puns, metaphors, slogans, etc.<br />
Professional translators understand the idiomatic differences between their languages<br />
Humans can spot pieces of content where literal translation isn’t possible and find the most suitable alternative<br />
The disadvantages of human translation<br />
Turnaround time is longer <br />
Translators rarely work for free <br />
Unless you use a translation agency, with access to thousands of translators, you’re limited to the languages any one translator can work with<br />
Simply put, human translation is your best option when accuracy is even remotely important. Other considerations to make are the complexity of your source material and the two languages you’re translating between – both of which can render machines pretty useless.<br />
<br />
When to use machine and human translation<br />
The truth is, the debate over machine vs human translation is an unnecessary distraction. What we should really be talking about is when to use these two different types of translation services, because they both serve a very valid purpose.<br />
<br />
Examples of when to use machine translation<br />
When you have a large bulk of content to translate and the general meaning is enough<br />
When your translation never reaches the final audience, e.g. you’re translating a resource as research for another piece of content<br />
Translating documents for internal use within a company, provided 100% accuracy isn’t needed<br />
To partially translate large chunks of content for a human translator to improve upon<br />
Examples of when to use human translation<br />
When accuracy is important<br />
Most cases where your translated content is received by a consumer audience<br />
When you have a duty of care to provide accurate translations (e.g. legal documents, product instructions, medical guidelines or health and safety content)<br />
When translating marketing material or other texts for creative language uses.<br />
<br />
types of machine translation.<br />
<br />
What is Machine Translation? Rule Based Machine Translation vs. Statistical Machine Translation. Machine translation (MT) is automated translation. It is the process by which computer software is used to translate a text from one natural language (such as English) to another (such as Spanish).<br />
<br />
To process any translation, human or automated, the meaning of a text in the original (source) language must be fully restored in the target language, i.e. the translation. While on the surface this seems straightforward, it is far more complex. Translation is not a mere word-for-word substitution. A translator must interpret and analyze all of the elements in the text and know how each word may influence another. This requires extensive expertise in grammar, syntax (sentence structure), semantics (meanings), etc., in the source and target languages, as well as familiarity with each local region.<br />
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Human and machine translation each have their share of challenges. For example, no two individual translators can produce identical translations of the same text in the same language pair, and it may take several rounds of revisions to meet customer satisfaction. But the greater challenge lies in how machine translation can produce publishable quality translations.<br />
<br />
Rule-Based Machine Translation Technology<br />
Rule-based machine translation relies on countless built-in linguistic rules and millions of bilingual dictionaries for each language pair.<br />
The software parses text and creates a transitional representation from which the text in the target language is generated. This process requires extensive lexicons with morphological, syntactic, and semantic information, and large sets of rules. The software uses these complex rule sets and then transfers the grammatical structure of the source language into the target language.<br />
Translations are built on gigantic dictionaries and sophisticated linguistic rules. Users can improve the out-of-the-box translation quality by adding their terminology into the translation process. They create user-defined dictionaries which override the system’s default settings.<br />
In most cases, there are two steps: an initial investment that significantly increases the quality at a limited cost, and an ongoing investment to increase quality incrementally. While rule-based MT brings companies to the quality threshold and beyond, the quality improvement process may be long and expensive.<br />
<br />
Statistical Machine Translation Technology<br />
Statistical machine translation utilizes statistical translation models whose parameters stem from the analysis of monolingual and bilingual corpora. Building statistical translation models is a quick process, but the technology relies heavily on existing multilingual corpora. A minimum of 2 million words for a specific domain and even more for general language are required. Theoretically it is possible to reach the quality threshold but most companies do not have such large amounts of existing multilingual corpora to build the necessary translation models. Additionally, statistical machine translation is CPU intensive and requires an extensive hardware configuration to run translation models for average performance levels.<br />
<br />
Rule-Based MT vs. Statistical MT<br />
Rule-based MT provides good out-of-domain quality and is by nature predictable. Dictionary-based customization guarantees improved quality and compliance with corporate terminology. But translation results may lack the fluency readers expect. In terms of investment, the customization cycle needed to reach the quality threshold can be long and costly. The performance is high even on standard hardware.<br />
<br />
Statistical MT provides good quality when large and qualified corpora are available. The translation is fluent, meaning it reads well and therefore meets user expectations. However, the translation is neither predictable nor consistent. Training from good corpora is automated and cheaper. But training on general language corpora, meaning text other than the specified domain, is poor. Furthermore, statistical MT requires significant hardware to build and manage large translation models.<br />
<br />
Rule-Based MT Statistical MT<br />
+ Consistent and predictable quality – Unpredictable translation quality<br />
+ Out-of-domain translation quality – Poor out-of-domain quality<br />
+ Knows grammatical rules – Does not know grammar <br />
+ High performance and robustness – High CPU and disk space requirements<br />
+ Consistency between versions – Inconsistency between versions <br />
– Lack of fluency + Good fluency<br />
– Hard to handle exceptions to rules + Good for catching exceptions to rules <br />
– High development and customization costs + Rapid and cost-effective development costs provided the required corpus exists<br />
Given the overall requirements, there is a clear need for a third approach through which users would reach better translation quality and high performance (similar to rule-based MT), with less investment (similar to statistical MT).<br />
Post-Edited Machine Translation (PEMT)<br />
Often, PEMT is used to bridge the gap between the speed of machine translation and the quality of human translation, as translators review, edit and improve machine-translated texts. PEMT services cost more than plain machine translations but less than 100% human translation, especially since the post-editors don’t have to be fluently bilingual—they just have to be skilled proofreaders with some experience in the language and target region.<br />
Successful translation is about more than just the words, which is why we advocate for not just human translation by skilled linguists, but for translation by people deeply familiar with the cultures they’re writing for. Life experience, study and the knowledge that only comes from living in a geographic region can make the difference between words that are understandable and language that is capable of having real, positive impact. <br />
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PacTranz<br />
The HUGE list of 51 translation types, methods and techniques<br />
Upper section of infographic of 51 common types of translation classified in 4 broad categoriesThere are a bewildering number of different types of translation.<br />
So we’ve identified the 51 types you’re most likely to come across, and explain exactly what each one means.<br />
This includes all the main translation methods, techniques, strategies, procedures and areas of specialisation.<br />
It’s our way of helping you make sense of the many different kinds of translation – and deciding which ones are right for you.<br />
Don’t miss our free summary pdf download later in the article!<br />
The 51 types of translation we’ve identified fall neatly into four distinct categories.<br />
Translation Category A: 15 types of translation based on the technical field or subject area of the text<br />
Icons representing 15 types of translation categorised by the technical field or subject area of the textTranslation companies often define the various kinds of translation they provide according to the subject area of the text.<br />
This is a useful way of classifying translation types because specialist texts normally require translators with specialist knowledge.<br />
Here are the most common types you’re like to come across in this category.<br />
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1. General Translation<br />
What is it?<br />
The translation of non-specialised text. That is, text that we can all understand without needing specialist knowledge in some area.<br />
The text may still contain some technical terms and jargon, but these will either be widely understood, or easily researched.<br />
What this means<br />
The implication is that you don’t need someone with specialist knowledge for this type of translation – any professional translator can handle them.<br />
Translators who only do this kind of translation (don’t have a specialist field) are sometimes referred to as ‘generalist’ or ‘general purpose’ translators.<br />
Examples<br />
Most business correspondence, website content, company and product/service info, non-technical reports.<br />
Most of the rest of the translation types in this Category do require specialist translators.<br />
Check out our video on 13 types of translation requiring special translator expertise:<br />
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2. Technical Translation<br />
What is it?<br />
We use the term “technical translation” in two different ways:<br />
Broad meaning: any translation where the translator needs specialist knowledge in some domain or area.<br />
This definition would include almost all the translation types described in this section.<br />
Narrow meaning: limited to the translation of engineering (in all its forms), IT and industrial texts.<br />
This narrower meaning would exclude legal, financial and medical translations for example, where these would be included in the broader definition.<br />
What this means<br />
Technical translations require knowledge of the specialist field or domain of the text.<br />
That’s because without it translators won’t completely understand the text and its implications. And this is essential if we want a fully accurate and appropriate translation.Good to know Many technical translation projects also have a typesetting/dtp requirement. Be sure your translation provider can handle this component, and that you’ve allowed for it in your project costings and time frames.<br />
Examples<br />
Manuals, specialist reports, product brochures<br />
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3. Scientific Translation<br />
What is it?<br />
The translation of scientific research or documents relating to it.<br />
What this means<br />
These texts invariably contain domain-specific terminology, and often involve cutting edge research.<br />
So it’s imperative the translator has the necessary knowledge of the field to fully understand the text. That’s why scientific translators are typically either experts in the field who have turned to translation, or professionally qualified translators who also have qualifications and/or experience in that domain.<br />
On occasion the translator may have to consult either with the author or other domain experts to fully comprehend the material and so translate it appropriately.<br />
Examples<br />
Research papers, journal articles, experiment/trial results<br />
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4. Medical Translation<br />
What is it?<br />
The translation of healthcare, medical product, pharmaceutical and biotechnology materials.<br />
Medical translation is a very broad term covering a wide variety of specialist areas and materials – everything from patient information to regulatory, marketing and technical documents.<br />
As a result, this translation type has numerous potential sub-categories – ‘medical device translations’ and ‘clinical trial translations’, for example.<br />
What this means<br />
As with any text, the translators need to fully understand the materials they’re translating. That means sound knowledge of medical terminology and they’ll often also need specific subject-matter expertise.<br />
Good to know<br />
Many countries have specific requirements governing the translation of medical device and pharmaceutical documentation. This includes both your client-facing and product-related materials.<br />
Examples<br />
Medical reports, product instructions, labeling, clinical trial documentation<br />
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5. Financial Translation<br />
What is it?<br />
In broad terms, the translation of banking, stock exchange, forex, financing and financial reporting documents.<br />
However, the term is generally used only for the more technical of these documents that require translators with knowledge of the field.<br />
Any competent translator could translate a bank statement, for example, so that wouldn’t typically be considered a financial translation.<br />
What this means<br />
You need translators with domain expertise to correctly understand and translate the financial terminology in these texts.<br />
Examples<br />
Company accounts, annual reports, fund or product prospectuses, audit reports, IPO documentation<br />
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6. Economic Translations<br />
What is it?<br />
1. Sometimes used as a synonym for financial translations.<br />
2. Other times used somewhat loosely to refer to any area of economic activity – so combining business/commercial, financial and some types of technical translations.<br />
3. More narrowly, the translation of documents relating specifically to the economy and the field of economics.<br />
What this means<br />
As always, you need translators with the relevant expertise and knowledge for this type of translation.<br />
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7. Legal Translation<br />
What is it?<br />
The translation of documents relating to the law and legal process.<br />
What this means<br />
Legal texts require translators with a legal background.<br />
That’s because without it, a translator may not:<br />
– fully understand the legal concepts<br />
– write in legal style<br />
– understand the differences between legal systems, and how best to translate concepts that don’t correspond.<br />
And we need all that to produce professional quality legal translations – translations that are accurate, terminologically correct and stylistically appropriate.<br />
Examples<br />
Contracts, legal reports, court judgments, expert opinions, legislation<br />
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8. Juridical Translation<br />
What is it?<br />
1. Generally used as a synonym for legal translations.<br />
2. Alternatively, can refer to translations requiring some form of legal verification, certification or notarization that is common in many jurisdictions.<br />
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9. Judicial Translation<br />
What is it?<br />
1. Most commonly a synonym for legal translations.<br />
2. Rarely, used to refer specifically to the translation of court proceeding documentation – so judgments, minutes, testimonies, etc. <br />
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10. Patent Translation<br />
What is it?<br />
The translation of intellectual property and patent-related documents.<br />
Key features<br />
Patents have a specific structure, established terminology and a requirement for complete consistency throughout – read more on this here. These are key aspects to patent translations that translators need to get right.<br />
In addition, subject matter can be highly technical.<br />
What this means<br />
You need translators who have been trained in the specific requirements for translating patent documents. And with the domain expertise needed to handle any technical content.<br />
Examples<br />
Patent specifications, prior art documents, oppositions, opinions<br />
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11. Literary Translation<br />
What is it?<br />
The translation of literary works – novels, short stories, plays, essays, poems.<br />
Key features<br />
Literary translation is widely regarded as the most difficult form of translation.<br />
That’s because it involves much more than simply conveying all meaning in an appropriate style. The translator’s challenge is to also reproduce the character, subtlety and impact of the original – the essence of what makes that work unique.<br />
This is a monumental task, and why it’s often said that the translation of a literary work should be a literary work in its own right.<br />
What this means<br />
Literary translators must be talented wordsmiths with exceptional creative writing skills.<br />
Because few translators have this skillset, you should only consider dedicated literary translators for this type of translation.<br />
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<br />
12. Commercial Translation<br />
What is it?<br />
The translation of documents relating to the world of business.<br />
This is a very generic, wide-reaching translation type. It includes other more specialised forms of translation – legal, financial and technical, for example. And all types of more general business documentation.<br />
Also, some documents will require familiarity with business jargon and an ability to write in that style.<br />
What this means<br />
Different translators will be required for different document types – specialists should handle materials involving technical and specialist fields, whereas generalist translators can translate non-specialist materials.<br />
Examples<br />
Business correspondence, reports, marketing and promotional materials, sales proposals<br />
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13. Business Translations<br />
What is it?<br />
A synonym for Commercial Translations.<br />
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14. Administrative Translations<br />
What is it?<br />
The translation of business management and administration documents.<br />
So it’s a subset of business / commercial translations.<br />
What this means<br />
The implication is these documents will include business jargon and ‘management speak’, so require a translator familiar with, and practised at, writing in that style.<br />
Examples<br />
Management reports and proposals<br />
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15. Marketing Translations<br />
What is it?<br />
The translation of advertising, marketing and promotional materials.<br />
This is a subset of business or commercial translations.<br />
Key features<br />
Marketing copy is designed to have a specific impact on the audience – to appeal and persuade.<br />
So the translated copy must do this too.<br />
But a direct translation will seldom achieve this – so translators need to adapt their wording to produce the impact the text is seeking.<br />
And sometimes a completely new message might be needed – see transcreation in our next category of translation types.<br />
What this means<br />
Marketing translations require translators who are skilled writers with a flair for producing persuasive, impactful copy.<br />
As relatively few translators have these skills, engaging the right translator is key.<br />
Good to know<br />
This type of translation often comes with a typesetting or dtp requirement – particularly for adverts, posters, brochures, etc.<br />
Its best for your translation provider to handle this component. That’s because multilingual typesetters understand the design and aesthetic conventions in other languages/cultures. And these are essential to ensure your materials have the desired impact and appeal in your target markets.<br />
Examples<br />
Advertising, brochures, some website/social media text.<br />
Translation Category B: 14 types of translation based on the end product or use of the translation<br />
This category is all about how the translation is going to be used or the end product that’s produced.<br />
Most of these types involve either adapting or processing a completed translation in some way, or converting or incorporating it into another program or format.<br />
You’ll see that some are very specialised, and complex.<br />
It’s another way translation providers refer to the range of services they provide.<br />
Check out our video of the most specialised of these types of translation:<br />
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16. Document Translations<br />
What is it?<br />
The translation of documents of all sorts.<br />
Here the translation itself is the end product and needs no further processing beyond standard formatting and layout.<br />
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17. Text Translations<br />
What is it?<br />
A synonym for document translation.<br />
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18. Certified Translations<br />
What is it?<br />
A translation with some form of certification.<br />
Key features<br />
The certification can take many forms. It can be a statement by the translation company, signed and dated, and optionally with their company seal. Or a similar certification by the translator.<br />
The exact format and wording will depend on what clients and authorities require – here’s an example.<br />
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19. Official Translations<br />
What is it?<br />
1. Generally used as a synonym for certified translations.<br />
2. Can also refer to the translation of ‘official’ documents issued by the authorities in a foreign country. These will almost always need to be certified.<br />
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20. Software Localisation<br />
What is it?<br />
Adapting software for another language/culture.<br />
Key features<br />
The goal of software localisation is not just to make the program or product available in other languages. It’s also about ensuring the user experience in those languages is as natural and effective as possible.<br />
Translating the user interface, messaging, documentation, etc is a major part of the process.<br />
Also key is a customisation process to ensure everything matches the conventions, norms and expectations of the target cultures.<br />
Adjusting time, date and currency formats are examples of simple customisations. Others might involve adapting symbols, graphics, colours and even concepts and ideas.<br />
Localisation is often preceded by internationalisation – a review process to ensure the software is optimally designed to handle other languages.<br />
And it’s almost always followed by thorough testing – to ensure all text is in the correct place and fits the space, and that everything makes sense, functions as intended and is culturally appropriate.<br />
Localisation is often abbreviated to L10N, internationalisation to i18n.<br />
What this means<br />
Software localisation is a specialised kind of translation, and you should always engage a company that specialises in it.<br />
They’ll have the systems, tools, personnel and experience needed to achieve top quality outcomes for your product.<br />
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21. Game Localisation<br />
What is it?<br />
Adapting games for other languages and markets.<br />
<br />
It’s a subset of software localisation.<br />
Key features<br />
The goal of game localisation is to provide an engaging and fun gaming experience for speakers of other languages.<br />
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It involves translating all text and recording any required foreign language audio.<br />
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But also adapting anything that would clash with the target culture’s customs, sensibilities and regulations.<br />
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For example, content involving alcohol, violence or gambling may either be censored or inappropriate in the target market.<br />
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And at a more basic level, anything that makes users feel uncomfortable or awkward will detract from their experience and thus the success of the game in that market.<br />
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So portions of the game may have to be removed, added to or re-worked.<br />
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Game localisation involves at least the steps of translation, adaptation, integrating the translations and adaptations into the game, and testing.<br />
<br />
What this means<br />
Game localisation is a very specialised type of translation best left to those with specific expertise and experience in this area.<br />
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22. Multimedia Localisation<br />
What is it?<br />
Adapting multimedia for other languages and cultures.<br />
<br />
Multimedia refers to any material that combines visual, audio and/or interactive elements. So videos and movies, on-line presentations, e-Learning courses, etc.<br />
Key features<br />
Anything a user can see or hear may need localising.<br />
<br />
That means the audio and any text appearing on screen or in images and animations.<br />
<br />
Plus it can mean reviewing and adapting the visuals and/or script if these aren’t suitable for the target culture.<br />
<br />
The localisation process will typical involve:<br />
– Translation<br />
– Modifying the translation for cultural reasons and/or to meet technical requirements<br />
– Producing the other language versions<br />
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Audio output may be voice-overs, dubbing or subtitling.<br />
<br />
And output for visuals can involve re-creating elements, or supplying the translated text for the designers/engineers to incorporate.<br />
<br />
What this means<br />
Multimedia localisation projects vary hugely, and it’s essential your translation providers have the specific expertise needed for your materials.<br />
<br />
<br />
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23. Script Translations<br />
What is it?<br />
Preparing the text of recorded material for recording in other languages.<br />
Key features<br />
There are several issues with script translation.<br />
<br />
One is that translations typically end up longer than the original script. So voicing the translation would take up more space/time on the video than the original language.<br />
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Sometimes that space will be available and this will be OK.<br />
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But generally it won’t be. So the translation has to be edited back until it can be comfortably voiced within the time available on the video.<br />
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Another challenge is the translation may have to synchronise with specific actions, animations or text on screen.<br />
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Also, some scripts also deal with technical subject areas involving specialist technical terminology.<br />
<br />
Finally, some scripts may be very culture-specific – featuring humour, customs or activities that won’t work well in another language. Here the script, and sometimes also the associated visuals, may need to be adjusted before beginning the translation process.<br />
<br />
It goes without saying that a script translation must be done well. If it’s not, there’ll be problems producing a good foreign language audio, which will compromise the effectiveness of the video.<br />
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Translators typically work from a time-coded transcript. This is the original script marked to show the time available for each section of the translation.<br />
<br />
What this means<br />
There are several potential pitfalls in script translations. So it’s vital your translation provider is practiced at this type of translation and able to handle any technical content.<br />
<br />
<br />
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24. Voice-over and Dubbing Projects<br />
What is it?<br />
Translation and recording of scripts in other languages.<br />
<br />
Voice-overs vs dubbing<br />
There is a technical difference.<br />
A voice-over adds a new track to the production, dubbing replaces an existing one.<br />
<br />
<br />
Key features<br />
These projects involve two parts:<br />
– a script translation (as described above), and<br />
– producing the audio<br />
<br />
So they involve the combined efforts of translators and voice artists.<br />
The task for the voice artist is to produce a high quality read. That’s one that matches the style, tone and richness of the original.<br />
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Often each section of the new audio will need to be the same length as the original.<br />
<br />
But sometimes the segments will need to be shorter – for example where the voice-over lags the original by a second or two. This is common in interviews etc, where the original voice is heard initially then drops out.<br />
<br />
The most difficult form of dubbing is lip-syncing – where the new audio needs to synchronise with the original speaker’s lip movements, gestures and actions.<br />
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Lip-syncing requires an exceptionally skilled voice talent and considerable time spent rehearsing and fine tuning the translation.<br />
<br />
What this means<br />
You need to use experienced professionals every step of the way in this type of project.<br />
<br />
That’s to ensure firstly that your foreign-language scripts are first class, then that the voicing is of high professional standard.<br />
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Anything less will mean your foreign language versions will be way less effective and appealing to your target audience.<br />
<br />
<br />
<br />
25. Subtitle Translations<br />
What is it?<br />
Producing foreign language captions for sub or surtitles.<br />
Key features<br />
The goal with subtitling is to produce captions that viewers can comfortably read in the time available and still follow what’s happening on the video.<br />
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To achieve this, languages have “rules” governing the number of characters per line and the minimum time each subtitle should display.<br />
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Sticking to these guidelines is essential if your subtitles are to be effective.<br />
<br />
But this is no easy task – it requires simple language, short words, and a very succinct style. Translators will spend considerable time mulling over and re-working their translation to get it just right.<br />
<br />
Most subtitle translators use specialised software that will output the captions in the format sound engineers need for incorporation into the video.<br />
<br />
What this means<br />
As with other specialised types of translation, you should only use translators with specific expertise and experience in subtitling.<br />
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<br />
26. Website Localisation<br />
What is it?<br />
The translation and adapting of relevant content on a website to best suit the target language and culture.<br />
<br />
Note: Many providers use the term website translation as a synonym for localisation. Strictly speaking though, translation is just one part of localisation.<br />
Key features<br />
<br />
Not all pages on a website may need to be localised – clients should review their content to identify what’s relevant for the other language versions.<br />
Some content may need specialist translators – legal and technical pages for example.<br />
There may also be videos, linked documents, and text or captions in graphics to translate.<br />
Adaptation can mean changing date, time, currency and number formats, units of measure, etc.<br />
But also images, colours and even the overall site design and style if these won’t have the desired impact in the target culture.<br />
Translated files can be supplied in a wide range of formats – translators usually coordinate output with the site webmasters.<br />
New language versions are normally thoroughly reviewed and tested before going live to confirm everything is displaying correctly, works as intended and is cultural appropriate.<br />
What this means<br />
The first step should be to review your content and identify what needs to be translated. This might lead you to modify some pages for the foreign language versions.<br />
<br />
In choosing your translation providers be sure they can:<br />
– handle any technical or legal content,<br />
– provide your webmaster with the file types they want.<br />
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And you should always get your translators to systematically review the foreign language versions before going live.<br />
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<br />
<br />
27. Transcreation<br />
What is it?<br />
Adapting a message to elicit the same emotional response in another language and culture.<br />
Translation is all about conveying the message or meaning of a text in another language. But sometimes that message or meaning won’t have the desired effect in the target culture.<br />
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This is where transcreation comes in. Transcreation creates a new message that will get the desired emotional response in that culture, while preserving the style and tone of the original.<br />
<br />
So it’s a sort of creative translation – which is where the word comes from, a combination of ‘translation’ and ‘creation’.<br />
<br />
At one level transcreation may be as simple as choosing an appropriate idiom to convey the same intent in the target language – something translators do all the time.<br />
<br />
But mostly the term is used to refer to adapting key advertising and marketing messaging. Which requires copywriting skills, cultural awareness and an excellent knowledge of the target market.<br />
<br />
Who does it?<br />
Some translation companies have suitably skilled personnel and offer transcreation services.<br />
<br />
Often though it’s done in the target country by specialist copywriters or an advertising or marketing agency – particularly for significant campaigns and to establish a brand in the target marketplace.<br />
<br />
What this means<br />
Most general marketing and promotional texts won’t need transcreation – they can be handled by a translator with excellent creative writing skills.<br />
<br />
But slogans, by-lines, advertising copy and branding statements often do.<br />
<br />
Whether you should opt for a translation company or an in-market agency will depend on the nature and importance of the material, and of course your budget.<br />
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<br />
<br />
28. Audio Translations<br />
What is it?<br />
Broad meaning: the translation of any type of recorded material into another language.<br />
<br />
More commonly: the translation of a foreign language video or audio recording into your own language. So this is where you want to know and document what a recording says.<br />
Key features<br />
The first challenge with audio translations is it’s often impossible to pick up every word that’s said. That’s because audio quality, speech clarity and speaking speed can all vary enormously.<br />
<br />
It’s also a mentally challenging task to listen to an audio and translate it directly into another language. It’s easy to miss a word or an aspect of meaning.<br />
<br />
So best practice is to first transcribe the audio (type up exactly what is said in the language it is spoken in), then translate that transcription.<br />
<br />
However, this is time consuming and therefore costly, and there are other options if lesser precision is acceptable.<br />
<br />
What this means<br />
It’s best to discuss your requirements for this kind of translation with your translation provider. They’ll be able to suggest the best translation process for your needs.<br />
<br />
Examples<br />
Interviews, product videos, police recordings, social media videos.<br />
<br />
<br />
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29. Translations with DTP<br />
What is it?<br />
Translation incorporated into graphic design files.multilingual dtp example in the form of a Rubik's Cube with foreign text on each square<br />
Key features<br />
Graphic design programs are used by professional designers and graphic artists to combine text and images to create brochures, books, posters, packaging, etc.<br />
<br />
Translation plus dtp projects involve 3 steps – translation, typesetting, output.<br />
<br />
The typesetting component requires specific expertise and resources – software and fonts, typesetting know-how, an appreciation of foreign language display conventions and aesthetics.<br />
<br />
What this means<br />
Make sure your translation company has the required multilingual typesetting/desktop publishing expertise whenever you’re translating a document created in a graphic design program.<br />
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Translation Category C: 13 types of translation based on the translation method employed<br />
This category has two sub-groups:<br />
– the practical methods translation providers use to produce their translations, and<br />
– the translation strategies/methods identified and discussed within academia.<br />
<br />
The translation methods translation providers use<br />
There are 4 main methods used in the translation industry today. We have an overview of each below, but for more detail, including when to use each one, see our comprehensive blog article.<br />
<br />
Or watch our video.<br />
<br />
Important: If you’re a client you need to understand these 4 methods – choose the wrong one and the translation you end up with may not meet your needs!<br />
<br />
<br />
30. Machine Translation (MT)<br />
What is it?<br />
A translation produced entirely by a software program with no human intervention.<br />
<br />
A widely used, and free, example is Google Translate. And there are also commercial MT engines, generally tailored to specific domains, languages and/or clients.<br />
<br />
Pros and cons<br />
There are two limitations to MT:<br />
– they make mistakes (incorrect translations), and<br />
– quality of wording is patchy (some parts good, others unnatural or even nonsensical)<br />
<br />
On they positive side they are virtually instantaneous and many are free.<br />
<br />
Best suited for:<br />
Getting the general idea of what a text says.<br />
<br />
This method should never be relied on when high accuracy and/or good quality wording is needed.<br />
<br />
<br />
<br />
31. Machine Translation plus Human Editing (PEMT)<br />
What is it?<br />
A machine translation subsequently edited by a human translator or editor (often called Post-editing Machine Translation = PEMT).<br />
<br />
The editing process is designed to rectify some of the deficiencies of a machine translation.<br />
<br />
This process can take different forms, with different desired outcomes. Probably most common is a ‘light editing’ process where the editor ensures the text is understandable, without trying to fix quality of expression.<br />
<br />
Pros and cons<br />
This method won’t necessarily eliminate all translation mistakes. That’s because the program may have chosen a wrong word (meaning) that wasn’t obvious to the editor.<br />
<br />
And wording won’t generally be as good as a professional human translator would produce.<br />
<br />
Its advantage is it’s generally quicker and a little cheaper than a full translation by a professional translator.<br />
<br />
Best suited for:<br />
Translations for information purposes only.<br />
<br />
Again, this method shouldn’t be used when full accuracy and/or consistent, natural wording is needed.<br />
<br />
<br />
<br />
32. Human Translation<br />
What is it?<br />
Translation by a professional human translator.<br />
Pros and cons<br />
Professional translators should produce translations that are fully accurate and well-worded.<br />
<br />
That said, there is always the possibility of ‘human error’, which is why translation companies like us typically offer an additional review process – see next method.<br />
<br />
This method will take a little longer and likely cost more than the PEMT method.<br />
<br />
Best suited for:<br />
Most if not all translation purposes.<br />
<br />
<br />
<br />
33. Human Translation + Revision<br />
What is it?<br />
A human translation with an additional review by a second translator.<br />
<br />
The review is essentially a safety check – designed to pick up any translation errors and refine wording if need be.<br />
<br />
Pros and cons<br />
This produces the highest level of translation quality.<br />
<br />
It’s also the most expensive of the 4 methods, and takes the longest.<br />
<br />
Best suited for:<br />
All translation purposes.<br />
<br />
Gearwheel with 5 practical translation methods written on the teeth <br />
There’s also one other common term used by practitioners and academics alike to describe a type (method) of translation:<br />
<br />
34. Computer-Assisted Translation (CAT)<br />
What is it?<br />
A human translator using computer tools to aid the translation process.<br />
<br />
Key features<br />
Virtually all translators use such tools these days.<br />
<br />
The most prevalent tool is Translation Memory (TM) software. This creates a database of previous translations that can be accessed for future work.<br />
<br />
TM software is particularly useful when dealing with repeated and closely-matching text, and for ensuring consistency of terminology. For certain projects it can speed up the translation process.<br />
<br />
<br />
<br />
The translation methods described by academia<br />
A great deal has been written within academia analysing how human translators go about their craft.<br />
<br />
Seminal has been the work of Newmark, and the following methods of translation attributed to him are widely discussed in the literature.Gearwheel with Newmark's 8 translation methods written on the teeth <br />
These methods are approaches and strategies for translating the text as a whole, not techniques for handling smaller text units, which we discuss in our final translation category.<br />
<br />
<br />
<br />
35. Word-for-word Translation<br />
This method translates each word into the other language using its most common meaning and keeping the word order of the original language.<br />
<br />
So the translator deliberately ignores context and target language grammar and syntax.<br />
<br />
Its main purpose is to help understand the source language structure and word use.<br />
<br />
Often the translation will be placed below the original text to aid comparison.<br />
<br />
<br />
<br />
36. Literal Translation<br />
Words are again translated independently using their most common meanings and out of context, but word order changed to the closest acceptable target language grammatical structure to the original.<br />
<br />
Its main suggested purpose is to help someone read the original text.<br />
<br />
<br />
<br />
37. Faithful Translation<br />
Faithful translation focuses on the intention of the author and seeks to convey the precise meaning of the original text.<br />
<br />
It uses correct target language structures, but structure is less important than meaning.<br />
<br />
<br />
<br />
38. Semantic Translation<br />
Semantic translation is also author-focused and seeks to convey the exact meaning.<br />
<br />
Where it differs from faithful translation is that it places equal emphasis on aesthetics, ie the ‘sounds’ of the text – repetition, word play, assonance, etc.<br />
<br />
In this method form is as important as meaning as it seeks to “recreate the precise flavour and tone of the original” (Newmark).slide showing definition of semantic translation as a translation method<br />
<br />
39. Communicative Translation<br />
Seeks to communicate the message and meaning of the text in a natural and easily understood way.<br />
<br />
It’s described as reader-focused, seeking to produce the same effect on the reader as the original text.<br />
<br />
A good comparison of Communicative and Semantic translation can be found here.<br />
<br />
40. Free Translation<br />
Here conveying the meaning and effect of the original are all important.<br />
<br />
There are no constraints on grammatical form or word choice to achieve this.<br />
<br />
Often the translation will paraphrase, so may be of markedly different length to the original.<br />
<br />
41. Adaptation<br />
Mainly used for poetry and plays, this method involves re-writing the text where the translation would otherwise lack the same resonance and impact on the audience.<br />
<br />
Themes, storylines and characters will generally be retained, but cultural references, acts and situations adapted to relevant target culture ones.<br />
<br />
So this is effectively a re-creation of the work for the target culture.<br />
<br />
42. Idiomatic Translation<br />
Reproduces the meaning or message of the text using idioms and colloquial expressions and language wherever possible.<br />
<br />
The goal is to produce a translation with language that is as natural as possible.<br />
<br />
Translation Category D: 9 types of translation based on the translation technique used<br />
These translation types are specific strategies, techniques and procedures for dealing with short chunks of text – generally words or phrases.<br />
<br />
They’re often thought of as techniques for solving translation problems.<br />
<br />
They differ from the translation methods of the previous category which deal with the text as a whole.<br />
9 translation techniques as titles of books in a bookcase<br />
<br />
43. Borrowing<br />
What is it?<br />
Using a word or phrase from the original text unchanged in the translation.<br />
<br />
Key features<br />
With this procedure we don’t translate the word or phrase at all – we simply ‘borrow’ it from the source language.<br />
<br />
Borrowing is a very common strategy across languages. Initially, borrowed words seem clearly ‘foreign’, but as they become more familiar, they can lose that ‘foreignness’.<br />
<br />
Translators use this technique:<br />
– when it’s the best word to use – either because it has become the standard, or it’s the most precise term, or<br />
– for stylist effect – borrowings can add a prestigious or scholarly flavour.<br />
<br />
Borrowed words or phrases are often italicised in English.<br />
<br />
Examples of borrowings in English<br />
grand prix, kindergarten, tango, perestroika, barista, sampan, karaoke, tofu<br />
<br />
<br />
44. Transliteration<br />
What is it?<br />
Reproducing the approximate sounds of a name or term from a language with a different writing system.<br />
<br />
Key features<br />
In English we use the Roman (Latin) alphabet in common with many other languages including almost all European languages.<br />
<br />
Other writing systems include Arabic, Cyrillic, Chinese, Japanese, Korean, Thai, and the Indian languages.<br />
<br />
Transliteration from such systems into the Roman alphabet is also called romanisation.<br />
<br />
There are accepted systems for how individual letters/sounds should be romanised from most other languages – there are three common systems for Chinese, for example.<br />
<br />
English borrowings from languages using non-Roman writing systems also require transliteration – perestroika, sampan, karaoke, tofu are examples from the above list.<br />
<br />
Translators mostly use transliteration as a procedure for translating proper names.<br />
<br />
Examples<br />
毛泽东 Mao Tse-tung or Mao Zedong<br />
Владимир Путин Vladimir Putin<br />
서울 Seoul<br />
ភ្នំពេញ Phnom Penh<br />
<br />
45. Calque or Loan Translation<br />
What is it?<br />
A literal translation of a foreign word or phrase to create a new term with the same meaning in the target language.<br />
<br />
So a calque is a borrowing with translation if you like. The new term may be changed slightly to reflect target language structures.<br />
<br />
Examples<br />
German ‘Kindergarten’ has been calqued as детский сад in Russian, literally ‘children garden’ in both languages.<br />
<br />
Chinese 洗腦 ‘wash’ + ‘brain’ is the origin of ‘brainwash’ in English.<br />
<br />
English skyscraper is calqued as gratte-ciel in French and rascacielos in Spanish, literally ‘scratches sky’ in both languages.<br />
<br />
46. Word-for-word translation<br />
What is it?<br />
A literal translation that is natural and correct in the target language.<br />
<br />
Alternative names are ‘literal translation’ or ‘metaphrase’.<br />
<br />
Note: this technique is different to the translation method of the same name, which does not produce correct and natural text and has a different purpose.<br />
<br />
Key features<br />
This translation strategy will only work between languages that have very similar grammatical structures.<br />
<br />
And even then, only sometimes.<br />
<br />
For example, standard word order in Turkish is Subject-Object-Verb whereas in English it’s Subject-Verb-Object. So a literal translation between these two will seldom work:<br />
– Yusuf elmayı yedi is literally ‘Joseph the apple ate’.<br />
<br />
When word-for-word translations don’t produce natural and correct text, translators resort to some of the other techniques described below.<br />
Examples<br />
French ‘Quelle heure est-il?’ works into English as ‘What time is it?’.<br />
<br />
Russian ‘Oн хочет что-нибудь поесть’ is ‘He wants something to eat’.<br />
<br />
47. Transposition<br />
What is it?<br />
Translation with a change of grammatical structure.<br />
<br />
This technique gives the translation more natural wording and/or makes it grammatically correct.<br />
<br />
Examples<br />
A change in word order:<br />
Our Turkish example Yusuf elmayı yedi (literally ‘Joseph the apple ate’) –> Joseph ate the apple.<br />
<br />
Spanish La Casa Blanca (literally ‘The House White’) –> The White House<br />
<br />
A change in grammatical category:<br />
German Er hört gerne Musik (literally ‘he listens gladly [to] music’)<br />
= subject pronoun + verb + adverb + noun<br />
becomes Spanish Le gusta escuchar música (literally ‘[to] him [it] pleases to listen [to] music’)<br />
= indirect object pronoun + verb + infinitive + noun<br />
and English He likes listening to music<br />
= subject pronoun + verb + gerund + noun.<br />
<br />
48. Modulation<br />
What is it?<br />
Translation with a change of focus or point of view in the target language.<br />
<br />
This technique makes the translation more idiomatic – how people would normally say it in the language.<br />
<br />
Examples<br />
English talks of the ‘top floor’ of a building, French the dernier étage = last floor. ‘Last floor’ would be unnatural in English, so too ‘top floor’ in French.<br />
<br />
German uses the term Lebensgefahr (literally ‘danger to life’) where in English we’d be more likely to say ‘risk of death’.<br />
In English we’d say ‘I dropped the key’, in Spanish se me cayó la llave, literally ‘the key fell from me’. The English perspective is that I did something (dropped the key), whereas in Spanish something happened to me – I’m the recipient of the action.<br />
<br />
49. Equivalence or Reformulation<br />
What is it?<br />
Translating the underlying concept or meaning using a totally different expression.<br />
<br />
This technique is widely used when translating idioms and proverbs.<br />
<br />
And it’s common in titles and advertising slogans.<br />
<br />
It’s a common strategy where a direct translation either wouldn’t make sense or wouldn’t resonate in the same way.<br />
<br />
Examples<br />
Here are some equivalents of the English saying “Pigs may fly”, meaning something will never happen, or “you’re being unrealistic” (Source):<br />
– Thai: ชาติหน้าตอนบ่าย ๆ – literally, ‘One afternoon in your next reincarnation’<br />
– French: Quand les poules auront des dents – literally, ‘When hens have teeth’<br />
– Russian, Когда рак на горе свистнет – literally, ‘When a lobster whistles on top of a mountain’<br />
– Dutch, Als de koeien op het ijs dansen – literally, ‘When the cows dance on the ice’<br />
– Chinese: 除非太陽從西邊出來！– literally, ‘Only if the sun rises in the west’<br />
<br />
50. Adaptation<br />
What is it?<br />
A translation that substitutes a culturally-specific reference with something that’s more relevant or meaningful in the target language.<br />
<br />
It’s also known as cultural substitution or cultural equivalence.<br />
<br />
It’s a useful technique when a reference wouldn’t be understood at all, or the associated nuances or connotations would be lost in the target language.<br />
<br />
Note: the translation method of the same name is a similar concept but applied to the text as a whole.<br />
<br />
Examples<br />
Different cultures celebrate different coming of age birthdays – 21 in many cultures, 20, 15 or 16 in others. A translator might consider changing the age to the target culture custom where the coming of age implications were important in the original text.<br />
Animals have different connotations across languages and cultures. Owls for example are associated with wisdom in English, but are a bad omen to Vietnamese. A translator might want to remove or amend an animal reference where this would create a different image in the target language.<br />
<br />
51. Compensation<br />
What is it?<br />
A meaning or nuance that can’t be directly translated is expressed in another way in the text.<br />
Example<br />
Many languages have ways of expressing social status (honorifics) encoded into their grammatical structures.<br />
<br />
So you can convey different levels of respect, politeness, humility, etc simply by choosing different forms of words or grammatical elements.<br />
But these nuances will be lost when translating into languages that don’t have these structures.<br />
Then translating into languages that don’t have these structures<br />
Then translating into languages that don’t have these structures.<br />
<br />
===Conclusion ===<br />
<br />
In the light of above mentioned facts and figures here by we would say that machine translation is challenge for human translators because it can reduces the wokload of translation but can't give accurate and exact translation of the traget language.It can be less reliable than human translation..</div>Chen Huinihttps://bou.de/u/index.php?title=Machine_translation&diff=131947Machine translation2021-12-13T13:03:18Z<p>Chen Huini: /* References */</p>
<hr />
<div>'''Machine Translation - A challenge or a chance for human translators?'''<br />
<br />
[[Machine_translation|Overview Page of Machine Translation]]<br />
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30 Chapters（0/30)<br />
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[[Machine_Trans_EN_1]] [[Machine_Trans_EN_2]] [[Machine_Trans_EN_3]] [[Machine_Trans_EN_4]] [[Machine_Trans_EN_5]] [[Machine_Trans_EN_6]] [[Machine_Trans_EN_7]] [[Machine_Trans_EN_8]] [[Machine_Trans_EN_9]] [[Machine_Trans_EN_10]] [[Machine_Trans_EN_11]] [[Machine_Trans_EN_12]] [[Machine_Trans_EN_13]] [[Machine_Trans_EN_14]] [[Machine_Trans_EN_15]] [[Machine_Trans_EN_16]] [[Machine_Trans_EN_17]] [[Machine_Trans_EN_18]] [[Machine_Trans_EN_19]] [[Machine_Trans_EN_20]] [[Machine_Trans_EN_21]] [[Machine_Trans_EN_22]] [[Machine_Trans_EN_23]] [[Machine_Trans_EN_24]] [[Machine_Trans_EN_25]] [[Machine_Trans_EN_26]] [[Machine_Trans_EN_27]] [[Machine_Trans_EN_28]] [[Machine_Trans_EN_29]] [[Machine_Trans_EN_30]] ...<br />
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[[Book_projects|Back to translation project overview]]<br />
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[[DCG-To-Do|To the To Do list]]<br />
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=Chapter 1:A Comparison Between the Quality of Machine Translation and Human Translation——A Case Study of the Application of artificial intelligence in Sports Events=<br />
'''论机器翻译与人工翻译的质量对比——以人工智能在体育赛事领域的应用为例'''<br />
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卫怡雯 Wei Yiwen, Hunan Normal University, China<br />
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[[Machine_Trans_EN_1]]<br />
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=Chapter 3：On the Realm Advantages And Mutual Development of Machine Translation And Huamn Translation)=<br />
"'论机器翻译与人工翻译的领域优势及共同发展'"<br />
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肖毅瑶 Xiao Yiyao, Hunan Normal University, China<br />
[[Machine_Trans_EN_3]]<br />
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=Chapter 4 : A Comparison Between Machine Translation of Netease and Traditional Human Translation—A Case Study of The Economist Articles)= <br />
'''网易有道机器翻译与人工翻译的译文对比——以经济学人语料为例'''<br />
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王李菲 Wang Lifei, Hunan Normal University<br />
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[[Machine_Trans_EN_4]]<br />
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=Chapter 5: Muhammad Saqib Mehran - Problems in translation studies=<br />
[[Machine_Trans_EN_5]]<br />
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=6 徐敏赟(Machine Translation Based on Neural Network --Challenge or Chance?)=<br />
[[Machine_Trans_EN_6]]<br />
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=Chapter 7: The Cultivation of artificial intelligence and translation talents in the Belt and Road Initiative=<br />
[[Machine_Trans_EN_7]]<br />
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一带一路背景下人工智能与翻译人才的培养<br />
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颜莉莉 Yan Lili, Hunan Normal University, China<br />
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=Chapter 8: On Machine Translation Under Lanuguage Intelligence——An Option and Opportunity for Human Translators=<br />
'''论语言智能下的机器翻译——译者的选择与机遇'''<br />
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颜静 Yan Jing, Hunan Normal University, China<br />
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[[Machine_Trans_EN_8]]<br />
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=9 谢佳芬（ Machine Translation and Artificial Translation in the Era of Artificial Intelligence）=<br />
[[Machine_Trans_EN_9]]<br />
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=10 熊敏(Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts)=<br />
[[Machine_Trans_EN_10]]<br />
===Abstract===<br />
The history of machine translation can be traced to 1940s, undergoing germination, recession, revival and flourish. Nowadays, with the rapid development of information technology, machine translation technology emerged and is gradually becoming mature. Whether manual translation can be replaced by machine translation becomes a widely-disputed topic. So in order to explore the ability of machine translation, I adopts two versions of translation, which are manual translation and machine translation(this paper uses Youdao translation) for different types of texts(according to Peter Newmark's types of text：informative text, expressive text and vocative text). The results are quite different in terms of quality and accuracy. The results show that machine translation is more suitable for informative text for its brevity, while the expressive texts especially literary works and vocative texts are difficult to be translated, because there are too many loaded words and cultural images in expressive text and dependence on the readers. To draw a conclusion, the relationship between machine translation and manual translation should be complementary rather than competitive.<br />
<br />
===Key words===<br />
machine translation; manual translation; Newmark's type of texts<br />
<br />
===题目===<br />
Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts<br />
<br />
===摘要===<br />
机器翻译的历史可以追溯到上个世纪40年代，经历了萌芽期、萧条期、复兴期和繁荣期四个阶段。如今，随着信息技术的高速发展，机器翻译技术日益成熟，于是机器翻译能不能替代人工翻译这个话题引起了广泛热议。为了探究机器翻译的能力水平是否足以替代人工翻译，本人根据皮特纽马克的文本类型分类理论（信息类文本，表达文本，呼吁文本），选择了相应的译文类型，并且将其机器翻译的版本以及人工翻译的版本进行对比。结果发现，就质量和准确度而言，译文的水平大相径庭。因为其简洁的特性，机器比较适合翻译信息文本。而就表达文本，尤其是文学作品，因其存在文化负载词及相应的文化现象，所以机器翻译这类文本存在难度。而呼唤文本因其目的性以及对读者的依赖性较强，翻译难度较大。由此得知，机器翻译和人工翻译的关系应该是互补的，而不是竞争的。<br />
<br />
===关键词===<br />
机器翻译；人工翻译；纽马克文本类型<br />
<br />
===1. Introduction===<br />
====1.1.Introduction to machine translation====<br />
Machine translation, also known as computer translation is a technique to translating through machine translator, such as Google translation and Youdao translation. Machine translation is one of the branches of computational linguistics, ranging from computer science, statistics, information science and so on. Machine translation plays an important role in all aspects.<br />
Machine translation can be traced back to 1940s, when British engineer Booth and American engineer Weaver proposed using computer to translate and started to study machines used for translation. And the first machine translating system launched in 1954, used in English and Russian translation. And this means machine translation becomes a reality. However, the arrival of new things always accompanies barriers and setbacks. In the 1960s, reports from ALPAC (Automated Language Processing Advisory Committee) showed studies on machine translation had stagnated for a decade. At that time, due to the high cost of machine, choosing human translators to finish translating works was more economical for most companies. What’s worse, machine had difficulty in understanding semantic and pragmatic meanings. Fortunately, in the 1970s, with the advance and popularity of computer, machine translation was gradually back on track. With the development of science and technology, machine translation has better technological guarantee, such as artificial intelligence and computer technology. In the last decades, from the late 90s till now, machine translation is becoming more and more mature. The initial rule-oriented machine translation has been updated and Corpus-aided machine translation appears. And nowadays, technology in voice recognition has been further developed. This technique is frequently applied in speech translation products. Users only need to speak source language to the online translator and instantly it will speak out the target version. But machine can’t fully provide precise and accurate translation without any grammatical or semantic problems. Machine can understand the literal meaning of texts, but not the meaning in context. For example, there is polysemy in English, which refers to words having multiple meanings. So when translating these words, translators should take contexts into consideration. But machine has troubles in this way. In addition, machine has no feeling or intention. Hence, it is also difficult to convey the feelings from the source language.<br />
<br />
====1.2.Process of machine translation====<br />
The process of manual translation is different from that of machine translation. Here is the process of the former. (1) Understand source language. (2) Use target language to organize language. (3) Generate translation. Unlike manual translation, machine translation tends to analyze and code source language first, then look for related codes in corpus, and work out the code that represents target language, generating translation. But they share a common feature, which is that Lexicon, grammatical rules and syntactic structure are taken into consideration. This is one of the biggest challenges for machine translation.<br />
<br />
===2.Theorectical framework===<br />
====2.1Newmark’s text typology====<br />
Text typology is a theory from linguistics. It undergoes several phrases. Karl Buhler, a German psychologist and linguist defines communicative functions into expressive, information and vocative. Then Roman Jakobson proposes categorization of texts, which are intralingual translation, interlingual translation and intersemiotic translation. Accordingly, he defines six main functions of language, including the referential function, conative function, emotive function, poetic function, metalingual function and the phatic function. Based on the two theories, Peter Newmark, a translation theorist, summarizes the language function into six parts: the informative function, the vocative function, the expressive function, the phatic function, the aesthetic function, and the metalingual function. Later, he further divided texts into informative type, expressive text and vocative type according to the linguistic functions of various texts. <br />
The core of the informative texts is the truth. It is to convey facts, information, knowledge of certain topics, reality and theories. The language style of the text is objective, logical and standardized. Reports, papers, scientific and technological textbooks are all attributed to informative texts. Translators are required to take format into account for different styles.<br />
The core of the expressive text is the sender’s emotions and attitudes. It is to express sender’s preferences, feelings, views and so on. The language style of it is subjective. Imaginative literature, including fictions, poems and dramas, autobiography and authoritative statements belong to expressive text. It requires translators to be faithful to the source language and maintain the style.<br />
The core of the vocative text is readership. It is to call upon readers to act, think, feel and react in the way intended by the text. So it is reader-oriented. Such texts as advertisement, propaganda and notices are of vocative text. Newmark noted that translators need to consider cultural background of the source language and the semantic and pragmatic effect of target language. If readers can comprehend the meaning at once and do as the text says, then the goal of information communication is achieved.<br />
To draw a conclusion, informative texts focus on the information and facts. Expressive texts center on the mind of addressers, including feelings, prejudices and so on. And vocative texts are oriented on readers and call on them to practice as the texts say.<br />
<br />
====2.2Study method====<br />
Manual translations of the three texts are selected from authoritative versions and universally acknowledged. And machine translations of those come from Youdao Translator. And in this thesis I will compare and evaluate the two methods in word diction, sentence structure, word order and redundancy.<br />
<br />
===3.Comparison and analysis of machine translation and manual translation ===<br />
====3.1Informative text ====<br />
（1）English into Chinese<br />
<br />
①Source language:<br />
<br />
Keep the tip of Apple Pencil clean, as dirt and other small particles may cause excessive wear to the tip or damage the screen of i-pad.<br />
<br />
Target language:<br />
<br />
Machine translation: Apple Pencil笔尖应保持清洁，灰尘等小颗粒可能会导致笔尖过度磨损或损坏ipad屏幕。<br />
<br />
Manual translation: 保持Apple Pencil铅笔的笔尖干净，因为灰尘和其他微粒可能会导致笔尖的过度磨损或损坏iPad屏幕。<br />
<br />
Analysis: Here is the instruction of Apple Pencil. And the manual translation is the Chinese version on the instruction.Product instruction tends to be professional, since there are many terms for some concepts. Machine can easily identify these terms and provide related words to translate. The machine version is faithful and expressive to the source language. So it is well-qualified and readable for readers to understand the instruction. So we can use machine to translate informative text.<br />
<br />
②Source language:<br />
<br />
China on Saturday launched a rocket carrying three astronauts-two men and one woman - to the core module of a future space station where they will live and work for six months, the longest orbit for Chinese astronauts.<br />
<br />
Target language:<br />
<br />
Machine translation: 周六，中国发射了一枚运载三名宇航员(两男一女)的火箭，进入未来空间站的核心舱，他们将在那里生活和工作6个月，这是中国宇航员最长的轨道。<br />
<br />
Manual translation: 周六，中国发射了一枚搭载三名宇航员(两男一女)的火箭，进入未来空间站的核心舱，他们将在那里生活和工作6个月，这是中国宇航员最漫长的一次轨道飞行。<br />
<br />
Analysis: This is a news from Reuters, reporting that China has launched a rocket.The meaning of the two translations is almost the same, except for some word diction. But there are some details dealt with different choice. For example, the last sentence of the machine translation is a bit of obscure and direct. There are some ambiguous words and expressions.<br />
<br />
(2)Chinese into English<br />
<br />
Source language:湖南省博物馆是湖南省最大的历史艺术类博物馆，占地面积4.9万平方米，总建筑面积为9.1万平方米，是首批国家一级博物馆，中央地方共建的八个国家级重点博物馆之一、全国文化系统先进集体、文化强省建设有突出贡献先进集体。<br />
<br />
Target language:<br />
Manual translation: As the largest history and art museum in Hunan province, the Hunan Museum covers an area of 49,000㎡, with the building area reaching 91,000㎡. It is one of the first batch of national first-level museums and one of the first eight national museums co-funded by central and local governments.<br />
<br />
Machine translation: Museum in hunan province is one of the largest historical art museum in hunan province, covers an area of 49000 square meters, a total construction area of 91000 square meters, is the first national museum, the central place to build one of the eight national key museum, national cultural system advanced collectives, strong culture began with outstanding contribution of advanced collective.<br />
<br />
Analysis: Machine translation is not faithful enough in content. For instance, “首批国家一级博物馆” is translated into “first national museum”, which is not the meaning of the source language. And there are some obvious grammar mistakes in the machine translation. For example, machine translates it into just one sentence but there are multiple predicates in it. So it is not grammatically permissible. What’s more, the sentence structure of machine translation is confusing and the focus is not specific enough.<br />
<br />
====3.2Expressive text ====<br />
(1)English into Chinese<br />
<br />
Source language:<br />
<br />
An individual human existence should be like a river- small at first, narrowly contained within its banks, and rushing passionately past rocks and over waterfalls. Gradually the river grows wider, the banks recede, the waters flow more quietly, and in the end, without any visible breaks, they become merged in the sea, and painlessly lose their individual being.<br />
<br />
Target language:<br />
<br />
Machine translation: 一个人的存在应该像一条河流——开始很小，被紧紧地夹在两岸中间，然后热情奔放地冲过岩石，飞下瀑布。渐渐地，河面变宽，两岸后退，水流更加平缓，最后，没有任何明显的停顿，它们汇入大海，毫无痛苦地失去了自己的存在。<br />
<br />
Manual translation:人生在世，如若河流；河口初始狭窄，河岸虬曲，而后狂涛击石，飞泻成瀑。河道渐趋开阔，峡岸退去，水流潺缓，终了，一马平川，汇于大海，消逝无影。<br />
<br />
Analysis: Here is a well-known metaphor in the prose How to Grow Old written by Bertrand Russell. The manual translation is written by Tian Rongchang.This is a philosophical prose with graceful language. Literary translation is a most important and difficult branch of translation. Translator should focus on the literal meaning, culture, writing style and so on. It is a combination of beauty and elegance. Therefore, translators find it in a dilemma of beauty and faithfulness, let alone translating machine. Compared with manual translation, machine translation has difficulty in word choice. It is faithful and expressive, but not elegant enough.<br />
<br />
(2)Chinese into English<br />
<br />
Source language:没有一个人将小草叫做“大力士”，但是它的力量之大，的确是世界无比。这种力，是一般人看不见的生命力，只要生命存在，这种力就要显现，上面的石块，丝毫不足以阻挡。因为它是一种“长期抗战”的力，有弹性，能屈能伸的力，有韧性，不达目的不止的力。<br />
<br />
Target language:<br />
<br />
Machine translation: No one calls the little grass "hercules", but its power is truly matchless in the world. This force is invisible life force. As long as there is life, this force will show itself. The stone above is not strong enough to stop it. Because it is a "long-term resistance" of the force, elastic, can bend and extend force, tenacity, not to achieve the purpose of the force.<br />
<br />
Manual translation: Though nobody describes the little grass as a “husky”, yet its herculean strength is unrivalled. It is the force of life invisible to naked eye. It will display itself so long as there is life. The rock is utterly helpless before this force- a force that will forever remain militant, a force that is resilient and can take temporary setbacks calmly, a force that is tenacity itself and will never give up until the goal is reached. (by Zhang Peiji)<br />
<br />
Analysis:This is the excerpt of a well-known Chinese prose written by Xia Yan. It is written during the war of Resistance Against Japan. So the prose holds symbolic meaning, eulogizing the invisible tenacious vitality so as to encourage Chinese to have confidence in the anti-aggression war. Compared with manual translation, machine translation is much more abstract and confusing, especially for the word diction. For example, “大力士” is translated into “hercules” which is a man of exceptional strength and size in Greek and Roman Mythology, making it difficult to understand if readers of target language have no idea of the allusion. What’s worse, the machine version doesn’t reveal the symbolic meaning of the text, which is the core of this prose.<br />
<br />
====3.3Vocative text ====<br />
<br />
(1)English into Chinese<br />
<br />
①Source language:<br />
<br />
iPhone went to film school, so you don’t have to. (Advertisement of iPhone13)<br />
<br />
Target language:<br />
<br />
Machine translation: iPhone上的是电影学院，所以你不用去。<br />
<br />
Manual translation:电影专业课，iPhone同学替你上完了。<br />
<br />
Analysis：Here are advertisements of iPhone on Apple official website. There is a personification in the source language. It is used to stress the advancement and proficiency in camera, which is an appealing selling point to potential buyers. Compared with manual translation, machine translation is plain and not eye-catching enough for customers.<br />
<br />
②Source language: <br />
<br />
5G speed OMGGGGG<br />
<br />
Machine language: 5克的速度 OMGGGGG<br />
<br />
Manual translation:<br />
<br />
iPhone的5G 巨巨巨巨巨5G<br />
<br />
Analysis: The “G” in the source language is the unit of speed, standing for generation. However, it is mistaken as a unit of weight, representing gram in the machine translation. So the meaning is not faithful to the source language at all. As for manual translation, it complies with the source in form. Specifically speaking, five “G”s in the former complies with five characters “巨”in the latter. And the pronunciation of the two is similar. There are two layers of meaning for the 5 “G”s. One exclaims the fast speed of 5 generation network and the other new technology. In the manual version, “巨”can be used to show degree, meaning “quite” or “very”. <br />
<br />
③Source language: <br />
<br />
History, faith and reason show the way, the way of unity. We can see each other not as adversaries but as neighbors. We can treat each other with dignity and respect, we can join forces, stop the shouting and lower the temperature. For without unity, there is no peace, only bitterness and fury."<br />
<br />
Target language:<br />
<br />
Machine translation: 历史、信仰和理性指明了团结的道路。我们可以把彼此视为邻居，而不是对手。我们可以尊严地对待彼此，我们可以联合起来，停止大喊大叫，降低温度。因为没有团结，就没有和平，只有痛苦和愤怒。<br />
<br />
Manual translation:历史、信仰和理性为我们指明道路。那是团结之路。我们可以把彼此视为邻居，而不是对手。我们可以有尊严地相互尊重。我们可以联合起来，停止喊叫，减少愤怒。因为没有团结就没有和平，只有痛苦和愤怒<br />
<br />
Analysis: Speech is a way to propagate some activity in public. It is an art to inspire emotion of the audience. The source language is the excerpt of Joe Biden’s inaugural speech. The speech should be inspiring and logic. The machine translation has some misunderstanding. Taking the translation of “lower the temperature” for example, machine only translates its literal meaning, relating to the temperature itself, without considering the context. What’s more, it is less logic than the manual one. Therefore, it adds difficulty to inspire the audience and infect their emotion.<br />
<br />
===4.Common mistakes in machine translation ===<br />
<br />
====4.1 lexical mistakes ====<br />
<br />
Common lexical mistakes include misunderstandings in word category, lexical meaning and emotive and evaluative meaning. Misunderstanding in word category shows in the classification of word in the source language. As for misunderstanding in lexical meaning, machine has difficulty in precisely reflecting the meaning of the original texts, due to different cultural background and different language system. And for misunderstanding in emotive meaning, machine has no intention and emotion like human-beings. Therefore, it’s impossible for it to know writers’ feelings and their writing purposes. So sometimes, it may translate something negative into something positive.<br />
<br />
====4.2 grammatical mistakes====<br />
<br />
Grammatical analysis plays an important part in translation. Normally speaking, every language has its own unique grammatical rules. So in the process of translation, if translators don’t know the formation rule well, the sentence meaning will be affected. Even though all the lexical meanings are well-known by translators, the lack of consciousness of grammaticality makes it harder to arrange words according to sequential rule. English tends to be hypotactic, while Chinese tends to be paratactic. English sentences are connected through syntactic devices and lexical devices. While Chinese sentences are semantically connected, which means there are limited logical words and connection words in Chinese. So when translating English sentence, we should first analyze its grammaticality and logical structure and then rearrange its sequence. However, online translating machine has troubles in grammatical analysis, which makes its improvement more difficult.<br />
<br />
====4.3 other mistakes====<br />
<br />
The two mistakes above are the internal ones. Apart from mistakes in linguistic system, there are some mistakes in other aspects, such as cultural background.<br />
<br />
===5.Reasons for its common mistakes ===<br />
<br />
====5.1 Difference in two linguistic system====<br />
<br />
With different history, English and Chinese have different ways of expression. Commonly speaking, English is synthetic language which expresses grammatical meaning through inflection such as tense and Chinese is analytic language which expresses grammatical meaning through word order and function word. In addition, English is more compact with full sentences. Subordinate sentence is one of the most important features in modern English. Chinese, on the other hand, is more diffusive with minor sentences.<br />
<br />
====5.2 Difference in thinking patterns and cultural background====<br />
<br />
According to Sapir-Whorf’s Hypothesis, our language helps mould our way of thinking and consequently, different languages may probably express their unique ways of understanding the world. For two different speech communities, the greater their structural differentiations are, the more diverse their conceptualization of the world will be. For example, western culture is more direct and eastern culture more euphemistic. What’s more, English culture tends to be individualism, focusing on detail, through which it reflects the whole, while Chinese culture tends to be collective. Different thinking patterns will add difficulty for machine to translate texts.<br />
<br />
====5.3 Limitation of computer====<br />
<br />
Recently, there are some breakthroughs and innovation in machine translation. However, due to its own limitation, online translation has limitation in some ways. Firstly, compared with machine, human brain is much more complicated, consisting of ten billions of neuron, each of which has different function to affect human’s daily activities and help humans avoid some errors. However, computer can only function according to preset programming has no intention or consciousness. Until now, countless related scholars have invested much time in machine translation. They upload massive language database, which include almost all linguistic rules. But computers still fail to precisely reflect the meaning of source language for many times due to the complexity and flexibility of language. On the other hand, computers can’t take context into consideration. During translation, it is often the case that machine chooses the most-frequently used meaning of one word. So without the correct and exact meaning, readers are easier to feel confused and even misunderstand the meaning of source language.<br />
<br />
===6.Conclusion===<br />
From the analysis above, we can draw a conclusion that machine deals with informative text best, followed by non-literary translation of expressive text. What’s more, machine can be a useful tool to get to know the gist and main idea of a specific topic, for the simple sentence structure and numerous terms. And it can improve translating efficiency with high speed. But machine has difficulty in translating literary works, especially proses and poems.<br />
<br />
Machine translation has mixed future. From the perspective of commercial, machine translation boasts a bright future. With the process of globalization, the demand for translation is increasing accordingly. On one hand, if we only depend on human translator to deal with translating works, the quality and accuracy of translation can be greatly affected. On the other hand, if machine is used properly to do some basic work, human translators only need to make preparation before translating, progress, polish and other advanced work, contributing to highly-qualified translation and high working efficiency.<br />
<br />
However, compared with manual translation, machine translation has a bleak future. It is still impossible for machine to replace interpreter or translator in a short term. With intelligence and initiative, humans are able to learn new knowledge constantly, which machine will never accomplish. Besides, machine is not used to replace translators but to assist them in work. In other words, translators and machine carry out their own duties and they are not incompatible.<br />
<br />
To draw a conclusion, although there are certain limitations of machine translation, it can serve as a catalyst for translating works. Therefore, with the rapid development of artificial intelligence and related technology, there are still many opportunities for machine translation.<br />
<br />
===Reference ===<br />
<br />
Cui Zihan 崔子涵.机器翻译译文质量对比——以谷歌翻译和DeepL为例[J] [Comparison among Machine Translation--Taking Google Translation and Deepl for Example].Overseas English 海外英语,2021(15):182-183.<br />
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Li Deyi 李德毅. (2018). 人工智能导论 [Introduction to Artificial Intelligence]. Beijing: China Science and Technology Press 中国科学技术出版社.<br />
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Qiu Quanju 仇全菊.大数据时代背景下机器翻译及其发展趋势[J][Machine Translation and its Development Trend under the Background of Big Data Era]. English Teachers 英语教师,2021,21(16):60-62.<br />
<br />
Zhuo Jianbin 卓键滨,Liu Wenxian 刘文娴,Peng Zili 彭子莉.机器翻译对各类型文本的德汉翻译能力探究[J][Research on the German Chinese Translation Ability of Machine Translation for Various Types of Texts]. Comparative Study of Cultural innovation 文化创新比较研究,2021,5(28):122-125.<br />
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(英) Peter Newmark A Textbook of Translation[M] Shanghai Foreign Education Press, 2002<br />
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Wang Hongqi 王红旗. (2008). 语言学概论 [Introduction to Linguistics]. Beijing: Peking University Press 北京大学出版社.<br />
<br />
Liu Qin刘琴.功能目的论对于不同文本类型的翻译解读[J][Analysis of Translations in Different Types of Text based on Functionalist Approaches].Overseas Engliosh 海外英语,2021(17):8-9.<br />
<br />
Zhang Peiji 张培基.英译中国现代散文选[M][Selected Modern Chinese Prose Writings]. Shanghai Foreign Languages Education Press 上海外语教育出版社, 2002.<br />
<br />
Chen Cheng陈诚.机器翻译技术的综述[J][Overview of Machine Translation Technology].Electronic Techonology 电子技术,2021,50(11):290-291.<br />
<br />
He Xinyu何馨宇.机器翻译的发展及其对翻译职业化的影响研究[J] [The Development of Machine Translation and its Effect on Professional Transltors].Overseas English 海外英语,2021(20):48-49.<br />
<br />
He Wen 何雯, Wang Xiufeng 王秀峰.信息型文本的在线机器翻译错误研究[J][Research on Errors in Online Machine Translation of Informative text ].Overseas English海外英语,2021(15):188-189.<br />
<br />
Li Hanji 李晗佶. (2021). 人工智能时代翻译技术与译者关系演变与重构 [Evolution and reconstruction of the relationship between translation technology and translators in the era of artificial intelligence]. 西华师范大学学报(哲学社会科学版) Journal of West China Normal University (PHILOSOPHY AND SOCIAL SCIENCES EDITION) (2021-12-04) 1-6.<br />
<br />
Wei Guang魏光. 人工翻译与机器翻译译文编辑比较研究[J][Comparative Study of Translation Editing between Manual Translation and Machine Translation]. Overseas English 海外英语,2021(19):18-19+21.<br />
<br />
=Chapter 11 陈惠妮=Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts=<br />
<br />
机器翻译的译前编辑研究——以医学类文摘为例<br />
<br />
陈惠妮 Chen Huini, Hunan Normal University<br />
<br />
[[Machine_Trans_EN_11]]<br />
<br />
===Abstract===<br />
At present, globalization is accelerating and the market demand for language services is rapidly increasing . Machine translation, as an important translation method, can greatly improve translation efficiency due to its low cost and high speed. However, because of the limitations of machine translation and the differences between Chinese and English language, machine translation is not accurate enough. In order to balance translation efficiency and translation quality, a great number of manual revisions in translation are required for the machine translating texts. Medical papers are specialized, special and purposeful, so it requires accurate,qualified and professional translation. However, the quality of translations by machine is inefficient to meet the high-quality requirements of medical papers translation. Therefore, the introduction of pre-editing can greatly improve the efficiency and quality of machine translation.<br />
<br />
===Key words===<br />
Pre-editing, Machine translation, Medical texts<br />
<br />
===题目===<br />
Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts<br />
<br />
===摘要===<br />
在全球化加速发展的今天，市场对语言服务的需求迅速增加。机器翻译作为一种重要的翻译途径，由于其成本低、速度快，可以大大提高翻译效率。然而，由于机器翻译的局限性以及中英文语言的差异，机器翻译的准确性不高。为了平衡翻译效率和翻译质量，机器翻译文本需要大量的手工修改。<br />
医学论文具有专业性、特殊性和目的性，要求其译文准确、合格、专业。然而，机器翻译的质量较低，无法满足医学论文对翻译的高质量要求。因此，译前编辑的引入可以大大提高机器翻译的效率和质量。<br />
<br />
===关键词===<br />
译前编辑；机器翻译；医学文本<br />
<br />
===1. Introduction===<br />
===1.1 Definition of Machine Translation===<br />
As Cronin(2013) revealed: "Translation is undergoing a revolutionary upheaval. The influence of digital technology and the Internet on translation is continuous, extensive and profound. From the popularity of automatic online translation applications, translation revolution is everywhere (Cronin,2013) However,the concept of machine translation was firstly proposed in the 1930s. Since 1940s, the machine translation technology has been evolving from rule-based machine translation (RBMT) to statistical machine translation (SMT), and to neural machine translation (NMT). Machine translation refers to the automatic translation of source language into target language by using a computer system. That is, machine translation refers to the automatic translation of text from one language into another natural language by computer software or other online translation webs. Machine translation is also defined as the process of “using a computer system to automatically translate text or speech from one natural language to another” according to the definition by ISO (Cui 2014:68-73).O 'Brien (2002) defines it as "the behavior of modifying errors in machine translation to ensure that the target translation meets certain quality requirements". On the basic level, machine translation performs mechanical substitution of words in one language for words in another language, but that rarely produces a good translation, therefore, recognition of the whole phrases and their closest counterparts in the target language is needed. Not all words in one language have equivalents in another language, and many words have more than one meaning. A huge demand for translation is greatly needed in today’s global world, which creates new opportunities for the development of machine translation, attracts more and more attention and becomes one of the current research focuses.<br />
<br />
===1.2 Definition of Pre-editing===<br />
Pre-editing means to adjust and modify the source language to make it fit more with the characteristics of the machine translation software before putting the source language before the into machine translation, so as to improve the quality of the translation machine translation (Wei Changhong 2008:93-94). Pre-editing is to modify the original text before putting it into machine translation software, in order to improve the recognition rate of machine translation, optimize the output quality of translated text and reduce the workload of post-editing. Because pre-translation editing only needs to be modified in one language, the operation is simpler than post-translation editing, which can realize the double improvement of quality and efficiency. A good pre-editing translation can help machine translation more smoothly, thus improving the machine readability and quality of the output translation. Pre-editing is mostly applied in the following situations: one is when the original text is of poor quality and the machine is difficult to recognize the meaning of the sentence, such as user generated content with poor readability and translatability (Gerlach et al 2003:45-53); Documents that need to be published in multiple languages; Next is when the original text contains a lot of jargon; The last is the original text has a corresponding translation memory bank. If the original text is edited, it can better match the content of the translation memory bank. Pre-editing is a process of identifying problems. It requires to pre-edit the source texts before putting it into machine translation according to the requirements, listing the expressions or sentences that may have trouble in machine translation and then pre-edit it by human. The purpose is to enable the computer to translate better, improve the translatability of machine translation. (Slype G V & Guinet J F & Seitz F 1984:115)<br />
<br />
===1.3 Machine Translation Mode===<br />
According to the different knowledge acquisition methods, machine translation modes can be classified as follows: one is rule-based machine translation, which is based on bilingual dictionaries and a library of language rules for each language. The quality of translation depends on whether the source language conforms to the existing rules, but the inexhaustible rules are the hinders of this model. The second mode is machine translation based on statistics. This translation model relies on the principles of mathematics and statistics to find various existing translations corresponding to the translation tasks through the employment of corpus, analyzing the frequency of their occurrence and selecting the translation with the highest frequency for output. The disadvantage of this translation model is that it ignores the flexibility of language and the importance of context. The last one is neural network language model. This model is different from previous translation models in that it uses end-to-end neural network to realize automatic translation between natural languages. At present, the quality of its translation is much higher than that of the previous translation models.<br />
===1.4 Source of Study Abstracts===<br />
The core medical journals from domestic are selected in order to make the paper more representative and authoritative, such as National Medical Journal of China, Journal of Peking University (Health Science), and Journal of Third Military Medical University. All of them include basic medical science, biomedical technology, laboratory medical science and other fields. In this way, the pre-editing approaches included are applicable enough to machine translation of medical abstracts.<br />
===1.5 Selection of Translation Software===<br />
Generally, machine translation software includes Google Translation, Youdao Translator and Niu Translation, which has their own special use in translation. Take Google as an example, it is more like neurons in human brain, enabling to learn and collect information to establish connections with its neural machine translator’s neurons. However, it also causes many errors because of the lack of enough information. In this paper, contrastive analysis will be carried on by using Google translation. On the one hand, being a pioneer in the translation of NET, it is inevitably to sue Google as the translation software to translate medical texts in this paper. Comparing the program called “Google brain”, other NMT of translation software are relatively disadvantages. On the other hand, the Google translation enjoys the largest users in the world, with its downloads of more than one billion. The output quality of Google translation is more correct and complete than other machine translation software. With years of development and improvement, Google Translation has been greatly promoted. In this paper, Chinese medical abstract will be automatically translated by Google translation, and then the translation output will be compared with the translation by human and the publication of English abstracts. The main purpose is to prove that the improvement and promotion of quality and accuracy in the medical abstracts will be obtained through the pre-editing approaches.<br />
<br />
===2.Language Characteristics and Error Division===<br />
Since Chinese and English are two different languages, it is quite neccessary to identify their own characteristics so as to better analyze and understand the two languages. Tytler (1978: 118-119) argued in his Essay on the Principles of Translation that there are three principles of translation that in all the translation should give readers the same feelings as the source text, except for complete transcript of ideas. There actually also exit some mistakes of these two languages. So the following will make some clarrifications of these two languages to ensure more accurate translation.<br />
<br />
===2.1 Language Characteristics of Medical Abstracts===<br />
Chinese and English belong to different language systems, so there are differences in their language structure and the way users think of the languages. When using machine translation from Chinese to English, due to the unequal language levels, there will be many mistakes in the translation process, especially for ESP texts, such as medical papers. <br />
Here, these differences mainly refer to the linguistic characteristics of medical abstracts. In medical abstracts, it usually includes structured and unstructured abstracts. Although in different forms, they both describe the purpose, methods and conclusions of the research.<br />
In the method section of medial abstract, several Chinese sentences can be connected with commas, but each sentence may convey different information. In contrast, English sentences contain a great deal of information, but in order to ensure clarity, some modifiers need to be isolated and then reconstructed. However, in Chinese-English machine translation, a lot of information is put into the sentence because the machine segments the sentence on the basis of the full comma.<br />
In addition, subjectless sentence is used in the objective and method parts of Chinese medical abstracts. The subjectless sentence means the sentence without or free from subject and is usually employed in two contexts. The first is "needless to say". In Chinese sentences, it is common to omit the subject of the sentence. Chinese sentences can convey meanings by using incomplete sentence structures, so Chinese speakers can understand the meanings of the sentences even though the sentence subject is omitted. The second is "emphasis of action". In this context, subjectless sentences are used to describe behaviors, especially in the study of traditional Chinese medicine abstracts. Comparatively speaking, it should be avoided in English medical abstracts. Abstract sentences are more subjective when describing the learning process, while the essential requirement of English medical abstract is objectivity. From this point, sentences without subject should be avoided in English medical abstracts. Another feature is voice. Few words with passive meanings appear in Chinese abstracts. English sentences are more favoured in using passive voice. When translating from Chinese to English, passive voice should be used to make the contents objective, which is also the basic requirement of medical papers.<br />
These are the linguistic features of Chinese medical abstracts. There are great differences in sentence structure and expression between Chinese and English medical abstracts. These differences may reduce the accuracy of machine translation, so it is necessary to introduce pre-editing to edit the source text to ensure that the source text can be accurately recognized by the machine and fully translated into English.<br />
===2.2 Error Division of Machine Translation in Translating Abstracts of Medical Papers from Chinese to English===<br />
In this paper, errors in machine translation are listed out after analyzing. Some are due to the principles, such as statistical-based MT and NMT, employed by machine translation. Based on the original errors, they can be studied from two levels. One is from the macro-level, referring to mistakes caused by objective factors. These are not human factors, but the disadvantage of machine translation and the limitations of language. The limitations of machine translation are derived from the principles and models adopted and manifests itself as a reliance on reference sources. In this case, semantic ambiguity and textual incoherence may occur in the absence of reference sources.<br />
However, for ESP texts such as medical papers, the requirements for terms and sentence patterns are far beyond the existing corpora. For unfamiliar texts (that is, the corresponding texts cannot be found in the corpora). The quality of output translation will be relatively low, which is determined by the principles of machine translation. As mentioned above, machine translation has evolved over the past few decades from rules-based to corp-based statistics to NMT. However, there still exist some limitations in machine translation.<br />
Mistakes on micro-level are mainly caused by the variations and differences of linguistic structure between Chinese and English. Chinese is an implicit language, while English is an explicit one (Lian, 2010) To put it in another way, Chinese expression does not depend on language structures, while English does the opposite. This may result in mismatches between the original and machine translated sentences. This kind of error is mainly divided into two types: component fragment and component missing. For a medical paper, such errors are very serious. As a kind of ESP discourse, medical paper has the characteristics of fixed, objective and accurate language structure. In order to reproduce the characteristics of medical abstracts in translation, it is necessary to avoid errors at the level of words and sentences and pay attention to logic and consistency.<br />
Lexical errors here refer to the inconsistency of fixed expressions in the translation of terms. Although these terms are machine translation based on the corpus, the corpus may not be perfect for ESP texts such as medical papers. Therefore, fixed expressions are not used in term translation. This is also the most common mistake in machine translation (the term here includes but is not limited to nouns). For ESP texts, medical text in particular, the fixed expressions and the accuracy of terms are of great importance.<br />
<br />
===3.Approaches Proposed for Pre-editing ===<br />
Generally speaking, there is a close relationship between pre-editing and post-editing, both of which aim to convey information and ensure high or publishable translation quality. Proper pre-editing can improve the quality of machine translation in terms of adequacy and consistency. <br />
Complexity of natural language and people use language arbitrarily, bringing many difficulties to Chinese-English machine translation, Hu Qingping (2005:24) has proposed "the research of translation software and the development of the controlled language are the two directions to improve the quality of machine translation : the former aims at the difficulty in the natural language processing, the latter overcomes the arbitrariness of natural language". Feng Quangong and Gao Lin (2017: 63-68) put forward: "The writing principles of controlled language can be applied to pre-editing of machine translation. Pre-editing based on controlled language can effectively reduce the complexity and ambiguity of source text, improve the identifiability of machine translation (the translatability of source text itself), and thus reduce (fully) the workload of post-editing".<br />
The central task of pre-editing is to transform human-friendly content into machine-friendly content, so words and sentences need to be repositioned or even changed. Based on the analysis of the language characteristics of Chinese and English, the Chinese ideographic group should be split before the Chinese source text is input into the machine software to translate so that the sentence structure is complete which can be easily recognized by the machine translation software.<br />
===3.1 Extraction and Replacement of Terms in the Source Text===<br />
As a branch of applied English, medical English is the product of the combination of English language knowledge and medical knowledge. The terms in medical papers have the characteristics of fixed and complex language structure. Although Google Translation is based on a corpus, the language structure is not fixed due to the limited size of the corpus. Therefore, the following two steps should be followed before machine translation. The first is to extract terms and create a glossary, and then replace the Chinese terms in the source text with the corresponding English expressions in the glossary. Of course, the terms of extraction should be searched and verified according to the actual situation. All of these words are verified before they can be replaced. This method can not only ensure the accuracy of term translation, but also maintain the consistency of expression, especially when dealing with long texts.<br />
Example1<br />
Source abstract: 口腔白斑病癌变相关缺氧应答基因和微小RNA的芯片及表达验证。<br />
Google translation: Microarray detection/Chip detection and expression verification of hypoxia response genes and microRNAs related to oral leukoplakia canceration.<br />
Published translation:Transcriptome array screening and verification of oral leukoplakia carcinogenesis-related hypoxia-responsive gene and microRNA. <br />
Analysis: The expression “ Affymetrix GeneChip” empolyed in this thesis is a human transcription array for transcriptome array. In the source abstract, “芯片检测“ can be meant “screening” but not detection, so the proper and appropriate translation of these expression should be “transcription array screening”, but the Google translates it into “microarry detection of chip detection”. Therefore, term extraction is essential and inevitable before putting the source abstracts into the machine translation software.<br />
After pre-editing source abstracts: 对 oral leukoplakia carcinogenesis 相关 hypoxia-responsive gene 和微小RNA进行 transcriptome array screening 及表达验证。<br />
After pre-editing translation: Transcriptome array screening and expression- verification of hypoxia-responsive genes and microRNAs related to oral leukoplakia carcinogenesis.<br />
===3.2 Explication of Subordination===<br />
As mentioned above, the subordination of Chinese sentences is judged by certain specific words in machine translation . Chinese is a paratactic language, and sentences are often connected by internal logical relationships; while English depends on sentence structure, so sentences are often closely linked by various language forms. In order to improve the accuracy of machine translation, we can adjust the sentence structure and adjust the position of adjectives and their modifiers. <br />
Example 2<br />
Source abstracts:回顾性分析南京医科大学附属儿童医院中重度HIE患儿49例及同期就诊的无神经系统症状体征的足月新生儿为对照组31例的头颅磁共振成像（MRI）资料。<br />
Google translation: A retrospective analysis that the brain magnetic resonance imaging (MRI) data of 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University and full-term neonates with no neurological symptoms and signs during the same period were included in the control group.<br />
Published translation: A total of 49 children with moderate to severe HIE admitted to the children’s Hospital Affiliated to Nanjing Medical University were retrospectively analyzed. Cranial magnetic resonance imaging (MRI) date of 31 full-term neonates without neurological symptoms and signs who visited the hospital during the same period were recruited as the control group. <br />
Analysis: As the above example shows that “中重度HIE患儿” and “足月新生儿” in the source abstracts are from the Children’s Hospital Affiliated Nanjing Medical University. The Google translation shows that 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University. There is an error due to the misuse of subordination. There are some advice in order to solve the problem. That is, first to segment the sentence and then reconstruct the sentence. For instance, the Chinese expression “南京医科大学附属儿童医院” carries many modifiers, which requires to cut the modifiers and reconstruct the sentence. Therefore, the Chinese expression “南京医科大学附属儿童医院’can be divided into abstractions, leaving two sentences instead of one long sentence with modifiers..<br />
After pre-editing source abstracts: 对49例中重度HIE患儿以及31例无神经系统症状体征的足月新生儿的MRI资料进行回顾性分析。这些患者收治于南京医科大学儿童附属医院。<br />
After pre-editing translation: The MRI data of 49 children with moderate to severe HIE and 31 full-term newborns without neurological symptoms and signs were retrospectively analyzed. These patients were admitted to the Children’s Hospital of Nanjing Medical University.<br />
===3.3 Explication of Subject through Voice Changing===<br />
The extensive use of subjectless sentences are quite common in the Chinese abstracts, while English prefers to employ passive voice in the sentences so as to make them object and accurate. In order to take the characteristics of English sentences into great and careful consideration, the sentences written in passive voice in particular, it will be helpful for machine translation to find out the subject and reconstruct a sentence in passive voice (Wang Yan: 2008). The first is to discover the “doee” in a sentence and then is to reconstruct the sentence structure and put the “doee” before the verb. The last is to explicate the passive relation between the noun and the verb.<br />
Example 3<br />
Source abstract: 回顾性分析2018年1月至2020年12月解放军总医院第七医学中心收治的500例老年髋部骨折患者的资料。<br />
Google translation: A retrospective analysis of the data of 500 elderly patients with hip fractures admitted to the Seventh Medical Center of the PLA General Hospital from January 2018 to December 2020.<br />
Published translation: From January 2018 to December 2020, the data of 500 elderly patients with hip fracture treated in the Seventh Medical Center of PLA General Hospital were analyzed retrospectively.<br />
Analysis: The above example indicates that the “回顾性分析”in the Google Translate is a noun phrase, but the source abstracts actually describes an action or a behavior. The reason for such a mistake is that the machine can’t recognize the Chinese subjetless sentences. To find the subject of the sentence, the source abstracts can be revised and adjusted. Finding the “doee” is the first step, which refers to “500例老年髋部骨折患者的资料”in the source abstracts. And next is to put it in front of the verb, that is to place it in the beginning of the sentence. Last but not least, the passive voice should be explicated in the sentence. <br />
After pre-editing source abstract: 500例老年髋部骨折患者的资料被回顾性分析，他们在2018年1月之2020年12月期间收治于解放军总医院第七医学中心。<br />
After pre-editing translation: The data of 500 elderly hip fracture patients were retrospectively analyzed. They were admitted to the Seventh Medical Center of the PLA General Hospital between January 2018 and December 2020.<br />
===3.4 Relocation of Modifiers===<br />
Modifiers, including noun, adverbial and attributive are constantly employed in the Chinese medical papers in order to add information to subject and object in the sentence. By doing this, complicated sentences can be structured, thus causing obstacles to machine translation for recognizing this complex sentence structures when translating Chinese-English sentences. To make the machine translation successfully recognize the sentence structure, simplifying the sentence structure is quite necessary. The key is to moving the location of the modifiers and thus making the modifiers an independent sentence. In other words, the modifiers ought to be put before or behind the main part of the sentence to satisfy the common use of English. <br />
Usually, the modifiers in Chinese sentence can only be placed in front of the core word, while in English, modifiers are very flexible. It is all right to place the modifiers in front of or behind the major part of the sentences with adjective or a connective noun.<br />
Example 4<br />
Source abstracts: 利用DNA重组技术以pET-28a表达系统在E.coli BL21(DE3) 中重组表达Hepl。<br />
Google Translation: Hepl is recombined in E.coli BL21 (DE3) using DNA recombination technology with pET-28a expression system.<br />
Published translation: The recombinant Hcpl protein was expressed by using DNA recombination technology through pET-28a expression system in E. coli BL21 (De3).<br />
Analysis: The Chinese medical text includes two modifiers, “利用DNA”and “以pET-28a)表达系统”. These two modifiers will be translated by machine, which catches more attention to the two constituents. From the Google translation above, one failure is obvious that it misplaces the location of the two modifiers and presents it in not accurate form. To make it translate correctly by machine translation, dividing the sentences is very important so that the source abstracts can be correctly recognized by machine translation.<br />
After pre-editing source abstract: 利用DNA重组技术，Hcp1重组表达在E.coli BL21 (DE3)中， 通过pET-28a表达系统在E. coli BL21 (DE3)中。<br />
Google translation: Using DNA recombination technology, Hcp1 recombination is expressed in E.coli BL21 (DE3), via pET-28a expression system in E. coli BL21 (DE3).<br />
The second is the independence of modifiers, that is, modifiers can also be reconstructed into clauses to modify core words. Compared with English, There is no subordinate clause in Chinese, so redundant Chinese modifiers need to be reconstructed into subordinate clauses in Chinese-English translation to meet the characteristics of English. English, especially sentences with multiple modifiers. Otherwise, sentence structure may be confused, such as scattered modifiers of core words. To avoid this mistake, it is necessary to separate the modifier from the main part of the sentence. These modifiers should be reconstituted into clauses. Also, keep your sentences simple and easy to understand.<br />
===3.5 Proper Omission and Deletion of Category Words===<br />
Category words are commonly used in Chinese. Category words complement the meaning of words, including problems, positions, situations and jobs. Adding this supplementary word is more in line with Chinese custom. In many cases, it has no real meaning, so it can be omitted in translation. In Chinese, category words are frequently used. However, it is rarely used in English, which is one of the differences between Chinese and English. There are many kinds of category words. Considering objectivity and the fixed structure of language, redundant category words should be deleted in Chinese-English translation. In the general, the most commonly used category of words in the Chinese medical abstracts are "process", "behavior", and "situation". These categories of words hinder the language conversion of Chinese and English, resulting in redundancy. <br />
Example 5<br />
Source abstract: 探讨口腔白斑病癌变进程中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia response genes and associated microRNAs in the process of oral leukoplakia cancer was discussed.<br />
Published translation: To study the hypoxia response gene and microRNA (miRNA)expression profiles in the pathogenesis and progression of oral leukoplakia (OLK).<br />
Analysis: As the above example shows, the Chinese words “进程” belongs to a category word. However, the Chinese expression “癌变”contains the process, so the “进程” expression can be deleted before placing it into the machine translation, because the meaning of it has been overlapping between the expression “癌变”. Therefore, its place can be replaced with preposition “during”.<br />
After pre-editing source abstract: 探讨口腔白斑病癌变中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia responsive genes and miRNA in oral leukoplakia cancer was investigated.<br />
<br />
===Conclusion===<br />
After years of development, machine translation has made great progress. The accuracy of machine translation has been greatly improved in both text recognition and sentence pattern conversion. However, machine translation has its own limitations. In other words, it needs to rely on the parallel corpus as a reference source for improving its accuracy. ESP text, in particular, is harder to get the high quality by machine translation. <br />
<br />
As one of the research papers, the characteristics of medical abstracts are fixed language structures, objectivity and accuracy (Qin Yi 2004:421-423). Therefore, medical translation must be accurate, object and understandable to follow the specific demands of the medical paper. Being an important field in the human society, medical paper translation is on a great demand, which means that it needs a huge demand for human labor. However, with the machine translation promoting, it will be more efficient to translate medical papers combining the effort by human and machine. The improvement and development of machine translation requires the joint efforts of computer science, information science, statistics, linguistics and other academic circles to achieve more mature human-computer mutual assistance translation (Li Yafei, Zhang Ruihua 2019:38-45).<br />
<br />
However, errors can occur during the process of machine translation of Chinese- English, because of the differences of the Chinese and English and the processing of the machine. Errors from the perspective of linguistic or grammar can affect the machine translation a lot. After division and recognition of errors, some pre-editing approaches are put forward to help the machine translation more accurate and readable, that are, extraction and replacement of terms in the source text, relocation of modifiers, explication of subordination, proper omission, deletion of category words and explication of subject through voice changing. <br />
<br />
The paper mainly focuses on the pre-editing machine translation by using medical papers as a case study. The errors of machine translation occurring in the translation of medical abstracts and pre-editing approaches for machine translation. The quality of machine translation of medical papers is greatly improved after employing the pre-editing methods. However, machine translation is not as flexible and accurate as human brain, so it is of importance to combine pre-editing and post-editing approaches with machine translation in order to produce more accurate, more object machine translation of medical papers.<br />
<br />
===References===<br />
Cronin, Michael (2013). ''Translation in the Digital Age''[M]. New York & London: Routledge. 86<br />
<br />
Cui Qiliang崔启亮(2014).论机器翻译的译后编辑[J] ''On Post-Editing of Machine Translatio''. 中国翻译 Chinese Translators Journal, 035(006):68-73<br />
<br />
Feng Quangong, Gao Lin冯全功,高琳 (2017). 基于受控语言的译前编辑对机器翻译的影响[J] ''Influence of Pre-editing Based on Controlled Language on Machine Translation''. 当代外语研究Contemporary Foreign Language Research,(2): 63-68+87+110.<br />
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GERLACH J, et al ( 2013). ''Combining Pre-editing and Post-editing to Improve SMT of User-generated Content''[M]// Proceedings of MT Summit XIV Workshop on Post-editing Technology and Practice. 45-53<br />
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Hu Qingping胡清平(2005). 机器翻译中的受控语言[J] ''Controlled Language in Machine Translation''. 中国科技翻译 Chinese Science and Technology Translation, (03): 24-27. <br />
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Lian Shuneng连淑能 (2010). 英汉对比研究增订本[M]''An Updated Version of English-Chinese Contrastive Studies'' . 北京:高等教育出版社Beijing: Higher Education Publishing House. 35-36.<br />
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Li Yafei, Zhang Ruihua黎亚飞,张瑞华 (2019). 机器翻译发展与现状[J]''The Development and Current Situation of Machine Translation''. 中国轻工教育 China Light Industry Education, (5):38-45. <br />
<br />
Qin Yi秦毅(2004),从翻译基本标准议医学英语的翻译[J] ''On the Translation of Medical English from the Basic Standard of Translation''. 遵义医学院学报 Journal of Zunyi Medical College,27 (4): 421-423. <br />
<br />
Slype G V & Guinet J F & Seitz F (1984). ''Better Translation for Better Communication'' [M] . Oxford: Pergamon Press Ltd (U.K.). 90-93<br />
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O'Brien S (2002). Teaching Post-editing: A Proposal for Course Con-tent [EB/OL]. http://mt-archive. Info/EAMT-2002-0brien. Pdf.<br />
<br />
Tytler, A. F. (1978). ''Essay On The Principles of Translation''[M]. Amsterdam: JohnBenjamins Publishing. 118-119<br />
<br />
Wang Yan王燕 (2008). 医学英语翻译与写作教程[M] ''Medical English Translation and Writing Course''. 重庆:重庆大学出版社 Chongqing: Chongqing University Press. 60-61<br />
<br />
=12 蔡珠凤=(The Mistranslation of C-J Machine Translation of Political Statements)=<br />
[[Machine_Trans_EN_12]]<br />
===Abstract===<br />
Language is the main way of communication between people. With the continuous development of globalization, the scale of cross-border exchanges is also expanding. However, due to cultural differences and diversity, the languages of different countries and regions are very different, which seriously hinders people's communication. The demand for efficient and convenient translation tools is increasing. At the same time, with the development of network technology and artificial intelligence, recognition technology based on deep learning is more and more widely used in English, Japanese and other fields.<br />
<br />
===Key words===<br />
machine translation; political statements; mistranslation of C-J machine translation<br />
===题目===<br />
The Mistranslation of C-J Machine Translation of Political Statements<br />
===摘要===<br />
语言是人与人之间交流的主要方式。随着全球化的不断发展，跨境交流的规模也在不断扩大。然而，由于文化的差异和多样性，不同国家和地区的语言差异很大，这严重阻碍了人们的交流。对高效便捷的翻译工具的需求正在增加。同时，随着网络技术和人工智能的发展，基于深度学习的识别技术在英语、日语等领域的应用越来越广泛。<br />
<br />
===关键词===<br />
机器翻译；政治发言；政治发言中译日的误译<br />
===1. Introduction===<br />
===Introduction to machine translation===<br />
Machine translation, also known as automatic translation, is a process of using computers to convert one natural language (source language) into another natural language (target language). It is a branch of computational linguistics, one of the ultimate goals of artificial intelligence, and has important scientific research value.At the same time, machine translation has important practical value. With the rapid development of economic globalization and the Internet, machine translation technology plays a more and more important role in promoting political, economic and cultural exchanges.The development of machine translation technology has been closely accompanied by the development of computer technology, information theory, linguistics and other disciplines. From the early dictionary matching, to the rule translation of dictionaries combined with linguistic expert knowledge, and then to the statistical machine translation based on corpus, with the improvement of computer computing power and the explosive growth of multilingual information, machine translation technology gradually stepped out of the ivory tower and began to provide real-time and convenient translation services for general users.（Zhang 2019:5-6)<br />
<br />
=== C-J machine translation software===<br />
Today's online machine translation software includes Baidu translation, Tencent translation, Google translation, Youdao translation, Bing translation and so on. Google was the first company to launch the machine translation system, and Baidu was the first company to import the machine translation system in China. In addition, Tencent and Youdao have attracted much attention.Machine translation is the process of using computers to convert one natural language into another. It usually refers to sentence and full-text translation between natural languages. In order to continuously improve the translation quality, R & D personnel have added artificial intelligence technologies such as speech recognition, image processing and deep neural network to machine translation on the basis of traditional machine translation based on rules, statistics and examples.With the increase of using machine translation, the joint cooperation between manual translation and machine translation will also increase significantly in the future. What criteria should be used to evaluate the quality of machine translation? In the evolving field of machine translation, there is an urgent need to clarify the unsolvable questions and solved problems.<br />
<br />
===The history of machine translation===<br />
The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. alchuni put forward the idea of using machines for translation. In 1933, Soviet inventor П.П. Trojansky designed a machine to translate one language into another, and registered his invention on September 5 of the same year; However, due to the low technical level in the 1930s, his translation machine was not made. In 1946, the first modern electronic computer ENIAC was born. Shortly after that, W. weaver, an American scientist and A. D. booth, a British engineer, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947 when discussing the application scope of electronic computer. In 1949, W. Weaver published the translation memorandum, which formally put forward the idea of machine translation. After 60 years of ups and downs, machine translation has experienced a tortuous and long development path. The academic community generally divides it into the following four stages:<br />
<br />
Pioneering period（1947-1964）<br />
<br />
In 1954, with the cooperation of IBM, Georgetown University completed the English Russian machine translation experiment with ibm-701 computer for the first time, showing the feasibility of machine translation to the public and the scientific community, thus opening the prelude to the study of machine translation.<br />
It is not too late for China to start this research. As early as 1956, the state included this research in the national scientific work development plan. The topic name is "machine translation, the construction of natural language translation rules and the mathematical theory of natural language". In 1957, the Institute of language and the Institute of computing technology of the Chinese Academy of Sciences cooperated in the Russian Chinese machine translation experiment, translating 9 different types of more complex sentences.From the 1950s to the first half of the 1960s, machine translation research has been on the rise. The United States and the former Soviet Union, two superpowers, have provided a lot of financial support for machine translation projects for military, political and economic purposes, while European countries have also paid considerable attention to machine translation research due to geopolitical and economic needs, and machine translation has become an upsurge for a time. In this period, although machine translation is just in the pioneering stage, it has entered an optimistic period of prosperity.<br />
<br />
Frustrated period（1964-1975）<br />
<br />
In 1964, in order to evaluate the research progress of machine translation, the American Academy of Sciences established the automatic language processing Advisory Committee (Alpac Committee) and began a two-year comprehensive investigation, analysis and test.In November 1966, the committee published a report entitled "language and machine" (Alpac report for short), which comprehensively denied the feasibility of machine translation and suggested stopping the financial support for machine translation projects. The publication of this report has dealt a blow to the booming machine translation, and the research of machine translation has fallen into a standstill. Coincidentally, during this period, China broke out the "ten-year Cultural Revolution", and basically these studies also stagnated. Machine translation has entered a depression.<br />
<br />
convalescence（1975-1989）<br />
<br />
Since the 1970s, with the development of science and technology and the increasingly frequent exchange of scientific and technological information among countries, the language barriers between countries have become more serious. The traditional manual operation mode has been far from meeting the needs, and there is an urgent need for computers to engage in translation. At the same time, the development of computer science and linguistics, especially the substantial improvement of computer hardware technology and the application of artificial intelligence in natural language processing, have promoted the recovery of machine translation research from the technical level. Machine translation projects have begun to develop again, and various practical and experimental systems have been launched successively, such as weinder system Eurpotra multilingual translation system, taum-meteo system, etc.However, after the end of the "ten-year holocaust", China has perked up again, and machine translation research has been put on the agenda again. The "784" project has paid enough attention to machine translation research. After the mid-1980s, the development of machine translation research in China has further accelerated. Firstly, two English Chinese machine translation systems, ky-1 and MT / ec863, have been successfully developed, indicating that China has made great progress in machine translation technology.<br />
<br />
New period(1990 present)<br />
<br />
With the universal application of the Internet, the acceleration of the process of world economic integration and the increasingly frequent exchanges in the international community, the traditional way of manual operation is far from meeting the rapidly growing needs of translation. People's demand for machine translation has increased unprecedentedly, and machine translation has ushered in a new development opportunity. International conferences on machine translation research have been held frequently, and China has made unprecedented achievements. A series of machine translation software have been launched, such as "Yixing", "Yaxin", "Tongyi", "Huajian", etc. Driven by the market demand, the commercial machine translation system has entered the practical stage, entered the market and came to the users.Since the new century, with the emergence and popularization of the Internet, the amount of data has increased sharply, and statistical methods have been fully applied. Internet companies have set up machine translation research groups and developed machine translation systems based on Internet big data, so as to make machine translation really practical, such as "Baidu translation", "Google translation", etc. In recent years, with the progress of in-depth learning, machine translation technology has further developed, which has promoted the rapid improvement of translation quality, and the translation in oral and other fields is more authentic and fluent.<br />
<br />
===The problem of machine translation at present===<br />
Error is inevitable<br />
Many people have misunderstandings about machine translation. They think that machine translation has great deviation and can't help people solve any problems. In fact, the error is inevitable. The reason is that machine translation uses linguistic principles. The machine automatically recognizes grammar, calls the stored thesaurus and automatically performs corresponding translation. However, errors are inevitable due to changes or irregularities in grammar, morphology and syntax, such as sentences with adverbials after "give me a reason to kill you first" in Dahua journey to the West. After all, a machine is a machine. No one has special feelings for language. How can it feel the lasting charm of "the tenderness of lowering its head, like the shame of a water lotus? After all, the meaning of Chinese is very different due to the changes of morphology, grammar and syntax and the change of context. Even many Chinese people are zhanger monks - they can't touch their heads, let alone machines.<br />
Bottleneck<br />
In fact, no matter which method, the biggest factor affecting the development of machine translation lies in the quality of translation. Judging from the achievements, the quality of machine translation is still far from the ultimate goal.<br />
Chinese mathematician and linguist Zhou Haizhong once pointed out in his paper "fifty years of machine translation": to improve the quality of machine translation, the first thing to solve is the problem of language itself rather than programming; It is certainly impossible to improve the quality of machine translation by relying on several programs alone. At the same time, he also pointed out that it is impossible for machine translation to achieve the degree of "faithfulness, expressiveness and elegance" when human beings have not yet understood how the brain performs fuzzy recognition and logical judgment of language. This view may reveal the bottleneck restricting the quality of translation. <br />
It is worth mentioning that American inventor and futurist ray cozwell predicted in an interview with Huffington Post that the quality of machine translation will reach the level of human translation by 2029. There are still many disputes about this thesis in the academic circles.<br />
<br />
===2.Mistranslation of Chinese Japanese machine translation===<br />
===2.1Vocabulary mistranslation in Chinese Japanese machine translation===<br />
===2.1.1Mistranslation of proper nouns===<br />
In political materials, there are often political dignitaries' names, place names or a large number of proper nouns in the political field. The morphemes of such words are definite and inseparable. Mistranslation will make the source language lose its specific meaning. <br />
<br />
Japanese translation into Chinese Chinese translation into Japanese<br />
<br />
original text translation by Youdao reference translation original text translation by Youdao reference translation<br />
<br />
朱鎔基 朱基 朱镕基 栗战书 栗戰史書 栗戰書<br />
<br />
労安 劳安 劳安 李克强 李克強 李克強 <br />
<br />
筑紫哲也 筑紫哲也 筑紫哲也 习近平 習近平 習近平<br />
<br />
山口百惠 山口百惠 山口百惠 韩正 韓中 韓正<br />
<br />
田中角栄 田中角荣 田中角荣 王沪宁 王上海氏 王滬寧<br />
<br />
東条英機 东条英社 东条英机 汪洋 汪洋 汪洋<br />
<br />
毛沢东 毛泽东 毛泽东 赵乐际 趙樂南 趙樂際<br />
<br />
トウ・ショウヘイ 大酱 邓小平 江泽民 江沢民 江沢民<br />
<br />
周恩来 周恩来 周恩来<br />
<br />
クリントン 克林顿 克林顿<br />
<br />
The above table counts 18 special names in the two texts, and 7 machine translation errors. In terms of mistranslation, there are not only "战书" but also "戰史" and "史書" in Chinese. "沪" is the abbreviation of Shanghai. In other words, since "战书" and "沪" are originally common nouns, this disrupts the choice of target language in machine translation. Except Katakana interference (except for トウシゃウヘイ, most of the mistranslations appear in the new Standing Committee. It can be seen that the machine translation system does not update the thesaurus in time. Different from other words, people's names have particularity. Especially as members of the Standing Committee of the Political Bureau of the CPC Central Committee, the translation of their names is officially unified. In this regard, public figures such as political dignitaries, stars, famous hosts and important. In addition, the machine also translates Japanese surnames such as "タ二モト" (谷本), "アンドウ" (安藤) into "塔尼莫特" and "龙胆". It can be found that the language feature that Japanese will be written together with Chinese characters and Hiragana also directly affects the translation quality of Japanese Chinese machine translation. Japanese people often use Katakana pronunciation. For example, when Japanese people talk with Premier Zhu, they often use "二ーハウ" (Hello). However, the machine only recognizes the two pseudonyms "二" and "ハ" and transliterates them into "尼哈" , ignoring the long sound after "二" and "ハ".<br />
<br />
original text Translation by Youdao reference translation<br />
<br />
日美安全体制 日米の安全体制 日米安保体制<br />
<br />
中国共产党第十九次全国代表大会 中国共産党第19回全国代表大会 中国共産党第19回全国代表大会（第19回党大会）<br />
<br />
十八大 十八大 第18回党大会中国特色社会主义<br />
<br />
中国特色社会主義 中国の特色ある社会主義 第18回党大会<br />
<br />
中国共产党中央委员会 中国共産党中央委員会 中国共産党中央委員会<br />
<br />
中国共産党中央委員会十八届中共中央政治局常委 第18代中国共產党中央政治局常務委員 第18期中共中央政治局常務委員<br />
<br />
十八届中共中央政治局委员 18期の中国共產党中央政治局委員 第18期中共中央政治局委員<br />
<br />
十九届中共中央政治局常委 十九回中国共產党中央政治局常務委員 第19期中央政治局常務委員<br />
<br />
中共十九届一中全会 中国共產党第十九回一中央委員会 第19期中央委員会第1回全体会議<br />
<br />
The above table is a comparison of the original and translated versions of some proper nouns. As shown in the table, the mistranslation problems are mainly reflected in the mismatch of numerals + quantifiers, the wrong addition of case auxiliary word "の", the lack of connectors, the Mistranslation of abbreviations, dead translation, and the writing errors of Chinese characters. The following is a specific analysis one by one.<br />
<br />
"十八届中共中央政治局常委", "十八届中共中央政治局委员", "十九届中共中央政治局常委" and "中共十九届一中全会" all have quantifiers "届", which are translated into "代", "期" and "回" respectively. The meaning is vague and should be uniformly translated into "期"; Among them, the translation of the last three proper nouns lacks the Chinese conjunction "第". The case auxiliary word "の" was added by mistake in the translation of "十八届中共中央政治局委员" and "日美安全体制". The "中国共产党中央委员会" did not write in the form of Japanese Ming Dynasty characters, but directly used simplified Chinese characters.<br />
<br />
The full name of "中共十九届一中全会" is "中国共产党第十九届中央委员会第一次全体会议", in which "一" stands for "第一次", which is an ordinal word rather than a cardinal word. Machine translation does not produce a correct translation. The fundamental reason is that there are no "规则" for translating such words in machine translation. If we can formulate corresponding rules for such words (for example, 中共m'1届m2 中全会→第m1期中央委员会第m2回全体会议), the translation system must be able to translate well no matter how many plenary sessions of the CPC Central Committee.<br />
<br />
===2.1.2Mistranslation of Polysemy===<br />
original text Translation by Youdao reference translation<br />
<br />
スタジオ 摄影棚/工作室 直播现场/演播厅<br />
<br />
日中関係の話 中日关系的故事 就中日关系(话题)<br />
<br />
溝 水沟 鸿沟<br />
<br />
それでは日中の問題について質問のある方。 那么对白天的问题有提问的人。 关于中日问题的话题，举手提问。<br />
<br />
私たちのクラスは20人ちょっとですが、 我们班有20人左右， 我们班二十多人的意见统一很难，<br />
<br />
いろいろな意見が出て、まとめるのは大変です。 但是又各种各样的意见，总结起来很困难。 <br />
<br />
一体どうやって、13億人もの人をまとめているんですか。 到底是怎么处理13亿的人的呢？ 中国是怎样把13亿人凝聚在一起的？<br />
<br />
In the original text, the word "スタジオ" appeared four times and was translated into "摄影棚" or "工作室" respectively, but the context is on-site interview, not the production site of photography or film. "話" has many semantics, such as "说话", "事情", "道理", etc. In accordance with the practice of the interview program, Premier Zhu Rongji was invited to answer five questions on the topic of China Japan relations. Obviously, the meaning of the word "故事" is very abrupt. The inherent Japanese word "日中" means "晌午、白天". At the same time, it is also the abbreviation of the names of the two countries. The machine failed to deal with it correctly according to the context. "溝" refers to the gap between China and Japan, not a "水沟". The former "まとめる" appears in the original text with the word "意见", which is intended to describe that it is difficult to unify everyone's opinions. The latter "まとめている" also refers to unifying the thoughts of 1.3 billion people, not "处理". Therefore, it is more appropriate to use "统一" and "处理" in human translation, rather than "总结意见" or "处理意见".<br />
<br />
Mistranslation of polysemy has always been a difficult problem in machine translation research. The translation of each word is correct, but it is often very different from the original expression in the context. Zhang Zhengzeng said that ambiguity is a common phenomenon in natural language. Its essence is that the same language form may have different meanings, which is also one of the differences between natural language and artificial language. Therefore, one of the difficulties faced by machine translation is language disambiguation (Zhang Zheng, 2005:60). In this regard, we can mark all the meanings of polysemous words and judge which meaning to choose by common collocation with other words. At the same time, strengthen the text recognition ability of the machine to avoid the translation inconsistent with the current context. In this way, we can avoid the mistake of "大酱" in the place where famous figures such as Deng Xiaoping should have appeared in the previous political articles.<br />
<br />
===2.1.3Mistranslation of compound words===<br />
Multi class words refer to a word with two or more parts of speech, also known as the same word and different classes.<br />
<br />
original text Translation by Youdao reference translation<br />
<br />
1998年江泽民主席曾经访问日本, 1998年の江沢民国家主席の日本訪問し、 1998年、江沢民総書記が日本を訪問し、かつ<br />
<br />
同已故小渊首相签署了联合宣言。 かつて同じ故小渕首相が署名した共同宣言 亡くなられた小渕総理と宣言に調印されました<br />
<br />
Chinese "同" has two parts of speech: prepositions and conjunctions: "故" has many parts of speech, such as nouns, verbs, adjectives and conjunctions. This directly affects the judgment of the machine at the source language level. The word "同" in the above table is used as a conjunction to indicate the other party of a common act. "故" is used as a verb with the semantic meaning of "死亡", which is a modifier of "小渊首相". The machine regards "同" as an adjective and "故" as a noun "原因", which leads to confusion in the structure and unclear semantics of the translation. Different from the polysemy problem, multi category words have at least two parts of speech, and there is often not only one meaning under each part of speech. In this regard, software R & D personnel should fully consider the existence of multi category words, so that the translation machine can distinguish the meaning of words on the basis of marking the part of speech, so as to select the translation through the context and the components of the word in the sentence. Of course, the realization of this function is difficult, and we need to give full play to the wisdom of R & D personnel.<br />
<br />
===2.2 Syntactic mistranslation in Chinese Japanese machine translation===<br />
===2.2.1Mistranslation of tenses===<br />
original text：History is written by the people, and all achievements are attributed to the people. <br />
<br />
Translation by Youdao：歴史は人民が書いたものであり、すべての成果は人民のためである。<br />
<br />
reference translation：歴史は人民が綴っていくものであり、すべての成果は人民に帰することとなります。<br />
<br />
The original sentence meaning is "历史是人民书写的历史" or "历史是人民书写的东西". When translated into Japanese, due to the influence of Japanese language habits, the formal noun "もの" should be supplemented accordingly. In this sentence, both the machine and the interpreter have translated correctly. However, the machine's recognition of "的" is biased, resulting in tense translation errors. The past, present and future will become "history", and this is a continuous action. The form translated as "~ ていく" not only reflects that the action is a continuous action, but also conforms to the tense and semantic information of the original text. In addition, the machine's treatment of the preposition "于" is also inappropriate. In the original text, "人民" is the recipient of action, not the target language. As the most obvious feature of isolated language, function words in Chinese play an important role in semantic expression, and their translation should be regarded as a focus of machine translation research.<br />
<br />
original text：李克强同志是十六届中共中央政治局常委,其他五位同志都是十六届中共中央政治局委员。<br />
<br />
Translation by Youdao：李克強総理は第16代中国共産党中央政治局常務委員であり、他の５人の同志はいずれも16期の中国共産党中央政治局委員である。<br />
<br />
reference translation：李克強同志は第16期中国共産党中央政治局常務委員を務め、他の５人は第16期中共中央政治局委員を務めました。<br />
<br />
Judging the verb "是" in the original text plays its most basic positive role, but due to the complexity of Chinese language, "是" often can not be completely transformed into the form of "だ / である" in the process of translation. This sentence is a judgment of what has happened. Compared with the 19th session, the 18th session has become history. This is an implicit temporal information. It is very difficult for machines without human brain to recognize this implicit information. "中共中央政治局常委" is the abbreviation of "中共中央政治局常务委员会委员" and it is a kind of position. In Japanese, often use it with verbs such as "務める","担当する",etc. The translator adopts the past tense of "務める", which conforms to the expression habit of Japanese and deals with the problem of tense at the same time. However, machine translation only mechanically translates this sentence into judgment sentence, and fails to correctly deal with the past information implied by the word "十八届".<br />
<br />
===2.2.2Mistranslation of honorifics===<br />
Honorific language is a language means to show respect to the listener. Different from Japanese, there is no grammatical category of honorifics in Chinese. There is no specific fixed grammatical form to express honorifics, self modesty and politeness. Instead, specific words such as "您", "请" and "劳驾" are used to express all kinds of respect or self modesty. <br />
<br />
Original text translation by Youdao reference translation<br />
<br />
女士们，先生们，同志们，朋友们 さんたち、先生たち、同志たち、お友达さん ご列席の皆さん<br />
<br />
谢谢大家！ ありがとうございます！ ご清聴ありがとうございました。<br />
<br />
これはどうされますか。 这是怎么回事呢? 您将如何解决这一问题？<br />
<br />
こうした問題をどうお考えでしょうか。 我们会如何考虑这些问题呢？ 您如何看待这一问题？<br />
<br />
For example, for the processing of "女士们，先生们，同志们，朋友们", the machine makes the translation correspond to the original one by one based on the principle of unchanged format, but there are errors in semantic communication and pragmatic habits. When speaking on formal occasions, Chinese expression tends to be comprehensive and detailed, as well as the address of the audience. In contrast, Japanese usually uses general terms such as "皆様", "ご臨席の皆さん" or "代表団の方々" as the opening remarks. In terms of Japanese Chinese translation, the machine also failed to recognize the usage of Japanese honorifics such as "さ れ る" and "お考え". It can be seen that Chinese Japanese machine translation has a low ability to deal with honorific expressions. The occasion is more formal. A good translation should not only be fluent in meaning, but also conform to the expression habits of the target language and match the current translation environment.<br />
<br />
===Conclusion===<br />
In the process of discussing the Mistranslation of machine translation, this paper mainly cites the Mistranslation of Youdao translator. When I studied the problem of machine translation mistranslation, I also used a large number of translation software such as Google and Baidu for parallel comparison, and found that these translation software also had similar problems with Youdao translator. For example, the "新世界新未来" in Chinese ABAC structural phrases is translated by Baidu as "新し世界の新しい未来", which, like Youdao, is not translated in the form of parallel phrases; Google online translation translates the word "“全心全意" into a completely wrong "全面的に"; The original sentence "我吃了很多亏" in the Mistranslation of the unique expression in the language is translated by Google online as "私はたくさんの損失を食べました". Because the "吃" and "亏" in the original text are not closely adjacent, the machine can not recognize this echo and mistakenly treats "亏" as a kind of food, so the machine translates the predicate as "食べました"; Japanese "こうした問題をどう考えでしょうか", Google's online translation is "你如何看待这些问题?", "你" tone can not reflect the tone of Japanese honorific, while Baidu translates as "你怎么想这样的问题呢?" Although the meaning can be understood, it is an irregular spoken language. Google and Baidu also made the same mistakes as Youdao in the translation of proper nouns. For example, they translated "十七届(中共中央政治局常委)”" into "第17回..." .In short, similar mistranslations of Youdao are also common in Baidu and Google. Due to space constraints, they will not be listed one by one here.<br />
<br />
Mr. Liu Yongquan, Institute of language, Chinese Academy of Social Sciences (1997) It has been pointed out that machine translation is a linguistic problem in the final analysis. Although corpus based machine translation does not require a lot of linguistic knowledge, language expression is ever-changing. Machine translation based solely on statistical ideas can not avoid the translation quality problems caused by the lack of language rules. The following is based on the analysis of lexical, syntactic and other mistranslations From the perspective of the characteristics of the source language, this paper summarizes some difficulties of Chinese and Japanese in machine translation.<br />
<br />
(1) The difficulties of Chinese in machine translation <br />
<br />
Chinese is a typical isolated language. The relationship between words needs to be reflected by word order and function words. We should pay attention to the transformation of function words such as "的", "在", "向" and "了". Some special verbs, such as "是", "做" and "作", are widely used. How to translate on the basis of conforming to Japanese pragmatic habits and expressions is a difficulty in machine translation research. In terms of word order, Chinese is basically "subject→predicate→object". At the same time, Chinese pays attention to parataxis, and there is no need to be clear in expression with meaningful cohesion, which increases the difficulty of Chinese Japanese machine translation. When there are multiple verbs or modifier components in complex long sentences, machine translation usually can not accurately divide the components of the sentence, resulting in the result that the translation is completely unreadable. <br />
<br />
(2) Difficulties of Japanese in machine translation <br />
<br />
Both Chinese and Japanese languages use Chinese characters, and most machines produce the target language through the corresponding translation of Chinese characters. However, pseudonyms in Japanese play an important role in judging the part of speech and meaning, and can not only recognize Chinese characters and judge the structure and semantics of sentences. Japanese vocabulary is composed of Chinese vocabulary, inherent vocabulary and foreign vocabulary. Among them, the first two have a great impact on Chinese Japanese machine translation. For example "提出" is translated as "提出する" or "打ち出す"; and "重视" is translated as "重視する" or "大切にする". Japanese is an adhesive language, which contains a lot of "けど", "が" and "けれども" Some of them are transitional and progressive structures, but there are also some sequential expressions that do not need translation, and these translation software often can not accurately grasp them.<br />
<br />
===References===<br />
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[10]Lv Yinqiu 呂寅秋(1996).機械翻訳の言語規則と伝統文法との相違点.【D】The language rules of mechanical translation, the traditional grammar, and the points of contradiction.日本学研究.Japanese Studies.1996(00):21-22 <br />
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[11]Liu Jun 刘君(2014).基于语料库的中日同形词词义用法对比及其日中机器翻译研究【D】.A Corpus-based Comparison of the Meanings of Chinese and Japanese Homographs and Research on Japanese-Chinese Machine Translation.广西大学.(03) <br />
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[13]Zhang Yi 张义(2019).机器翻译的译文分析【D】.Translation analysis of machine translation.西安外国语大学.(10) <br />
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[14]Zhang Linqian 张琳婧(2019).在线机器翻译中日翻译错误原因及对策【D】.Causes and countermeasures of online machine translation errors in Chinese-Japanese translation.山西大学.(02)<br />
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[15]Wang Dan 王丹(2020).基于机器翻译的专利文本译后编辑对策研究【D】.Research on countermeasures for post-translational editing of patent texts based on machine translation.大连理工大学.(06)<br />
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[16]Yang Xiaokun 杨晓琨(2020).日中机器翻译中的前编辑规则与效果验证【D】.Pre-editing rules and effect verification in Japanese-Chinese machine translation.大连理工大学.(06)<br />
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[17]Zuo Jia 左嘉(2021). 机器翻译日译汉误译研究【D】. Research on Mistranslation of Machine Translation from Japanese to Chinese.北京第二外国语学院.<br />
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[19]Che Tong 车彤(2021).汉译日机器翻译质量评估及译后编辑策略研究【D】.Research on Quality Evaluation of Chinese-Japanese Machine Translation and Post-translation Editing Strategies.北京外国语大学.(09)<br />
<br />
Networking Linking<br />
<br />
http://www.elecfans.com/rengongzhineng/692245.html<br />
<br />
https://baike.baidu.com/item/%E6%9C%BA%E5%99%A8%E7%BF%BB%E8%AF%91/411793<br />
<br />
=13 陈湘琼Chen Xiangqiong（Study on Post-editing from the Perspective of Functional Equivalence Theory ）=<br />
[[Machine_Trans_EN_13]]<br />
<br />
=14 Bi bi Nadia（Machine Translation a Challenge for Human Translators)=<br />
[[Machine_Trans_EN_14]]<br />
<br />
===Abstract===<br />
Machine translation is a big obstacle in the way of Human translators or interpretors although it is quick and less time consuming.people are trying to get translation of their target language using through source language.For this purpose they are using digital apps like Google translation Google translation does not give accurate and exact interpretation.Google translator is translates word to word translate that doesn't clarifies it's true and actual meaning.On the other hand human translators can give exact and accurate translation ,they take care of grammatical errors , diction and sentence structure.They clarify the purpose of target language through using source language.<br />
<br />
===Keywords===<br />
Descriptive translation, academic, interdiscipline, comparative literature, localization,Translatology,school of thought,translation , studies, linguistics, corresponding.<br />
<br />
===Body of article===<br />
Translation is the process of reworking text from one language into another to maintain the original message and communication. But, like everything else, there are different methods of translation, and they vary in form and function. What is translation? The term has become widely used among knowledge transfer researchers and practitioners, especially in the fields of health and health care. In a landmark review, Jonathan Lomas began to argue that 'The tasks… may be defined as to establish and maintain links between researchers and their audience, via the appropriate translation of research findings' (Lomas 1997, p 4). In 2004, the World Health Organization's World Report on Knowledge for Better Health suggested that 'One of the key contributions of research to health systems is the translation of knowledge into actions' (WHO 2004, p 33 and p 100). By 2006, special issues of WHO's Bulletin as well as the journals Evaluation and the Health Professions and the Journal of Continuing Education in the Health Professions were dedicated to translation.But what does 'translation' mean? It may be a new word for an old problem, meaning nothing more than 'transfer'. The rapidly emerging field of 'translational medicine' seems to take translation to mean generally what transfer might have meant, that is the transmission of knowledge and evidence 'from bench to bedside'. As one commentator has observed, "Translational medicine" as a fashionable term is being increasingly used to describe the wish of biomedical researchers to ultimately help patients' (Wehling 2008, abstract). <br />
Semantic uncertainty persists, not least because of the different interests of scientists, clinicians, patients and commercial firms (Littman et al 2007): 'Translational research means different things to different people, but it seems important to almost everyone' (Woolf 2008, p 211).<br />
In related fields, such as public health (Armstrong et al 2006), 'translation' seems to signify dissatisfaction with 'transfer'. It wants to move away from thinking of knowledge transfer as a form of technology transfer or dissemination, rejecting if only by implication its mechanistic assumptions and its model of linear messaging from A to B. But still, what does it signify?<br />
<br />
===Why translation?===<br />
'Translation' indicates a closer attention to the problem of shared meaning and how it might be developed. It seems to represent some new epistemological lubricant, facilitating the dissemination of texts and the application and use of the knowledge and information they in. Simply, translation might be the key to transfer. And yet, when we stop to think, we are more ambivalent. What is translated often seems somehow inferior, not real or original. Note how readily commentators reach for the idea that things might be 'lost in translation'. Knowing at a distance – made in and mediated by translation - makes for incomplete renditions, blurred images, partial truths. So what might 'translation' really mean? The purpose of this paper is to set out, for policy makers and practitioners, the theoretical and conceptual resources translation holds and seems to represent. In doing so, it explores understandings of translation in the fields of literature and linguistics and in the sociology of science and technology. It begins by setting out just why this idea of translation should make immediate, intuitive sense in relation to research, policy and practice.<br />
1translation in research, policy and practice research as translation<br />
Research often entails translation from one language to another: where data is collected from more than one ethnic group, for example, or where the language of the researcher is other than that of the research subject. It may draw on a secondary literature or source documents written in different languages, and may be published and disseminated in languages other than the one in which it is first written up.<br />
2In a different way, to conduct an interview is to ask for an account of experience and its meanings, but it is also to construct and translate that experience in terms defined at least in part by the researcher. In representing what is said, transcripts then select data, usually excluding significant gesture and eye-contact, for example. Often, certain characteristics of speech-acts (such as hesitations) will be edited out. In turn, the format of the transcript shapes the analytic use the researcher may make of it. The basis of research 'findings', then, is an artefact, a transcript or translation, not an original interaction (Ochs 1979, Barnes, Bloor and Henry 1996, Ross 2009).<br />
3In this way, the researcher recasts aspects of his or her problem or topic in new, scientific form: 'All researchers "translate" the experiences of others' (Temple 1997, p 609). Research is invariably conducted in a sort of 'metalanguage' (Hantrais and Ager 1985): the research process can be conceived as one of successive translations (from theoretical formulation to operationalization, transcription, interpretation and dissemination). Theorization is a process of reciprocal back and forth between theory and fact, in which conceptions of each are revised in order that one fit the other (Baldamus 1974).<br />
4 It is a kind of translation: a rereading, re-use, re-application or re-representation of what we know in new terms (Turner 1980). Referencing, too, is an act of translation, a form of appropriation and incorporation of one text by another (Gilbert 1977).policy as translation Each of the fields covered by the paper is diverse and ill-defined, and there is no intention here to provide a comprehensive account of any them. Sources have been chosen for their relevance: main references are cited in the text, and additional sources listed in footnotes.<br />
<br />
2For a brief introduction to the technical issues involved in social research in more than one language, see Birbili (2000). For translation issues in survey research and question design in general, see Ervin and Bower (1952) and Deutscher (1968); on translating survey research instruments (in this instance health-related quality of life measures), Bowden and Fox-Rushby (2003). On the use of translators and interpreters, see Temple (1997) and Jentsch (1998).<br />
3For an interesting discussion of this problem, see Bourdieu (1999), esp pp 621-626, 'The risks of writing'. <br />
===Difference between machine translation and human translators===<br />
Few people disagree on the differences between the two, but many argue over the quality of the translations. How accurate are machine translations? How reliable are human translations? Some say machine translation produces near-perfect translations while others are adamant that translations are incomprehensible and cause more problems than they solve. Results will, of course, vary depending on the source and target languages, the machine translation service used (e.g. Google Translate), and the complexity of the original text.<br />
Machine translation, love it or hate it, is here to stay. In fact, the machine translation market is growing at such a fast pace that it is predicted to reach $980 million by 2022.<br />
===Machine translation: the pros and cons===<br />
The advantages of machine translation generally come down to two factors: it’s faster and cheaper. The downside to this is the standard of translation can be anywhere from inaccurate, to incomprehensible, and potentially dangerous (more on that shortly).<br />
==The advantages of machine translation==<br />
Many free tools are readily available (Google Translate, Skype Translator, etc.)<br />
Quick turnaround time <br />
You can translate between multiple languages using one tool<br />
Translation technology is constantly improving<br />
The disadvantages of machine translation<br />
Level of accuracy can be very low <br />
Accuracy is also very inconsistent across different languages<br />
==Machines can't translate context ==<br />
Mistakes are sometimes costly <br />
Sometimes translation simply doesn’t work<br />
The most important thing to consider with any kind of translation is the cost of potential mistakes. Translating instructions for medical equipment, aviation manuals, legal documents and many other kinds of content require 100% accuracy. In such cases, mistakes can cost lives, huge amounts of money and irreparable damage to your company’s image. So choose carefully!<br />
Human translation: the pros and cons<br />
Human translation essentially switches the table in terms of pros and cons. A higher standard of accuracy comes at the price of longer turnaround times and higher costs. What you have to decide is whether that initial investment outweighs the potential cost of mistakes. Alternatively, whether mistakes simply aren’t an option, like the cases we looked at in the previous section.<br />
<br />
==The advantages of human translation ==<br />
It’s a translator’s job to ensure the highest accuracy<br />
Humans can interpret context and capture the same meaning, rather than simply translating words<br />
Human translators can review their work and provide a quality process<br />
Humans can interpret the creative use of language, e.g. puns, metaphors, slogans, etc.<br />
Professional translators understand the idiomatic differences between their languages<br />
Humans can spot pieces of content where literal translation isn’t possible and find the most suitable alternative<br />
The disadvantages of human translation<br />
Turnaround time is longer <br />
Translators rarely work for free <br />
Unless you use a translation agency, with access to thousands of translators, you’re limited to the languages any one translator can work with<br />
Simply put, human translation is your best option when accuracy is even remotely important. Other considerations to make are the complexity of your source material and the two languages you’re translating between – both of which can render machines pretty useless.<br />
<br />
When to use machine and human translation<br />
The truth is, the debate over machine vs human translation is an unnecessary distraction. What we should really be talking about is when to use these two different types of translation services, because they both serve a very valid purpose.<br />
<br />
Examples of when to use machine translation<br />
When you have a large bulk of content to translate and the general meaning is enough<br />
When your translation never reaches the final audience, e.g. you’re translating a resource as research for another piece of content<br />
Translating documents for internal use within a company, provided 100% accuracy isn’t needed<br />
To partially translate large chunks of content for a human translator to improve upon<br />
Examples of when to use human translation<br />
When accuracy is important<br />
Most cases where your translated content is received by a consumer audience<br />
When you have a duty of care to provide accurate translations (e.g. legal documents, product instructions, medical guidelines or health and safety content)<br />
When translating marketing material or other texts for creative language uses.<br />
<br />
types of machine translation.<br />
<br />
What is Machine Translation? Rule Based Machine Translation vs. Statistical Machine Translation. Machine translation (MT) is automated translation. It is the process by which computer software is used to translate a text from one natural language (such as English) to another (such as Spanish).<br />
<br />
To process any translation, human or automated, the meaning of a text in the original (source) language must be fully restored in the target language, i.e. the translation. While on the surface this seems straightforward, it is far more complex. Translation is not a mere word-for-word substitution. A translator must interpret and analyze all of the elements in the text and know how each word may influence another. This requires extensive expertise in grammar, syntax (sentence structure), semantics (meanings), etc., in the source and target languages, as well as familiarity with each local region.<br />
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Human and machine translation each have their share of challenges. For example, no two individual translators can produce identical translations of the same text in the same language pair, and it may take several rounds of revisions to meet customer satisfaction. But the greater challenge lies in how machine translation can produce publishable quality translations.<br />
<br />
Rule-Based Machine Translation Technology<br />
Rule-based machine translation relies on countless built-in linguistic rules and millions of bilingual dictionaries for each language pair.<br />
The software parses text and creates a transitional representation from which the text in the target language is generated. This process requires extensive lexicons with morphological, syntactic, and semantic information, and large sets of rules. The software uses these complex rule sets and then transfers the grammatical structure of the source language into the target language.<br />
Translations are built on gigantic dictionaries and sophisticated linguistic rules. Users can improve the out-of-the-box translation quality by adding their terminology into the translation process. They create user-defined dictionaries which override the system’s default settings.<br />
In most cases, there are two steps: an initial investment that significantly increases the quality at a limited cost, and an ongoing investment to increase quality incrementally. While rule-based MT brings companies to the quality threshold and beyond, the quality improvement process may be long and expensive.<br />
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Statistical Machine Translation Technology<br />
Statistical machine translation utilizes statistical translation models whose parameters stem from the analysis of monolingual and bilingual corpora. Building statistical translation models is a quick process, but the technology relies heavily on existing multilingual corpora. A minimum of 2 million words for a specific domain and even more for general language are required. Theoretically it is possible to reach the quality threshold but most companies do not have such large amounts of existing multilingual corpora to build the necessary translation models. Additionally, statistical machine translation is CPU intensive and requires an extensive hardware configuration to run translation models for average performance levels.<br />
<br />
Rule-Based MT vs. Statistical MT<br />
Rule-based MT provides good out-of-domain quality and is by nature predictable. Dictionary-based customization guarantees improved quality and compliance with corporate terminology. But translation results may lack the fluency readers expect. In terms of investment, the customization cycle needed to reach the quality threshold can be long and costly. The performance is high even on standard hardware.<br />
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Statistical MT provides good quality when large and qualified corpora are available. The translation is fluent, meaning it reads well and therefore meets user expectations. However, the translation is neither predictable nor consistent. Training from good corpora is automated and cheaper. But training on general language corpora, meaning text other than the specified domain, is poor. Furthermore, statistical MT requires significant hardware to build and manage large translation models.<br />
<br />
Rule-Based MT Statistical MT<br />
+ Consistent and predictable quality – Unpredictable translation quality<br />
+ Out-of-domain translation quality – Poor out-of-domain quality<br />
+ Knows grammatical rules – Does not know grammar <br />
+ High performance and robustness – High CPU and disk space requirements<br />
+ Consistency between versions – Inconsistency between versions <br />
– Lack of fluency + Good fluency<br />
– Hard to handle exceptions to rules + Good for catching exceptions to rules <br />
– High development and customization costs + Rapid and cost-effective development costs provided the required corpus exists<br />
Given the overall requirements, there is a clear need for a third approach through which users would reach better translation quality and high performance (similar to rule-based MT), with less investment (similar to statistical MT).<br />
Post-Edited Machine Translation (PEMT)<br />
Often, PEMT is used to bridge the gap between the speed of machine translation and the quality of human translation, as translators review, edit and improve machine-translated texts. PEMT services cost more than plain machine translations but less than 100% human translation, especially since the post-editors don’t have to be fluently bilingual—they just have to be skilled proofreaders with some experience in the language and target region.<br />
Successful translation is about more than just the words, which is why we advocate for not just human translation by skilled linguists, but for translation by people deeply familiar with the cultures they’re writing for. Life experience, study and the knowledge that only comes from living in a geographic region can make the difference between words that are understandable and language that is capable of having real, positive impact. <br />
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PacTranz<br />
The HUGE list of 51 translation types, methods and techniques<br />
Upper section of infographic of 51 common types of translation classified in 4 broad categoriesThere are a bewildering number of different types of translation.<br />
So we’ve identified the 51 types you’re most likely to come across, and explain exactly what each one means.<br />
This includes all the main translation methods, techniques, strategies, procedures and areas of specialisation.<br />
It’s our way of helping you make sense of the many different kinds of translation – and deciding which ones are right for you.<br />
Don’t miss our free summary pdf download later in the article!<br />
The 51 types of translation we’ve identified fall neatly into four distinct categories.<br />
Translation Category A: 15 types of translation based on the technical field or subject area of the text<br />
Icons representing 15 types of translation categorised by the technical field or subject area of the textTranslation companies often define the various kinds of translation they provide according to the subject area of the text.<br />
This is a useful way of classifying translation types because specialist texts normally require translators with specialist knowledge.<br />
Here are the most common types you’re like to come across in this category.<br />
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1. General Translation<br />
What is it?<br />
The translation of non-specialised text. That is, text that we can all understand without needing specialist knowledge in some area.<br />
The text may still contain some technical terms and jargon, but these will either be widely understood, or easily researched.<br />
What this means<br />
The implication is that you don’t need someone with specialist knowledge for this type of translation – any professional translator can handle them.<br />
Translators who only do this kind of translation (don’t have a specialist field) are sometimes referred to as ‘generalist’ or ‘general purpose’ translators.<br />
Examples<br />
Most business correspondence, website content, company and product/service info, non-technical reports.<br />
Most of the rest of the translation types in this Category do require specialist translators.<br />
Check out our video on 13 types of translation requiring special translator expertise:<br />
<br />
2. Technical Translation<br />
What is it?<br />
We use the term “technical translation” in two different ways:<br />
Broad meaning: any translation where the translator needs specialist knowledge in some domain or area.<br />
This definition would include almost all the translation types described in this section.<br />
Narrow meaning: limited to the translation of engineering (in all its forms), IT and industrial texts.<br />
This narrower meaning would exclude legal, financial and medical translations for example, where these would be included in the broader definition.<br />
What this means<br />
Technical translations require knowledge of the specialist field or domain of the text.<br />
That’s because without it translators won’t completely understand the text and its implications. And this is essential if we want a fully accurate and appropriate translation.Good to know Many technical translation projects also have a typesetting/dtp requirement. Be sure your translation provider can handle this component, and that you’ve allowed for it in your project costings and time frames.<br />
Examples<br />
Manuals, specialist reports, product brochures<br />
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3. Scientific Translation<br />
What is it?<br />
The translation of scientific research or documents relating to it.<br />
What this means<br />
These texts invariably contain domain-specific terminology, and often involve cutting edge research.<br />
So it’s imperative the translator has the necessary knowledge of the field to fully understand the text. That’s why scientific translators are typically either experts in the field who have turned to translation, or professionally qualified translators who also have qualifications and/or experience in that domain.<br />
On occasion the translator may have to consult either with the author or other domain experts to fully comprehend the material and so translate it appropriately.<br />
Examples<br />
Research papers, journal articles, experiment/trial results<br />
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4. Medical Translation<br />
What is it?<br />
The translation of healthcare, medical product, pharmaceutical and biotechnology materials.<br />
Medical translation is a very broad term covering a wide variety of specialist areas and materials – everything from patient information to regulatory, marketing and technical documents.<br />
As a result, this translation type has numerous potential sub-categories – ‘medical device translations’ and ‘clinical trial translations’, for example.<br />
What this means<br />
As with any text, the translators need to fully understand the materials they’re translating. That means sound knowledge of medical terminology and they’ll often also need specific subject-matter expertise.<br />
Good to know<br />
Many countries have specific requirements governing the translation of medical device and pharmaceutical documentation. This includes both your client-facing and product-related materials.<br />
Examples<br />
Medical reports, product instructions, labeling, clinical trial documentation<br />
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5. Financial Translation<br />
What is it?<br />
In broad terms, the translation of banking, stock exchange, forex, financing and financial reporting documents.<br />
However, the term is generally used only for the more technical of these documents that require translators with knowledge of the field.<br />
Any competent translator could translate a bank statement, for example, so that wouldn’t typically be considered a financial translation.<br />
What this means<br />
You need translators with domain expertise to correctly understand and translate the financial terminology in these texts.<br />
Examples<br />
Company accounts, annual reports, fund or product prospectuses, audit reports, IPO documentation<br />
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6. Economic Translations<br />
What is it?<br />
1. Sometimes used as a synonym for financial translations.<br />
2. Other times used somewhat loosely to refer to any area of economic activity – so combining business/commercial, financial and some types of technical translations.<br />
3. More narrowly, the translation of documents relating specifically to the economy and the field of economics.<br />
What this means<br />
As always, you need translators with the relevant expertise and knowledge for this type of translation.<br />
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7. Legal Translation<br />
What is it?<br />
The translation of documents relating to the law and legal process.<br />
What this means<br />
Legal texts require translators with a legal background.<br />
That’s because without it, a translator may not:<br />
– fully understand the legal concepts<br />
– write in legal style<br />
– understand the differences between legal systems, and how best to translate concepts that don’t correspond.<br />
And we need all that to produce professional quality legal translations – translations that are accurate, terminologically correct and stylistically appropriate.<br />
Examples<br />
Contracts, legal reports, court judgments, expert opinions, legislation<br />
<br />
8. Juridical Translation<br />
What is it?<br />
1. Generally used as a synonym for legal translations.<br />
2. Alternatively, can refer to translations requiring some form of legal verification, certification or notarization that is common in many jurisdictions.<br />
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9. Judicial Translation<br />
What is it?<br />
1. Most commonly a synonym for legal translations.<br />
2. Rarely, used to refer specifically to the translation of court proceeding documentation – so judgments, minutes, testimonies, etc. <br />
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10. Patent Translation<br />
What is it?<br />
The translation of intellectual property and patent-related documents.<br />
Key features<br />
Patents have a specific structure, established terminology and a requirement for complete consistency throughout – read more on this here. These are key aspects to patent translations that translators need to get right.<br />
In addition, subject matter can be highly technical.<br />
What this means<br />
You need translators who have been trained in the specific requirements for translating patent documents. And with the domain expertise needed to handle any technical content.<br />
Examples<br />
Patent specifications, prior art documents, oppositions, opinions<br />
<br />
11. Literary Translation<br />
What is it?<br />
The translation of literary works – novels, short stories, plays, essays, poems.<br />
Key features<br />
Literary translation is widely regarded as the most difficult form of translation.<br />
That’s because it involves much more than simply conveying all meaning in an appropriate style. The translator’s challenge is to also reproduce the character, subtlety and impact of the original – the essence of what makes that work unique.<br />
This is a monumental task, and why it’s often said that the translation of a literary work should be a literary work in its own right.<br />
What this means<br />
Literary translators must be talented wordsmiths with exceptional creative writing skills.<br />
Because few translators have this skillset, you should only consider dedicated literary translators for this type of translation.<br />
<br />
<br />
12. Commercial Translation<br />
What is it?<br />
The translation of documents relating to the world of business.<br />
This is a very generic, wide-reaching translation type. It includes other more specialised forms of translation – legal, financial and technical, for example. And all types of more general business documentation.<br />
Also, some documents will require familiarity with business jargon and an ability to write in that style.<br />
What this means<br />
Different translators will be required for different document types – specialists should handle materials involving technical and specialist fields, whereas generalist translators can translate non-specialist materials.<br />
Examples<br />
Business correspondence, reports, marketing and promotional materials, sales proposals<br />
<br />
13. Business Translations<br />
What is it?<br />
A synonym for Commercial Translations.<br />
<br />
14. Administrative Translations<br />
What is it?<br />
The translation of business management and administration documents.<br />
So it’s a subset of business / commercial translations.<br />
What this means<br />
The implication is these documents will include business jargon and ‘management speak’, so require a translator familiar with, and practised at, writing in that style.<br />
Examples<br />
Management reports and proposals<br />
<br />
15. Marketing Translations<br />
What is it?<br />
The translation of advertising, marketing and promotional materials.<br />
This is a subset of business or commercial translations.<br />
Key features<br />
Marketing copy is designed to have a specific impact on the audience – to appeal and persuade.<br />
So the translated copy must do this too.<br />
But a direct translation will seldom achieve this – so translators need to adapt their wording to produce the impact the text is seeking.<br />
And sometimes a completely new message might be needed – see transcreation in our next category of translation types.<br />
What this means<br />
Marketing translations require translators who are skilled writers with a flair for producing persuasive, impactful copy.<br />
As relatively few translators have these skills, engaging the right translator is key.<br />
Good to know<br />
This type of translation often comes with a typesetting or dtp requirement – particularly for adverts, posters, brochures, etc.<br />
Its best for your translation provider to handle this component. That’s because multilingual typesetters understand the design and aesthetic conventions in other languages/cultures. And these are essential to ensure your materials have the desired impact and appeal in your target markets.<br />
Examples<br />
Advertising, brochures, some website/social media text.<br />
Translation Category B: 14 types of translation based on the end product or use of the translation<br />
This category is all about how the translation is going to be used or the end product that’s produced.<br />
Most of these types involve either adapting or processing a completed translation in some way, or converting or incorporating it into another program or format.<br />
You’ll see that some are very specialised, and complex.<br />
It’s another way translation providers refer to the range of services they provide.<br />
Check out our video of the most specialised of these types of translation:<br />
<br />
16. Document Translations<br />
What is it?<br />
The translation of documents of all sorts.<br />
Here the translation itself is the end product and needs no further processing beyond standard formatting and layout.<br />
<br />
17. Text Translations<br />
What is it?<br />
A synonym for document translation.<br />
<br />
18. Certified Translations<br />
What is it?<br />
A translation with some form of certification.<br />
Key features<br />
The certification can take many forms. It can be a statement by the translation company, signed and dated, and optionally with their company seal. Or a similar certification by the translator.<br />
The exact format and wording will depend on what clients and authorities require – here’s an example.<br />
<br />
19. Official Translations<br />
What is it?<br />
1. Generally used as a synonym for certified translations.<br />
2. Can also refer to the translation of ‘official’ documents issued by the authorities in a foreign country. These will almost always need to be certified.<br />
<br />
20. Software Localisation<br />
What is it?<br />
Adapting software for another language/culture.<br />
Key features<br />
The goal of software localisation is not just to make the program or product available in other languages. It’s also about ensuring the user experience in those languages is as natural and effective as possible.<br />
Translating the user interface, messaging, documentation, etc is a major part of the process.<br />
Also key is a customisation process to ensure everything matches the conventions, norms and expectations of the target cultures.<br />
Adjusting time, date and currency formats are examples of simple customisations. Others might involve adapting symbols, graphics, colours and even concepts and ideas.<br />
Localisation is often preceded by internationalisation – a review process to ensure the software is optimally designed to handle other languages.<br />
And it’s almost always followed by thorough testing – to ensure all text is in the correct place and fits the space, and that everything makes sense, functions as intended and is culturally appropriate.<br />
Localisation is often abbreviated to L10N, internationalisation to i18n.<br />
What this means<br />
Software localisation is a specialised kind of translation, and you should always engage a company that specialises in it.<br />
They’ll have the systems, tools, personnel and experience needed to achieve top quality outcomes for your product.<br />
<br />
21. Game Localisation<br />
What is it?<br />
Adapting games for other languages and markets.<br />
<br />
It’s a subset of software localisation.<br />
Key features<br />
The goal of game localisation is to provide an engaging and fun gaming experience for speakers of other languages.<br />
<br />
It involves translating all text and recording any required foreign language audio.<br />
<br />
But also adapting anything that would clash with the target culture’s customs, sensibilities and regulations.<br />
<br />
For example, content involving alcohol, violence or gambling may either be censored or inappropriate in the target market.<br />
<br />
And at a more basic level, anything that makes users feel uncomfortable or awkward will detract from their experience and thus the success of the game in that market.<br />
<br />
So portions of the game may have to be removed, added to or re-worked.<br />
<br />
Game localisation involves at least the steps of translation, adaptation, integrating the translations and adaptations into the game, and testing.<br />
<br />
What this means<br />
Game localisation is a very specialised type of translation best left to those with specific expertise and experience in this area.<br />
<br />
22. Multimedia Localisation<br />
What is it?<br />
Adapting multimedia for other languages and cultures.<br />
<br />
Multimedia refers to any material that combines visual, audio and/or interactive elements. So videos and movies, on-line presentations, e-Learning courses, etc.<br />
Key features<br />
Anything a user can see or hear may need localising.<br />
<br />
That means the audio and any text appearing on screen or in images and animations.<br />
<br />
Plus it can mean reviewing and adapting the visuals and/or script if these aren’t suitable for the target culture.<br />
<br />
The localisation process will typical involve:<br />
– Translation<br />
– Modifying the translation for cultural reasons and/or to meet technical requirements<br />
– Producing the other language versions<br />
<br />
Audio output may be voice-overs, dubbing or subtitling.<br />
<br />
And output for visuals can involve re-creating elements, or supplying the translated text for the designers/engineers to incorporate.<br />
<br />
What this means<br />
Multimedia localisation projects vary hugely, and it’s essential your translation providers have the specific expertise needed for your materials.<br />
<br />
<br />
<br />
23. Script Translations<br />
What is it?<br />
Preparing the text of recorded material for recording in other languages.<br />
Key features<br />
There are several issues with script translation.<br />
<br />
One is that translations typically end up longer than the original script. So voicing the translation would take up more space/time on the video than the original language.<br />
<br />
Sometimes that space will be available and this will be OK.<br />
<br />
But generally it won’t be. So the translation has to be edited back until it can be comfortably voiced within the time available on the video.<br />
<br />
Another challenge is the translation may have to synchronise with specific actions, animations or text on screen.<br />
<br />
Also, some scripts also deal with technical subject areas involving specialist technical terminology.<br />
<br />
Finally, some scripts may be very culture-specific – featuring humour, customs or activities that won’t work well in another language. Here the script, and sometimes also the associated visuals, may need to be adjusted before beginning the translation process.<br />
<br />
It goes without saying that a script translation must be done well. If it’s not, there’ll be problems producing a good foreign language audio, which will compromise the effectiveness of the video.<br />
<br />
Translators typically work from a time-coded transcript. This is the original script marked to show the time available for each section of the translation.<br />
<br />
What this means<br />
There are several potential pitfalls in script translations. So it’s vital your translation provider is practiced at this type of translation and able to handle any technical content.<br />
<br />
<br />
<br />
24. Voice-over and Dubbing Projects<br />
What is it?<br />
Translation and recording of scripts in other languages.<br />
<br />
Voice-overs vs dubbing<br />
There is a technical difference.<br />
A voice-over adds a new track to the production, dubbing replaces an existing one.<br />
<br />
<br />
Key features<br />
These projects involve two parts:<br />
– a script translation (as described above), and<br />
– producing the audio<br />
<br />
So they involve the combined efforts of translators and voice artists.<br />
The task for the voice artist is to produce a high quality read. That’s one that matches the style, tone and richness of the original.<br />
<br />
Often each section of the new audio will need to be the same length as the original.<br />
<br />
But sometimes the segments will need to be shorter – for example where the voice-over lags the original by a second or two. This is common in interviews etc, where the original voice is heard initially then drops out.<br />
<br />
The most difficult form of dubbing is lip-syncing – where the new audio needs to synchronise with the original speaker’s lip movements, gestures and actions.<br />
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Lip-syncing requires an exceptionally skilled voice talent and considerable time spent rehearsing and fine tuning the translation.<br />
<br />
What this means<br />
You need to use experienced professionals every step of the way in this type of project.<br />
<br />
That’s to ensure firstly that your foreign-language scripts are first class, then that the voicing is of high professional standard.<br />
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Anything less will mean your foreign language versions will be way less effective and appealing to your target audience.<br />
<br />
<br />
<br />
25. Subtitle Translations<br />
What is it?<br />
Producing foreign language captions for sub or surtitles.<br />
Key features<br />
The goal with subtitling is to produce captions that viewers can comfortably read in the time available and still follow what’s happening on the video.<br />
<br />
To achieve this, languages have “rules” governing the number of characters per line and the minimum time each subtitle should display.<br />
<br />
Sticking to these guidelines is essential if your subtitles are to be effective.<br />
<br />
But this is no easy task – it requires simple language, short words, and a very succinct style. Translators will spend considerable time mulling over and re-working their translation to get it just right.<br />
<br />
Most subtitle translators use specialised software that will output the captions in the format sound engineers need for incorporation into the video.<br />
<br />
What this means<br />
As with other specialised types of translation, you should only use translators with specific expertise and experience in subtitling.<br />
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<br />
<br />
26. Website Localisation<br />
What is it?<br />
The translation and adapting of relevant content on a website to best suit the target language and culture.<br />
<br />
Note: Many providers use the term website translation as a synonym for localisation. Strictly speaking though, translation is just one part of localisation.<br />
Key features<br />
<br />
Not all pages on a website may need to be localised – clients should review their content to identify what’s relevant for the other language versions.<br />
Some content may need specialist translators – legal and technical pages for example.<br />
There may also be videos, linked documents, and text or captions in graphics to translate.<br />
Adaptation can mean changing date, time, currency and number formats, units of measure, etc.<br />
But also images, colours and even the overall site design and style if these won’t have the desired impact in the target culture.<br />
Translated files can be supplied in a wide range of formats – translators usually coordinate output with the site webmasters.<br />
New language versions are normally thoroughly reviewed and tested before going live to confirm everything is displaying correctly, works as intended and is cultural appropriate.<br />
What this means<br />
The first step should be to review your content and identify what needs to be translated. This might lead you to modify some pages for the foreign language versions.<br />
<br />
In choosing your translation providers be sure they can:<br />
– handle any technical or legal content,<br />
– provide your webmaster with the file types they want.<br />
<br />
And you should always get your translators to systematically review the foreign language versions before going live.<br />
<br />
<br />
<br />
27. Transcreation<br />
What is it?<br />
Adapting a message to elicit the same emotional response in another language and culture.<br />
Translation is all about conveying the message or meaning of a text in another language. But sometimes that message or meaning won’t have the desired effect in the target culture.<br />
<br />
This is where transcreation comes in. Transcreation creates a new message that will get the desired emotional response in that culture, while preserving the style and tone of the original.<br />
<br />
So it’s a sort of creative translation – which is where the word comes from, a combination of ‘translation’ and ‘creation’.<br />
<br />
At one level transcreation may be as simple as choosing an appropriate idiom to convey the same intent in the target language – something translators do all the time.<br />
<br />
But mostly the term is used to refer to adapting key advertising and marketing messaging. Which requires copywriting skills, cultural awareness and an excellent knowledge of the target market.<br />
<br />
Who does it?<br />
Some translation companies have suitably skilled personnel and offer transcreation services.<br />
<br />
Often though it’s done in the target country by specialist copywriters or an advertising or marketing agency – particularly for significant campaigns and to establish a brand in the target marketplace.<br />
<br />
What this means<br />
Most general marketing and promotional texts won’t need transcreation – they can be handled by a translator with excellent creative writing skills.<br />
<br />
But slogans, by-lines, advertising copy and branding statements often do.<br />
<br />
Whether you should opt for a translation company or an in-market agency will depend on the nature and importance of the material, and of course your budget.<br />
<br />
<br />
<br />
28. Audio Translations<br />
What is it?<br />
Broad meaning: the translation of any type of recorded material into another language.<br />
<br />
More commonly: the translation of a foreign language video or audio recording into your own language. So this is where you want to know and document what a recording says.<br />
Key features<br />
The first challenge with audio translations is it’s often impossible to pick up every word that’s said. That’s because audio quality, speech clarity and speaking speed can all vary enormously.<br />
<br />
It’s also a mentally challenging task to listen to an audio and translate it directly into another language. It’s easy to miss a word or an aspect of meaning.<br />
<br />
So best practice is to first transcribe the audio (type up exactly what is said in the language it is spoken in), then translate that transcription.<br />
<br />
However, this is time consuming and therefore costly, and there are other options if lesser precision is acceptable.<br />
<br />
What this means<br />
It’s best to discuss your requirements for this kind of translation with your translation provider. They’ll be able to suggest the best translation process for your needs.<br />
<br />
Examples<br />
Interviews, product videos, police recordings, social media videos.<br />
<br />
<br />
<br />
29. Translations with DTP<br />
What is it?<br />
Translation incorporated into graphic design files.multilingual dtp example in the form of a Rubik's Cube with foreign text on each square<br />
Key features<br />
Graphic design programs are used by professional designers and graphic artists to combine text and images to create brochures, books, posters, packaging, etc.<br />
<br />
Translation plus dtp projects involve 3 steps – translation, typesetting, output.<br />
<br />
The typesetting component requires specific expertise and resources – software and fonts, typesetting know-how, an appreciation of foreign language display conventions and aesthetics.<br />
<br />
What this means<br />
Make sure your translation company has the required multilingual typesetting/desktop publishing expertise whenever you’re translating a document created in a graphic design program.<br />
<br />
Translation Category C: 13 types of translation based on the translation method employed<br />
This category has two sub-groups:<br />
– the practical methods translation providers use to produce their translations, and<br />
– the translation strategies/methods identified and discussed within academia.<br />
<br />
The translation methods translation providers use<br />
There are 4 main methods used in the translation industry today. We have an overview of each below, but for more detail, including when to use each one, see our comprehensive blog article.<br />
<br />
Or watch our video.<br />
<br />
Important: If you’re a client you need to understand these 4 methods – choose the wrong one and the translation you end up with may not meet your needs!<br />
<br />
<br />
30. Machine Translation (MT)<br />
What is it?<br />
A translation produced entirely by a software program with no human intervention.<br />
<br />
A widely used, and free, example is Google Translate. And there are also commercial MT engines, generally tailored to specific domains, languages and/or clients.<br />
<br />
Pros and cons<br />
There are two limitations to MT:<br />
– they make mistakes (incorrect translations), and<br />
– quality of wording is patchy (some parts good, others unnatural or even nonsensical)<br />
<br />
On they positive side they are virtually instantaneous and many are free.<br />
<br />
Best suited for:<br />
Getting the general idea of what a text says.<br />
<br />
This method should never be relied on when high accuracy and/or good quality wording is needed.<br />
<br />
<br />
<br />
31. Machine Translation plus Human Editing (PEMT)<br />
What is it?<br />
A machine translation subsequently edited by a human translator or editor (often called Post-editing Machine Translation = PEMT).<br />
<br />
The editing process is designed to rectify some of the deficiencies of a machine translation.<br />
<br />
This process can take different forms, with different desired outcomes. Probably most common is a ‘light editing’ process where the editor ensures the text is understandable, without trying to fix quality of expression.<br />
<br />
Pros and cons<br />
This method won’t necessarily eliminate all translation mistakes. That’s because the program may have chosen a wrong word (meaning) that wasn’t obvious to the editor.<br />
<br />
And wording won’t generally be as good as a professional human translator would produce.<br />
<br />
Its advantage is it’s generally quicker and a little cheaper than a full translation by a professional translator.<br />
<br />
Best suited for:<br />
Translations for information purposes only.<br />
<br />
Again, this method shouldn’t be used when full accuracy and/or consistent, natural wording is needed.<br />
<br />
<br />
<br />
32. Human Translation<br />
What is it?<br />
Translation by a professional human translator.<br />
Pros and cons<br />
Professional translators should produce translations that are fully accurate and well-worded.<br />
<br />
That said, there is always the possibility of ‘human error’, which is why translation companies like us typically offer an additional review process – see next method.<br />
<br />
This method will take a little longer and likely cost more than the PEMT method.<br />
<br />
Best suited for:<br />
Most if not all translation purposes.<br />
<br />
<br />
<br />
33. Human Translation + Revision<br />
What is it?<br />
A human translation with an additional review by a second translator.<br />
<br />
The review is essentially a safety check – designed to pick up any translation errors and refine wording if need be.<br />
<br />
Pros and cons<br />
This produces the highest level of translation quality.<br />
<br />
It’s also the most expensive of the 4 methods, and takes the longest.<br />
<br />
Best suited for:<br />
All translation purposes.<br />
<br />
Gearwheel with 5 practical translation methods written on the teeth <br />
There’s also one other common term used by practitioners and academics alike to describe a type (method) of translation:<br />
<br />
34. Computer-Assisted Translation (CAT)<br />
What is it?<br />
A human translator using computer tools to aid the translation process.<br />
<br />
Key features<br />
Virtually all translators use such tools these days.<br />
<br />
The most prevalent tool is Translation Memory (TM) software. This creates a database of previous translations that can be accessed for future work.<br />
<br />
TM software is particularly useful when dealing with repeated and closely-matching text, and for ensuring consistency of terminology. For certain projects it can speed up the translation process.<br />
<br />
<br />
<br />
The translation methods described by academia<br />
A great deal has been written within academia analysing how human translators go about their craft.<br />
<br />
Seminal has been the work of Newmark, and the following methods of translation attributed to him are widely discussed in the literature.Gearwheel with Newmark's 8 translation methods written on the teeth <br />
These methods are approaches and strategies for translating the text as a whole, not techniques for handling smaller text units, which we discuss in our final translation category.<br />
<br />
<br />
<br />
35. Word-for-word Translation<br />
This method translates each word into the other language using its most common meaning and keeping the word order of the original language.<br />
<br />
So the translator deliberately ignores context and target language grammar and syntax.<br />
<br />
Its main purpose is to help understand the source language structure and word use.<br />
<br />
Often the translation will be placed below the original text to aid comparison.<br />
<br />
<br />
<br />
36. Literal Translation<br />
Words are again translated independently using their most common meanings and out of context, but word order changed to the closest acceptable target language grammatical structure to the original.<br />
<br />
Its main suggested purpose is to help someone read the original text.<br />
<br />
<br />
<br />
37. Faithful Translation<br />
Faithful translation focuses on the intention of the author and seeks to convey the precise meaning of the original text.<br />
<br />
It uses correct target language structures, but structure is less important than meaning.<br />
<br />
<br />
<br />
38. Semantic Translation<br />
Semantic translation is also author-focused and seeks to convey the exact meaning.<br />
<br />
Where it differs from faithful translation is that it places equal emphasis on aesthetics, ie the ‘sounds’ of the text – repetition, word play, assonance, etc.<br />
<br />
In this method form is as important as meaning as it seeks to “recreate the precise flavour and tone of the original” (Newmark).slide showing definition of semantic translation as a translation method<br />
<br />
39. Communicative Translation<br />
Seeks to communicate the message and meaning of the text in a natural and easily understood way.<br />
<br />
It’s described as reader-focused, seeking to produce the same effect on the reader as the original text.<br />
<br />
A good comparison of Communicative and Semantic translation can be found here.<br />
<br />
40. Free Translation<br />
Here conveying the meaning and effect of the original are all important.<br />
<br />
There are no constraints on grammatical form or word choice to achieve this.<br />
<br />
Often the translation will paraphrase, so may be of markedly different length to the original.<br />
<br />
41. Adaptation<br />
Mainly used for poetry and plays, this method involves re-writing the text where the translation would otherwise lack the same resonance and impact on the audience.<br />
<br />
Themes, storylines and characters will generally be retained, but cultural references, acts and situations adapted to relevant target culture ones.<br />
<br />
So this is effectively a re-creation of the work for the target culture.<br />
<br />
42. Idiomatic Translation<br />
Reproduces the meaning or message of the text using idioms and colloquial expressions and language wherever possible.<br />
<br />
The goal is to produce a translation with language that is as natural as possible.<br />
<br />
Translation Category D: 9 types of translation based on the translation technique used<br />
These translation types are specific strategies, techniques and procedures for dealing with short chunks of text – generally words or phrases.<br />
<br />
They’re often thought of as techniques for solving translation problems.<br />
<br />
They differ from the translation methods of the previous category which deal with the text as a whole.<br />
9 translation techniques as titles of books in a bookcase<br />
<br />
43. Borrowing<br />
What is it?<br />
Using a word or phrase from the original text unchanged in the translation.<br />
<br />
Key features<br />
With this procedure we don’t translate the word or phrase at all – we simply ‘borrow’ it from the source language.<br />
<br />
Borrowing is a very common strategy across languages. Initially, borrowed words seem clearly ‘foreign’, but as they become more familiar, they can lose that ‘foreignness’.<br />
<br />
Translators use this technique:<br />
– when it’s the best word to use – either because it has become the standard, or it’s the most precise term, or<br />
– for stylist effect – borrowings can add a prestigious or scholarly flavour.<br />
<br />
Borrowed words or phrases are often italicised in English.<br />
<br />
Examples of borrowings in English<br />
grand prix, kindergarten, tango, perestroika, barista, sampan, karaoke, tofu<br />
<br />
<br />
44. Transliteration<br />
What is it?<br />
Reproducing the approximate sounds of a name or term from a language with a different writing system.<br />
<br />
Key features<br />
In English we use the Roman (Latin) alphabet in common with many other languages including almost all European languages.<br />
<br />
Other writing systems include Arabic, Cyrillic, Chinese, Japanese, Korean, Thai, and the Indian languages.<br />
<br />
Transliteration from such systems into the Roman alphabet is also called romanisation.<br />
<br />
There are accepted systems for how individual letters/sounds should be romanised from most other languages – there are three common systems for Chinese, for example.<br />
<br />
English borrowings from languages using non-Roman writing systems also require transliteration – perestroika, sampan, karaoke, tofu are examples from the above list.<br />
<br />
Translators mostly use transliteration as a procedure for translating proper names.<br />
<br />
Examples<br />
毛泽东 Mao Tse-tung or Mao Zedong<br />
Владимир Путин Vladimir Putin<br />
서울 Seoul<br />
ភ្នំពេញ Phnom Penh<br />
<br />
45. Calque or Loan Translation<br />
What is it?<br />
A literal translation of a foreign word or phrase to create a new term with the same meaning in the target language.<br />
<br />
So a calque is a borrowing with translation if you like. The new term may be changed slightly to reflect target language structures.<br />
<br />
Examples<br />
German ‘Kindergarten’ has been calqued as детский сад in Russian, literally ‘children garden’ in both languages.<br />
<br />
Chinese 洗腦 ‘wash’ + ‘brain’ is the origin of ‘brainwash’ in English.<br />
<br />
English skyscraper is calqued as gratte-ciel in French and rascacielos in Spanish, literally ‘scratches sky’ in both languages.<br />
<br />
46. Word-for-word translation<br />
What is it?<br />
A literal translation that is natural and correct in the target language.<br />
<br />
Alternative names are ‘literal translation’ or ‘metaphrase’.<br />
<br />
Note: this technique is different to the translation method of the same name, which does not produce correct and natural text and has a different purpose.<br />
<br />
Key features<br />
This translation strategy will only work between languages that have very similar grammatical structures.<br />
<br />
And even then, only sometimes.<br />
<br />
For example, standard word order in Turkish is Subject-Object-Verb whereas in English it’s Subject-Verb-Object. So a literal translation between these two will seldom work:<br />
– Yusuf elmayı yedi is literally ‘Joseph the apple ate’.<br />
<br />
When word-for-word translations don’t produce natural and correct text, translators resort to some of the other techniques described below.<br />
Examples<br />
French ‘Quelle heure est-il?’ works into English as ‘What time is it?’.<br />
<br />
Russian ‘Oн хочет что-нибудь поесть’ is ‘He wants something to eat’.<br />
<br />
47. Transposition<br />
What is it?<br />
Translation with a change of grammatical structure.<br />
<br />
This technique gives the translation more natural wording and/or makes it grammatically correct.<br />
<br />
Examples<br />
A change in word order:<br />
Our Turkish example Yusuf elmayı yedi (literally ‘Joseph the apple ate’) –> Joseph ate the apple.<br />
<br />
Spanish La Casa Blanca (literally ‘The House White’) –> The White House<br />
<br />
A change in grammatical category:<br />
German Er hört gerne Musik (literally ‘he listens gladly [to] music’)<br />
= subject pronoun + verb + adverb + noun<br />
becomes Spanish Le gusta escuchar música (literally ‘[to] him [it] pleases to listen [to] music’)<br />
= indirect object pronoun + verb + infinitive + noun<br />
and English He likes listening to music<br />
= subject pronoun + verb + gerund + noun.<br />
<br />
48. Modulation<br />
What is it?<br />
Translation with a change of focus or point of view in the target language.<br />
<br />
This technique makes the translation more idiomatic – how people would normally say it in the language.<br />
<br />
Examples<br />
English talks of the ‘top floor’ of a building, French the dernier étage = last floor. ‘Last floor’ would be unnatural in English, so too ‘top floor’ in French.<br />
<br />
German uses the term Lebensgefahr (literally ‘danger to life’) where in English we’d be more likely to say ‘risk of death’.<br />
In English we’d say ‘I dropped the key’, in Spanish se me cayó la llave, literally ‘the key fell from me’. The English perspective is that I did something (dropped the key), whereas in Spanish something happened to me – I’m the recipient of the action.<br />
<br />
49. Equivalence or Reformulation<br />
What is it?<br />
Translating the underlying concept or meaning using a totally different expression.<br />
<br />
This technique is widely used when translating idioms and proverbs.<br />
<br />
And it’s common in titles and advertising slogans.<br />
<br />
It’s a common strategy where a direct translation either wouldn’t make sense or wouldn’t resonate in the same way.<br />
<br />
Examples<br />
Here are some equivalents of the English saying “Pigs may fly”, meaning something will never happen, or “you’re being unrealistic” (Source):<br />
– Thai: ชาติหน้าตอนบ่าย ๆ – literally, ‘One afternoon in your next reincarnation’<br />
– French: Quand les poules auront des dents – literally, ‘When hens have teeth’<br />
– Russian, Когда рак на горе свистнет – literally, ‘When a lobster whistles on top of a mountain’<br />
– Dutch, Als de koeien op het ijs dansen – literally, ‘When the cows dance on the ice’<br />
– Chinese: 除非太陽從西邊出來！– literally, ‘Only if the sun rises in the west’<br />
<br />
50. Adaptation<br />
What is it?<br />
A translation that substitutes a culturally-specific reference with something that’s more relevant or meaningful in the target language.<br />
<br />
It’s also known as cultural substitution or cultural equivalence.<br />
<br />
It’s a useful technique when a reference wouldn’t be understood at all, or the associated nuances or connotations would be lost in the target language.<br />
<br />
Note: the translation method of the same name is a similar concept but applied to the text as a whole.<br />
<br />
Examples<br />
Different cultures celebrate different coming of age birthdays – 21 in many cultures, 20, 15 or 16 in others. A translator might consider changing the age to the target culture custom where the coming of age implications were important in the original text.<br />
Animals have different connotations across languages and cultures. Owls for example are associated with wisdom in English, but are a bad omen to Vietnamese. A translator might want to remove or amend an animal reference where this would create a different image in the target language.<br />
<br />
51. Compensation<br />
What is it?<br />
A meaning or nuance that can’t be directly translated is expressed in another way in the text.<br />
Example<br />
Many languages have ways of expressing social status (honorifics) encoded into their grammatical structures.<br />
<br />
So you can convey different levels of respect, politeness, humility, etc simply by choosing different forms of words or grammatical elements.<br />
But these nuances will be lost when translating into languages that don’t have these structures.<br />
Then translating into languages that don’t have these structures<br />
Then translating into languages that don’t have these structures.<br />
<br />
===Conclusion ===<br />
<br />
In the light of above mentioned facts and figures here by we would say that machine translation is challenge for human translators because it can reduces the wokload of translation but can't give accurate and exact translation of the traget language.It can be less reliable than human translation..</div>Chen Huinihttps://bou.de/u/index.php?title=Machine_translation&diff=131939Machine translation2021-12-13T13:00:33Z<p>Chen Huini: /* Conclusion */</p>
<hr />
<div>'''Machine Translation - A challenge or a chance for human translators?'''<br />
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[[Machine_translation|Overview Page of Machine Translation]]<br />
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30 Chapters（0/30)<br />
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[[Machine_Trans_EN_1]] [[Machine_Trans_EN_2]] [[Machine_Trans_EN_3]] [[Machine_Trans_EN_4]] [[Machine_Trans_EN_5]] [[Machine_Trans_EN_6]] [[Machine_Trans_EN_7]] [[Machine_Trans_EN_8]] [[Machine_Trans_EN_9]] [[Machine_Trans_EN_10]] [[Machine_Trans_EN_11]] [[Machine_Trans_EN_12]] [[Machine_Trans_EN_13]] [[Machine_Trans_EN_14]] [[Machine_Trans_EN_15]] [[Machine_Trans_EN_16]] [[Machine_Trans_EN_17]] [[Machine_Trans_EN_18]] [[Machine_Trans_EN_19]] [[Machine_Trans_EN_20]] [[Machine_Trans_EN_21]] [[Machine_Trans_EN_22]] [[Machine_Trans_EN_23]] [[Machine_Trans_EN_24]] [[Machine_Trans_EN_25]] [[Machine_Trans_EN_26]] [[Machine_Trans_EN_27]] [[Machine_Trans_EN_28]] [[Machine_Trans_EN_29]] [[Machine_Trans_EN_30]] ...<br />
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[[Book_projects|Back to translation project overview]]<br />
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[[DCG-To-Do|To the To Do list]]<br />
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=Chapter 1:A Comparison Between the Quality of Machine Translation and Human Translation——A Case Study of the Application of artificial intelligence in Sports Events=<br />
'''论机器翻译与人工翻译的质量对比——以人工智能在体育赛事领域的应用为例'''<br />
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卫怡雯 Wei Yiwen, Hunan Normal University, China<br />
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[[Machine_Trans_EN_1]]<br />
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=Chapter 3：On the Realm Advantages And Mutual Development of Machine Translation And Huamn Translation)=<br />
"'论机器翻译与人工翻译的领域优势及共同发展'"<br />
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肖毅瑶 Xiao Yiyao, Hunan Normal University, China<br />
[[Machine_Trans_EN_3]]<br />
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=Chapter 4 : A Comparison Between Machine Translation of Netease and Traditional Human Translation—A Case Study of The Economist Articles)= <br />
'''网易有道机器翻译与人工翻译的译文对比——以经济学人语料为例'''<br />
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王李菲 Wang Lifei, Hunan Normal University<br />
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[[Machine_Trans_EN_4]]<br />
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=Chapter 5: Muhammad Saqib Mehran - Problems in translation studies=<br />
[[Machine_Trans_EN_5]]<br />
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=6 徐敏赟(Machine Translation Based on Neural Network --Challenge or Chance?)=<br />
[[Machine_Trans_EN_6]]<br />
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=Chapter 7: The Cultivation of artificial intelligence and translation talents in the Belt and Road Initiative=<br />
[[Machine_Trans_EN_7]]<br />
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一带一路背景下人工智能与翻译人才的培养<br />
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颜莉莉 Yan Lili, Hunan Normal University, China<br />
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=Chapter 8: On Machine Translation Under Lanuguage Intelligence——An Option and Opportunity for Human Translators=<br />
'''论语言智能下的机器翻译——译者的选择与机遇'''<br />
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颜静 Yan Jing, Hunan Normal University, China<br />
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[[Machine_Trans_EN_8]]<br />
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=9 谢佳芬（ Machine Translation and Artificial Translation in the Era of Artificial Intelligence）=<br />
[[Machine_Trans_EN_9]]<br />
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=10 熊敏(Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts)=<br />
[[Machine_Trans_EN_10]]<br />
===Abstract===<br />
The history of machine translation can be traced to 1940s, undergoing germination, recession, revival and flourish. Nowadays, with the rapid development of information technology, machine translation technology emerged and is gradually becoming mature. Whether manual translation can be replaced by machine translation becomes a widely-disputed topic. So in order to explore the ability of machine translation, I adopts two versions of translation, which are manual translation and machine translation(this paper uses Youdao translation) for different types of texts(according to Peter Newmark's types of text：informative text, expressive text and vocative text). The results are quite different in terms of quality and accuracy. The results show that machine translation is more suitable for informative text for its brevity, while the expressive texts especially literary works and vocative texts are difficult to be translated, because there are too many loaded words and cultural images in expressive text and dependence on the readers. To draw a conclusion, the relationship between machine translation and manual translation should be complementary rather than competitive.<br />
<br />
===Key words===<br />
machine translation; manual translation; Newmark's type of texts<br />
<br />
===题目===<br />
Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts<br />
<br />
===摘要===<br />
机器翻译的历史可以追溯到上个世纪40年代，经历了萌芽期、萧条期、复兴期和繁荣期四个阶段。如今，随着信息技术的高速发展，机器翻译技术日益成熟，于是机器翻译能不能替代人工翻译这个话题引起了广泛热议。为了探究机器翻译的能力水平是否足以替代人工翻译，本人根据皮特纽马克的文本类型分类理论（信息类文本，表达文本，呼吁文本），选择了相应的译文类型，并且将其机器翻译的版本以及人工翻译的版本进行对比。结果发现，就质量和准确度而言，译文的水平大相径庭。因为其简洁的特性，机器比较适合翻译信息文本。而就表达文本，尤其是文学作品，因其存在文化负载词及相应的文化现象，所以机器翻译这类文本存在难度。而呼唤文本因其目的性以及对读者的依赖性较强，翻译难度较大。由此得知，机器翻译和人工翻译的关系应该是互补的，而不是竞争的。<br />
<br />
===关键词===<br />
机器翻译；人工翻译；纽马克文本类型<br />
<br />
===1. Introduction===<br />
====1.1.Introduction to machine translation====<br />
Machine translation, also known as computer translation is a technique to translating through machine translator, such as Google translation and Youdao translation. Machine translation is one of the branches of computational linguistics, ranging from computer science, statistics, information science and so on. Machine translation plays an important role in all aspects.<br />
Machine translation can be traced back to 1940s, when British engineer Booth and American engineer Weaver proposed using computer to translate and started to study machines used for translation. And the first machine translating system launched in 1954, used in English and Russian translation. And this means machine translation becomes a reality. However, the arrival of new things always accompanies barriers and setbacks. In the 1960s, reports from ALPAC (Automated Language Processing Advisory Committee) showed studies on machine translation had stagnated for a decade. At that time, due to the high cost of machine, choosing human translators to finish translating works was more economical for most companies. What’s worse, machine had difficulty in understanding semantic and pragmatic meanings. Fortunately, in the 1970s, with the advance and popularity of computer, machine translation was gradually back on track. With the development of science and technology, machine translation has better technological guarantee, such as artificial intelligence and computer technology. In the last decades, from the late 90s till now, machine translation is becoming more and more mature. The initial rule-oriented machine translation has been updated and Corpus-aided machine translation appears. And nowadays, technology in voice recognition has been further developed. This technique is frequently applied in speech translation products. Users only need to speak source language to the online translator and instantly it will speak out the target version. But machine can’t fully provide precise and accurate translation without any grammatical or semantic problems. Machine can understand the literal meaning of texts, but not the meaning in context. For example, there is polysemy in English, which refers to words having multiple meanings. So when translating these words, translators should take contexts into consideration. But machine has troubles in this way. In addition, machine has no feeling or intention. Hence, it is also difficult to convey the feelings from the source language.<br />
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====1.2.Process of machine translation====<br />
The process of manual translation is different from that of machine translation. Here is the process of the former. (1) Understand source language. (2) Use target language to organize language. (3) Generate translation. Unlike manual translation, machine translation tends to analyze and code source language first, then look for related codes in corpus, and work out the code that represents target language, generating translation. But they share a common feature, which is that Lexicon, grammatical rules and syntactic structure are taken into consideration. This is one of the biggest challenges for machine translation.<br />
<br />
===2.Theorectical framework===<br />
====2.1Newmark’s text typology====<br />
Text typology is a theory from linguistics. It undergoes several phrases. Karl Buhler, a German psychologist and linguist defines communicative functions into expressive, information and vocative. Then Roman Jakobson proposes categorization of texts, which are intralingual translation, interlingual translation and intersemiotic translation. Accordingly, he defines six main functions of language, including the referential function, conative function, emotive function, poetic function, metalingual function and the phatic function. Based on the two theories, Peter Newmark, a translation theorist, summarizes the language function into six parts: the informative function, the vocative function, the expressive function, the phatic function, the aesthetic function, and the metalingual function. Later, he further divided texts into informative type, expressive text and vocative type according to the linguistic functions of various texts. <br />
The core of the informative texts is the truth. It is to convey facts, information, knowledge of certain topics, reality and theories. The language style of the text is objective, logical and standardized. Reports, papers, scientific and technological textbooks are all attributed to informative texts. Translators are required to take format into account for different styles.<br />
The core of the expressive text is the sender’s emotions and attitudes. It is to express sender’s preferences, feelings, views and so on. The language style of it is subjective. Imaginative literature, including fictions, poems and dramas, autobiography and authoritative statements belong to expressive text. It requires translators to be faithful to the source language and maintain the style.<br />
The core of the vocative text is readership. It is to call upon readers to act, think, feel and react in the way intended by the text. So it is reader-oriented. Such texts as advertisement, propaganda and notices are of vocative text. Newmark noted that translators need to consider cultural background of the source language and the semantic and pragmatic effect of target language. If readers can comprehend the meaning at once and do as the text says, then the goal of information communication is achieved.<br />
To draw a conclusion, informative texts focus on the information and facts. Expressive texts center on the mind of addressers, including feelings, prejudices and so on. And vocative texts are oriented on readers and call on them to practice as the texts say.<br />
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====2.2Study method====<br />
Manual translations of the three texts are selected from authoritative versions and universally acknowledged. And machine translations of those come from Youdao Translator. And in this thesis I will compare and evaluate the two methods in word diction, sentence structure, word order and redundancy.<br />
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===3.Comparison and analysis of machine translation and manual translation ===<br />
====3.1Informative text ====<br />
（1）English into Chinese<br />
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①Source language:<br />
<br />
Keep the tip of Apple Pencil clean, as dirt and other small particles may cause excessive wear to the tip or damage the screen of i-pad.<br />
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Target language:<br />
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Machine translation: Apple Pencil笔尖应保持清洁，灰尘等小颗粒可能会导致笔尖过度磨损或损坏ipad屏幕。<br />
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Manual translation: 保持Apple Pencil铅笔的笔尖干净，因为灰尘和其他微粒可能会导致笔尖的过度磨损或损坏iPad屏幕。<br />
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Analysis: Here is the instruction of Apple Pencil. And the manual translation is the Chinese version on the instruction.Product instruction tends to be professional, since there are many terms for some concepts. Machine can easily identify these terms and provide related words to translate. The machine version is faithful and expressive to the source language. So it is well-qualified and readable for readers to understand the instruction. So we can use machine to translate informative text.<br />
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②Source language:<br />
<br />
China on Saturday launched a rocket carrying three astronauts-two men and one woman - to the core module of a future space station where they will live and work for six months, the longest orbit for Chinese astronauts.<br />
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Target language:<br />
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Machine translation: 周六，中国发射了一枚运载三名宇航员(两男一女)的火箭，进入未来空间站的核心舱，他们将在那里生活和工作6个月，这是中国宇航员最长的轨道。<br />
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Manual translation: 周六，中国发射了一枚搭载三名宇航员(两男一女)的火箭，进入未来空间站的核心舱，他们将在那里生活和工作6个月，这是中国宇航员最漫长的一次轨道飞行。<br />
<br />
Analysis: This is a news from Reuters, reporting that China has launched a rocket.The meaning of the two translations is almost the same, except for some word diction. But there are some details dealt with different choice. For example, the last sentence of the machine translation is a bit of obscure and direct. There are some ambiguous words and expressions.<br />
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(2)Chinese into English<br />
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Source language:湖南省博物馆是湖南省最大的历史艺术类博物馆，占地面积4.9万平方米，总建筑面积为9.1万平方米，是首批国家一级博物馆，中央地方共建的八个国家级重点博物馆之一、全国文化系统先进集体、文化强省建设有突出贡献先进集体。<br />
<br />
Target language:<br />
Manual translation: As the largest history and art museum in Hunan province, the Hunan Museum covers an area of 49,000㎡, with the building area reaching 91,000㎡. It is one of the first batch of national first-level museums and one of the first eight national museums co-funded by central and local governments.<br />
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Machine translation: Museum in hunan province is one of the largest historical art museum in hunan province, covers an area of 49000 square meters, a total construction area of 91000 square meters, is the first national museum, the central place to build one of the eight national key museum, national cultural system advanced collectives, strong culture began with outstanding contribution of advanced collective.<br />
<br />
Analysis: Machine translation is not faithful enough in content. For instance, “首批国家一级博物馆” is translated into “first national museum”, which is not the meaning of the source language. And there are some obvious grammar mistakes in the machine translation. For example, machine translates it into just one sentence but there are multiple predicates in it. So it is not grammatically permissible. What’s more, the sentence structure of machine translation is confusing and the focus is not specific enough.<br />
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====3.2Expressive text ====<br />
(1)English into Chinese<br />
<br />
Source language:<br />
<br />
An individual human existence should be like a river- small at first, narrowly contained within its banks, and rushing passionately past rocks and over waterfalls. Gradually the river grows wider, the banks recede, the waters flow more quietly, and in the end, without any visible breaks, they become merged in the sea, and painlessly lose their individual being.<br />
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Target language:<br />
<br />
Machine translation: 一个人的存在应该像一条河流——开始很小，被紧紧地夹在两岸中间，然后热情奔放地冲过岩石，飞下瀑布。渐渐地，河面变宽，两岸后退，水流更加平缓，最后，没有任何明显的停顿，它们汇入大海，毫无痛苦地失去了自己的存在。<br />
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Manual translation:人生在世，如若河流；河口初始狭窄，河岸虬曲，而后狂涛击石，飞泻成瀑。河道渐趋开阔，峡岸退去，水流潺缓，终了，一马平川，汇于大海，消逝无影。<br />
<br />
Analysis: Here is a well-known metaphor in the prose How to Grow Old written by Bertrand Russell. The manual translation is written by Tian Rongchang.This is a philosophical prose with graceful language. Literary translation is a most important and difficult branch of translation. Translator should focus on the literal meaning, culture, writing style and so on. It is a combination of beauty and elegance. Therefore, translators find it in a dilemma of beauty and faithfulness, let alone translating machine. Compared with manual translation, machine translation has difficulty in word choice. It is faithful and expressive, but not elegant enough.<br />
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(2)Chinese into English<br />
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Source language:没有一个人将小草叫做“大力士”，但是它的力量之大，的确是世界无比。这种力，是一般人看不见的生命力，只要生命存在，这种力就要显现，上面的石块，丝毫不足以阻挡。因为它是一种“长期抗战”的力，有弹性，能屈能伸的力，有韧性，不达目的不止的力。<br />
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Target language:<br />
<br />
Machine translation: No one calls the little grass "hercules", but its power is truly matchless in the world. This force is invisible life force. As long as there is life, this force will show itself. The stone above is not strong enough to stop it. Because it is a "long-term resistance" of the force, elastic, can bend and extend force, tenacity, not to achieve the purpose of the force.<br />
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Manual translation: Though nobody describes the little grass as a “husky”, yet its herculean strength is unrivalled. It is the force of life invisible to naked eye. It will display itself so long as there is life. The rock is utterly helpless before this force- a force that will forever remain militant, a force that is resilient and can take temporary setbacks calmly, a force that is tenacity itself and will never give up until the goal is reached. (by Zhang Peiji)<br />
<br />
Analysis:This is the excerpt of a well-known Chinese prose written by Xia Yan. It is written during the war of Resistance Against Japan. So the prose holds symbolic meaning, eulogizing the invisible tenacious vitality so as to encourage Chinese to have confidence in the anti-aggression war. Compared with manual translation, machine translation is much more abstract and confusing, especially for the word diction. For example, “大力士” is translated into “hercules” which is a man of exceptional strength and size in Greek and Roman Mythology, making it difficult to understand if readers of target language have no idea of the allusion. What’s worse, the machine version doesn’t reveal the symbolic meaning of the text, which is the core of this prose.<br />
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====3.3Vocative text ====<br />
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(1)English into Chinese<br />
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①Source language:<br />
<br />
iPhone went to film school, so you don’t have to. (Advertisement of iPhone13)<br />
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Target language:<br />
<br />
Machine translation: iPhone上的是电影学院，所以你不用去。<br />
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Manual translation:电影专业课，iPhone同学替你上完了。<br />
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Analysis：Here are advertisements of iPhone on Apple official website. There is a personification in the source language. It is used to stress the advancement and proficiency in camera, which is an appealing selling point to potential buyers. Compared with manual translation, machine translation is plain and not eye-catching enough for customers.<br />
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②Source language: <br />
<br />
5G speed OMGGGGG<br />
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Machine language: 5克的速度 OMGGGGG<br />
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Manual translation:<br />
<br />
iPhone的5G 巨巨巨巨巨5G<br />
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Analysis: The “G” in the source language is the unit of speed, standing for generation. However, it is mistaken as a unit of weight, representing gram in the machine translation. So the meaning is not faithful to the source language at all. As for manual translation, it complies with the source in form. Specifically speaking, five “G”s in the former complies with five characters “巨”in the latter. And the pronunciation of the two is similar. There are two layers of meaning for the 5 “G”s. One exclaims the fast speed of 5 generation network and the other new technology. In the manual version, “巨”can be used to show degree, meaning “quite” or “very”. <br />
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③Source language: <br />
<br />
History, faith and reason show the way, the way of unity. We can see each other not as adversaries but as neighbors. We can treat each other with dignity and respect, we can join forces, stop the shouting and lower the temperature. For without unity, there is no peace, only bitterness and fury."<br />
<br />
Target language:<br />
<br />
Machine translation: 历史、信仰和理性指明了团结的道路。我们可以把彼此视为邻居，而不是对手。我们可以尊严地对待彼此，我们可以联合起来，停止大喊大叫，降低温度。因为没有团结，就没有和平，只有痛苦和愤怒。<br />
<br />
Manual translation:历史、信仰和理性为我们指明道路。那是团结之路。我们可以把彼此视为邻居，而不是对手。我们可以有尊严地相互尊重。我们可以联合起来，停止喊叫，减少愤怒。因为没有团结就没有和平，只有痛苦和愤怒<br />
<br />
Analysis: Speech is a way to propagate some activity in public. It is an art to inspire emotion of the audience. The source language is the excerpt of Joe Biden’s inaugural speech. The speech should be inspiring and logic. The machine translation has some misunderstanding. Taking the translation of “lower the temperature” for example, machine only translates its literal meaning, relating to the temperature itself, without considering the context. What’s more, it is less logic than the manual one. Therefore, it adds difficulty to inspire the audience and infect their emotion.<br />
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===4.Common mistakes in machine translation ===<br />
<br />
====4.1 lexical mistakes ====<br />
<br />
Common lexical mistakes include misunderstandings in word category, lexical meaning and emotive and evaluative meaning. Misunderstanding in word category shows in the classification of word in the source language. As for misunderstanding in lexical meaning, machine has difficulty in precisely reflecting the meaning of the original texts, due to different cultural background and different language system. And for misunderstanding in emotive meaning, machine has no intention and emotion like human-beings. Therefore, it’s impossible for it to know writers’ feelings and their writing purposes. So sometimes, it may translate something negative into something positive.<br />
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====4.2 grammatical mistakes====<br />
<br />
Grammatical analysis plays an important part in translation. Normally speaking, every language has its own unique grammatical rules. So in the process of translation, if translators don’t know the formation rule well, the sentence meaning will be affected. Even though all the lexical meanings are well-known by translators, the lack of consciousness of grammaticality makes it harder to arrange words according to sequential rule. English tends to be hypotactic, while Chinese tends to be paratactic. English sentences are connected through syntactic devices and lexical devices. While Chinese sentences are semantically connected, which means there are limited logical words and connection words in Chinese. So when translating English sentence, we should first analyze its grammaticality and logical structure and then rearrange its sequence. However, online translating machine has troubles in grammatical analysis, which makes its improvement more difficult.<br />
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====4.3 other mistakes====<br />
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The two mistakes above are the internal ones. Apart from mistakes in linguistic system, there are some mistakes in other aspects, such as cultural background.<br />
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===5.Reasons for its common mistakes ===<br />
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====5.1 Difference in two linguistic system====<br />
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With different history, English and Chinese have different ways of expression. Commonly speaking, English is synthetic language which expresses grammatical meaning through inflection such as tense and Chinese is analytic language which expresses grammatical meaning through word order and function word. In addition, English is more compact with full sentences. Subordinate sentence is one of the most important features in modern English. Chinese, on the other hand, is more diffusive with minor sentences.<br />
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====5.2 Difference in thinking patterns and cultural background====<br />
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According to Sapir-Whorf’s Hypothesis, our language helps mould our way of thinking and consequently, different languages may probably express their unique ways of understanding the world. For two different speech communities, the greater their structural differentiations are, the more diverse their conceptualization of the world will be. For example, western culture is more direct and eastern culture more euphemistic. What’s more, English culture tends to be individualism, focusing on detail, through which it reflects the whole, while Chinese culture tends to be collective. Different thinking patterns will add difficulty for machine to translate texts.<br />
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====5.3 Limitation of computer====<br />
<br />
Recently, there are some breakthroughs and innovation in machine translation. However, due to its own limitation, online translation has limitation in some ways. Firstly, compared with machine, human brain is much more complicated, consisting of ten billions of neuron, each of which has different function to affect human’s daily activities and help humans avoid some errors. However, computer can only function according to preset programming has no intention or consciousness. Until now, countless related scholars have invested much time in machine translation. They upload massive language database, which include almost all linguistic rules. But computers still fail to precisely reflect the meaning of source language for many times due to the complexity and flexibility of language. On the other hand, computers can’t take context into consideration. During translation, it is often the case that machine chooses the most-frequently used meaning of one word. So without the correct and exact meaning, readers are easier to feel confused and even misunderstand the meaning of source language.<br />
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===6.Conclusion===<br />
From the analysis above, we can draw a conclusion that machine deals with informative text best, followed by non-literary translation of expressive text. What’s more, machine can be a useful tool to get to know the gist and main idea of a specific topic, for the simple sentence structure and numerous terms. And it can improve translating efficiency with high speed. But machine has difficulty in translating literary works, especially proses and poems.<br />
<br />
Machine translation has mixed future. From the perspective of commercial, machine translation boasts a bright future. With the process of globalization, the demand for translation is increasing accordingly. On one hand, if we only depend on human translator to deal with translating works, the quality and accuracy of translation can be greatly affected. On the other hand, if machine is used properly to do some basic work, human translators only need to make preparation before translating, progress, polish and other advanced work, contributing to highly-qualified translation and high working efficiency.<br />
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However, compared with manual translation, machine translation has a bleak future. It is still impossible for machine to replace interpreter or translator in a short term. With intelligence and initiative, humans are able to learn new knowledge constantly, which machine will never accomplish. Besides, machine is not used to replace translators but to assist them in work. In other words, translators and machine carry out their own duties and they are not incompatible.<br />
<br />
To draw a conclusion, although there are certain limitations of machine translation, it can serve as a catalyst for translating works. Therefore, with the rapid development of artificial intelligence and related technology, there are still many opportunities for machine translation.<br />
<br />
===Reference ===<br />
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Cui Zihan 崔子涵.机器翻译译文质量对比——以谷歌翻译和DeepL为例[J] [Comparison among Machine Translation--Taking Google Translation and Deepl for Example].Overseas English 海外英语,2021(15):182-183.<br />
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Li Deyi 李德毅. (2018). 人工智能导论 [Introduction to Artificial Intelligence]. Beijing: China Science and Technology Press 中国科学技术出版社.<br />
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Qiu Quanju 仇全菊.大数据时代背景下机器翻译及其发展趋势[J][Machine Translation and its Development Trend under the Background of Big Data Era]. English Teachers 英语教师,2021,21(16):60-62.<br />
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Zhuo Jianbin 卓键滨,Liu Wenxian 刘文娴,Peng Zili 彭子莉.机器翻译对各类型文本的德汉翻译能力探究[J][Research on the German Chinese Translation Ability of Machine Translation for Various Types of Texts]. Comparative Study of Cultural innovation 文化创新比较研究,2021,5(28):122-125.<br />
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Liu Qin刘琴.功能目的论对于不同文本类型的翻译解读[J][Analysis of Translations in Different Types of Text based on Functionalist Approaches].Overseas Engliosh 海外英语,2021(17):8-9.<br />
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Zhang Peiji 张培基.英译中国现代散文选[M][Selected Modern Chinese Prose Writings]. Shanghai Foreign Languages Education Press 上海外语教育出版社, 2002.<br />
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Chen Cheng陈诚.机器翻译技术的综述[J][Overview of Machine Translation Technology].Electronic Techonology 电子技术,2021,50(11):290-291.<br />
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He Xinyu何馨宇.机器翻译的发展及其对翻译职业化的影响研究[J] [The Development of Machine Translation and its Effect on Professional Transltors].Overseas English 海外英语,2021(20):48-49.<br />
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He Wen 何雯, Wang Xiufeng 王秀峰.信息型文本的在线机器翻译错误研究[J][Research on Errors in Online Machine Translation of Informative text ].Overseas English海外英语,2021(15):188-189.<br />
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Li Hanji 李晗佶. (2021). 人工智能时代翻译技术与译者关系演变与重构 [Evolution and reconstruction of the relationship between translation technology and translators in the era of artificial intelligence]. 西华师范大学学报(哲学社会科学版) Journal of West China Normal University (PHILOSOPHY AND SOCIAL SCIENCES EDITION) (2021-12-04) 1-6.<br />
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Wei Guang魏光. 人工翻译与机器翻译译文编辑比较研究[J][Comparative Study of Translation Editing between Manual Translation and Machine Translation]. Overseas English 海外英语,2021(19):18-19+21.<br />
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=Chapter 11 陈惠妮=Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts=<br />
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机器翻译的译前编辑研究——以医学类文摘为例<br />
<br />
陈惠妮 Chen Huini, Hunan Normal University<br />
<br />
[[Machine_Trans_EN_11]]<br />
<br />
===Abstract===<br />
At present, globalization is accelerating and the market demand for language services is rapidly increasing . Machine translation, as an important translation method, can greatly improve translation efficiency due to its low cost and high speed. However, because of the limitations of machine translation and the differences between Chinese and English language, machine translation is not accurate enough. In order to balance translation efficiency and translation quality, a great number of manual revisions in translation are required for the machine translating texts. Medical papers are specialized, special and purposeful, so it requires accurate,qualified and professional translation. However, the quality of translations by machine is inefficient to meet the high-quality requirements of medical papers translation. Therefore, the introduction of pre-editing can greatly improve the efficiency and quality of machine translation.<br />
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===Key words===<br />
Pre-editing, Machine translation, Medical texts<br />
<br />
===题目===<br />
Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts<br />
<br />
===摘要===<br />
在全球化加速发展的今天，市场对语言服务的需求迅速增加。机器翻译作为一种重要的翻译途径，由于其成本低、速度快，可以大大提高翻译效率。然而，由于机器翻译的局限性以及中英文语言的差异，机器翻译的准确性不高。为了平衡翻译效率和翻译质量，机器翻译文本需要大量的手工修改。<br />
医学论文具有专业性、特殊性和目的性，要求其译文准确、合格、专业。然而，机器翻译的质量较低，无法满足医学论文对翻译的高质量要求。因此，译前编辑的引入可以大大提高机器翻译的效率和质量。<br />
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===关键词===<br />
译前编辑；机器翻译；医学文本<br />
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===1. Introduction===<br />
===1.1 Definition of Machine Translation===<br />
As Cronin(2013) revealed: "Translation is undergoing a revolutionary upheaval. The influence of digital technology and the Internet on translation is continuous, extensive and profound. From the popularity of automatic online translation applications, translation revolution is everywhere (Cronin,2013) However,the concept of machine translation was firstly proposed in the 1930s. Since 1940s, the machine translation technology has been evolving from rule-based machine translation (RBMT) to statistical machine translation (SMT), and to neural machine translation (NMT). Machine translation refers to the automatic translation of source language into target language by using a computer system. That is, machine translation refers to the automatic translation of text from one language into another natural language by computer software or other online translation webs. Machine translation is also defined as the process of “using a computer system to automatically translate text or speech from one natural language to another” according to the definition by ISO (Cui 2014:68-73).O 'Brien (2002) defines it as "the behavior of modifying errors in machine translation to ensure that the target translation meets certain quality requirements". On the basic level, machine translation performs mechanical substitution of words in one language for words in another language, but that rarely produces a good translation, therefore, recognition of the whole phrases and their closest counterparts in the target language is needed. Not all words in one language have equivalents in another language, and many words have more than one meaning. A huge demand for translation is greatly needed in today’s global world, which creates new opportunities for the development of machine translation, attracts more and more attention and becomes one of the current research focuses.<br />
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===1.2 Definition of Pre-editing===<br />
Pre-editing means to adjust and modify the source language to make it fit more with the characteristics of the machine translation software before putting the source language before the into machine translation, so as to improve the quality of the translation machine translation (Wei Changhong 2008:93-94). Pre-editing is to modify the original text before putting it into machine translation software, in order to improve the recognition rate of machine translation, optimize the output quality of translated text and reduce the workload of post-editing. Because pre-translation editing only needs to be modified in one language, the operation is simpler than post-translation editing, which can realize the double improvement of quality and efficiency. A good pre-editing translation can help machine translation more smoothly, thus improving the machine readability and quality of the output translation. Pre-editing is mostly applied in the following situations: one is when the original text is of poor quality and the machine is difficult to recognize the meaning of the sentence, such as user generated content with poor readability and translatability (Gerlach et al 2003:45-53); Documents that need to be published in multiple languages; Next is when the original text contains a lot of jargon; The last is the original text has a corresponding translation memory bank. If the original text is edited, it can better match the content of the translation memory bank. Pre-editing is a process of identifying problems. It requires to pre-edit the source texts before putting it into machine translation according to the requirements, listing the expressions or sentences that may have trouble in machine translation and then pre-edit it by human. The purpose is to enable the computer to translate better, improve the translatability of machine translation. (Slype G V & Guinet J F & Seitz F 1984:115)<br />
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===1.3 Machine Translation Mode===<br />
According to the different knowledge acquisition methods, machine translation modes can be classified as follows: one is rule-based machine translation, which is based on bilingual dictionaries and a library of language rules for each language. The quality of translation depends on whether the source language conforms to the existing rules, but the inexhaustible rules are the hinders of this model. The second mode is machine translation based on statistics. This translation model relies on the principles of mathematics and statistics to find various existing translations corresponding to the translation tasks through the employment of corpus, analyzing the frequency of their occurrence and selecting the translation with the highest frequency for output. The disadvantage of this translation model is that it ignores the flexibility of language and the importance of context. The last one is neural network language model. This model is different from previous translation models in that it uses end-to-end neural network to realize automatic translation between natural languages. At present, the quality of its translation is much higher than that of the previous translation models.<br />
===1.4 Source of Study Abstracts===<br />
The core medical journals from domestic are selected in order to make the paper more representative and authoritative, such as National Medical Journal of China, Journal of Peking University (Health Science), and Journal of Third Military Medical University. All of them include basic medical science, biomedical technology, laboratory medical science and other fields. In this way, the pre-editing approaches included are applicable enough to machine translation of medical abstracts.<br />
===1.5 Selection of Translation Software===<br />
Generally, machine translation software includes Google Translation, Youdao Translator and Niu Translation, which has their own special use in translation. Take Google as an example, it is more like neurons in human brain, enabling to learn and collect information to establish connections with its neural machine translator’s neurons. However, it also causes many errors because of the lack of enough information. In this paper, contrastive analysis will be carried on by using Google translation. On the one hand, being a pioneer in the translation of NET, it is inevitably to sue Google as the translation software to translate medical texts in this paper. Comparing the program called “Google brain”, other NMT of translation software are relatively disadvantages. On the other hand, the Google translation enjoys the largest users in the world, with its downloads of more than one billion. The output quality of Google translation is more correct and complete than other machine translation software. With years of development and improvement, Google Translation has been greatly promoted. In this paper, Chinese medical abstract will be automatically translated by Google translation, and then the translation output will be compared with the translation by human and the publication of English abstracts. The main purpose is to prove that the improvement and promotion of quality and accuracy in the medical abstracts will be obtained through the pre-editing approaches.<br />
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===2.Language Characteristics and Error Division===<br />
Since Chinese and English are two different languages, it is quite neccessary to identify their own characteristics so as to better analyze and understand the two languages. Tytler (1978: 118-119) argued in his Essay on the Principles of Translation that there are three principles of translation that in all the translation should give readers the same feelings as the source text, except for complete transcript of ideas. There actually also exit some mistakes of these two languages. So the following will make some clarrifications of these two languages to ensure more accurate translation.<br />
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===2.1 Language Characteristics of Medical Abstracts===<br />
Chinese and English belong to different language systems, so there are differences in their language structure and the way users think of the languages. When using machine translation from Chinese to English, due to the unequal language levels, there will be many mistakes in the translation process, especially for ESP texts, such as medical papers. <br />
Here, these differences mainly refer to the linguistic characteristics of medical abstracts. In medical abstracts, it usually includes structured and unstructured abstracts. Although in different forms, they both describe the purpose, methods and conclusions of the research.<br />
In the method section of medial abstract, several Chinese sentences can be connected with commas, but each sentence may convey different information. In contrast, English sentences contain a great deal of information, but in order to ensure clarity, some modifiers need to be isolated and then reconstructed. However, in Chinese-English machine translation, a lot of information is put into the sentence because the machine segments the sentence on the basis of the full comma.<br />
In addition, subjectless sentence is used in the objective and method parts of Chinese medical abstracts. The subjectless sentence means the sentence without or free from subject and is usually employed in two contexts. The first is "needless to say". In Chinese sentences, it is common to omit the subject of the sentence. Chinese sentences can convey meanings by using incomplete sentence structures, so Chinese speakers can understand the meanings of the sentences even though the sentence subject is omitted. The second is "emphasis of action". In this context, subjectless sentences are used to describe behaviors, especially in the study of traditional Chinese medicine abstracts. Comparatively speaking, it should be avoided in English medical abstracts. Abstract sentences are more subjective when describing the learning process, while the essential requirement of English medical abstract is objectivity. From this point, sentences without subject should be avoided in English medical abstracts. Another feature is voice. Few words with passive meanings appear in Chinese abstracts. English sentences are more favoured in using passive voice. When translating from Chinese to English, passive voice should be used to make the contents objective, which is also the basic requirement of medical papers.<br />
These are the linguistic features of Chinese medical abstracts. There are great differences in sentence structure and expression between Chinese and English medical abstracts. These differences may reduce the accuracy of machine translation, so it is necessary to introduce pre-editing to edit the source text to ensure that the source text can be accurately recognized by the machine and fully translated into English.<br />
===2.2 Error Division of Machine Translation in Translating Abstracts of Medical Papers from Chinese to English===<br />
In this paper, errors in machine translation are listed out after analyzing. Some are due to the principles, such as statistical-based MT and NMT, employed by machine translation. Based on the original errors, they can be studied from two levels. One is from the macro-level, referring to mistakes caused by objective factors. These are not human factors, but the disadvantage of machine translation and the limitations of language. The limitations of machine translation are derived from the principles and models adopted and manifests itself as a reliance on reference sources. In this case, semantic ambiguity and textual incoherence may occur in the absence of reference sources.<br />
However, for ESP texts such as medical papers, the requirements for terms and sentence patterns are far beyond the existing corpora. For unfamiliar texts (that is, the corresponding texts cannot be found in the corpora). The quality of output translation will be relatively low, which is determined by the principles of machine translation. As mentioned above, machine translation has evolved over the past few decades from rules-based to corp-based statistics to NMT. However, there still exist some limitations in machine translation.<br />
Mistakes on micro-level are mainly caused by the variations and differences of linguistic structure between Chinese and English. Chinese is an implicit language, while English is an explicit one (Lian, 2010) To put it in another way, Chinese expression does not depend on language structures, while English does the opposite. This may result in mismatches between the original and machine translated sentences. This kind of error is mainly divided into two types: component fragment and component missing. For a medical paper, such errors are very serious. As a kind of ESP discourse, medical paper has the characteristics of fixed, objective and accurate language structure. In order to reproduce the characteristics of medical abstracts in translation, it is necessary to avoid errors at the level of words and sentences and pay attention to logic and consistency.<br />
Lexical errors here refer to the inconsistency of fixed expressions in the translation of terms. Although these terms are machine translation based on the corpus, the corpus may not be perfect for ESP texts such as medical papers. Therefore, fixed expressions are not used in term translation. This is also the most common mistake in machine translation (the term here includes but is not limited to nouns). For ESP texts, medical text in particular, the fixed expressions and the accuracy of terms are of great importance.<br />
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===3.Approaches Proposed for Pre-editing ===<br />
Generally speaking, there is a close relationship between pre-editing and post-editing, both of which aim to convey information and ensure high or publishable translation quality. Proper pre-editing can improve the quality of machine translation in terms of adequacy and consistency. <br />
Complexity of natural language and people use language arbitrarily, bringing many difficulties to Chinese-English machine translation, Hu Qingping (2005:24) has proposed "the research of translation software and the development of the controlled language are the two directions to improve the quality of machine translation : the former aims at the difficulty in the natural language processing, the latter overcomes the arbitrariness of natural language". Feng Quangong and Gao Lin (2017: 63-68) put forward: "The writing principles of controlled language can be applied to pre-editing of machine translation. Pre-editing based on controlled language can effectively reduce the complexity and ambiguity of source text, improve the identifiability of machine translation (the translatability of source text itself), and thus reduce (fully) the workload of post-editing".<br />
The central task of pre-editing is to transform human-friendly content into machine-friendly content, so words and sentences need to be repositioned or even changed. Based on the analysis of the language characteristics of Chinese and English, the Chinese ideographic group should be split before the Chinese source text is input into the machine software to translate so that the sentence structure is complete which can be easily recognized by the machine translation software.<br />
===3.1 Extraction and Replacement of Terms in the Source Text===<br />
As a branch of applied English, medical English is the product of the combination of English language knowledge and medical knowledge. The terms in medical papers have the characteristics of fixed and complex language structure. Although Google Translation is based on a corpus, the language structure is not fixed due to the limited size of the corpus. Therefore, the following two steps should be followed before machine translation. The first is to extract terms and create a glossary, and then replace the Chinese terms in the source text with the corresponding English expressions in the glossary. Of course, the terms of extraction should be searched and verified according to the actual situation. All of these words are verified before they can be replaced. This method can not only ensure the accuracy of term translation, but also maintain the consistency of expression, especially when dealing with long texts.<br />
Example1<br />
Source abstract: 口腔白斑病癌变相关缺氧应答基因和微小RNA的芯片及表达验证。<br />
Google translation: Microarray detection/Chip detection and expression verification of hypoxia response genes and microRNAs related to oral leukoplakia canceration.<br />
Published translation:Transcriptome array screening and verification of oral leukoplakia carcinogenesis-related hypoxia-responsive gene and microRNA. <br />
Analysis: The expression “ Affymetrix GeneChip” empolyed in this thesis is a human transcription array for transcriptome array. In the source abstract, “芯片检测“ can be meant “screening” but not detection, so the proper and appropriate translation of these expression should be “transcription array screening”, but the Google translates it into “microarry detection of chip detection”. Therefore, term extraction is essential and inevitable before putting the source abstracts into the machine translation software.<br />
After pre-editing source abstracts: 对 oral leukoplakia carcinogenesis 相关 hypoxia-responsive gene 和微小RNA进行 transcriptome array screening 及表达验证。<br />
After pre-editing translation: Transcriptome array screening and expression- verification of hypoxia-responsive genes and microRNAs related to oral leukoplakia carcinogenesis.<br />
===3.2 Explication of Subordination===<br />
As mentioned above, the subordination of Chinese sentences is judged by certain specific words in machine translation . Chinese is a paratactic language, and sentences are often connected by internal logical relationships; while English depends on sentence structure, so sentences are often closely linked by various language forms. In order to improve the accuracy of machine translation, we can adjust the sentence structure and adjust the position of adjectives and their modifiers. <br />
Example 2<br />
Source abstracts:回顾性分析南京医科大学附属儿童医院中重度HIE患儿49例及同期就诊的无神经系统症状体征的足月新生儿为对照组31例的头颅磁共振成像（MRI）资料。<br />
Google translation: A retrospective analysis that the brain magnetic resonance imaging (MRI) data of 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University and full-term neonates with no neurological symptoms and signs during the same period were included in the control group.<br />
Published translation: A total of 49 children with moderate to severe HIE admitted to the children’s Hospital Affiliated to Nanjing Medical University were retrospectively analyzed. Cranial magnetic resonance imaging (MRI) date of 31 full-term neonates without neurological symptoms and signs who visited the hospital during the same period were recruited as the control group. <br />
Analysis: As the above example shows that “中重度HIE患儿” and “足月新生儿” in the source abstracts are from the Children’s Hospital Affiliated Nanjing Medical University. The Google translation shows that 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University. There is an error due to the misuse of subordination. There are some advice in order to solve the problem. That is, first to segment the sentence and then reconstruct the sentence. For instance, the Chinese expression “南京医科大学附属儿童医院” carries many modifiers, which requires to cut the modifiers and reconstruct the sentence. Therefore, the Chinese expression “南京医科大学附属儿童医院’can be divided into abstractions, leaving two sentences instead of one long sentence with modifiers..<br />
After pre-editing source abstracts: 对49例中重度HIE患儿以及31例无神经系统症状体征的足月新生儿的MRI资料进行回顾性分析。这些患者收治于南京医科大学儿童附属医院。<br />
After pre-editing translation: The MRI data of 49 children with moderate to severe HIE and 31 full-term newborns without neurological symptoms and signs were retrospectively analyzed. These patients were admitted to the Children’s Hospital of Nanjing Medical University.<br />
===3.3 Explication of Subject through Voice Changing===<br />
The extensive use of subjectless sentences are quite common in the Chinese abstracts, while English prefers to employ passive voice in the sentences so as to make them object and accurate. In order to take the characteristics of English sentences into great and careful consideration, the sentences written in passive voice in particular, it will be helpful for machine translation to find out the subject and reconstruct a sentence in passive voice (Wang Yan: 2008). The first is to discover the “doee” in a sentence and then is to reconstruct the sentence structure and put the “doee” before the verb. The last is to explicate the passive relation between the noun and the verb.<br />
Example 3<br />
Source abstract: 回顾性分析2018年1月至2020年12月解放军总医院第七医学中心收治的500例老年髋部骨折患者的资料。<br />
Google translation: A retrospective analysis of the data of 500 elderly patients with hip fractures admitted to the Seventh Medical Center of the PLA General Hospital from January 2018 to December 2020.<br />
Published translation: From January 2018 to December 2020, the data of 500 elderly patients with hip fracture treated in the Seventh Medical Center of PLA General Hospital were analyzed retrospectively.<br />
Analysis: The above example indicates that the “回顾性分析”in the Google Translate is a noun phrase, but the source abstracts actually describes an action or a behavior. The reason for such a mistake is that the machine can’t recognize the Chinese subjetless sentences. To find the subject of the sentence, the source abstracts can be revised and adjusted. Finding the “doee” is the first step, which refers to “500例老年髋部骨折患者的资料”in the source abstracts. And next is to put it in front of the verb, that is to place it in the beginning of the sentence. Last but not least, the passive voice should be explicated in the sentence. <br />
After pre-editing source abstract: 500例老年髋部骨折患者的资料被回顾性分析，他们在2018年1月之2020年12月期间收治于解放军总医院第七医学中心。<br />
After pre-editing translation: The data of 500 elderly hip fracture patients were retrospectively analyzed. They were admitted to the Seventh Medical Center of the PLA General Hospital between January 2018 and December 2020.<br />
===3.4 Relocation of Modifiers===<br />
Modifiers, including noun, adverbial and attributive are constantly employed in the Chinese medical papers in order to add information to subject and object in the sentence. By doing this, complicated sentences can be structured, thus causing obstacles to machine translation for recognizing this complex sentence structures when translating Chinese-English sentences. To make the machine translation successfully recognize the sentence structure, simplifying the sentence structure is quite necessary. The key is to moving the location of the modifiers and thus making the modifiers an independent sentence. In other words, the modifiers ought to be put before or behind the main part of the sentence to satisfy the common use of English. <br />
Usually, the modifiers in Chinese sentence can only be placed in front of the core word, while in English, modifiers are very flexible. It is all right to place the modifiers in front of or behind the major part of the sentences with adjective or a connective noun.<br />
Example 4<br />
Source abstracts: 利用DNA重组技术以pET-28a表达系统在E.coli BL21(DE3) 中重组表达Hepl。<br />
Google Translation: Hepl is recombined in E.coli BL21 (DE3) using DNA recombination technology with pET-28a expression system.<br />
Published translation: The recombinant Hcpl protein was expressed by using DNA recombination technology through pET-28a expression system in E. coli BL21 (De3).<br />
Analysis: The Chinese medical text includes two modifiers, “利用DNA”and “以pET-28a)表达系统”. These two modifiers will be translated by machine, which catches more attention to the two constituents. From the Google translation above, one failure is obvious that it misplaces the location of the two modifiers and presents it in not accurate form. To make it translate correctly by machine translation, dividing the sentences is very important so that the source abstracts can be correctly recognized by machine translation.<br />
After pre-editing source abstract: 利用DNA重组技术，Hcp1重组表达在E.coli BL21 (DE3)中， 通过pET-28a表达系统在E. coli BL21 (DE3)中。<br />
Google translation: Using DNA recombination technology, Hcp1 recombination is expressed in E.coli BL21 (DE3), via pET-28a expression system in E. coli BL21 (DE3).<br />
The second is the independence of modifiers, that is, modifiers can also be reconstructed into clauses to modify core words. Compared with English, There is no subordinate clause in Chinese, so redundant Chinese modifiers need to be reconstructed into subordinate clauses in Chinese-English translation to meet the characteristics of English. English, especially sentences with multiple modifiers. Otherwise, sentence structure may be confused, such as scattered modifiers of core words. To avoid this mistake, it is necessary to separate the modifier from the main part of the sentence. These modifiers should be reconstituted into clauses. Also, keep your sentences simple and easy to understand.<br />
===3.5 Proper Omission and Deletion of Category Words===<br />
Category words are commonly used in Chinese. Category words complement the meaning of words, including problems, positions, situations and jobs. Adding this supplementary word is more in line with Chinese custom. In many cases, it has no real meaning, so it can be omitted in translation. In Chinese, category words are frequently used. However, it is rarely used in English, which is one of the differences between Chinese and English. There are many kinds of category words. Considering objectivity and the fixed structure of language, redundant category words should be deleted in Chinese-English translation. In the general, the most commonly used category of words in the Chinese medical abstracts are "process", "behavior", and "situation". These categories of words hinder the language conversion of Chinese and English, resulting in redundancy. <br />
Example 5<br />
Source abstract: 探讨口腔白斑病癌变进程中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia response genes and associated microRNAs in the process of oral leukoplakia cancer was discussed.<br />
Published translation: To study the hypoxia response gene and microRNA (miRNA)expression profiles in the pathogenesis and progression of oral leukoplakia (OLK).<br />
Analysis: As the above example shows, the Chinese words “进程” belongs to a category word. However, the Chinese expression “癌变”contains the process, so the “进程” expression can be deleted before placing it into the machine translation, because the meaning of it has been overlapping between the expression “癌变”. Therefore, its place can be replaced with preposition “during”.<br />
After pre-editing source abstract: 探讨口腔白斑病癌变中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia responsive genes and miRNA in oral leukoplakia cancer was investigated.<br />
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===Conclusion===<br />
After years of development, machine translation has made great progress. The accuracy of machine translation has been greatly improved in both text recognition and sentence pattern conversion. However, machine translation has its own limitations. In other words, it needs to rely on the parallel corpus as a reference source for improving its accuracy. ESP text, in particular, is harder to get the high quality by machine translation. <br />
<br />
As one of the research papers, the characteristics of medical abstracts are fixed language structures, objectivity and accuracy (Qin Yi 2004:421-423). Therefore, medical translation must be accurate, object and understandable to follow the specific demands of the medical paper. Being an important field in the human society, medical paper translation is on a great demand, which means that it needs a huge demand for human labor. However, with the machine translation promoting, it will be more efficient to translate medical papers combining the effort by human and machine. The improvement and development of machine translation requires the joint efforts of computer science, information science, statistics, linguistics and other academic circles to achieve more mature human-computer mutual assistance translation (Li Yafei, Zhang Ruihua 2019:38-45).<br />
<br />
However, errors can occur during the process of machine translation of Chinese- English, because of the differences of the Chinese and English and the processing of the machine. Errors from the perspective of linguistic or grammar can affect the machine translation a lot. After division and recognition of errors, some pre-editing approaches are put forward to help the machine translation more accurate and readable, that are, extraction and replacement of terms in the source text, relocation of modifiers, explication of subordination, proper omission, deletion of category words and explication of subject through voice changing. <br />
<br />
The paper mainly focuses on the pre-editing machine translation by using medical papers as a case study. The errors of machine translation occurring in the translation of medical abstracts and pre-editing approaches for machine translation. The quality of machine translation of medical papers is greatly improved after employing the pre-editing methods. However, machine translation is not as flexible and accurate as human brain, so it is of importance to combine pre-editing and post-editing approaches with machine translation in order to produce more accurate, more object machine translation of medical papers.<br />
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===References===<br />
[1]. Cronin, Michael (2013). Translation in the Digital Age[M]. New York&London: Routledge.<br />
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[2]. GERLACH J, et al ( 2013). Combining Pre-editing and Post-editing to Improve SMT of User-generated Content[M]// Proceedings of MT Summit XIV Workshop on Post-editing Technology and Practice. 45-53.<br />
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[3]. Slype G V & Guinet J F & Seitz F (1984). Better Translation for Better Communication [M] .Oxford: Pergamon Press Ltd (U.K.), <br />
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[4]. O'Brien S (2002). Teaching Post-editing: A Proposal for Course Con-tent [EB/OL]. http://mt-archive. Info/EAMT-2002-0brien. Pdf.<br />
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[5]. Tytler, A. F. (1978). Essay On The Principles of Translation[M]. Amsterdam: JohnBenjamins Publishing.<br />
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[7] 冯全功,高琳 (2017) 基于受控语言的译前编辑对机器翻译的影响[J]. 当代外语研究,(2): 63-68+87+110.<br />
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[8] 胡清平(2005). 机器翻译中的受控语言[J]. 中国科技翻译, (03): 24-27. <br />
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[11] 秦毅(2004),从翻译基本标准议医学英语的翻译[J]. 遵义医学院学报,27 (4): 421-423. <br />
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=12 蔡珠凤=(The Mistranslation of C-J Machine Translation of Political Statements)=<br />
[[Machine_Trans_EN_12]]<br />
===Abstract===<br />
Language is the main way of communication between people. With the continuous development of globalization, the scale of cross-border exchanges is also expanding. However, due to cultural differences and diversity, the languages of different countries and regions are very different, which seriously hinders people's communication. The demand for efficient and convenient translation tools is increasing. At the same time, with the development of network technology and artificial intelligence, recognition technology based on deep learning is more and more widely used in English, Japanese and other fields.<br />
<br />
===Key words===<br />
machine translation; political statements; mistranslation of C-J machine translation<br />
===题目===<br />
The Mistranslation of C-J Machine Translation of Political Statements<br />
===摘要===<br />
语言是人与人之间交流的主要方式。随着全球化的不断发展，跨境交流的规模也在不断扩大。然而，由于文化的差异和多样性，不同国家和地区的语言差异很大，这严重阻碍了人们的交流。对高效便捷的翻译工具的需求正在增加。同时，随着网络技术和人工智能的发展，基于深度学习的识别技术在英语、日语等领域的应用越来越广泛。<br />
<br />
===关键词===<br />
机器翻译；政治发言；政治发言中译日的误译<br />
===1. Introduction===<br />
===Introduction to machine translation===<br />
Machine translation, also known as automatic translation, is a process of using computers to convert one natural language (source language) into another natural language (target language). It is a branch of computational linguistics, one of the ultimate goals of artificial intelligence, and has important scientific research value.At the same time, machine translation has important practical value. With the rapid development of economic globalization and the Internet, machine translation technology plays a more and more important role in promoting political, economic and cultural exchanges.The development of machine translation technology has been closely accompanied by the development of computer technology, information theory, linguistics and other disciplines. From the early dictionary matching, to the rule translation of dictionaries combined with linguistic expert knowledge, and then to the statistical machine translation based on corpus, with the improvement of computer computing power and the explosive growth of multilingual information, machine translation technology gradually stepped out of the ivory tower and began to provide real-time and convenient translation services for general users.（Zhang 2019:5-6)<br />
<br />
=== C-J machine translation software===<br />
Today's online machine translation software includes Baidu translation, Tencent translation, Google translation, Youdao translation, Bing translation and so on. Google was the first company to launch the machine translation system, and Baidu was the first company to import the machine translation system in China. In addition, Tencent and Youdao have attracted much attention.Machine translation is the process of using computers to convert one natural language into another. It usually refers to sentence and full-text translation between natural languages. In order to continuously improve the translation quality, R & D personnel have added artificial intelligence technologies such as speech recognition, image processing and deep neural network to machine translation on the basis of traditional machine translation based on rules, statistics and examples.With the increase of using machine translation, the joint cooperation between manual translation and machine translation will also increase significantly in the future. What criteria should be used to evaluate the quality of machine translation? In the evolving field of machine translation, there is an urgent need to clarify the unsolvable questions and solved problems.<br />
<br />
===The history of machine translation===<br />
The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. alchuni put forward the idea of using machines for translation. In 1933, Soviet inventor П.П. Trojansky designed a machine to translate one language into another, and registered his invention on September 5 of the same year; However, due to the low technical level in the 1930s, his translation machine was not made. In 1946, the first modern electronic computer ENIAC was born. Shortly after that, W. weaver, an American scientist and A. D. booth, a British engineer, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947 when discussing the application scope of electronic computer. In 1949, W. Weaver published the translation memorandum, which formally put forward the idea of machine translation. After 60 years of ups and downs, machine translation has experienced a tortuous and long development path. The academic community generally divides it into the following four stages:<br />
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Pioneering period（1947-1964）<br />
<br />
In 1954, with the cooperation of IBM, Georgetown University completed the English Russian machine translation experiment with ibm-701 computer for the first time, showing the feasibility of machine translation to the public and the scientific community, thus opening the prelude to the study of machine translation.<br />
It is not too late for China to start this research. As early as 1956, the state included this research in the national scientific work development plan. The topic name is "machine translation, the construction of natural language translation rules and the mathematical theory of natural language". In 1957, the Institute of language and the Institute of computing technology of the Chinese Academy of Sciences cooperated in the Russian Chinese machine translation experiment, translating 9 different types of more complex sentences.From the 1950s to the first half of the 1960s, machine translation research has been on the rise. The United States and the former Soviet Union, two superpowers, have provided a lot of financial support for machine translation projects for military, political and economic purposes, while European countries have also paid considerable attention to machine translation research due to geopolitical and economic needs, and machine translation has become an upsurge for a time. In this period, although machine translation is just in the pioneering stage, it has entered an optimistic period of prosperity.<br />
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Frustrated period（1964-1975）<br />
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In 1964, in order to evaluate the research progress of machine translation, the American Academy of Sciences established the automatic language processing Advisory Committee (Alpac Committee) and began a two-year comprehensive investigation, analysis and test.In November 1966, the committee published a report entitled "language and machine" (Alpac report for short), which comprehensively denied the feasibility of machine translation and suggested stopping the financial support for machine translation projects. The publication of this report has dealt a blow to the booming machine translation, and the research of machine translation has fallen into a standstill. Coincidentally, during this period, China broke out the "ten-year Cultural Revolution", and basically these studies also stagnated. Machine translation has entered a depression.<br />
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convalescence（1975-1989）<br />
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Since the 1970s, with the development of science and technology and the increasingly frequent exchange of scientific and technological information among countries, the language barriers between countries have become more serious. The traditional manual operation mode has been far from meeting the needs, and there is an urgent need for computers to engage in translation. At the same time, the development of computer science and linguistics, especially the substantial improvement of computer hardware technology and the application of artificial intelligence in natural language processing, have promoted the recovery of machine translation research from the technical level. Machine translation projects have begun to develop again, and various practical and experimental systems have been launched successively, such as weinder system Eurpotra multilingual translation system, taum-meteo system, etc.However, after the end of the "ten-year holocaust", China has perked up again, and machine translation research has been put on the agenda again. The "784" project has paid enough attention to machine translation research. After the mid-1980s, the development of machine translation research in China has further accelerated. Firstly, two English Chinese machine translation systems, ky-1 and MT / ec863, have been successfully developed, indicating that China has made great progress in machine translation technology.<br />
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New period(1990 present)<br />
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With the universal application of the Internet, the acceleration of the process of world economic integration and the increasingly frequent exchanges in the international community, the traditional way of manual operation is far from meeting the rapidly growing needs of translation. People's demand for machine translation has increased unprecedentedly, and machine translation has ushered in a new development opportunity. International conferences on machine translation research have been held frequently, and China has made unprecedented achievements. A series of machine translation software have been launched, such as "Yixing", "Yaxin", "Tongyi", "Huajian", etc. Driven by the market demand, the commercial machine translation system has entered the practical stage, entered the market and came to the users.Since the new century, with the emergence and popularization of the Internet, the amount of data has increased sharply, and statistical methods have been fully applied. Internet companies have set up machine translation research groups and developed machine translation systems based on Internet big data, so as to make machine translation really practical, such as "Baidu translation", "Google translation", etc. In recent years, with the progress of in-depth learning, machine translation technology has further developed, which has promoted the rapid improvement of translation quality, and the translation in oral and other fields is more authentic and fluent.<br />
<br />
===The problem of machine translation at present===<br />
Error is inevitable<br />
Many people have misunderstandings about machine translation. They think that machine translation has great deviation and can't help people solve any problems. In fact, the error is inevitable. The reason is that machine translation uses linguistic principles. The machine automatically recognizes grammar, calls the stored thesaurus and automatically performs corresponding translation. However, errors are inevitable due to changes or irregularities in grammar, morphology and syntax, such as sentences with adverbials after "give me a reason to kill you first" in Dahua journey to the West. After all, a machine is a machine. No one has special feelings for language. How can it feel the lasting charm of "the tenderness of lowering its head, like the shame of a water lotus? After all, the meaning of Chinese is very different due to the changes of morphology, grammar and syntax and the change of context. Even many Chinese people are zhanger monks - they can't touch their heads, let alone machines.<br />
Bottleneck<br />
In fact, no matter which method, the biggest factor affecting the development of machine translation lies in the quality of translation. Judging from the achievements, the quality of machine translation is still far from the ultimate goal.<br />
Chinese mathematician and linguist Zhou Haizhong once pointed out in his paper "fifty years of machine translation": to improve the quality of machine translation, the first thing to solve is the problem of language itself rather than programming; It is certainly impossible to improve the quality of machine translation by relying on several programs alone. At the same time, he also pointed out that it is impossible for machine translation to achieve the degree of "faithfulness, expressiveness and elegance" when human beings have not yet understood how the brain performs fuzzy recognition and logical judgment of language. This view may reveal the bottleneck restricting the quality of translation. <br />
It is worth mentioning that American inventor and futurist ray cozwell predicted in an interview with Huffington Post that the quality of machine translation will reach the level of human translation by 2029. There are still many disputes about this thesis in the academic circles.<br />
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===2.Mistranslation of Chinese Japanese machine translation===<br />
===2.1Vocabulary mistranslation in Chinese Japanese machine translation===<br />
===2.1.1Mistranslation of proper nouns===<br />
In political materials, there are often political dignitaries' names, place names or a large number of proper nouns in the political field. The morphemes of such words are definite and inseparable. Mistranslation will make the source language lose its specific meaning. <br />
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Japanese translation into Chinese Chinese translation into Japanese<br />
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original text translation by Youdao reference translation original text translation by Youdao reference translation<br />
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朱鎔基 朱基 朱镕基 栗战书 栗戰史書 栗戰書<br />
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労安 劳安 劳安 李克强 李克強 李克強 <br />
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筑紫哲也 筑紫哲也 筑紫哲也 习近平 習近平 習近平<br />
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山口百惠 山口百惠 山口百惠 韩正 韓中 韓正<br />
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田中角栄 田中角荣 田中角荣 王沪宁 王上海氏 王滬寧<br />
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東条英機 东条英社 东条英机 汪洋 汪洋 汪洋<br />
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毛沢东 毛泽东 毛泽东 赵乐际 趙樂南 趙樂際<br />
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トウ・ショウヘイ 大酱 邓小平 江泽民 江沢民 江沢民<br />
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周恩来 周恩来 周恩来<br />
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クリントン 克林顿 克林顿<br />
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The above table counts 18 special names in the two texts, and 7 machine translation errors. In terms of mistranslation, there are not only "战书" but also "戰史" and "史書" in Chinese. "沪" is the abbreviation of Shanghai. In other words, since "战书" and "沪" are originally common nouns, this disrupts the choice of target language in machine translation. Except Katakana interference (except for トウシゃウヘイ, most of the mistranslations appear in the new Standing Committee. It can be seen that the machine translation system does not update the thesaurus in time. Different from other words, people's names have particularity. Especially as members of the Standing Committee of the Political Bureau of the CPC Central Committee, the translation of their names is officially unified. In this regard, public figures such as political dignitaries, stars, famous hosts and important. In addition, the machine also translates Japanese surnames such as "タ二モト" (谷本), "アンドウ" (安藤) into "塔尼莫特" and "龙胆". It can be found that the language feature that Japanese will be written together with Chinese characters and Hiragana also directly affects the translation quality of Japanese Chinese machine translation. Japanese people often use Katakana pronunciation. For example, when Japanese people talk with Premier Zhu, they often use "二ーハウ" (Hello). However, the machine only recognizes the two pseudonyms "二" and "ハ" and transliterates them into "尼哈" , ignoring the long sound after "二" and "ハ".<br />
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original text Translation by Youdao reference translation<br />
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日美安全体制 日米の安全体制 日米安保体制<br />
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中国共产党第十九次全国代表大会 中国共産党第19回全国代表大会 中国共産党第19回全国代表大会（第19回党大会）<br />
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十八大 十八大 第18回党大会中国特色社会主义<br />
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中国特色社会主義 中国の特色ある社会主義 第18回党大会<br />
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中国共产党中央委员会 中国共産党中央委員会 中国共産党中央委員会<br />
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中国共産党中央委員会十八届中共中央政治局常委 第18代中国共產党中央政治局常務委員 第18期中共中央政治局常務委員<br />
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十八届中共中央政治局委员 18期の中国共產党中央政治局委員 第18期中共中央政治局委員<br />
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十九届中共中央政治局常委 十九回中国共產党中央政治局常務委員 第19期中央政治局常務委員<br />
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中共十九届一中全会 中国共產党第十九回一中央委員会 第19期中央委員会第1回全体会議<br />
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The above table is a comparison of the original and translated versions of some proper nouns. As shown in the table, the mistranslation problems are mainly reflected in the mismatch of numerals + quantifiers, the wrong addition of case auxiliary word "の", the lack of connectors, the Mistranslation of abbreviations, dead translation, and the writing errors of Chinese characters. The following is a specific analysis one by one.<br />
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"十八届中共中央政治局常委", "十八届中共中央政治局委员", "十九届中共中央政治局常委" and "中共十九届一中全会" all have quantifiers "届", which are translated into "代", "期" and "回" respectively. The meaning is vague and should be uniformly translated into "期"; Among them, the translation of the last three proper nouns lacks the Chinese conjunction "第". The case auxiliary word "の" was added by mistake in the translation of "十八届中共中央政治局委员" and "日美安全体制". The "中国共产党中央委员会" did not write in the form of Japanese Ming Dynasty characters, but directly used simplified Chinese characters.<br />
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The full name of "中共十九届一中全会" is "中国共产党第十九届中央委员会第一次全体会议", in which "一" stands for "第一次", which is an ordinal word rather than a cardinal word. Machine translation does not produce a correct translation. The fundamental reason is that there are no "规则" for translating such words in machine translation. If we can formulate corresponding rules for such words (for example, 中共m'1届m2 中全会→第m1期中央委员会第m2回全体会议), the translation system must be able to translate well no matter how many plenary sessions of the CPC Central Committee.<br />
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===2.1.2Mistranslation of Polysemy===<br />
original text Translation by Youdao reference translation<br />
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スタジオ 摄影棚/工作室 直播现场/演播厅<br />
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日中関係の話 中日关系的故事 就中日关系(话题)<br />
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溝 水沟 鸿沟<br />
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それでは日中の問題について質問のある方。 那么对白天的问题有提问的人。 关于中日问题的话题，举手提问。<br />
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私たちのクラスは20人ちょっとですが、 我们班有20人左右， 我们班二十多人的意见统一很难，<br />
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いろいろな意見が出て、まとめるのは大変です。 但是又各种各样的意见，总结起来很困难。 <br />
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一体どうやって、13億人もの人をまとめているんですか。 到底是怎么处理13亿的人的呢？ 中国是怎样把13亿人凝聚在一起的？<br />
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In the original text, the word "スタジオ" appeared four times and was translated into "摄影棚" or "工作室" respectively, but the context is on-site interview, not the production site of photography or film. "話" has many semantics, such as "说话", "事情", "道理", etc. In accordance with the practice of the interview program, Premier Zhu Rongji was invited to answer five questions on the topic of China Japan relations. Obviously, the meaning of the word "故事" is very abrupt. The inherent Japanese word "日中" means "晌午、白天". At the same time, it is also the abbreviation of the names of the two countries. The machine failed to deal with it correctly according to the context. "溝" refers to the gap between China and Japan, not a "水沟". The former "まとめる" appears in the original text with the word "意见", which is intended to describe that it is difficult to unify everyone's opinions. The latter "まとめている" also refers to unifying the thoughts of 1.3 billion people, not "处理". Therefore, it is more appropriate to use "统一" and "处理" in human translation, rather than "总结意见" or "处理意见".<br />
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Mistranslation of polysemy has always been a difficult problem in machine translation research. The translation of each word is correct, but it is often very different from the original expression in the context. Zhang Zhengzeng said that ambiguity is a common phenomenon in natural language. Its essence is that the same language form may have different meanings, which is also one of the differences between natural language and artificial language. Therefore, one of the difficulties faced by machine translation is language disambiguation (Zhang Zheng, 2005:60). In this regard, we can mark all the meanings of polysemous words and judge which meaning to choose by common collocation with other words. At the same time, strengthen the text recognition ability of the machine to avoid the translation inconsistent with the current context. In this way, we can avoid the mistake of "大酱" in the place where famous figures such as Deng Xiaoping should have appeared in the previous political articles.<br />
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===2.1.3Mistranslation of compound words===<br />
Multi class words refer to a word with two or more parts of speech, also known as the same word and different classes.<br />
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original text Translation by Youdao reference translation<br />
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1998年江泽民主席曾经访问日本, 1998年の江沢民国家主席の日本訪問し、 1998年、江沢民総書記が日本を訪問し、かつ<br />
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同已故小渊首相签署了联合宣言。 かつて同じ故小渕首相が署名した共同宣言 亡くなられた小渕総理と宣言に調印されました<br />
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Chinese "同" has two parts of speech: prepositions and conjunctions: "故" has many parts of speech, such as nouns, verbs, adjectives and conjunctions. This directly affects the judgment of the machine at the source language level. The word "同" in the above table is used as a conjunction to indicate the other party of a common act. "故" is used as a verb with the semantic meaning of "死亡", which is a modifier of "小渊首相". The machine regards "同" as an adjective and "故" as a noun "原因", which leads to confusion in the structure and unclear semantics of the translation. Different from the polysemy problem, multi category words have at least two parts of speech, and there is often not only one meaning under each part of speech. In this regard, software R & D personnel should fully consider the existence of multi category words, so that the translation machine can distinguish the meaning of words on the basis of marking the part of speech, so as to select the translation through the context and the components of the word in the sentence. Of course, the realization of this function is difficult, and we need to give full play to the wisdom of R & D personnel.<br />
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===2.2 Syntactic mistranslation in Chinese Japanese machine translation===<br />
===2.2.1Mistranslation of tenses===<br />
original text：History is written by the people, and all achievements are attributed to the people. <br />
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Translation by Youdao：歴史は人民が書いたものであり、すべての成果は人民のためである。<br />
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reference translation：歴史は人民が綴っていくものであり、すべての成果は人民に帰することとなります。<br />
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The original sentence meaning is "历史是人民书写的历史" or "历史是人民书写的东西". When translated into Japanese, due to the influence of Japanese language habits, the formal noun "もの" should be supplemented accordingly. In this sentence, both the machine and the interpreter have translated correctly. However, the machine's recognition of "的" is biased, resulting in tense translation errors. The past, present and future will become "history", and this is a continuous action. The form translated as "~ ていく" not only reflects that the action is a continuous action, but also conforms to the tense and semantic information of the original text. In addition, the machine's treatment of the preposition "于" is also inappropriate. In the original text, "人民" is the recipient of action, not the target language. As the most obvious feature of isolated language, function words in Chinese play an important role in semantic expression, and their translation should be regarded as a focus of machine translation research.<br />
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original text：李克强同志是十六届中共中央政治局常委,其他五位同志都是十六届中共中央政治局委员。<br />
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Translation by Youdao：李克強総理は第16代中国共産党中央政治局常務委員であり、他の５人の同志はいずれも16期の中国共産党中央政治局委員である。<br />
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reference translation：李克強同志は第16期中国共産党中央政治局常務委員を務め、他の５人は第16期中共中央政治局委員を務めました。<br />
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Judging the verb "是" in the original text plays its most basic positive role, but due to the complexity of Chinese language, "是" often can not be completely transformed into the form of "だ / である" in the process of translation. This sentence is a judgment of what has happened. Compared with the 19th session, the 18th session has become history. This is an implicit temporal information. It is very difficult for machines without human brain to recognize this implicit information. "中共中央政治局常委" is the abbreviation of "中共中央政治局常务委员会委员" and it is a kind of position. In Japanese, often use it with verbs such as "務める","担当する",etc. The translator adopts the past tense of "務める", which conforms to the expression habit of Japanese and deals with the problem of tense at the same time. However, machine translation only mechanically translates this sentence into judgment sentence, and fails to correctly deal with the past information implied by the word "十八届".<br />
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===2.2.2Mistranslation of honorifics===<br />
Honorific language is a language means to show respect to the listener. Different from Japanese, there is no grammatical category of honorifics in Chinese. There is no specific fixed grammatical form to express honorifics, self modesty and politeness. Instead, specific words such as "您", "请" and "劳驾" are used to express all kinds of respect or self modesty. <br />
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Original text translation by Youdao reference translation<br />
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女士们，先生们，同志们，朋友们 さんたち、先生たち、同志たち、お友达さん ご列席の皆さん<br />
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谢谢大家！ ありがとうございます！ ご清聴ありがとうございました。<br />
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これはどうされますか。 这是怎么回事呢? 您将如何解决这一问题？<br />
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こうした問題をどうお考えでしょうか。 我们会如何考虑这些问题呢？ 您如何看待这一问题？<br />
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For example, for the processing of "女士们，先生们，同志们，朋友们", the machine makes the translation correspond to the original one by one based on the principle of unchanged format, but there are errors in semantic communication and pragmatic habits. When speaking on formal occasions, Chinese expression tends to be comprehensive and detailed, as well as the address of the audience. In contrast, Japanese usually uses general terms such as "皆様", "ご臨席の皆さん" or "代表団の方々" as the opening remarks. In terms of Japanese Chinese translation, the machine also failed to recognize the usage of Japanese honorifics such as "さ れ る" and "お考え". It can be seen that Chinese Japanese machine translation has a low ability to deal with honorific expressions. The occasion is more formal. A good translation should not only be fluent in meaning, but also conform to the expression habits of the target language and match the current translation environment.<br />
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===Conclusion===<br />
In the process of discussing the Mistranslation of machine translation, this paper mainly cites the Mistranslation of Youdao translator. When I studied the problem of machine translation mistranslation, I also used a large number of translation software such as Google and Baidu for parallel comparison, and found that these translation software also had similar problems with Youdao translator. For example, the "新世界新未来" in Chinese ABAC structural phrases is translated by Baidu as "新し世界の新しい未来", which, like Youdao, is not translated in the form of parallel phrases; Google online translation translates the word "“全心全意" into a completely wrong "全面的に"; The original sentence "我吃了很多亏" in the Mistranslation of the unique expression in the language is translated by Google online as "私はたくさんの損失を食べました". Because the "吃" and "亏" in the original text are not closely adjacent, the machine can not recognize this echo and mistakenly treats "亏" as a kind of food, so the machine translates the predicate as "食べました"; Japanese "こうした問題をどう考えでしょうか", Google's online translation is "你如何看待这些问题?", "你" tone can not reflect the tone of Japanese honorific, while Baidu translates as "你怎么想这样的问题呢?" Although the meaning can be understood, it is an irregular spoken language. Google and Baidu also made the same mistakes as Youdao in the translation of proper nouns. For example, they translated "十七届(中共中央政治局常委)”" into "第17回..." .In short, similar mistranslations of Youdao are also common in Baidu and Google. Due to space constraints, they will not be listed one by one here.<br />
<br />
Mr. Liu Yongquan, Institute of language, Chinese Academy of Social Sciences (1997) It has been pointed out that machine translation is a linguistic problem in the final analysis. Although corpus based machine translation does not require a lot of linguistic knowledge, language expression is ever-changing. Machine translation based solely on statistical ideas can not avoid the translation quality problems caused by the lack of language rules. The following is based on the analysis of lexical, syntactic and other mistranslations From the perspective of the characteristics of the source language, this paper summarizes some difficulties of Chinese and Japanese in machine translation.<br />
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(1) The difficulties of Chinese in machine translation <br />
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Chinese is a typical isolated language. The relationship between words needs to be reflected by word order and function words. We should pay attention to the transformation of function words such as "的", "在", "向" and "了". Some special verbs, such as "是", "做" and "作", are widely used. How to translate on the basis of conforming to Japanese pragmatic habits and expressions is a difficulty in machine translation research. In terms of word order, Chinese is basically "subject→predicate→object". At the same time, Chinese pays attention to parataxis, and there is no need to be clear in expression with meaningful cohesion, which increases the difficulty of Chinese Japanese machine translation. When there are multiple verbs or modifier components in complex long sentences, machine translation usually can not accurately divide the components of the sentence, resulting in the result that the translation is completely unreadable. <br />
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(2) Difficulties of Japanese in machine translation <br />
<br />
Both Chinese and Japanese languages use Chinese characters, and most machines produce the target language through the corresponding translation of Chinese characters. However, pseudonyms in Japanese play an important role in judging the part of speech and meaning, and can not only recognize Chinese characters and judge the structure and semantics of sentences. Japanese vocabulary is composed of Chinese vocabulary, inherent vocabulary and foreign vocabulary. Among them, the first two have a great impact on Chinese Japanese machine translation. For example "提出" is translated as "提出する" or "打ち出す"; and "重视" is translated as "重視する" or "大切にする". Japanese is an adhesive language, which contains a lot of "けど", "が" and "けれども" Some of them are transitional and progressive structures, but there are also some sequential expressions that do not need translation, and these translation software often can not accurately grasp them.<br />
<br />
===References===<br />
[1] Navroz Kaur Kahlon,(2021(prepublish));Williamjeet Singh.Machine translation from text to sign language: a systematic review[J].Universal Access in the Information Society,1-35.<br />
<br />
[2] Cao Qianyu;Hao Hanmei,(2021);Ahmed Syed Hassan.A Chaotic Neural Network Model for English Machine Translation Based on Big Data Analysis[J].Computational Intelligence and Neuroscience,3274326-3274326.<br />
<br />
[3]Hwang Yongkeun;Kim Yanghoon;Jung Kyomin.(2021)Context-Aware Neural Machine Translation for Korean Honorific Expressions[J].Electronics,10(13):1589-1589.<br />
<br />
[4]Zakaryia Almahasees.(2021)Analysing English-Arabic Machine Translation:Google Translate, Microsoft Translator and Sakhr.<br />
<br />
[5](2021)Machine learning in translation[J].Nature Biomedical Engineering,5(6):485-486.<br />
<br />
[6]Shaimaa Marzouk.(2021(prepublish))An in-depth analysis of the individual impact of controlled language rules on machine translation output: a mixed-methods approach[J].Machine Translation,1-37.<br />
<br />
[7]Welnitzová Katarína;Munková Daša.(2021)Sentence-structure errors of machine translation into Slovak[J].Topics in Linguistics,22(1):78-92.<br />
<br />
[8]Xu Xueyuan.(2021).Machine learning-based prediction of urban soil environment and corpus translation teaching[J].Arabian Journal of Geosciences,14(11). <br />
<br />
[9]Chen Bingchang 陈丙昌(2016).機械翻訳の誤訳分析【D】.Error analysis of mechanical translation.贵州大学.2016(05) <br />
<br />
[10]Lv Yinqiu 呂寅秋(1996).機械翻訳の言語規則と伝統文法との相違点.【D】The language rules of mechanical translation, the traditional grammar, and the points of contradiction.日本学研究.Japanese Studies.1996(00):21-22 <br />
<br />
[11]Liu Jun 刘君(2014).基于语料库的中日同形词词义用法对比及其日中机器翻译研究【D】.A Corpus-based Comparison of the Meanings of Chinese and Japanese Homographs and Research on Japanese-Chinese Machine Translation.广西大学.(03) <br />
<br />
[12]Cun Qianqian 崔倩倩(2019).机器翻译错误与译后编辑策略研究【D】.Research on Machine Translation Errors and Post-Editing Strategies.北京外国语大学.(09) <br />
<br />
[13]Zhang Yi 张义(2019).机器翻译的译文分析【D】.Translation analysis of machine translation.西安外国语大学.(10) <br />
<br />
[14]Zhang Linqian 张琳婧(2019).在线机器翻译中日翻译错误原因及对策【D】.Causes and countermeasures of online machine translation errors in Chinese-Japanese translation.山西大学.(02)<br />
<br />
[15]Wang Dan 王丹(2020).基于机器翻译的专利文本译后编辑对策研究【D】.Research on countermeasures for post-translational editing of patent texts based on machine translation.大连理工大学.(06)<br />
<br />
[16]Yang Xiaokun 杨晓琨(2020).日中机器翻译中的前编辑规则与效果验证【D】.Pre-editing rules and effect verification in Japanese-Chinese machine translation.大连理工大学.(06)<br />
<br />
[17]Zuo Jia 左嘉(2021). 机器翻译日译汉误译研究【D】. Research on Mistranslation of Machine Translation from Japanese to Chinese.北京第二外国语学院.<br />
<br />
[18]Guan Biying 关碧莹(2018).关于政治类发言的汉日机器翻译误译分析【D】.Analysis of Chinese-Japanese Machine Translation Mistranslations of Political Speeches.哈尔滨理工大学.<br />
<br />
[19]Che Tong 车彤(2021).汉译日机器翻译质量评估及译后编辑策略研究【D】.Research on Quality Evaluation of Chinese-Japanese Machine Translation and Post-translation Editing Strategies.北京外国语大学.(09)<br />
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Networking Linking<br />
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http://www.elecfans.com/rengongzhineng/692245.html<br />
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https://baike.baidu.com/item/%E6%9C%BA%E5%99%A8%E7%BF%BB%E8%AF%91/411793<br />
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=13 陈湘琼Chen Xiangqiong（Study on Post-editing from the Perspective of Functional Equivalence Theory ）=<br />
[[Machine_Trans_EN_13]]<br />
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=14 Bi bi Nadia（Machine Translation a Challenge for Human Translators)=<br />
[[Machine_Trans_EN_14]]<br />
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===Abstract===<br />
Machine translation is a big obstacle in the way of Human translators or interpretors although it is quick and less time consuming.people are trying to get translation of their target language using through source language.For this purpose they are using digital apps like Google translation Google translation does not give accurate and exact interpretation.Google translator is translates word to word translate that doesn't clarifies it's true and actual meaning.On the other hand human translators can give exact and accurate translation ,they take care of grammatical errors , diction and sentence structure.They clarify the purpose of target language through using source language.<br />
<br />
===Keywords===<br />
Descriptive translation, academic, interdiscipline, comparative literature, localization,Translatology,school of thought,translation , studies, linguistics, corresponding.<br />
<br />
===Body of article===<br />
Translation is the process of reworking text from one language into another to maintain the original message and communication. But, like everything else, there are different methods of translation, and they vary in form and function. What is translation? The term has become widely used among knowledge transfer researchers and practitioners, especially in the fields of health and health care. In a landmark review, Jonathan Lomas began to argue that 'The tasks… may be defined as to establish and maintain links between researchers and their audience, via the appropriate translation of research findings' (Lomas 1997, p 4). In 2004, the World Health Organization's World Report on Knowledge for Better Health suggested that 'One of the key contributions of research to health systems is the translation of knowledge into actions' (WHO 2004, p 33 and p 100). By 2006, special issues of WHO's Bulletin as well as the journals Evaluation and the Health Professions and the Journal of Continuing Education in the Health Professions were dedicated to translation.But what does 'translation' mean? It may be a new word for an old problem, meaning nothing more than 'transfer'. The rapidly emerging field of 'translational medicine' seems to take translation to mean generally what transfer might have meant, that is the transmission of knowledge and evidence 'from bench to bedside'. As one commentator has observed, "Translational medicine" as a fashionable term is being increasingly used to describe the wish of biomedical researchers to ultimately help patients' (Wehling 2008, abstract). <br />
Semantic uncertainty persists, not least because of the different interests of scientists, clinicians, patients and commercial firms (Littman et al 2007): 'Translational research means different things to different people, but it seems important to almost everyone' (Woolf 2008, p 211).<br />
In related fields, such as public health (Armstrong et al 2006), 'translation' seems to signify dissatisfaction with 'transfer'. It wants to move away from thinking of knowledge transfer as a form of technology transfer or dissemination, rejecting if only by implication its mechanistic assumptions and its model of linear messaging from A to B. But still, what does it signify?<br />
<br />
===Why translation?===<br />
'Translation' indicates a closer attention to the problem of shared meaning and how it might be developed. It seems to represent some new epistemological lubricant, facilitating the dissemination of texts and the application and use of the knowledge and information they in. Simply, translation might be the key to transfer. And yet, when we stop to think, we are more ambivalent. What is translated often seems somehow inferior, not real or original. Note how readily commentators reach for the idea that things might be 'lost in translation'. Knowing at a distance – made in and mediated by translation - makes for incomplete renditions, blurred images, partial truths. So what might 'translation' really mean? The purpose of this paper is to set out, for policy makers and practitioners, the theoretical and conceptual resources translation holds and seems to represent. In doing so, it explores understandings of translation in the fields of literature and linguistics and in the sociology of science and technology. It begins by setting out just why this idea of translation should make immediate, intuitive sense in relation to research, policy and practice.<br />
1translation in research, policy and practice research as translation<br />
Research often entails translation from one language to another: where data is collected from more than one ethnic group, for example, or where the language of the researcher is other than that of the research subject. It may draw on a secondary literature or source documents written in different languages, and may be published and disseminated in languages other than the one in which it is first written up.<br />
2In a different way, to conduct an interview is to ask for an account of experience and its meanings, but it is also to construct and translate that experience in terms defined at least in part by the researcher. In representing what is said, transcripts then select data, usually excluding significant gesture and eye-contact, for example. Often, certain characteristics of speech-acts (such as hesitations) will be edited out. In turn, the format of the transcript shapes the analytic use the researcher may make of it. The basis of research 'findings', then, is an artefact, a transcript or translation, not an original interaction (Ochs 1979, Barnes, Bloor and Henry 1996, Ross 2009).<br />
3In this way, the researcher recasts aspects of his or her problem or topic in new, scientific form: 'All researchers "translate" the experiences of others' (Temple 1997, p 609). Research is invariably conducted in a sort of 'metalanguage' (Hantrais and Ager 1985): the research process can be conceived as one of successive translations (from theoretical formulation to operationalization, transcription, interpretation and dissemination). Theorization is a process of reciprocal back and forth between theory and fact, in which conceptions of each are revised in order that one fit the other (Baldamus 1974).<br />
4 It is a kind of translation: a rereading, re-use, re-application or re-representation of what we know in new terms (Turner 1980). Referencing, too, is an act of translation, a form of appropriation and incorporation of one text by another (Gilbert 1977).policy as translation Each of the fields covered by the paper is diverse and ill-defined, and there is no intention here to provide a comprehensive account of any them. Sources have been chosen for their relevance: main references are cited in the text, and additional sources listed in footnotes.<br />
<br />
2For a brief introduction to the technical issues involved in social research in more than one language, see Birbili (2000). For translation issues in survey research and question design in general, see Ervin and Bower (1952) and Deutscher (1968); on translating survey research instruments (in this instance health-related quality of life measures), Bowden and Fox-Rushby (2003). On the use of translators and interpreters, see Temple (1997) and Jentsch (1998).<br />
3For an interesting discussion of this problem, see Bourdieu (1999), esp pp 621-626, 'The risks of writing'. <br />
===Difference between machine translation and human translators===<br />
Few people disagree on the differences between the two, but many argue over the quality of the translations. How accurate are machine translations? How reliable are human translations? Some say machine translation produces near-perfect translations while others are adamant that translations are incomprehensible and cause more problems than they solve. Results will, of course, vary depending on the source and target languages, the machine translation service used (e.g. Google Translate), and the complexity of the original text.<br />
Machine translation, love it or hate it, is here to stay. In fact, the machine translation market is growing at such a fast pace that it is predicted to reach $980 million by 2022.<br />
===Machine translation: the pros and cons===<br />
The advantages of machine translation generally come down to two factors: it’s faster and cheaper. The downside to this is the standard of translation can be anywhere from inaccurate, to incomprehensible, and potentially dangerous (more on that shortly).<br />
==The advantages of machine translation==<br />
Many free tools are readily available (Google Translate, Skype Translator, etc.)<br />
Quick turnaround time <br />
You can translate between multiple languages using one tool<br />
Translation technology is constantly improving<br />
The disadvantages of machine translation<br />
Level of accuracy can be very low <br />
Accuracy is also very inconsistent across different languages<br />
==Machines can't translate context ==<br />
Mistakes are sometimes costly <br />
Sometimes translation simply doesn’t work<br />
The most important thing to consider with any kind of translation is the cost of potential mistakes. Translating instructions for medical equipment, aviation manuals, legal documents and many other kinds of content require 100% accuracy. In such cases, mistakes can cost lives, huge amounts of money and irreparable damage to your company’s image. So choose carefully!<br />
Human translation: the pros and cons<br />
Human translation essentially switches the table in terms of pros and cons. A higher standard of accuracy comes at the price of longer turnaround times and higher costs. What you have to decide is whether that initial investment outweighs the potential cost of mistakes. Alternatively, whether mistakes simply aren’t an option, like the cases we looked at in the previous section.<br />
<br />
==The advantages of human translation ==<br />
It’s a translator’s job to ensure the highest accuracy<br />
Humans can interpret context and capture the same meaning, rather than simply translating words<br />
Human translators can review their work and provide a quality process<br />
Humans can interpret the creative use of language, e.g. puns, metaphors, slogans, etc.<br />
Professional translators understand the idiomatic differences between their languages<br />
Humans can spot pieces of content where literal translation isn’t possible and find the most suitable alternative<br />
The disadvantages of human translation<br />
Turnaround time is longer <br />
Translators rarely work for free <br />
Unless you use a translation agency, with access to thousands of translators, you’re limited to the languages any one translator can work with<br />
Simply put, human translation is your best option when accuracy is even remotely important. Other considerations to make are the complexity of your source material and the two languages you’re translating between – both of which can render machines pretty useless.<br />
<br />
When to use machine and human translation<br />
The truth is, the debate over machine vs human translation is an unnecessary distraction. What we should really be talking about is when to use these two different types of translation services, because they both serve a very valid purpose.<br />
<br />
Examples of when to use machine translation<br />
When you have a large bulk of content to translate and the general meaning is enough<br />
When your translation never reaches the final audience, e.g. you’re translating a resource as research for another piece of content<br />
Translating documents for internal use within a company, provided 100% accuracy isn’t needed<br />
To partially translate large chunks of content for a human translator to improve upon<br />
Examples of when to use human translation<br />
When accuracy is important<br />
Most cases where your translated content is received by a consumer audience<br />
When you have a duty of care to provide accurate translations (e.g. legal documents, product instructions, medical guidelines or health and safety content)<br />
When translating marketing material or other texts for creative language uses.<br />
<br />
types of machine translation.<br />
<br />
What is Machine Translation? Rule Based Machine Translation vs. Statistical Machine Translation. Machine translation (MT) is automated translation. It is the process by which computer software is used to translate a text from one natural language (such as English) to another (such as Spanish).<br />
<br />
To process any translation, human or automated, the meaning of a text in the original (source) language must be fully restored in the target language, i.e. the translation. While on the surface this seems straightforward, it is far more complex. Translation is not a mere word-for-word substitution. A translator must interpret and analyze all of the elements in the text and know how each word may influence another. This requires extensive expertise in grammar, syntax (sentence structure), semantics (meanings), etc., in the source and target languages, as well as familiarity with each local region.<br />
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Human and machine translation each have their share of challenges. For example, no two individual translators can produce identical translations of the same text in the same language pair, and it may take several rounds of revisions to meet customer satisfaction. But the greater challenge lies in how machine translation can produce publishable quality translations.<br />
<br />
Rule-Based Machine Translation Technology<br />
Rule-based machine translation relies on countless built-in linguistic rules and millions of bilingual dictionaries for each language pair.<br />
The software parses text and creates a transitional representation from which the text in the target language is generated. This process requires extensive lexicons with morphological, syntactic, and semantic information, and large sets of rules. The software uses these complex rule sets and then transfers the grammatical structure of the source language into the target language.<br />
Translations are built on gigantic dictionaries and sophisticated linguistic rules. Users can improve the out-of-the-box translation quality by adding their terminology into the translation process. They create user-defined dictionaries which override the system’s default settings.<br />
In most cases, there are two steps: an initial investment that significantly increases the quality at a limited cost, and an ongoing investment to increase quality incrementally. While rule-based MT brings companies to the quality threshold and beyond, the quality improvement process may be long and expensive.<br />
<br />
Statistical Machine Translation Technology<br />
Statistical machine translation utilizes statistical translation models whose parameters stem from the analysis of monolingual and bilingual corpora. Building statistical translation models is a quick process, but the technology relies heavily on existing multilingual corpora. A minimum of 2 million words for a specific domain and even more for general language are required. Theoretically it is possible to reach the quality threshold but most companies do not have such large amounts of existing multilingual corpora to build the necessary translation models. Additionally, statistical machine translation is CPU intensive and requires an extensive hardware configuration to run translation models for average performance levels.<br />
<br />
Rule-Based MT vs. Statistical MT<br />
Rule-based MT provides good out-of-domain quality and is by nature predictable. Dictionary-based customization guarantees improved quality and compliance with corporate terminology. But translation results may lack the fluency readers expect. In terms of investment, the customization cycle needed to reach the quality threshold can be long and costly. The performance is high even on standard hardware.<br />
<br />
Statistical MT provides good quality when large and qualified corpora are available. The translation is fluent, meaning it reads well and therefore meets user expectations. However, the translation is neither predictable nor consistent. Training from good corpora is automated and cheaper. But training on general language corpora, meaning text other than the specified domain, is poor. Furthermore, statistical MT requires significant hardware to build and manage large translation models.<br />
<br />
Rule-Based MT Statistical MT<br />
+ Consistent and predictable quality – Unpredictable translation quality<br />
+ Out-of-domain translation quality – Poor out-of-domain quality<br />
+ Knows grammatical rules – Does not know grammar <br />
+ High performance and robustness – High CPU and disk space requirements<br />
+ Consistency between versions – Inconsistency between versions <br />
– Lack of fluency + Good fluency<br />
– Hard to handle exceptions to rules + Good for catching exceptions to rules <br />
– High development and customization costs + Rapid and cost-effective development costs provided the required corpus exists<br />
Given the overall requirements, there is a clear need for a third approach through which users would reach better translation quality and high performance (similar to rule-based MT), with less investment (similar to statistical MT).<br />
Post-Edited Machine Translation (PEMT)<br />
Often, PEMT is used to bridge the gap between the speed of machine translation and the quality of human translation, as translators review, edit and improve machine-translated texts. PEMT services cost more than plain machine translations but less than 100% human translation, especially since the post-editors don’t have to be fluently bilingual—they just have to be skilled proofreaders with some experience in the language and target region.<br />
Successful translation is about more than just the words, which is why we advocate for not just human translation by skilled linguists, but for translation by people deeply familiar with the cultures they’re writing for. Life experience, study and the knowledge that only comes from living in a geographic region can make the difference between words that are understandable and language that is capable of having real, positive impact. <br />
<br />
PacTranz<br />
The HUGE list of 51 translation types, methods and techniques<br />
Upper section of infographic of 51 common types of translation classified in 4 broad categoriesThere are a bewildering number of different types of translation.<br />
So we’ve identified the 51 types you’re most likely to come across, and explain exactly what each one means.<br />
This includes all the main translation methods, techniques, strategies, procedures and areas of specialisation.<br />
It’s our way of helping you make sense of the many different kinds of translation – and deciding which ones are right for you.<br />
Don’t miss our free summary pdf download later in the article!<br />
The 51 types of translation we’ve identified fall neatly into four distinct categories.<br />
Translation Category A: 15 types of translation based on the technical field or subject area of the text<br />
Icons representing 15 types of translation categorised by the technical field or subject area of the textTranslation companies often define the various kinds of translation they provide according to the subject area of the text.<br />
This is a useful way of classifying translation types because specialist texts normally require translators with specialist knowledge.<br />
Here are the most common types you’re like to come across in this category.<br />
<br />
1. General Translation<br />
What is it?<br />
The translation of non-specialised text. That is, text that we can all understand without needing specialist knowledge in some area.<br />
The text may still contain some technical terms and jargon, but these will either be widely understood, or easily researched.<br />
What this means<br />
The implication is that you don’t need someone with specialist knowledge for this type of translation – any professional translator can handle them.<br />
Translators who only do this kind of translation (don’t have a specialist field) are sometimes referred to as ‘generalist’ or ‘general purpose’ translators.<br />
Examples<br />
Most business correspondence, website content, company and product/service info, non-technical reports.<br />
Most of the rest of the translation types in this Category do require specialist translators.<br />
Check out our video on 13 types of translation requiring special translator expertise:<br />
<br />
2. Technical Translation<br />
What is it?<br />
We use the term “technical translation” in two different ways:<br />
Broad meaning: any translation where the translator needs specialist knowledge in some domain or area.<br />
This definition would include almost all the translation types described in this section.<br />
Narrow meaning: limited to the translation of engineering (in all its forms), IT and industrial texts.<br />
This narrower meaning would exclude legal, financial and medical translations for example, where these would be included in the broader definition.<br />
What this means<br />
Technical translations require knowledge of the specialist field or domain of the text.<br />
That’s because without it translators won’t completely understand the text and its implications. And this is essential if we want a fully accurate and appropriate translation.Good to know Many technical translation projects also have a typesetting/dtp requirement. Be sure your translation provider can handle this component, and that you’ve allowed for it in your project costings and time frames.<br />
Examples<br />
Manuals, specialist reports, product brochures<br />
<br />
3. Scientific Translation<br />
What is it?<br />
The translation of scientific research or documents relating to it.<br />
What this means<br />
These texts invariably contain domain-specific terminology, and often involve cutting edge research.<br />
So it’s imperative the translator has the necessary knowledge of the field to fully understand the text. That’s why scientific translators are typically either experts in the field who have turned to translation, or professionally qualified translators who also have qualifications and/or experience in that domain.<br />
On occasion the translator may have to consult either with the author or other domain experts to fully comprehend the material and so translate it appropriately.<br />
Examples<br />
Research papers, journal articles, experiment/trial results<br />
<br />
4. Medical Translation<br />
What is it?<br />
The translation of healthcare, medical product, pharmaceutical and biotechnology materials.<br />
Medical translation is a very broad term covering a wide variety of specialist areas and materials – everything from patient information to regulatory, marketing and technical documents.<br />
As a result, this translation type has numerous potential sub-categories – ‘medical device translations’ and ‘clinical trial translations’, for example.<br />
What this means<br />
As with any text, the translators need to fully understand the materials they’re translating. That means sound knowledge of medical terminology and they’ll often also need specific subject-matter expertise.<br />
Good to know<br />
Many countries have specific requirements governing the translation of medical device and pharmaceutical documentation. This includes both your client-facing and product-related materials.<br />
Examples<br />
Medical reports, product instructions, labeling, clinical trial documentation<br />
<br />
5. Financial Translation<br />
What is it?<br />
In broad terms, the translation of banking, stock exchange, forex, financing and financial reporting documents.<br />
However, the term is generally used only for the more technical of these documents that require translators with knowledge of the field.<br />
Any competent translator could translate a bank statement, for example, so that wouldn’t typically be considered a financial translation.<br />
What this means<br />
You need translators with domain expertise to correctly understand and translate the financial terminology in these texts.<br />
Examples<br />
Company accounts, annual reports, fund or product prospectuses, audit reports, IPO documentation<br />
<br />
6. Economic Translations<br />
What is it?<br />
1. Sometimes used as a synonym for financial translations.<br />
2. Other times used somewhat loosely to refer to any area of economic activity – so combining business/commercial, financial and some types of technical translations.<br />
3. More narrowly, the translation of documents relating specifically to the economy and the field of economics.<br />
What this means<br />
As always, you need translators with the relevant expertise and knowledge for this type of translation.<br />
<br />
7. Legal Translation<br />
What is it?<br />
The translation of documents relating to the law and legal process.<br />
What this means<br />
Legal texts require translators with a legal background.<br />
That’s because without it, a translator may not:<br />
– fully understand the legal concepts<br />
– write in legal style<br />
– understand the differences between legal systems, and how best to translate concepts that don’t correspond.<br />
And we need all that to produce professional quality legal translations – translations that are accurate, terminologically correct and stylistically appropriate.<br />
Examples<br />
Contracts, legal reports, court judgments, expert opinions, legislation<br />
<br />
8. Juridical Translation<br />
What is it?<br />
1. Generally used as a synonym for legal translations.<br />
2. Alternatively, can refer to translations requiring some form of legal verification, certification or notarization that is common in many jurisdictions.<br />
<br />
9. Judicial Translation<br />
What is it?<br />
1. Most commonly a synonym for legal translations.<br />
2. Rarely, used to refer specifically to the translation of court proceeding documentation – so judgments, minutes, testimonies, etc. <br />
<br />
10. Patent Translation<br />
What is it?<br />
The translation of intellectual property and patent-related documents.<br />
Key features<br />
Patents have a specific structure, established terminology and a requirement for complete consistency throughout – read more on this here. These are key aspects to patent translations that translators need to get right.<br />
In addition, subject matter can be highly technical.<br />
What this means<br />
You need translators who have been trained in the specific requirements for translating patent documents. And with the domain expertise needed to handle any technical content.<br />
Examples<br />
Patent specifications, prior art documents, oppositions, opinions<br />
<br />
11. Literary Translation<br />
What is it?<br />
The translation of literary works – novels, short stories, plays, essays, poems.<br />
Key features<br />
Literary translation is widely regarded as the most difficult form of translation.<br />
That’s because it involves much more than simply conveying all meaning in an appropriate style. The translator’s challenge is to also reproduce the character, subtlety and impact of the original – the essence of what makes that work unique.<br />
This is a monumental task, and why it’s often said that the translation of a literary work should be a literary work in its own right.<br />
What this means<br />
Literary translators must be talented wordsmiths with exceptional creative writing skills.<br />
Because few translators have this skillset, you should only consider dedicated literary translators for this type of translation.<br />
<br />
<br />
12. Commercial Translation<br />
What is it?<br />
The translation of documents relating to the world of business.<br />
This is a very generic, wide-reaching translation type. It includes other more specialised forms of translation – legal, financial and technical, for example. And all types of more general business documentation.<br />
Also, some documents will require familiarity with business jargon and an ability to write in that style.<br />
What this means<br />
Different translators will be required for different document types – specialists should handle materials involving technical and specialist fields, whereas generalist translators can translate non-specialist materials.<br />
Examples<br />
Business correspondence, reports, marketing and promotional materials, sales proposals<br />
<br />
13. Business Translations<br />
What is it?<br />
A synonym for Commercial Translations.<br />
<br />
14. Administrative Translations<br />
What is it?<br />
The translation of business management and administration documents.<br />
So it’s a subset of business / commercial translations.<br />
What this means<br />
The implication is these documents will include business jargon and ‘management speak’, so require a translator familiar with, and practised at, writing in that style.<br />
Examples<br />
Management reports and proposals<br />
<br />
15. Marketing Translations<br />
What is it?<br />
The translation of advertising, marketing and promotional materials.<br />
This is a subset of business or commercial translations.<br />
Key features<br />
Marketing copy is designed to have a specific impact on the audience – to appeal and persuade.<br />
So the translated copy must do this too.<br />
But a direct translation will seldom achieve this – so translators need to adapt their wording to produce the impact the text is seeking.<br />
And sometimes a completely new message might be needed – see transcreation in our next category of translation types.<br />
What this means<br />
Marketing translations require translators who are skilled writers with a flair for producing persuasive, impactful copy.<br />
As relatively few translators have these skills, engaging the right translator is key.<br />
Good to know<br />
This type of translation often comes with a typesetting or dtp requirement – particularly for adverts, posters, brochures, etc.<br />
Its best for your translation provider to handle this component. That’s because multilingual typesetters understand the design and aesthetic conventions in other languages/cultures. And these are essential to ensure your materials have the desired impact and appeal in your target markets.<br />
Examples<br />
Advertising, brochures, some website/social media text.<br />
Translation Category B: 14 types of translation based on the end product or use of the translation<br />
This category is all about how the translation is going to be used or the end product that’s produced.<br />
Most of these types involve either adapting or processing a completed translation in some way, or converting or incorporating it into another program or format.<br />
You’ll see that some are very specialised, and complex.<br />
It’s another way translation providers refer to the range of services they provide.<br />
Check out our video of the most specialised of these types of translation:<br />
<br />
16. Document Translations<br />
What is it?<br />
The translation of documents of all sorts.<br />
Here the translation itself is the end product and needs no further processing beyond standard formatting and layout.<br />
<br />
17. Text Translations<br />
What is it?<br />
A synonym for document translation.<br />
<br />
18. Certified Translations<br />
What is it?<br />
A translation with some form of certification.<br />
Key features<br />
The certification can take many forms. It can be a statement by the translation company, signed and dated, and optionally with their company seal. Or a similar certification by the translator.<br />
The exact format and wording will depend on what clients and authorities require – here’s an example.<br />
<br />
19. Official Translations<br />
What is it?<br />
1. Generally used as a synonym for certified translations.<br />
2. Can also refer to the translation of ‘official’ documents issued by the authorities in a foreign country. These will almost always need to be certified.<br />
<br />
20. Software Localisation<br />
What is it?<br />
Adapting software for another language/culture.<br />
Key features<br />
The goal of software localisation is not just to make the program or product available in other languages. It’s also about ensuring the user experience in those languages is as natural and effective as possible.<br />
Translating the user interface, messaging, documentation, etc is a major part of the process.<br />
Also key is a customisation process to ensure everything matches the conventions, norms and expectations of the target cultures.<br />
Adjusting time, date and currency formats are examples of simple customisations. Others might involve adapting symbols, graphics, colours and even concepts and ideas.<br />
Localisation is often preceded by internationalisation – a review process to ensure the software is optimally designed to handle other languages.<br />
And it’s almost always followed by thorough testing – to ensure all text is in the correct place and fits the space, and that everything makes sense, functions as intended and is culturally appropriate.<br />
Localisation is often abbreviated to L10N, internationalisation to i18n.<br />
What this means<br />
Software localisation is a specialised kind of translation, and you should always engage a company that specialises in it.<br />
They’ll have the systems, tools, personnel and experience needed to achieve top quality outcomes for your product.<br />
<br />
21. Game Localisation<br />
What is it?<br />
Adapting games for other languages and markets.<br />
<br />
It’s a subset of software localisation.<br />
Key features<br />
The goal of game localisation is to provide an engaging and fun gaming experience for speakers of other languages.<br />
<br />
It involves translating all text and recording any required foreign language audio.<br />
<br />
But also adapting anything that would clash with the target culture’s customs, sensibilities and regulations.<br />
<br />
For example, content involving alcohol, violence or gambling may either be censored or inappropriate in the target market.<br />
<br />
And at a more basic level, anything that makes users feel uncomfortable or awkward will detract from their experience and thus the success of the game in that market.<br />
<br />
So portions of the game may have to be removed, added to or re-worked.<br />
<br />
Game localisation involves at least the steps of translation, adaptation, integrating the translations and adaptations into the game, and testing.<br />
<br />
What this means<br />
Game localisation is a very specialised type of translation best left to those with specific expertise and experience in this area.<br />
<br />
22. Multimedia Localisation<br />
What is it?<br />
Adapting multimedia for other languages and cultures.<br />
<br />
Multimedia refers to any material that combines visual, audio and/or interactive elements. So videos and movies, on-line presentations, e-Learning courses, etc.<br />
Key features<br />
Anything a user can see or hear may need localising.<br />
<br />
That means the audio and any text appearing on screen or in images and animations.<br />
<br />
Plus it can mean reviewing and adapting the visuals and/or script if these aren’t suitable for the target culture.<br />
<br />
The localisation process will typical involve:<br />
– Translation<br />
– Modifying the translation for cultural reasons and/or to meet technical requirements<br />
– Producing the other language versions<br />
<br />
Audio output may be voice-overs, dubbing or subtitling.<br />
<br />
And output for visuals can involve re-creating elements, or supplying the translated text for the designers/engineers to incorporate.<br />
<br />
What this means<br />
Multimedia localisation projects vary hugely, and it’s essential your translation providers have the specific expertise needed for your materials.<br />
<br />
<br />
<br />
23. Script Translations<br />
What is it?<br />
Preparing the text of recorded material for recording in other languages.<br />
Key features<br />
There are several issues with script translation.<br />
<br />
One is that translations typically end up longer than the original script. So voicing the translation would take up more space/time on the video than the original language.<br />
<br />
Sometimes that space will be available and this will be OK.<br />
<br />
But generally it won’t be. So the translation has to be edited back until it can be comfortably voiced within the time available on the video.<br />
<br />
Another challenge is the translation may have to synchronise with specific actions, animations or text on screen.<br />
<br />
Also, some scripts also deal with technical subject areas involving specialist technical terminology.<br />
<br />
Finally, some scripts may be very culture-specific – featuring humour, customs or activities that won’t work well in another language. Here the script, and sometimes also the associated visuals, may need to be adjusted before beginning the translation process.<br />
<br />
It goes without saying that a script translation must be done well. If it’s not, there’ll be problems producing a good foreign language audio, which will compromise the effectiveness of the video.<br />
<br />
Translators typically work from a time-coded transcript. This is the original script marked to show the time available for each section of the translation.<br />
<br />
What this means<br />
There are several potential pitfalls in script translations. So it’s vital your translation provider is practiced at this type of translation and able to handle any technical content.<br />
<br />
<br />
<br />
24. Voice-over and Dubbing Projects<br />
What is it?<br />
Translation and recording of scripts in other languages.<br />
<br />
Voice-overs vs dubbing<br />
There is a technical difference.<br />
A voice-over adds a new track to the production, dubbing replaces an existing one.<br />
<br />
<br />
Key features<br />
These projects involve two parts:<br />
– a script translation (as described above), and<br />
– producing the audio<br />
<br />
So they involve the combined efforts of translators and voice artists.<br />
The task for the voice artist is to produce a high quality read. That’s one that matches the style, tone and richness of the original.<br />
<br />
Often each section of the new audio will need to be the same length as the original.<br />
<br />
But sometimes the segments will need to be shorter – for example where the voice-over lags the original by a second or two. This is common in interviews etc, where the original voice is heard initially then drops out.<br />
<br />
The most difficult form of dubbing is lip-syncing – where the new audio needs to synchronise with the original speaker’s lip movements, gestures and actions.<br />
<br />
Lip-syncing requires an exceptionally skilled voice talent and considerable time spent rehearsing and fine tuning the translation.<br />
<br />
What this means<br />
You need to use experienced professionals every step of the way in this type of project.<br />
<br />
That’s to ensure firstly that your foreign-language scripts are first class, then that the voicing is of high professional standard.<br />
<br />
Anything less will mean your foreign language versions will be way less effective and appealing to your target audience.<br />
<br />
<br />
<br />
25. Subtitle Translations<br />
What is it?<br />
Producing foreign language captions for sub or surtitles.<br />
Key features<br />
The goal with subtitling is to produce captions that viewers can comfortably read in the time available and still follow what’s happening on the video.<br />
<br />
To achieve this, languages have “rules” governing the number of characters per line and the minimum time each subtitle should display.<br />
<br />
Sticking to these guidelines is essential if your subtitles are to be effective.<br />
<br />
But this is no easy task – it requires simple language, short words, and a very succinct style. Translators will spend considerable time mulling over and re-working their translation to get it just right.<br />
<br />
Most subtitle translators use specialised software that will output the captions in the format sound engineers need for incorporation into the video.<br />
<br />
What this means<br />
As with other specialised types of translation, you should only use translators with specific expertise and experience in subtitling.<br />
<br />
<br />
<br />
26. Website Localisation<br />
What is it?<br />
The translation and adapting of relevant content on a website to best suit the target language and culture.<br />
<br />
Note: Many providers use the term website translation as a synonym for localisation. Strictly speaking though, translation is just one part of localisation.<br />
Key features<br />
<br />
Not all pages on a website may need to be localised – clients should review their content to identify what’s relevant for the other language versions.<br />
Some content may need specialist translators – legal and technical pages for example.<br />
There may also be videos, linked documents, and text or captions in graphics to translate.<br />
Adaptation can mean changing date, time, currency and number formats, units of measure, etc.<br />
But also images, colours and even the overall site design and style if these won’t have the desired impact in the target culture.<br />
Translated files can be supplied in a wide range of formats – translators usually coordinate output with the site webmasters.<br />
New language versions are normally thoroughly reviewed and tested before going live to confirm everything is displaying correctly, works as intended and is cultural appropriate.<br />
What this means<br />
The first step should be to review your content and identify what needs to be translated. This might lead you to modify some pages for the foreign language versions.<br />
<br />
In choosing your translation providers be sure they can:<br />
– handle any technical or legal content,<br />
– provide your webmaster with the file types they want.<br />
<br />
And you should always get your translators to systematically review the foreign language versions before going live.<br />
<br />
<br />
<br />
27. Transcreation<br />
What is it?<br />
Adapting a message to elicit the same emotional response in another language and culture.<br />
Translation is all about conveying the message or meaning of a text in another language. But sometimes that message or meaning won’t have the desired effect in the target culture.<br />
<br />
This is where transcreation comes in. Transcreation creates a new message that will get the desired emotional response in that culture, while preserving the style and tone of the original.<br />
<br />
So it’s a sort of creative translation – which is where the word comes from, a combination of ‘translation’ and ‘creation’.<br />
<br />
At one level transcreation may be as simple as choosing an appropriate idiom to convey the same intent in the target language – something translators do all the time.<br />
<br />
But mostly the term is used to refer to adapting key advertising and marketing messaging. Which requires copywriting skills, cultural awareness and an excellent knowledge of the target market.<br />
<br />
Who does it?<br />
Some translation companies have suitably skilled personnel and offer transcreation services.<br />
<br />
Often though it’s done in the target country by specialist copywriters or an advertising or marketing agency – particularly for significant campaigns and to establish a brand in the target marketplace.<br />
<br />
What this means<br />
Most general marketing and promotional texts won’t need transcreation – they can be handled by a translator with excellent creative writing skills.<br />
<br />
But slogans, by-lines, advertising copy and branding statements often do.<br />
<br />
Whether you should opt for a translation company or an in-market agency will depend on the nature and importance of the material, and of course your budget.<br />
<br />
<br />
<br />
28. Audio Translations<br />
What is it?<br />
Broad meaning: the translation of any type of recorded material into another language.<br />
<br />
More commonly: the translation of a foreign language video or audio recording into your own language. So this is where you want to know and document what a recording says.<br />
Key features<br />
The first challenge with audio translations is it’s often impossible to pick up every word that’s said. That’s because audio quality, speech clarity and speaking speed can all vary enormously.<br />
<br />
It’s also a mentally challenging task to listen to an audio and translate it directly into another language. It’s easy to miss a word or an aspect of meaning.<br />
<br />
So best practice is to first transcribe the audio (type up exactly what is said in the language it is spoken in), then translate that transcription.<br />
<br />
However, this is time consuming and therefore costly, and there are other options if lesser precision is acceptable.<br />
<br />
What this means<br />
It’s best to discuss your requirements for this kind of translation with your translation provider. They’ll be able to suggest the best translation process for your needs.<br />
<br />
Examples<br />
Interviews, product videos, police recordings, social media videos.<br />
<br />
<br />
<br />
29. Translations with DTP<br />
What is it?<br />
Translation incorporated into graphic design files.multilingual dtp example in the form of a Rubik's Cube with foreign text on each square<br />
Key features<br />
Graphic design programs are used by professional designers and graphic artists to combine text and images to create brochures, books, posters, packaging, etc.<br />
<br />
Translation plus dtp projects involve 3 steps – translation, typesetting, output.<br />
<br />
The typesetting component requires specific expertise and resources – software and fonts, typesetting know-how, an appreciation of foreign language display conventions and aesthetics.<br />
<br />
What this means<br />
Make sure your translation company has the required multilingual typesetting/desktop publishing expertise whenever you’re translating a document created in a graphic design program.<br />
<br />
Translation Category C: 13 types of translation based on the translation method employed<br />
This category has two sub-groups:<br />
– the practical methods translation providers use to produce their translations, and<br />
– the translation strategies/methods identified and discussed within academia.<br />
<br />
The translation methods translation providers use<br />
There are 4 main methods used in the translation industry today. We have an overview of each below, but for more detail, including when to use each one, see our comprehensive blog article.<br />
<br />
Or watch our video.<br />
<br />
Important: If you’re a client you need to understand these 4 methods – choose the wrong one and the translation you end up with may not meet your needs!<br />
<br />
<br />
30. Machine Translation (MT)<br />
What is it?<br />
A translation produced entirely by a software program with no human intervention.<br />
<br />
A widely used, and free, example is Google Translate. And there are also commercial MT engines, generally tailored to specific domains, languages and/or clients.<br />
<br />
Pros and cons<br />
There are two limitations to MT:<br />
– they make mistakes (incorrect translations), and<br />
– quality of wording is patchy (some parts good, others unnatural or even nonsensical)<br />
<br />
On they positive side they are virtually instantaneous and many are free.<br />
<br />
Best suited for:<br />
Getting the general idea of what a text says.<br />
<br />
This method should never be relied on when high accuracy and/or good quality wording is needed.<br />
<br />
<br />
<br />
31. Machine Translation plus Human Editing (PEMT)<br />
What is it?<br />
A machine translation subsequently edited by a human translator or editor (often called Post-editing Machine Translation = PEMT).<br />
<br />
The editing process is designed to rectify some of the deficiencies of a machine translation.<br />
<br />
This process can take different forms, with different desired outcomes. Probably most common is a ‘light editing’ process where the editor ensures the text is understandable, without trying to fix quality of expression.<br />
<br />
Pros and cons<br />
This method won’t necessarily eliminate all translation mistakes. That’s because the program may have chosen a wrong word (meaning) that wasn’t obvious to the editor.<br />
<br />
And wording won’t generally be as good as a professional human translator would produce.<br />
<br />
Its advantage is it’s generally quicker and a little cheaper than a full translation by a professional translator.<br />
<br />
Best suited for:<br />
Translations for information purposes only.<br />
<br />
Again, this method shouldn’t be used when full accuracy and/or consistent, natural wording is needed.<br />
<br />
<br />
<br />
32. Human Translation<br />
What is it?<br />
Translation by a professional human translator.<br />
Pros and cons<br />
Professional translators should produce translations that are fully accurate and well-worded.<br />
<br />
That said, there is always the possibility of ‘human error’, which is why translation companies like us typically offer an additional review process – see next method.<br />
<br />
This method will take a little longer and likely cost more than the PEMT method.<br />
<br />
Best suited for:<br />
Most if not all translation purposes.<br />
<br />
<br />
<br />
33. Human Translation + Revision<br />
What is it?<br />
A human translation with an additional review by a second translator.<br />
<br />
The review is essentially a safety check – designed to pick up any translation errors and refine wording if need be.<br />
<br />
Pros and cons<br />
This produces the highest level of translation quality.<br />
<br />
It’s also the most expensive of the 4 methods, and takes the longest.<br />
<br />
Best suited for:<br />
All translation purposes.<br />
<br />
Gearwheel with 5 practical translation methods written on the teeth <br />
There’s also one other common term used by practitioners and academics alike to describe a type (method) of translation:<br />
<br />
34. Computer-Assisted Translation (CAT)<br />
What is it?<br />
A human translator using computer tools to aid the translation process.<br />
<br />
Key features<br />
Virtually all translators use such tools these days.<br />
<br />
The most prevalent tool is Translation Memory (TM) software. This creates a database of previous translations that can be accessed for future work.<br />
<br />
TM software is particularly useful when dealing with repeated and closely-matching text, and for ensuring consistency of terminology. For certain projects it can speed up the translation process.<br />
<br />
<br />
<br />
The translation methods described by academia<br />
A great deal has been written within academia analysing how human translators go about their craft.<br />
<br />
Seminal has been the work of Newmark, and the following methods of translation attributed to him are widely discussed in the literature.Gearwheel with Newmark's 8 translation methods written on the teeth <br />
These methods are approaches and strategies for translating the text as a whole, not techniques for handling smaller text units, which we discuss in our final translation category.<br />
<br />
<br />
<br />
35. Word-for-word Translation<br />
This method translates each word into the other language using its most common meaning and keeping the word order of the original language.<br />
<br />
So the translator deliberately ignores context and target language grammar and syntax.<br />
<br />
Its main purpose is to help understand the source language structure and word use.<br />
<br />
Often the translation will be placed below the original text to aid comparison.<br />
<br />
<br />
<br />
36. Literal Translation<br />
Words are again translated independently using their most common meanings and out of context, but word order changed to the closest acceptable target language grammatical structure to the original.<br />
<br />
Its main suggested purpose is to help someone read the original text.<br />
<br />
<br />
<br />
37. Faithful Translation<br />
Faithful translation focuses on the intention of the author and seeks to convey the precise meaning of the original text.<br />
<br />
It uses correct target language structures, but structure is less important than meaning.<br />
<br />
<br />
<br />
38. Semantic Translation<br />
Semantic translation is also author-focused and seeks to convey the exact meaning.<br />
<br />
Where it differs from faithful translation is that it places equal emphasis on aesthetics, ie the ‘sounds’ of the text – repetition, word play, assonance, etc.<br />
<br />
In this method form is as important as meaning as it seeks to “recreate the precise flavour and tone of the original” (Newmark).slide showing definition of semantic translation as a translation method<br />
<br />
39. Communicative Translation<br />
Seeks to communicate the message and meaning of the text in a natural and easily understood way.<br />
<br />
It’s described as reader-focused, seeking to produce the same effect on the reader as the original text.<br />
<br />
A good comparison of Communicative and Semantic translation can be found here.<br />
<br />
40. Free Translation<br />
Here conveying the meaning and effect of the original are all important.<br />
<br />
There are no constraints on grammatical form or word choice to achieve this.<br />
<br />
Often the translation will paraphrase, so may be of markedly different length to the original.<br />
<br />
41. Adaptation<br />
Mainly used for poetry and plays, this method involves re-writing the text where the translation would otherwise lack the same resonance and impact on the audience.<br />
<br />
Themes, storylines and characters will generally be retained, but cultural references, acts and situations adapted to relevant target culture ones.<br />
<br />
So this is effectively a re-creation of the work for the target culture.<br />
<br />
42. Idiomatic Translation<br />
Reproduces the meaning or message of the text using idioms and colloquial expressions and language wherever possible.<br />
<br />
The goal is to produce a translation with language that is as natural as possible.<br />
<br />
Translation Category D: 9 types of translation based on the translation technique used<br />
These translation types are specific strategies, techniques and procedures for dealing with short chunks of text – generally words or phrases.<br />
<br />
They’re often thought of as techniques for solving translation problems.<br />
<br />
They differ from the translation methods of the previous category which deal with the text as a whole.<br />
9 translation techniques as titles of books in a bookcase<br />
<br />
43. Borrowing<br />
What is it?<br />
Using a word or phrase from the original text unchanged in the translation.<br />
<br />
Key features<br />
With this procedure we don’t translate the word or phrase at all – we simply ‘borrow’ it from the source language.<br />
<br />
Borrowing is a very common strategy across languages. Initially, borrowed words seem clearly ‘foreign’, but as they become more familiar, they can lose that ‘foreignness’.<br />
<br />
Translators use this technique:<br />
– when it’s the best word to use – either because it has become the standard, or it’s the most precise term, or<br />
– for stylist effect – borrowings can add a prestigious or scholarly flavour.<br />
<br />
Borrowed words or phrases are often italicised in English.<br />
<br />
Examples of borrowings in English<br />
grand prix, kindergarten, tango, perestroika, barista, sampan, karaoke, tofu<br />
<br />
<br />
44. Transliteration<br />
What is it?<br />
Reproducing the approximate sounds of a name or term from a language with a different writing system.<br />
<br />
Key features<br />
In English we use the Roman (Latin) alphabet in common with many other languages including almost all European languages.<br />
<br />
Other writing systems include Arabic, Cyrillic, Chinese, Japanese, Korean, Thai, and the Indian languages.<br />
<br />
Transliteration from such systems into the Roman alphabet is also called romanisation.<br />
<br />
There are accepted systems for how individual letters/sounds should be romanised from most other languages – there are three common systems for Chinese, for example.<br />
<br />
English borrowings from languages using non-Roman writing systems also require transliteration – perestroika, sampan, karaoke, tofu are examples from the above list.<br />
<br />
Translators mostly use transliteration as a procedure for translating proper names.<br />
<br />
Examples<br />
毛泽东 Mao Tse-tung or Mao Zedong<br />
Владимир Путин Vladimir Putin<br />
서울 Seoul<br />
ភ្នំពេញ Phnom Penh<br />
<br />
45. Calque or Loan Translation<br />
What is it?<br />
A literal translation of a foreign word or phrase to create a new term with the same meaning in the target language.<br />
<br />
So a calque is a borrowing with translation if you like. The new term may be changed slightly to reflect target language structures.<br />
<br />
Examples<br />
German ‘Kindergarten’ has been calqued as детский сад in Russian, literally ‘children garden’ in both languages.<br />
<br />
Chinese 洗腦 ‘wash’ + ‘brain’ is the origin of ‘brainwash’ in English.<br />
<br />
English skyscraper is calqued as gratte-ciel in French and rascacielos in Spanish, literally ‘scratches sky’ in both languages.<br />
<br />
46. Word-for-word translation<br />
What is it?<br />
A literal translation that is natural and correct in the target language.<br />
<br />
Alternative names are ‘literal translation’ or ‘metaphrase’.<br />
<br />
Note: this technique is different to the translation method of the same name, which does not produce correct and natural text and has a different purpose.<br />
<br />
Key features<br />
This translation strategy will only work between languages that have very similar grammatical structures.<br />
<br />
And even then, only sometimes.<br />
<br />
For example, standard word order in Turkish is Subject-Object-Verb whereas in English it’s Subject-Verb-Object. So a literal translation between these two will seldom work:<br />
– Yusuf elmayı yedi is literally ‘Joseph the apple ate’.<br />
<br />
When word-for-word translations don’t produce natural and correct text, translators resort to some of the other techniques described below.<br />
Examples<br />
French ‘Quelle heure est-il?’ works into English as ‘What time is it?’.<br />
<br />
Russian ‘Oн хочет что-нибудь поесть’ is ‘He wants something to eat’.<br />
<br />
47. Transposition<br />
What is it?<br />
Translation with a change of grammatical structure.<br />
<br />
This technique gives the translation more natural wording and/or makes it grammatically correct.<br />
<br />
Examples<br />
A change in word order:<br />
Our Turkish example Yusuf elmayı yedi (literally ‘Joseph the apple ate’) –> Joseph ate the apple.<br />
<br />
Spanish La Casa Blanca (literally ‘The House White’) –> The White House<br />
<br />
A change in grammatical category:<br />
German Er hört gerne Musik (literally ‘he listens gladly [to] music’)<br />
= subject pronoun + verb + adverb + noun<br />
becomes Spanish Le gusta escuchar música (literally ‘[to] him [it] pleases to listen [to] music’)<br />
= indirect object pronoun + verb + infinitive + noun<br />
and English He likes listening to music<br />
= subject pronoun + verb + gerund + noun.<br />
<br />
48. Modulation<br />
What is it?<br />
Translation with a change of focus or point of view in the target language.<br />
<br />
This technique makes the translation more idiomatic – how people would normally say it in the language.<br />
<br />
Examples<br />
English talks of the ‘top floor’ of a building, French the dernier étage = last floor. ‘Last floor’ would be unnatural in English, so too ‘top floor’ in French.<br />
<br />
German uses the term Lebensgefahr (literally ‘danger to life’) where in English we’d be more likely to say ‘risk of death’.<br />
In English we’d say ‘I dropped the key’, in Spanish se me cayó la llave, literally ‘the key fell from me’. The English perspective is that I did something (dropped the key), whereas in Spanish something happened to me – I’m the recipient of the action.<br />
<br />
49. Equivalence or Reformulation<br />
What is it?<br />
Translating the underlying concept or meaning using a totally different expression.<br />
<br />
This technique is widely used when translating idioms and proverbs.<br />
<br />
And it’s common in titles and advertising slogans.<br />
<br />
It’s a common strategy where a direct translation either wouldn’t make sense or wouldn’t resonate in the same way.<br />
<br />
Examples<br />
Here are some equivalents of the English saying “Pigs may fly”, meaning something will never happen, or “you’re being unrealistic” (Source):<br />
– Thai: ชาติหน้าตอนบ่าย ๆ – literally, ‘One afternoon in your next reincarnation’<br />
– French: Quand les poules auront des dents – literally, ‘When hens have teeth’<br />
– Russian, Когда рак на горе свистнет – literally, ‘When a lobster whistles on top of a mountain’<br />
– Dutch, Als de koeien op het ijs dansen – literally, ‘When the cows dance on the ice’<br />
– Chinese: 除非太陽從西邊出來！– literally, ‘Only if the sun rises in the west’<br />
<br />
50. Adaptation<br />
What is it?<br />
A translation that substitutes a culturally-specific reference with something that’s more relevant or meaningful in the target language.<br />
<br />
It’s also known as cultural substitution or cultural equivalence.<br />
<br />
It’s a useful technique when a reference wouldn’t be understood at all, or the associated nuances or connotations would be lost in the target language.<br />
<br />
Note: the translation method of the same name is a similar concept but applied to the text as a whole.<br />
<br />
Examples<br />
Different cultures celebrate different coming of age birthdays – 21 in many cultures, 20, 15 or 16 in others. A translator might consider changing the age to the target culture custom where the coming of age implications were important in the original text.<br />
Animals have different connotations across languages and cultures. Owls for example are associated with wisdom in English, but are a bad omen to Vietnamese. A translator might want to remove or amend an animal reference where this would create a different image in the target language.<br />
<br />
51. Compensation<br />
What is it?<br />
A meaning or nuance that can’t be directly translated is expressed in another way in the text.<br />
Example<br />
Many languages have ways of expressing social status (honorifics) encoded into their grammatical structures.<br />
<br />
So you can convey different levels of respect, politeness, humility, etc simply by choosing different forms of words or grammatical elements.<br />
But these nuances will be lost when translating into languages that don’t have these structures.<br />
Then translating into languages that don’t have these structures<br />
Then translating into languages that don’t have these structures.<br />
<br />
===Conclusion ===<br />
<br />
In the light of above mentioned facts and figures here by we would say that machine translation is challenge for human translators because it can reduces the wokload of translation but can't give accurate and exact translation of the traget language.It can be less reliable than human translation..</div>Chen Huinihttps://bou.de/u/index.php?title=Machine_translation&diff=131928Machine translation2021-12-13T12:57:22Z<p>Chen Huini: /* 2.Language Characteristics and Error Division */</p>
<hr />
<div>'''Machine Translation - A challenge or a chance for human translators?'''<br />
<br />
[[Machine_translation|Overview Page of Machine Translation]]<br />
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30 Chapters（0/30)<br />
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[[Machine_Trans_EN_1]] [[Machine_Trans_EN_2]] [[Machine_Trans_EN_3]] [[Machine_Trans_EN_4]] [[Machine_Trans_EN_5]] [[Machine_Trans_EN_6]] [[Machine_Trans_EN_7]] [[Machine_Trans_EN_8]] [[Machine_Trans_EN_9]] [[Machine_Trans_EN_10]] [[Machine_Trans_EN_11]] [[Machine_Trans_EN_12]] [[Machine_Trans_EN_13]] [[Machine_Trans_EN_14]] [[Machine_Trans_EN_15]] [[Machine_Trans_EN_16]] [[Machine_Trans_EN_17]] [[Machine_Trans_EN_18]] [[Machine_Trans_EN_19]] [[Machine_Trans_EN_20]] [[Machine_Trans_EN_21]] [[Machine_Trans_EN_22]] [[Machine_Trans_EN_23]] [[Machine_Trans_EN_24]] [[Machine_Trans_EN_25]] [[Machine_Trans_EN_26]] [[Machine_Trans_EN_27]] [[Machine_Trans_EN_28]] [[Machine_Trans_EN_29]] [[Machine_Trans_EN_30]] ...<br />
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[[Book_projects|Back to translation project overview]]<br />
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[[DCG-To-Do|To the To Do list]]<br />
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=Chapter 1:A Comparison Between the Quality of Machine Translation and Human Translation——A Case Study of the Application of artificial intelligence in Sports Events=<br />
'''论机器翻译与人工翻译的质量对比——以人工智能在体育赛事领域的应用为例'''<br />
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卫怡雯 Wei Yiwen, Hunan Normal University, China<br />
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[[Machine_Trans_EN_1]]<br />
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=Chapter 3：On the Realm Advantages And Mutual Development of Machine Translation And Huamn Translation)=<br />
"'论机器翻译与人工翻译的领域优势及共同发展'"<br />
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肖毅瑶 Xiao Yiyao, Hunan Normal University, China<br />
[[Machine_Trans_EN_3]]<br />
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=Chapter 4 : A Comparison Between Machine Translation of Netease and Traditional Human Translation—A Case Study of The Economist Articles)= <br />
'''网易有道机器翻译与人工翻译的译文对比——以经济学人语料为例'''<br />
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王李菲 Wang Lifei, Hunan Normal University<br />
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[[Machine_Trans_EN_4]]<br />
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=Chapter 5: Muhammad Saqib Mehran - Problems in translation studies=<br />
[[Machine_Trans_EN_5]]<br />
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=6 徐敏赟(Machine Translation Based on Neural Network --Challenge or Chance?)=<br />
[[Machine_Trans_EN_6]]<br />
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=Chapter 7: The Cultivation of artificial intelligence and translation talents in the Belt and Road Initiative=<br />
[[Machine_Trans_EN_7]]<br />
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一带一路背景下人工智能与翻译人才的培养<br />
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颜莉莉 Yan Lili, Hunan Normal University, China<br />
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=Chapter 8: On Machine Translation Under Lanuguage Intelligence——An Option and Opportunity for Human Translators=<br />
'''论语言智能下的机器翻译——译者的选择与机遇'''<br />
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颜静 Yan Jing, Hunan Normal University, China<br />
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[[Machine_Trans_EN_8]]<br />
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=9 谢佳芬（ Machine Translation and Artificial Translation in the Era of Artificial Intelligence）=<br />
[[Machine_Trans_EN_9]]<br />
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=10 熊敏(Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts)=<br />
[[Machine_Trans_EN_10]]<br />
===Abstract===<br />
The history of machine translation can be traced to 1940s, undergoing germination, recession, revival and flourish. Nowadays, with the rapid development of information technology, machine translation technology emerged and is gradually becoming mature. Whether manual translation can be replaced by machine translation becomes a widely-disputed topic. So in order to explore the ability of machine translation, I adopts two versions of translation, which are manual translation and machine translation(this paper uses Youdao translation) for different types of texts(according to Peter Newmark's types of text：informative text, expressive text and vocative text). The results are quite different in terms of quality and accuracy. The results show that machine translation is more suitable for informative text for its brevity, while the expressive texts especially literary works and vocative texts are difficult to be translated, because there are too many loaded words and cultural images in expressive text and dependence on the readers. To draw a conclusion, the relationship between machine translation and manual translation should be complementary rather than competitive.<br />
<br />
===Key words===<br />
machine translation; manual translation; Newmark's type of texts<br />
<br />
===题目===<br />
Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts<br />
<br />
===摘要===<br />
机器翻译的历史可以追溯到上个世纪40年代，经历了萌芽期、萧条期、复兴期和繁荣期四个阶段。如今，随着信息技术的高速发展，机器翻译技术日益成熟，于是机器翻译能不能替代人工翻译这个话题引起了广泛热议。为了探究机器翻译的能力水平是否足以替代人工翻译，本人根据皮特纽马克的文本类型分类理论（信息类文本，表达文本，呼吁文本），选择了相应的译文类型，并且将其机器翻译的版本以及人工翻译的版本进行对比。结果发现，就质量和准确度而言，译文的水平大相径庭。因为其简洁的特性，机器比较适合翻译信息文本。而就表达文本，尤其是文学作品，因其存在文化负载词及相应的文化现象，所以机器翻译这类文本存在难度。而呼唤文本因其目的性以及对读者的依赖性较强，翻译难度较大。由此得知，机器翻译和人工翻译的关系应该是互补的，而不是竞争的。<br />
<br />
===关键词===<br />
机器翻译；人工翻译；纽马克文本类型<br />
<br />
===1. Introduction===<br />
====1.1.Introduction to machine translation====<br />
Machine translation, also known as computer translation is a technique to translating through machine translator, such as Google translation and Youdao translation. Machine translation is one of the branches of computational linguistics, ranging from computer science, statistics, information science and so on. Machine translation plays an important role in all aspects.<br />
Machine translation can be traced back to 1940s, when British engineer Booth and American engineer Weaver proposed using computer to translate and started to study machines used for translation. And the first machine translating system launched in 1954, used in English and Russian translation. And this means machine translation becomes a reality. However, the arrival of new things always accompanies barriers and setbacks. In the 1960s, reports from ALPAC (Automated Language Processing Advisory Committee) showed studies on machine translation had stagnated for a decade. At that time, due to the high cost of machine, choosing human translators to finish translating works was more economical for most companies. What’s worse, machine had difficulty in understanding semantic and pragmatic meanings. Fortunately, in the 1970s, with the advance and popularity of computer, machine translation was gradually back on track. With the development of science and technology, machine translation has better technological guarantee, such as artificial intelligence and computer technology. In the last decades, from the late 90s till now, machine translation is becoming more and more mature. The initial rule-oriented machine translation has been updated and Corpus-aided machine translation appears. And nowadays, technology in voice recognition has been further developed. This technique is frequently applied in speech translation products. Users only need to speak source language to the online translator and instantly it will speak out the target version. But machine can’t fully provide precise and accurate translation without any grammatical or semantic problems. Machine can understand the literal meaning of texts, but not the meaning in context. For example, there is polysemy in English, which refers to words having multiple meanings. So when translating these words, translators should take contexts into consideration. But machine has troubles in this way. In addition, machine has no feeling or intention. Hence, it is also difficult to convey the feelings from the source language.<br />
<br />
====1.2.Process of machine translation====<br />
The process of manual translation is different from that of machine translation. Here is the process of the former. (1) Understand source language. (2) Use target language to organize language. (3) Generate translation. Unlike manual translation, machine translation tends to analyze and code source language first, then look for related codes in corpus, and work out the code that represents target language, generating translation. But they share a common feature, which is that Lexicon, grammatical rules and syntactic structure are taken into consideration. This is one of the biggest challenges for machine translation.<br />
<br />
===2.Theorectical framework===<br />
====2.1Newmark’s text typology====<br />
Text typology is a theory from linguistics. It undergoes several phrases. Karl Buhler, a German psychologist and linguist defines communicative functions into expressive, information and vocative. Then Roman Jakobson proposes categorization of texts, which are intralingual translation, interlingual translation and intersemiotic translation. Accordingly, he defines six main functions of language, including the referential function, conative function, emotive function, poetic function, metalingual function and the phatic function. Based on the two theories, Peter Newmark, a translation theorist, summarizes the language function into six parts: the informative function, the vocative function, the expressive function, the phatic function, the aesthetic function, and the metalingual function. Later, he further divided texts into informative type, expressive text and vocative type according to the linguistic functions of various texts. <br />
The core of the informative texts is the truth. It is to convey facts, information, knowledge of certain topics, reality and theories. The language style of the text is objective, logical and standardized. Reports, papers, scientific and technological textbooks are all attributed to informative texts. Translators are required to take format into account for different styles.<br />
The core of the expressive text is the sender’s emotions and attitudes. It is to express sender’s preferences, feelings, views and so on. The language style of it is subjective. Imaginative literature, including fictions, poems and dramas, autobiography and authoritative statements belong to expressive text. It requires translators to be faithful to the source language and maintain the style.<br />
The core of the vocative text is readership. It is to call upon readers to act, think, feel and react in the way intended by the text. So it is reader-oriented. Such texts as advertisement, propaganda and notices are of vocative text. Newmark noted that translators need to consider cultural background of the source language and the semantic and pragmatic effect of target language. If readers can comprehend the meaning at once and do as the text says, then the goal of information communication is achieved.<br />
To draw a conclusion, informative texts focus on the information and facts. Expressive texts center on the mind of addressers, including feelings, prejudices and so on. And vocative texts are oriented on readers and call on them to practice as the texts say.<br />
<br />
====2.2Study method====<br />
Manual translations of the three texts are selected from authoritative versions and universally acknowledged. And machine translations of those come from Youdao Translator. And in this thesis I will compare and evaluate the two methods in word diction, sentence structure, word order and redundancy.<br />
<br />
===3.Comparison and analysis of machine translation and manual translation ===<br />
====3.1Informative text ====<br />
（1）English into Chinese<br />
<br />
①Source language:<br />
<br />
Keep the tip of Apple Pencil clean, as dirt and other small particles may cause excessive wear to the tip or damage the screen of i-pad.<br />
<br />
Target language:<br />
<br />
Machine translation: Apple Pencil笔尖应保持清洁，灰尘等小颗粒可能会导致笔尖过度磨损或损坏ipad屏幕。<br />
<br />
Manual translation: 保持Apple Pencil铅笔的笔尖干净，因为灰尘和其他微粒可能会导致笔尖的过度磨损或损坏iPad屏幕。<br />
<br />
Analysis: Here is the instruction of Apple Pencil. And the manual translation is the Chinese version on the instruction.Product instruction tends to be professional, since there are many terms for some concepts. Machine can easily identify these terms and provide related words to translate. The machine version is faithful and expressive to the source language. So it is well-qualified and readable for readers to understand the instruction. So we can use machine to translate informative text.<br />
<br />
②Source language:<br />
<br />
China on Saturday launched a rocket carrying three astronauts-two men and one woman - to the core module of a future space station where they will live and work for six months, the longest orbit for Chinese astronauts.<br />
<br />
Target language:<br />
<br />
Machine translation: 周六，中国发射了一枚运载三名宇航员(两男一女)的火箭，进入未来空间站的核心舱，他们将在那里生活和工作6个月，这是中国宇航员最长的轨道。<br />
<br />
Manual translation: 周六，中国发射了一枚搭载三名宇航员(两男一女)的火箭，进入未来空间站的核心舱，他们将在那里生活和工作6个月，这是中国宇航员最漫长的一次轨道飞行。<br />
<br />
Analysis: This is a news from Reuters, reporting that China has launched a rocket.The meaning of the two translations is almost the same, except for some word diction. But there are some details dealt with different choice. For example, the last sentence of the machine translation is a bit of obscure and direct. There are some ambiguous words and expressions.<br />
<br />
(2)Chinese into English<br />
<br />
Source language:湖南省博物馆是湖南省最大的历史艺术类博物馆，占地面积4.9万平方米，总建筑面积为9.1万平方米，是首批国家一级博物馆，中央地方共建的八个国家级重点博物馆之一、全国文化系统先进集体、文化强省建设有突出贡献先进集体。<br />
<br />
Target language:<br />
Manual translation: As the largest history and art museum in Hunan province, the Hunan Museum covers an area of 49,000㎡, with the building area reaching 91,000㎡. It is one of the first batch of national first-level museums and one of the first eight national museums co-funded by central and local governments.<br />
<br />
Machine translation: Museum in hunan province is one of the largest historical art museum in hunan province, covers an area of 49000 square meters, a total construction area of 91000 square meters, is the first national museum, the central place to build one of the eight national key museum, national cultural system advanced collectives, strong culture began with outstanding contribution of advanced collective.<br />
<br />
Analysis: Machine translation is not faithful enough in content. For instance, “首批国家一级博物馆” is translated into “first national museum”, which is not the meaning of the source language. And there are some obvious grammar mistakes in the machine translation. For example, machine translates it into just one sentence but there are multiple predicates in it. So it is not grammatically permissible. What’s more, the sentence structure of machine translation is confusing and the focus is not specific enough.<br />
<br />
====3.2Expressive text ====<br />
(1)English into Chinese<br />
<br />
Source language:<br />
<br />
An individual human existence should be like a river- small at first, narrowly contained within its banks, and rushing passionately past rocks and over waterfalls. Gradually the river grows wider, the banks recede, the waters flow more quietly, and in the end, without any visible breaks, they become merged in the sea, and painlessly lose their individual being.<br />
<br />
Target language:<br />
<br />
Machine translation: 一个人的存在应该像一条河流——开始很小，被紧紧地夹在两岸中间，然后热情奔放地冲过岩石，飞下瀑布。渐渐地，河面变宽，两岸后退，水流更加平缓，最后，没有任何明显的停顿，它们汇入大海，毫无痛苦地失去了自己的存在。<br />
<br />
Manual translation:人生在世，如若河流；河口初始狭窄，河岸虬曲，而后狂涛击石，飞泻成瀑。河道渐趋开阔，峡岸退去，水流潺缓，终了，一马平川，汇于大海，消逝无影。<br />
<br />
Analysis: Here is a well-known metaphor in the prose How to Grow Old written by Bertrand Russell. The manual translation is written by Tian Rongchang.This is a philosophical prose with graceful language. Literary translation is a most important and difficult branch of translation. Translator should focus on the literal meaning, culture, writing style and so on. It is a combination of beauty and elegance. Therefore, translators find it in a dilemma of beauty and faithfulness, let alone translating machine. Compared with manual translation, machine translation has difficulty in word choice. It is faithful and expressive, but not elegant enough.<br />
<br />
(2)Chinese into English<br />
<br />
Source language:没有一个人将小草叫做“大力士”，但是它的力量之大，的确是世界无比。这种力，是一般人看不见的生命力，只要生命存在，这种力就要显现，上面的石块，丝毫不足以阻挡。因为它是一种“长期抗战”的力，有弹性，能屈能伸的力，有韧性，不达目的不止的力。<br />
<br />
Target language:<br />
<br />
Machine translation: No one calls the little grass "hercules", but its power is truly matchless in the world. This force is invisible life force. As long as there is life, this force will show itself. The stone above is not strong enough to stop it. Because it is a "long-term resistance" of the force, elastic, can bend and extend force, tenacity, not to achieve the purpose of the force.<br />
<br />
Manual translation: Though nobody describes the little grass as a “husky”, yet its herculean strength is unrivalled. It is the force of life invisible to naked eye. It will display itself so long as there is life. The rock is utterly helpless before this force- a force that will forever remain militant, a force that is resilient and can take temporary setbacks calmly, a force that is tenacity itself and will never give up until the goal is reached. (by Zhang Peiji)<br />
<br />
Analysis:This is the excerpt of a well-known Chinese prose written by Xia Yan. It is written during the war of Resistance Against Japan. So the prose holds symbolic meaning, eulogizing the invisible tenacious vitality so as to encourage Chinese to have confidence in the anti-aggression war. Compared with manual translation, machine translation is much more abstract and confusing, especially for the word diction. For example, “大力士” is translated into “hercules” which is a man of exceptional strength and size in Greek and Roman Mythology, making it difficult to understand if readers of target language have no idea of the allusion. What’s worse, the machine version doesn’t reveal the symbolic meaning of the text, which is the core of this prose.<br />
<br />
====3.3Vocative text ====<br />
<br />
(1)English into Chinese<br />
<br />
①Source language:<br />
<br />
iPhone went to film school, so you don’t have to. (Advertisement of iPhone13)<br />
<br />
Target language:<br />
<br />
Machine translation: iPhone上的是电影学院，所以你不用去。<br />
<br />
Manual translation:电影专业课，iPhone同学替你上完了。<br />
<br />
Analysis：Here are advertisements of iPhone on Apple official website. There is a personification in the source language. It is used to stress the advancement and proficiency in camera, which is an appealing selling point to potential buyers. Compared with manual translation, machine translation is plain and not eye-catching enough for customers.<br />
<br />
②Source language: <br />
<br />
5G speed OMGGGGG<br />
<br />
Machine language: 5克的速度 OMGGGGG<br />
<br />
Manual translation:<br />
<br />
iPhone的5G 巨巨巨巨巨5G<br />
<br />
Analysis: The “G” in the source language is the unit of speed, standing for generation. However, it is mistaken as a unit of weight, representing gram in the machine translation. So the meaning is not faithful to the source language at all. As for manual translation, it complies with the source in form. Specifically speaking, five “G”s in the former complies with five characters “巨”in the latter. And the pronunciation of the two is similar. There are two layers of meaning for the 5 “G”s. One exclaims the fast speed of 5 generation network and the other new technology. In the manual version, “巨”can be used to show degree, meaning “quite” or “very”. <br />
<br />
③Source language: <br />
<br />
History, faith and reason show the way, the way of unity. We can see each other not as adversaries but as neighbors. We can treat each other with dignity and respect, we can join forces, stop the shouting and lower the temperature. For without unity, there is no peace, only bitterness and fury."<br />
<br />
Target language:<br />
<br />
Machine translation: 历史、信仰和理性指明了团结的道路。我们可以把彼此视为邻居，而不是对手。我们可以尊严地对待彼此，我们可以联合起来，停止大喊大叫，降低温度。因为没有团结，就没有和平，只有痛苦和愤怒。<br />
<br />
Manual translation:历史、信仰和理性为我们指明道路。那是团结之路。我们可以把彼此视为邻居，而不是对手。我们可以有尊严地相互尊重。我们可以联合起来，停止喊叫，减少愤怒。因为没有团结就没有和平，只有痛苦和愤怒<br />
<br />
Analysis: Speech is a way to propagate some activity in public. It is an art to inspire emotion of the audience. The source language is the excerpt of Joe Biden’s inaugural speech. The speech should be inspiring and logic. The machine translation has some misunderstanding. Taking the translation of “lower the temperature” for example, machine only translates its literal meaning, relating to the temperature itself, without considering the context. What’s more, it is less logic than the manual one. Therefore, it adds difficulty to inspire the audience and infect their emotion.<br />
<br />
===4.Common mistakes in machine translation ===<br />
<br />
====4.1 lexical mistakes ====<br />
<br />
Common lexical mistakes include misunderstandings in word category, lexical meaning and emotive and evaluative meaning. Misunderstanding in word category shows in the classification of word in the source language. As for misunderstanding in lexical meaning, machine has difficulty in precisely reflecting the meaning of the original texts, due to different cultural background and different language system. And for misunderstanding in emotive meaning, machine has no intention and emotion like human-beings. Therefore, it’s impossible for it to know writers’ feelings and their writing purposes. So sometimes, it may translate something negative into something positive.<br />
<br />
====4.2 grammatical mistakes====<br />
<br />
Grammatical analysis plays an important part in translation. Normally speaking, every language has its own unique grammatical rules. So in the process of translation, if translators don’t know the formation rule well, the sentence meaning will be affected. Even though all the lexical meanings are well-known by translators, the lack of consciousness of grammaticality makes it harder to arrange words according to sequential rule. English tends to be hypotactic, while Chinese tends to be paratactic. English sentences are connected through syntactic devices and lexical devices. While Chinese sentences are semantically connected, which means there are limited logical words and connection words in Chinese. So when translating English sentence, we should first analyze its grammaticality and logical structure and then rearrange its sequence. However, online translating machine has troubles in grammatical analysis, which makes its improvement more difficult.<br />
<br />
====4.3 other mistakes====<br />
<br />
The two mistakes above are the internal ones. Apart from mistakes in linguistic system, there are some mistakes in other aspects, such as cultural background.<br />
<br />
===5.Reasons for its common mistakes ===<br />
<br />
====5.1 Difference in two linguistic system====<br />
<br />
With different history, English and Chinese have different ways of expression. Commonly speaking, English is synthetic language which expresses grammatical meaning through inflection such as tense and Chinese is analytic language which expresses grammatical meaning through word order and function word. In addition, English is more compact with full sentences. Subordinate sentence is one of the most important features in modern English. Chinese, on the other hand, is more diffusive with minor sentences.<br />
<br />
====5.2 Difference in thinking patterns and cultural background====<br />
<br />
According to Sapir-Whorf’s Hypothesis, our language helps mould our way of thinking and consequently, different languages may probably express their unique ways of understanding the world. For two different speech communities, the greater their structural differentiations are, the more diverse their conceptualization of the world will be. For example, western culture is more direct and eastern culture more euphemistic. What’s more, English culture tends to be individualism, focusing on detail, through which it reflects the whole, while Chinese culture tends to be collective. Different thinking patterns will add difficulty for machine to translate texts.<br />
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====5.3 Limitation of computer====<br />
<br />
Recently, there are some breakthroughs and innovation in machine translation. However, due to its own limitation, online translation has limitation in some ways. Firstly, compared with machine, human brain is much more complicated, consisting of ten billions of neuron, each of which has different function to affect human’s daily activities and help humans avoid some errors. However, computer can only function according to preset programming has no intention or consciousness. Until now, countless related scholars have invested much time in machine translation. They upload massive language database, which include almost all linguistic rules. But computers still fail to precisely reflect the meaning of source language for many times due to the complexity and flexibility of language. On the other hand, computers can’t take context into consideration. During translation, it is often the case that machine chooses the most-frequently used meaning of one word. So without the correct and exact meaning, readers are easier to feel confused and even misunderstand the meaning of source language.<br />
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===6.Conclusion===<br />
From the analysis above, we can draw a conclusion that machine deals with informative text best, followed by non-literary translation of expressive text. What’s more, machine can be a useful tool to get to know the gist and main idea of a specific topic, for the simple sentence structure and numerous terms. And it can improve translating efficiency with high speed. But machine has difficulty in translating literary works, especially proses and poems.<br />
<br />
Machine translation has mixed future. From the perspective of commercial, machine translation boasts a bright future. With the process of globalization, the demand for translation is increasing accordingly. On one hand, if we only depend on human translator to deal with translating works, the quality and accuracy of translation can be greatly affected. On the other hand, if machine is used properly to do some basic work, human translators only need to make preparation before translating, progress, polish and other advanced work, contributing to highly-qualified translation and high working efficiency.<br />
<br />
However, compared with manual translation, machine translation has a bleak future. It is still impossible for machine to replace interpreter or translator in a short term. With intelligence and initiative, humans are able to learn new knowledge constantly, which machine will never accomplish. Besides, machine is not used to replace translators but to assist them in work. In other words, translators and machine carry out their own duties and they are not incompatible.<br />
<br />
To draw a conclusion, although there are certain limitations of machine translation, it can serve as a catalyst for translating works. Therefore, with the rapid development of artificial intelligence and related technology, there are still many opportunities for machine translation.<br />
<br />
===Reference ===<br />
<br />
Cui Zihan 崔子涵.机器翻译译文质量对比——以谷歌翻译和DeepL为例[J] [Comparison among Machine Translation--Taking Google Translation and Deepl for Example].Overseas English 海外英语,2021(15):182-183.<br />
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Li Deyi 李德毅. (2018). 人工智能导论 [Introduction to Artificial Intelligence]. Beijing: China Science and Technology Press 中国科学技术出版社.<br />
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Qiu Quanju 仇全菊.大数据时代背景下机器翻译及其发展趋势[J][Machine Translation and its Development Trend under the Background of Big Data Era]. English Teachers 英语教师,2021,21(16):60-62.<br />
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Zhuo Jianbin 卓键滨,Liu Wenxian 刘文娴,Peng Zili 彭子莉.机器翻译对各类型文本的德汉翻译能力探究[J][Research on the German Chinese Translation Ability of Machine Translation for Various Types of Texts]. Comparative Study of Cultural innovation 文化创新比较研究,2021,5(28):122-125.<br />
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(英) Peter Newmark A Textbook of Translation[M] Shanghai Foreign Education Press, 2002<br />
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Wang Hongqi 王红旗. (2008). 语言学概论 [Introduction to Linguistics]. Beijing: Peking University Press 北京大学出版社.<br />
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Liu Qin刘琴.功能目的论对于不同文本类型的翻译解读[J][Analysis of Translations in Different Types of Text based on Functionalist Approaches].Overseas Engliosh 海外英语,2021(17):8-9.<br />
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Zhang Peiji 张培基.英译中国现代散文选[M][Selected Modern Chinese Prose Writings]. Shanghai Foreign Languages Education Press 上海外语教育出版社, 2002.<br />
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Chen Cheng陈诚.机器翻译技术的综述[J][Overview of Machine Translation Technology].Electronic Techonology 电子技术,2021,50(11):290-291.<br />
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He Xinyu何馨宇.机器翻译的发展及其对翻译职业化的影响研究[J] [The Development of Machine Translation and its Effect on Professional Transltors].Overseas English 海外英语,2021(20):48-49.<br />
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He Wen 何雯, Wang Xiufeng 王秀峰.信息型文本的在线机器翻译错误研究[J][Research on Errors in Online Machine Translation of Informative text ].Overseas English海外英语,2021(15):188-189.<br />
<br />
Li Hanji 李晗佶. (2021). 人工智能时代翻译技术与译者关系演变与重构 [Evolution and reconstruction of the relationship between translation technology and translators in the era of artificial intelligence]. 西华师范大学学报(哲学社会科学版) Journal of West China Normal University (PHILOSOPHY AND SOCIAL SCIENCES EDITION) (2021-12-04) 1-6.<br />
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Wei Guang魏光. 人工翻译与机器翻译译文编辑比较研究[J][Comparative Study of Translation Editing between Manual Translation and Machine Translation]. Overseas English 海外英语,2021(19):18-19+21.<br />
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=Chapter 11 陈惠妮=Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts=<br />
<br />
机器翻译的译前编辑研究——以医学类文摘为例<br />
<br />
陈惠妮 Chen Huini, Hunan Normal University<br />
<br />
[[Machine_Trans_EN_11]]<br />
<br />
===Abstract===<br />
At present, globalization is accelerating and the market demand for language services is rapidly increasing . Machine translation, as an important translation method, can greatly improve translation efficiency due to its low cost and high speed. However, because of the limitations of machine translation and the differences between Chinese and English language, machine translation is not accurate enough. In order to balance translation efficiency and translation quality, a great number of manual revisions in translation are required for the machine translating texts. Medical papers are specialized, special and purposeful, so it requires accurate,qualified and professional translation. However, the quality of translations by machine is inefficient to meet the high-quality requirements of medical papers translation. Therefore, the introduction of pre-editing can greatly improve the efficiency and quality of machine translation.<br />
<br />
===Key words===<br />
Pre-editing, Machine translation, Medical texts<br />
<br />
===题目===<br />
Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts<br />
<br />
===摘要===<br />
在全球化加速发展的今天，市场对语言服务的需求迅速增加。机器翻译作为一种重要的翻译途径，由于其成本低、速度快，可以大大提高翻译效率。然而，由于机器翻译的局限性以及中英文语言的差异，机器翻译的准确性不高。为了平衡翻译效率和翻译质量，机器翻译文本需要大量的手工修改。<br />
医学论文具有专业性、特殊性和目的性，要求其译文准确、合格、专业。然而，机器翻译的质量较低，无法满足医学论文对翻译的高质量要求。因此，译前编辑的引入可以大大提高机器翻译的效率和质量。<br />
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===关键词===<br />
译前编辑；机器翻译；医学文本<br />
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===1. Introduction===<br />
===1.1 Definition of Machine Translation===<br />
As Cronin(2013) revealed: "Translation is undergoing a revolutionary upheaval. The influence of digital technology and the Internet on translation is continuous, extensive and profound. From the popularity of automatic online translation applications, translation revolution is everywhere (Cronin,2013) However,the concept of machine translation was firstly proposed in the 1930s. Since 1940s, the machine translation technology has been evolving from rule-based machine translation (RBMT) to statistical machine translation (SMT), and to neural machine translation (NMT). Machine translation refers to the automatic translation of source language into target language by using a computer system. That is, machine translation refers to the automatic translation of text from one language into another natural language by computer software or other online translation webs. Machine translation is also defined as the process of “using a computer system to automatically translate text or speech from one natural language to another” according to the definition by ISO (Cui 2014:68-73).O 'Brien (2002) defines it as "the behavior of modifying errors in machine translation to ensure that the target translation meets certain quality requirements". On the basic level, machine translation performs mechanical substitution of words in one language for words in another language, but that rarely produces a good translation, therefore, recognition of the whole phrases and their closest counterparts in the target language is needed. Not all words in one language have equivalents in another language, and many words have more than one meaning. A huge demand for translation is greatly needed in today’s global world, which creates new opportunities for the development of machine translation, attracts more and more attention and becomes one of the current research focuses.<br />
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===1.2 Definition of Pre-editing===<br />
Pre-editing means to adjust and modify the source language to make it fit more with the characteristics of the machine translation software before putting the source language before the into machine translation, so as to improve the quality of the translation machine translation (Wei Changhong 2008:93-94). Pre-editing is to modify the original text before putting it into machine translation software, in order to improve the recognition rate of machine translation, optimize the output quality of translated text and reduce the workload of post-editing. Because pre-translation editing only needs to be modified in one language, the operation is simpler than post-translation editing, which can realize the double improvement of quality and efficiency. A good pre-editing translation can help machine translation more smoothly, thus improving the machine readability and quality of the output translation. Pre-editing is mostly applied in the following situations: one is when the original text is of poor quality and the machine is difficult to recognize the meaning of the sentence, such as user generated content with poor readability and translatability (Gerlach et al 2003:45-53); Documents that need to be published in multiple languages; Next is when the original text contains a lot of jargon; The last is the original text has a corresponding translation memory bank. If the original text is edited, it can better match the content of the translation memory bank. Pre-editing is a process of identifying problems. It requires to pre-edit the source texts before putting it into machine translation according to the requirements, listing the expressions or sentences that may have trouble in machine translation and then pre-edit it by human. The purpose is to enable the computer to translate better, improve the translatability of machine translation. (Slype G V & Guinet J F & Seitz F 1984:115)<br />
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===1.3 Machine Translation Mode===<br />
According to the different knowledge acquisition methods, machine translation modes can be classified as follows: one is rule-based machine translation, which is based on bilingual dictionaries and a library of language rules for each language. The quality of translation depends on whether the source language conforms to the existing rules, but the inexhaustible rules are the hinders of this model. The second mode is machine translation based on statistics. This translation model relies on the principles of mathematics and statistics to find various existing translations corresponding to the translation tasks through the employment of corpus, analyzing the frequency of their occurrence and selecting the translation with the highest frequency for output. The disadvantage of this translation model is that it ignores the flexibility of language and the importance of context. The last one is neural network language model. This model is different from previous translation models in that it uses end-to-end neural network to realize automatic translation between natural languages. At present, the quality of its translation is much higher than that of the previous translation models.<br />
===1.4 Source of Study Abstracts===<br />
The core medical journals from domestic are selected in order to make the paper more representative and authoritative, such as National Medical Journal of China, Journal of Peking University (Health Science), and Journal of Third Military Medical University. All of them include basic medical science, biomedical technology, laboratory medical science and other fields. In this way, the pre-editing approaches included are applicable enough to machine translation of medical abstracts.<br />
===1.5 Selection of Translation Software===<br />
Generally, machine translation software includes Google Translation, Youdao Translator and Niu Translation, which has their own special use in translation. Take Google as an example, it is more like neurons in human brain, enabling to learn and collect information to establish connections with its neural machine translator’s neurons. However, it also causes many errors because of the lack of enough information. In this paper, contrastive analysis will be carried on by using Google translation. On the one hand, being a pioneer in the translation of NET, it is inevitably to sue Google as the translation software to translate medical texts in this paper. Comparing the program called “Google brain”, other NMT of translation software are relatively disadvantages. On the other hand, the Google translation enjoys the largest users in the world, with its downloads of more than one billion. The output quality of Google translation is more correct and complete than other machine translation software. With years of development and improvement, Google Translation has been greatly promoted. In this paper, Chinese medical abstract will be automatically translated by Google translation, and then the translation output will be compared with the translation by human and the publication of English abstracts. The main purpose is to prove that the improvement and promotion of quality and accuracy in the medical abstracts will be obtained through the pre-editing approaches.<br />
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===2.Language Characteristics and Error Division===<br />
Since Chinese and English are two different languages, it is quite neccessary to identify their own characteristics so as to better analyze and understand the two languages. Tytler (1978: 118-119) argued in his Essay on the Principles of Translation that there are three principles of translation that in all the translation should give readers the same feelings as the source text, except for complete transcript of ideas. There actually also exit some mistakes of these two languages. So the following will make some clarrifications of these two languages to ensure more accurate translation.<br />
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===2.1 Language Characteristics of Medical Abstracts===<br />
Chinese and English belong to different language systems, so there are differences in their language structure and the way users think of the languages. When using machine translation from Chinese to English, due to the unequal language levels, there will be many mistakes in the translation process, especially for ESP texts, such as medical papers. <br />
Here, these differences mainly refer to the linguistic characteristics of medical abstracts. In medical abstracts, it usually includes structured and unstructured abstracts. Although in different forms, they both describe the purpose, methods and conclusions of the research.<br />
In the method section of medial abstract, several Chinese sentences can be connected with commas, but each sentence may convey different information. In contrast, English sentences contain a great deal of information, but in order to ensure clarity, some modifiers need to be isolated and then reconstructed. However, in Chinese-English machine translation, a lot of information is put into the sentence because the machine segments the sentence on the basis of the full comma.<br />
In addition, subjectless sentence is used in the objective and method parts of Chinese medical abstracts. The subjectless sentence means the sentence without or free from subject and is usually employed in two contexts. The first is "needless to say". In Chinese sentences, it is common to omit the subject of the sentence. Chinese sentences can convey meanings by using incomplete sentence structures, so Chinese speakers can understand the meanings of the sentences even though the sentence subject is omitted. The second is "emphasis of action". In this context, subjectless sentences are used to describe behaviors, especially in the study of traditional Chinese medicine abstracts. Comparatively speaking, it should be avoided in English medical abstracts. Abstract sentences are more subjective when describing the learning process, while the essential requirement of English medical abstract is objectivity. From this point, sentences without subject should be avoided in English medical abstracts. Another feature is voice. Few words with passive meanings appear in Chinese abstracts. English sentences are more favoured in using passive voice. When translating from Chinese to English, passive voice should be used to make the contents objective, which is also the basic requirement of medical papers.<br />
These are the linguistic features of Chinese medical abstracts. There are great differences in sentence structure and expression between Chinese and English medical abstracts. These differences may reduce the accuracy of machine translation, so it is necessary to introduce pre-editing to edit the source text to ensure that the source text can be accurately recognized by the machine and fully translated into English.<br />
===2.2 Error Division of Machine Translation in Translating Abstracts of Medical Papers from Chinese to English===<br />
In this paper, errors in machine translation are listed out after analyzing. Some are due to the principles, such as statistical-based MT and NMT, employed by machine translation. Based on the original errors, they can be studied from two levels. One is from the macro-level, referring to mistakes caused by objective factors. These are not human factors, but the disadvantage of machine translation and the limitations of language. The limitations of machine translation are derived from the principles and models adopted and manifests itself as a reliance on reference sources. In this case, semantic ambiguity and textual incoherence may occur in the absence of reference sources.<br />
However, for ESP texts such as medical papers, the requirements for terms and sentence patterns are far beyond the existing corpora. For unfamiliar texts (that is, the corresponding texts cannot be found in the corpora). The quality of output translation will be relatively low, which is determined by the principles of machine translation. As mentioned above, machine translation has evolved over the past few decades from rules-based to corp-based statistics to NMT. However, there still exist some limitations in machine translation.<br />
Mistakes on micro-level are mainly caused by the variations and differences of linguistic structure between Chinese and English. Chinese is an implicit language, while English is an explicit one (Lian, 2010) To put it in another way, Chinese expression does not depend on language structures, while English does the opposite. This may result in mismatches between the original and machine translated sentences. This kind of error is mainly divided into two types: component fragment and component missing. For a medical paper, such errors are very serious. As a kind of ESP discourse, medical paper has the characteristics of fixed, objective and accurate language structure. In order to reproduce the characteristics of medical abstracts in translation, it is necessary to avoid errors at the level of words and sentences and pay attention to logic and consistency.<br />
Lexical errors here refer to the inconsistency of fixed expressions in the translation of terms. Although these terms are machine translation based on the corpus, the corpus may not be perfect for ESP texts such as medical papers. Therefore, fixed expressions are not used in term translation. This is also the most common mistake in machine translation (the term here includes but is not limited to nouns). For ESP texts, medical text in particular, the fixed expressions and the accuracy of terms are of great importance.<br />
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===3.Approaches Proposed for Pre-editing ===<br />
Generally speaking, there is a close relationship between pre-editing and post-editing, both of which aim to convey information and ensure high or publishable translation quality. Proper pre-editing can improve the quality of machine translation in terms of adequacy and consistency. <br />
Complexity of natural language and people use language arbitrarily, bringing many difficulties to Chinese-English machine translation, Hu Qingping (2005:24) has proposed "the research of translation software and the development of the controlled language are the two directions to improve the quality of machine translation : the former aims at the difficulty in the natural language processing, the latter overcomes the arbitrariness of natural language". Feng Quangong and Gao Lin (2017: 63-68) put forward: "The writing principles of controlled language can be applied to pre-editing of machine translation. Pre-editing based on controlled language can effectively reduce the complexity and ambiguity of source text, improve the identifiability of machine translation (the translatability of source text itself), and thus reduce (fully) the workload of post-editing".<br />
The central task of pre-editing is to transform human-friendly content into machine-friendly content, so words and sentences need to be repositioned or even changed. Based on the analysis of the language characteristics of Chinese and English, the Chinese ideographic group should be split before the Chinese source text is input into the machine software to translate so that the sentence structure is complete which can be easily recognized by the machine translation software.<br />
===3.1 Extraction and Replacement of Terms in the Source Text===<br />
As a branch of applied English, medical English is the product of the combination of English language knowledge and medical knowledge. The terms in medical papers have the characteristics of fixed and complex language structure. Although Google Translation is based on a corpus, the language structure is not fixed due to the limited size of the corpus. Therefore, the following two steps should be followed before machine translation. The first is to extract terms and create a glossary, and then replace the Chinese terms in the source text with the corresponding English expressions in the glossary. Of course, the terms of extraction should be searched and verified according to the actual situation. All of these words are verified before they can be replaced. This method can not only ensure the accuracy of term translation, but also maintain the consistency of expression, especially when dealing with long texts.<br />
Example1<br />
Source abstract: 口腔白斑病癌变相关缺氧应答基因和微小RNA的芯片及表达验证。<br />
Google translation: Microarray detection/Chip detection and expression verification of hypoxia response genes and microRNAs related to oral leukoplakia canceration.<br />
Published translation:Transcriptome array screening and verification of oral leukoplakia carcinogenesis-related hypoxia-responsive gene and microRNA. <br />
Analysis: The expression “ Affymetrix GeneChip” empolyed in this thesis is a human transcription array for transcriptome array. In the source abstract, “芯片检测“ can be meant “screening” but not detection, so the proper and appropriate translation of these expression should be “transcription array screening”, but the Google translates it into “microarry detection of chip detection”. Therefore, term extraction is essential and inevitable before putting the source abstracts into the machine translation software.<br />
After pre-editing source abstracts: 对 oral leukoplakia carcinogenesis 相关 hypoxia-responsive gene 和微小RNA进行 transcriptome array screening 及表达验证。<br />
After pre-editing translation: Transcriptome array screening and expression- verification of hypoxia-responsive genes and microRNAs related to oral leukoplakia carcinogenesis.<br />
===3.2 Explication of Subordination===<br />
As mentioned above, the subordination of Chinese sentences is judged by certain specific words in machine translation . Chinese is a paratactic language, and sentences are often connected by internal logical relationships; while English depends on sentence structure, so sentences are often closely linked by various language forms. In order to improve the accuracy of machine translation, we can adjust the sentence structure and adjust the position of adjectives and their modifiers. <br />
Example 2<br />
Source abstracts:回顾性分析南京医科大学附属儿童医院中重度HIE患儿49例及同期就诊的无神经系统症状体征的足月新生儿为对照组31例的头颅磁共振成像（MRI）资料。<br />
Google translation: A retrospective analysis that the brain magnetic resonance imaging (MRI) data of 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University and full-term neonates with no neurological symptoms and signs during the same period were included in the control group.<br />
Published translation: A total of 49 children with moderate to severe HIE admitted to the children’s Hospital Affiliated to Nanjing Medical University were retrospectively analyzed. Cranial magnetic resonance imaging (MRI) date of 31 full-term neonates without neurological symptoms and signs who visited the hospital during the same period were recruited as the control group. <br />
Analysis: As the above example shows that “中重度HIE患儿” and “足月新生儿” in the source abstracts are from the Children’s Hospital Affiliated Nanjing Medical University. The Google translation shows that 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University. There is an error due to the misuse of subordination. There are some advice in order to solve the problem. That is, first to segment the sentence and then reconstruct the sentence. For instance, the Chinese expression “南京医科大学附属儿童医院” carries many modifiers, which requires to cut the modifiers and reconstruct the sentence. Therefore, the Chinese expression “南京医科大学附属儿童医院’can be divided into abstractions, leaving two sentences instead of one long sentence with modifiers..<br />
After pre-editing source abstracts: 对49例中重度HIE患儿以及31例无神经系统症状体征的足月新生儿的MRI资料进行回顾性分析。这些患者收治于南京医科大学儿童附属医院。<br />
After pre-editing translation: The MRI data of 49 children with moderate to severe HIE and 31 full-term newborns without neurological symptoms and signs were retrospectively analyzed. These patients were admitted to the Children’s Hospital of Nanjing Medical University.<br />
===3.3 Explication of Subject through Voice Changing===<br />
The extensive use of subjectless sentences are quite common in the Chinese abstracts, while English prefers to employ passive voice in the sentences so as to make them object and accurate. In order to take the characteristics of English sentences into great and careful consideration, the sentences written in passive voice in particular, it will be helpful for machine translation to find out the subject and reconstruct a sentence in passive voice (Wang Yan: 2008). The first is to discover the “doee” in a sentence and then is to reconstruct the sentence structure and put the “doee” before the verb. The last is to explicate the passive relation between the noun and the verb.<br />
Example 3<br />
Source abstract: 回顾性分析2018年1月至2020年12月解放军总医院第七医学中心收治的500例老年髋部骨折患者的资料。<br />
Google translation: A retrospective analysis of the data of 500 elderly patients with hip fractures admitted to the Seventh Medical Center of the PLA General Hospital from January 2018 to December 2020.<br />
Published translation: From January 2018 to December 2020, the data of 500 elderly patients with hip fracture treated in the Seventh Medical Center of PLA General Hospital were analyzed retrospectively.<br />
Analysis: The above example indicates that the “回顾性分析”in the Google Translate is a noun phrase, but the source abstracts actually describes an action or a behavior. The reason for such a mistake is that the machine can’t recognize the Chinese subjetless sentences. To find the subject of the sentence, the source abstracts can be revised and adjusted. Finding the “doee” is the first step, which refers to “500例老年髋部骨折患者的资料”in the source abstracts. And next is to put it in front of the verb, that is to place it in the beginning of the sentence. Last but not least, the passive voice should be explicated in the sentence. <br />
After pre-editing source abstract: 500例老年髋部骨折患者的资料被回顾性分析，他们在2018年1月之2020年12月期间收治于解放军总医院第七医学中心。<br />
After pre-editing translation: The data of 500 elderly hip fracture patients were retrospectively analyzed. They were admitted to the Seventh Medical Center of the PLA General Hospital between January 2018 and December 2020.<br />
===3.4 Relocation of Modifiers===<br />
Modifiers, including noun, adverbial and attributive are constantly employed in the Chinese medical papers in order to add information to subject and object in the sentence. By doing this, complicated sentences can be structured, thus causing obstacles to machine translation for recognizing this complex sentence structures when translating Chinese-English sentences. To make the machine translation successfully recognize the sentence structure, simplifying the sentence structure is quite necessary. The key is to moving the location of the modifiers and thus making the modifiers an independent sentence. In other words, the modifiers ought to be put before or behind the main part of the sentence to satisfy the common use of English. <br />
Usually, the modifiers in Chinese sentence can only be placed in front of the core word, while in English, modifiers are very flexible. It is all right to place the modifiers in front of or behind the major part of the sentences with adjective or a connective noun.<br />
Example 4<br />
Source abstracts: 利用DNA重组技术以pET-28a表达系统在E.coli BL21(DE3) 中重组表达Hepl。<br />
Google Translation: Hepl is recombined in E.coli BL21 (DE3) using DNA recombination technology with pET-28a expression system.<br />
Published translation: The recombinant Hcpl protein was expressed by using DNA recombination technology through pET-28a expression system in E. coli BL21 (De3).<br />
Analysis: The Chinese medical text includes two modifiers, “利用DNA”and “以pET-28a)表达系统”. These two modifiers will be translated by machine, which catches more attention to the two constituents. From the Google translation above, one failure is obvious that it misplaces the location of the two modifiers and presents it in not accurate form. To make it translate correctly by machine translation, dividing the sentences is very important so that the source abstracts can be correctly recognized by machine translation.<br />
After pre-editing source abstract: 利用DNA重组技术，Hcp1重组表达在E.coli BL21 (DE3)中， 通过pET-28a表达系统在E. coli BL21 (DE3)中。<br />
Google translation: Using DNA recombination technology, Hcp1 recombination is expressed in E.coli BL21 (DE3), via pET-28a expression system in E. coli BL21 (DE3).<br />
The second is the independence of modifiers, that is, modifiers can also be reconstructed into clauses to modify core words. Compared with English, There is no subordinate clause in Chinese, so redundant Chinese modifiers need to be reconstructed into subordinate clauses in Chinese-English translation to meet the characteristics of English. English, especially sentences with multiple modifiers. Otherwise, sentence structure may be confused, such as scattered modifiers of core words. To avoid this mistake, it is necessary to separate the modifier from the main part of the sentence. These modifiers should be reconstituted into clauses. Also, keep your sentences simple and easy to understand.<br />
===3.5 Proper Omission and Deletion of Category Words===<br />
Category words are commonly used in Chinese. Category words complement the meaning of words, including problems, positions, situations and jobs. Adding this supplementary word is more in line with Chinese custom. In many cases, it has no real meaning, so it can be omitted in translation. In Chinese, category words are frequently used. However, it is rarely used in English, which is one of the differences between Chinese and English. There are many kinds of category words. Considering objectivity and the fixed structure of language, redundant category words should be deleted in Chinese-English translation. In the general, the most commonly used category of words in the Chinese medical abstracts are "process", "behavior", and "situation". These categories of words hinder the language conversion of Chinese and English, resulting in redundancy. <br />
Example 5<br />
Source abstract: 探讨口腔白斑病癌变进程中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia response genes and associated microRNAs in the process of oral leukoplakia cancer was discussed.<br />
Published translation: To study the hypoxia response gene and microRNA (miRNA)expression profiles in the pathogenesis and progression of oral leukoplakia (OLK).<br />
Analysis: As the above example shows, the Chinese words “进程” belongs to a category word. However, the Chinese expression “癌变”contains the process, so the “进程” expression can be deleted before placing it into the machine translation, because the meaning of it has been overlapping between the expression “癌变”. Therefore, its place can be replaced with preposition “during”.<br />
After pre-editing source abstract: 探讨口腔白斑病癌变中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia responsive genes and miRNA in oral leukoplakia cancer was investigated.<br />
<br />
===Conclusion===<br />
After years of development, machine translation has made great progress. The accuracy of machine translation has been greatly improved in both text recognition and sentence pattern conversion. However, machine translation has its own limitations. In other words, it needs to rely on the parallel corpus as a reference source for improving its accuracy. ESP text, in particular, is harder to get the high quality by machine translation. <br />
As one of the research papers, the characteristics of medical abstracts are fixed language structures, objectivity and accuracy (Qin Yi:2004). Therefore, medical translation must be accurate, object and understandable to follow the specific demands of the medical paper. Being an important field in the human society, medical paper translation is on a great demand, which means that it needs a huge demand for human labor. However, with the machine translation promoting, it will be more efficient to translate medical papers combining the effort by human and machine. The improvement and development of machine translation requires the joint efforts of computer science, information science, statistics, linguistics and other academic circles to achieve more mature human-computer mutual assistance translation (Li Yafei, Zhang Ruihua : 2019).<br />
However, errors can occur during the process of machine translation of Chinese- English, because of the differences of the Chinese and English and the processing of the machine. Errors from the perspective of linguistic or grammar can affect the machine translation a lot. After division and recognition of errors, some pre-editing approaches are put forward to help the machine translation more accurate and readable, that are, extraction and replacement of terms in the source text, relocation of modifiers, explication of subordination, proper omission, deletion of category words and explication of subject through voice changing. <br />
The paper mainly focuses on the pre-editing machine translation by using medical papers as a case study. The errors of machine translation occurring in the translation of medical abstracts and pre-editing approaches for machine translation. The quality of machine translation of medical papers is greatly improved after employing the pre-editing methods. However, machine translation is not as flexible and accurate as human brain, so it is of importance to combine pre-editing and post-editing approaches with machine translation in order to produce more accurate, more object machine translation of medical papers.<br />
<br />
===References===<br />
[1]. Cronin, Michael (2013). Translation in the Digital Age[M]. New York&London: Routledge.<br />
<br />
[2]. GERLACH J, et al ( 2013). Combining Pre-editing and Post-editing to Improve SMT of User-generated Content[M]// Proceedings of MT Summit XIV Workshop on Post-editing Technology and Practice. 45-53.<br />
<br />
[3]. Slype G V & Guinet J F & Seitz F (1984). Better Translation for Better Communication [M] .Oxford: Pergamon Press Ltd (U.K.), <br />
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[4]. O'Brien S (2002). Teaching Post-editing: A Proposal for Course Con-tent [EB/OL]. http://mt-archive. Info/EAMT-2002-0brien. Pdf.<br />
<br />
[5]. Tytler, A. F. (1978). Essay On The Principles of Translation[M]. Amsterdam: JohnBenjamins Publishing.<br />
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[6] 崔启亮. (2014), 论机器翻译的译后编辑[J], 中国翻译, 035(006):68-73.<br />
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[7] 冯全功,高琳 (2017) 基于受控语言的译前编辑对机器翻译的影响[J]. 当代外语研究,(2): 63-68+87+110.<br />
<br />
[8] 胡清平(2005). 机器翻译中的受控语言[J]. 中国科技翻译, (03): 24-27. <br />
<br />
[9] 连淑能 (2010). 英汉对比研究增订本[M]. 北京:高等教育出版社.<br />
<br />
[10] 黎亚飞,张瑞华 (2019). 机器翻译发展与现状[J]. 中国轻工教育,(5):38-45. <br />
<br />
[11] 秦毅(2004),从翻译基本标准议医学英语的翻译[J]. 遵义医学院学报,27 (4): 421-423. <br />
<br />
[12] 王燕 (2008). 医学英语翻译与写作教程[M]. 重庆:重庆大学出版社<br />
<br />
=12 蔡珠凤=(The Mistranslation of C-J Machine Translation of Political Statements)=<br />
[[Machine_Trans_EN_12]]<br />
===Abstract===<br />
Language is the main way of communication between people. With the continuous development of globalization, the scale of cross-border exchanges is also expanding. However, due to cultural differences and diversity, the languages of different countries and regions are very different, which seriously hinders people's communication. The demand for efficient and convenient translation tools is increasing. At the same time, with the development of network technology and artificial intelligence, recognition technology based on deep learning is more and more widely used in English, Japanese and other fields.<br />
<br />
===Key words===<br />
machine translation; political statements; mistranslation of C-J machine translation<br />
===题目===<br />
The Mistranslation of C-J Machine Translation of Political Statements<br />
===摘要===<br />
语言是人与人之间交流的主要方式。随着全球化的不断发展，跨境交流的规模也在不断扩大。然而，由于文化的差异和多样性，不同国家和地区的语言差异很大，这严重阻碍了人们的交流。对高效便捷的翻译工具的需求正在增加。同时，随着网络技术和人工智能的发展，基于深度学习的识别技术在英语、日语等领域的应用越来越广泛。<br />
<br />
===关键词===<br />
机器翻译；政治发言；政治发言中译日的误译<br />
===1. Introduction===<br />
===Introduction to machine translation===<br />
Machine translation, also known as automatic translation, is a process of using computers to convert one natural language (source language) into another natural language (target language). It is a branch of computational linguistics, one of the ultimate goals of artificial intelligence, and has important scientific research value.At the same time, machine translation has important practical value. With the rapid development of economic globalization and the Internet, machine translation technology plays a more and more important role in promoting political, economic and cultural exchanges.The development of machine translation technology has been closely accompanied by the development of computer technology, information theory, linguistics and other disciplines. From the early dictionary matching, to the rule translation of dictionaries combined with linguistic expert knowledge, and then to the statistical machine translation based on corpus, with the improvement of computer computing power and the explosive growth of multilingual information, machine translation technology gradually stepped out of the ivory tower and began to provide real-time and convenient translation services for general users.（Zhang 2019:5-6)<br />
<br />
=== C-J machine translation software===<br />
Today's online machine translation software includes Baidu translation, Tencent translation, Google translation, Youdao translation, Bing translation and so on. Google was the first company to launch the machine translation system, and Baidu was the first company to import the machine translation system in China. In addition, Tencent and Youdao have attracted much attention.Machine translation is the process of using computers to convert one natural language into another. It usually refers to sentence and full-text translation between natural languages. In order to continuously improve the translation quality, R & D personnel have added artificial intelligence technologies such as speech recognition, image processing and deep neural network to machine translation on the basis of traditional machine translation based on rules, statistics and examples.With the increase of using machine translation, the joint cooperation between manual translation and machine translation will also increase significantly in the future. What criteria should be used to evaluate the quality of machine translation? In the evolving field of machine translation, there is an urgent need to clarify the unsolvable questions and solved problems.<br />
<br />
===The history of machine translation===<br />
The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. alchuni put forward the idea of using machines for translation. In 1933, Soviet inventor П.П. Trojansky designed a machine to translate one language into another, and registered his invention on September 5 of the same year; However, due to the low technical level in the 1930s, his translation machine was not made. In 1946, the first modern electronic computer ENIAC was born. Shortly after that, W. weaver, an American scientist and A. D. booth, a British engineer, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947 when discussing the application scope of electronic computer. In 1949, W. Weaver published the translation memorandum, which formally put forward the idea of machine translation. After 60 years of ups and downs, machine translation has experienced a tortuous and long development path. The academic community generally divides it into the following four stages:<br />
<br />
Pioneering period（1947-1964）<br />
<br />
In 1954, with the cooperation of IBM, Georgetown University completed the English Russian machine translation experiment with ibm-701 computer for the first time, showing the feasibility of machine translation to the public and the scientific community, thus opening the prelude to the study of machine translation.<br />
It is not too late for China to start this research. As early as 1956, the state included this research in the national scientific work development plan. The topic name is "machine translation, the construction of natural language translation rules and the mathematical theory of natural language". In 1957, the Institute of language and the Institute of computing technology of the Chinese Academy of Sciences cooperated in the Russian Chinese machine translation experiment, translating 9 different types of more complex sentences.From the 1950s to the first half of the 1960s, machine translation research has been on the rise. The United States and the former Soviet Union, two superpowers, have provided a lot of financial support for machine translation projects for military, political and economic purposes, while European countries have also paid considerable attention to machine translation research due to geopolitical and economic needs, and machine translation has become an upsurge for a time. In this period, although machine translation is just in the pioneering stage, it has entered an optimistic period of prosperity.<br />
<br />
Frustrated period（1964-1975）<br />
<br />
In 1964, in order to evaluate the research progress of machine translation, the American Academy of Sciences established the automatic language processing Advisory Committee (Alpac Committee) and began a two-year comprehensive investigation, analysis and test.In November 1966, the committee published a report entitled "language and machine" (Alpac report for short), which comprehensively denied the feasibility of machine translation and suggested stopping the financial support for machine translation projects. The publication of this report has dealt a blow to the booming machine translation, and the research of machine translation has fallen into a standstill. Coincidentally, during this period, China broke out the "ten-year Cultural Revolution", and basically these studies also stagnated. Machine translation has entered a depression.<br />
<br />
convalescence（1975-1989）<br />
<br />
Since the 1970s, with the development of science and technology and the increasingly frequent exchange of scientific and technological information among countries, the language barriers between countries have become more serious. The traditional manual operation mode has been far from meeting the needs, and there is an urgent need for computers to engage in translation. At the same time, the development of computer science and linguistics, especially the substantial improvement of computer hardware technology and the application of artificial intelligence in natural language processing, have promoted the recovery of machine translation research from the technical level. Machine translation projects have begun to develop again, and various practical and experimental systems have been launched successively, such as weinder system Eurpotra multilingual translation system, taum-meteo system, etc.However, after the end of the "ten-year holocaust", China has perked up again, and machine translation research has been put on the agenda again. The "784" project has paid enough attention to machine translation research. After the mid-1980s, the development of machine translation research in China has further accelerated. Firstly, two English Chinese machine translation systems, ky-1 and MT / ec863, have been successfully developed, indicating that China has made great progress in machine translation technology.<br />
<br />
New period(1990 present)<br />
<br />
With the universal application of the Internet, the acceleration of the process of world economic integration and the increasingly frequent exchanges in the international community, the traditional way of manual operation is far from meeting the rapidly growing needs of translation. People's demand for machine translation has increased unprecedentedly, and machine translation has ushered in a new development opportunity. International conferences on machine translation research have been held frequently, and China has made unprecedented achievements. A series of machine translation software have been launched, such as "Yixing", "Yaxin", "Tongyi", "Huajian", etc. Driven by the market demand, the commercial machine translation system has entered the practical stage, entered the market and came to the users.Since the new century, with the emergence and popularization of the Internet, the amount of data has increased sharply, and statistical methods have been fully applied. Internet companies have set up machine translation research groups and developed machine translation systems based on Internet big data, so as to make machine translation really practical, such as "Baidu translation", "Google translation", etc. In recent years, with the progress of in-depth learning, machine translation technology has further developed, which has promoted the rapid improvement of translation quality, and the translation in oral and other fields is more authentic and fluent.<br />
<br />
===The problem of machine translation at present===<br />
Error is inevitable<br />
Many people have misunderstandings about machine translation. They think that machine translation has great deviation and can't help people solve any problems. In fact, the error is inevitable. The reason is that machine translation uses linguistic principles. The machine automatically recognizes grammar, calls the stored thesaurus and automatically performs corresponding translation. However, errors are inevitable due to changes or irregularities in grammar, morphology and syntax, such as sentences with adverbials after "give me a reason to kill you first" in Dahua journey to the West. After all, a machine is a machine. No one has special feelings for language. How can it feel the lasting charm of "the tenderness of lowering its head, like the shame of a water lotus? After all, the meaning of Chinese is very different due to the changes of morphology, grammar and syntax and the change of context. Even many Chinese people are zhanger monks - they can't touch their heads, let alone machines.<br />
Bottleneck<br />
In fact, no matter which method, the biggest factor affecting the development of machine translation lies in the quality of translation. Judging from the achievements, the quality of machine translation is still far from the ultimate goal.<br />
Chinese mathematician and linguist Zhou Haizhong once pointed out in his paper "fifty years of machine translation": to improve the quality of machine translation, the first thing to solve is the problem of language itself rather than programming; It is certainly impossible to improve the quality of machine translation by relying on several programs alone. At the same time, he also pointed out that it is impossible for machine translation to achieve the degree of "faithfulness, expressiveness and elegance" when human beings have not yet understood how the brain performs fuzzy recognition and logical judgment of language. This view may reveal the bottleneck restricting the quality of translation. <br />
It is worth mentioning that American inventor and futurist ray cozwell predicted in an interview with Huffington Post that the quality of machine translation will reach the level of human translation by 2029. There are still many disputes about this thesis in the academic circles.<br />
<br />
===2.Mistranslation of Chinese Japanese machine translation===<br />
===2.1Vocabulary mistranslation in Chinese Japanese machine translation===<br />
===2.1.1Mistranslation of proper nouns===<br />
In political materials, there are often political dignitaries' names, place names or a large number of proper nouns in the political field. The morphemes of such words are definite and inseparable. Mistranslation will make the source language lose its specific meaning. <br />
<br />
Japanese translation into Chinese Chinese translation into Japanese<br />
<br />
original text translation by Youdao reference translation original text translation by Youdao reference translation<br />
<br />
朱鎔基 朱基 朱镕基 栗战书 栗戰史書 栗戰書<br />
<br />
労安 劳安 劳安 李克强 李克強 李克強 <br />
<br />
筑紫哲也 筑紫哲也 筑紫哲也 习近平 習近平 習近平<br />
<br />
山口百惠 山口百惠 山口百惠 韩正 韓中 韓正<br />
<br />
田中角栄 田中角荣 田中角荣 王沪宁 王上海氏 王滬寧<br />
<br />
東条英機 东条英社 东条英机 汪洋 汪洋 汪洋<br />
<br />
毛沢东 毛泽东 毛泽东 赵乐际 趙樂南 趙樂際<br />
<br />
トウ・ショウヘイ 大酱 邓小平 江泽民 江沢民 江沢民<br />
<br />
周恩来 周恩来 周恩来<br />
<br />
クリントン 克林顿 克林顿<br />
<br />
The above table counts 18 special names in the two texts, and 7 machine translation errors. In terms of mistranslation, there are not only "战书" but also "戰史" and "史書" in Chinese. "沪" is the abbreviation of Shanghai. In other words, since "战书" and "沪" are originally common nouns, this disrupts the choice of target language in machine translation. Except Katakana interference (except for トウシゃウヘイ, most of the mistranslations appear in the new Standing Committee. It can be seen that the machine translation system does not update the thesaurus in time. Different from other words, people's names have particularity. Especially as members of the Standing Committee of the Political Bureau of the CPC Central Committee, the translation of their names is officially unified. In this regard, public figures such as political dignitaries, stars, famous hosts and important. In addition, the machine also translates Japanese surnames such as "タ二モト" (谷本), "アンドウ" (安藤) into "塔尼莫特" and "龙胆". It can be found that the language feature that Japanese will be written together with Chinese characters and Hiragana also directly affects the translation quality of Japanese Chinese machine translation. Japanese people often use Katakana pronunciation. For example, when Japanese people talk with Premier Zhu, they often use "二ーハウ" (Hello). However, the machine only recognizes the two pseudonyms "二" and "ハ" and transliterates them into "尼哈" , ignoring the long sound after "二" and "ハ".<br />
<br />
original text Translation by Youdao reference translation<br />
<br />
日美安全体制 日米の安全体制 日米安保体制<br />
<br />
中国共产党第十九次全国代表大会 中国共産党第19回全国代表大会 中国共産党第19回全国代表大会（第19回党大会）<br />
<br />
十八大 十八大 第18回党大会中国特色社会主义<br />
<br />
中国特色社会主義 中国の特色ある社会主義 第18回党大会<br />
<br />
中国共产党中央委员会 中国共産党中央委員会 中国共産党中央委員会<br />
<br />
中国共産党中央委員会十八届中共中央政治局常委 第18代中国共產党中央政治局常務委員 第18期中共中央政治局常務委員<br />
<br />
十八届中共中央政治局委员 18期の中国共產党中央政治局委員 第18期中共中央政治局委員<br />
<br />
十九届中共中央政治局常委 十九回中国共產党中央政治局常務委員 第19期中央政治局常務委員<br />
<br />
中共十九届一中全会 中国共產党第十九回一中央委員会 第19期中央委員会第1回全体会議<br />
<br />
The above table is a comparison of the original and translated versions of some proper nouns. As shown in the table, the mistranslation problems are mainly reflected in the mismatch of numerals + quantifiers, the wrong addition of case auxiliary word "の", the lack of connectors, the Mistranslation of abbreviations, dead translation, and the writing errors of Chinese characters. The following is a specific analysis one by one.<br />
<br />
"十八届中共中央政治局常委", "十八届中共中央政治局委员", "十九届中共中央政治局常委" and "中共十九届一中全会" all have quantifiers "届", which are translated into "代", "期" and "回" respectively. The meaning is vague and should be uniformly translated into "期"; Among them, the translation of the last three proper nouns lacks the Chinese conjunction "第". The case auxiliary word "の" was added by mistake in the translation of "十八届中共中央政治局委员" and "日美安全体制". The "中国共产党中央委员会" did not write in the form of Japanese Ming Dynasty characters, but directly used simplified Chinese characters.<br />
<br />
The full name of "中共十九届一中全会" is "中国共产党第十九届中央委员会第一次全体会议", in which "一" stands for "第一次", which is an ordinal word rather than a cardinal word. Machine translation does not produce a correct translation. The fundamental reason is that there are no "规则" for translating such words in machine translation. If we can formulate corresponding rules for such words (for example, 中共m'1届m2 中全会→第m1期中央委员会第m2回全体会议), the translation system must be able to translate well no matter how many plenary sessions of the CPC Central Committee.<br />
<br />
===2.1.2Mistranslation of Polysemy===<br />
original text Translation by Youdao reference translation<br />
<br />
スタジオ 摄影棚/工作室 直播现场/演播厅<br />
<br />
日中関係の話 中日关系的故事 就中日关系(话题)<br />
<br />
溝 水沟 鸿沟<br />
<br />
それでは日中の問題について質問のある方。 那么对白天的问题有提问的人。 关于中日问题的话题，举手提问。<br />
<br />
私たちのクラスは20人ちょっとですが、 我们班有20人左右， 我们班二十多人的意见统一很难，<br />
<br />
いろいろな意見が出て、まとめるのは大変です。 但是又各种各样的意见，总结起来很困难。 <br />
<br />
一体どうやって、13億人もの人をまとめているんですか。 到底是怎么处理13亿的人的呢？ 中国是怎样把13亿人凝聚在一起的？<br />
<br />
In the original text, the word "スタジオ" appeared four times and was translated into "摄影棚" or "工作室" respectively, but the context is on-site interview, not the production site of photography or film. "話" has many semantics, such as "说话", "事情", "道理", etc. In accordance with the practice of the interview program, Premier Zhu Rongji was invited to answer five questions on the topic of China Japan relations. Obviously, the meaning of the word "故事" is very abrupt. The inherent Japanese word "日中" means "晌午、白天". At the same time, it is also the abbreviation of the names of the two countries. The machine failed to deal with it correctly according to the context. "溝" refers to the gap between China and Japan, not a "水沟". The former "まとめる" appears in the original text with the word "意见", which is intended to describe that it is difficult to unify everyone's opinions. The latter "まとめている" also refers to unifying the thoughts of 1.3 billion people, not "处理". Therefore, it is more appropriate to use "统一" and "处理" in human translation, rather than "总结意见" or "处理意见".<br />
<br />
Mistranslation of polysemy has always been a difficult problem in machine translation research. The translation of each word is correct, but it is often very different from the original expression in the context. Zhang Zhengzeng said that ambiguity is a common phenomenon in natural language. Its essence is that the same language form may have different meanings, which is also one of the differences between natural language and artificial language. Therefore, one of the difficulties faced by machine translation is language disambiguation (Zhang Zheng, 2005:60). In this regard, we can mark all the meanings of polysemous words and judge which meaning to choose by common collocation with other words. At the same time, strengthen the text recognition ability of the machine to avoid the translation inconsistent with the current context. In this way, we can avoid the mistake of "大酱" in the place where famous figures such as Deng Xiaoping should have appeared in the previous political articles.<br />
<br />
===2.1.3Mistranslation of compound words===<br />
Multi class words refer to a word with two or more parts of speech, also known as the same word and different classes.<br />
<br />
original text Translation by Youdao reference translation<br />
<br />
1998年江泽民主席曾经访问日本, 1998年の江沢民国家主席の日本訪問し、 1998年、江沢民総書記が日本を訪問し、かつ<br />
<br />
同已故小渊首相签署了联合宣言。 かつて同じ故小渕首相が署名した共同宣言 亡くなられた小渕総理と宣言に調印されました<br />
<br />
Chinese "同" has two parts of speech: prepositions and conjunctions: "故" has many parts of speech, such as nouns, verbs, adjectives and conjunctions. This directly affects the judgment of the machine at the source language level. The word "同" in the above table is used as a conjunction to indicate the other party of a common act. "故" is used as a verb with the semantic meaning of "死亡", which is a modifier of "小渊首相". The machine regards "同" as an adjective and "故" as a noun "原因", which leads to confusion in the structure and unclear semantics of the translation. Different from the polysemy problem, multi category words have at least two parts of speech, and there is often not only one meaning under each part of speech. In this regard, software R & D personnel should fully consider the existence of multi category words, so that the translation machine can distinguish the meaning of words on the basis of marking the part of speech, so as to select the translation through the context and the components of the word in the sentence. Of course, the realization of this function is difficult, and we need to give full play to the wisdom of R & D personnel.<br />
<br />
===2.2 Syntactic mistranslation in Chinese Japanese machine translation===<br />
===2.2.1Mistranslation of tenses===<br />
original text：History is written by the people, and all achievements are attributed to the people. <br />
<br />
Translation by Youdao：歴史は人民が書いたものであり、すべての成果は人民のためである。<br />
<br />
reference translation：歴史は人民が綴っていくものであり、すべての成果は人民に帰することとなります。<br />
<br />
The original sentence meaning is "历史是人民书写的历史" or "历史是人民书写的东西". When translated into Japanese, due to the influence of Japanese language habits, the formal noun "もの" should be supplemented accordingly. In this sentence, both the machine and the interpreter have translated correctly. However, the machine's recognition of "的" is biased, resulting in tense translation errors. The past, present and future will become "history", and this is a continuous action. The form translated as "~ ていく" not only reflects that the action is a continuous action, but also conforms to the tense and semantic information of the original text. In addition, the machine's treatment of the preposition "于" is also inappropriate. In the original text, "人民" is the recipient of action, not the target language. As the most obvious feature of isolated language, function words in Chinese play an important role in semantic expression, and their translation should be regarded as a focus of machine translation research.<br />
<br />
original text：李克强同志是十六届中共中央政治局常委,其他五位同志都是十六届中共中央政治局委员。<br />
<br />
Translation by Youdao：李克強総理は第16代中国共産党中央政治局常務委員であり、他の５人の同志はいずれも16期の中国共産党中央政治局委員である。<br />
<br />
reference translation：李克強同志は第16期中国共産党中央政治局常務委員を務め、他の５人は第16期中共中央政治局委員を務めました。<br />
<br />
Judging the verb "是" in the original text plays its most basic positive role, but due to the complexity of Chinese language, "是" often can not be completely transformed into the form of "だ / である" in the process of translation. This sentence is a judgment of what has happened. Compared with the 19th session, the 18th session has become history. This is an implicit temporal information. It is very difficult for machines without human brain to recognize this implicit information. "中共中央政治局常委" is the abbreviation of "中共中央政治局常务委员会委员" and it is a kind of position. In Japanese, often use it with verbs such as "務める","担当する",etc. The translator adopts the past tense of "務める", which conforms to the expression habit of Japanese and deals with the problem of tense at the same time. However, machine translation only mechanically translates this sentence into judgment sentence, and fails to correctly deal with the past information implied by the word "十八届".<br />
<br />
===2.2.2Mistranslation of honorifics===<br />
Honorific language is a language means to show respect to the listener. Different from Japanese, there is no grammatical category of honorifics in Chinese. There is no specific fixed grammatical form to express honorifics, self modesty and politeness. Instead, specific words such as "您", "请" and "劳驾" are used to express all kinds of respect or self modesty. <br />
<br />
Original text translation by Youdao reference translation<br />
<br />
女士们，先生们，同志们，朋友们 さんたち、先生たち、同志たち、お友达さん ご列席の皆さん<br />
<br />
谢谢大家！ ありがとうございます！ ご清聴ありがとうございました。<br />
<br />
これはどうされますか。 这是怎么回事呢? 您将如何解决这一问题？<br />
<br />
こうした問題をどうお考えでしょうか。 我们会如何考虑这些问题呢？ 您如何看待这一问题？<br />
<br />
For example, for the processing of "女士们，先生们，同志们，朋友们", the machine makes the translation correspond to the original one by one based on the principle of unchanged format, but there are errors in semantic communication and pragmatic habits. When speaking on formal occasions, Chinese expression tends to be comprehensive and detailed, as well as the address of the audience. In contrast, Japanese usually uses general terms such as "皆様", "ご臨席の皆さん" or "代表団の方々" as the opening remarks. In terms of Japanese Chinese translation, the machine also failed to recognize the usage of Japanese honorifics such as "さ れ る" and "お考え". It can be seen that Chinese Japanese machine translation has a low ability to deal with honorific expressions. The occasion is more formal. A good translation should not only be fluent in meaning, but also conform to the expression habits of the target language and match the current translation environment.<br />
<br />
===Conclusion===<br />
In the process of discussing the Mistranslation of machine translation, this paper mainly cites the Mistranslation of Youdao translator. When I studied the problem of machine translation mistranslation, I also used a large number of translation software such as Google and Baidu for parallel comparison, and found that these translation software also had similar problems with Youdao translator. For example, the "新世界新未来" in Chinese ABAC structural phrases is translated by Baidu as "新し世界の新しい未来", which, like Youdao, is not translated in the form of parallel phrases; Google online translation translates the word "“全心全意" into a completely wrong "全面的に"; The original sentence "我吃了很多亏" in the Mistranslation of the unique expression in the language is translated by Google online as "私はたくさんの損失を食べました". Because the "吃" and "亏" in the original text are not closely adjacent, the machine can not recognize this echo and mistakenly treats "亏" as a kind of food, so the machine translates the predicate as "食べました"; Japanese "こうした問題をどう考えでしょうか", Google's online translation is "你如何看待这些问题?", "你" tone can not reflect the tone of Japanese honorific, while Baidu translates as "你怎么想这样的问题呢?" Although the meaning can be understood, it is an irregular spoken language. Google and Baidu also made the same mistakes as Youdao in the translation of proper nouns. For example, they translated "十七届(中共中央政治局常委)”" into "第17回..." .In short, similar mistranslations of Youdao are also common in Baidu and Google. Due to space constraints, they will not be listed one by one here.<br />
<br />
Mr. Liu Yongquan, Institute of language, Chinese Academy of Social Sciences (1997) It has been pointed out that machine translation is a linguistic problem in the final analysis. Although corpus based machine translation does not require a lot of linguistic knowledge, language expression is ever-changing. Machine translation based solely on statistical ideas can not avoid the translation quality problems caused by the lack of language rules. The following is based on the analysis of lexical, syntactic and other mistranslations From the perspective of the characteristics of the source language, this paper summarizes some difficulties of Chinese and Japanese in machine translation.<br />
<br />
(1) The difficulties of Chinese in machine translation <br />
<br />
Chinese is a typical isolated language. The relationship between words needs to be reflected by word order and function words. We should pay attention to the transformation of function words such as "的", "在", "向" and "了". Some special verbs, such as "是", "做" and "作", are widely used. How to translate on the basis of conforming to Japanese pragmatic habits and expressions is a difficulty in machine translation research. In terms of word order, Chinese is basically "subject→predicate→object". At the same time, Chinese pays attention to parataxis, and there is no need to be clear in expression with meaningful cohesion, which increases the difficulty of Chinese Japanese machine translation. When there are multiple verbs or modifier components in complex long sentences, machine translation usually can not accurately divide the components of the sentence, resulting in the result that the translation is completely unreadable. <br />
<br />
(2) Difficulties of Japanese in machine translation <br />
<br />
Both Chinese and Japanese languages use Chinese characters, and most machines produce the target language through the corresponding translation of Chinese characters. However, pseudonyms in Japanese play an important role in judging the part of speech and meaning, and can not only recognize Chinese characters and judge the structure and semantics of sentences. Japanese vocabulary is composed of Chinese vocabulary, inherent vocabulary and foreign vocabulary. Among them, the first two have a great impact on Chinese Japanese machine translation. For example "提出" is translated as "提出する" or "打ち出す"; and "重视" is translated as "重視する" or "大切にする". Japanese is an adhesive language, which contains a lot of "けど", "が" and "けれども" Some of them are transitional and progressive structures, but there are also some sequential expressions that do not need translation, and these translation software often can not accurately grasp them.<br />
<br />
===References===<br />
[1] Navroz Kaur Kahlon,(2021(prepublish));Williamjeet Singh.Machine translation from text to sign language: a systematic review[J].Universal Access in the Information Society,1-35.<br />
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[2] Cao Qianyu;Hao Hanmei,(2021);Ahmed Syed Hassan.A Chaotic Neural Network Model for English Machine Translation Based on Big Data Analysis[J].Computational Intelligence and Neuroscience,3274326-3274326.<br />
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[3]Hwang Yongkeun;Kim Yanghoon;Jung Kyomin.(2021)Context-Aware Neural Machine Translation for Korean Honorific Expressions[J].Electronics,10(13):1589-1589.<br />
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[4]Zakaryia Almahasees.(2021)Analysing English-Arabic Machine Translation:Google Translate, Microsoft Translator and Sakhr.<br />
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[5](2021)Machine learning in translation[J].Nature Biomedical Engineering,5(6):485-486.<br />
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[6]Shaimaa Marzouk.(2021(prepublish))An in-depth analysis of the individual impact of controlled language rules on machine translation output: a mixed-methods approach[J].Machine Translation,1-37.<br />
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[7]Welnitzová Katarína;Munková Daša.(2021)Sentence-structure errors of machine translation into Slovak[J].Topics in Linguistics,22(1):78-92.<br />
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[8]Xu Xueyuan.(2021).Machine learning-based prediction of urban soil environment and corpus translation teaching[J].Arabian Journal of Geosciences,14(11). <br />
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[9]Chen Bingchang 陈丙昌(2016).機械翻訳の誤訳分析【D】.Error analysis of mechanical translation.贵州大学.2016(05) <br />
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[10]Lv Yinqiu 呂寅秋(1996).機械翻訳の言語規則と伝統文法との相違点.【D】The language rules of mechanical translation, the traditional grammar, and the points of contradiction.日本学研究.Japanese Studies.1996(00):21-22 <br />
<br />
[11]Liu Jun 刘君(2014).基于语料库的中日同形词词义用法对比及其日中机器翻译研究【D】.A Corpus-based Comparison of the Meanings of Chinese and Japanese Homographs and Research on Japanese-Chinese Machine Translation.广西大学.(03) <br />
<br />
[12]Cun Qianqian 崔倩倩(2019).机器翻译错误与译后编辑策略研究【D】.Research on Machine Translation Errors and Post-Editing Strategies.北京外国语大学.(09) <br />
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[13]Zhang Yi 张义(2019).机器翻译的译文分析【D】.Translation analysis of machine translation.西安外国语大学.(10) <br />
<br />
[14]Zhang Linqian 张琳婧(2019).在线机器翻译中日翻译错误原因及对策【D】.Causes and countermeasures of online machine translation errors in Chinese-Japanese translation.山西大学.(02)<br />
<br />
[15]Wang Dan 王丹(2020).基于机器翻译的专利文本译后编辑对策研究【D】.Research on countermeasures for post-translational editing of patent texts based on machine translation.大连理工大学.(06)<br />
<br />
[16]Yang Xiaokun 杨晓琨(2020).日中机器翻译中的前编辑规则与效果验证【D】.Pre-editing rules and effect verification in Japanese-Chinese machine translation.大连理工大学.(06)<br />
<br />
[17]Zuo Jia 左嘉(2021). 机器翻译日译汉误译研究【D】. Research on Mistranslation of Machine Translation from Japanese to Chinese.北京第二外国语学院.<br />
<br />
[18]Guan Biying 关碧莹(2018).关于政治类发言的汉日机器翻译误译分析【D】.Analysis of Chinese-Japanese Machine Translation Mistranslations of Political Speeches.哈尔滨理工大学.<br />
<br />
[19]Che Tong 车彤(2021).汉译日机器翻译质量评估及译后编辑策略研究【D】.Research on Quality Evaluation of Chinese-Japanese Machine Translation and Post-translation Editing Strategies.北京外国语大学.(09)<br />
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Networking Linking<br />
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http://www.elecfans.com/rengongzhineng/692245.html<br />
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https://baike.baidu.com/item/%E6%9C%BA%E5%99%A8%E7%BF%BB%E8%AF%91/411793<br />
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=13 陈湘琼Chen Xiangqiong（Study on Post-editing from the Perspective of Functional Equivalence Theory ）=<br />
[[Machine_Trans_EN_13]]<br />
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=14 Bi bi Nadia（Machine Translation a Challenge for Human Translators)=<br />
[[Machine_Trans_EN_14]]<br />
<br />
===Abstract===<br />
Machine translation is a big obstacle in the way of Human translators or interpretors although it is quick and less time consuming.people are trying to get translation of their target language using through source language.For this purpose they are using digital apps like Google translation Google translation does not give accurate and exact interpretation.Google translator is translates word to word translate that doesn't clarifies it's true and actual meaning.On the other hand human translators can give exact and accurate translation ,they take care of grammatical errors , diction and sentence structure.They clarify the purpose of target language through using source language.<br />
<br />
===Keywords===<br />
Descriptive translation, academic, interdiscipline, comparative literature, localization,Translatology,school of thought,translation , studies, linguistics, corresponding.<br />
<br />
===Body of article===<br />
Translation is the process of reworking text from one language into another to maintain the original message and communication. But, like everything else, there are different methods of translation, and they vary in form and function. What is translation? The term has become widely used among knowledge transfer researchers and practitioners, especially in the fields of health and health care. In a landmark review, Jonathan Lomas began to argue that 'The tasks… may be defined as to establish and maintain links between researchers and their audience, via the appropriate translation of research findings' (Lomas 1997, p 4). In 2004, the World Health Organization's World Report on Knowledge for Better Health suggested that 'One of the key contributions of research to health systems is the translation of knowledge into actions' (WHO 2004, p 33 and p 100). By 2006, special issues of WHO's Bulletin as well as the journals Evaluation and the Health Professions and the Journal of Continuing Education in the Health Professions were dedicated to translation.But what does 'translation' mean? It may be a new word for an old problem, meaning nothing more than 'transfer'. The rapidly emerging field of 'translational medicine' seems to take translation to mean generally what transfer might have meant, that is the transmission of knowledge and evidence 'from bench to bedside'. As one commentator has observed, "Translational medicine" as a fashionable term is being increasingly used to describe the wish of biomedical researchers to ultimately help patients' (Wehling 2008, abstract). <br />
Semantic uncertainty persists, not least because of the different interests of scientists, clinicians, patients and commercial firms (Littman et al 2007): 'Translational research means different things to different people, but it seems important to almost everyone' (Woolf 2008, p 211).<br />
In related fields, such as public health (Armstrong et al 2006), 'translation' seems to signify dissatisfaction with 'transfer'. It wants to move away from thinking of knowledge transfer as a form of technology transfer or dissemination, rejecting if only by implication its mechanistic assumptions and its model of linear messaging from A to B. But still, what does it signify?<br />
<br />
===Why translation?===<br />
'Translation' indicates a closer attention to the problem of shared meaning and how it might be developed. It seems to represent some new epistemological lubricant, facilitating the dissemination of texts and the application and use of the knowledge and information they in. Simply, translation might be the key to transfer. And yet, when we stop to think, we are more ambivalent. What is translated often seems somehow inferior, not real or original. Note how readily commentators reach for the idea that things might be 'lost in translation'. Knowing at a distance – made in and mediated by translation - makes for incomplete renditions, blurred images, partial truths. So what might 'translation' really mean? The purpose of this paper is to set out, for policy makers and practitioners, the theoretical and conceptual resources translation holds and seems to represent. In doing so, it explores understandings of translation in the fields of literature and linguistics and in the sociology of science and technology. It begins by setting out just why this idea of translation should make immediate, intuitive sense in relation to research, policy and practice.<br />
1translation in research, policy and practice research as translation<br />
Research often entails translation from one language to another: where data is collected from more than one ethnic group, for example, or where the language of the researcher is other than that of the research subject. It may draw on a secondary literature or source documents written in different languages, and may be published and disseminated in languages other than the one in which it is first written up.<br />
2In a different way, to conduct an interview is to ask for an account of experience and its meanings, but it is also to construct and translate that experience in terms defined at least in part by the researcher. In representing what is said, transcripts then select data, usually excluding significant gesture and eye-contact, for example. Often, certain characteristics of speech-acts (such as hesitations) will be edited out. In turn, the format of the transcript shapes the analytic use the researcher may make of it. The basis of research 'findings', then, is an artefact, a transcript or translation, not an original interaction (Ochs 1979, Barnes, Bloor and Henry 1996, Ross 2009).<br />
3In this way, the researcher recasts aspects of his or her problem or topic in new, scientific form: 'All researchers "translate" the experiences of others' (Temple 1997, p 609). Research is invariably conducted in a sort of 'metalanguage' (Hantrais and Ager 1985): the research process can be conceived as one of successive translations (from theoretical formulation to operationalization, transcription, interpretation and dissemination). Theorization is a process of reciprocal back and forth between theory and fact, in which conceptions of each are revised in order that one fit the other (Baldamus 1974).<br />
4 It is a kind of translation: a rereading, re-use, re-application or re-representation of what we know in new terms (Turner 1980). Referencing, too, is an act of translation, a form of appropriation and incorporation of one text by another (Gilbert 1977).policy as translation Each of the fields covered by the paper is diverse and ill-defined, and there is no intention here to provide a comprehensive account of any them. Sources have been chosen for their relevance: main references are cited in the text, and additional sources listed in footnotes.<br />
<br />
2For a brief introduction to the technical issues involved in social research in more than one language, see Birbili (2000). For translation issues in survey research and question design in general, see Ervin and Bower (1952) and Deutscher (1968); on translating survey research instruments (in this instance health-related quality of life measures), Bowden and Fox-Rushby (2003). On the use of translators and interpreters, see Temple (1997) and Jentsch (1998).<br />
3For an interesting discussion of this problem, see Bourdieu (1999), esp pp 621-626, 'The risks of writing'. <br />
===Difference between machine translation and human translators===<br />
Few people disagree on the differences between the two, but many argue over the quality of the translations. How accurate are machine translations? How reliable are human translations? Some say machine translation produces near-perfect translations while others are adamant that translations are incomprehensible and cause more problems than they solve. Results will, of course, vary depending on the source and target languages, the machine translation service used (e.g. Google Translate), and the complexity of the original text.<br />
Machine translation, love it or hate it, is here to stay. In fact, the machine translation market is growing at such a fast pace that it is predicted to reach $980 million by 2022.<br />
===Machine translation: the pros and cons===<br />
The advantages of machine translation generally come down to two factors: it’s faster and cheaper. The downside to this is the standard of translation can be anywhere from inaccurate, to incomprehensible, and potentially dangerous (more on that shortly).<br />
==The advantages of machine translation==<br />
Many free tools are readily available (Google Translate, Skype Translator, etc.)<br />
Quick turnaround time <br />
You can translate between multiple languages using one tool<br />
Translation technology is constantly improving<br />
The disadvantages of machine translation<br />
Level of accuracy can be very low <br />
Accuracy is also very inconsistent across different languages<br />
==Machines can't translate context ==<br />
Mistakes are sometimes costly <br />
Sometimes translation simply doesn’t work<br />
The most important thing to consider with any kind of translation is the cost of potential mistakes. Translating instructions for medical equipment, aviation manuals, legal documents and many other kinds of content require 100% accuracy. In such cases, mistakes can cost lives, huge amounts of money and irreparable damage to your company’s image. So choose carefully!<br />
Human translation: the pros and cons<br />
Human translation essentially switches the table in terms of pros and cons. A higher standard of accuracy comes at the price of longer turnaround times and higher costs. What you have to decide is whether that initial investment outweighs the potential cost of mistakes. Alternatively, whether mistakes simply aren’t an option, like the cases we looked at in the previous section.<br />
<br />
==The advantages of human translation ==<br />
It’s a translator’s job to ensure the highest accuracy<br />
Humans can interpret context and capture the same meaning, rather than simply translating words<br />
Human translators can review their work and provide a quality process<br />
Humans can interpret the creative use of language, e.g. puns, metaphors, slogans, etc.<br />
Professional translators understand the idiomatic differences between their languages<br />
Humans can spot pieces of content where literal translation isn’t possible and find the most suitable alternative<br />
The disadvantages of human translation<br />
Turnaround time is longer <br />
Translators rarely work for free <br />
Unless you use a translation agency, with access to thousands of translators, you’re limited to the languages any one translator can work with<br />
Simply put, human translation is your best option when accuracy is even remotely important. Other considerations to make are the complexity of your source material and the two languages you’re translating between – both of which can render machines pretty useless.<br />
<br />
When to use machine and human translation<br />
The truth is, the debate over machine vs human translation is an unnecessary distraction. What we should really be talking about is when to use these two different types of translation services, because they both serve a very valid purpose.<br />
<br />
Examples of when to use machine translation<br />
When you have a large bulk of content to translate and the general meaning is enough<br />
When your translation never reaches the final audience, e.g. you’re translating a resource as research for another piece of content<br />
Translating documents for internal use within a company, provided 100% accuracy isn’t needed<br />
To partially translate large chunks of content for a human translator to improve upon<br />
Examples of when to use human translation<br />
When accuracy is important<br />
Most cases where your translated content is received by a consumer audience<br />
When you have a duty of care to provide accurate translations (e.g. legal documents, product instructions, medical guidelines or health and safety content)<br />
When translating marketing material or other texts for creative language uses.<br />
<br />
types of machine translation.<br />
<br />
What is Machine Translation? Rule Based Machine Translation vs. Statistical Machine Translation. Machine translation (MT) is automated translation. It is the process by which computer software is used to translate a text from one natural language (such as English) to another (such as Spanish).<br />
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To process any translation, human or automated, the meaning of a text in the original (source) language must be fully restored in the target language, i.e. the translation. While on the surface this seems straightforward, it is far more complex. Translation is not a mere word-for-word substitution. A translator must interpret and analyze all of the elements in the text and know how each word may influence another. This requires extensive expertise in grammar, syntax (sentence structure), semantics (meanings), etc., in the source and target languages, as well as familiarity with each local region.<br />
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Human and machine translation each have their share of challenges. For example, no two individual translators can produce identical translations of the same text in the same language pair, and it may take several rounds of revisions to meet customer satisfaction. But the greater challenge lies in how machine translation can produce publishable quality translations.<br />
<br />
Rule-Based Machine Translation Technology<br />
Rule-based machine translation relies on countless built-in linguistic rules and millions of bilingual dictionaries for each language pair.<br />
The software parses text and creates a transitional representation from which the text in the target language is generated. This process requires extensive lexicons with morphological, syntactic, and semantic information, and large sets of rules. The software uses these complex rule sets and then transfers the grammatical structure of the source language into the target language.<br />
Translations are built on gigantic dictionaries and sophisticated linguistic rules. Users can improve the out-of-the-box translation quality by adding their terminology into the translation process. They create user-defined dictionaries which override the system’s default settings.<br />
In most cases, there are two steps: an initial investment that significantly increases the quality at a limited cost, and an ongoing investment to increase quality incrementally. While rule-based MT brings companies to the quality threshold and beyond, the quality improvement process may be long and expensive.<br />
<br />
Statistical Machine Translation Technology<br />
Statistical machine translation utilizes statistical translation models whose parameters stem from the analysis of monolingual and bilingual corpora. Building statistical translation models is a quick process, but the technology relies heavily on existing multilingual corpora. A minimum of 2 million words for a specific domain and even more for general language are required. Theoretically it is possible to reach the quality threshold but most companies do not have such large amounts of existing multilingual corpora to build the necessary translation models. Additionally, statistical machine translation is CPU intensive and requires an extensive hardware configuration to run translation models for average performance levels.<br />
<br />
Rule-Based MT vs. Statistical MT<br />
Rule-based MT provides good out-of-domain quality and is by nature predictable. Dictionary-based customization guarantees improved quality and compliance with corporate terminology. But translation results may lack the fluency readers expect. In terms of investment, the customization cycle needed to reach the quality threshold can be long and costly. The performance is high even on standard hardware.<br />
<br />
Statistical MT provides good quality when large and qualified corpora are available. The translation is fluent, meaning it reads well and therefore meets user expectations. However, the translation is neither predictable nor consistent. Training from good corpora is automated and cheaper. But training on general language corpora, meaning text other than the specified domain, is poor. Furthermore, statistical MT requires significant hardware to build and manage large translation models.<br />
<br />
Rule-Based MT Statistical MT<br />
+ Consistent and predictable quality – Unpredictable translation quality<br />
+ Out-of-domain translation quality – Poor out-of-domain quality<br />
+ Knows grammatical rules – Does not know grammar <br />
+ High performance and robustness – High CPU and disk space requirements<br />
+ Consistency between versions – Inconsistency between versions <br />
– Lack of fluency + Good fluency<br />
– Hard to handle exceptions to rules + Good for catching exceptions to rules <br />
– High development and customization costs + Rapid and cost-effective development costs provided the required corpus exists<br />
Given the overall requirements, there is a clear need for a third approach through which users would reach better translation quality and high performance (similar to rule-based MT), with less investment (similar to statistical MT).<br />
Post-Edited Machine Translation (PEMT)<br />
Often, PEMT is used to bridge the gap between the speed of machine translation and the quality of human translation, as translators review, edit and improve machine-translated texts. PEMT services cost more than plain machine translations but less than 100% human translation, especially since the post-editors don’t have to be fluently bilingual—they just have to be skilled proofreaders with some experience in the language and target region.<br />
Successful translation is about more than just the words, which is why we advocate for not just human translation by skilled linguists, but for translation by people deeply familiar with the cultures they’re writing for. Life experience, study and the knowledge that only comes from living in a geographic region can make the difference between words that are understandable and language that is capable of having real, positive impact. <br />
<br />
PacTranz<br />
The HUGE list of 51 translation types, methods and techniques<br />
Upper section of infographic of 51 common types of translation classified in 4 broad categoriesThere are a bewildering number of different types of translation.<br />
So we’ve identified the 51 types you’re most likely to come across, and explain exactly what each one means.<br />
This includes all the main translation methods, techniques, strategies, procedures and areas of specialisation.<br />
It’s our way of helping you make sense of the many different kinds of translation – and deciding which ones are right for you.<br />
Don’t miss our free summary pdf download later in the article!<br />
The 51 types of translation we’ve identified fall neatly into four distinct categories.<br />
Translation Category A: 15 types of translation based on the technical field or subject area of the text<br />
Icons representing 15 types of translation categorised by the technical field or subject area of the textTranslation companies often define the various kinds of translation they provide according to the subject area of the text.<br />
This is a useful way of classifying translation types because specialist texts normally require translators with specialist knowledge.<br />
Here are the most common types you’re like to come across in this category.<br />
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1. General Translation<br />
What is it?<br />
The translation of non-specialised text. That is, text that we can all understand without needing specialist knowledge in some area.<br />
The text may still contain some technical terms and jargon, but these will either be widely understood, or easily researched.<br />
What this means<br />
The implication is that you don’t need someone with specialist knowledge for this type of translation – any professional translator can handle them.<br />
Translators who only do this kind of translation (don’t have a specialist field) are sometimes referred to as ‘generalist’ or ‘general purpose’ translators.<br />
Examples<br />
Most business correspondence, website content, company and product/service info, non-technical reports.<br />
Most of the rest of the translation types in this Category do require specialist translators.<br />
Check out our video on 13 types of translation requiring special translator expertise:<br />
<br />
2. Technical Translation<br />
What is it?<br />
We use the term “technical translation” in two different ways:<br />
Broad meaning: any translation where the translator needs specialist knowledge in some domain or area.<br />
This definition would include almost all the translation types described in this section.<br />
Narrow meaning: limited to the translation of engineering (in all its forms), IT and industrial texts.<br />
This narrower meaning would exclude legal, financial and medical translations for example, where these would be included in the broader definition.<br />
What this means<br />
Technical translations require knowledge of the specialist field or domain of the text.<br />
That’s because without it translators won’t completely understand the text and its implications. And this is essential if we want a fully accurate and appropriate translation.Good to know Many technical translation projects also have a typesetting/dtp requirement. Be sure your translation provider can handle this component, and that you’ve allowed for it in your project costings and time frames.<br />
Examples<br />
Manuals, specialist reports, product brochures<br />
<br />
3. Scientific Translation<br />
What is it?<br />
The translation of scientific research or documents relating to it.<br />
What this means<br />
These texts invariably contain domain-specific terminology, and often involve cutting edge research.<br />
So it’s imperative the translator has the necessary knowledge of the field to fully understand the text. That’s why scientific translators are typically either experts in the field who have turned to translation, or professionally qualified translators who also have qualifications and/or experience in that domain.<br />
On occasion the translator may have to consult either with the author or other domain experts to fully comprehend the material and so translate it appropriately.<br />
Examples<br />
Research papers, journal articles, experiment/trial results<br />
<br />
4. Medical Translation<br />
What is it?<br />
The translation of healthcare, medical product, pharmaceutical and biotechnology materials.<br />
Medical translation is a very broad term covering a wide variety of specialist areas and materials – everything from patient information to regulatory, marketing and technical documents.<br />
As a result, this translation type has numerous potential sub-categories – ‘medical device translations’ and ‘clinical trial translations’, for example.<br />
What this means<br />
As with any text, the translators need to fully understand the materials they’re translating. That means sound knowledge of medical terminology and they’ll often also need specific subject-matter expertise.<br />
Good to know<br />
Many countries have specific requirements governing the translation of medical device and pharmaceutical documentation. This includes both your client-facing and product-related materials.<br />
Examples<br />
Medical reports, product instructions, labeling, clinical trial documentation<br />
<br />
5. Financial Translation<br />
What is it?<br />
In broad terms, the translation of banking, stock exchange, forex, financing and financial reporting documents.<br />
However, the term is generally used only for the more technical of these documents that require translators with knowledge of the field.<br />
Any competent translator could translate a bank statement, for example, so that wouldn’t typically be considered a financial translation.<br />
What this means<br />
You need translators with domain expertise to correctly understand and translate the financial terminology in these texts.<br />
Examples<br />
Company accounts, annual reports, fund or product prospectuses, audit reports, IPO documentation<br />
<br />
6. Economic Translations<br />
What is it?<br />
1. Sometimes used as a synonym for financial translations.<br />
2. Other times used somewhat loosely to refer to any area of economic activity – so combining business/commercial, financial and some types of technical translations.<br />
3. More narrowly, the translation of documents relating specifically to the economy and the field of economics.<br />
What this means<br />
As always, you need translators with the relevant expertise and knowledge for this type of translation.<br />
<br />
7. Legal Translation<br />
What is it?<br />
The translation of documents relating to the law and legal process.<br />
What this means<br />
Legal texts require translators with a legal background.<br />
That’s because without it, a translator may not:<br />
– fully understand the legal concepts<br />
– write in legal style<br />
– understand the differences between legal systems, and how best to translate concepts that don’t correspond.<br />
And we need all that to produce professional quality legal translations – translations that are accurate, terminologically correct and stylistically appropriate.<br />
Examples<br />
Contracts, legal reports, court judgments, expert opinions, legislation<br />
<br />
8. Juridical Translation<br />
What is it?<br />
1. Generally used as a synonym for legal translations.<br />
2. Alternatively, can refer to translations requiring some form of legal verification, certification or notarization that is common in many jurisdictions.<br />
<br />
9. Judicial Translation<br />
What is it?<br />
1. Most commonly a synonym for legal translations.<br />
2. Rarely, used to refer specifically to the translation of court proceeding documentation – so judgments, minutes, testimonies, etc. <br />
<br />
10. Patent Translation<br />
What is it?<br />
The translation of intellectual property and patent-related documents.<br />
Key features<br />
Patents have a specific structure, established terminology and a requirement for complete consistency throughout – read more on this here. These are key aspects to patent translations that translators need to get right.<br />
In addition, subject matter can be highly technical.<br />
What this means<br />
You need translators who have been trained in the specific requirements for translating patent documents. And with the domain expertise needed to handle any technical content.<br />
Examples<br />
Patent specifications, prior art documents, oppositions, opinions<br />
<br />
11. Literary Translation<br />
What is it?<br />
The translation of literary works – novels, short stories, plays, essays, poems.<br />
Key features<br />
Literary translation is widely regarded as the most difficult form of translation.<br />
That’s because it involves much more than simply conveying all meaning in an appropriate style. The translator’s challenge is to also reproduce the character, subtlety and impact of the original – the essence of what makes that work unique.<br />
This is a monumental task, and why it’s often said that the translation of a literary work should be a literary work in its own right.<br />
What this means<br />
Literary translators must be talented wordsmiths with exceptional creative writing skills.<br />
Because few translators have this skillset, you should only consider dedicated literary translators for this type of translation.<br />
<br />
<br />
12. Commercial Translation<br />
What is it?<br />
The translation of documents relating to the world of business.<br />
This is a very generic, wide-reaching translation type. It includes other more specialised forms of translation – legal, financial and technical, for example. And all types of more general business documentation.<br />
Also, some documents will require familiarity with business jargon and an ability to write in that style.<br />
What this means<br />
Different translators will be required for different document types – specialists should handle materials involving technical and specialist fields, whereas generalist translators can translate non-specialist materials.<br />
Examples<br />
Business correspondence, reports, marketing and promotional materials, sales proposals<br />
<br />
13. Business Translations<br />
What is it?<br />
A synonym for Commercial Translations.<br />
<br />
14. Administrative Translations<br />
What is it?<br />
The translation of business management and administration documents.<br />
So it’s a subset of business / commercial translations.<br />
What this means<br />
The implication is these documents will include business jargon and ‘management speak’, so require a translator familiar with, and practised at, writing in that style.<br />
Examples<br />
Management reports and proposals<br />
<br />
15. Marketing Translations<br />
What is it?<br />
The translation of advertising, marketing and promotional materials.<br />
This is a subset of business or commercial translations.<br />
Key features<br />
Marketing copy is designed to have a specific impact on the audience – to appeal and persuade.<br />
So the translated copy must do this too.<br />
But a direct translation will seldom achieve this – so translators need to adapt their wording to produce the impact the text is seeking.<br />
And sometimes a completely new message might be needed – see transcreation in our next category of translation types.<br />
What this means<br />
Marketing translations require translators who are skilled writers with a flair for producing persuasive, impactful copy.<br />
As relatively few translators have these skills, engaging the right translator is key.<br />
Good to know<br />
This type of translation often comes with a typesetting or dtp requirement – particularly for adverts, posters, brochures, etc.<br />
Its best for your translation provider to handle this component. That’s because multilingual typesetters understand the design and aesthetic conventions in other languages/cultures. And these are essential to ensure your materials have the desired impact and appeal in your target markets.<br />
Examples<br />
Advertising, brochures, some website/social media text.<br />
Translation Category B: 14 types of translation based on the end product or use of the translation<br />
This category is all about how the translation is going to be used or the end product that’s produced.<br />
Most of these types involve either adapting or processing a completed translation in some way, or converting or incorporating it into another program or format.<br />
You’ll see that some are very specialised, and complex.<br />
It’s another way translation providers refer to the range of services they provide.<br />
Check out our video of the most specialised of these types of translation:<br />
<br />
16. Document Translations<br />
What is it?<br />
The translation of documents of all sorts.<br />
Here the translation itself is the end product and needs no further processing beyond standard formatting and layout.<br />
<br />
17. Text Translations<br />
What is it?<br />
A synonym for document translation.<br />
<br />
18. Certified Translations<br />
What is it?<br />
A translation with some form of certification.<br />
Key features<br />
The certification can take many forms. It can be a statement by the translation company, signed and dated, and optionally with their company seal. Or a similar certification by the translator.<br />
The exact format and wording will depend on what clients and authorities require – here’s an example.<br />
<br />
19. Official Translations<br />
What is it?<br />
1. Generally used as a synonym for certified translations.<br />
2. Can also refer to the translation of ‘official’ documents issued by the authorities in a foreign country. These will almost always need to be certified.<br />
<br />
20. Software Localisation<br />
What is it?<br />
Adapting software for another language/culture.<br />
Key features<br />
The goal of software localisation is not just to make the program or product available in other languages. It’s also about ensuring the user experience in those languages is as natural and effective as possible.<br />
Translating the user interface, messaging, documentation, etc is a major part of the process.<br />
Also key is a customisation process to ensure everything matches the conventions, norms and expectations of the target cultures.<br />
Adjusting time, date and currency formats are examples of simple customisations. Others might involve adapting symbols, graphics, colours and even concepts and ideas.<br />
Localisation is often preceded by internationalisation – a review process to ensure the software is optimally designed to handle other languages.<br />
And it’s almost always followed by thorough testing – to ensure all text is in the correct place and fits the space, and that everything makes sense, functions as intended and is culturally appropriate.<br />
Localisation is often abbreviated to L10N, internationalisation to i18n.<br />
What this means<br />
Software localisation is a specialised kind of translation, and you should always engage a company that specialises in it.<br />
They’ll have the systems, tools, personnel and experience needed to achieve top quality outcomes for your product.<br />
<br />
21. Game Localisation<br />
What is it?<br />
Adapting games for other languages and markets.<br />
<br />
It’s a subset of software localisation.<br />
Key features<br />
The goal of game localisation is to provide an engaging and fun gaming experience for speakers of other languages.<br />
<br />
It involves translating all text and recording any required foreign language audio.<br />
<br />
But also adapting anything that would clash with the target culture’s customs, sensibilities and regulations.<br />
<br />
For example, content involving alcohol, violence or gambling may either be censored or inappropriate in the target market.<br />
<br />
And at a more basic level, anything that makes users feel uncomfortable or awkward will detract from their experience and thus the success of the game in that market.<br />
<br />
So portions of the game may have to be removed, added to or re-worked.<br />
<br />
Game localisation involves at least the steps of translation, adaptation, integrating the translations and adaptations into the game, and testing.<br />
<br />
What this means<br />
Game localisation is a very specialised type of translation best left to those with specific expertise and experience in this area.<br />
<br />
22. Multimedia Localisation<br />
What is it?<br />
Adapting multimedia for other languages and cultures.<br />
<br />
Multimedia refers to any material that combines visual, audio and/or interactive elements. So videos and movies, on-line presentations, e-Learning courses, etc.<br />
Key features<br />
Anything a user can see or hear may need localising.<br />
<br />
That means the audio and any text appearing on screen or in images and animations.<br />
<br />
Plus it can mean reviewing and adapting the visuals and/or script if these aren’t suitable for the target culture.<br />
<br />
The localisation process will typical involve:<br />
– Translation<br />
– Modifying the translation for cultural reasons and/or to meet technical requirements<br />
– Producing the other language versions<br />
<br />
Audio output may be voice-overs, dubbing or subtitling.<br />
<br />
And output for visuals can involve re-creating elements, or supplying the translated text for the designers/engineers to incorporate.<br />
<br />
What this means<br />
Multimedia localisation projects vary hugely, and it’s essential your translation providers have the specific expertise needed for your materials.<br />
<br />
<br />
<br />
23. Script Translations<br />
What is it?<br />
Preparing the text of recorded material for recording in other languages.<br />
Key features<br />
There are several issues with script translation.<br />
<br />
One is that translations typically end up longer than the original script. So voicing the translation would take up more space/time on the video than the original language.<br />
<br />
Sometimes that space will be available and this will be OK.<br />
<br />
But generally it won’t be. So the translation has to be edited back until it can be comfortably voiced within the time available on the video.<br />
<br />
Another challenge is the translation may have to synchronise with specific actions, animations or text on screen.<br />
<br />
Also, some scripts also deal with technical subject areas involving specialist technical terminology.<br />
<br />
Finally, some scripts may be very culture-specific – featuring humour, customs or activities that won’t work well in another language. Here the script, and sometimes also the associated visuals, may need to be adjusted before beginning the translation process.<br />
<br />
It goes without saying that a script translation must be done well. If it’s not, there’ll be problems producing a good foreign language audio, which will compromise the effectiveness of the video.<br />
<br />
Translators typically work from a time-coded transcript. This is the original script marked to show the time available for each section of the translation.<br />
<br />
What this means<br />
There are several potential pitfalls in script translations. So it’s vital your translation provider is practiced at this type of translation and able to handle any technical content.<br />
<br />
<br />
<br />
24. Voice-over and Dubbing Projects<br />
What is it?<br />
Translation and recording of scripts in other languages.<br />
<br />
Voice-overs vs dubbing<br />
There is a technical difference.<br />
A voice-over adds a new track to the production, dubbing replaces an existing one.<br />
<br />
<br />
Key features<br />
These projects involve two parts:<br />
– a script translation (as described above), and<br />
– producing the audio<br />
<br />
So they involve the combined efforts of translators and voice artists.<br />
The task for the voice artist is to produce a high quality read. That’s one that matches the style, tone and richness of the original.<br />
<br />
Often each section of the new audio will need to be the same length as the original.<br />
<br />
But sometimes the segments will need to be shorter – for example where the voice-over lags the original by a second or two. This is common in interviews etc, where the original voice is heard initially then drops out.<br />
<br />
The most difficult form of dubbing is lip-syncing – where the new audio needs to synchronise with the original speaker’s lip movements, gestures and actions.<br />
<br />
Lip-syncing requires an exceptionally skilled voice talent and considerable time spent rehearsing and fine tuning the translation.<br />
<br />
What this means<br />
You need to use experienced professionals every step of the way in this type of project.<br />
<br />
That’s to ensure firstly that your foreign-language scripts are first class, then that the voicing is of high professional standard.<br />
<br />
Anything less will mean your foreign language versions will be way less effective and appealing to your target audience.<br />
<br />
<br />
<br />
25. Subtitle Translations<br />
What is it?<br />
Producing foreign language captions for sub or surtitles.<br />
Key features<br />
The goal with subtitling is to produce captions that viewers can comfortably read in the time available and still follow what’s happening on the video.<br />
<br />
To achieve this, languages have “rules” governing the number of characters per line and the minimum time each subtitle should display.<br />
<br />
Sticking to these guidelines is essential if your subtitles are to be effective.<br />
<br />
But this is no easy task – it requires simple language, short words, and a very succinct style. Translators will spend considerable time mulling over and re-working their translation to get it just right.<br />
<br />
Most subtitle translators use specialised software that will output the captions in the format sound engineers need for incorporation into the video.<br />
<br />
What this means<br />
As with other specialised types of translation, you should only use translators with specific expertise and experience in subtitling.<br />
<br />
<br />
<br />
26. Website Localisation<br />
What is it?<br />
The translation and adapting of relevant content on a website to best suit the target language and culture.<br />
<br />
Note: Many providers use the term website translation as a synonym for localisation. Strictly speaking though, translation is just one part of localisation.<br />
Key features<br />
<br />
Not all pages on a website may need to be localised – clients should review their content to identify what’s relevant for the other language versions.<br />
Some content may need specialist translators – legal and technical pages for example.<br />
There may also be videos, linked documents, and text or captions in graphics to translate.<br />
Adaptation can mean changing date, time, currency and number formats, units of measure, etc.<br />
But also images, colours and even the overall site design and style if these won’t have the desired impact in the target culture.<br />
Translated files can be supplied in a wide range of formats – translators usually coordinate output with the site webmasters.<br />
New language versions are normally thoroughly reviewed and tested before going live to confirm everything is displaying correctly, works as intended and is cultural appropriate.<br />
What this means<br />
The first step should be to review your content and identify what needs to be translated. This might lead you to modify some pages for the foreign language versions.<br />
<br />
In choosing your translation providers be sure they can:<br />
– handle any technical or legal content,<br />
– provide your webmaster with the file types they want.<br />
<br />
And you should always get your translators to systematically review the foreign language versions before going live.<br />
<br />
<br />
<br />
27. Transcreation<br />
What is it?<br />
Adapting a message to elicit the same emotional response in another language and culture.<br />
Translation is all about conveying the message or meaning of a text in another language. But sometimes that message or meaning won’t have the desired effect in the target culture.<br />
<br />
This is where transcreation comes in. Transcreation creates a new message that will get the desired emotional response in that culture, while preserving the style and tone of the original.<br />
<br />
So it’s a sort of creative translation – which is where the word comes from, a combination of ‘translation’ and ‘creation’.<br />
<br />
At one level transcreation may be as simple as choosing an appropriate idiom to convey the same intent in the target language – something translators do all the time.<br />
<br />
But mostly the term is used to refer to adapting key advertising and marketing messaging. Which requires copywriting skills, cultural awareness and an excellent knowledge of the target market.<br />
<br />
Who does it?<br />
Some translation companies have suitably skilled personnel and offer transcreation services.<br />
<br />
Often though it’s done in the target country by specialist copywriters or an advertising or marketing agency – particularly for significant campaigns and to establish a brand in the target marketplace.<br />
<br />
What this means<br />
Most general marketing and promotional texts won’t need transcreation – they can be handled by a translator with excellent creative writing skills.<br />
<br />
But slogans, by-lines, advertising copy and branding statements often do.<br />
<br />
Whether you should opt for a translation company or an in-market agency will depend on the nature and importance of the material, and of course your budget.<br />
<br />
<br />
<br />
28. Audio Translations<br />
What is it?<br />
Broad meaning: the translation of any type of recorded material into another language.<br />
<br />
More commonly: the translation of a foreign language video or audio recording into your own language. So this is where you want to know and document what a recording says.<br />
Key features<br />
The first challenge with audio translations is it’s often impossible to pick up every word that’s said. That’s because audio quality, speech clarity and speaking speed can all vary enormously.<br />
<br />
It’s also a mentally challenging task to listen to an audio and translate it directly into another language. It’s easy to miss a word or an aspect of meaning.<br />
<br />
So best practice is to first transcribe the audio (type up exactly what is said in the language it is spoken in), then translate that transcription.<br />
<br />
However, this is time consuming and therefore costly, and there are other options if lesser precision is acceptable.<br />
<br />
What this means<br />
It’s best to discuss your requirements for this kind of translation with your translation provider. They’ll be able to suggest the best translation process for your needs.<br />
<br />
Examples<br />
Interviews, product videos, police recordings, social media videos.<br />
<br />
<br />
<br />
29. Translations with DTP<br />
What is it?<br />
Translation incorporated into graphic design files.multilingual dtp example in the form of a Rubik's Cube with foreign text on each square<br />
Key features<br />
Graphic design programs are used by professional designers and graphic artists to combine text and images to create brochures, books, posters, packaging, etc.<br />
<br />
Translation plus dtp projects involve 3 steps – translation, typesetting, output.<br />
<br />
The typesetting component requires specific expertise and resources – software and fonts, typesetting know-how, an appreciation of foreign language display conventions and aesthetics.<br />
<br />
What this means<br />
Make sure your translation company has the required multilingual typesetting/desktop publishing expertise whenever you’re translating a document created in a graphic design program.<br />
<br />
Translation Category C: 13 types of translation based on the translation method employed<br />
This category has two sub-groups:<br />
– the practical methods translation providers use to produce their translations, and<br />
– the translation strategies/methods identified and discussed within academia.<br />
<br />
The translation methods translation providers use<br />
There are 4 main methods used in the translation industry today. We have an overview of each below, but for more detail, including when to use each one, see our comprehensive blog article.<br />
<br />
Or watch our video.<br />
<br />
Important: If you’re a client you need to understand these 4 methods – choose the wrong one and the translation you end up with may not meet your needs!<br />
<br />
<br />
30. Machine Translation (MT)<br />
What is it?<br />
A translation produced entirely by a software program with no human intervention.<br />
<br />
A widely used, and free, example is Google Translate. And there are also commercial MT engines, generally tailored to specific domains, languages and/or clients.<br />
<br />
Pros and cons<br />
There are two limitations to MT:<br />
– they make mistakes (incorrect translations), and<br />
– quality of wording is patchy (some parts good, others unnatural or even nonsensical)<br />
<br />
On they positive side they are virtually instantaneous and many are free.<br />
<br />
Best suited for:<br />
Getting the general idea of what a text says.<br />
<br />
This method should never be relied on when high accuracy and/or good quality wording is needed.<br />
<br />
<br />
<br />
31. Machine Translation plus Human Editing (PEMT)<br />
What is it?<br />
A machine translation subsequently edited by a human translator or editor (often called Post-editing Machine Translation = PEMT).<br />
<br />
The editing process is designed to rectify some of the deficiencies of a machine translation.<br />
<br />
This process can take different forms, with different desired outcomes. Probably most common is a ‘light editing’ process where the editor ensures the text is understandable, without trying to fix quality of expression.<br />
<br />
Pros and cons<br />
This method won’t necessarily eliminate all translation mistakes. That’s because the program may have chosen a wrong word (meaning) that wasn’t obvious to the editor.<br />
<br />
And wording won’t generally be as good as a professional human translator would produce.<br />
<br />
Its advantage is it’s generally quicker and a little cheaper than a full translation by a professional translator.<br />
<br />
Best suited for:<br />
Translations for information purposes only.<br />
<br />
Again, this method shouldn’t be used when full accuracy and/or consistent, natural wording is needed.<br />
<br />
<br />
<br />
32. Human Translation<br />
What is it?<br />
Translation by a professional human translator.<br />
Pros and cons<br />
Professional translators should produce translations that are fully accurate and well-worded.<br />
<br />
That said, there is always the possibility of ‘human error’, which is why translation companies like us typically offer an additional review process – see next method.<br />
<br />
This method will take a little longer and likely cost more than the PEMT method.<br />
<br />
Best suited for:<br />
Most if not all translation purposes.<br />
<br />
<br />
<br />
33. Human Translation + Revision<br />
What is it?<br />
A human translation with an additional review by a second translator.<br />
<br />
The review is essentially a safety check – designed to pick up any translation errors and refine wording if need be.<br />
<br />
Pros and cons<br />
This produces the highest level of translation quality.<br />
<br />
It’s also the most expensive of the 4 methods, and takes the longest.<br />
<br />
Best suited for:<br />
All translation purposes.<br />
<br />
Gearwheel with 5 practical translation methods written on the teeth <br />
There’s also one other common term used by practitioners and academics alike to describe a type (method) of translation:<br />
<br />
34. Computer-Assisted Translation (CAT)<br />
What is it?<br />
A human translator using computer tools to aid the translation process.<br />
<br />
Key features<br />
Virtually all translators use such tools these days.<br />
<br />
The most prevalent tool is Translation Memory (TM) software. This creates a database of previous translations that can be accessed for future work.<br />
<br />
TM software is particularly useful when dealing with repeated and closely-matching text, and for ensuring consistency of terminology. For certain projects it can speed up the translation process.<br />
<br />
<br />
<br />
The translation methods described by academia<br />
A great deal has been written within academia analysing how human translators go about their craft.<br />
<br />
Seminal has been the work of Newmark, and the following methods of translation attributed to him are widely discussed in the literature.Gearwheel with Newmark's 8 translation methods written on the teeth <br />
These methods are approaches and strategies for translating the text as a whole, not techniques for handling smaller text units, which we discuss in our final translation category.<br />
<br />
<br />
<br />
35. Word-for-word Translation<br />
This method translates each word into the other language using its most common meaning and keeping the word order of the original language.<br />
<br />
So the translator deliberately ignores context and target language grammar and syntax.<br />
<br />
Its main purpose is to help understand the source language structure and word use.<br />
<br />
Often the translation will be placed below the original text to aid comparison.<br />
<br />
<br />
<br />
36. Literal Translation<br />
Words are again translated independently using their most common meanings and out of context, but word order changed to the closest acceptable target language grammatical structure to the original.<br />
<br />
Its main suggested purpose is to help someone read the original text.<br />
<br />
<br />
<br />
37. Faithful Translation<br />
Faithful translation focuses on the intention of the author and seeks to convey the precise meaning of the original text.<br />
<br />
It uses correct target language structures, but structure is less important than meaning.<br />
<br />
<br />
<br />
38. Semantic Translation<br />
Semantic translation is also author-focused and seeks to convey the exact meaning.<br />
<br />
Where it differs from faithful translation is that it places equal emphasis on aesthetics, ie the ‘sounds’ of the text – repetition, word play, assonance, etc.<br />
<br />
In this method form is as important as meaning as it seeks to “recreate the precise flavour and tone of the original” (Newmark).slide showing definition of semantic translation as a translation method<br />
<br />
39. Communicative Translation<br />
Seeks to communicate the message and meaning of the text in a natural and easily understood way.<br />
<br />
It’s described as reader-focused, seeking to produce the same effect on the reader as the original text.<br />
<br />
A good comparison of Communicative and Semantic translation can be found here.<br />
<br />
40. Free Translation<br />
Here conveying the meaning and effect of the original are all important.<br />
<br />
There are no constraints on grammatical form or word choice to achieve this.<br />
<br />
Often the translation will paraphrase, so may be of markedly different length to the original.<br />
<br />
41. Adaptation<br />
Mainly used for poetry and plays, this method involves re-writing the text where the translation would otherwise lack the same resonance and impact on the audience.<br />
<br />
Themes, storylines and characters will generally be retained, but cultural references, acts and situations adapted to relevant target culture ones.<br />
<br />
So this is effectively a re-creation of the work for the target culture.<br />
<br />
42. Idiomatic Translation<br />
Reproduces the meaning or message of the text using idioms and colloquial expressions and language wherever possible.<br />
<br />
The goal is to produce a translation with language that is as natural as possible.<br />
<br />
Translation Category D: 9 types of translation based on the translation technique used<br />
These translation types are specific strategies, techniques and procedures for dealing with short chunks of text – generally words or phrases.<br />
<br />
They’re often thought of as techniques for solving translation problems.<br />
<br />
They differ from the translation methods of the previous category which deal with the text as a whole.<br />
9 translation techniques as titles of books in a bookcase<br />
<br />
43. Borrowing<br />
What is it?<br />
Using a word or phrase from the original text unchanged in the translation.<br />
<br />
Key features<br />
With this procedure we don’t translate the word or phrase at all – we simply ‘borrow’ it from the source language.<br />
<br />
Borrowing is a very common strategy across languages. Initially, borrowed words seem clearly ‘foreign’, but as they become more familiar, they can lose that ‘foreignness’.<br />
<br />
Translators use this technique:<br />
– when it’s the best word to use – either because it has become the standard, or it’s the most precise term, or<br />
– for stylist effect – borrowings can add a prestigious or scholarly flavour.<br />
<br />
Borrowed words or phrases are often italicised in English.<br />
<br />
Examples of borrowings in English<br />
grand prix, kindergarten, tango, perestroika, barista, sampan, karaoke, tofu<br />
<br />
<br />
44. Transliteration<br />
What is it?<br />
Reproducing the approximate sounds of a name or term from a language with a different writing system.<br />
<br />
Key features<br />
In English we use the Roman (Latin) alphabet in common with many other languages including almost all European languages.<br />
<br />
Other writing systems include Arabic, Cyrillic, Chinese, Japanese, Korean, Thai, and the Indian languages.<br />
<br />
Transliteration from such systems into the Roman alphabet is also called romanisation.<br />
<br />
There are accepted systems for how individual letters/sounds should be romanised from most other languages – there are three common systems for Chinese, for example.<br />
<br />
English borrowings from languages using non-Roman writing systems also require transliteration – perestroika, sampan, karaoke, tofu are examples from the above list.<br />
<br />
Translators mostly use transliteration as a procedure for translating proper names.<br />
<br />
Examples<br />
毛泽东 Mao Tse-tung or Mao Zedong<br />
Владимир Путин Vladimir Putin<br />
서울 Seoul<br />
ភ្នំពេញ Phnom Penh<br />
<br />
45. Calque or Loan Translation<br />
What is it?<br />
A literal translation of a foreign word or phrase to create a new term with the same meaning in the target language.<br />
<br />
So a calque is a borrowing with translation if you like. The new term may be changed slightly to reflect target language structures.<br />
<br />
Examples<br />
German ‘Kindergarten’ has been calqued as детский сад in Russian, literally ‘children garden’ in both languages.<br />
<br />
Chinese 洗腦 ‘wash’ + ‘brain’ is the origin of ‘brainwash’ in English.<br />
<br />
English skyscraper is calqued as gratte-ciel in French and rascacielos in Spanish, literally ‘scratches sky’ in both languages.<br />
<br />
46. Word-for-word translation<br />
What is it?<br />
A literal translation that is natural and correct in the target language.<br />
<br />
Alternative names are ‘literal translation’ or ‘metaphrase’.<br />
<br />
Note: this technique is different to the translation method of the same name, which does not produce correct and natural text and has a different purpose.<br />
<br />
Key features<br />
This translation strategy will only work between languages that have very similar grammatical structures.<br />
<br />
And even then, only sometimes.<br />
<br />
For example, standard word order in Turkish is Subject-Object-Verb whereas in English it’s Subject-Verb-Object. So a literal translation between these two will seldom work:<br />
– Yusuf elmayı yedi is literally ‘Joseph the apple ate’.<br />
<br />
When word-for-word translations don’t produce natural and correct text, translators resort to some of the other techniques described below.<br />
Examples<br />
French ‘Quelle heure est-il?’ works into English as ‘What time is it?’.<br />
<br />
Russian ‘Oн хочет что-нибудь поесть’ is ‘He wants something to eat’.<br />
<br />
47. Transposition<br />
What is it?<br />
Translation with a change of grammatical structure.<br />
<br />
This technique gives the translation more natural wording and/or makes it grammatically correct.<br />
<br />
Examples<br />
A change in word order:<br />
Our Turkish example Yusuf elmayı yedi (literally ‘Joseph the apple ate’) –> Joseph ate the apple.<br />
<br />
Spanish La Casa Blanca (literally ‘The House White’) –> The White House<br />
<br />
A change in grammatical category:<br />
German Er hört gerne Musik (literally ‘he listens gladly [to] music’)<br />
= subject pronoun + verb + adverb + noun<br />
becomes Spanish Le gusta escuchar música (literally ‘[to] him [it] pleases to listen [to] music’)<br />
= indirect object pronoun + verb + infinitive + noun<br />
and English He likes listening to music<br />
= subject pronoun + verb + gerund + noun.<br />
<br />
48. Modulation<br />
What is it?<br />
Translation with a change of focus or point of view in the target language.<br />
<br />
This technique makes the translation more idiomatic – how people would normally say it in the language.<br />
<br />
Examples<br />
English talks of the ‘top floor’ of a building, French the dernier étage = last floor. ‘Last floor’ would be unnatural in English, so too ‘top floor’ in French.<br />
<br />
German uses the term Lebensgefahr (literally ‘danger to life’) where in English we’d be more likely to say ‘risk of death’.<br />
In English we’d say ‘I dropped the key’, in Spanish se me cayó la llave, literally ‘the key fell from me’. The English perspective is that I did something (dropped the key), whereas in Spanish something happened to me – I’m the recipient of the action.<br />
<br />
49. Equivalence or Reformulation<br />
What is it?<br />
Translating the underlying concept or meaning using a totally different expression.<br />
<br />
This technique is widely used when translating idioms and proverbs.<br />
<br />
And it’s common in titles and advertising slogans.<br />
<br />
It’s a common strategy where a direct translation either wouldn’t make sense or wouldn’t resonate in the same way.<br />
<br />
Examples<br />
Here are some equivalents of the English saying “Pigs may fly”, meaning something will never happen, or “you’re being unrealistic” (Source):<br />
– Thai: ชาติหน้าตอนบ่าย ๆ – literally, ‘One afternoon in your next reincarnation’<br />
– French: Quand les poules auront des dents – literally, ‘When hens have teeth’<br />
– Russian, Когда рак на горе свистнет – literally, ‘When a lobster whistles on top of a mountain’<br />
– Dutch, Als de koeien op het ijs dansen – literally, ‘When the cows dance on the ice’<br />
– Chinese: 除非太陽從西邊出來！– literally, ‘Only if the sun rises in the west’<br />
<br />
50. Adaptation<br />
What is it?<br />
A translation that substitutes a culturally-specific reference with something that’s more relevant or meaningful in the target language.<br />
<br />
It’s also known as cultural substitution or cultural equivalence.<br />
<br />
It’s a useful technique when a reference wouldn’t be understood at all, or the associated nuances or connotations would be lost in the target language.<br />
<br />
Note: the translation method of the same name is a similar concept but applied to the text as a whole.<br />
<br />
Examples<br />
Different cultures celebrate different coming of age birthdays – 21 in many cultures, 20, 15 or 16 in others. A translator might consider changing the age to the target culture custom where the coming of age implications were important in the original text.<br />
Animals have different connotations across languages and cultures. Owls for example are associated with wisdom in English, but are a bad omen to Vietnamese. A translator might want to remove or amend an animal reference where this would create a different image in the target language.<br />
<br />
51. Compensation<br />
What is it?<br />
A meaning or nuance that can’t be directly translated is expressed in another way in the text.<br />
Example<br />
Many languages have ways of expressing social status (honorifics) encoded into their grammatical structures.<br />
<br />
So you can convey different levels of respect, politeness, humility, etc simply by choosing different forms of words or grammatical elements.<br />
But these nuances will be lost when translating into languages that don’t have these structures.<br />
Then translating into languages that don’t have these structures<br />
Then translating into languages that don’t have these structures.<br />
<br />
===Conclusion ===<br />
<br />
In the light of above mentioned facts and figures here by we would say that machine translation is challenge for human translators because it can reduces the wokload of translation but can't give accurate and exact translation of the traget language.It can be less reliable than human translation..</div>Chen Huinihttps://bou.de/u/index.php?title=Machine_translation&diff=131924Machine translation2021-12-13T12:56:04Z<p>Chen Huini: /* 1.2 Definition of Pre-editing */</p>
<hr />
<div>'''Machine Translation - A challenge or a chance for human translators?'''<br />
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[[Machine_translation|Overview Page of Machine Translation]]<br />
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30 Chapters（0/30)<br />
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[[Machine_Trans_EN_1]] [[Machine_Trans_EN_2]] [[Machine_Trans_EN_3]] [[Machine_Trans_EN_4]] [[Machine_Trans_EN_5]] [[Machine_Trans_EN_6]] [[Machine_Trans_EN_7]] [[Machine_Trans_EN_8]] [[Machine_Trans_EN_9]] [[Machine_Trans_EN_10]] [[Machine_Trans_EN_11]] [[Machine_Trans_EN_12]] [[Machine_Trans_EN_13]] [[Machine_Trans_EN_14]] [[Machine_Trans_EN_15]] [[Machine_Trans_EN_16]] [[Machine_Trans_EN_17]] [[Machine_Trans_EN_18]] [[Machine_Trans_EN_19]] [[Machine_Trans_EN_20]] [[Machine_Trans_EN_21]] [[Machine_Trans_EN_22]] [[Machine_Trans_EN_23]] [[Machine_Trans_EN_24]] [[Machine_Trans_EN_25]] [[Machine_Trans_EN_26]] [[Machine_Trans_EN_27]] [[Machine_Trans_EN_28]] [[Machine_Trans_EN_29]] [[Machine_Trans_EN_30]] ...<br />
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[[Book_projects|Back to translation project overview]]<br />
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[[DCG-To-Do|To the To Do list]]<br />
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=Chapter 1:A Comparison Between the Quality of Machine Translation and Human Translation——A Case Study of the Application of artificial intelligence in Sports Events=<br />
'''论机器翻译与人工翻译的质量对比——以人工智能在体育赛事领域的应用为例'''<br />
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卫怡雯 Wei Yiwen, Hunan Normal University, China<br />
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[[Machine_Trans_EN_1]]<br />
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=Chapter 3：On the Realm Advantages And Mutual Development of Machine Translation And Huamn Translation)=<br />
"'论机器翻译与人工翻译的领域优势及共同发展'"<br />
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肖毅瑶 Xiao Yiyao, Hunan Normal University, China<br />
[[Machine_Trans_EN_3]]<br />
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=Chapter 4 : A Comparison Between Machine Translation of Netease and Traditional Human Translation—A Case Study of The Economist Articles)= <br />
'''网易有道机器翻译与人工翻译的译文对比——以经济学人语料为例'''<br />
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王李菲 Wang Lifei, Hunan Normal University<br />
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[[Machine_Trans_EN_4]]<br />
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=Chapter 5: Muhammad Saqib Mehran - Problems in translation studies=<br />
[[Machine_Trans_EN_5]]<br />
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=6 徐敏赟(Machine Translation Based on Neural Network --Challenge or Chance?)=<br />
[[Machine_Trans_EN_6]]<br />
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=Chapter 7: The Cultivation of artificial intelligence and translation talents in the Belt and Road Initiative=<br />
[[Machine_Trans_EN_7]]<br />
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一带一路背景下人工智能与翻译人才的培养<br />
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颜莉莉 Yan Lili, Hunan Normal University, China<br />
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=Chapter 8: On Machine Translation Under Lanuguage Intelligence——An Option and Opportunity for Human Translators=<br />
'''论语言智能下的机器翻译——译者的选择与机遇'''<br />
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颜静 Yan Jing, Hunan Normal University, China<br />
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[[Machine_Trans_EN_8]]<br />
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=9 谢佳芬（ Machine Translation and Artificial Translation in the Era of Artificial Intelligence）=<br />
[[Machine_Trans_EN_9]]<br />
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=10 熊敏(Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts)=<br />
[[Machine_Trans_EN_10]]<br />
===Abstract===<br />
The history of machine translation can be traced to 1940s, undergoing germination, recession, revival and flourish. Nowadays, with the rapid development of information technology, machine translation technology emerged and is gradually becoming mature. Whether manual translation can be replaced by machine translation becomes a widely-disputed topic. So in order to explore the ability of machine translation, I adopts two versions of translation, which are manual translation and machine translation(this paper uses Youdao translation) for different types of texts(according to Peter Newmark's types of text：informative text, expressive text and vocative text). The results are quite different in terms of quality and accuracy. The results show that machine translation is more suitable for informative text for its brevity, while the expressive texts especially literary works and vocative texts are difficult to be translated, because there are too many loaded words and cultural images in expressive text and dependence on the readers. To draw a conclusion, the relationship between machine translation and manual translation should be complementary rather than competitive.<br />
<br />
===Key words===<br />
machine translation; manual translation; Newmark's type of texts<br />
<br />
===题目===<br />
Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts<br />
<br />
===摘要===<br />
机器翻译的历史可以追溯到上个世纪40年代，经历了萌芽期、萧条期、复兴期和繁荣期四个阶段。如今，随着信息技术的高速发展，机器翻译技术日益成熟，于是机器翻译能不能替代人工翻译这个话题引起了广泛热议。为了探究机器翻译的能力水平是否足以替代人工翻译，本人根据皮特纽马克的文本类型分类理论（信息类文本，表达文本，呼吁文本），选择了相应的译文类型，并且将其机器翻译的版本以及人工翻译的版本进行对比。结果发现，就质量和准确度而言，译文的水平大相径庭。因为其简洁的特性，机器比较适合翻译信息文本。而就表达文本，尤其是文学作品，因其存在文化负载词及相应的文化现象，所以机器翻译这类文本存在难度。而呼唤文本因其目的性以及对读者的依赖性较强，翻译难度较大。由此得知，机器翻译和人工翻译的关系应该是互补的，而不是竞争的。<br />
<br />
===关键词===<br />
机器翻译；人工翻译；纽马克文本类型<br />
<br />
===1. Introduction===<br />
====1.1.Introduction to machine translation====<br />
Machine translation, also known as computer translation is a technique to translating through machine translator, such as Google translation and Youdao translation. Machine translation is one of the branches of computational linguistics, ranging from computer science, statistics, information science and so on. Machine translation plays an important role in all aspects.<br />
Machine translation can be traced back to 1940s, when British engineer Booth and American engineer Weaver proposed using computer to translate and started to study machines used for translation. And the first machine translating system launched in 1954, used in English and Russian translation. And this means machine translation becomes a reality. However, the arrival of new things always accompanies barriers and setbacks. In the 1960s, reports from ALPAC (Automated Language Processing Advisory Committee) showed studies on machine translation had stagnated for a decade. At that time, due to the high cost of machine, choosing human translators to finish translating works was more economical for most companies. What’s worse, machine had difficulty in understanding semantic and pragmatic meanings. Fortunately, in the 1970s, with the advance and popularity of computer, machine translation was gradually back on track. With the development of science and technology, machine translation has better technological guarantee, such as artificial intelligence and computer technology. In the last decades, from the late 90s till now, machine translation is becoming more and more mature. The initial rule-oriented machine translation has been updated and Corpus-aided machine translation appears. And nowadays, technology in voice recognition has been further developed. This technique is frequently applied in speech translation products. Users only need to speak source language to the online translator and instantly it will speak out the target version. But machine can’t fully provide precise and accurate translation without any grammatical or semantic problems. Machine can understand the literal meaning of texts, but not the meaning in context. For example, there is polysemy in English, which refers to words having multiple meanings. So when translating these words, translators should take contexts into consideration. But machine has troubles in this way. In addition, machine has no feeling or intention. Hence, it is also difficult to convey the feelings from the source language.<br />
<br />
====1.2.Process of machine translation====<br />
The process of manual translation is different from that of machine translation. Here is the process of the former. (1) Understand source language. (2) Use target language to organize language. (3) Generate translation. Unlike manual translation, machine translation tends to analyze and code source language first, then look for related codes in corpus, and work out the code that represents target language, generating translation. But they share a common feature, which is that Lexicon, grammatical rules and syntactic structure are taken into consideration. This is one of the biggest challenges for machine translation.<br />
<br />
===2.Theorectical framework===<br />
====2.1Newmark’s text typology====<br />
Text typology is a theory from linguistics. It undergoes several phrases. Karl Buhler, a German psychologist and linguist defines communicative functions into expressive, information and vocative. Then Roman Jakobson proposes categorization of texts, which are intralingual translation, interlingual translation and intersemiotic translation. Accordingly, he defines six main functions of language, including the referential function, conative function, emotive function, poetic function, metalingual function and the phatic function. Based on the two theories, Peter Newmark, a translation theorist, summarizes the language function into six parts: the informative function, the vocative function, the expressive function, the phatic function, the aesthetic function, and the metalingual function. Later, he further divided texts into informative type, expressive text and vocative type according to the linguistic functions of various texts. <br />
The core of the informative texts is the truth. It is to convey facts, information, knowledge of certain topics, reality and theories. The language style of the text is objective, logical and standardized. Reports, papers, scientific and technological textbooks are all attributed to informative texts. Translators are required to take format into account for different styles.<br />
The core of the expressive text is the sender’s emotions and attitudes. It is to express sender’s preferences, feelings, views and so on. The language style of it is subjective. Imaginative literature, including fictions, poems and dramas, autobiography and authoritative statements belong to expressive text. It requires translators to be faithful to the source language and maintain the style.<br />
The core of the vocative text is readership. It is to call upon readers to act, think, feel and react in the way intended by the text. So it is reader-oriented. Such texts as advertisement, propaganda and notices are of vocative text. Newmark noted that translators need to consider cultural background of the source language and the semantic and pragmatic effect of target language. If readers can comprehend the meaning at once and do as the text says, then the goal of information communication is achieved.<br />
To draw a conclusion, informative texts focus on the information and facts. Expressive texts center on the mind of addressers, including feelings, prejudices and so on. And vocative texts are oriented on readers and call on them to practice as the texts say.<br />
<br />
====2.2Study method====<br />
Manual translations of the three texts are selected from authoritative versions and universally acknowledged. And machine translations of those come from Youdao Translator. And in this thesis I will compare and evaluate the two methods in word diction, sentence structure, word order and redundancy.<br />
<br />
===3.Comparison and analysis of machine translation and manual translation ===<br />
====3.1Informative text ====<br />
（1）English into Chinese<br />
<br />
①Source language:<br />
<br />
Keep the tip of Apple Pencil clean, as dirt and other small particles may cause excessive wear to the tip or damage the screen of i-pad.<br />
<br />
Target language:<br />
<br />
Machine translation: Apple Pencil笔尖应保持清洁，灰尘等小颗粒可能会导致笔尖过度磨损或损坏ipad屏幕。<br />
<br />
Manual translation: 保持Apple Pencil铅笔的笔尖干净，因为灰尘和其他微粒可能会导致笔尖的过度磨损或损坏iPad屏幕。<br />
<br />
Analysis: Here is the instruction of Apple Pencil. And the manual translation is the Chinese version on the instruction.Product instruction tends to be professional, since there are many terms for some concepts. Machine can easily identify these terms and provide related words to translate. The machine version is faithful and expressive to the source language. So it is well-qualified and readable for readers to understand the instruction. So we can use machine to translate informative text.<br />
<br />
②Source language:<br />
<br />
China on Saturday launched a rocket carrying three astronauts-two men and one woman - to the core module of a future space station where they will live and work for six months, the longest orbit for Chinese astronauts.<br />
<br />
Target language:<br />
<br />
Machine translation: 周六，中国发射了一枚运载三名宇航员(两男一女)的火箭，进入未来空间站的核心舱，他们将在那里生活和工作6个月，这是中国宇航员最长的轨道。<br />
<br />
Manual translation: 周六，中国发射了一枚搭载三名宇航员(两男一女)的火箭，进入未来空间站的核心舱，他们将在那里生活和工作6个月，这是中国宇航员最漫长的一次轨道飞行。<br />
<br />
Analysis: This is a news from Reuters, reporting that China has launched a rocket.The meaning of the two translations is almost the same, except for some word diction. But there are some details dealt with different choice. For example, the last sentence of the machine translation is a bit of obscure and direct. There are some ambiguous words and expressions.<br />
<br />
(2)Chinese into English<br />
<br />
Source language:湖南省博物馆是湖南省最大的历史艺术类博物馆，占地面积4.9万平方米，总建筑面积为9.1万平方米，是首批国家一级博物馆，中央地方共建的八个国家级重点博物馆之一、全国文化系统先进集体、文化强省建设有突出贡献先进集体。<br />
<br />
Target language:<br />
Manual translation: As the largest history and art museum in Hunan province, the Hunan Museum covers an area of 49,000㎡, with the building area reaching 91,000㎡. It is one of the first batch of national first-level museums and one of the first eight national museums co-funded by central and local governments.<br />
<br />
Machine translation: Museum in hunan province is one of the largest historical art museum in hunan province, covers an area of 49000 square meters, a total construction area of 91000 square meters, is the first national museum, the central place to build one of the eight national key museum, national cultural system advanced collectives, strong culture began with outstanding contribution of advanced collective.<br />
<br />
Analysis: Machine translation is not faithful enough in content. For instance, “首批国家一级博物馆” is translated into “first national museum”, which is not the meaning of the source language. And there are some obvious grammar mistakes in the machine translation. For example, machine translates it into just one sentence but there are multiple predicates in it. So it is not grammatically permissible. What’s more, the sentence structure of machine translation is confusing and the focus is not specific enough.<br />
<br />
====3.2Expressive text ====<br />
(1)English into Chinese<br />
<br />
Source language:<br />
<br />
An individual human existence should be like a river- small at first, narrowly contained within its banks, and rushing passionately past rocks and over waterfalls. Gradually the river grows wider, the banks recede, the waters flow more quietly, and in the end, without any visible breaks, they become merged in the sea, and painlessly lose their individual being.<br />
<br />
Target language:<br />
<br />
Machine translation: 一个人的存在应该像一条河流——开始很小，被紧紧地夹在两岸中间，然后热情奔放地冲过岩石，飞下瀑布。渐渐地，河面变宽，两岸后退，水流更加平缓，最后，没有任何明显的停顿，它们汇入大海，毫无痛苦地失去了自己的存在。<br />
<br />
Manual translation:人生在世，如若河流；河口初始狭窄，河岸虬曲，而后狂涛击石，飞泻成瀑。河道渐趋开阔，峡岸退去，水流潺缓，终了，一马平川，汇于大海，消逝无影。<br />
<br />
Analysis: Here is a well-known metaphor in the prose How to Grow Old written by Bertrand Russell. The manual translation is written by Tian Rongchang.This is a philosophical prose with graceful language. Literary translation is a most important and difficult branch of translation. Translator should focus on the literal meaning, culture, writing style and so on. It is a combination of beauty and elegance. Therefore, translators find it in a dilemma of beauty and faithfulness, let alone translating machine. Compared with manual translation, machine translation has difficulty in word choice. It is faithful and expressive, but not elegant enough.<br />
<br />
(2)Chinese into English<br />
<br />
Source language:没有一个人将小草叫做“大力士”，但是它的力量之大，的确是世界无比。这种力，是一般人看不见的生命力，只要生命存在，这种力就要显现，上面的石块，丝毫不足以阻挡。因为它是一种“长期抗战”的力，有弹性，能屈能伸的力，有韧性，不达目的不止的力。<br />
<br />
Target language:<br />
<br />
Machine translation: No one calls the little grass "hercules", but its power is truly matchless in the world. This force is invisible life force. As long as there is life, this force will show itself. The stone above is not strong enough to stop it. Because it is a "long-term resistance" of the force, elastic, can bend and extend force, tenacity, not to achieve the purpose of the force.<br />
<br />
Manual translation: Though nobody describes the little grass as a “husky”, yet its herculean strength is unrivalled. It is the force of life invisible to naked eye. It will display itself so long as there is life. The rock is utterly helpless before this force- a force that will forever remain militant, a force that is resilient and can take temporary setbacks calmly, a force that is tenacity itself and will never give up until the goal is reached. (by Zhang Peiji)<br />
<br />
Analysis:This is the excerpt of a well-known Chinese prose written by Xia Yan. It is written during the war of Resistance Against Japan. So the prose holds symbolic meaning, eulogizing the invisible tenacious vitality so as to encourage Chinese to have confidence in the anti-aggression war. Compared with manual translation, machine translation is much more abstract and confusing, especially for the word diction. For example, “大力士” is translated into “hercules” which is a man of exceptional strength and size in Greek and Roman Mythology, making it difficult to understand if readers of target language have no idea of the allusion. What’s worse, the machine version doesn’t reveal the symbolic meaning of the text, which is the core of this prose.<br />
<br />
====3.3Vocative text ====<br />
<br />
(1)English into Chinese<br />
<br />
①Source language:<br />
<br />
iPhone went to film school, so you don’t have to. (Advertisement of iPhone13)<br />
<br />
Target language:<br />
<br />
Machine translation: iPhone上的是电影学院，所以你不用去。<br />
<br />
Manual translation:电影专业课，iPhone同学替你上完了。<br />
<br />
Analysis：Here are advertisements of iPhone on Apple official website. There is a personification in the source language. It is used to stress the advancement and proficiency in camera, which is an appealing selling point to potential buyers. Compared with manual translation, machine translation is plain and not eye-catching enough for customers.<br />
<br />
②Source language: <br />
<br />
5G speed OMGGGGG<br />
<br />
Machine language: 5克的速度 OMGGGGG<br />
<br />
Manual translation:<br />
<br />
iPhone的5G 巨巨巨巨巨5G<br />
<br />
Analysis: The “G” in the source language is the unit of speed, standing for generation. However, it is mistaken as a unit of weight, representing gram in the machine translation. So the meaning is not faithful to the source language at all. As for manual translation, it complies with the source in form. Specifically speaking, five “G”s in the former complies with five characters “巨”in the latter. And the pronunciation of the two is similar. There are two layers of meaning for the 5 “G”s. One exclaims the fast speed of 5 generation network and the other new technology. In the manual version, “巨”can be used to show degree, meaning “quite” or “very”. <br />
<br />
③Source language: <br />
<br />
History, faith and reason show the way, the way of unity. We can see each other not as adversaries but as neighbors. We can treat each other with dignity and respect, we can join forces, stop the shouting and lower the temperature. For without unity, there is no peace, only bitterness and fury."<br />
<br />
Target language:<br />
<br />
Machine translation: 历史、信仰和理性指明了团结的道路。我们可以把彼此视为邻居，而不是对手。我们可以尊严地对待彼此，我们可以联合起来，停止大喊大叫，降低温度。因为没有团结，就没有和平，只有痛苦和愤怒。<br />
<br />
Manual translation:历史、信仰和理性为我们指明道路。那是团结之路。我们可以把彼此视为邻居，而不是对手。我们可以有尊严地相互尊重。我们可以联合起来，停止喊叫，减少愤怒。因为没有团结就没有和平，只有痛苦和愤怒<br />
<br />
Analysis: Speech is a way to propagate some activity in public. It is an art to inspire emotion of the audience. The source language is the excerpt of Joe Biden’s inaugural speech. The speech should be inspiring and logic. The machine translation has some misunderstanding. Taking the translation of “lower the temperature” for example, machine only translates its literal meaning, relating to the temperature itself, without considering the context. What’s more, it is less logic than the manual one. Therefore, it adds difficulty to inspire the audience and infect their emotion.<br />
<br />
===4.Common mistakes in machine translation ===<br />
<br />
====4.1 lexical mistakes ====<br />
<br />
Common lexical mistakes include misunderstandings in word category, lexical meaning and emotive and evaluative meaning. Misunderstanding in word category shows in the classification of word in the source language. As for misunderstanding in lexical meaning, machine has difficulty in precisely reflecting the meaning of the original texts, due to different cultural background and different language system. And for misunderstanding in emotive meaning, machine has no intention and emotion like human-beings. Therefore, it’s impossible for it to know writers’ feelings and their writing purposes. So sometimes, it may translate something negative into something positive.<br />
<br />
====4.2 grammatical mistakes====<br />
<br />
Grammatical analysis plays an important part in translation. Normally speaking, every language has its own unique grammatical rules. So in the process of translation, if translators don’t know the formation rule well, the sentence meaning will be affected. Even though all the lexical meanings are well-known by translators, the lack of consciousness of grammaticality makes it harder to arrange words according to sequential rule. English tends to be hypotactic, while Chinese tends to be paratactic. English sentences are connected through syntactic devices and lexical devices. While Chinese sentences are semantically connected, which means there are limited logical words and connection words in Chinese. So when translating English sentence, we should first analyze its grammaticality and logical structure and then rearrange its sequence. However, online translating machine has troubles in grammatical analysis, which makes its improvement more difficult.<br />
<br />
====4.3 other mistakes====<br />
<br />
The two mistakes above are the internal ones. Apart from mistakes in linguistic system, there are some mistakes in other aspects, such as cultural background.<br />
<br />
===5.Reasons for its common mistakes ===<br />
<br />
====5.1 Difference in two linguistic system====<br />
<br />
With different history, English and Chinese have different ways of expression. Commonly speaking, English is synthetic language which expresses grammatical meaning through inflection such as tense and Chinese is analytic language which expresses grammatical meaning through word order and function word. In addition, English is more compact with full sentences. Subordinate sentence is one of the most important features in modern English. Chinese, on the other hand, is more diffusive with minor sentences.<br />
<br />
====5.2 Difference in thinking patterns and cultural background====<br />
<br />
According to Sapir-Whorf’s Hypothesis, our language helps mould our way of thinking and consequently, different languages may probably express their unique ways of understanding the world. For two different speech communities, the greater their structural differentiations are, the more diverse their conceptualization of the world will be. For example, western culture is more direct and eastern culture more euphemistic. What’s more, English culture tends to be individualism, focusing on detail, through which it reflects the whole, while Chinese culture tends to be collective. Different thinking patterns will add difficulty for machine to translate texts.<br />
<br />
====5.3 Limitation of computer====<br />
<br />
Recently, there are some breakthroughs and innovation in machine translation. However, due to its own limitation, online translation has limitation in some ways. Firstly, compared with machine, human brain is much more complicated, consisting of ten billions of neuron, each of which has different function to affect human’s daily activities and help humans avoid some errors. However, computer can only function according to preset programming has no intention or consciousness. Until now, countless related scholars have invested much time in machine translation. They upload massive language database, which include almost all linguistic rules. But computers still fail to precisely reflect the meaning of source language for many times due to the complexity and flexibility of language. On the other hand, computers can’t take context into consideration. During translation, it is often the case that machine chooses the most-frequently used meaning of one word. So without the correct and exact meaning, readers are easier to feel confused and even misunderstand the meaning of source language.<br />
<br />
===6.Conclusion===<br />
From the analysis above, we can draw a conclusion that machine deals with informative text best, followed by non-literary translation of expressive text. What’s more, machine can be a useful tool to get to know the gist and main idea of a specific topic, for the simple sentence structure and numerous terms. And it can improve translating efficiency with high speed. But machine has difficulty in translating literary works, especially proses and poems.<br />
<br />
Machine translation has mixed future. From the perspective of commercial, machine translation boasts a bright future. With the process of globalization, the demand for translation is increasing accordingly. On one hand, if we only depend on human translator to deal with translating works, the quality and accuracy of translation can be greatly affected. On the other hand, if machine is used properly to do some basic work, human translators only need to make preparation before translating, progress, polish and other advanced work, contributing to highly-qualified translation and high working efficiency.<br />
<br />
However, compared with manual translation, machine translation has a bleak future. It is still impossible for machine to replace interpreter or translator in a short term. With intelligence and initiative, humans are able to learn new knowledge constantly, which machine will never accomplish. Besides, machine is not used to replace translators but to assist them in work. In other words, translators and machine carry out their own duties and they are not incompatible.<br />
<br />
To draw a conclusion, although there are certain limitations of machine translation, it can serve as a catalyst for translating works. Therefore, with the rapid development of artificial intelligence and related technology, there are still many opportunities for machine translation.<br />
<br />
===Reference ===<br />
<br />
Cui Zihan 崔子涵.机器翻译译文质量对比——以谷歌翻译和DeepL为例[J] [Comparison among Machine Translation--Taking Google Translation and Deepl for Example].Overseas English 海外英语,2021(15):182-183.<br />
<br />
Li Deyi 李德毅. (2018). 人工智能导论 [Introduction to Artificial Intelligence]. Beijing: China Science and Technology Press 中国科学技术出版社.<br />
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Qiu Quanju 仇全菊.大数据时代背景下机器翻译及其发展趋势[J][Machine Translation and its Development Trend under the Background of Big Data Era]. English Teachers 英语教师,2021,21(16):60-62.<br />
<br />
Zhuo Jianbin 卓键滨,Liu Wenxian 刘文娴,Peng Zili 彭子莉.机器翻译对各类型文本的德汉翻译能力探究[J][Research on the German Chinese Translation Ability of Machine Translation for Various Types of Texts]. Comparative Study of Cultural innovation 文化创新比较研究,2021,5(28):122-125.<br />
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(英) Peter Newmark A Textbook of Translation[M] Shanghai Foreign Education Press, 2002<br />
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Wang Hongqi 王红旗. (2008). 语言学概论 [Introduction to Linguistics]. Beijing: Peking University Press 北京大学出版社.<br />
<br />
Liu Qin刘琴.功能目的论对于不同文本类型的翻译解读[J][Analysis of Translations in Different Types of Text based on Functionalist Approaches].Overseas Engliosh 海外英语,2021(17):8-9.<br />
<br />
Zhang Peiji 张培基.英译中国现代散文选[M][Selected Modern Chinese Prose Writings]. Shanghai Foreign Languages Education Press 上海外语教育出版社, 2002.<br />
<br />
Chen Cheng陈诚.机器翻译技术的综述[J][Overview of Machine Translation Technology].Electronic Techonology 电子技术,2021,50(11):290-291.<br />
<br />
He Xinyu何馨宇.机器翻译的发展及其对翻译职业化的影响研究[J] [The Development of Machine Translation and its Effect on Professional Transltors].Overseas English 海外英语,2021(20):48-49.<br />
<br />
He Wen 何雯, Wang Xiufeng 王秀峰.信息型文本的在线机器翻译错误研究[J][Research on Errors in Online Machine Translation of Informative text ].Overseas English海外英语,2021(15):188-189.<br />
<br />
Li Hanji 李晗佶. (2021). 人工智能时代翻译技术与译者关系演变与重构 [Evolution and reconstruction of the relationship between translation technology and translators in the era of artificial intelligence]. 西华师范大学学报(哲学社会科学版) Journal of West China Normal University (PHILOSOPHY AND SOCIAL SCIENCES EDITION) (2021-12-04) 1-6.<br />
<br />
Wei Guang魏光. 人工翻译与机器翻译译文编辑比较研究[J][Comparative Study of Translation Editing between Manual Translation and Machine Translation]. Overseas English 海外英语,2021(19):18-19+21.<br />
<br />
=Chapter 11 陈惠妮=Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts=<br />
<br />
机器翻译的译前编辑研究——以医学类文摘为例<br />
<br />
陈惠妮 Chen Huini, Hunan Normal University<br />
<br />
[[Machine_Trans_EN_11]]<br />
<br />
===Abstract===<br />
At present, globalization is accelerating and the market demand for language services is rapidly increasing . Machine translation, as an important translation method, can greatly improve translation efficiency due to its low cost and high speed. However, because of the limitations of machine translation and the differences between Chinese and English language, machine translation is not accurate enough. In order to balance translation efficiency and translation quality, a great number of manual revisions in translation are required for the machine translating texts. Medical papers are specialized, special and purposeful, so it requires accurate,qualified and professional translation. However, the quality of translations by machine is inefficient to meet the high-quality requirements of medical papers translation. Therefore, the introduction of pre-editing can greatly improve the efficiency and quality of machine translation.<br />
<br />
===Key words===<br />
Pre-editing, Machine translation, Medical texts<br />
<br />
===题目===<br />
Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts<br />
<br />
===摘要===<br />
在全球化加速发展的今天，市场对语言服务的需求迅速增加。机器翻译作为一种重要的翻译途径，由于其成本低、速度快，可以大大提高翻译效率。然而，由于机器翻译的局限性以及中英文语言的差异，机器翻译的准确性不高。为了平衡翻译效率和翻译质量，机器翻译文本需要大量的手工修改。<br />
医学论文具有专业性、特殊性和目的性，要求其译文准确、合格、专业。然而，机器翻译的质量较低，无法满足医学论文对翻译的高质量要求。因此，译前编辑的引入可以大大提高机器翻译的效率和质量。<br />
<br />
===关键词===<br />
译前编辑；机器翻译；医学文本<br />
<br />
===1. Introduction===<br />
===1.1 Definition of Machine Translation===<br />
As Cronin(2013) revealed: "Translation is undergoing a revolutionary upheaval. The influence of digital technology and the Internet on translation is continuous, extensive and profound. From the popularity of automatic online translation applications, translation revolution is everywhere (Cronin,2013) However,the concept of machine translation was firstly proposed in the 1930s. Since 1940s, the machine translation technology has been evolving from rule-based machine translation (RBMT) to statistical machine translation (SMT), and to neural machine translation (NMT). Machine translation refers to the automatic translation of source language into target language by using a computer system. That is, machine translation refers to the automatic translation of text from one language into another natural language by computer software or other online translation webs. Machine translation is also defined as the process of “using a computer system to automatically translate text or speech from one natural language to another” according to the definition by ISO (Cui 2014:68-73).O 'Brien (2002) defines it as "the behavior of modifying errors in machine translation to ensure that the target translation meets certain quality requirements". On the basic level, machine translation performs mechanical substitution of words in one language for words in another language, but that rarely produces a good translation, therefore, recognition of the whole phrases and their closest counterparts in the target language is needed. Not all words in one language have equivalents in another language, and many words have more than one meaning. A huge demand for translation is greatly needed in today’s global world, which creates new opportunities for the development of machine translation, attracts more and more attention and becomes one of the current research focuses.<br />
<br />
===1.2 Definition of Pre-editing===<br />
Pre-editing means to adjust and modify the source language to make it fit more with the characteristics of the machine translation software before putting the source language before the into machine translation, so as to improve the quality of the translation machine translation (Wei Changhong 2008:93-94). Pre-editing is to modify the original text before putting it into machine translation software, in order to improve the recognition rate of machine translation, optimize the output quality of translated text and reduce the workload of post-editing. Because pre-translation editing only needs to be modified in one language, the operation is simpler than post-translation editing, which can realize the double improvement of quality and efficiency. A good pre-editing translation can help machine translation more smoothly, thus improving the machine readability and quality of the output translation. Pre-editing is mostly applied in the following situations: one is when the original text is of poor quality and the machine is difficult to recognize the meaning of the sentence, such as user generated content with poor readability and translatability (Gerlach et al 2003:45-53); Documents that need to be published in multiple languages; Next is when the original text contains a lot of jargon; The last is the original text has a corresponding translation memory bank. If the original text is edited, it can better match the content of the translation memory bank. Pre-editing is a process of identifying problems. It requires to pre-edit the source texts before putting it into machine translation according to the requirements, listing the expressions or sentences that may have trouble in machine translation and then pre-edit it by human. The purpose is to enable the computer to translate better, improve the translatability of machine translation. (Slype G V & Guinet J F & Seitz F 1984:115)<br />
<br />
===1.3 Machine Translation Mode===<br />
According to the different knowledge acquisition methods, machine translation modes can be classified as follows: one is rule-based machine translation, which is based on bilingual dictionaries and a library of language rules for each language. The quality of translation depends on whether the source language conforms to the existing rules, but the inexhaustible rules are the hinders of this model. The second mode is machine translation based on statistics. This translation model relies on the principles of mathematics and statistics to find various existing translations corresponding to the translation tasks through the employment of corpus, analyzing the frequency of their occurrence and selecting the translation with the highest frequency for output. The disadvantage of this translation model is that it ignores the flexibility of language and the importance of context. The last one is neural network language model. This model is different from previous translation models in that it uses end-to-end neural network to realize automatic translation between natural languages. At present, the quality of its translation is much higher than that of the previous translation models.<br />
===1.4 Source of Study Abstracts===<br />
The core medical journals from domestic are selected in order to make the paper more representative and authoritative, such as National Medical Journal of China, Journal of Peking University (Health Science), and Journal of Third Military Medical University. All of them include basic medical science, biomedical technology, laboratory medical science and other fields. In this way, the pre-editing approaches included are applicable enough to machine translation of medical abstracts.<br />
===1.5 Selection of Translation Software===<br />
Generally, machine translation software includes Google Translation, Youdao Translator and Niu Translation, which has their own special use in translation. Take Google as an example, it is more like neurons in human brain, enabling to learn and collect information to establish connections with its neural machine translator’s neurons. However, it also causes many errors because of the lack of enough information. In this paper, contrastive analysis will be carried on by using Google translation. On the one hand, being a pioneer in the translation of NET, it is inevitably to sue Google as the translation software to translate medical texts in this paper. Comparing the program called “Google brain”, other NMT of translation software are relatively disadvantages. On the other hand, the Google translation enjoys the largest users in the world, with its downloads of more than one billion. The output quality of Google translation is more correct and complete than other machine translation software. With years of development and improvement, Google Translation has been greatly promoted. In this paper, Chinese medical abstract will be automatically translated by Google translation, and then the translation output will be compared with the translation by human and the publication of English abstracts. The main purpose is to prove that the improvement and promotion of quality and accuracy in the medical abstracts will be obtained through the pre-editing approaches.<br />
<br />
===2.Language Characteristics and Error Division===<br />
Since Chinese and English are two different languages, it is quite neccessary to identify their own characteristics so as to better analyze and understand the two languages. Tytler (1978) argued in his Essay on the Principles of Translation that there are three principles of translation that in all the translation should give readers the same feelings as the source text, except for complete transcript of ideas. There actually also exit some mistakes of these two languages. So the following will make some clarrifications of these two languages to ensure more accurate translation.<br />
<br />
===2.1 Language Characteristics of Medical Abstracts===<br />
Chinese and English belong to different language systems, so there are differences in their language structure and the way users think of the languages. When using machine translation from Chinese to English, due to the unequal language levels, there will be many mistakes in the translation process, especially for ESP texts, such as medical papers. <br />
Here, these differences mainly refer to the linguistic characteristics of medical abstracts. In medical abstracts, it usually includes structured and unstructured abstracts. Although in different forms, they both describe the purpose, methods and conclusions of the research.<br />
In the method section of medial abstract, several Chinese sentences can be connected with commas, but each sentence may convey different information. In contrast, English sentences contain a great deal of information, but in order to ensure clarity, some modifiers need to be isolated and then reconstructed. However, in Chinese-English machine translation, a lot of information is put into the sentence because the machine segments the sentence on the basis of the full comma.<br />
In addition, subjectless sentence is used in the objective and method parts of Chinese medical abstracts. The subjectless sentence means the sentence without or free from subject and is usually employed in two contexts. The first is "needless to say". In Chinese sentences, it is common to omit the subject of the sentence. Chinese sentences can convey meanings by using incomplete sentence structures, so Chinese speakers can understand the meanings of the sentences even though the sentence subject is omitted. The second is "emphasis of action". In this context, subjectless sentences are used to describe behaviors, especially in the study of traditional Chinese medicine abstracts. Comparatively speaking, it should be avoided in English medical abstracts. Abstract sentences are more subjective when describing the learning process, while the essential requirement of English medical abstract is objectivity. From this point, sentences without subject should be avoided in English medical abstracts. Another feature is voice. Few words with passive meanings appear in Chinese abstracts. English sentences are more favoured in using passive voice. When translating from Chinese to English, passive voice should be used to make the contents objective, which is also the basic requirement of medical papers.<br />
These are the linguistic features of Chinese medical abstracts. There are great differences in sentence structure and expression between Chinese and English medical abstracts. These differences may reduce the accuracy of machine translation, so it is necessary to introduce pre-editing to edit the source text to ensure that the source text can be accurately recognized by the machine and fully translated into English.<br />
===2.2 Error Division of Machine Translation in Translating Abstracts of Medical Papers from Chinese to English===<br />
In this paper, errors in machine translation are listed out after analyzing. Some are due to the principles, such as statistical-based MT and NMT, employed by machine translation. Based on the original errors, they can be studied from two levels. One is from the macro-level, referring to mistakes caused by objective factors. These are not human factors, but the disadvantage of machine translation and the limitations of language. The limitations of machine translation are derived from the principles and models adopted and manifests itself as a reliance on reference sources. In this case, semantic ambiguity and textual incoherence may occur in the absence of reference sources.<br />
However, for ESP texts such as medical papers, the requirements for terms and sentence patterns are far beyond the existing corpora. For unfamiliar texts (that is, the corresponding texts cannot be found in the corpora). The quality of output translation will be relatively low, which is determined by the principles of machine translation. As mentioned above, machine translation has evolved over the past few decades from rules-based to corp-based statistics to NMT. However, there still exist some limitations in machine translation.<br />
Mistakes on micro-level are mainly caused by the variations and differences of linguistic structure between Chinese and English. Chinese is an implicit language, while English is an explicit one (Lian, 2010) To put it in another way, Chinese expression does not depend on language structures, while English does the opposite. This may result in mismatches between the original and machine translated sentences. This kind of error is mainly divided into two types: component fragment and component missing. For a medical paper, such errors are very serious. As a kind of ESP discourse, medical paper has the characteristics of fixed, objective and accurate language structure. In order to reproduce the characteristics of medical abstracts in translation, it is necessary to avoid errors at the level of words and sentences and pay attention to logic and consistency.<br />
Lexical errors here refer to the inconsistency of fixed expressions in the translation of terms. Although these terms are machine translation based on the corpus, the corpus may not be perfect for ESP texts such as medical papers. Therefore, fixed expressions are not used in term translation. This is also the most common mistake in machine translation (the term here includes but is not limited to nouns). For ESP texts, medical text in particular, the fixed expressions and the accuracy of terms are of great importance.<br />
<br />
===3.Approaches Proposed for Pre-editing ===<br />
Generally speaking, there is a close relationship between pre-editing and post-editing, both of which aim to convey information and ensure high or publishable translation quality. Proper pre-editing can improve the quality of machine translation in terms of adequacy and consistency. <br />
Complexity of natural language and people use language arbitrarily, bringing many difficulties to Chinese-English machine translation, Hu Qingping (2005:24) has proposed "the research of translation software and the development of the controlled language are the two directions to improve the quality of machine translation : the former aims at the difficulty in the natural language processing, the latter overcomes the arbitrariness of natural language". Feng Quangong and Gao Lin (2017: 63-68) put forward: "The writing principles of controlled language can be applied to pre-editing of machine translation. Pre-editing based on controlled language can effectively reduce the complexity and ambiguity of source text, improve the identifiability of machine translation (the translatability of source text itself), and thus reduce (fully) the workload of post-editing".<br />
The central task of pre-editing is to transform human-friendly content into machine-friendly content, so words and sentences need to be repositioned or even changed. Based on the analysis of the language characteristics of Chinese and English, the Chinese ideographic group should be split before the Chinese source text is input into the machine software to translate so that the sentence structure is complete which can be easily recognized by the machine translation software.<br />
===3.1 Extraction and Replacement of Terms in the Source Text===<br />
As a branch of applied English, medical English is the product of the combination of English language knowledge and medical knowledge. The terms in medical papers have the characteristics of fixed and complex language structure. Although Google Translation is based on a corpus, the language structure is not fixed due to the limited size of the corpus. Therefore, the following two steps should be followed before machine translation. The first is to extract terms and create a glossary, and then replace the Chinese terms in the source text with the corresponding English expressions in the glossary. Of course, the terms of extraction should be searched and verified according to the actual situation. All of these words are verified before they can be replaced. This method can not only ensure the accuracy of term translation, but also maintain the consistency of expression, especially when dealing with long texts.<br />
Example1<br />
Source abstract: 口腔白斑病癌变相关缺氧应答基因和微小RNA的芯片及表达验证。<br />
Google translation: Microarray detection/Chip detection and expression verification of hypoxia response genes and microRNAs related to oral leukoplakia canceration.<br />
Published translation:Transcriptome array screening and verification of oral leukoplakia carcinogenesis-related hypoxia-responsive gene and microRNA. <br />
Analysis: The expression “ Affymetrix GeneChip” empolyed in this thesis is a human transcription array for transcriptome array. In the source abstract, “芯片检测“ can be meant “screening” but not detection, so the proper and appropriate translation of these expression should be “transcription array screening”, but the Google translates it into “microarry detection of chip detection”. Therefore, term extraction is essential and inevitable before putting the source abstracts into the machine translation software.<br />
After pre-editing source abstracts: 对 oral leukoplakia carcinogenesis 相关 hypoxia-responsive gene 和微小RNA进行 transcriptome array screening 及表达验证。<br />
After pre-editing translation: Transcriptome array screening and expression- verification of hypoxia-responsive genes and microRNAs related to oral leukoplakia carcinogenesis.<br />
===3.2 Explication of Subordination===<br />
As mentioned above, the subordination of Chinese sentences is judged by certain specific words in machine translation . Chinese is a paratactic language, and sentences are often connected by internal logical relationships; while English depends on sentence structure, so sentences are often closely linked by various language forms. In order to improve the accuracy of machine translation, we can adjust the sentence structure and adjust the position of adjectives and their modifiers. <br />
Example 2<br />
Source abstracts:回顾性分析南京医科大学附属儿童医院中重度HIE患儿49例及同期就诊的无神经系统症状体征的足月新生儿为对照组31例的头颅磁共振成像（MRI）资料。<br />
Google translation: A retrospective analysis that the brain magnetic resonance imaging (MRI) data of 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University and full-term neonates with no neurological symptoms and signs during the same period were included in the control group.<br />
Published translation: A total of 49 children with moderate to severe HIE admitted to the children’s Hospital Affiliated to Nanjing Medical University were retrospectively analyzed. Cranial magnetic resonance imaging (MRI) date of 31 full-term neonates without neurological symptoms and signs who visited the hospital during the same period were recruited as the control group. <br />
Analysis: As the above example shows that “中重度HIE患儿” and “足月新生儿” in the source abstracts are from the Children’s Hospital Affiliated Nanjing Medical University. The Google translation shows that 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University. There is an error due to the misuse of subordination. There are some advice in order to solve the problem. That is, first to segment the sentence and then reconstruct the sentence. For instance, the Chinese expression “南京医科大学附属儿童医院” carries many modifiers, which requires to cut the modifiers and reconstruct the sentence. Therefore, the Chinese expression “南京医科大学附属儿童医院’can be divided into abstractions, leaving two sentences instead of one long sentence with modifiers..<br />
After pre-editing source abstracts: 对49例中重度HIE患儿以及31例无神经系统症状体征的足月新生儿的MRI资料进行回顾性分析。这些患者收治于南京医科大学儿童附属医院。<br />
After pre-editing translation: The MRI data of 49 children with moderate to severe HIE and 31 full-term newborns without neurological symptoms and signs were retrospectively analyzed. These patients were admitted to the Children’s Hospital of Nanjing Medical University.<br />
===3.3 Explication of Subject through Voice Changing===<br />
The extensive use of subjectless sentences are quite common in the Chinese abstracts, while English prefers to employ passive voice in the sentences so as to make them object and accurate. In order to take the characteristics of English sentences into great and careful consideration, the sentences written in passive voice in particular, it will be helpful for machine translation to find out the subject and reconstruct a sentence in passive voice (Wang Yan: 2008). The first is to discover the “doee” in a sentence and then is to reconstruct the sentence structure and put the “doee” before the verb. The last is to explicate the passive relation between the noun and the verb.<br />
Example 3<br />
Source abstract: 回顾性分析2018年1月至2020年12月解放军总医院第七医学中心收治的500例老年髋部骨折患者的资料。<br />
Google translation: A retrospective analysis of the data of 500 elderly patients with hip fractures admitted to the Seventh Medical Center of the PLA General Hospital from January 2018 to December 2020.<br />
Published translation: From January 2018 to December 2020, the data of 500 elderly patients with hip fracture treated in the Seventh Medical Center of PLA General Hospital were analyzed retrospectively.<br />
Analysis: The above example indicates that the “回顾性分析”in the Google Translate is a noun phrase, but the source abstracts actually describes an action or a behavior. The reason for such a mistake is that the machine can’t recognize the Chinese subjetless sentences. To find the subject of the sentence, the source abstracts can be revised and adjusted. Finding the “doee” is the first step, which refers to “500例老年髋部骨折患者的资料”in the source abstracts. And next is to put it in front of the verb, that is to place it in the beginning of the sentence. Last but not least, the passive voice should be explicated in the sentence. <br />
After pre-editing source abstract: 500例老年髋部骨折患者的资料被回顾性分析，他们在2018年1月之2020年12月期间收治于解放军总医院第七医学中心。<br />
After pre-editing translation: The data of 500 elderly hip fracture patients were retrospectively analyzed. They were admitted to the Seventh Medical Center of the PLA General Hospital between January 2018 and December 2020.<br />
===3.4 Relocation of Modifiers===<br />
Modifiers, including noun, adverbial and attributive are constantly employed in the Chinese medical papers in order to add information to subject and object in the sentence. By doing this, complicated sentences can be structured, thus causing obstacles to machine translation for recognizing this complex sentence structures when translating Chinese-English sentences. To make the machine translation successfully recognize the sentence structure, simplifying the sentence structure is quite necessary. The key is to moving the location of the modifiers and thus making the modifiers an independent sentence. In other words, the modifiers ought to be put before or behind the main part of the sentence to satisfy the common use of English. <br />
Usually, the modifiers in Chinese sentence can only be placed in front of the core word, while in English, modifiers are very flexible. It is all right to place the modifiers in front of or behind the major part of the sentences with adjective or a connective noun.<br />
Example 4<br />
Source abstracts: 利用DNA重组技术以pET-28a表达系统在E.coli BL21(DE3) 中重组表达Hepl。<br />
Google Translation: Hepl is recombined in E.coli BL21 (DE3) using DNA recombination technology with pET-28a expression system.<br />
Published translation: The recombinant Hcpl protein was expressed by using DNA recombination technology through pET-28a expression system in E. coli BL21 (De3).<br />
Analysis: The Chinese medical text includes two modifiers, “利用DNA”and “以pET-28a)表达系统”. These two modifiers will be translated by machine, which catches more attention to the two constituents. From the Google translation above, one failure is obvious that it misplaces the location of the two modifiers and presents it in not accurate form. To make it translate correctly by machine translation, dividing the sentences is very important so that the source abstracts can be correctly recognized by machine translation.<br />
After pre-editing source abstract: 利用DNA重组技术，Hcp1重组表达在E.coli BL21 (DE3)中， 通过pET-28a表达系统在E. coli BL21 (DE3)中。<br />
Google translation: Using DNA recombination technology, Hcp1 recombination is expressed in E.coli BL21 (DE3), via pET-28a expression system in E. coli BL21 (DE3).<br />
The second is the independence of modifiers, that is, modifiers can also be reconstructed into clauses to modify core words. Compared with English, There is no subordinate clause in Chinese, so redundant Chinese modifiers need to be reconstructed into subordinate clauses in Chinese-English translation to meet the characteristics of English. English, especially sentences with multiple modifiers. Otherwise, sentence structure may be confused, such as scattered modifiers of core words. To avoid this mistake, it is necessary to separate the modifier from the main part of the sentence. These modifiers should be reconstituted into clauses. Also, keep your sentences simple and easy to understand.<br />
===3.5 Proper Omission and Deletion of Category Words===<br />
Category words are commonly used in Chinese. Category words complement the meaning of words, including problems, positions, situations and jobs. Adding this supplementary word is more in line with Chinese custom. In many cases, it has no real meaning, so it can be omitted in translation. In Chinese, category words are frequently used. However, it is rarely used in English, which is one of the differences between Chinese and English. There are many kinds of category words. Considering objectivity and the fixed structure of language, redundant category words should be deleted in Chinese-English translation. In the general, the most commonly used category of words in the Chinese medical abstracts are "process", "behavior", and "situation". These categories of words hinder the language conversion of Chinese and English, resulting in redundancy. <br />
Example 5<br />
Source abstract: 探讨口腔白斑病癌变进程中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia response genes and associated microRNAs in the process of oral leukoplakia cancer was discussed.<br />
Published translation: To study the hypoxia response gene and microRNA (miRNA)expression profiles in the pathogenesis and progression of oral leukoplakia (OLK).<br />
Analysis: As the above example shows, the Chinese words “进程” belongs to a category word. However, the Chinese expression “癌变”contains the process, so the “进程” expression can be deleted before placing it into the machine translation, because the meaning of it has been overlapping between the expression “癌变”. Therefore, its place can be replaced with preposition “during”.<br />
After pre-editing source abstract: 探讨口腔白斑病癌变中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia responsive genes and miRNA in oral leukoplakia cancer was investigated.<br />
<br />
===Conclusion===<br />
After years of development, machine translation has made great progress. The accuracy of machine translation has been greatly improved in both text recognition and sentence pattern conversion. However, machine translation has its own limitations. In other words, it needs to rely on the parallel corpus as a reference source for improving its accuracy. ESP text, in particular, is harder to get the high quality by machine translation. <br />
As one of the research papers, the characteristics of medical abstracts are fixed language structures, objectivity and accuracy (Qin Yi:2004). Therefore, medical translation must be accurate, object and understandable to follow the specific demands of the medical paper. Being an important field in the human society, medical paper translation is on a great demand, which means that it needs a huge demand for human labor. However, with the machine translation promoting, it will be more efficient to translate medical papers combining the effort by human and machine. The improvement and development of machine translation requires the joint efforts of computer science, information science, statistics, linguistics and other academic circles to achieve more mature human-computer mutual assistance translation (Li Yafei, Zhang Ruihua : 2019).<br />
However, errors can occur during the process of machine translation of Chinese- English, because of the differences of the Chinese and English and the processing of the machine. Errors from the perspective of linguistic or grammar can affect the machine translation a lot. After division and recognition of errors, some pre-editing approaches are put forward to help the machine translation more accurate and readable, that are, extraction and replacement of terms in the source text, relocation of modifiers, explication of subordination, proper omission, deletion of category words and explication of subject through voice changing. <br />
The paper mainly focuses on the pre-editing machine translation by using medical papers as a case study. The errors of machine translation occurring in the translation of medical abstracts and pre-editing approaches for machine translation. The quality of machine translation of medical papers is greatly improved after employing the pre-editing methods. However, machine translation is not as flexible and accurate as human brain, so it is of importance to combine pre-editing and post-editing approaches with machine translation in order to produce more accurate, more object machine translation of medical papers.<br />
<br />
===References===<br />
[1]. Cronin, Michael (2013). Translation in the Digital Age[M]. New York&London: Routledge.<br />
<br />
[2]. GERLACH J, et al ( 2013). Combining Pre-editing and Post-editing to Improve SMT of User-generated Content[M]// Proceedings of MT Summit XIV Workshop on Post-editing Technology and Practice. 45-53.<br />
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[3]. Slype G V & Guinet J F & Seitz F (1984). Better Translation for Better Communication [M] .Oxford: Pergamon Press Ltd (U.K.), <br />
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[4]. O'Brien S (2002). Teaching Post-editing: A Proposal for Course Con-tent [EB/OL]. http://mt-archive. Info/EAMT-2002-0brien. Pdf.<br />
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[5]. Tytler, A. F. (1978). Essay On The Principles of Translation[M]. Amsterdam: JohnBenjamins Publishing.<br />
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[6] 崔启亮. (2014), 论机器翻译的译后编辑[J], 中国翻译, 035(006):68-73.<br />
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[7] 冯全功,高琳 (2017) 基于受控语言的译前编辑对机器翻译的影响[J]. 当代外语研究,(2): 63-68+87+110.<br />
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[8] 胡清平(2005). 机器翻译中的受控语言[J]. 中国科技翻译, (03): 24-27. <br />
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[9] 连淑能 (2010). 英汉对比研究增订本[M]. 北京:高等教育出版社.<br />
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[10] 黎亚飞,张瑞华 (2019). 机器翻译发展与现状[J]. 中国轻工教育,(5):38-45. <br />
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[11] 秦毅(2004),从翻译基本标准议医学英语的翻译[J]. 遵义医学院学报,27 (4): 421-423. <br />
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[12] 王燕 (2008). 医学英语翻译与写作教程[M]. 重庆:重庆大学出版社<br />
<br />
=12 蔡珠凤=(The Mistranslation of C-J Machine Translation of Political Statements)=<br />
[[Machine_Trans_EN_12]]<br />
===Abstract===<br />
Language is the main way of communication between people. With the continuous development of globalization, the scale of cross-border exchanges is also expanding. However, due to cultural differences and diversity, the languages of different countries and regions are very different, which seriously hinders people's communication. The demand for efficient and convenient translation tools is increasing. At the same time, with the development of network technology and artificial intelligence, recognition technology based on deep learning is more and more widely used in English, Japanese and other fields.<br />
<br />
===Key words===<br />
machine translation; political statements; mistranslation of C-J machine translation<br />
===题目===<br />
The Mistranslation of C-J Machine Translation of Political Statements<br />
===摘要===<br />
语言是人与人之间交流的主要方式。随着全球化的不断发展，跨境交流的规模也在不断扩大。然而，由于文化的差异和多样性，不同国家和地区的语言差异很大，这严重阻碍了人们的交流。对高效便捷的翻译工具的需求正在增加。同时，随着网络技术和人工智能的发展，基于深度学习的识别技术在英语、日语等领域的应用越来越广泛。<br />
<br />
===关键词===<br />
机器翻译；政治发言；政治发言中译日的误译<br />
===1. Introduction===<br />
===Introduction to machine translation===<br />
Machine translation, also known as automatic translation, is a process of using computers to convert one natural language (source language) into another natural language (target language). It is a branch of computational linguistics, one of the ultimate goals of artificial intelligence, and has important scientific research value.At the same time, machine translation has important practical value. With the rapid development of economic globalization and the Internet, machine translation technology plays a more and more important role in promoting political, economic and cultural exchanges.The development of machine translation technology has been closely accompanied by the development of computer technology, information theory, linguistics and other disciplines. From the early dictionary matching, to the rule translation of dictionaries combined with linguistic expert knowledge, and then to the statistical machine translation based on corpus, with the improvement of computer computing power and the explosive growth of multilingual information, machine translation technology gradually stepped out of the ivory tower and began to provide real-time and convenient translation services for general users.（Zhang 2019:5-6)<br />
<br />
=== C-J machine translation software===<br />
Today's online machine translation software includes Baidu translation, Tencent translation, Google translation, Youdao translation, Bing translation and so on. Google was the first company to launch the machine translation system, and Baidu was the first company to import the machine translation system in China. In addition, Tencent and Youdao have attracted much attention.Machine translation is the process of using computers to convert one natural language into another. It usually refers to sentence and full-text translation between natural languages. In order to continuously improve the translation quality, R & D personnel have added artificial intelligence technologies such as speech recognition, image processing and deep neural network to machine translation on the basis of traditional machine translation based on rules, statistics and examples.With the increase of using machine translation, the joint cooperation between manual translation and machine translation will also increase significantly in the future. What criteria should be used to evaluate the quality of machine translation? In the evolving field of machine translation, there is an urgent need to clarify the unsolvable questions and solved problems.<br />
<br />
===The history of machine translation===<br />
The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. alchuni put forward the idea of using machines for translation. In 1933, Soviet inventor П.П. Trojansky designed a machine to translate one language into another, and registered his invention on September 5 of the same year; However, due to the low technical level in the 1930s, his translation machine was not made. In 1946, the first modern electronic computer ENIAC was born. Shortly after that, W. weaver, an American scientist and A. D. booth, a British engineer, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947 when discussing the application scope of electronic computer. In 1949, W. Weaver published the translation memorandum, which formally put forward the idea of machine translation. After 60 years of ups and downs, machine translation has experienced a tortuous and long development path. The academic community generally divides it into the following four stages:<br />
<br />
Pioneering period（1947-1964）<br />
<br />
In 1954, with the cooperation of IBM, Georgetown University completed the English Russian machine translation experiment with ibm-701 computer for the first time, showing the feasibility of machine translation to the public and the scientific community, thus opening the prelude to the study of machine translation.<br />
It is not too late for China to start this research. As early as 1956, the state included this research in the national scientific work development plan. The topic name is "machine translation, the construction of natural language translation rules and the mathematical theory of natural language". In 1957, the Institute of language and the Institute of computing technology of the Chinese Academy of Sciences cooperated in the Russian Chinese machine translation experiment, translating 9 different types of more complex sentences.From the 1950s to the first half of the 1960s, machine translation research has been on the rise. The United States and the former Soviet Union, two superpowers, have provided a lot of financial support for machine translation projects for military, political and economic purposes, while European countries have also paid considerable attention to machine translation research due to geopolitical and economic needs, and machine translation has become an upsurge for a time. In this period, although machine translation is just in the pioneering stage, it has entered an optimistic period of prosperity.<br />
<br />
Frustrated period（1964-1975）<br />
<br />
In 1964, in order to evaluate the research progress of machine translation, the American Academy of Sciences established the automatic language processing Advisory Committee (Alpac Committee) and began a two-year comprehensive investigation, analysis and test.In November 1966, the committee published a report entitled "language and machine" (Alpac report for short), which comprehensively denied the feasibility of machine translation and suggested stopping the financial support for machine translation projects. The publication of this report has dealt a blow to the booming machine translation, and the research of machine translation has fallen into a standstill. Coincidentally, during this period, China broke out the "ten-year Cultural Revolution", and basically these studies also stagnated. Machine translation has entered a depression.<br />
<br />
convalescence（1975-1989）<br />
<br />
Since the 1970s, with the development of science and technology and the increasingly frequent exchange of scientific and technological information among countries, the language barriers between countries have become more serious. The traditional manual operation mode has been far from meeting the needs, and there is an urgent need for computers to engage in translation. At the same time, the development of computer science and linguistics, especially the substantial improvement of computer hardware technology and the application of artificial intelligence in natural language processing, have promoted the recovery of machine translation research from the technical level. Machine translation projects have begun to develop again, and various practical and experimental systems have been launched successively, such as weinder system Eurpotra multilingual translation system, taum-meteo system, etc.However, after the end of the "ten-year holocaust", China has perked up again, and machine translation research has been put on the agenda again. The "784" project has paid enough attention to machine translation research. After the mid-1980s, the development of machine translation research in China has further accelerated. Firstly, two English Chinese machine translation systems, ky-1 and MT / ec863, have been successfully developed, indicating that China has made great progress in machine translation technology.<br />
<br />
New period(1990 present)<br />
<br />
With the universal application of the Internet, the acceleration of the process of world economic integration and the increasingly frequent exchanges in the international community, the traditional way of manual operation is far from meeting the rapidly growing needs of translation. People's demand for machine translation has increased unprecedentedly, and machine translation has ushered in a new development opportunity. International conferences on machine translation research have been held frequently, and China has made unprecedented achievements. A series of machine translation software have been launched, such as "Yixing", "Yaxin", "Tongyi", "Huajian", etc. Driven by the market demand, the commercial machine translation system has entered the practical stage, entered the market and came to the users.Since the new century, with the emergence and popularization of the Internet, the amount of data has increased sharply, and statistical methods have been fully applied. Internet companies have set up machine translation research groups and developed machine translation systems based on Internet big data, so as to make machine translation really practical, such as "Baidu translation", "Google translation", etc. In recent years, with the progress of in-depth learning, machine translation technology has further developed, which has promoted the rapid improvement of translation quality, and the translation in oral and other fields is more authentic and fluent.<br />
<br />
===The problem of machine translation at present===<br />
Error is inevitable<br />
Many people have misunderstandings about machine translation. They think that machine translation has great deviation and can't help people solve any problems. In fact, the error is inevitable. The reason is that machine translation uses linguistic principles. The machine automatically recognizes grammar, calls the stored thesaurus and automatically performs corresponding translation. However, errors are inevitable due to changes or irregularities in grammar, morphology and syntax, such as sentences with adverbials after "give me a reason to kill you first" in Dahua journey to the West. After all, a machine is a machine. No one has special feelings for language. How can it feel the lasting charm of "the tenderness of lowering its head, like the shame of a water lotus? After all, the meaning of Chinese is very different due to the changes of morphology, grammar and syntax and the change of context. Even many Chinese people are zhanger monks - they can't touch their heads, let alone machines.<br />
Bottleneck<br />
In fact, no matter which method, the biggest factor affecting the development of machine translation lies in the quality of translation. Judging from the achievements, the quality of machine translation is still far from the ultimate goal.<br />
Chinese mathematician and linguist Zhou Haizhong once pointed out in his paper "fifty years of machine translation": to improve the quality of machine translation, the first thing to solve is the problem of language itself rather than programming; It is certainly impossible to improve the quality of machine translation by relying on several programs alone. At the same time, he also pointed out that it is impossible for machine translation to achieve the degree of "faithfulness, expressiveness and elegance" when human beings have not yet understood how the brain performs fuzzy recognition and logical judgment of language. This view may reveal the bottleneck restricting the quality of translation. <br />
It is worth mentioning that American inventor and futurist ray cozwell predicted in an interview with Huffington Post that the quality of machine translation will reach the level of human translation by 2029. There are still many disputes about this thesis in the academic circles.<br />
<br />
===2.Mistranslation of Chinese Japanese machine translation===<br />
===2.1Vocabulary mistranslation in Chinese Japanese machine translation===<br />
===2.1.1Mistranslation of proper nouns===<br />
In political materials, there are often political dignitaries' names, place names or a large number of proper nouns in the political field. The morphemes of such words are definite and inseparable. Mistranslation will make the source language lose its specific meaning. <br />
<br />
Japanese translation into Chinese Chinese translation into Japanese<br />
<br />
original text translation by Youdao reference translation original text translation by Youdao reference translation<br />
<br />
朱鎔基 朱基 朱镕基 栗战书 栗戰史書 栗戰書<br />
<br />
労安 劳安 劳安 李克强 李克強 李克強 <br />
<br />
筑紫哲也 筑紫哲也 筑紫哲也 习近平 習近平 習近平<br />
<br />
山口百惠 山口百惠 山口百惠 韩正 韓中 韓正<br />
<br />
田中角栄 田中角荣 田中角荣 王沪宁 王上海氏 王滬寧<br />
<br />
東条英機 东条英社 东条英机 汪洋 汪洋 汪洋<br />
<br />
毛沢东 毛泽东 毛泽东 赵乐际 趙樂南 趙樂際<br />
<br />
トウ・ショウヘイ 大酱 邓小平 江泽民 江沢民 江沢民<br />
<br />
周恩来 周恩来 周恩来<br />
<br />
クリントン 克林顿 克林顿<br />
<br />
The above table counts 18 special names in the two texts, and 7 machine translation errors. In terms of mistranslation, there are not only "战书" but also "戰史" and "史書" in Chinese. "沪" is the abbreviation of Shanghai. In other words, since "战书" and "沪" are originally common nouns, this disrupts the choice of target language in machine translation. Except Katakana interference (except for トウシゃウヘイ, most of the mistranslations appear in the new Standing Committee. It can be seen that the machine translation system does not update the thesaurus in time. Different from other words, people's names have particularity. Especially as members of the Standing Committee of the Political Bureau of the CPC Central Committee, the translation of their names is officially unified. In this regard, public figures such as political dignitaries, stars, famous hosts and important. In addition, the machine also translates Japanese surnames such as "タ二モト" (谷本), "アンドウ" (安藤) into "塔尼莫特" and "龙胆". It can be found that the language feature that Japanese will be written together with Chinese characters and Hiragana also directly affects the translation quality of Japanese Chinese machine translation. Japanese people often use Katakana pronunciation. For example, when Japanese people talk with Premier Zhu, they often use "二ーハウ" (Hello). However, the machine only recognizes the two pseudonyms "二" and "ハ" and transliterates them into "尼哈" , ignoring the long sound after "二" and "ハ".<br />
<br />
original text Translation by Youdao reference translation<br />
<br />
日美安全体制 日米の安全体制 日米安保体制<br />
<br />
中国共产党第十九次全国代表大会 中国共産党第19回全国代表大会 中国共産党第19回全国代表大会（第19回党大会）<br />
<br />
十八大 十八大 第18回党大会中国特色社会主义<br />
<br />
中国特色社会主義 中国の特色ある社会主義 第18回党大会<br />
<br />
中国共产党中央委员会 中国共産党中央委員会 中国共産党中央委員会<br />
<br />
中国共産党中央委員会十八届中共中央政治局常委 第18代中国共產党中央政治局常務委員 第18期中共中央政治局常務委員<br />
<br />
十八届中共中央政治局委员 18期の中国共產党中央政治局委員 第18期中共中央政治局委員<br />
<br />
十九届中共中央政治局常委 十九回中国共產党中央政治局常務委員 第19期中央政治局常務委員<br />
<br />
中共十九届一中全会 中国共產党第十九回一中央委員会 第19期中央委員会第1回全体会議<br />
<br />
The above table is a comparison of the original and translated versions of some proper nouns. As shown in the table, the mistranslation problems are mainly reflected in the mismatch of numerals + quantifiers, the wrong addition of case auxiliary word "の", the lack of connectors, the Mistranslation of abbreviations, dead translation, and the writing errors of Chinese characters. The following is a specific analysis one by one.<br />
<br />
"十八届中共中央政治局常委", "十八届中共中央政治局委员", "十九届中共中央政治局常委" and "中共十九届一中全会" all have quantifiers "届", which are translated into "代", "期" and "回" respectively. The meaning is vague and should be uniformly translated into "期"; Among them, the translation of the last three proper nouns lacks the Chinese conjunction "第". The case auxiliary word "の" was added by mistake in the translation of "十八届中共中央政治局委员" and "日美安全体制". The "中国共产党中央委员会" did not write in the form of Japanese Ming Dynasty characters, but directly used simplified Chinese characters.<br />
<br />
The full name of "中共十九届一中全会" is "中国共产党第十九届中央委员会第一次全体会议", in which "一" stands for "第一次", which is an ordinal word rather than a cardinal word. Machine translation does not produce a correct translation. The fundamental reason is that there are no "规则" for translating such words in machine translation. If we can formulate corresponding rules for such words (for example, 中共m'1届m2 中全会→第m1期中央委员会第m2回全体会议), the translation system must be able to translate well no matter how many plenary sessions of the CPC Central Committee.<br />
<br />
===2.1.2Mistranslation of Polysemy===<br />
original text Translation by Youdao reference translation<br />
<br />
スタジオ 摄影棚/工作室 直播现场/演播厅<br />
<br />
日中関係の話 中日关系的故事 就中日关系(话题)<br />
<br />
溝 水沟 鸿沟<br />
<br />
それでは日中の問題について質問のある方。 那么对白天的问题有提问的人。 关于中日问题的话题，举手提问。<br />
<br />
私たちのクラスは20人ちょっとですが、 我们班有20人左右， 我们班二十多人的意见统一很难，<br />
<br />
いろいろな意見が出て、まとめるのは大変です。 但是又各种各样的意见，总结起来很困难。 <br />
<br />
一体どうやって、13億人もの人をまとめているんですか。 到底是怎么处理13亿的人的呢？ 中国是怎样把13亿人凝聚在一起的？<br />
<br />
In the original text, the word "スタジオ" appeared four times and was translated into "摄影棚" or "工作室" respectively, but the context is on-site interview, not the production site of photography or film. "話" has many semantics, such as "说话", "事情", "道理", etc. In accordance with the practice of the interview program, Premier Zhu Rongji was invited to answer five questions on the topic of China Japan relations. Obviously, the meaning of the word "故事" is very abrupt. The inherent Japanese word "日中" means "晌午、白天". At the same time, it is also the abbreviation of the names of the two countries. The machine failed to deal with it correctly according to the context. "溝" refers to the gap between China and Japan, not a "水沟". The former "まとめる" appears in the original text with the word "意见", which is intended to describe that it is difficult to unify everyone's opinions. The latter "まとめている" also refers to unifying the thoughts of 1.3 billion people, not "处理". Therefore, it is more appropriate to use "统一" and "处理" in human translation, rather than "总结意见" or "处理意见".<br />
<br />
Mistranslation of polysemy has always been a difficult problem in machine translation research. The translation of each word is correct, but it is often very different from the original expression in the context. Zhang Zhengzeng said that ambiguity is a common phenomenon in natural language. Its essence is that the same language form may have different meanings, which is also one of the differences between natural language and artificial language. Therefore, one of the difficulties faced by machine translation is language disambiguation (Zhang Zheng, 2005:60). In this regard, we can mark all the meanings of polysemous words and judge which meaning to choose by common collocation with other words. At the same time, strengthen the text recognition ability of the machine to avoid the translation inconsistent with the current context. In this way, we can avoid the mistake of "大酱" in the place where famous figures such as Deng Xiaoping should have appeared in the previous political articles.<br />
<br />
===2.1.3Mistranslation of compound words===<br />
Multi class words refer to a word with two or more parts of speech, also known as the same word and different classes.<br />
<br />
original text Translation by Youdao reference translation<br />
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1998年江泽民主席曾经访问日本, 1998年の江沢民国家主席の日本訪問し、 1998年、江沢民総書記が日本を訪問し、かつ<br />
<br />
同已故小渊首相签署了联合宣言。 かつて同じ故小渕首相が署名した共同宣言 亡くなられた小渕総理と宣言に調印されました<br />
<br />
Chinese "同" has two parts of speech: prepositions and conjunctions: "故" has many parts of speech, such as nouns, verbs, adjectives and conjunctions. This directly affects the judgment of the machine at the source language level. The word "同" in the above table is used as a conjunction to indicate the other party of a common act. "故" is used as a verb with the semantic meaning of "死亡", which is a modifier of "小渊首相". The machine regards "同" as an adjective and "故" as a noun "原因", which leads to confusion in the structure and unclear semantics of the translation. Different from the polysemy problem, multi category words have at least two parts of speech, and there is often not only one meaning under each part of speech. In this regard, software R & D personnel should fully consider the existence of multi category words, so that the translation machine can distinguish the meaning of words on the basis of marking the part of speech, so as to select the translation through the context and the components of the word in the sentence. Of course, the realization of this function is difficult, and we need to give full play to the wisdom of R & D personnel.<br />
<br />
===2.2 Syntactic mistranslation in Chinese Japanese machine translation===<br />
===2.2.1Mistranslation of tenses===<br />
original text：History is written by the people, and all achievements are attributed to the people. <br />
<br />
Translation by Youdao：歴史は人民が書いたものであり、すべての成果は人民のためである。<br />
<br />
reference translation：歴史は人民が綴っていくものであり、すべての成果は人民に帰することとなります。<br />
<br />
The original sentence meaning is "历史是人民书写的历史" or "历史是人民书写的东西". When translated into Japanese, due to the influence of Japanese language habits, the formal noun "もの" should be supplemented accordingly. In this sentence, both the machine and the interpreter have translated correctly. However, the machine's recognition of "的" is biased, resulting in tense translation errors. The past, present and future will become "history", and this is a continuous action. The form translated as "~ ていく" not only reflects that the action is a continuous action, but also conforms to the tense and semantic information of the original text. In addition, the machine's treatment of the preposition "于" is also inappropriate. In the original text, "人民" is the recipient of action, not the target language. As the most obvious feature of isolated language, function words in Chinese play an important role in semantic expression, and their translation should be regarded as a focus of machine translation research.<br />
<br />
original text：李克强同志是十六届中共中央政治局常委,其他五位同志都是十六届中共中央政治局委员。<br />
<br />
Translation by Youdao：李克強総理は第16代中国共産党中央政治局常務委員であり、他の５人の同志はいずれも16期の中国共産党中央政治局委員である。<br />
<br />
reference translation：李克強同志は第16期中国共産党中央政治局常務委員を務め、他の５人は第16期中共中央政治局委員を務めました。<br />
<br />
Judging the verb "是" in the original text plays its most basic positive role, but due to the complexity of Chinese language, "是" often can not be completely transformed into the form of "だ / である" in the process of translation. This sentence is a judgment of what has happened. Compared with the 19th session, the 18th session has become history. This is an implicit temporal information. It is very difficult for machines without human brain to recognize this implicit information. "中共中央政治局常委" is the abbreviation of "中共中央政治局常务委员会委员" and it is a kind of position. In Japanese, often use it with verbs such as "務める","担当する",etc. The translator adopts the past tense of "務める", which conforms to the expression habit of Japanese and deals with the problem of tense at the same time. However, machine translation only mechanically translates this sentence into judgment sentence, and fails to correctly deal with the past information implied by the word "十八届".<br />
<br />
===2.2.2Mistranslation of honorifics===<br />
Honorific language is a language means to show respect to the listener. Different from Japanese, there is no grammatical category of honorifics in Chinese. There is no specific fixed grammatical form to express honorifics, self modesty and politeness. Instead, specific words such as "您", "请" and "劳驾" are used to express all kinds of respect or self modesty. <br />
<br />
Original text translation by Youdao reference translation<br />
<br />
女士们，先生们，同志们，朋友们 さんたち、先生たち、同志たち、お友达さん ご列席の皆さん<br />
<br />
谢谢大家！ ありがとうございます！ ご清聴ありがとうございました。<br />
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これはどうされますか。 这是怎么回事呢? 您将如何解决这一问题？<br />
<br />
こうした問題をどうお考えでしょうか。 我们会如何考虑这些问题呢？ 您如何看待这一问题？<br />
<br />
For example, for the processing of "女士们，先生们，同志们，朋友们", the machine makes the translation correspond to the original one by one based on the principle of unchanged format, but there are errors in semantic communication and pragmatic habits. When speaking on formal occasions, Chinese expression tends to be comprehensive and detailed, as well as the address of the audience. In contrast, Japanese usually uses general terms such as "皆様", "ご臨席の皆さん" or "代表団の方々" as the opening remarks. In terms of Japanese Chinese translation, the machine also failed to recognize the usage of Japanese honorifics such as "さ れ る" and "お考え". It can be seen that Chinese Japanese machine translation has a low ability to deal with honorific expressions. The occasion is more formal. A good translation should not only be fluent in meaning, but also conform to the expression habits of the target language and match the current translation environment.<br />
<br />
===Conclusion===<br />
In the process of discussing the Mistranslation of machine translation, this paper mainly cites the Mistranslation of Youdao translator. When I studied the problem of machine translation mistranslation, I also used a large number of translation software such as Google and Baidu for parallel comparison, and found that these translation software also had similar problems with Youdao translator. For example, the "新世界新未来" in Chinese ABAC structural phrases is translated by Baidu as "新し世界の新しい未来", which, like Youdao, is not translated in the form of parallel phrases; Google online translation translates the word "“全心全意" into a completely wrong "全面的に"; The original sentence "我吃了很多亏" in the Mistranslation of the unique expression in the language is translated by Google online as "私はたくさんの損失を食べました". Because the "吃" and "亏" in the original text are not closely adjacent, the machine can not recognize this echo and mistakenly treats "亏" as a kind of food, so the machine translates the predicate as "食べました"; Japanese "こうした問題をどう考えでしょうか", Google's online translation is "你如何看待这些问题?", "你" tone can not reflect the tone of Japanese honorific, while Baidu translates as "你怎么想这样的问题呢?" Although the meaning can be understood, it is an irregular spoken language. Google and Baidu also made the same mistakes as Youdao in the translation of proper nouns. For example, they translated "十七届(中共中央政治局常委)”" into "第17回..." .In short, similar mistranslations of Youdao are also common in Baidu and Google. Due to space constraints, they will not be listed one by one here.<br />
<br />
Mr. Liu Yongquan, Institute of language, Chinese Academy of Social Sciences (1997) It has been pointed out that machine translation is a linguistic problem in the final analysis. Although corpus based machine translation does not require a lot of linguistic knowledge, language expression is ever-changing. Machine translation based solely on statistical ideas can not avoid the translation quality problems caused by the lack of language rules. The following is based on the analysis of lexical, syntactic and other mistranslations From the perspective of the characteristics of the source language, this paper summarizes some difficulties of Chinese and Japanese in machine translation.<br />
<br />
(1) The difficulties of Chinese in machine translation <br />
<br />
Chinese is a typical isolated language. The relationship between words needs to be reflected by word order and function words. We should pay attention to the transformation of function words such as "的", "在", "向" and "了". Some special verbs, such as "是", "做" and "作", are widely used. How to translate on the basis of conforming to Japanese pragmatic habits and expressions is a difficulty in machine translation research. In terms of word order, Chinese is basically "subject→predicate→object". At the same time, Chinese pays attention to parataxis, and there is no need to be clear in expression with meaningful cohesion, which increases the difficulty of Chinese Japanese machine translation. When there are multiple verbs or modifier components in complex long sentences, machine translation usually can not accurately divide the components of the sentence, resulting in the result that the translation is completely unreadable. <br />
<br />
(2) Difficulties of Japanese in machine translation <br />
<br />
Both Chinese and Japanese languages use Chinese characters, and most machines produce the target language through the corresponding translation of Chinese characters. However, pseudonyms in Japanese play an important role in judging the part of speech and meaning, and can not only recognize Chinese characters and judge the structure and semantics of sentences. Japanese vocabulary is composed of Chinese vocabulary, inherent vocabulary and foreign vocabulary. Among them, the first two have a great impact on Chinese Japanese machine translation. For example "提出" is translated as "提出する" or "打ち出す"; and "重视" is translated as "重視する" or "大切にする". Japanese is an adhesive language, which contains a lot of "けど", "が" and "けれども" Some of them are transitional and progressive structures, but there are also some sequential expressions that do not need translation, and these translation software often can not accurately grasp them.<br />
<br />
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[9]Chen Bingchang 陈丙昌(2016).機械翻訳の誤訳分析【D】.Error analysis of mechanical translation.贵州大学.2016(05) <br />
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[10]Lv Yinqiu 呂寅秋(1996).機械翻訳の言語規則と伝統文法との相違点.【D】The language rules of mechanical translation, the traditional grammar, and the points of contradiction.日本学研究.Japanese Studies.1996(00):21-22 <br />
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[11]Liu Jun 刘君(2014).基于语料库的中日同形词词义用法对比及其日中机器翻译研究【D】.A Corpus-based Comparison of the Meanings of Chinese and Japanese Homographs and Research on Japanese-Chinese Machine Translation.广西大学.(03) <br />
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[12]Cun Qianqian 崔倩倩(2019).机器翻译错误与译后编辑策略研究【D】.Research on Machine Translation Errors and Post-Editing Strategies.北京外国语大学.(09) <br />
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[13]Zhang Yi 张义(2019).机器翻译的译文分析【D】.Translation analysis of machine translation.西安外国语大学.(10) <br />
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[14]Zhang Linqian 张琳婧(2019).在线机器翻译中日翻译错误原因及对策【D】.Causes and countermeasures of online machine translation errors in Chinese-Japanese translation.山西大学.(02)<br />
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[15]Wang Dan 王丹(2020).基于机器翻译的专利文本译后编辑对策研究【D】.Research on countermeasures for post-translational editing of patent texts based on machine translation.大连理工大学.(06)<br />
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[16]Yang Xiaokun 杨晓琨(2020).日中机器翻译中的前编辑规则与效果验证【D】.Pre-editing rules and effect verification in Japanese-Chinese machine translation.大连理工大学.(06)<br />
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[17]Zuo Jia 左嘉(2021). 机器翻译日译汉误译研究【D】. Research on Mistranslation of Machine Translation from Japanese to Chinese.北京第二外国语学院.<br />
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[18]Guan Biying 关碧莹(2018).关于政治类发言的汉日机器翻译误译分析【D】.Analysis of Chinese-Japanese Machine Translation Mistranslations of Political Speeches.哈尔滨理工大学.<br />
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[19]Che Tong 车彤(2021).汉译日机器翻译质量评估及译后编辑策略研究【D】.Research on Quality Evaluation of Chinese-Japanese Machine Translation and Post-translation Editing Strategies.北京外国语大学.(09)<br />
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Networking Linking<br />
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http://www.elecfans.com/rengongzhineng/692245.html<br />
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https://baike.baidu.com/item/%E6%9C%BA%E5%99%A8%E7%BF%BB%E8%AF%91/411793<br />
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=13 陈湘琼Chen Xiangqiong（Study on Post-editing from the Perspective of Functional Equivalence Theory ）=<br />
[[Machine_Trans_EN_13]]<br />
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=14 Bi bi Nadia（Machine Translation a Challenge for Human Translators)=<br />
[[Machine_Trans_EN_14]]<br />
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===Abstract===<br />
Machine translation is a big obstacle in the way of Human translators or interpretors although it is quick and less time consuming.people are trying to get translation of their target language using through source language.For this purpose they are using digital apps like Google translation Google translation does not give accurate and exact interpretation.Google translator is translates word to word translate that doesn't clarifies it's true and actual meaning.On the other hand human translators can give exact and accurate translation ,they take care of grammatical errors , diction and sentence structure.They clarify the purpose of target language through using source language.<br />
<br />
===Keywords===<br />
Descriptive translation, academic, interdiscipline, comparative literature, localization,Translatology,school of thought,translation , studies, linguistics, corresponding.<br />
<br />
===Body of article===<br />
Translation is the process of reworking text from one language into another to maintain the original message and communication. But, like everything else, there are different methods of translation, and they vary in form and function. What is translation? The term has become widely used among knowledge transfer researchers and practitioners, especially in the fields of health and health care. In a landmark review, Jonathan Lomas began to argue that 'The tasks… may be defined as to establish and maintain links between researchers and their audience, via the appropriate translation of research findings' (Lomas 1997, p 4). In 2004, the World Health Organization's World Report on Knowledge for Better Health suggested that 'One of the key contributions of research to health systems is the translation of knowledge into actions' (WHO 2004, p 33 and p 100). By 2006, special issues of WHO's Bulletin as well as the journals Evaluation and the Health Professions and the Journal of Continuing Education in the Health Professions were dedicated to translation.But what does 'translation' mean? It may be a new word for an old problem, meaning nothing more than 'transfer'. The rapidly emerging field of 'translational medicine' seems to take translation to mean generally what transfer might have meant, that is the transmission of knowledge and evidence 'from bench to bedside'. As one commentator has observed, "Translational medicine" as a fashionable term is being increasingly used to describe the wish of biomedical researchers to ultimately help patients' (Wehling 2008, abstract). <br />
Semantic uncertainty persists, not least because of the different interests of scientists, clinicians, patients and commercial firms (Littman et al 2007): 'Translational research means different things to different people, but it seems important to almost everyone' (Woolf 2008, p 211).<br />
In related fields, such as public health (Armstrong et al 2006), 'translation' seems to signify dissatisfaction with 'transfer'. It wants to move away from thinking of knowledge transfer as a form of technology transfer or dissemination, rejecting if only by implication its mechanistic assumptions and its model of linear messaging from A to B. But still, what does it signify?<br />
<br />
===Why translation?===<br />
'Translation' indicates a closer attention to the problem of shared meaning and how it might be developed. It seems to represent some new epistemological lubricant, facilitating the dissemination of texts and the application and use of the knowledge and information they in. Simply, translation might be the key to transfer. And yet, when we stop to think, we are more ambivalent. What is translated often seems somehow inferior, not real or original. Note how readily commentators reach for the idea that things might be 'lost in translation'. Knowing at a distance – made in and mediated by translation - makes for incomplete renditions, blurred images, partial truths. So what might 'translation' really mean? The purpose of this paper is to set out, for policy makers and practitioners, the theoretical and conceptual resources translation holds and seems to represent. In doing so, it explores understandings of translation in the fields of literature and linguistics and in the sociology of science and technology. It begins by setting out just why this idea of translation should make immediate, intuitive sense in relation to research, policy and practice.<br />
1translation in research, policy and practice research as translation<br />
Research often entails translation from one language to another: where data is collected from more than one ethnic group, for example, or where the language of the researcher is other than that of the research subject. It may draw on a secondary literature or source documents written in different languages, and may be published and disseminated in languages other than the one in which it is first written up.<br />
2In a different way, to conduct an interview is to ask for an account of experience and its meanings, but it is also to construct and translate that experience in terms defined at least in part by the researcher. In representing what is said, transcripts then select data, usually excluding significant gesture and eye-contact, for example. Often, certain characteristics of speech-acts (such as hesitations) will be edited out. In turn, the format of the transcript shapes the analytic use the researcher may make of it. The basis of research 'findings', then, is an artefact, a transcript or translation, not an original interaction (Ochs 1979, Barnes, Bloor and Henry 1996, Ross 2009).<br />
3In this way, the researcher recasts aspects of his or her problem or topic in new, scientific form: 'All researchers "translate" the experiences of others' (Temple 1997, p 609). Research is invariably conducted in a sort of 'metalanguage' (Hantrais and Ager 1985): the research process can be conceived as one of successive translations (from theoretical formulation to operationalization, transcription, interpretation and dissemination). Theorization is a process of reciprocal back and forth between theory and fact, in which conceptions of each are revised in order that one fit the other (Baldamus 1974).<br />
4 It is a kind of translation: a rereading, re-use, re-application or re-representation of what we know in new terms (Turner 1980). Referencing, too, is an act of translation, a form of appropriation and incorporation of one text by another (Gilbert 1977).policy as translation Each of the fields covered by the paper is diverse and ill-defined, and there is no intention here to provide a comprehensive account of any them. Sources have been chosen for their relevance: main references are cited in the text, and additional sources listed in footnotes.<br />
<br />
2For a brief introduction to the technical issues involved in social research in more than one language, see Birbili (2000). For translation issues in survey research and question design in general, see Ervin and Bower (1952) and Deutscher (1968); on translating survey research instruments (in this instance health-related quality of life measures), Bowden and Fox-Rushby (2003). On the use of translators and interpreters, see Temple (1997) and Jentsch (1998).<br />
3For an interesting discussion of this problem, see Bourdieu (1999), esp pp 621-626, 'The risks of writing'. <br />
===Difference between machine translation and human translators===<br />
Few people disagree on the differences between the two, but many argue over the quality of the translations. How accurate are machine translations? How reliable are human translations? Some say machine translation produces near-perfect translations while others are adamant that translations are incomprehensible and cause more problems than they solve. Results will, of course, vary depending on the source and target languages, the machine translation service used (e.g. Google Translate), and the complexity of the original text.<br />
Machine translation, love it or hate it, is here to stay. In fact, the machine translation market is growing at such a fast pace that it is predicted to reach $980 million by 2022.<br />
===Machine translation: the pros and cons===<br />
The advantages of machine translation generally come down to two factors: it’s faster and cheaper. The downside to this is the standard of translation can be anywhere from inaccurate, to incomprehensible, and potentially dangerous (more on that shortly).<br />
==The advantages of machine translation==<br />
Many free tools are readily available (Google Translate, Skype Translator, etc.)<br />
Quick turnaround time <br />
You can translate between multiple languages using one tool<br />
Translation technology is constantly improving<br />
The disadvantages of machine translation<br />
Level of accuracy can be very low <br />
Accuracy is also very inconsistent across different languages<br />
==Machines can't translate context ==<br />
Mistakes are sometimes costly <br />
Sometimes translation simply doesn’t work<br />
The most important thing to consider with any kind of translation is the cost of potential mistakes. Translating instructions for medical equipment, aviation manuals, legal documents and many other kinds of content require 100% accuracy. In such cases, mistakes can cost lives, huge amounts of money and irreparable damage to your company’s image. So choose carefully!<br />
Human translation: the pros and cons<br />
Human translation essentially switches the table in terms of pros and cons. A higher standard of accuracy comes at the price of longer turnaround times and higher costs. What you have to decide is whether that initial investment outweighs the potential cost of mistakes. Alternatively, whether mistakes simply aren’t an option, like the cases we looked at in the previous section.<br />
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==The advantages of human translation ==<br />
It’s a translator’s job to ensure the highest accuracy<br />
Humans can interpret context and capture the same meaning, rather than simply translating words<br />
Human translators can review their work and provide a quality process<br />
Humans can interpret the creative use of language, e.g. puns, metaphors, slogans, etc.<br />
Professional translators understand the idiomatic differences between their languages<br />
Humans can spot pieces of content where literal translation isn’t possible and find the most suitable alternative<br />
The disadvantages of human translation<br />
Turnaround time is longer <br />
Translators rarely work for free <br />
Unless you use a translation agency, with access to thousands of translators, you’re limited to the languages any one translator can work with<br />
Simply put, human translation is your best option when accuracy is even remotely important. Other considerations to make are the complexity of your source material and the two languages you’re translating between – both of which can render machines pretty useless.<br />
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When to use machine and human translation<br />
The truth is, the debate over machine vs human translation is an unnecessary distraction. What we should really be talking about is when to use these two different types of translation services, because they both serve a very valid purpose.<br />
<br />
Examples of when to use machine translation<br />
When you have a large bulk of content to translate and the general meaning is enough<br />
When your translation never reaches the final audience, e.g. you’re translating a resource as research for another piece of content<br />
Translating documents for internal use within a company, provided 100% accuracy isn’t needed<br />
To partially translate large chunks of content for a human translator to improve upon<br />
Examples of when to use human translation<br />
When accuracy is important<br />
Most cases where your translated content is received by a consumer audience<br />
When you have a duty of care to provide accurate translations (e.g. legal documents, product instructions, medical guidelines or health and safety content)<br />
When translating marketing material or other texts for creative language uses.<br />
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types of machine translation.<br />
<br />
What is Machine Translation? Rule Based Machine Translation vs. Statistical Machine Translation. Machine translation (MT) is automated translation. It is the process by which computer software is used to translate a text from one natural language (such as English) to another (such as Spanish).<br />
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To process any translation, human or automated, the meaning of a text in the original (source) language must be fully restored in the target language, i.e. the translation. While on the surface this seems straightforward, it is far more complex. Translation is not a mere word-for-word substitution. A translator must interpret and analyze all of the elements in the text and know how each word may influence another. This requires extensive expertise in grammar, syntax (sentence structure), semantics (meanings), etc., in the source and target languages, as well as familiarity with each local region.<br />
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Human and machine translation each have their share of challenges. For example, no two individual translators can produce identical translations of the same text in the same language pair, and it may take several rounds of revisions to meet customer satisfaction. But the greater challenge lies in how machine translation can produce publishable quality translations.<br />
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Rule-Based Machine Translation Technology<br />
Rule-based machine translation relies on countless built-in linguistic rules and millions of bilingual dictionaries for each language pair.<br />
The software parses text and creates a transitional representation from which the text in the target language is generated. This process requires extensive lexicons with morphological, syntactic, and semantic information, and large sets of rules. The software uses these complex rule sets and then transfers the grammatical structure of the source language into the target language.<br />
Translations are built on gigantic dictionaries and sophisticated linguistic rules. Users can improve the out-of-the-box translation quality by adding their terminology into the translation process. They create user-defined dictionaries which override the system’s default settings.<br />
In most cases, there are two steps: an initial investment that significantly increases the quality at a limited cost, and an ongoing investment to increase quality incrementally. While rule-based MT brings companies to the quality threshold and beyond, the quality improvement process may be long and expensive.<br />
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Statistical Machine Translation Technology<br />
Statistical machine translation utilizes statistical translation models whose parameters stem from the analysis of monolingual and bilingual corpora. Building statistical translation models is a quick process, but the technology relies heavily on existing multilingual corpora. A minimum of 2 million words for a specific domain and even more for general language are required. Theoretically it is possible to reach the quality threshold but most companies do not have such large amounts of existing multilingual corpora to build the necessary translation models. Additionally, statistical machine translation is CPU intensive and requires an extensive hardware configuration to run translation models for average performance levels.<br />
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Rule-Based MT vs. Statistical MT<br />
Rule-based MT provides good out-of-domain quality and is by nature predictable. Dictionary-based customization guarantees improved quality and compliance with corporate terminology. But translation results may lack the fluency readers expect. In terms of investment, the customization cycle needed to reach the quality threshold can be long and costly. The performance is high even on standard hardware.<br />
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Statistical MT provides good quality when large and qualified corpora are available. The translation is fluent, meaning it reads well and therefore meets user expectations. However, the translation is neither predictable nor consistent. Training from good corpora is automated and cheaper. But training on general language corpora, meaning text other than the specified domain, is poor. Furthermore, statistical MT requires significant hardware to build and manage large translation models.<br />
<br />
Rule-Based MT Statistical MT<br />
+ Consistent and predictable quality – Unpredictable translation quality<br />
+ Out-of-domain translation quality – Poor out-of-domain quality<br />
+ Knows grammatical rules – Does not know grammar <br />
+ High performance and robustness – High CPU and disk space requirements<br />
+ Consistency between versions – Inconsistency between versions <br />
– Lack of fluency + Good fluency<br />
– Hard to handle exceptions to rules + Good for catching exceptions to rules <br />
– High development and customization costs + Rapid and cost-effective development costs provided the required corpus exists<br />
Given the overall requirements, there is a clear need for a third approach through which users would reach better translation quality and high performance (similar to rule-based MT), with less investment (similar to statistical MT).<br />
Post-Edited Machine Translation (PEMT)<br />
Often, PEMT is used to bridge the gap between the speed of machine translation and the quality of human translation, as translators review, edit and improve machine-translated texts. PEMT services cost more than plain machine translations but less than 100% human translation, especially since the post-editors don’t have to be fluently bilingual—they just have to be skilled proofreaders with some experience in the language and target region.<br />
Successful translation is about more than just the words, which is why we advocate for not just human translation by skilled linguists, but for translation by people deeply familiar with the cultures they’re writing for. Life experience, study and the knowledge that only comes from living in a geographic region can make the difference between words that are understandable and language that is capable of having real, positive impact. <br />
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PacTranz<br />
The HUGE list of 51 translation types, methods and techniques<br />
Upper section of infographic of 51 common types of translation classified in 4 broad categoriesThere are a bewildering number of different types of translation.<br />
So we’ve identified the 51 types you’re most likely to come across, and explain exactly what each one means.<br />
This includes all the main translation methods, techniques, strategies, procedures and areas of specialisation.<br />
It’s our way of helping you make sense of the many different kinds of translation – and deciding which ones are right for you.<br />
Don’t miss our free summary pdf download later in the article!<br />
The 51 types of translation we’ve identified fall neatly into four distinct categories.<br />
Translation Category A: 15 types of translation based on the technical field or subject area of the text<br />
Icons representing 15 types of translation categorised by the technical field or subject area of the textTranslation companies often define the various kinds of translation they provide according to the subject area of the text.<br />
This is a useful way of classifying translation types because specialist texts normally require translators with specialist knowledge.<br />
Here are the most common types you’re like to come across in this category.<br />
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1. General Translation<br />
What is it?<br />
The translation of non-specialised text. That is, text that we can all understand without needing specialist knowledge in some area.<br />
The text may still contain some technical terms and jargon, but these will either be widely understood, or easily researched.<br />
What this means<br />
The implication is that you don’t need someone with specialist knowledge for this type of translation – any professional translator can handle them.<br />
Translators who only do this kind of translation (don’t have a specialist field) are sometimes referred to as ‘generalist’ or ‘general purpose’ translators.<br />
Examples<br />
Most business correspondence, website content, company and product/service info, non-technical reports.<br />
Most of the rest of the translation types in this Category do require specialist translators.<br />
Check out our video on 13 types of translation requiring special translator expertise:<br />
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2. Technical Translation<br />
What is it?<br />
We use the term “technical translation” in two different ways:<br />
Broad meaning: any translation where the translator needs specialist knowledge in some domain or area.<br />
This definition would include almost all the translation types described in this section.<br />
Narrow meaning: limited to the translation of engineering (in all its forms), IT and industrial texts.<br />
This narrower meaning would exclude legal, financial and medical translations for example, where these would be included in the broader definition.<br />
What this means<br />
Technical translations require knowledge of the specialist field or domain of the text.<br />
That’s because without it translators won’t completely understand the text and its implications. And this is essential if we want a fully accurate and appropriate translation.Good to know Many technical translation projects also have a typesetting/dtp requirement. Be sure your translation provider can handle this component, and that you’ve allowed for it in your project costings and time frames.<br />
Examples<br />
Manuals, specialist reports, product brochures<br />
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3. Scientific Translation<br />
What is it?<br />
The translation of scientific research or documents relating to it.<br />
What this means<br />
These texts invariably contain domain-specific terminology, and often involve cutting edge research.<br />
So it’s imperative the translator has the necessary knowledge of the field to fully understand the text. That’s why scientific translators are typically either experts in the field who have turned to translation, or professionally qualified translators who also have qualifications and/or experience in that domain.<br />
On occasion the translator may have to consult either with the author or other domain experts to fully comprehend the material and so translate it appropriately.<br />
Examples<br />
Research papers, journal articles, experiment/trial results<br />
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4. Medical Translation<br />
What is it?<br />
The translation of healthcare, medical product, pharmaceutical and biotechnology materials.<br />
Medical translation is a very broad term covering a wide variety of specialist areas and materials – everything from patient information to regulatory, marketing and technical documents.<br />
As a result, this translation type has numerous potential sub-categories – ‘medical device translations’ and ‘clinical trial translations’, for example.<br />
What this means<br />
As with any text, the translators need to fully understand the materials they’re translating. That means sound knowledge of medical terminology and they’ll often also need specific subject-matter expertise.<br />
Good to know<br />
Many countries have specific requirements governing the translation of medical device and pharmaceutical documentation. This includes both your client-facing and product-related materials.<br />
Examples<br />
Medical reports, product instructions, labeling, clinical trial documentation<br />
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5. Financial Translation<br />
What is it?<br />
In broad terms, the translation of banking, stock exchange, forex, financing and financial reporting documents.<br />
However, the term is generally used only for the more technical of these documents that require translators with knowledge of the field.<br />
Any competent translator could translate a bank statement, for example, so that wouldn’t typically be considered a financial translation.<br />
What this means<br />
You need translators with domain expertise to correctly understand and translate the financial terminology in these texts.<br />
Examples<br />
Company accounts, annual reports, fund or product prospectuses, audit reports, IPO documentation<br />
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6. Economic Translations<br />
What is it?<br />
1. Sometimes used as a synonym for financial translations.<br />
2. Other times used somewhat loosely to refer to any area of economic activity – so combining business/commercial, financial and some types of technical translations.<br />
3. More narrowly, the translation of documents relating specifically to the economy and the field of economics.<br />
What this means<br />
As always, you need translators with the relevant expertise and knowledge for this type of translation.<br />
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7. Legal Translation<br />
What is it?<br />
The translation of documents relating to the law and legal process.<br />
What this means<br />
Legal texts require translators with a legal background.<br />
That’s because without it, a translator may not:<br />
– fully understand the legal concepts<br />
– write in legal style<br />
– understand the differences between legal systems, and how best to translate concepts that don’t correspond.<br />
And we need all that to produce professional quality legal translations – translations that are accurate, terminologically correct and stylistically appropriate.<br />
Examples<br />
Contracts, legal reports, court judgments, expert opinions, legislation<br />
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8. Juridical Translation<br />
What is it?<br />
1. Generally used as a synonym for legal translations.<br />
2. Alternatively, can refer to translations requiring some form of legal verification, certification or notarization that is common in many jurisdictions.<br />
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9. Judicial Translation<br />
What is it?<br />
1. Most commonly a synonym for legal translations.<br />
2. Rarely, used to refer specifically to the translation of court proceeding documentation – so judgments, minutes, testimonies, etc. <br />
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10. Patent Translation<br />
What is it?<br />
The translation of intellectual property and patent-related documents.<br />
Key features<br />
Patents have a specific structure, established terminology and a requirement for complete consistency throughout – read more on this here. These are key aspects to patent translations that translators need to get right.<br />
In addition, subject matter can be highly technical.<br />
What this means<br />
You need translators who have been trained in the specific requirements for translating patent documents. And with the domain expertise needed to handle any technical content.<br />
Examples<br />
Patent specifications, prior art documents, oppositions, opinions<br />
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11. Literary Translation<br />
What is it?<br />
The translation of literary works – novels, short stories, plays, essays, poems.<br />
Key features<br />
Literary translation is widely regarded as the most difficult form of translation.<br />
That’s because it involves much more than simply conveying all meaning in an appropriate style. The translator’s challenge is to also reproduce the character, subtlety and impact of the original – the essence of what makes that work unique.<br />
This is a monumental task, and why it’s often said that the translation of a literary work should be a literary work in its own right.<br />
What this means<br />
Literary translators must be talented wordsmiths with exceptional creative writing skills.<br />
Because few translators have this skillset, you should only consider dedicated literary translators for this type of translation.<br />
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12. Commercial Translation<br />
What is it?<br />
The translation of documents relating to the world of business.<br />
This is a very generic, wide-reaching translation type. It includes other more specialised forms of translation – legal, financial and technical, for example. And all types of more general business documentation.<br />
Also, some documents will require familiarity with business jargon and an ability to write in that style.<br />
What this means<br />
Different translators will be required for different document types – specialists should handle materials involving technical and specialist fields, whereas generalist translators can translate non-specialist materials.<br />
Examples<br />
Business correspondence, reports, marketing and promotional materials, sales proposals<br />
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13. Business Translations<br />
What is it?<br />
A synonym for Commercial Translations.<br />
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14. Administrative Translations<br />
What is it?<br />
The translation of business management and administration documents.<br />
So it’s a subset of business / commercial translations.<br />
What this means<br />
The implication is these documents will include business jargon and ‘management speak’, so require a translator familiar with, and practised at, writing in that style.<br />
Examples<br />
Management reports and proposals<br />
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15. Marketing Translations<br />
What is it?<br />
The translation of advertising, marketing and promotional materials.<br />
This is a subset of business or commercial translations.<br />
Key features<br />
Marketing copy is designed to have a specific impact on the audience – to appeal and persuade.<br />
So the translated copy must do this too.<br />
But a direct translation will seldom achieve this – so translators need to adapt their wording to produce the impact the text is seeking.<br />
And sometimes a completely new message might be needed – see transcreation in our next category of translation types.<br />
What this means<br />
Marketing translations require translators who are skilled writers with a flair for producing persuasive, impactful copy.<br />
As relatively few translators have these skills, engaging the right translator is key.<br />
Good to know<br />
This type of translation often comes with a typesetting or dtp requirement – particularly for adverts, posters, brochures, etc.<br />
Its best for your translation provider to handle this component. That’s because multilingual typesetters understand the design and aesthetic conventions in other languages/cultures. And these are essential to ensure your materials have the desired impact and appeal in your target markets.<br />
Examples<br />
Advertising, brochures, some website/social media text.<br />
Translation Category B: 14 types of translation based on the end product or use of the translation<br />
This category is all about how the translation is going to be used or the end product that’s produced.<br />
Most of these types involve either adapting or processing a completed translation in some way, or converting or incorporating it into another program or format.<br />
You’ll see that some are very specialised, and complex.<br />
It’s another way translation providers refer to the range of services they provide.<br />
Check out our video of the most specialised of these types of translation:<br />
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16. Document Translations<br />
What is it?<br />
The translation of documents of all sorts.<br />
Here the translation itself is the end product and needs no further processing beyond standard formatting and layout.<br />
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17. Text Translations<br />
What is it?<br />
A synonym for document translation.<br />
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18. Certified Translations<br />
What is it?<br />
A translation with some form of certification.<br />
Key features<br />
The certification can take many forms. It can be a statement by the translation company, signed and dated, and optionally with their company seal. Or a similar certification by the translator.<br />
The exact format and wording will depend on what clients and authorities require – here’s an example.<br />
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19. Official Translations<br />
What is it?<br />
1. Generally used as a synonym for certified translations.<br />
2. Can also refer to the translation of ‘official’ documents issued by the authorities in a foreign country. These will almost always need to be certified.<br />
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20. Software Localisation<br />
What is it?<br />
Adapting software for another language/culture.<br />
Key features<br />
The goal of software localisation is not just to make the program or product available in other languages. It’s also about ensuring the user experience in those languages is as natural and effective as possible.<br />
Translating the user interface, messaging, documentation, etc is a major part of the process.<br />
Also key is a customisation process to ensure everything matches the conventions, norms and expectations of the target cultures.<br />
Adjusting time, date and currency formats are examples of simple customisations. Others might involve adapting symbols, graphics, colours and even concepts and ideas.<br />
Localisation is often preceded by internationalisation – a review process to ensure the software is optimally designed to handle other languages.<br />
And it’s almost always followed by thorough testing – to ensure all text is in the correct place and fits the space, and that everything makes sense, functions as intended and is culturally appropriate.<br />
Localisation is often abbreviated to L10N, internationalisation to i18n.<br />
What this means<br />
Software localisation is a specialised kind of translation, and you should always engage a company that specialises in it.<br />
They’ll have the systems, tools, personnel and experience needed to achieve top quality outcomes for your product.<br />
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21. Game Localisation<br />
What is it?<br />
Adapting games for other languages and markets.<br />
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It’s a subset of software localisation.<br />
Key features<br />
The goal of game localisation is to provide an engaging and fun gaming experience for speakers of other languages.<br />
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It involves translating all text and recording any required foreign language audio.<br />
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But also adapting anything that would clash with the target culture’s customs, sensibilities and regulations.<br />
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For example, content involving alcohol, violence or gambling may either be censored or inappropriate in the target market.<br />
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And at a more basic level, anything that makes users feel uncomfortable or awkward will detract from their experience and thus the success of the game in that market.<br />
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So portions of the game may have to be removed, added to or re-worked.<br />
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Game localisation involves at least the steps of translation, adaptation, integrating the translations and adaptations into the game, and testing.<br />
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What this means<br />
Game localisation is a very specialised type of translation best left to those with specific expertise and experience in this area.<br />
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22. Multimedia Localisation<br />
What is it?<br />
Adapting multimedia for other languages and cultures.<br />
<br />
Multimedia refers to any material that combines visual, audio and/or interactive elements. So videos and movies, on-line presentations, e-Learning courses, etc.<br />
Key features<br />
Anything a user can see or hear may need localising.<br />
<br />
That means the audio and any text appearing on screen or in images and animations.<br />
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Plus it can mean reviewing and adapting the visuals and/or script if these aren’t suitable for the target culture.<br />
<br />
The localisation process will typical involve:<br />
– Translation<br />
– Modifying the translation for cultural reasons and/or to meet technical requirements<br />
– Producing the other language versions<br />
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Audio output may be voice-overs, dubbing or subtitling.<br />
<br />
And output for visuals can involve re-creating elements, or supplying the translated text for the designers/engineers to incorporate.<br />
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What this means<br />
Multimedia localisation projects vary hugely, and it’s essential your translation providers have the specific expertise needed for your materials.<br />
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23. Script Translations<br />
What is it?<br />
Preparing the text of recorded material for recording in other languages.<br />
Key features<br />
There are several issues with script translation.<br />
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One is that translations typically end up longer than the original script. So voicing the translation would take up more space/time on the video than the original language.<br />
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Sometimes that space will be available and this will be OK.<br />
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But generally it won’t be. So the translation has to be edited back until it can be comfortably voiced within the time available on the video.<br />
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Another challenge is the translation may have to synchronise with specific actions, animations or text on screen.<br />
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Also, some scripts also deal with technical subject areas involving specialist technical terminology.<br />
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Finally, some scripts may be very culture-specific – featuring humour, customs or activities that won’t work well in another language. Here the script, and sometimes also the associated visuals, may need to be adjusted before beginning the translation process.<br />
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It goes without saying that a script translation must be done well. If it’s not, there’ll be problems producing a good foreign language audio, which will compromise the effectiveness of the video.<br />
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Translators typically work from a time-coded transcript. This is the original script marked to show the time available for each section of the translation.<br />
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What this means<br />
There are several potential pitfalls in script translations. So it’s vital your translation provider is practiced at this type of translation and able to handle any technical content.<br />
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24. Voice-over and Dubbing Projects<br />
What is it?<br />
Translation and recording of scripts in other languages.<br />
<br />
Voice-overs vs dubbing<br />
There is a technical difference.<br />
A voice-over adds a new track to the production, dubbing replaces an existing one.<br />
<br />
<br />
Key features<br />
These projects involve two parts:<br />
– a script translation (as described above), and<br />
– producing the audio<br />
<br />
So they involve the combined efforts of translators and voice artists.<br />
The task for the voice artist is to produce a high quality read. That’s one that matches the style, tone and richness of the original.<br />
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Often each section of the new audio will need to be the same length as the original.<br />
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But sometimes the segments will need to be shorter – for example where the voice-over lags the original by a second or two. This is common in interviews etc, where the original voice is heard initially then drops out.<br />
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The most difficult form of dubbing is lip-syncing – where the new audio needs to synchronise with the original speaker’s lip movements, gestures and actions.<br />
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Lip-syncing requires an exceptionally skilled voice talent and considerable time spent rehearsing and fine tuning the translation.<br />
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What this means<br />
You need to use experienced professionals every step of the way in this type of project.<br />
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That’s to ensure firstly that your foreign-language scripts are first class, then that the voicing is of high professional standard.<br />
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Anything less will mean your foreign language versions will be way less effective and appealing to your target audience.<br />
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<br />
25. Subtitle Translations<br />
What is it?<br />
Producing foreign language captions for sub or surtitles.<br />
Key features<br />
The goal with subtitling is to produce captions that viewers can comfortably read in the time available and still follow what’s happening on the video.<br />
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To achieve this, languages have “rules” governing the number of characters per line and the minimum time each subtitle should display.<br />
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Sticking to these guidelines is essential if your subtitles are to be effective.<br />
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But this is no easy task – it requires simple language, short words, and a very succinct style. Translators will spend considerable time mulling over and re-working their translation to get it just right.<br />
<br />
Most subtitle translators use specialised software that will output the captions in the format sound engineers need for incorporation into the video.<br />
<br />
What this means<br />
As with other specialised types of translation, you should only use translators with specific expertise and experience in subtitling.<br />
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26. Website Localisation<br />
What is it?<br />
The translation and adapting of relevant content on a website to best suit the target language and culture.<br />
<br />
Note: Many providers use the term website translation as a synonym for localisation. Strictly speaking though, translation is just one part of localisation.<br />
Key features<br />
<br />
Not all pages on a website may need to be localised – clients should review their content to identify what’s relevant for the other language versions.<br />
Some content may need specialist translators – legal and technical pages for example.<br />
There may also be videos, linked documents, and text or captions in graphics to translate.<br />
Adaptation can mean changing date, time, currency and number formats, units of measure, etc.<br />
But also images, colours and even the overall site design and style if these won’t have the desired impact in the target culture.<br />
Translated files can be supplied in a wide range of formats – translators usually coordinate output with the site webmasters.<br />
New language versions are normally thoroughly reviewed and tested before going live to confirm everything is displaying correctly, works as intended and is cultural appropriate.<br />
What this means<br />
The first step should be to review your content and identify what needs to be translated. This might lead you to modify some pages for the foreign language versions.<br />
<br />
In choosing your translation providers be sure they can:<br />
– handle any technical or legal content,<br />
– provide your webmaster with the file types they want.<br />
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And you should always get your translators to systematically review the foreign language versions before going live.<br />
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<br />
27. Transcreation<br />
What is it?<br />
Adapting a message to elicit the same emotional response in another language and culture.<br />
Translation is all about conveying the message or meaning of a text in another language. But sometimes that message or meaning won’t have the desired effect in the target culture.<br />
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This is where transcreation comes in. Transcreation creates a new message that will get the desired emotional response in that culture, while preserving the style and tone of the original.<br />
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So it’s a sort of creative translation – which is where the word comes from, a combination of ‘translation’ and ‘creation’.<br />
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At one level transcreation may be as simple as choosing an appropriate idiom to convey the same intent in the target language – something translators do all the time.<br />
<br />
But mostly the term is used to refer to adapting key advertising and marketing messaging. Which requires copywriting skills, cultural awareness and an excellent knowledge of the target market.<br />
<br />
Who does it?<br />
Some translation companies have suitably skilled personnel and offer transcreation services.<br />
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Often though it’s done in the target country by specialist copywriters or an advertising or marketing agency – particularly for significant campaigns and to establish a brand in the target marketplace.<br />
<br />
What this means<br />
Most general marketing and promotional texts won’t need transcreation – they can be handled by a translator with excellent creative writing skills.<br />
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But slogans, by-lines, advertising copy and branding statements often do.<br />
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Whether you should opt for a translation company or an in-market agency will depend on the nature and importance of the material, and of course your budget.<br />
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28. Audio Translations<br />
What is it?<br />
Broad meaning: the translation of any type of recorded material into another language.<br />
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More commonly: the translation of a foreign language video or audio recording into your own language. So this is where you want to know and document what a recording says.<br />
Key features<br />
The first challenge with audio translations is it’s often impossible to pick up every word that’s said. That’s because audio quality, speech clarity and speaking speed can all vary enormously.<br />
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It’s also a mentally challenging task to listen to an audio and translate it directly into another language. It’s easy to miss a word or an aspect of meaning.<br />
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So best practice is to first transcribe the audio (type up exactly what is said in the language it is spoken in), then translate that transcription.<br />
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However, this is time consuming and therefore costly, and there are other options if lesser precision is acceptable.<br />
<br />
What this means<br />
It’s best to discuss your requirements for this kind of translation with your translation provider. They’ll be able to suggest the best translation process for your needs.<br />
<br />
Examples<br />
Interviews, product videos, police recordings, social media videos.<br />
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29. Translations with DTP<br />
What is it?<br />
Translation incorporated into graphic design files.multilingual dtp example in the form of a Rubik's Cube with foreign text on each square<br />
Key features<br />
Graphic design programs are used by professional designers and graphic artists to combine text and images to create brochures, books, posters, packaging, etc.<br />
<br />
Translation plus dtp projects involve 3 steps – translation, typesetting, output.<br />
<br />
The typesetting component requires specific expertise and resources – software and fonts, typesetting know-how, an appreciation of foreign language display conventions and aesthetics.<br />
<br />
What this means<br />
Make sure your translation company has the required multilingual typesetting/desktop publishing expertise whenever you’re translating a document created in a graphic design program.<br />
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Translation Category C: 13 types of translation based on the translation method employed<br />
This category has two sub-groups:<br />
– the practical methods translation providers use to produce their translations, and<br />
– the translation strategies/methods identified and discussed within academia.<br />
<br />
The translation methods translation providers use<br />
There are 4 main methods used in the translation industry today. We have an overview of each below, but for more detail, including when to use each one, see our comprehensive blog article.<br />
<br />
Or watch our video.<br />
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Important: If you’re a client you need to understand these 4 methods – choose the wrong one and the translation you end up with may not meet your needs!<br />
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30. Machine Translation (MT)<br />
What is it?<br />
A translation produced entirely by a software program with no human intervention.<br />
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A widely used, and free, example is Google Translate. And there are also commercial MT engines, generally tailored to specific domains, languages and/or clients.<br />
<br />
Pros and cons<br />
There are two limitations to MT:<br />
– they make mistakes (incorrect translations), and<br />
– quality of wording is patchy (some parts good, others unnatural or even nonsensical)<br />
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On they positive side they are virtually instantaneous and many are free.<br />
<br />
Best suited for:<br />
Getting the general idea of what a text says.<br />
<br />
This method should never be relied on when high accuracy and/or good quality wording is needed.<br />
<br />
<br />
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31. Machine Translation plus Human Editing (PEMT)<br />
What is it?<br />
A machine translation subsequently edited by a human translator or editor (often called Post-editing Machine Translation = PEMT).<br />
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The editing process is designed to rectify some of the deficiencies of a machine translation.<br />
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This process can take different forms, with different desired outcomes. Probably most common is a ‘light editing’ process where the editor ensures the text is understandable, without trying to fix quality of expression.<br />
<br />
Pros and cons<br />
This method won’t necessarily eliminate all translation mistakes. That’s because the program may have chosen a wrong word (meaning) that wasn’t obvious to the editor.<br />
<br />
And wording won’t generally be as good as a professional human translator would produce.<br />
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Its advantage is it’s generally quicker and a little cheaper than a full translation by a professional translator.<br />
<br />
Best suited for:<br />
Translations for information purposes only.<br />
<br />
Again, this method shouldn’t be used when full accuracy and/or consistent, natural wording is needed.<br />
<br />
<br />
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32. Human Translation<br />
What is it?<br />
Translation by a professional human translator.<br />
Pros and cons<br />
Professional translators should produce translations that are fully accurate and well-worded.<br />
<br />
That said, there is always the possibility of ‘human error’, which is why translation companies like us typically offer an additional review process – see next method.<br />
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This method will take a little longer and likely cost more than the PEMT method.<br />
<br />
Best suited for:<br />
Most if not all translation purposes.<br />
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<br />
<br />
33. Human Translation + Revision<br />
What is it?<br />
A human translation with an additional review by a second translator.<br />
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The review is essentially a safety check – designed to pick up any translation errors and refine wording if need be.<br />
<br />
Pros and cons<br />
This produces the highest level of translation quality.<br />
<br />
It’s also the most expensive of the 4 methods, and takes the longest.<br />
<br />
Best suited for:<br />
All translation purposes.<br />
<br />
Gearwheel with 5 practical translation methods written on the teeth <br />
There’s also one other common term used by practitioners and academics alike to describe a type (method) of translation:<br />
<br />
34. Computer-Assisted Translation (CAT)<br />
What is it?<br />
A human translator using computer tools to aid the translation process.<br />
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Key features<br />
Virtually all translators use such tools these days.<br />
<br />
The most prevalent tool is Translation Memory (TM) software. This creates a database of previous translations that can be accessed for future work.<br />
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TM software is particularly useful when dealing with repeated and closely-matching text, and for ensuring consistency of terminology. For certain projects it can speed up the translation process.<br />
<br />
<br />
<br />
The translation methods described by academia<br />
A great deal has been written within academia analysing how human translators go about their craft.<br />
<br />
Seminal has been the work of Newmark, and the following methods of translation attributed to him are widely discussed in the literature.Gearwheel with Newmark's 8 translation methods written on the teeth <br />
These methods are approaches and strategies for translating the text as a whole, not techniques for handling smaller text units, which we discuss in our final translation category.<br />
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<br />
<br />
35. Word-for-word Translation<br />
This method translates each word into the other language using its most common meaning and keeping the word order of the original language.<br />
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So the translator deliberately ignores context and target language grammar and syntax.<br />
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Its main purpose is to help understand the source language structure and word use.<br />
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Often the translation will be placed below the original text to aid comparison.<br />
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<br />
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36. Literal Translation<br />
Words are again translated independently using their most common meanings and out of context, but word order changed to the closest acceptable target language grammatical structure to the original.<br />
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Its main suggested purpose is to help someone read the original text.<br />
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<br />
<br />
37. Faithful Translation<br />
Faithful translation focuses on the intention of the author and seeks to convey the precise meaning of the original text.<br />
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It uses correct target language structures, but structure is less important than meaning.<br />
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<br />
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38. Semantic Translation<br />
Semantic translation is also author-focused and seeks to convey the exact meaning.<br />
<br />
Where it differs from faithful translation is that it places equal emphasis on aesthetics, ie the ‘sounds’ of the text – repetition, word play, assonance, etc.<br />
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In this method form is as important as meaning as it seeks to “recreate the precise flavour and tone of the original” (Newmark).slide showing definition of semantic translation as a translation method<br />
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39. Communicative Translation<br />
Seeks to communicate the message and meaning of the text in a natural and easily understood way.<br />
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It’s described as reader-focused, seeking to produce the same effect on the reader as the original text.<br />
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A good comparison of Communicative and Semantic translation can be found here.<br />
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40. Free Translation<br />
Here conveying the meaning and effect of the original are all important.<br />
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There are no constraints on grammatical form or word choice to achieve this.<br />
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Often the translation will paraphrase, so may be of markedly different length to the original.<br />
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41. Adaptation<br />
Mainly used for poetry and plays, this method involves re-writing the text where the translation would otherwise lack the same resonance and impact on the audience.<br />
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Themes, storylines and characters will generally be retained, but cultural references, acts and situations adapted to relevant target culture ones.<br />
<br />
So this is effectively a re-creation of the work for the target culture.<br />
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42. Idiomatic Translation<br />
Reproduces the meaning or message of the text using idioms and colloquial expressions and language wherever possible.<br />
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The goal is to produce a translation with language that is as natural as possible.<br />
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Translation Category D: 9 types of translation based on the translation technique used<br />
These translation types are specific strategies, techniques and procedures for dealing with short chunks of text – generally words or phrases.<br />
<br />
They’re often thought of as techniques for solving translation problems.<br />
<br />
They differ from the translation methods of the previous category which deal with the text as a whole.<br />
9 translation techniques as titles of books in a bookcase<br />
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43. Borrowing<br />
What is it?<br />
Using a word or phrase from the original text unchanged in the translation.<br />
<br />
Key features<br />
With this procedure we don’t translate the word or phrase at all – we simply ‘borrow’ it from the source language.<br />
<br />
Borrowing is a very common strategy across languages. Initially, borrowed words seem clearly ‘foreign’, but as they become more familiar, they can lose that ‘foreignness’.<br />
<br />
Translators use this technique:<br />
– when it’s the best word to use – either because it has become the standard, or it’s the most precise term, or<br />
– for stylist effect – borrowings can add a prestigious or scholarly flavour.<br />
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Borrowed words or phrases are often italicised in English.<br />
<br />
Examples of borrowings in English<br />
grand prix, kindergarten, tango, perestroika, barista, sampan, karaoke, tofu<br />
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44. Transliteration<br />
What is it?<br />
Reproducing the approximate sounds of a name or term from a language with a different writing system.<br />
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Key features<br />
In English we use the Roman (Latin) alphabet in common with many other languages including almost all European languages.<br />
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Other writing systems include Arabic, Cyrillic, Chinese, Japanese, Korean, Thai, and the Indian languages.<br />
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Transliteration from such systems into the Roman alphabet is also called romanisation.<br />
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There are accepted systems for how individual letters/sounds should be romanised from most other languages – there are three common systems for Chinese, for example.<br />
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English borrowings from languages using non-Roman writing systems also require transliteration – perestroika, sampan, karaoke, tofu are examples from the above list.<br />
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Translators mostly use transliteration as a procedure for translating proper names.<br />
<br />
Examples<br />
毛泽东 Mao Tse-tung or Mao Zedong<br />
Владимир Путин Vladimir Putin<br />
서울 Seoul<br />
ភ្នំពេញ Phnom Penh<br />
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45. Calque or Loan Translation<br />
What is it?<br />
A literal translation of a foreign word or phrase to create a new term with the same meaning in the target language.<br />
<br />
So a calque is a borrowing with translation if you like. The new term may be changed slightly to reflect target language structures.<br />
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Examples<br />
German ‘Kindergarten’ has been calqued as детский сад in Russian, literally ‘children garden’ in both languages.<br />
<br />
Chinese 洗腦 ‘wash’ + ‘brain’ is the origin of ‘brainwash’ in English.<br />
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English skyscraper is calqued as gratte-ciel in French and rascacielos in Spanish, literally ‘scratches sky’ in both languages.<br />
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46. Word-for-word translation<br />
What is it?<br />
A literal translation that is natural and correct in the target language.<br />
<br />
Alternative names are ‘literal translation’ or ‘metaphrase’.<br />
<br />
Note: this technique is different to the translation method of the same name, which does not produce correct and natural text and has a different purpose.<br />
<br />
Key features<br />
This translation strategy will only work between languages that have very similar grammatical structures.<br />
<br />
And even then, only sometimes.<br />
<br />
For example, standard word order in Turkish is Subject-Object-Verb whereas in English it’s Subject-Verb-Object. So a literal translation between these two will seldom work:<br />
– Yusuf elmayı yedi is literally ‘Joseph the apple ate’.<br />
<br />
When word-for-word translations don’t produce natural and correct text, translators resort to some of the other techniques described below.<br />
Examples<br />
French ‘Quelle heure est-il?’ works into English as ‘What time is it?’.<br />
<br />
Russian ‘Oн хочет что-нибудь поесть’ is ‘He wants something to eat’.<br />
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47. Transposition<br />
What is it?<br />
Translation with a change of grammatical structure.<br />
<br />
This technique gives the translation more natural wording and/or makes it grammatically correct.<br />
<br />
Examples<br />
A change in word order:<br />
Our Turkish example Yusuf elmayı yedi (literally ‘Joseph the apple ate’) –> Joseph ate the apple.<br />
<br />
Spanish La Casa Blanca (literally ‘The House White’) –> The White House<br />
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A change in grammatical category:<br />
German Er hört gerne Musik (literally ‘he listens gladly [to] music’)<br />
= subject pronoun + verb + adverb + noun<br />
becomes Spanish Le gusta escuchar música (literally ‘[to] him [it] pleases to listen [to] music’)<br />
= indirect object pronoun + verb + infinitive + noun<br />
and English He likes listening to music<br />
= subject pronoun + verb + gerund + noun.<br />
<br />
48. Modulation<br />
What is it?<br />
Translation with a change of focus or point of view in the target language.<br />
<br />
This technique makes the translation more idiomatic – how people would normally say it in the language.<br />
<br />
Examples<br />
English talks of the ‘top floor’ of a building, French the dernier étage = last floor. ‘Last floor’ would be unnatural in English, so too ‘top floor’ in French.<br />
<br />
German uses the term Lebensgefahr (literally ‘danger to life’) where in English we’d be more likely to say ‘risk of death’.<br />
In English we’d say ‘I dropped the key’, in Spanish se me cayó la llave, literally ‘the key fell from me’. The English perspective is that I did something (dropped the key), whereas in Spanish something happened to me – I’m the recipient of the action.<br />
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49. Equivalence or Reformulation<br />
What is it?<br />
Translating the underlying concept or meaning using a totally different expression.<br />
<br />
This technique is widely used when translating idioms and proverbs.<br />
<br />
And it’s common in titles and advertising slogans.<br />
<br />
It’s a common strategy where a direct translation either wouldn’t make sense or wouldn’t resonate in the same way.<br />
<br />
Examples<br />
Here are some equivalents of the English saying “Pigs may fly”, meaning something will never happen, or “you’re being unrealistic” (Source):<br />
– Thai: ชาติหน้าตอนบ่าย ๆ – literally, ‘One afternoon in your next reincarnation’<br />
– French: Quand les poules auront des dents – literally, ‘When hens have teeth’<br />
– Russian, Когда рак на горе свистнет – literally, ‘When a lobster whistles on top of a mountain’<br />
– Dutch, Als de koeien op het ijs dansen – literally, ‘When the cows dance on the ice’<br />
– Chinese: 除非太陽從西邊出來！– literally, ‘Only if the sun rises in the west’<br />
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50. Adaptation<br />
What is it?<br />
A translation that substitutes a culturally-specific reference with something that’s more relevant or meaningful in the target language.<br />
<br />
It’s also known as cultural substitution or cultural equivalence.<br />
<br />
It’s a useful technique when a reference wouldn’t be understood at all, or the associated nuances or connotations would be lost in the target language.<br />
<br />
Note: the translation method of the same name is a similar concept but applied to the text as a whole.<br />
<br />
Examples<br />
Different cultures celebrate different coming of age birthdays – 21 in many cultures, 20, 15 or 16 in others. A translator might consider changing the age to the target culture custom where the coming of age implications were important in the original text.<br />
Animals have different connotations across languages and cultures. Owls for example are associated with wisdom in English, but are a bad omen to Vietnamese. A translator might want to remove or amend an animal reference where this would create a different image in the target language.<br />
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51. Compensation<br />
What is it?<br />
A meaning or nuance that can’t be directly translated is expressed in another way in the text.<br />
Example<br />
Many languages have ways of expressing social status (honorifics) encoded into their grammatical structures.<br />
<br />
So you can convey different levels of respect, politeness, humility, etc simply by choosing different forms of words or grammatical elements.<br />
But these nuances will be lost when translating into languages that don’t have these structures.<br />
Then translating into languages that don’t have these structures<br />
Then translating into languages that don’t have these structures.<br />
<br />
===Conclusion ===<br />
<br />
In the light of above mentioned facts and figures here by we would say that machine translation is challenge for human translators because it can reduces the wokload of translation but can't give accurate and exact translation of the traget language.It can be less reliable than human translation..</div>Chen Huinihttps://bou.de/u/index.php?title=Machine_translation&diff=131922Machine translation2021-12-13T12:54:59Z<p>Chen Huini: /* 1.1 Definition of Machine Translation */</p>
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<div>'''Machine Translation - A challenge or a chance for human translators?'''<br />
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[[Machine_translation|Overview Page of Machine Translation]]<br />
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30 Chapters（0/30)<br />
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[[Machine_Trans_EN_1]] [[Machine_Trans_EN_2]] [[Machine_Trans_EN_3]] [[Machine_Trans_EN_4]] [[Machine_Trans_EN_5]] [[Machine_Trans_EN_6]] [[Machine_Trans_EN_7]] [[Machine_Trans_EN_8]] [[Machine_Trans_EN_9]] [[Machine_Trans_EN_10]] [[Machine_Trans_EN_11]] [[Machine_Trans_EN_12]] [[Machine_Trans_EN_13]] [[Machine_Trans_EN_14]] [[Machine_Trans_EN_15]] [[Machine_Trans_EN_16]] [[Machine_Trans_EN_17]] [[Machine_Trans_EN_18]] [[Machine_Trans_EN_19]] [[Machine_Trans_EN_20]] [[Machine_Trans_EN_21]] [[Machine_Trans_EN_22]] [[Machine_Trans_EN_23]] [[Machine_Trans_EN_24]] [[Machine_Trans_EN_25]] [[Machine_Trans_EN_26]] [[Machine_Trans_EN_27]] [[Machine_Trans_EN_28]] [[Machine_Trans_EN_29]] [[Machine_Trans_EN_30]] ...<br />
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[[Book_projects|Back to translation project overview]]<br />
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[[DCG-To-Do|To the To Do list]]<br />
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=Chapter 1:A Comparison Between the Quality of Machine Translation and Human Translation——A Case Study of the Application of artificial intelligence in Sports Events=<br />
'''论机器翻译与人工翻译的质量对比——以人工智能在体育赛事领域的应用为例'''<br />
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卫怡雯 Wei Yiwen, Hunan Normal University, China<br />
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[[Machine_Trans_EN_1]]<br />
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=Chapter 3：On the Realm Advantages And Mutual Development of Machine Translation And Huamn Translation)=<br />
"'论机器翻译与人工翻译的领域优势及共同发展'"<br />
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肖毅瑶 Xiao Yiyao, Hunan Normal University, China<br />
[[Machine_Trans_EN_3]]<br />
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=Chapter 4 : A Comparison Between Machine Translation of Netease and Traditional Human Translation—A Case Study of The Economist Articles)= <br />
'''网易有道机器翻译与人工翻译的译文对比——以经济学人语料为例'''<br />
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王李菲 Wang Lifei, Hunan Normal University<br />
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[[Machine_Trans_EN_4]]<br />
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=Chapter 5: Muhammad Saqib Mehran - Problems in translation studies=<br />
[[Machine_Trans_EN_5]]<br />
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=6 徐敏赟(Machine Translation Based on Neural Network --Challenge or Chance?)=<br />
[[Machine_Trans_EN_6]]<br />
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=Chapter 7: The Cultivation of artificial intelligence and translation talents in the Belt and Road Initiative=<br />
[[Machine_Trans_EN_7]]<br />
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一带一路背景下人工智能与翻译人才的培养<br />
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颜莉莉 Yan Lili, Hunan Normal University, China<br />
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=Chapter 8: On Machine Translation Under Lanuguage Intelligence——An Option and Opportunity for Human Translators=<br />
'''论语言智能下的机器翻译——译者的选择与机遇'''<br />
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颜静 Yan Jing, Hunan Normal University, China<br />
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[[Machine_Trans_EN_8]]<br />
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=9 谢佳芬（ Machine Translation and Artificial Translation in the Era of Artificial Intelligence）=<br />
[[Machine_Trans_EN_9]]<br />
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=10 熊敏(Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts)=<br />
[[Machine_Trans_EN_10]]<br />
===Abstract===<br />
The history of machine translation can be traced to 1940s, undergoing germination, recession, revival and flourish. Nowadays, with the rapid development of information technology, machine translation technology emerged and is gradually becoming mature. Whether manual translation can be replaced by machine translation becomes a widely-disputed topic. So in order to explore the ability of machine translation, I adopts two versions of translation, which are manual translation and machine translation(this paper uses Youdao translation) for different types of texts(according to Peter Newmark's types of text：informative text, expressive text and vocative text). The results are quite different in terms of quality and accuracy. The results show that machine translation is more suitable for informative text for its brevity, while the expressive texts especially literary works and vocative texts are difficult to be translated, because there are too many loaded words and cultural images in expressive text and dependence on the readers. To draw a conclusion, the relationship between machine translation and manual translation should be complementary rather than competitive.<br />
<br />
===Key words===<br />
machine translation; manual translation; Newmark's type of texts<br />
<br />
===题目===<br />
Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts<br />
<br />
===摘要===<br />
机器翻译的历史可以追溯到上个世纪40年代，经历了萌芽期、萧条期、复兴期和繁荣期四个阶段。如今，随着信息技术的高速发展，机器翻译技术日益成熟，于是机器翻译能不能替代人工翻译这个话题引起了广泛热议。为了探究机器翻译的能力水平是否足以替代人工翻译，本人根据皮特纽马克的文本类型分类理论（信息类文本，表达文本，呼吁文本），选择了相应的译文类型，并且将其机器翻译的版本以及人工翻译的版本进行对比。结果发现，就质量和准确度而言，译文的水平大相径庭。因为其简洁的特性，机器比较适合翻译信息文本。而就表达文本，尤其是文学作品，因其存在文化负载词及相应的文化现象，所以机器翻译这类文本存在难度。而呼唤文本因其目的性以及对读者的依赖性较强，翻译难度较大。由此得知，机器翻译和人工翻译的关系应该是互补的，而不是竞争的。<br />
<br />
===关键词===<br />
机器翻译；人工翻译；纽马克文本类型<br />
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===1. Introduction===<br />
====1.1.Introduction to machine translation====<br />
Machine translation, also known as computer translation is a technique to translating through machine translator, such as Google translation and Youdao translation. Machine translation is one of the branches of computational linguistics, ranging from computer science, statistics, information science and so on. Machine translation plays an important role in all aspects.<br />
Machine translation can be traced back to 1940s, when British engineer Booth and American engineer Weaver proposed using computer to translate and started to study machines used for translation. And the first machine translating system launched in 1954, used in English and Russian translation. And this means machine translation becomes a reality. However, the arrival of new things always accompanies barriers and setbacks. In the 1960s, reports from ALPAC (Automated Language Processing Advisory Committee) showed studies on machine translation had stagnated for a decade. At that time, due to the high cost of machine, choosing human translators to finish translating works was more economical for most companies. What’s worse, machine had difficulty in understanding semantic and pragmatic meanings. Fortunately, in the 1970s, with the advance and popularity of computer, machine translation was gradually back on track. With the development of science and technology, machine translation has better technological guarantee, such as artificial intelligence and computer technology. In the last decades, from the late 90s till now, machine translation is becoming more and more mature. The initial rule-oriented machine translation has been updated and Corpus-aided machine translation appears. And nowadays, technology in voice recognition has been further developed. This technique is frequently applied in speech translation products. Users only need to speak source language to the online translator and instantly it will speak out the target version. But machine can’t fully provide precise and accurate translation without any grammatical or semantic problems. Machine can understand the literal meaning of texts, but not the meaning in context. For example, there is polysemy in English, which refers to words having multiple meanings. So when translating these words, translators should take contexts into consideration. But machine has troubles in this way. In addition, machine has no feeling or intention. Hence, it is also difficult to convey the feelings from the source language.<br />
<br />
====1.2.Process of machine translation====<br />
The process of manual translation is different from that of machine translation. Here is the process of the former. (1) Understand source language. (2) Use target language to organize language. (3) Generate translation. Unlike manual translation, machine translation tends to analyze and code source language first, then look for related codes in corpus, and work out the code that represents target language, generating translation. But they share a common feature, which is that Lexicon, grammatical rules and syntactic structure are taken into consideration. This is one of the biggest challenges for machine translation.<br />
<br />
===2.Theorectical framework===<br />
====2.1Newmark’s text typology====<br />
Text typology is a theory from linguistics. It undergoes several phrases. Karl Buhler, a German psychologist and linguist defines communicative functions into expressive, information and vocative. Then Roman Jakobson proposes categorization of texts, which are intralingual translation, interlingual translation and intersemiotic translation. Accordingly, he defines six main functions of language, including the referential function, conative function, emotive function, poetic function, metalingual function and the phatic function. Based on the two theories, Peter Newmark, a translation theorist, summarizes the language function into six parts: the informative function, the vocative function, the expressive function, the phatic function, the aesthetic function, and the metalingual function. Later, he further divided texts into informative type, expressive text and vocative type according to the linguistic functions of various texts. <br />
The core of the informative texts is the truth. It is to convey facts, information, knowledge of certain topics, reality and theories. The language style of the text is objective, logical and standardized. Reports, papers, scientific and technological textbooks are all attributed to informative texts. Translators are required to take format into account for different styles.<br />
The core of the expressive text is the sender’s emotions and attitudes. It is to express sender’s preferences, feelings, views and so on. The language style of it is subjective. Imaginative literature, including fictions, poems and dramas, autobiography and authoritative statements belong to expressive text. It requires translators to be faithful to the source language and maintain the style.<br />
The core of the vocative text is readership. It is to call upon readers to act, think, feel and react in the way intended by the text. So it is reader-oriented. Such texts as advertisement, propaganda and notices are of vocative text. Newmark noted that translators need to consider cultural background of the source language and the semantic and pragmatic effect of target language. If readers can comprehend the meaning at once and do as the text says, then the goal of information communication is achieved.<br />
To draw a conclusion, informative texts focus on the information and facts. Expressive texts center on the mind of addressers, including feelings, prejudices and so on. And vocative texts are oriented on readers and call on them to practice as the texts say.<br />
<br />
====2.2Study method====<br />
Manual translations of the three texts are selected from authoritative versions and universally acknowledged. And machine translations of those come from Youdao Translator. And in this thesis I will compare and evaluate the two methods in word diction, sentence structure, word order and redundancy.<br />
<br />
===3.Comparison and analysis of machine translation and manual translation ===<br />
====3.1Informative text ====<br />
（1）English into Chinese<br />
<br />
①Source language:<br />
<br />
Keep the tip of Apple Pencil clean, as dirt and other small particles may cause excessive wear to the tip or damage the screen of i-pad.<br />
<br />
Target language:<br />
<br />
Machine translation: Apple Pencil笔尖应保持清洁，灰尘等小颗粒可能会导致笔尖过度磨损或损坏ipad屏幕。<br />
<br />
Manual translation: 保持Apple Pencil铅笔的笔尖干净，因为灰尘和其他微粒可能会导致笔尖的过度磨损或损坏iPad屏幕。<br />
<br />
Analysis: Here is the instruction of Apple Pencil. And the manual translation is the Chinese version on the instruction.Product instruction tends to be professional, since there are many terms for some concepts. Machine can easily identify these terms and provide related words to translate. The machine version is faithful and expressive to the source language. So it is well-qualified and readable for readers to understand the instruction. So we can use machine to translate informative text.<br />
<br />
②Source language:<br />
<br />
China on Saturday launched a rocket carrying three astronauts-two men and one woman - to the core module of a future space station where they will live and work for six months, the longest orbit for Chinese astronauts.<br />
<br />
Target language:<br />
<br />
Machine translation: 周六，中国发射了一枚运载三名宇航员(两男一女)的火箭，进入未来空间站的核心舱，他们将在那里生活和工作6个月，这是中国宇航员最长的轨道。<br />
<br />
Manual translation: 周六，中国发射了一枚搭载三名宇航员(两男一女)的火箭，进入未来空间站的核心舱，他们将在那里生活和工作6个月，这是中国宇航员最漫长的一次轨道飞行。<br />
<br />
Analysis: This is a news from Reuters, reporting that China has launched a rocket.The meaning of the two translations is almost the same, except for some word diction. But there are some details dealt with different choice. For example, the last sentence of the machine translation is a bit of obscure and direct. There are some ambiguous words and expressions.<br />
<br />
(2)Chinese into English<br />
<br />
Source language:湖南省博物馆是湖南省最大的历史艺术类博物馆，占地面积4.9万平方米，总建筑面积为9.1万平方米，是首批国家一级博物馆，中央地方共建的八个国家级重点博物馆之一、全国文化系统先进集体、文化强省建设有突出贡献先进集体。<br />
<br />
Target language:<br />
Manual translation: As the largest history and art museum in Hunan province, the Hunan Museum covers an area of 49,000㎡, with the building area reaching 91,000㎡. It is one of the first batch of national first-level museums and one of the first eight national museums co-funded by central and local governments.<br />
<br />
Machine translation: Museum in hunan province is one of the largest historical art museum in hunan province, covers an area of 49000 square meters, a total construction area of 91000 square meters, is the first national museum, the central place to build one of the eight national key museum, national cultural system advanced collectives, strong culture began with outstanding contribution of advanced collective.<br />
<br />
Analysis: Machine translation is not faithful enough in content. For instance, “首批国家一级博物馆” is translated into “first national museum”, which is not the meaning of the source language. And there are some obvious grammar mistakes in the machine translation. For example, machine translates it into just one sentence but there are multiple predicates in it. So it is not grammatically permissible. What’s more, the sentence structure of machine translation is confusing and the focus is not specific enough.<br />
<br />
====3.2Expressive text ====<br />
(1)English into Chinese<br />
<br />
Source language:<br />
<br />
An individual human existence should be like a river- small at first, narrowly contained within its banks, and rushing passionately past rocks and over waterfalls. Gradually the river grows wider, the banks recede, the waters flow more quietly, and in the end, without any visible breaks, they become merged in the sea, and painlessly lose their individual being.<br />
<br />
Target language:<br />
<br />
Machine translation: 一个人的存在应该像一条河流——开始很小，被紧紧地夹在两岸中间，然后热情奔放地冲过岩石，飞下瀑布。渐渐地，河面变宽，两岸后退，水流更加平缓，最后，没有任何明显的停顿，它们汇入大海，毫无痛苦地失去了自己的存在。<br />
<br />
Manual translation:人生在世，如若河流；河口初始狭窄，河岸虬曲，而后狂涛击石，飞泻成瀑。河道渐趋开阔，峡岸退去，水流潺缓，终了，一马平川，汇于大海，消逝无影。<br />
<br />
Analysis: Here is a well-known metaphor in the prose How to Grow Old written by Bertrand Russell. The manual translation is written by Tian Rongchang.This is a philosophical prose with graceful language. Literary translation is a most important and difficult branch of translation. Translator should focus on the literal meaning, culture, writing style and so on. It is a combination of beauty and elegance. Therefore, translators find it in a dilemma of beauty and faithfulness, let alone translating machine. Compared with manual translation, machine translation has difficulty in word choice. It is faithful and expressive, but not elegant enough.<br />
<br />
(2)Chinese into English<br />
<br />
Source language:没有一个人将小草叫做“大力士”，但是它的力量之大，的确是世界无比。这种力，是一般人看不见的生命力，只要生命存在，这种力就要显现，上面的石块，丝毫不足以阻挡。因为它是一种“长期抗战”的力，有弹性，能屈能伸的力，有韧性，不达目的不止的力。<br />
<br />
Target language:<br />
<br />
Machine translation: No one calls the little grass "hercules", but its power is truly matchless in the world. This force is invisible life force. As long as there is life, this force will show itself. The stone above is not strong enough to stop it. Because it is a "long-term resistance" of the force, elastic, can bend and extend force, tenacity, not to achieve the purpose of the force.<br />
<br />
Manual translation: Though nobody describes the little grass as a “husky”, yet its herculean strength is unrivalled. It is the force of life invisible to naked eye. It will display itself so long as there is life. The rock is utterly helpless before this force- a force that will forever remain militant, a force that is resilient and can take temporary setbacks calmly, a force that is tenacity itself and will never give up until the goal is reached. (by Zhang Peiji)<br />
<br />
Analysis:This is the excerpt of a well-known Chinese prose written by Xia Yan. It is written during the war of Resistance Against Japan. So the prose holds symbolic meaning, eulogizing the invisible tenacious vitality so as to encourage Chinese to have confidence in the anti-aggression war. Compared with manual translation, machine translation is much more abstract and confusing, especially for the word diction. For example, “大力士” is translated into “hercules” which is a man of exceptional strength and size in Greek and Roman Mythology, making it difficult to understand if readers of target language have no idea of the allusion. What’s worse, the machine version doesn’t reveal the symbolic meaning of the text, which is the core of this prose.<br />
<br />
====3.3Vocative text ====<br />
<br />
(1)English into Chinese<br />
<br />
①Source language:<br />
<br />
iPhone went to film school, so you don’t have to. (Advertisement of iPhone13)<br />
<br />
Target language:<br />
<br />
Machine translation: iPhone上的是电影学院，所以你不用去。<br />
<br />
Manual translation:电影专业课，iPhone同学替你上完了。<br />
<br />
Analysis：Here are advertisements of iPhone on Apple official website. There is a personification in the source language. It is used to stress the advancement and proficiency in camera, which is an appealing selling point to potential buyers. Compared with manual translation, machine translation is plain and not eye-catching enough for customers.<br />
<br />
②Source language: <br />
<br />
5G speed OMGGGGG<br />
<br />
Machine language: 5克的速度 OMGGGGG<br />
<br />
Manual translation:<br />
<br />
iPhone的5G 巨巨巨巨巨5G<br />
<br />
Analysis: The “G” in the source language is the unit of speed, standing for generation. However, it is mistaken as a unit of weight, representing gram in the machine translation. So the meaning is not faithful to the source language at all. As for manual translation, it complies with the source in form. Specifically speaking, five “G”s in the former complies with five characters “巨”in the latter. And the pronunciation of the two is similar. There are two layers of meaning for the 5 “G”s. One exclaims the fast speed of 5 generation network and the other new technology. In the manual version, “巨”can be used to show degree, meaning “quite” or “very”. <br />
<br />
③Source language: <br />
<br />
History, faith and reason show the way, the way of unity. We can see each other not as adversaries but as neighbors. We can treat each other with dignity and respect, we can join forces, stop the shouting and lower the temperature. For without unity, there is no peace, only bitterness and fury."<br />
<br />
Target language:<br />
<br />
Machine translation: 历史、信仰和理性指明了团结的道路。我们可以把彼此视为邻居，而不是对手。我们可以尊严地对待彼此，我们可以联合起来，停止大喊大叫，降低温度。因为没有团结，就没有和平，只有痛苦和愤怒。<br />
<br />
Manual translation:历史、信仰和理性为我们指明道路。那是团结之路。我们可以把彼此视为邻居，而不是对手。我们可以有尊严地相互尊重。我们可以联合起来，停止喊叫，减少愤怒。因为没有团结就没有和平，只有痛苦和愤怒<br />
<br />
Analysis: Speech is a way to propagate some activity in public. It is an art to inspire emotion of the audience. The source language is the excerpt of Joe Biden’s inaugural speech. The speech should be inspiring and logic. The machine translation has some misunderstanding. Taking the translation of “lower the temperature” for example, machine only translates its literal meaning, relating to the temperature itself, without considering the context. What’s more, it is less logic than the manual one. Therefore, it adds difficulty to inspire the audience and infect their emotion.<br />
<br />
===4.Common mistakes in machine translation ===<br />
<br />
====4.1 lexical mistakes ====<br />
<br />
Common lexical mistakes include misunderstandings in word category, lexical meaning and emotive and evaluative meaning. Misunderstanding in word category shows in the classification of word in the source language. As for misunderstanding in lexical meaning, machine has difficulty in precisely reflecting the meaning of the original texts, due to different cultural background and different language system. And for misunderstanding in emotive meaning, machine has no intention and emotion like human-beings. Therefore, it’s impossible for it to know writers’ feelings and their writing purposes. So sometimes, it may translate something negative into something positive.<br />
<br />
====4.2 grammatical mistakes====<br />
<br />
Grammatical analysis plays an important part in translation. Normally speaking, every language has its own unique grammatical rules. So in the process of translation, if translators don’t know the formation rule well, the sentence meaning will be affected. Even though all the lexical meanings are well-known by translators, the lack of consciousness of grammaticality makes it harder to arrange words according to sequential rule. English tends to be hypotactic, while Chinese tends to be paratactic. English sentences are connected through syntactic devices and lexical devices. While Chinese sentences are semantically connected, which means there are limited logical words and connection words in Chinese. So when translating English sentence, we should first analyze its grammaticality and logical structure and then rearrange its sequence. However, online translating machine has troubles in grammatical analysis, which makes its improvement more difficult.<br />
<br />
====4.3 other mistakes====<br />
<br />
The two mistakes above are the internal ones. Apart from mistakes in linguistic system, there are some mistakes in other aspects, such as cultural background.<br />
<br />
===5.Reasons for its common mistakes ===<br />
<br />
====5.1 Difference in two linguistic system====<br />
<br />
With different history, English and Chinese have different ways of expression. Commonly speaking, English is synthetic language which expresses grammatical meaning through inflection such as tense and Chinese is analytic language which expresses grammatical meaning through word order and function word. In addition, English is more compact with full sentences. Subordinate sentence is one of the most important features in modern English. Chinese, on the other hand, is more diffusive with minor sentences.<br />
<br />
====5.2 Difference in thinking patterns and cultural background====<br />
<br />
According to Sapir-Whorf’s Hypothesis, our language helps mould our way of thinking and consequently, different languages may probably express their unique ways of understanding the world. For two different speech communities, the greater their structural differentiations are, the more diverse their conceptualization of the world will be. For example, western culture is more direct and eastern culture more euphemistic. What’s more, English culture tends to be individualism, focusing on detail, through which it reflects the whole, while Chinese culture tends to be collective. Different thinking patterns will add difficulty for machine to translate texts.<br />
<br />
====5.3 Limitation of computer====<br />
<br />
Recently, there are some breakthroughs and innovation in machine translation. However, due to its own limitation, online translation has limitation in some ways. Firstly, compared with machine, human brain is much more complicated, consisting of ten billions of neuron, each of which has different function to affect human’s daily activities and help humans avoid some errors. However, computer can only function according to preset programming has no intention or consciousness. Until now, countless related scholars have invested much time in machine translation. They upload massive language database, which include almost all linguistic rules. But computers still fail to precisely reflect the meaning of source language for many times due to the complexity and flexibility of language. On the other hand, computers can’t take context into consideration. During translation, it is often the case that machine chooses the most-frequently used meaning of one word. So without the correct and exact meaning, readers are easier to feel confused and even misunderstand the meaning of source language.<br />
<br />
===6.Conclusion===<br />
From the analysis above, we can draw a conclusion that machine deals with informative text best, followed by non-literary translation of expressive text. What’s more, machine can be a useful tool to get to know the gist and main idea of a specific topic, for the simple sentence structure and numerous terms. And it can improve translating efficiency with high speed. But machine has difficulty in translating literary works, especially proses and poems.<br />
<br />
Machine translation has mixed future. From the perspective of commercial, machine translation boasts a bright future. With the process of globalization, the demand for translation is increasing accordingly. On one hand, if we only depend on human translator to deal with translating works, the quality and accuracy of translation can be greatly affected. On the other hand, if machine is used properly to do some basic work, human translators only need to make preparation before translating, progress, polish and other advanced work, contributing to highly-qualified translation and high working efficiency.<br />
<br />
However, compared with manual translation, machine translation has a bleak future. It is still impossible for machine to replace interpreter or translator in a short term. With intelligence and initiative, humans are able to learn new knowledge constantly, which machine will never accomplish. Besides, machine is not used to replace translators but to assist them in work. In other words, translators and machine carry out their own duties and they are not incompatible.<br />
<br />
To draw a conclusion, although there are certain limitations of machine translation, it can serve as a catalyst for translating works. Therefore, with the rapid development of artificial intelligence and related technology, there are still many opportunities for machine translation.<br />
<br />
===Reference ===<br />
<br />
Cui Zihan 崔子涵.机器翻译译文质量对比——以谷歌翻译和DeepL为例[J] [Comparison among Machine Translation--Taking Google Translation and Deepl for Example].Overseas English 海外英语,2021(15):182-183.<br />
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Li Deyi 李德毅. (2018). 人工智能导论 [Introduction to Artificial Intelligence]. Beijing: China Science and Technology Press 中国科学技术出版社.<br />
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Qiu Quanju 仇全菊.大数据时代背景下机器翻译及其发展趋势[J][Machine Translation and its Development Trend under the Background of Big Data Era]. English Teachers 英语教师,2021,21(16):60-62.<br />
<br />
Zhuo Jianbin 卓键滨,Liu Wenxian 刘文娴,Peng Zili 彭子莉.机器翻译对各类型文本的德汉翻译能力探究[J][Research on the German Chinese Translation Ability of Machine Translation for Various Types of Texts]. Comparative Study of Cultural innovation 文化创新比较研究,2021,5(28):122-125.<br />
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(英) Peter Newmark A Textbook of Translation[M] Shanghai Foreign Education Press, 2002<br />
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Wang Hongqi 王红旗. (2008). 语言学概论 [Introduction to Linguistics]. Beijing: Peking University Press 北京大学出版社.<br />
<br />
Liu Qin刘琴.功能目的论对于不同文本类型的翻译解读[J][Analysis of Translations in Different Types of Text based on Functionalist Approaches].Overseas Engliosh 海外英语,2021(17):8-9.<br />
<br />
Zhang Peiji 张培基.英译中国现代散文选[M][Selected Modern Chinese Prose Writings]. Shanghai Foreign Languages Education Press 上海外语教育出版社, 2002.<br />
<br />
Chen Cheng陈诚.机器翻译技术的综述[J][Overview of Machine Translation Technology].Electronic Techonology 电子技术,2021,50(11):290-291.<br />
<br />
He Xinyu何馨宇.机器翻译的发展及其对翻译职业化的影响研究[J] [The Development of Machine Translation and its Effect on Professional Transltors].Overseas English 海外英语,2021(20):48-49.<br />
<br />
He Wen 何雯, Wang Xiufeng 王秀峰.信息型文本的在线机器翻译错误研究[J][Research on Errors in Online Machine Translation of Informative text ].Overseas English海外英语,2021(15):188-189.<br />
<br />
Li Hanji 李晗佶. (2021). 人工智能时代翻译技术与译者关系演变与重构 [Evolution and reconstruction of the relationship between translation technology and translators in the era of artificial intelligence]. 西华师范大学学报(哲学社会科学版) Journal of West China Normal University (PHILOSOPHY AND SOCIAL SCIENCES EDITION) (2021-12-04) 1-6.<br />
<br />
Wei Guang魏光. 人工翻译与机器翻译译文编辑比较研究[J][Comparative Study of Translation Editing between Manual Translation and Machine Translation]. Overseas English 海外英语,2021(19):18-19+21.<br />
<br />
=Chapter 11 陈惠妮=Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts=<br />
<br />
机器翻译的译前编辑研究——以医学类文摘为例<br />
<br />
陈惠妮 Chen Huini, Hunan Normal University<br />
<br />
[[Machine_Trans_EN_11]]<br />
<br />
===Abstract===<br />
At present, globalization is accelerating and the market demand for language services is rapidly increasing . Machine translation, as an important translation method, can greatly improve translation efficiency due to its low cost and high speed. However, because of the limitations of machine translation and the differences between Chinese and English language, machine translation is not accurate enough. In order to balance translation efficiency and translation quality, a great number of manual revisions in translation are required for the machine translating texts. Medical papers are specialized, special and purposeful, so it requires accurate,qualified and professional translation. However, the quality of translations by machine is inefficient to meet the high-quality requirements of medical papers translation. Therefore, the introduction of pre-editing can greatly improve the efficiency and quality of machine translation.<br />
<br />
===Key words===<br />
Pre-editing, Machine translation, Medical texts<br />
<br />
===题目===<br />
Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts<br />
<br />
===摘要===<br />
在全球化加速发展的今天，市场对语言服务的需求迅速增加。机器翻译作为一种重要的翻译途径，由于其成本低、速度快，可以大大提高翻译效率。然而，由于机器翻译的局限性以及中英文语言的差异，机器翻译的准确性不高。为了平衡翻译效率和翻译质量，机器翻译文本需要大量的手工修改。<br />
医学论文具有专业性、特殊性和目的性，要求其译文准确、合格、专业。然而，机器翻译的质量较低，无法满足医学论文对翻译的高质量要求。因此，译前编辑的引入可以大大提高机器翻译的效率和质量。<br />
<br />
===关键词===<br />
译前编辑；机器翻译；医学文本<br />
<br />
===1. Introduction===<br />
===1.1 Definition of Machine Translation===<br />
As Cronin(2013) revealed: "Translation is undergoing a revolutionary upheaval. The influence of digital technology and the Internet on translation is continuous, extensive and profound. From the popularity of automatic online translation applications, translation revolution is everywhere (Cronin,2013) However,the concept of machine translation was firstly proposed in the 1930s. Since 1940s, the machine translation technology has been evolving from rule-based machine translation (RBMT) to statistical machine translation (SMT), and to neural machine translation (NMT). Machine translation refers to the automatic translation of source language into target language by using a computer system. That is, machine translation refers to the automatic translation of text from one language into another natural language by computer software or other online translation webs. Machine translation is also defined as the process of “using a computer system to automatically translate text or speech from one natural language to another” according to the definition by ISO (Cui 2014:68-73).O 'Brien (2002) defines it as "the behavior of modifying errors in machine translation to ensure that the target translation meets certain quality requirements". On the basic level, machine translation performs mechanical substitution of words in one language for words in another language, but that rarely produces a good translation, therefore, recognition of the whole phrases and their closest counterparts in the target language is needed. Not all words in one language have equivalents in another language, and many words have more than one meaning. A huge demand for translation is greatly needed in today’s global world, which creates new opportunities for the development of machine translation, attracts more and more attention and becomes one of the current research focuses.<br />
<br />
===1.2 Definition of Pre-editing===<br />
Pre-editing means to adjust and modify the source language to make it fit more with the characteristics of the machine translation software before putting the source language before the into machine translation, so as to improve the quality of the translation machine translation (Wei Changhong, 2008:93-94). Pre-editing is to modify the original text before putting it into machine translation software, in order to improve the recognition rate of machine translation, optimize the output quality of translated text and reduce the workload of post-editing. Because pre-translation editing only needs to be modified in one language, the operation is simpler than post-translation editing, which can realize the double improvement of quality and efficiency. A good pre-editing translation can help machine translation more smoothly, thus improving the machine readability and quality of the output translation. Pre-editing is mostly applied in the following situations: one is when the original text is of poor quality and the machine is difficult to recognize the meaning of the sentence, such as user generated content with poor readability and translatability (Gerlach et al, 2003:45-53); Documents that need to be published in multiple languages; Next is when the original text contains a lot of jargon; The last is the original text has a corresponding translation memory bank. If the original text is edited, it can better match the content of the translation memory bank. Pre-editing is a process of identifying problems. It requires to pre-edit the source texts before putting it into machine translation according to the requirements, listing the expressions or sentences that may have trouble in machine translation and then pre-edit it by human. The purpose is to enable the computer to translate better, improve the translatability of machine translation. (Slype G V & Guinet J F & Seitz F,1984:115)<br />
<br />
===1.3 Machine Translation Mode===<br />
According to the different knowledge acquisition methods, machine translation modes can be classified as follows: one is rule-based machine translation, which is based on bilingual dictionaries and a library of language rules for each language. The quality of translation depends on whether the source language conforms to the existing rules, but the inexhaustible rules are the hinders of this model. The second mode is machine translation based on statistics. This translation model relies on the principles of mathematics and statistics to find various existing translations corresponding to the translation tasks through the employment of corpus, analyzing the frequency of their occurrence and selecting the translation with the highest frequency for output. The disadvantage of this translation model is that it ignores the flexibility of language and the importance of context. The last one is neural network language model. This model is different from previous translation models in that it uses end-to-end neural network to realize automatic translation between natural languages. At present, the quality of its translation is much higher than that of the previous translation models.<br />
===1.4 Source of Study Abstracts===<br />
The core medical journals from domestic are selected in order to make the paper more representative and authoritative, such as National Medical Journal of China, Journal of Peking University (Health Science), and Journal of Third Military Medical University. All of them include basic medical science, biomedical technology, laboratory medical science and other fields. In this way, the pre-editing approaches included are applicable enough to machine translation of medical abstracts.<br />
===1.5 Selection of Translation Software===<br />
Generally, machine translation software includes Google Translation, Youdao Translator and Niu Translation, which has their own special use in translation. Take Google as an example, it is more like neurons in human brain, enabling to learn and collect information to establish connections with its neural machine translator’s neurons. However, it also causes many errors because of the lack of enough information. In this paper, contrastive analysis will be carried on by using Google translation. On the one hand, being a pioneer in the translation of NET, it is inevitably to sue Google as the translation software to translate medical texts in this paper. Comparing the program called “Google brain”, other NMT of translation software are relatively disadvantages. On the other hand, the Google translation enjoys the largest users in the world, with its downloads of more than one billion. The output quality of Google translation is more correct and complete than other machine translation software. With years of development and improvement, Google Translation has been greatly promoted. In this paper, Chinese medical abstract will be automatically translated by Google translation, and then the translation output will be compared with the translation by human and the publication of English abstracts. The main purpose is to prove that the improvement and promotion of quality and accuracy in the medical abstracts will be obtained through the pre-editing approaches.<br />
<br />
===2.Language Characteristics and Error Division===<br />
Since Chinese and English are two different languages, it is quite neccessary to identify their own characteristics so as to better analyze and understand the two languages. Tytler (1978) argued in his Essay on the Principles of Translation that there are three principles of translation that in all the translation should give readers the same feelings as the source text, except for complete transcript of ideas. There actually also exit some mistakes of these two languages. So the following will make some clarrifications of these two languages to ensure more accurate translation.<br />
<br />
===2.1 Language Characteristics of Medical Abstracts===<br />
Chinese and English belong to different language systems, so there are differences in their language structure and the way users think of the languages. When using machine translation from Chinese to English, due to the unequal language levels, there will be many mistakes in the translation process, especially for ESP texts, such as medical papers. <br />
Here, these differences mainly refer to the linguistic characteristics of medical abstracts. In medical abstracts, it usually includes structured and unstructured abstracts. Although in different forms, they both describe the purpose, methods and conclusions of the research.<br />
In the method section of medial abstract, several Chinese sentences can be connected with commas, but each sentence may convey different information. In contrast, English sentences contain a great deal of information, but in order to ensure clarity, some modifiers need to be isolated and then reconstructed. However, in Chinese-English machine translation, a lot of information is put into the sentence because the machine segments the sentence on the basis of the full comma.<br />
In addition, subjectless sentence is used in the objective and method parts of Chinese medical abstracts. The subjectless sentence means the sentence without or free from subject and is usually employed in two contexts. The first is "needless to say". In Chinese sentences, it is common to omit the subject of the sentence. Chinese sentences can convey meanings by using incomplete sentence structures, so Chinese speakers can understand the meanings of the sentences even though the sentence subject is omitted. The second is "emphasis of action". In this context, subjectless sentences are used to describe behaviors, especially in the study of traditional Chinese medicine abstracts. Comparatively speaking, it should be avoided in English medical abstracts. Abstract sentences are more subjective when describing the learning process, while the essential requirement of English medical abstract is objectivity. From this point, sentences without subject should be avoided in English medical abstracts. Another feature is voice. Few words with passive meanings appear in Chinese abstracts. English sentences are more favoured in using passive voice. When translating from Chinese to English, passive voice should be used to make the contents objective, which is also the basic requirement of medical papers.<br />
These are the linguistic features of Chinese medical abstracts. There are great differences in sentence structure and expression between Chinese and English medical abstracts. These differences may reduce the accuracy of machine translation, so it is necessary to introduce pre-editing to edit the source text to ensure that the source text can be accurately recognized by the machine and fully translated into English.<br />
===2.2 Error Division of Machine Translation in Translating Abstracts of Medical Papers from Chinese to English===<br />
In this paper, errors in machine translation are listed out after analyzing. Some are due to the principles, such as statistical-based MT and NMT, employed by machine translation. Based on the original errors, they can be studied from two levels. One is from the macro-level, referring to mistakes caused by objective factors. These are not human factors, but the disadvantage of machine translation and the limitations of language. The limitations of machine translation are derived from the principles and models adopted and manifests itself as a reliance on reference sources. In this case, semantic ambiguity and textual incoherence may occur in the absence of reference sources.<br />
However, for ESP texts such as medical papers, the requirements for terms and sentence patterns are far beyond the existing corpora. For unfamiliar texts (that is, the corresponding texts cannot be found in the corpora). The quality of output translation will be relatively low, which is determined by the principles of machine translation. As mentioned above, machine translation has evolved over the past few decades from rules-based to corp-based statistics to NMT. However, there still exist some limitations in machine translation.<br />
Mistakes on micro-level are mainly caused by the variations and differences of linguistic structure between Chinese and English. Chinese is an implicit language, while English is an explicit one (Lian, 2010) To put it in another way, Chinese expression does not depend on language structures, while English does the opposite. This may result in mismatches between the original and machine translated sentences. This kind of error is mainly divided into two types: component fragment and component missing. For a medical paper, such errors are very serious. As a kind of ESP discourse, medical paper has the characteristics of fixed, objective and accurate language structure. In order to reproduce the characteristics of medical abstracts in translation, it is necessary to avoid errors at the level of words and sentences and pay attention to logic and consistency.<br />
Lexical errors here refer to the inconsistency of fixed expressions in the translation of terms. Although these terms are machine translation based on the corpus, the corpus may not be perfect for ESP texts such as medical papers. Therefore, fixed expressions are not used in term translation. This is also the most common mistake in machine translation (the term here includes but is not limited to nouns). For ESP texts, medical text in particular, the fixed expressions and the accuracy of terms are of great importance.<br />
<br />
===3.Approaches Proposed for Pre-editing ===<br />
Generally speaking, there is a close relationship between pre-editing and post-editing, both of which aim to convey information and ensure high or publishable translation quality. Proper pre-editing can improve the quality of machine translation in terms of adequacy and consistency. <br />
Complexity of natural language and people use language arbitrarily, bringing many difficulties to Chinese-English machine translation, Hu Qingping (2005:24) has proposed "the research of translation software and the development of the controlled language are the two directions to improve the quality of machine translation : the former aims at the difficulty in the natural language processing, the latter overcomes the arbitrariness of natural language". Feng Quangong and Gao Lin (2017: 63-68) put forward: "The writing principles of controlled language can be applied to pre-editing of machine translation. Pre-editing based on controlled language can effectively reduce the complexity and ambiguity of source text, improve the identifiability of machine translation (the translatability of source text itself), and thus reduce (fully) the workload of post-editing".<br />
The central task of pre-editing is to transform human-friendly content into machine-friendly content, so words and sentences need to be repositioned or even changed. Based on the analysis of the language characteristics of Chinese and English, the Chinese ideographic group should be split before the Chinese source text is input into the machine software to translate so that the sentence structure is complete which can be easily recognized by the machine translation software.<br />
===3.1 Extraction and Replacement of Terms in the Source Text===<br />
As a branch of applied English, medical English is the product of the combination of English language knowledge and medical knowledge. The terms in medical papers have the characteristics of fixed and complex language structure. Although Google Translation is based on a corpus, the language structure is not fixed due to the limited size of the corpus. Therefore, the following two steps should be followed before machine translation. The first is to extract terms and create a glossary, and then replace the Chinese terms in the source text with the corresponding English expressions in the glossary. Of course, the terms of extraction should be searched and verified according to the actual situation. All of these words are verified before they can be replaced. This method can not only ensure the accuracy of term translation, but also maintain the consistency of expression, especially when dealing with long texts.<br />
Example1<br />
Source abstract: 口腔白斑病癌变相关缺氧应答基因和微小RNA的芯片及表达验证。<br />
Google translation: Microarray detection/Chip detection and expression verification of hypoxia response genes and microRNAs related to oral leukoplakia canceration.<br />
Published translation:Transcriptome array screening and verification of oral leukoplakia carcinogenesis-related hypoxia-responsive gene and microRNA. <br />
Analysis: The expression “ Affymetrix GeneChip” empolyed in this thesis is a human transcription array for transcriptome array. In the source abstract, “芯片检测“ can be meant “screening” but not detection, so the proper and appropriate translation of these expression should be “transcription array screening”, but the Google translates it into “microarry detection of chip detection”. Therefore, term extraction is essential and inevitable before putting the source abstracts into the machine translation software.<br />
After pre-editing source abstracts: 对 oral leukoplakia carcinogenesis 相关 hypoxia-responsive gene 和微小RNA进行 transcriptome array screening 及表达验证。<br />
After pre-editing translation: Transcriptome array screening and expression- verification of hypoxia-responsive genes and microRNAs related to oral leukoplakia carcinogenesis.<br />
===3.2 Explication of Subordination===<br />
As mentioned above, the subordination of Chinese sentences is judged by certain specific words in machine translation . Chinese is a paratactic language, and sentences are often connected by internal logical relationships; while English depends on sentence structure, so sentences are often closely linked by various language forms. In order to improve the accuracy of machine translation, we can adjust the sentence structure and adjust the position of adjectives and their modifiers. <br />
Example 2<br />
Source abstracts:回顾性分析南京医科大学附属儿童医院中重度HIE患儿49例及同期就诊的无神经系统症状体征的足月新生儿为对照组31例的头颅磁共振成像（MRI）资料。<br />
Google translation: A retrospective analysis that the brain magnetic resonance imaging (MRI) data of 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University and full-term neonates with no neurological symptoms and signs during the same period were included in the control group.<br />
Published translation: A total of 49 children with moderate to severe HIE admitted to the children’s Hospital Affiliated to Nanjing Medical University were retrospectively analyzed. Cranial magnetic resonance imaging (MRI) date of 31 full-term neonates without neurological symptoms and signs who visited the hospital during the same period were recruited as the control group. <br />
Analysis: As the above example shows that “中重度HIE患儿” and “足月新生儿” in the source abstracts are from the Children’s Hospital Affiliated Nanjing Medical University. The Google translation shows that 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University. There is an error due to the misuse of subordination. There are some advice in order to solve the problem. That is, first to segment the sentence and then reconstruct the sentence. For instance, the Chinese expression “南京医科大学附属儿童医院” carries many modifiers, which requires to cut the modifiers and reconstruct the sentence. Therefore, the Chinese expression “南京医科大学附属儿童医院’can be divided into abstractions, leaving two sentences instead of one long sentence with modifiers..<br />
After pre-editing source abstracts: 对49例中重度HIE患儿以及31例无神经系统症状体征的足月新生儿的MRI资料进行回顾性分析。这些患者收治于南京医科大学儿童附属医院。<br />
After pre-editing translation: The MRI data of 49 children with moderate to severe HIE and 31 full-term newborns without neurological symptoms and signs were retrospectively analyzed. These patients were admitted to the Children’s Hospital of Nanjing Medical University.<br />
===3.3 Explication of Subject through Voice Changing===<br />
The extensive use of subjectless sentences are quite common in the Chinese abstracts, while English prefers to employ passive voice in the sentences so as to make them object and accurate. In order to take the characteristics of English sentences into great and careful consideration, the sentences written in passive voice in particular, it will be helpful for machine translation to find out the subject and reconstruct a sentence in passive voice (Wang Yan: 2008). The first is to discover the “doee” in a sentence and then is to reconstruct the sentence structure and put the “doee” before the verb. The last is to explicate the passive relation between the noun and the verb.<br />
Example 3<br />
Source abstract: 回顾性分析2018年1月至2020年12月解放军总医院第七医学中心收治的500例老年髋部骨折患者的资料。<br />
Google translation: A retrospective analysis of the data of 500 elderly patients with hip fractures admitted to the Seventh Medical Center of the PLA General Hospital from January 2018 to December 2020.<br />
Published translation: From January 2018 to December 2020, the data of 500 elderly patients with hip fracture treated in the Seventh Medical Center of PLA General Hospital were analyzed retrospectively.<br />
Analysis: The above example indicates that the “回顾性分析”in the Google Translate is a noun phrase, but the source abstracts actually describes an action or a behavior. The reason for such a mistake is that the machine can’t recognize the Chinese subjetless sentences. To find the subject of the sentence, the source abstracts can be revised and adjusted. Finding the “doee” is the first step, which refers to “500例老年髋部骨折患者的资料”in the source abstracts. And next is to put it in front of the verb, that is to place it in the beginning of the sentence. Last but not least, the passive voice should be explicated in the sentence. <br />
After pre-editing source abstract: 500例老年髋部骨折患者的资料被回顾性分析，他们在2018年1月之2020年12月期间收治于解放军总医院第七医学中心。<br />
After pre-editing translation: The data of 500 elderly hip fracture patients were retrospectively analyzed. They were admitted to the Seventh Medical Center of the PLA General Hospital between January 2018 and December 2020.<br />
===3.4 Relocation of Modifiers===<br />
Modifiers, including noun, adverbial and attributive are constantly employed in the Chinese medical papers in order to add information to subject and object in the sentence. By doing this, complicated sentences can be structured, thus causing obstacles to machine translation for recognizing this complex sentence structures when translating Chinese-English sentences. To make the machine translation successfully recognize the sentence structure, simplifying the sentence structure is quite necessary. The key is to moving the location of the modifiers and thus making the modifiers an independent sentence. In other words, the modifiers ought to be put before or behind the main part of the sentence to satisfy the common use of English. <br />
Usually, the modifiers in Chinese sentence can only be placed in front of the core word, while in English, modifiers are very flexible. It is all right to place the modifiers in front of or behind the major part of the sentences with adjective or a connective noun.<br />
Example 4<br />
Source abstracts: 利用DNA重组技术以pET-28a表达系统在E.coli BL21(DE3) 中重组表达Hepl。<br />
Google Translation: Hepl is recombined in E.coli BL21 (DE3) using DNA recombination technology with pET-28a expression system.<br />
Published translation: The recombinant Hcpl protein was expressed by using DNA recombination technology through pET-28a expression system in E. coli BL21 (De3).<br />
Analysis: The Chinese medical text includes two modifiers, “利用DNA”and “以pET-28a)表达系统”. These two modifiers will be translated by machine, which catches more attention to the two constituents. From the Google translation above, one failure is obvious that it misplaces the location of the two modifiers and presents it in not accurate form. To make it translate correctly by machine translation, dividing the sentences is very important so that the source abstracts can be correctly recognized by machine translation.<br />
After pre-editing source abstract: 利用DNA重组技术，Hcp1重组表达在E.coli BL21 (DE3)中， 通过pET-28a表达系统在E. coli BL21 (DE3)中。<br />
Google translation: Using DNA recombination technology, Hcp1 recombination is expressed in E.coli BL21 (DE3), via pET-28a expression system in E. coli BL21 (DE3).<br />
The second is the independence of modifiers, that is, modifiers can also be reconstructed into clauses to modify core words. Compared with English, There is no subordinate clause in Chinese, so redundant Chinese modifiers need to be reconstructed into subordinate clauses in Chinese-English translation to meet the characteristics of English. English, especially sentences with multiple modifiers. Otherwise, sentence structure may be confused, such as scattered modifiers of core words. To avoid this mistake, it is necessary to separate the modifier from the main part of the sentence. These modifiers should be reconstituted into clauses. Also, keep your sentences simple and easy to understand.<br />
===3.5 Proper Omission and Deletion of Category Words===<br />
Category words are commonly used in Chinese. Category words complement the meaning of words, including problems, positions, situations and jobs. Adding this supplementary word is more in line with Chinese custom. In many cases, it has no real meaning, so it can be omitted in translation. In Chinese, category words are frequently used. However, it is rarely used in English, which is one of the differences between Chinese and English. There are many kinds of category words. Considering objectivity and the fixed structure of language, redundant category words should be deleted in Chinese-English translation. In the general, the most commonly used category of words in the Chinese medical abstracts are "process", "behavior", and "situation". These categories of words hinder the language conversion of Chinese and English, resulting in redundancy. <br />
Example 5<br />
Source abstract: 探讨口腔白斑病癌变进程中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia response genes and associated microRNAs in the process of oral leukoplakia cancer was discussed.<br />
Published translation: To study the hypoxia response gene and microRNA (miRNA)expression profiles in the pathogenesis and progression of oral leukoplakia (OLK).<br />
Analysis: As the above example shows, the Chinese words “进程” belongs to a category word. However, the Chinese expression “癌变”contains the process, so the “进程” expression can be deleted before placing it into the machine translation, because the meaning of it has been overlapping between the expression “癌变”. Therefore, its place can be replaced with preposition “during”.<br />
After pre-editing source abstract: 探讨口腔白斑病癌变中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia responsive genes and miRNA in oral leukoplakia cancer was investigated.<br />
<br />
===Conclusion===<br />
After years of development, machine translation has made great progress. The accuracy of machine translation has been greatly improved in both text recognition and sentence pattern conversion. However, machine translation has its own limitations. In other words, it needs to rely on the parallel corpus as a reference source for improving its accuracy. ESP text, in particular, is harder to get the high quality by machine translation. <br />
As one of the research papers, the characteristics of medical abstracts are fixed language structures, objectivity and accuracy (Qin Yi:2004). Therefore, medical translation must be accurate, object and understandable to follow the specific demands of the medical paper. Being an important field in the human society, medical paper translation is on a great demand, which means that it needs a huge demand for human labor. However, with the machine translation promoting, it will be more efficient to translate medical papers combining the effort by human and machine. The improvement and development of machine translation requires the joint efforts of computer science, information science, statistics, linguistics and other academic circles to achieve more mature human-computer mutual assistance translation (Li Yafei, Zhang Ruihua : 2019).<br />
However, errors can occur during the process of machine translation of Chinese- English, because of the differences of the Chinese and English and the processing of the machine. Errors from the perspective of linguistic or grammar can affect the machine translation a lot. After division and recognition of errors, some pre-editing approaches are put forward to help the machine translation more accurate and readable, that are, extraction and replacement of terms in the source text, relocation of modifiers, explication of subordination, proper omission, deletion of category words and explication of subject through voice changing. <br />
The paper mainly focuses on the pre-editing machine translation by using medical papers as a case study. The errors of machine translation occurring in the translation of medical abstracts and pre-editing approaches for machine translation. The quality of machine translation of medical papers is greatly improved after employing the pre-editing methods. However, machine translation is not as flexible and accurate as human brain, so it is of importance to combine pre-editing and post-editing approaches with machine translation in order to produce more accurate, more object machine translation of medical papers.<br />
<br />
===References===<br />
[1]. Cronin, Michael (2013). Translation in the Digital Age[M]. New York&London: Routledge.<br />
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[2]. GERLACH J, et al ( 2013). Combining Pre-editing and Post-editing to Improve SMT of User-generated Content[M]// Proceedings of MT Summit XIV Workshop on Post-editing Technology and Practice. 45-53.<br />
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[3]. Slype G V & Guinet J F & Seitz F (1984). Better Translation for Better Communication [M] .Oxford: Pergamon Press Ltd (U.K.), <br />
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[4]. O'Brien S (2002). Teaching Post-editing: A Proposal for Course Con-tent [EB/OL]. http://mt-archive. Info/EAMT-2002-0brien. Pdf.<br />
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[5]. Tytler, A. F. (1978). Essay On The Principles of Translation[M]. Amsterdam: JohnBenjamins Publishing.<br />
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[6] 崔启亮. (2014), 论机器翻译的译后编辑[J], 中国翻译, 035(006):68-73.<br />
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[7] 冯全功,高琳 (2017) 基于受控语言的译前编辑对机器翻译的影响[J]. 当代外语研究,(2): 63-68+87+110.<br />
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[8] 胡清平(2005). 机器翻译中的受控语言[J]. 中国科技翻译, (03): 24-27. <br />
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[9] 连淑能 (2010). 英汉对比研究增订本[M]. 北京:高等教育出版社.<br />
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<br />
=12 蔡珠凤=(The Mistranslation of C-J Machine Translation of Political Statements)=<br />
[[Machine_Trans_EN_12]]<br />
===Abstract===<br />
Language is the main way of communication between people. With the continuous development of globalization, the scale of cross-border exchanges is also expanding. However, due to cultural differences and diversity, the languages of different countries and regions are very different, which seriously hinders people's communication. The demand for efficient and convenient translation tools is increasing. At the same time, with the development of network technology and artificial intelligence, recognition technology based on deep learning is more and more widely used in English, Japanese and other fields.<br />
<br />
===Key words===<br />
machine translation; political statements; mistranslation of C-J machine translation<br />
===题目===<br />
The Mistranslation of C-J Machine Translation of Political Statements<br />
===摘要===<br />
语言是人与人之间交流的主要方式。随着全球化的不断发展，跨境交流的规模也在不断扩大。然而，由于文化的差异和多样性，不同国家和地区的语言差异很大，这严重阻碍了人们的交流。对高效便捷的翻译工具的需求正在增加。同时，随着网络技术和人工智能的发展，基于深度学习的识别技术在英语、日语等领域的应用越来越广泛。<br />
<br />
===关键词===<br />
机器翻译；政治发言；政治发言中译日的误译<br />
===1. Introduction===<br />
===Introduction to machine translation===<br />
Machine translation, also known as automatic translation, is a process of using computers to convert one natural language (source language) into another natural language (target language). It is a branch of computational linguistics, one of the ultimate goals of artificial intelligence, and has important scientific research value.At the same time, machine translation has important practical value. With the rapid development of economic globalization and the Internet, machine translation technology plays a more and more important role in promoting political, economic and cultural exchanges.The development of machine translation technology has been closely accompanied by the development of computer technology, information theory, linguistics and other disciplines. From the early dictionary matching, to the rule translation of dictionaries combined with linguistic expert knowledge, and then to the statistical machine translation based on corpus, with the improvement of computer computing power and the explosive growth of multilingual information, machine translation technology gradually stepped out of the ivory tower and began to provide real-time and convenient translation services for general users.（Zhang 2019:5-6)<br />
<br />
=== C-J machine translation software===<br />
Today's online machine translation software includes Baidu translation, Tencent translation, Google translation, Youdao translation, Bing translation and so on. Google was the first company to launch the machine translation system, and Baidu was the first company to import the machine translation system in China. In addition, Tencent and Youdao have attracted much attention.Machine translation is the process of using computers to convert one natural language into another. It usually refers to sentence and full-text translation between natural languages. In order to continuously improve the translation quality, R & D personnel have added artificial intelligence technologies such as speech recognition, image processing and deep neural network to machine translation on the basis of traditional machine translation based on rules, statistics and examples.With the increase of using machine translation, the joint cooperation between manual translation and machine translation will also increase significantly in the future. What criteria should be used to evaluate the quality of machine translation? In the evolving field of machine translation, there is an urgent need to clarify the unsolvable questions and solved problems.<br />
<br />
===The history of machine translation===<br />
The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. alchuni put forward the idea of using machines for translation. In 1933, Soviet inventor П.П. Trojansky designed a machine to translate one language into another, and registered his invention on September 5 of the same year; However, due to the low technical level in the 1930s, his translation machine was not made. In 1946, the first modern electronic computer ENIAC was born. Shortly after that, W. weaver, an American scientist and A. D. booth, a British engineer, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947 when discussing the application scope of electronic computer. In 1949, W. Weaver published the translation memorandum, which formally put forward the idea of machine translation. After 60 years of ups and downs, machine translation has experienced a tortuous and long development path. The academic community generally divides it into the following four stages:<br />
<br />
Pioneering period（1947-1964）<br />
<br />
In 1954, with the cooperation of IBM, Georgetown University completed the English Russian machine translation experiment with ibm-701 computer for the first time, showing the feasibility of machine translation to the public and the scientific community, thus opening the prelude to the study of machine translation.<br />
It is not too late for China to start this research. As early as 1956, the state included this research in the national scientific work development plan. The topic name is "machine translation, the construction of natural language translation rules and the mathematical theory of natural language". In 1957, the Institute of language and the Institute of computing technology of the Chinese Academy of Sciences cooperated in the Russian Chinese machine translation experiment, translating 9 different types of more complex sentences.From the 1950s to the first half of the 1960s, machine translation research has been on the rise. The United States and the former Soviet Union, two superpowers, have provided a lot of financial support for machine translation projects for military, political and economic purposes, while European countries have also paid considerable attention to machine translation research due to geopolitical and economic needs, and machine translation has become an upsurge for a time. In this period, although machine translation is just in the pioneering stage, it has entered an optimistic period of prosperity.<br />
<br />
Frustrated period（1964-1975）<br />
<br />
In 1964, in order to evaluate the research progress of machine translation, the American Academy of Sciences established the automatic language processing Advisory Committee (Alpac Committee) and began a two-year comprehensive investigation, analysis and test.In November 1966, the committee published a report entitled "language and machine" (Alpac report for short), which comprehensively denied the feasibility of machine translation and suggested stopping the financial support for machine translation projects. The publication of this report has dealt a blow to the booming machine translation, and the research of machine translation has fallen into a standstill. Coincidentally, during this period, China broke out the "ten-year Cultural Revolution", and basically these studies also stagnated. Machine translation has entered a depression.<br />
<br />
convalescence（1975-1989）<br />
<br />
Since the 1970s, with the development of science and technology and the increasingly frequent exchange of scientific and technological information among countries, the language barriers between countries have become more serious. The traditional manual operation mode has been far from meeting the needs, and there is an urgent need for computers to engage in translation. At the same time, the development of computer science and linguistics, especially the substantial improvement of computer hardware technology and the application of artificial intelligence in natural language processing, have promoted the recovery of machine translation research from the technical level. Machine translation projects have begun to develop again, and various practical and experimental systems have been launched successively, such as weinder system Eurpotra multilingual translation system, taum-meteo system, etc.However, after the end of the "ten-year holocaust", China has perked up again, and machine translation research has been put on the agenda again. The "784" project has paid enough attention to machine translation research. After the mid-1980s, the development of machine translation research in China has further accelerated. Firstly, two English Chinese machine translation systems, ky-1 and MT / ec863, have been successfully developed, indicating that China has made great progress in machine translation technology.<br />
<br />
New period(1990 present)<br />
<br />
With the universal application of the Internet, the acceleration of the process of world economic integration and the increasingly frequent exchanges in the international community, the traditional way of manual operation is far from meeting the rapidly growing needs of translation. People's demand for machine translation has increased unprecedentedly, and machine translation has ushered in a new development opportunity. International conferences on machine translation research have been held frequently, and China has made unprecedented achievements. A series of machine translation software have been launched, such as "Yixing", "Yaxin", "Tongyi", "Huajian", etc. Driven by the market demand, the commercial machine translation system has entered the practical stage, entered the market and came to the users.Since the new century, with the emergence and popularization of the Internet, the amount of data has increased sharply, and statistical methods have been fully applied. Internet companies have set up machine translation research groups and developed machine translation systems based on Internet big data, so as to make machine translation really practical, such as "Baidu translation", "Google translation", etc. In recent years, with the progress of in-depth learning, machine translation technology has further developed, which has promoted the rapid improvement of translation quality, and the translation in oral and other fields is more authentic and fluent.<br />
<br />
===The problem of machine translation at present===<br />
Error is inevitable<br />
Many people have misunderstandings about machine translation. They think that machine translation has great deviation and can't help people solve any problems. In fact, the error is inevitable. The reason is that machine translation uses linguistic principles. The machine automatically recognizes grammar, calls the stored thesaurus and automatically performs corresponding translation. However, errors are inevitable due to changes or irregularities in grammar, morphology and syntax, such as sentences with adverbials after "give me a reason to kill you first" in Dahua journey to the West. After all, a machine is a machine. No one has special feelings for language. How can it feel the lasting charm of "the tenderness of lowering its head, like the shame of a water lotus? After all, the meaning of Chinese is very different due to the changes of morphology, grammar and syntax and the change of context. Even many Chinese people are zhanger monks - they can't touch their heads, let alone machines.<br />
Bottleneck<br />
In fact, no matter which method, the biggest factor affecting the development of machine translation lies in the quality of translation. Judging from the achievements, the quality of machine translation is still far from the ultimate goal.<br />
Chinese mathematician and linguist Zhou Haizhong once pointed out in his paper "fifty years of machine translation": to improve the quality of machine translation, the first thing to solve is the problem of language itself rather than programming; It is certainly impossible to improve the quality of machine translation by relying on several programs alone. At the same time, he also pointed out that it is impossible for machine translation to achieve the degree of "faithfulness, expressiveness and elegance" when human beings have not yet understood how the brain performs fuzzy recognition and logical judgment of language. This view may reveal the bottleneck restricting the quality of translation. <br />
It is worth mentioning that American inventor and futurist ray cozwell predicted in an interview with Huffington Post that the quality of machine translation will reach the level of human translation by 2029. There are still many disputes about this thesis in the academic circles.<br />
<br />
===2.Mistranslation of Chinese Japanese machine translation===<br />
===2.1Vocabulary mistranslation in Chinese Japanese machine translation===<br />
===2.1.1Mistranslation of proper nouns===<br />
In political materials, there are often political dignitaries' names, place names or a large number of proper nouns in the political field. The morphemes of such words are definite and inseparable. Mistranslation will make the source language lose its specific meaning. <br />
<br />
Japanese translation into Chinese Chinese translation into Japanese<br />
<br />
original text translation by Youdao reference translation original text translation by Youdao reference translation<br />
<br />
朱鎔基 朱基 朱镕基 栗战书 栗戰史書 栗戰書<br />
<br />
労安 劳安 劳安 李克强 李克強 李克強 <br />
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筑紫哲也 筑紫哲也 筑紫哲也 习近平 習近平 習近平<br />
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山口百惠 山口百惠 山口百惠 韩正 韓中 韓正<br />
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田中角栄 田中角荣 田中角荣 王沪宁 王上海氏 王滬寧<br />
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東条英機 东条英社 东条英机 汪洋 汪洋 汪洋<br />
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毛沢东 毛泽东 毛泽东 赵乐际 趙樂南 趙樂際<br />
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トウ・ショウヘイ 大酱 邓小平 江泽民 江沢民 江沢民<br />
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周恩来 周恩来 周恩来<br />
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クリントン 克林顿 克林顿<br />
<br />
The above table counts 18 special names in the two texts, and 7 machine translation errors. In terms of mistranslation, there are not only "战书" but also "戰史" and "史書" in Chinese. "沪" is the abbreviation of Shanghai. In other words, since "战书" and "沪" are originally common nouns, this disrupts the choice of target language in machine translation. Except Katakana interference (except for トウシゃウヘイ, most of the mistranslations appear in the new Standing Committee. It can be seen that the machine translation system does not update the thesaurus in time. Different from other words, people's names have particularity. Especially as members of the Standing Committee of the Political Bureau of the CPC Central Committee, the translation of their names is officially unified. In this regard, public figures such as political dignitaries, stars, famous hosts and important. In addition, the machine also translates Japanese surnames such as "タ二モト" (谷本), "アンドウ" (安藤) into "塔尼莫特" and "龙胆". It can be found that the language feature that Japanese will be written together with Chinese characters and Hiragana also directly affects the translation quality of Japanese Chinese machine translation. Japanese people often use Katakana pronunciation. For example, when Japanese people talk with Premier Zhu, they often use "二ーハウ" (Hello). However, the machine only recognizes the two pseudonyms "二" and "ハ" and transliterates them into "尼哈" , ignoring the long sound after "二" and "ハ".<br />
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original text Translation by Youdao reference translation<br />
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日美安全体制 日米の安全体制 日米安保体制<br />
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中国共产党第十九次全国代表大会 中国共産党第19回全国代表大会 中国共産党第19回全国代表大会（第19回党大会）<br />
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十八大 十八大 第18回党大会中国特色社会主义<br />
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中国特色社会主義 中国の特色ある社会主義 第18回党大会<br />
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中国共产党中央委员会 中国共産党中央委員会 中国共産党中央委員会<br />
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中国共産党中央委員会十八届中共中央政治局常委 第18代中国共產党中央政治局常務委員 第18期中共中央政治局常務委員<br />
<br />
十八届中共中央政治局委员 18期の中国共產党中央政治局委員 第18期中共中央政治局委員<br />
<br />
十九届中共中央政治局常委 十九回中国共產党中央政治局常務委員 第19期中央政治局常務委員<br />
<br />
中共十九届一中全会 中国共產党第十九回一中央委員会 第19期中央委員会第1回全体会議<br />
<br />
The above table is a comparison of the original and translated versions of some proper nouns. As shown in the table, the mistranslation problems are mainly reflected in the mismatch of numerals + quantifiers, the wrong addition of case auxiliary word "の", the lack of connectors, the Mistranslation of abbreviations, dead translation, and the writing errors of Chinese characters. The following is a specific analysis one by one.<br />
<br />
"十八届中共中央政治局常委", "十八届中共中央政治局委员", "十九届中共中央政治局常委" and "中共十九届一中全会" all have quantifiers "届", which are translated into "代", "期" and "回" respectively. The meaning is vague and should be uniformly translated into "期"; Among them, the translation of the last three proper nouns lacks the Chinese conjunction "第". The case auxiliary word "の" was added by mistake in the translation of "十八届中共中央政治局委员" and "日美安全体制". The "中国共产党中央委员会" did not write in the form of Japanese Ming Dynasty characters, but directly used simplified Chinese characters.<br />
<br />
The full name of "中共十九届一中全会" is "中国共产党第十九届中央委员会第一次全体会议", in which "一" stands for "第一次", which is an ordinal word rather than a cardinal word. Machine translation does not produce a correct translation. The fundamental reason is that there are no "规则" for translating such words in machine translation. If we can formulate corresponding rules for such words (for example, 中共m'1届m2 中全会→第m1期中央委员会第m2回全体会议), the translation system must be able to translate well no matter how many plenary sessions of the CPC Central Committee.<br />
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===2.1.2Mistranslation of Polysemy===<br />
original text Translation by Youdao reference translation<br />
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スタジオ 摄影棚/工作室 直播现场/演播厅<br />
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日中関係の話 中日关系的故事 就中日关系(话题)<br />
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溝 水沟 鸿沟<br />
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それでは日中の問題について質問のある方。 那么对白天的问题有提问的人。 关于中日问题的话题，举手提问。<br />
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私たちのクラスは20人ちょっとですが、 我们班有20人左右， 我们班二十多人的意见统一很难，<br />
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いろいろな意見が出て、まとめるのは大変です。 但是又各种各样的意见，总结起来很困难。 <br />
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一体どうやって、13億人もの人をまとめているんですか。 到底是怎么处理13亿的人的呢？ 中国是怎样把13亿人凝聚在一起的？<br />
<br />
In the original text, the word "スタジオ" appeared four times and was translated into "摄影棚" or "工作室" respectively, but the context is on-site interview, not the production site of photography or film. "話" has many semantics, such as "说话", "事情", "道理", etc. In accordance with the practice of the interview program, Premier Zhu Rongji was invited to answer five questions on the topic of China Japan relations. Obviously, the meaning of the word "故事" is very abrupt. The inherent Japanese word "日中" means "晌午、白天". At the same time, it is also the abbreviation of the names of the two countries. The machine failed to deal with it correctly according to the context. "溝" refers to the gap between China and Japan, not a "水沟". The former "まとめる" appears in the original text with the word "意见", which is intended to describe that it is difficult to unify everyone's opinions. The latter "まとめている" also refers to unifying the thoughts of 1.3 billion people, not "处理". Therefore, it is more appropriate to use "统一" and "处理" in human translation, rather than "总结意见" or "处理意见".<br />
<br />
Mistranslation of polysemy has always been a difficult problem in machine translation research. The translation of each word is correct, but it is often very different from the original expression in the context. Zhang Zhengzeng said that ambiguity is a common phenomenon in natural language. Its essence is that the same language form may have different meanings, which is also one of the differences between natural language and artificial language. Therefore, one of the difficulties faced by machine translation is language disambiguation (Zhang Zheng, 2005:60). In this regard, we can mark all the meanings of polysemous words and judge which meaning to choose by common collocation with other words. At the same time, strengthen the text recognition ability of the machine to avoid the translation inconsistent with the current context. In this way, we can avoid the mistake of "大酱" in the place where famous figures such as Deng Xiaoping should have appeared in the previous political articles.<br />
<br />
===2.1.3Mistranslation of compound words===<br />
Multi class words refer to a word with two or more parts of speech, also known as the same word and different classes.<br />
<br />
original text Translation by Youdao reference translation<br />
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1998年江泽民主席曾经访问日本, 1998年の江沢民国家主席の日本訪問し、 1998年、江沢民総書記が日本を訪問し、かつ<br />
<br />
同已故小渊首相签署了联合宣言。 かつて同じ故小渕首相が署名した共同宣言 亡くなられた小渕総理と宣言に調印されました<br />
<br />
Chinese "同" has two parts of speech: prepositions and conjunctions: "故" has many parts of speech, such as nouns, verbs, adjectives and conjunctions. This directly affects the judgment of the machine at the source language level. The word "同" in the above table is used as a conjunction to indicate the other party of a common act. "故" is used as a verb with the semantic meaning of "死亡", which is a modifier of "小渊首相". The machine regards "同" as an adjective and "故" as a noun "原因", which leads to confusion in the structure and unclear semantics of the translation. Different from the polysemy problem, multi category words have at least two parts of speech, and there is often not only one meaning under each part of speech. In this regard, software R & D personnel should fully consider the existence of multi category words, so that the translation machine can distinguish the meaning of words on the basis of marking the part of speech, so as to select the translation through the context and the components of the word in the sentence. Of course, the realization of this function is difficult, and we need to give full play to the wisdom of R & D personnel.<br />
<br />
===2.2 Syntactic mistranslation in Chinese Japanese machine translation===<br />
===2.2.1Mistranslation of tenses===<br />
original text：History is written by the people, and all achievements are attributed to the people. <br />
<br />
Translation by Youdao：歴史は人民が書いたものであり、すべての成果は人民のためである。<br />
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reference translation：歴史は人民が綴っていくものであり、すべての成果は人民に帰することとなります。<br />
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The original sentence meaning is "历史是人民书写的历史" or "历史是人民书写的东西". When translated into Japanese, due to the influence of Japanese language habits, the formal noun "もの" should be supplemented accordingly. In this sentence, both the machine and the interpreter have translated correctly. However, the machine's recognition of "的" is biased, resulting in tense translation errors. The past, present and future will become "history", and this is a continuous action. The form translated as "~ ていく" not only reflects that the action is a continuous action, but also conforms to the tense and semantic information of the original text. In addition, the machine's treatment of the preposition "于" is also inappropriate. In the original text, "人民" is the recipient of action, not the target language. As the most obvious feature of isolated language, function words in Chinese play an important role in semantic expression, and their translation should be regarded as a focus of machine translation research.<br />
<br />
original text：李克强同志是十六届中共中央政治局常委,其他五位同志都是十六届中共中央政治局委员。<br />
<br />
Translation by Youdao：李克強総理は第16代中国共産党中央政治局常務委員であり、他の５人の同志はいずれも16期の中国共産党中央政治局委員である。<br />
<br />
reference translation：李克強同志は第16期中国共産党中央政治局常務委員を務め、他の５人は第16期中共中央政治局委員を務めました。<br />
<br />
Judging the verb "是" in the original text plays its most basic positive role, but due to the complexity of Chinese language, "是" often can not be completely transformed into the form of "だ / である" in the process of translation. This sentence is a judgment of what has happened. Compared with the 19th session, the 18th session has become history. This is an implicit temporal information. It is very difficult for machines without human brain to recognize this implicit information. "中共中央政治局常委" is the abbreviation of "中共中央政治局常务委员会委员" and it is a kind of position. In Japanese, often use it with verbs such as "務める","担当する",etc. The translator adopts the past tense of "務める", which conforms to the expression habit of Japanese and deals with the problem of tense at the same time. However, machine translation only mechanically translates this sentence into judgment sentence, and fails to correctly deal with the past information implied by the word "十八届".<br />
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===2.2.2Mistranslation of honorifics===<br />
Honorific language is a language means to show respect to the listener. Different from Japanese, there is no grammatical category of honorifics in Chinese. There is no specific fixed grammatical form to express honorifics, self modesty and politeness. Instead, specific words such as "您", "请" and "劳驾" are used to express all kinds of respect or self modesty. <br />
<br />
Original text translation by Youdao reference translation<br />
<br />
女士们，先生们，同志们，朋友们 さんたち、先生たち、同志たち、お友达さん ご列席の皆さん<br />
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谢谢大家！ ありがとうございます！ ご清聴ありがとうございました。<br />
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これはどうされますか。 这是怎么回事呢? 您将如何解决这一问题？<br />
<br />
こうした問題をどうお考えでしょうか。 我们会如何考虑这些问题呢？ 您如何看待这一问题？<br />
<br />
For example, for the processing of "女士们，先生们，同志们，朋友们", the machine makes the translation correspond to the original one by one based on the principle of unchanged format, but there are errors in semantic communication and pragmatic habits. When speaking on formal occasions, Chinese expression tends to be comprehensive and detailed, as well as the address of the audience. In contrast, Japanese usually uses general terms such as "皆様", "ご臨席の皆さん" or "代表団の方々" as the opening remarks. In terms of Japanese Chinese translation, the machine also failed to recognize the usage of Japanese honorifics such as "さ れ る" and "お考え". It can be seen that Chinese Japanese machine translation has a low ability to deal with honorific expressions. The occasion is more formal. A good translation should not only be fluent in meaning, but also conform to the expression habits of the target language and match the current translation environment.<br />
<br />
===Conclusion===<br />
In the process of discussing the Mistranslation of machine translation, this paper mainly cites the Mistranslation of Youdao translator. When I studied the problem of machine translation mistranslation, I also used a large number of translation software such as Google and Baidu for parallel comparison, and found that these translation software also had similar problems with Youdao translator. For example, the "新世界新未来" in Chinese ABAC structural phrases is translated by Baidu as "新し世界の新しい未来", which, like Youdao, is not translated in the form of parallel phrases; Google online translation translates the word "“全心全意" into a completely wrong "全面的に"; The original sentence "我吃了很多亏" in the Mistranslation of the unique expression in the language is translated by Google online as "私はたくさんの損失を食べました". Because the "吃" and "亏" in the original text are not closely adjacent, the machine can not recognize this echo and mistakenly treats "亏" as a kind of food, so the machine translates the predicate as "食べました"; Japanese "こうした問題をどう考えでしょうか", Google's online translation is "你如何看待这些问题?", "你" tone can not reflect the tone of Japanese honorific, while Baidu translates as "你怎么想这样的问题呢?" Although the meaning can be understood, it is an irregular spoken language. Google and Baidu also made the same mistakes as Youdao in the translation of proper nouns. For example, they translated "十七届(中共中央政治局常委)”" into "第17回..." .In short, similar mistranslations of Youdao are also common in Baidu and Google. Due to space constraints, they will not be listed one by one here.<br />
<br />
Mr. Liu Yongquan, Institute of language, Chinese Academy of Social Sciences (1997) It has been pointed out that machine translation is a linguistic problem in the final analysis. Although corpus based machine translation does not require a lot of linguistic knowledge, language expression is ever-changing. Machine translation based solely on statistical ideas can not avoid the translation quality problems caused by the lack of language rules. The following is based on the analysis of lexical, syntactic and other mistranslations From the perspective of the characteristics of the source language, this paper summarizes some difficulties of Chinese and Japanese in machine translation.<br />
<br />
(1) The difficulties of Chinese in machine translation <br />
<br />
Chinese is a typical isolated language. The relationship between words needs to be reflected by word order and function words. We should pay attention to the transformation of function words such as "的", "在", "向" and "了". Some special verbs, such as "是", "做" and "作", are widely used. How to translate on the basis of conforming to Japanese pragmatic habits and expressions is a difficulty in machine translation research. In terms of word order, Chinese is basically "subject→predicate→object". At the same time, Chinese pays attention to parataxis, and there is no need to be clear in expression with meaningful cohesion, which increases the difficulty of Chinese Japanese machine translation. When there are multiple verbs or modifier components in complex long sentences, machine translation usually can not accurately divide the components of the sentence, resulting in the result that the translation is completely unreadable. <br />
<br />
(2) Difficulties of Japanese in machine translation <br />
<br />
Both Chinese and Japanese languages use Chinese characters, and most machines produce the target language through the corresponding translation of Chinese characters. However, pseudonyms in Japanese play an important role in judging the part of speech and meaning, and can not only recognize Chinese characters and judge the structure and semantics of sentences. Japanese vocabulary is composed of Chinese vocabulary, inherent vocabulary and foreign vocabulary. Among them, the first two have a great impact on Chinese Japanese machine translation. For example "提出" is translated as "提出する" or "打ち出す"; and "重视" is translated as "重視する" or "大切にする". Japanese is an adhesive language, which contains a lot of "けど", "が" and "けれども" Some of them are transitional and progressive structures, but there are also some sequential expressions that do not need translation, and these translation software often can not accurately grasp them.<br />
<br />
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http://www.elecfans.com/rengongzhineng/692245.html<br />
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https://baike.baidu.com/item/%E6%9C%BA%E5%99%A8%E7%BF%BB%E8%AF%91/411793<br />
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=13 陈湘琼Chen Xiangqiong（Study on Post-editing from the Perspective of Functional Equivalence Theory ）=<br />
[[Machine_Trans_EN_13]]<br />
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=14 Bi bi Nadia（Machine Translation a Challenge for Human Translators)=<br />
[[Machine_Trans_EN_14]]<br />
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===Abstract===<br />
Machine translation is a big obstacle in the way of Human translators or interpretors although it is quick and less time consuming.people are trying to get translation of their target language using through source language.For this purpose they are using digital apps like Google translation Google translation does not give accurate and exact interpretation.Google translator is translates word to word translate that doesn't clarifies it's true and actual meaning.On the other hand human translators can give exact and accurate translation ,they take care of grammatical errors , diction and sentence structure.They clarify the purpose of target language through using source language.<br />
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===Keywords===<br />
Descriptive translation, academic, interdiscipline, comparative literature, localization,Translatology,school of thought,translation , studies, linguistics, corresponding.<br />
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===Body of article===<br />
Translation is the process of reworking text from one language into another to maintain the original message and communication. But, like everything else, there are different methods of translation, and they vary in form and function. What is translation? The term has become widely used among knowledge transfer researchers and practitioners, especially in the fields of health and health care. In a landmark review, Jonathan Lomas began to argue that 'The tasks… may be defined as to establish and maintain links between researchers and their audience, via the appropriate translation of research findings' (Lomas 1997, p 4). In 2004, the World Health Organization's World Report on Knowledge for Better Health suggested that 'One of the key contributions of research to health systems is the translation of knowledge into actions' (WHO 2004, p 33 and p 100). By 2006, special issues of WHO's Bulletin as well as the journals Evaluation and the Health Professions and the Journal of Continuing Education in the Health Professions were dedicated to translation.But what does 'translation' mean? It may be a new word for an old problem, meaning nothing more than 'transfer'. The rapidly emerging field of 'translational medicine' seems to take translation to mean generally what transfer might have meant, that is the transmission of knowledge and evidence 'from bench to bedside'. As one commentator has observed, "Translational medicine" as a fashionable term is being increasingly used to describe the wish of biomedical researchers to ultimately help patients' (Wehling 2008, abstract). <br />
Semantic uncertainty persists, not least because of the different interests of scientists, clinicians, patients and commercial firms (Littman et al 2007): 'Translational research means different things to different people, but it seems important to almost everyone' (Woolf 2008, p 211).<br />
In related fields, such as public health (Armstrong et al 2006), 'translation' seems to signify dissatisfaction with 'transfer'. It wants to move away from thinking of knowledge transfer as a form of technology transfer or dissemination, rejecting if only by implication its mechanistic assumptions and its model of linear messaging from A to B. But still, what does it signify?<br />
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===Why translation?===<br />
'Translation' indicates a closer attention to the problem of shared meaning and how it might be developed. It seems to represent some new epistemological lubricant, facilitating the dissemination of texts and the application and use of the knowledge and information they in. Simply, translation might be the key to transfer. And yet, when we stop to think, we are more ambivalent. What is translated often seems somehow inferior, not real or original. Note how readily commentators reach for the idea that things might be 'lost in translation'. Knowing at a distance – made in and mediated by translation - makes for incomplete renditions, blurred images, partial truths. So what might 'translation' really mean? The purpose of this paper is to set out, for policy makers and practitioners, the theoretical and conceptual resources translation holds and seems to represent. In doing so, it explores understandings of translation in the fields of literature and linguistics and in the sociology of science and technology. It begins by setting out just why this idea of translation should make immediate, intuitive sense in relation to research, policy and practice.<br />
1translation in research, policy and practice research as translation<br />
Research often entails translation from one language to another: where data is collected from more than one ethnic group, for example, or where the language of the researcher is other than that of the research subject. It may draw on a secondary literature or source documents written in different languages, and may be published and disseminated in languages other than the one in which it is first written up.<br />
2In a different way, to conduct an interview is to ask for an account of experience and its meanings, but it is also to construct and translate that experience in terms defined at least in part by the researcher. In representing what is said, transcripts then select data, usually excluding significant gesture and eye-contact, for example. Often, certain characteristics of speech-acts (such as hesitations) will be edited out. In turn, the format of the transcript shapes the analytic use the researcher may make of it. The basis of research 'findings', then, is an artefact, a transcript or translation, not an original interaction (Ochs 1979, Barnes, Bloor and Henry 1996, Ross 2009).<br />
3In this way, the researcher recasts aspects of his or her problem or topic in new, scientific form: 'All researchers "translate" the experiences of others' (Temple 1997, p 609). Research is invariably conducted in a sort of 'metalanguage' (Hantrais and Ager 1985): the research process can be conceived as one of successive translations (from theoretical formulation to operationalization, transcription, interpretation and dissemination). Theorization is a process of reciprocal back and forth between theory and fact, in which conceptions of each are revised in order that one fit the other (Baldamus 1974).<br />
4 It is a kind of translation: a rereading, re-use, re-application or re-representation of what we know in new terms (Turner 1980). Referencing, too, is an act of translation, a form of appropriation and incorporation of one text by another (Gilbert 1977).policy as translation Each of the fields covered by the paper is diverse and ill-defined, and there is no intention here to provide a comprehensive account of any them. Sources have been chosen for their relevance: main references are cited in the text, and additional sources listed in footnotes.<br />
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2For a brief introduction to the technical issues involved in social research in more than one language, see Birbili (2000). For translation issues in survey research and question design in general, see Ervin and Bower (1952) and Deutscher (1968); on translating survey research instruments (in this instance health-related quality of life measures), Bowden and Fox-Rushby (2003). On the use of translators and interpreters, see Temple (1997) and Jentsch (1998).<br />
3For an interesting discussion of this problem, see Bourdieu (1999), esp pp 621-626, 'The risks of writing'. <br />
===Difference between machine translation and human translators===<br />
Few people disagree on the differences between the two, but many argue over the quality of the translations. How accurate are machine translations? How reliable are human translations? Some say machine translation produces near-perfect translations while others are adamant that translations are incomprehensible and cause more problems than they solve. Results will, of course, vary depending on the source and target languages, the machine translation service used (e.g. Google Translate), and the complexity of the original text.<br />
Machine translation, love it or hate it, is here to stay. In fact, the machine translation market is growing at such a fast pace that it is predicted to reach $980 million by 2022.<br />
===Machine translation: the pros and cons===<br />
The advantages of machine translation generally come down to two factors: it’s faster and cheaper. The downside to this is the standard of translation can be anywhere from inaccurate, to incomprehensible, and potentially dangerous (more on that shortly).<br />
==The advantages of machine translation==<br />
Many free tools are readily available (Google Translate, Skype Translator, etc.)<br />
Quick turnaround time <br />
You can translate between multiple languages using one tool<br />
Translation technology is constantly improving<br />
The disadvantages of machine translation<br />
Level of accuracy can be very low <br />
Accuracy is also very inconsistent across different languages<br />
==Machines can't translate context ==<br />
Mistakes are sometimes costly <br />
Sometimes translation simply doesn’t work<br />
The most important thing to consider with any kind of translation is the cost of potential mistakes. Translating instructions for medical equipment, aviation manuals, legal documents and many other kinds of content require 100% accuracy. In such cases, mistakes can cost lives, huge amounts of money and irreparable damage to your company’s image. So choose carefully!<br />
Human translation: the pros and cons<br />
Human translation essentially switches the table in terms of pros and cons. A higher standard of accuracy comes at the price of longer turnaround times and higher costs. What you have to decide is whether that initial investment outweighs the potential cost of mistakes. Alternatively, whether mistakes simply aren’t an option, like the cases we looked at in the previous section.<br />
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==The advantages of human translation ==<br />
It’s a translator’s job to ensure the highest accuracy<br />
Humans can interpret context and capture the same meaning, rather than simply translating words<br />
Human translators can review their work and provide a quality process<br />
Humans can interpret the creative use of language, e.g. puns, metaphors, slogans, etc.<br />
Professional translators understand the idiomatic differences between their languages<br />
Humans can spot pieces of content where literal translation isn’t possible and find the most suitable alternative<br />
The disadvantages of human translation<br />
Turnaround time is longer <br />
Translators rarely work for free <br />
Unless you use a translation agency, with access to thousands of translators, you’re limited to the languages any one translator can work with<br />
Simply put, human translation is your best option when accuracy is even remotely important. Other considerations to make are the complexity of your source material and the two languages you’re translating between – both of which can render machines pretty useless.<br />
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When to use machine and human translation<br />
The truth is, the debate over machine vs human translation is an unnecessary distraction. What we should really be talking about is when to use these two different types of translation services, because they both serve a very valid purpose.<br />
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Examples of when to use machine translation<br />
When you have a large bulk of content to translate and the general meaning is enough<br />
When your translation never reaches the final audience, e.g. you’re translating a resource as research for another piece of content<br />
Translating documents for internal use within a company, provided 100% accuracy isn’t needed<br />
To partially translate large chunks of content for a human translator to improve upon<br />
Examples of when to use human translation<br />
When accuracy is important<br />
Most cases where your translated content is received by a consumer audience<br />
When you have a duty of care to provide accurate translations (e.g. legal documents, product instructions, medical guidelines or health and safety content)<br />
When translating marketing material or other texts for creative language uses.<br />
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types of machine translation.<br />
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What is Machine Translation? Rule Based Machine Translation vs. Statistical Machine Translation. Machine translation (MT) is automated translation. It is the process by which computer software is used to translate a text from one natural language (such as English) to another (such as Spanish).<br />
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To process any translation, human or automated, the meaning of a text in the original (source) language must be fully restored in the target language, i.e. the translation. While on the surface this seems straightforward, it is far more complex. Translation is not a mere word-for-word substitution. A translator must interpret and analyze all of the elements in the text and know how each word may influence another. This requires extensive expertise in grammar, syntax (sentence structure), semantics (meanings), etc., in the source and target languages, as well as familiarity with each local region.<br />
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Human and machine translation each have their share of challenges. For example, no two individual translators can produce identical translations of the same text in the same language pair, and it may take several rounds of revisions to meet customer satisfaction. But the greater challenge lies in how machine translation can produce publishable quality translations.<br />
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Rule-Based Machine Translation Technology<br />
Rule-based machine translation relies on countless built-in linguistic rules and millions of bilingual dictionaries for each language pair.<br />
The software parses text and creates a transitional representation from which the text in the target language is generated. This process requires extensive lexicons with morphological, syntactic, and semantic information, and large sets of rules. The software uses these complex rule sets and then transfers the grammatical structure of the source language into the target language.<br />
Translations are built on gigantic dictionaries and sophisticated linguistic rules. Users can improve the out-of-the-box translation quality by adding their terminology into the translation process. They create user-defined dictionaries which override the system’s default settings.<br />
In most cases, there are two steps: an initial investment that significantly increases the quality at a limited cost, and an ongoing investment to increase quality incrementally. While rule-based MT brings companies to the quality threshold and beyond, the quality improvement process may be long and expensive.<br />
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Statistical Machine Translation Technology<br />
Statistical machine translation utilizes statistical translation models whose parameters stem from the analysis of monolingual and bilingual corpora. Building statistical translation models is a quick process, but the technology relies heavily on existing multilingual corpora. A minimum of 2 million words for a specific domain and even more for general language are required. Theoretically it is possible to reach the quality threshold but most companies do not have such large amounts of existing multilingual corpora to build the necessary translation models. Additionally, statistical machine translation is CPU intensive and requires an extensive hardware configuration to run translation models for average performance levels.<br />
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Rule-Based MT vs. Statistical MT<br />
Rule-based MT provides good out-of-domain quality and is by nature predictable. Dictionary-based customization guarantees improved quality and compliance with corporate terminology. But translation results may lack the fluency readers expect. In terms of investment, the customization cycle needed to reach the quality threshold can be long and costly. The performance is high even on standard hardware.<br />
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Statistical MT provides good quality when large and qualified corpora are available. The translation is fluent, meaning it reads well and therefore meets user expectations. However, the translation is neither predictable nor consistent. Training from good corpora is automated and cheaper. But training on general language corpora, meaning text other than the specified domain, is poor. Furthermore, statistical MT requires significant hardware to build and manage large translation models.<br />
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Rule-Based MT Statistical MT<br />
+ Consistent and predictable quality – Unpredictable translation quality<br />
+ Out-of-domain translation quality – Poor out-of-domain quality<br />
+ Knows grammatical rules – Does not know grammar <br />
+ High performance and robustness – High CPU and disk space requirements<br />
+ Consistency between versions – Inconsistency between versions <br />
– Lack of fluency + Good fluency<br />
– Hard to handle exceptions to rules + Good for catching exceptions to rules <br />
– High development and customization costs + Rapid and cost-effective development costs provided the required corpus exists<br />
Given the overall requirements, there is a clear need for a third approach through which users would reach better translation quality and high performance (similar to rule-based MT), with less investment (similar to statistical MT).<br />
Post-Edited Machine Translation (PEMT)<br />
Often, PEMT is used to bridge the gap between the speed of machine translation and the quality of human translation, as translators review, edit and improve machine-translated texts. PEMT services cost more than plain machine translations but less than 100% human translation, especially since the post-editors don’t have to be fluently bilingual—they just have to be skilled proofreaders with some experience in the language and target region.<br />
Successful translation is about more than just the words, which is why we advocate for not just human translation by skilled linguists, but for translation by people deeply familiar with the cultures they’re writing for. Life experience, study and the knowledge that only comes from living in a geographic region can make the difference between words that are understandable and language that is capable of having real, positive impact. <br />
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PacTranz<br />
The HUGE list of 51 translation types, methods and techniques<br />
Upper section of infographic of 51 common types of translation classified in 4 broad categoriesThere are a bewildering number of different types of translation.<br />
So we’ve identified the 51 types you’re most likely to come across, and explain exactly what each one means.<br />
This includes all the main translation methods, techniques, strategies, procedures and areas of specialisation.<br />
It’s our way of helping you make sense of the many different kinds of translation – and deciding which ones are right for you.<br />
Don’t miss our free summary pdf download later in the article!<br />
The 51 types of translation we’ve identified fall neatly into four distinct categories.<br />
Translation Category A: 15 types of translation based on the technical field or subject area of the text<br />
Icons representing 15 types of translation categorised by the technical field or subject area of the textTranslation companies often define the various kinds of translation they provide according to the subject area of the text.<br />
This is a useful way of classifying translation types because specialist texts normally require translators with specialist knowledge.<br />
Here are the most common types you’re like to come across in this category.<br />
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1. General Translation<br />
What is it?<br />
The translation of non-specialised text. That is, text that we can all understand without needing specialist knowledge in some area.<br />
The text may still contain some technical terms and jargon, but these will either be widely understood, or easily researched.<br />
What this means<br />
The implication is that you don’t need someone with specialist knowledge for this type of translation – any professional translator can handle them.<br />
Translators who only do this kind of translation (don’t have a specialist field) are sometimes referred to as ‘generalist’ or ‘general purpose’ translators.<br />
Examples<br />
Most business correspondence, website content, company and product/service info, non-technical reports.<br />
Most of the rest of the translation types in this Category do require specialist translators.<br />
Check out our video on 13 types of translation requiring special translator expertise:<br />
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2. Technical Translation<br />
What is it?<br />
We use the term “technical translation” in two different ways:<br />
Broad meaning: any translation where the translator needs specialist knowledge in some domain or area.<br />
This definition would include almost all the translation types described in this section.<br />
Narrow meaning: limited to the translation of engineering (in all its forms), IT and industrial texts.<br />
This narrower meaning would exclude legal, financial and medical translations for example, where these would be included in the broader definition.<br />
What this means<br />
Technical translations require knowledge of the specialist field or domain of the text.<br />
That’s because without it translators won’t completely understand the text and its implications. And this is essential if we want a fully accurate and appropriate translation.Good to know Many technical translation projects also have a typesetting/dtp requirement. Be sure your translation provider can handle this component, and that you’ve allowed for it in your project costings and time frames.<br />
Examples<br />
Manuals, specialist reports, product brochures<br />
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3. Scientific Translation<br />
What is it?<br />
The translation of scientific research or documents relating to it.<br />
What this means<br />
These texts invariably contain domain-specific terminology, and often involve cutting edge research.<br />
So it’s imperative the translator has the necessary knowledge of the field to fully understand the text. That’s why scientific translators are typically either experts in the field who have turned to translation, or professionally qualified translators who also have qualifications and/or experience in that domain.<br />
On occasion the translator may have to consult either with the author or other domain experts to fully comprehend the material and so translate it appropriately.<br />
Examples<br />
Research papers, journal articles, experiment/trial results<br />
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4. Medical Translation<br />
What is it?<br />
The translation of healthcare, medical product, pharmaceutical and biotechnology materials.<br />
Medical translation is a very broad term covering a wide variety of specialist areas and materials – everything from patient information to regulatory, marketing and technical documents.<br />
As a result, this translation type has numerous potential sub-categories – ‘medical device translations’ and ‘clinical trial translations’, for example.<br />
What this means<br />
As with any text, the translators need to fully understand the materials they’re translating. That means sound knowledge of medical terminology and they’ll often also need specific subject-matter expertise.<br />
Good to know<br />
Many countries have specific requirements governing the translation of medical device and pharmaceutical documentation. This includes both your client-facing and product-related materials.<br />
Examples<br />
Medical reports, product instructions, labeling, clinical trial documentation<br />
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5. Financial Translation<br />
What is it?<br />
In broad terms, the translation of banking, stock exchange, forex, financing and financial reporting documents.<br />
However, the term is generally used only for the more technical of these documents that require translators with knowledge of the field.<br />
Any competent translator could translate a bank statement, for example, so that wouldn’t typically be considered a financial translation.<br />
What this means<br />
You need translators with domain expertise to correctly understand and translate the financial terminology in these texts.<br />
Examples<br />
Company accounts, annual reports, fund or product prospectuses, audit reports, IPO documentation<br />
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6. Economic Translations<br />
What is it?<br />
1. Sometimes used as a synonym for financial translations.<br />
2. Other times used somewhat loosely to refer to any area of economic activity – so combining business/commercial, financial and some types of technical translations.<br />
3. More narrowly, the translation of documents relating specifically to the economy and the field of economics.<br />
What this means<br />
As always, you need translators with the relevant expertise and knowledge for this type of translation.<br />
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7. Legal Translation<br />
What is it?<br />
The translation of documents relating to the law and legal process.<br />
What this means<br />
Legal texts require translators with a legal background.<br />
That’s because without it, a translator may not:<br />
– fully understand the legal concepts<br />
– write in legal style<br />
– understand the differences between legal systems, and how best to translate concepts that don’t correspond.<br />
And we need all that to produce professional quality legal translations – translations that are accurate, terminologically correct and stylistically appropriate.<br />
Examples<br />
Contracts, legal reports, court judgments, expert opinions, legislation<br />
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8. Juridical Translation<br />
What is it?<br />
1. Generally used as a synonym for legal translations.<br />
2. Alternatively, can refer to translations requiring some form of legal verification, certification or notarization that is common in many jurisdictions.<br />
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9. Judicial Translation<br />
What is it?<br />
1. Most commonly a synonym for legal translations.<br />
2. Rarely, used to refer specifically to the translation of court proceeding documentation – so judgments, minutes, testimonies, etc. <br />
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10. Patent Translation<br />
What is it?<br />
The translation of intellectual property and patent-related documents.<br />
Key features<br />
Patents have a specific structure, established terminology and a requirement for complete consistency throughout – read more on this here. These are key aspects to patent translations that translators need to get right.<br />
In addition, subject matter can be highly technical.<br />
What this means<br />
You need translators who have been trained in the specific requirements for translating patent documents. And with the domain expertise needed to handle any technical content.<br />
Examples<br />
Patent specifications, prior art documents, oppositions, opinions<br />
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11. Literary Translation<br />
What is it?<br />
The translation of literary works – novels, short stories, plays, essays, poems.<br />
Key features<br />
Literary translation is widely regarded as the most difficult form of translation.<br />
That’s because it involves much more than simply conveying all meaning in an appropriate style. The translator’s challenge is to also reproduce the character, subtlety and impact of the original – the essence of what makes that work unique.<br />
This is a monumental task, and why it’s often said that the translation of a literary work should be a literary work in its own right.<br />
What this means<br />
Literary translators must be talented wordsmiths with exceptional creative writing skills.<br />
Because few translators have this skillset, you should only consider dedicated literary translators for this type of translation.<br />
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12. Commercial Translation<br />
What is it?<br />
The translation of documents relating to the world of business.<br />
This is a very generic, wide-reaching translation type. It includes other more specialised forms of translation – legal, financial and technical, for example. And all types of more general business documentation.<br />
Also, some documents will require familiarity with business jargon and an ability to write in that style.<br />
What this means<br />
Different translators will be required for different document types – specialists should handle materials involving technical and specialist fields, whereas generalist translators can translate non-specialist materials.<br />
Examples<br />
Business correspondence, reports, marketing and promotional materials, sales proposals<br />
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13. Business Translations<br />
What is it?<br />
A synonym for Commercial Translations.<br />
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14. Administrative Translations<br />
What is it?<br />
The translation of business management and administration documents.<br />
So it’s a subset of business / commercial translations.<br />
What this means<br />
The implication is these documents will include business jargon and ‘management speak’, so require a translator familiar with, and practised at, writing in that style.<br />
Examples<br />
Management reports and proposals<br />
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15. Marketing Translations<br />
What is it?<br />
The translation of advertising, marketing and promotional materials.<br />
This is a subset of business or commercial translations.<br />
Key features<br />
Marketing copy is designed to have a specific impact on the audience – to appeal and persuade.<br />
So the translated copy must do this too.<br />
But a direct translation will seldom achieve this – so translators need to adapt their wording to produce the impact the text is seeking.<br />
And sometimes a completely new message might be needed – see transcreation in our next category of translation types.<br />
What this means<br />
Marketing translations require translators who are skilled writers with a flair for producing persuasive, impactful copy.<br />
As relatively few translators have these skills, engaging the right translator is key.<br />
Good to know<br />
This type of translation often comes with a typesetting or dtp requirement – particularly for adverts, posters, brochures, etc.<br />
Its best for your translation provider to handle this component. That’s because multilingual typesetters understand the design and aesthetic conventions in other languages/cultures. And these are essential to ensure your materials have the desired impact and appeal in your target markets.<br />
Examples<br />
Advertising, brochures, some website/social media text.<br />
Translation Category B: 14 types of translation based on the end product or use of the translation<br />
This category is all about how the translation is going to be used or the end product that’s produced.<br />
Most of these types involve either adapting or processing a completed translation in some way, or converting or incorporating it into another program or format.<br />
You’ll see that some are very specialised, and complex.<br />
It’s another way translation providers refer to the range of services they provide.<br />
Check out our video of the most specialised of these types of translation:<br />
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16. Document Translations<br />
What is it?<br />
The translation of documents of all sorts.<br />
Here the translation itself is the end product and needs no further processing beyond standard formatting and layout.<br />
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17. Text Translations<br />
What is it?<br />
A synonym for document translation.<br />
<br />
18. Certified Translations<br />
What is it?<br />
A translation with some form of certification.<br />
Key features<br />
The certification can take many forms. It can be a statement by the translation company, signed and dated, and optionally with their company seal. Or a similar certification by the translator.<br />
The exact format and wording will depend on what clients and authorities require – here’s an example.<br />
<br />
19. Official Translations<br />
What is it?<br />
1. Generally used as a synonym for certified translations.<br />
2. Can also refer to the translation of ‘official’ documents issued by the authorities in a foreign country. These will almost always need to be certified.<br />
<br />
20. Software Localisation<br />
What is it?<br />
Adapting software for another language/culture.<br />
Key features<br />
The goal of software localisation is not just to make the program or product available in other languages. It’s also about ensuring the user experience in those languages is as natural and effective as possible.<br />
Translating the user interface, messaging, documentation, etc is a major part of the process.<br />
Also key is a customisation process to ensure everything matches the conventions, norms and expectations of the target cultures.<br />
Adjusting time, date and currency formats are examples of simple customisations. Others might involve adapting symbols, graphics, colours and even concepts and ideas.<br />
Localisation is often preceded by internationalisation – a review process to ensure the software is optimally designed to handle other languages.<br />
And it’s almost always followed by thorough testing – to ensure all text is in the correct place and fits the space, and that everything makes sense, functions as intended and is culturally appropriate.<br />
Localisation is often abbreviated to L10N, internationalisation to i18n.<br />
What this means<br />
Software localisation is a specialised kind of translation, and you should always engage a company that specialises in it.<br />
They’ll have the systems, tools, personnel and experience needed to achieve top quality outcomes for your product.<br />
<br />
21. Game Localisation<br />
What is it?<br />
Adapting games for other languages and markets.<br />
<br />
It’s a subset of software localisation.<br />
Key features<br />
The goal of game localisation is to provide an engaging and fun gaming experience for speakers of other languages.<br />
<br />
It involves translating all text and recording any required foreign language audio.<br />
<br />
But also adapting anything that would clash with the target culture’s customs, sensibilities and regulations.<br />
<br />
For example, content involving alcohol, violence or gambling may either be censored or inappropriate in the target market.<br />
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And at a more basic level, anything that makes users feel uncomfortable or awkward will detract from their experience and thus the success of the game in that market.<br />
<br />
So portions of the game may have to be removed, added to or re-worked.<br />
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Game localisation involves at least the steps of translation, adaptation, integrating the translations and adaptations into the game, and testing.<br />
<br />
What this means<br />
Game localisation is a very specialised type of translation best left to those with specific expertise and experience in this area.<br />
<br />
22. Multimedia Localisation<br />
What is it?<br />
Adapting multimedia for other languages and cultures.<br />
<br />
Multimedia refers to any material that combines visual, audio and/or interactive elements. So videos and movies, on-line presentations, e-Learning courses, etc.<br />
Key features<br />
Anything a user can see or hear may need localising.<br />
<br />
That means the audio and any text appearing on screen or in images and animations.<br />
<br />
Plus it can mean reviewing and adapting the visuals and/or script if these aren’t suitable for the target culture.<br />
<br />
The localisation process will typical involve:<br />
– Translation<br />
– Modifying the translation for cultural reasons and/or to meet technical requirements<br />
– Producing the other language versions<br />
<br />
Audio output may be voice-overs, dubbing or subtitling.<br />
<br />
And output for visuals can involve re-creating elements, or supplying the translated text for the designers/engineers to incorporate.<br />
<br />
What this means<br />
Multimedia localisation projects vary hugely, and it’s essential your translation providers have the specific expertise needed for your materials.<br />
<br />
<br />
<br />
23. Script Translations<br />
What is it?<br />
Preparing the text of recorded material for recording in other languages.<br />
Key features<br />
There are several issues with script translation.<br />
<br />
One is that translations typically end up longer than the original script. So voicing the translation would take up more space/time on the video than the original language.<br />
<br />
Sometimes that space will be available and this will be OK.<br />
<br />
But generally it won’t be. So the translation has to be edited back until it can be comfortably voiced within the time available on the video.<br />
<br />
Another challenge is the translation may have to synchronise with specific actions, animations or text on screen.<br />
<br />
Also, some scripts also deal with technical subject areas involving specialist technical terminology.<br />
<br />
Finally, some scripts may be very culture-specific – featuring humour, customs or activities that won’t work well in another language. Here the script, and sometimes also the associated visuals, may need to be adjusted before beginning the translation process.<br />
<br />
It goes without saying that a script translation must be done well. If it’s not, there’ll be problems producing a good foreign language audio, which will compromise the effectiveness of the video.<br />
<br />
Translators typically work from a time-coded transcript. This is the original script marked to show the time available for each section of the translation.<br />
<br />
What this means<br />
There are several potential pitfalls in script translations. So it’s vital your translation provider is practiced at this type of translation and able to handle any technical content.<br />
<br />
<br />
<br />
24. Voice-over and Dubbing Projects<br />
What is it?<br />
Translation and recording of scripts in other languages.<br />
<br />
Voice-overs vs dubbing<br />
There is a technical difference.<br />
A voice-over adds a new track to the production, dubbing replaces an existing one.<br />
<br />
<br />
Key features<br />
These projects involve two parts:<br />
– a script translation (as described above), and<br />
– producing the audio<br />
<br />
So they involve the combined efforts of translators and voice artists.<br />
The task for the voice artist is to produce a high quality read. That’s one that matches the style, tone and richness of the original.<br />
<br />
Often each section of the new audio will need to be the same length as the original.<br />
<br />
But sometimes the segments will need to be shorter – for example where the voice-over lags the original by a second or two. This is common in interviews etc, where the original voice is heard initially then drops out.<br />
<br />
The most difficult form of dubbing is lip-syncing – where the new audio needs to synchronise with the original speaker’s lip movements, gestures and actions.<br />
<br />
Lip-syncing requires an exceptionally skilled voice talent and considerable time spent rehearsing and fine tuning the translation.<br />
<br />
What this means<br />
You need to use experienced professionals every step of the way in this type of project.<br />
<br />
That’s to ensure firstly that your foreign-language scripts are first class, then that the voicing is of high professional standard.<br />
<br />
Anything less will mean your foreign language versions will be way less effective and appealing to your target audience.<br />
<br />
<br />
<br />
25. Subtitle Translations<br />
What is it?<br />
Producing foreign language captions for sub or surtitles.<br />
Key features<br />
The goal with subtitling is to produce captions that viewers can comfortably read in the time available and still follow what’s happening on the video.<br />
<br />
To achieve this, languages have “rules” governing the number of characters per line and the minimum time each subtitle should display.<br />
<br />
Sticking to these guidelines is essential if your subtitles are to be effective.<br />
<br />
But this is no easy task – it requires simple language, short words, and a very succinct style. Translators will spend considerable time mulling over and re-working their translation to get it just right.<br />
<br />
Most subtitle translators use specialised software that will output the captions in the format sound engineers need for incorporation into the video.<br />
<br />
What this means<br />
As with other specialised types of translation, you should only use translators with specific expertise and experience in subtitling.<br />
<br />
<br />
<br />
26. Website Localisation<br />
What is it?<br />
The translation and adapting of relevant content on a website to best suit the target language and culture.<br />
<br />
Note: Many providers use the term website translation as a synonym for localisation. Strictly speaking though, translation is just one part of localisation.<br />
Key features<br />
<br />
Not all pages on a website may need to be localised – clients should review their content to identify what’s relevant for the other language versions.<br />
Some content may need specialist translators – legal and technical pages for example.<br />
There may also be videos, linked documents, and text or captions in graphics to translate.<br />
Adaptation can mean changing date, time, currency and number formats, units of measure, etc.<br />
But also images, colours and even the overall site design and style if these won’t have the desired impact in the target culture.<br />
Translated files can be supplied in a wide range of formats – translators usually coordinate output with the site webmasters.<br />
New language versions are normally thoroughly reviewed and tested before going live to confirm everything is displaying correctly, works as intended and is cultural appropriate.<br />
What this means<br />
The first step should be to review your content and identify what needs to be translated. This might lead you to modify some pages for the foreign language versions.<br />
<br />
In choosing your translation providers be sure they can:<br />
– handle any technical or legal content,<br />
– provide your webmaster with the file types they want.<br />
<br />
And you should always get your translators to systematically review the foreign language versions before going live.<br />
<br />
<br />
<br />
27. Transcreation<br />
What is it?<br />
Adapting a message to elicit the same emotional response in another language and culture.<br />
Translation is all about conveying the message or meaning of a text in another language. But sometimes that message or meaning won’t have the desired effect in the target culture.<br />
<br />
This is where transcreation comes in. Transcreation creates a new message that will get the desired emotional response in that culture, while preserving the style and tone of the original.<br />
<br />
So it’s a sort of creative translation – which is where the word comes from, a combination of ‘translation’ and ‘creation’.<br />
<br />
At one level transcreation may be as simple as choosing an appropriate idiom to convey the same intent in the target language – something translators do all the time.<br />
<br />
But mostly the term is used to refer to adapting key advertising and marketing messaging. Which requires copywriting skills, cultural awareness and an excellent knowledge of the target market.<br />
<br />
Who does it?<br />
Some translation companies have suitably skilled personnel and offer transcreation services.<br />
<br />
Often though it’s done in the target country by specialist copywriters or an advertising or marketing agency – particularly for significant campaigns and to establish a brand in the target marketplace.<br />
<br />
What this means<br />
Most general marketing and promotional texts won’t need transcreation – they can be handled by a translator with excellent creative writing skills.<br />
<br />
But slogans, by-lines, advertising copy and branding statements often do.<br />
<br />
Whether you should opt for a translation company or an in-market agency will depend on the nature and importance of the material, and of course your budget.<br />
<br />
<br />
<br />
28. Audio Translations<br />
What is it?<br />
Broad meaning: the translation of any type of recorded material into another language.<br />
<br />
More commonly: the translation of a foreign language video or audio recording into your own language. So this is where you want to know and document what a recording says.<br />
Key features<br />
The first challenge with audio translations is it’s often impossible to pick up every word that’s said. That’s because audio quality, speech clarity and speaking speed can all vary enormously.<br />
<br />
It’s also a mentally challenging task to listen to an audio and translate it directly into another language. It’s easy to miss a word or an aspect of meaning.<br />
<br />
So best practice is to first transcribe the audio (type up exactly what is said in the language it is spoken in), then translate that transcription.<br />
<br />
However, this is time consuming and therefore costly, and there are other options if lesser precision is acceptable.<br />
<br />
What this means<br />
It’s best to discuss your requirements for this kind of translation with your translation provider. They’ll be able to suggest the best translation process for your needs.<br />
<br />
Examples<br />
Interviews, product videos, police recordings, social media videos.<br />
<br />
<br />
<br />
29. Translations with DTP<br />
What is it?<br />
Translation incorporated into graphic design files.multilingual dtp example in the form of a Rubik's Cube with foreign text on each square<br />
Key features<br />
Graphic design programs are used by professional designers and graphic artists to combine text and images to create brochures, books, posters, packaging, etc.<br />
<br />
Translation plus dtp projects involve 3 steps – translation, typesetting, output.<br />
<br />
The typesetting component requires specific expertise and resources – software and fonts, typesetting know-how, an appreciation of foreign language display conventions and aesthetics.<br />
<br />
What this means<br />
Make sure your translation company has the required multilingual typesetting/desktop publishing expertise whenever you’re translating a document created in a graphic design program.<br />
<br />
Translation Category C: 13 types of translation based on the translation method employed<br />
This category has two sub-groups:<br />
– the practical methods translation providers use to produce their translations, and<br />
– the translation strategies/methods identified and discussed within academia.<br />
<br />
The translation methods translation providers use<br />
There are 4 main methods used in the translation industry today. We have an overview of each below, but for more detail, including when to use each one, see our comprehensive blog article.<br />
<br />
Or watch our video.<br />
<br />
Important: If you’re a client you need to understand these 4 methods – choose the wrong one and the translation you end up with may not meet your needs!<br />
<br />
<br />
30. Machine Translation (MT)<br />
What is it?<br />
A translation produced entirely by a software program with no human intervention.<br />
<br />
A widely used, and free, example is Google Translate. And there are also commercial MT engines, generally tailored to specific domains, languages and/or clients.<br />
<br />
Pros and cons<br />
There are two limitations to MT:<br />
– they make mistakes (incorrect translations), and<br />
– quality of wording is patchy (some parts good, others unnatural or even nonsensical)<br />
<br />
On they positive side they are virtually instantaneous and many are free.<br />
<br />
Best suited for:<br />
Getting the general idea of what a text says.<br />
<br />
This method should never be relied on when high accuracy and/or good quality wording is needed.<br />
<br />
<br />
<br />
31. Machine Translation plus Human Editing (PEMT)<br />
What is it?<br />
A machine translation subsequently edited by a human translator or editor (often called Post-editing Machine Translation = PEMT).<br />
<br />
The editing process is designed to rectify some of the deficiencies of a machine translation.<br />
<br />
This process can take different forms, with different desired outcomes. Probably most common is a ‘light editing’ process where the editor ensures the text is understandable, without trying to fix quality of expression.<br />
<br />
Pros and cons<br />
This method won’t necessarily eliminate all translation mistakes. That’s because the program may have chosen a wrong word (meaning) that wasn’t obvious to the editor.<br />
<br />
And wording won’t generally be as good as a professional human translator would produce.<br />
<br />
Its advantage is it’s generally quicker and a little cheaper than a full translation by a professional translator.<br />
<br />
Best suited for:<br />
Translations for information purposes only.<br />
<br />
Again, this method shouldn’t be used when full accuracy and/or consistent, natural wording is needed.<br />
<br />
<br />
<br />
32. Human Translation<br />
What is it?<br />
Translation by a professional human translator.<br />
Pros and cons<br />
Professional translators should produce translations that are fully accurate and well-worded.<br />
<br />
That said, there is always the possibility of ‘human error’, which is why translation companies like us typically offer an additional review process – see next method.<br />
<br />
This method will take a little longer and likely cost more than the PEMT method.<br />
<br />
Best suited for:<br />
Most if not all translation purposes.<br />
<br />
<br />
<br />
33. Human Translation + Revision<br />
What is it?<br />
A human translation with an additional review by a second translator.<br />
<br />
The review is essentially a safety check – designed to pick up any translation errors and refine wording if need be.<br />
<br />
Pros and cons<br />
This produces the highest level of translation quality.<br />
<br />
It’s also the most expensive of the 4 methods, and takes the longest.<br />
<br />
Best suited for:<br />
All translation purposes.<br />
<br />
Gearwheel with 5 practical translation methods written on the teeth <br />
There’s also one other common term used by practitioners and academics alike to describe a type (method) of translation:<br />
<br />
34. Computer-Assisted Translation (CAT)<br />
What is it?<br />
A human translator using computer tools to aid the translation process.<br />
<br />
Key features<br />
Virtually all translators use such tools these days.<br />
<br />
The most prevalent tool is Translation Memory (TM) software. This creates a database of previous translations that can be accessed for future work.<br />
<br />
TM software is particularly useful when dealing with repeated and closely-matching text, and for ensuring consistency of terminology. For certain projects it can speed up the translation process.<br />
<br />
<br />
<br />
The translation methods described by academia<br />
A great deal has been written within academia analysing how human translators go about their craft.<br />
<br />
Seminal has been the work of Newmark, and the following methods of translation attributed to him are widely discussed in the literature.Gearwheel with Newmark's 8 translation methods written on the teeth <br />
These methods are approaches and strategies for translating the text as a whole, not techniques for handling smaller text units, which we discuss in our final translation category.<br />
<br />
<br />
<br />
35. Word-for-word Translation<br />
This method translates each word into the other language using its most common meaning and keeping the word order of the original language.<br />
<br />
So the translator deliberately ignores context and target language grammar and syntax.<br />
<br />
Its main purpose is to help understand the source language structure and word use.<br />
<br />
Often the translation will be placed below the original text to aid comparison.<br />
<br />
<br />
<br />
36. Literal Translation<br />
Words are again translated independently using their most common meanings and out of context, but word order changed to the closest acceptable target language grammatical structure to the original.<br />
<br />
Its main suggested purpose is to help someone read the original text.<br />
<br />
<br />
<br />
37. Faithful Translation<br />
Faithful translation focuses on the intention of the author and seeks to convey the precise meaning of the original text.<br />
<br />
It uses correct target language structures, but structure is less important than meaning.<br />
<br />
<br />
<br />
38. Semantic Translation<br />
Semantic translation is also author-focused and seeks to convey the exact meaning.<br />
<br />
Where it differs from faithful translation is that it places equal emphasis on aesthetics, ie the ‘sounds’ of the text – repetition, word play, assonance, etc.<br />
<br />
In this method form is as important as meaning as it seeks to “recreate the precise flavour and tone of the original” (Newmark).slide showing definition of semantic translation as a translation method<br />
<br />
39. Communicative Translation<br />
Seeks to communicate the message and meaning of the text in a natural and easily understood way.<br />
<br />
It’s described as reader-focused, seeking to produce the same effect on the reader as the original text.<br />
<br />
A good comparison of Communicative and Semantic translation can be found here.<br />
<br />
40. Free Translation<br />
Here conveying the meaning and effect of the original are all important.<br />
<br />
There are no constraints on grammatical form or word choice to achieve this.<br />
<br />
Often the translation will paraphrase, so may be of markedly different length to the original.<br />
<br />
41. Adaptation<br />
Mainly used for poetry and plays, this method involves re-writing the text where the translation would otherwise lack the same resonance and impact on the audience.<br />
<br />
Themes, storylines and characters will generally be retained, but cultural references, acts and situations adapted to relevant target culture ones.<br />
<br />
So this is effectively a re-creation of the work for the target culture.<br />
<br />
42. Idiomatic Translation<br />
Reproduces the meaning or message of the text using idioms and colloquial expressions and language wherever possible.<br />
<br />
The goal is to produce a translation with language that is as natural as possible.<br />
<br />
Translation Category D: 9 types of translation based on the translation technique used<br />
These translation types are specific strategies, techniques and procedures for dealing with short chunks of text – generally words or phrases.<br />
<br />
They’re often thought of as techniques for solving translation problems.<br />
<br />
They differ from the translation methods of the previous category which deal with the text as a whole.<br />
9 translation techniques as titles of books in a bookcase<br />
<br />
43. Borrowing<br />
What is it?<br />
Using a word or phrase from the original text unchanged in the translation.<br />
<br />
Key features<br />
With this procedure we don’t translate the word or phrase at all – we simply ‘borrow’ it from the source language.<br />
<br />
Borrowing is a very common strategy across languages. Initially, borrowed words seem clearly ‘foreign’, but as they become more familiar, they can lose that ‘foreignness’.<br />
<br />
Translators use this technique:<br />
– when it’s the best word to use – either because it has become the standard, or it’s the most precise term, or<br />
– for stylist effect – borrowings can add a prestigious or scholarly flavour.<br />
<br />
Borrowed words or phrases are often italicised in English.<br />
<br />
Examples of borrowings in English<br />
grand prix, kindergarten, tango, perestroika, barista, sampan, karaoke, tofu<br />
<br />
<br />
44. Transliteration<br />
What is it?<br />
Reproducing the approximate sounds of a name or term from a language with a different writing system.<br />
<br />
Key features<br />
In English we use the Roman (Latin) alphabet in common with many other languages including almost all European languages.<br />
<br />
Other writing systems include Arabic, Cyrillic, Chinese, Japanese, Korean, Thai, and the Indian languages.<br />
<br />
Transliteration from such systems into the Roman alphabet is also called romanisation.<br />
<br />
There are accepted systems for how individual letters/sounds should be romanised from most other languages – there are three common systems for Chinese, for example.<br />
<br />
English borrowings from languages using non-Roman writing systems also require transliteration – perestroika, sampan, karaoke, tofu are examples from the above list.<br />
<br />
Translators mostly use transliteration as a procedure for translating proper names.<br />
<br />
Examples<br />
毛泽东 Mao Tse-tung or Mao Zedong<br />
Владимир Путин Vladimir Putin<br />
서울 Seoul<br />
ភ្នំពេញ Phnom Penh<br />
<br />
45. Calque or Loan Translation<br />
What is it?<br />
A literal translation of a foreign word or phrase to create a new term with the same meaning in the target language.<br />
<br />
So a calque is a borrowing with translation if you like. The new term may be changed slightly to reflect target language structures.<br />
<br />
Examples<br />
German ‘Kindergarten’ has been calqued as детский сад in Russian, literally ‘children garden’ in both languages.<br />
<br />
Chinese 洗腦 ‘wash’ + ‘brain’ is the origin of ‘brainwash’ in English.<br />
<br />
English skyscraper is calqued as gratte-ciel in French and rascacielos in Spanish, literally ‘scratches sky’ in both languages.<br />
<br />
46. Word-for-word translation<br />
What is it?<br />
A literal translation that is natural and correct in the target language.<br />
<br />
Alternative names are ‘literal translation’ or ‘metaphrase’.<br />
<br />
Note: this technique is different to the translation method of the same name, which does not produce correct and natural text and has a different purpose.<br />
<br />
Key features<br />
This translation strategy will only work between languages that have very similar grammatical structures.<br />
<br />
And even then, only sometimes.<br />
<br />
For example, standard word order in Turkish is Subject-Object-Verb whereas in English it’s Subject-Verb-Object. So a literal translation between these two will seldom work:<br />
– Yusuf elmayı yedi is literally ‘Joseph the apple ate’.<br />
<br />
When word-for-word translations don’t produce natural and correct text, translators resort to some of the other techniques described below.<br />
Examples<br />
French ‘Quelle heure est-il?’ works into English as ‘What time is it?’.<br />
<br />
Russian ‘Oн хочет что-нибудь поесть’ is ‘He wants something to eat’.<br />
<br />
47. Transposition<br />
What is it?<br />
Translation with a change of grammatical structure.<br />
<br />
This technique gives the translation more natural wording and/or makes it grammatically correct.<br />
<br />
Examples<br />
A change in word order:<br />
Our Turkish example Yusuf elmayı yedi (literally ‘Joseph the apple ate’) –> Joseph ate the apple.<br />
<br />
Spanish La Casa Blanca (literally ‘The House White’) –> The White House<br />
<br />
A change in grammatical category:<br />
German Er hört gerne Musik (literally ‘he listens gladly [to] music’)<br />
= subject pronoun + verb + adverb + noun<br />
becomes Spanish Le gusta escuchar música (literally ‘[to] him [it] pleases to listen [to] music’)<br />
= indirect object pronoun + verb + infinitive + noun<br />
and English He likes listening to music<br />
= subject pronoun + verb + gerund + noun.<br />
<br />
48. Modulation<br />
What is it?<br />
Translation with a change of focus or point of view in the target language.<br />
<br />
This technique makes the translation more idiomatic – how people would normally say it in the language.<br />
<br />
Examples<br />
English talks of the ‘top floor’ of a building, French the dernier étage = last floor. ‘Last floor’ would be unnatural in English, so too ‘top floor’ in French.<br />
<br />
German uses the term Lebensgefahr (literally ‘danger to life’) where in English we’d be more likely to say ‘risk of death’.<br />
In English we’d say ‘I dropped the key’, in Spanish se me cayó la llave, literally ‘the key fell from me’. The English perspective is that I did something (dropped the key), whereas in Spanish something happened to me – I’m the recipient of the action.<br />
<br />
49. Equivalence or Reformulation<br />
What is it?<br />
Translating the underlying concept or meaning using a totally different expression.<br />
<br />
This technique is widely used when translating idioms and proverbs.<br />
<br />
And it’s common in titles and advertising slogans.<br />
<br />
It’s a common strategy where a direct translation either wouldn’t make sense or wouldn’t resonate in the same way.<br />
<br />
Examples<br />
Here are some equivalents of the English saying “Pigs may fly”, meaning something will never happen, or “you’re being unrealistic” (Source):<br />
– Thai: ชาติหน้าตอนบ่าย ๆ – literally, ‘One afternoon in your next reincarnation’<br />
– French: Quand les poules auront des dents – literally, ‘When hens have teeth’<br />
– Russian, Когда рак на горе свистнет – literally, ‘When a lobster whistles on top of a mountain’<br />
– Dutch, Als de koeien op het ijs dansen – literally, ‘When the cows dance on the ice’<br />
– Chinese: 除非太陽從西邊出來！– literally, ‘Only if the sun rises in the west’<br />
<br />
50. Adaptation<br />
What is it?<br />
A translation that substitutes a culturally-specific reference with something that’s more relevant or meaningful in the target language.<br />
<br />
It’s also known as cultural substitution or cultural equivalence.<br />
<br />
It’s a useful technique when a reference wouldn’t be understood at all, or the associated nuances or connotations would be lost in the target language.<br />
<br />
Note: the translation method of the same name is a similar concept but applied to the text as a whole.<br />
<br />
Examples<br />
Different cultures celebrate different coming of age birthdays – 21 in many cultures, 20, 15 or 16 in others. A translator might consider changing the age to the target culture custom where the coming of age implications were important in the original text.<br />
Animals have different connotations across languages and cultures. Owls for example are associated with wisdom in English, but are a bad omen to Vietnamese. A translator might want to remove or amend an animal reference where this would create a different image in the target language.<br />
<br />
51. Compensation<br />
What is it?<br />
A meaning or nuance that can’t be directly translated is expressed in another way in the text.<br />
Example<br />
Many languages have ways of expressing social status (honorifics) encoded into their grammatical structures.<br />
<br />
So you can convey different levels of respect, politeness, humility, etc simply by choosing different forms of words or grammatical elements.<br />
But these nuances will be lost when translating into languages that don’t have these structures.<br />
Then translating into languages that don’t have these structures<br />
Then translating into languages that don’t have these structures.<br />
<br />
===Conclusion ===<br />
<br />
In the light of above mentioned facts and figures here by we would say that machine translation is challenge for human translators because it can reduces the wokload of translation but can't give accurate and exact translation of the traget language.It can be less reliable than human translation..</div>Chen Huinihttps://bou.de/u/index.php?title=Machine_translation&diff=131893Machine translation2021-12-13T12:42:34Z<p>Chen Huini: /* Chapter 11 陈惠妮=Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts 机器翻译的译前编辑研究——以医学类文摘为例 */</p>
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<div>'''Machine Translation - A challenge or a chance for human translators?'''<br />
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[[Machine_translation|Overview Page of Machine Translation]]<br />
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[[Machine_Trans_EN_1]] [[Machine_Trans_EN_2]] [[Machine_Trans_EN_3]] [[Machine_Trans_EN_4]] [[Machine_Trans_EN_5]] [[Machine_Trans_EN_6]] [[Machine_Trans_EN_7]] [[Machine_Trans_EN_8]] [[Machine_Trans_EN_9]] [[Machine_Trans_EN_10]] [[Machine_Trans_EN_11]] [[Machine_Trans_EN_12]] [[Machine_Trans_EN_13]] [[Machine_Trans_EN_14]] [[Machine_Trans_EN_15]] [[Machine_Trans_EN_16]] [[Machine_Trans_EN_17]] [[Machine_Trans_EN_18]] [[Machine_Trans_EN_19]] [[Machine_Trans_EN_20]] [[Machine_Trans_EN_21]] [[Machine_Trans_EN_22]] [[Machine_Trans_EN_23]] [[Machine_Trans_EN_24]] [[Machine_Trans_EN_25]] [[Machine_Trans_EN_26]] [[Machine_Trans_EN_27]] [[Machine_Trans_EN_28]] [[Machine_Trans_EN_29]] [[Machine_Trans_EN_30]] ...<br />
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[[Book_projects|Back to translation project overview]]<br />
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[[DCG-To-Do|To the To Do list]]<br />
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=Chapter 1:A Comparison Between the Quality of Machine Translation and Human Translation——A Case Study of the Application of artificial intelligence in Sports Events=<br />
'''论机器翻译与人工翻译的质量对比——以人工智能在体育赛事领域的应用为例'''<br />
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卫怡雯 Wei Yiwen, Hunan Normal University, China<br />
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<br />
=Chapter 3：On the Realm Advantages And Mutual Development of Machine Translation And Huamn Translation)=<br />
"'论机器翻译与人工翻译的领域优势及共同发展'"<br />
<br />
肖毅瑶 Xiao Yiyao, Hunan Normal University, China<br />
[[Machine_Trans_EN_3]]<br />
<br />
=Chapter 4 : A Comparison Between Machine Translation of Netease and Traditional Human Translation—A Case Study of The Economist Articles)= <br />
'''网易有道机器翻译与人工翻译的译文对比——以经济学人语料为例'''<br />
<br />
王李菲 Wang Lifei, Hunan Normal University<br />
<br />
[[Machine_Trans_EN_4]]<br />
<br />
=Chapter 5: Muhammad Saqib Mehran - Problems in translation studies=<br />
[[Machine_Trans_EN_5]]<br />
<br />
=6 徐敏赟(Machine Translation Based on Neural Network --Challenge or Chance?)=<br />
[[Machine_Trans_EN_6]]<br />
<br />
=Chapter 7: The Cultivation of artificial intelligence and translation talents in the Belt and Road Initiative=<br />
[[Machine_Trans_EN_7]]<br />
<br />
一带一路背景下人工智能与翻译人才的培养<br />
<br />
颜莉莉 Yan Lili, Hunan Normal University, China<br />
<br />
=Chapter 8: On Machine Translation Under Lanuguage Intelligence——An Option and Opportunity for Human Translators=<br />
'''论语言智能下的机器翻译——译者的选择与机遇'''<br />
<br />
颜静 Yan Jing, Hunan Normal University, China<br />
<br />
[[Machine_Trans_EN_8]]<br />
<br />
=9 谢佳芬（ Machine Translation and Artificial Translation in the Era of Artificial Intelligence）=<br />
[[Machine_Trans_EN_9]]<br />
<br />
=10 熊敏(Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts)=<br />
[[Machine_Trans_EN_10]]<br />
===Abstract===<br />
The history of machine translation can be traced to 1940s, undergoing germination, recession, revival and flourish. Nowadays, with the rapid development of information technology, machine translation technology emerged and is gradually becoming mature. Whether manual translation can be replaced by machine translation becomes a widely-disputed topic. So in order to explore the ability of machine translation, I adopts two versions of translation, which are manual translation and machine translation(this paper uses Youdao translation) for different types of texts(according to Peter Newmark's types of text：informative text, expressive text and vocative text). The results are quite different in terms of quality and accuracy. The results show that machine translation is more suitable for informative text for its brevity, while the expressive texts especially literary works and vocative texts are difficult to be translated, because there are too many loaded words and cultural images in expressive text and dependence on the readers. To draw a conclusion, the relationship between machine translation and manual translation should be complementary rather than competitive.<br />
<br />
===Key words===<br />
machine translation; manual translation; Newmark's type of texts<br />
<br />
===题目===<br />
Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts<br />
<br />
===摘要===<br />
机器翻译的历史可以追溯到上个世纪40年代，经历了萌芽期、萧条期、复兴期和繁荣期四个阶段。如今，随着信息技术的高速发展，机器翻译技术日益成熟，于是机器翻译能不能替代人工翻译这个话题引起了广泛热议。为了探究机器翻译的能力水平是否足以替代人工翻译，本人根据皮特纽马克的文本类型分类理论（信息类文本，表达文本，呼吁文本），选择了相应的译文类型，并且将其机器翻译的版本以及人工翻译的版本进行对比。结果发现，就质量和准确度而言，译文的水平大相径庭。因为其简洁的特性，机器比较适合翻译信息文本。而就表达文本，尤其是文学作品，因其存在文化负载词及相应的文化现象，所以机器翻译这类文本存在难度。而呼唤文本因其目的性以及对读者的依赖性较强，翻译难度较大。由此得知，机器翻译和人工翻译的关系应该是互补的，而不是竞争的。<br />
<br />
===关键词===<br />
机器翻译；人工翻译；纽马克文本类型<br />
<br />
===1. Introduction===<br />
====1.1.Introduction to machine translation====<br />
Machine translation, also known as computer translation is a technique to translating through machine translator, such as Google translation and Youdao translation. Machine translation is one of the branches of computational linguistics, ranging from computer science, statistics, information science and so on. Machine translation plays an important role in all aspects.<br />
Machine translation can be traced back to 1940s, when British engineer Booth and American engineer Weaver proposed using computer to translate and started to study machines used for translation. And the first machine translating system launched in 1954, used in English and Russian translation. And this means machine translation becomes a reality. However, the arrival of new things always accompanies barriers and setbacks. In the 1960s, reports from ALPAC (Automated Language Processing Advisory Committee) showed studies on machine translation had stagnated for a decade. At that time, due to the high cost of machine, choosing human translators to finish translating works was more economical for most companies. What’s worse, machine had difficulty in understanding semantic and pragmatic meanings. Fortunately, in the 1970s, with the advance and popularity of computer, machine translation was gradually back on track. With the development of science and technology, machine translation has better technological guarantee, such as artificial intelligence and computer technology. In the last decades, from the late 90s till now, machine translation is becoming more and more mature. The initial rule-oriented machine translation has been updated and Corpus-aided machine translation appears. And nowadays, technology in voice recognition has been further developed. This technique is frequently applied in speech translation products. Users only need to speak source language to the online translator and instantly it will speak out the target version. But machine can’t fully provide precise and accurate translation without any grammatical or semantic problems. Machine can understand the literal meaning of texts, but not the meaning in context. For example, there is polysemy in English, which refers to words having multiple meanings. So when translating these words, translators should take contexts into consideration. But machine has troubles in this way. In addition, machine has no feeling or intention. Hence, it is also difficult to convey the feelings from the source language.<br />
<br />
====1.2.Process of machine translation====<br />
The process of manual translation is different from that of machine translation. Here is the process of the former. (1) Understand source language. (2) Use target language to organize language. (3) Generate translation. Unlike manual translation, machine translation tends to analyze and code source language first, then look for related codes in corpus, and work out the code that represents target language, generating translation. But they share a common feature, which is that Lexicon, grammatical rules and syntactic structure are taken into consideration. This is one of the biggest challenges for machine translation.<br />
<br />
===2.Theorectical framework===<br />
====2.1Newmark’s text typology====<br />
Text typology is a theory from linguistics. It undergoes several phrases. Karl Buhler, a German psychologist and linguist defines communicative functions into expressive, information and vocative. Then Roman Jakobson proposes categorization of texts, which are intralingual translation, interlingual translation and intersemiotic translation. Accordingly, he defines six main functions of language, including the referential function, conative function, emotive function, poetic function, metalingual function and the phatic function. Based on the two theories, Peter Newmark, a translation theorist, summarizes the language function into six parts: the informative function, the vocative function, the expressive function, the phatic function, the aesthetic function, and the metalingual function. Later, he further divided texts into informative type, expressive text and vocative type according to the linguistic functions of various texts. <br />
The core of the informative texts is the truth. It is to convey facts, information, knowledge of certain topics, reality and theories. The language style of the text is objective, logical and standardized. Reports, papers, scientific and technological textbooks are all attributed to informative texts. Translators are required to take format into account for different styles.<br />
The core of the expressive text is the sender’s emotions and attitudes. It is to express sender’s preferences, feelings, views and so on. The language style of it is subjective. Imaginative literature, including fictions, poems and dramas, autobiography and authoritative statements belong to expressive text. It requires translators to be faithful to the source language and maintain the style.<br />
The core of the vocative text is readership. It is to call upon readers to act, think, feel and react in the way intended by the text. So it is reader-oriented. Such texts as advertisement, propaganda and notices are of vocative text. Newmark noted that translators need to consider cultural background of the source language and the semantic and pragmatic effect of target language. If readers can comprehend the meaning at once and do as the text says, then the goal of information communication is achieved.<br />
To draw a conclusion, informative texts focus on the information and facts. Expressive texts center on the mind of addressers, including feelings, prejudices and so on. And vocative texts are oriented on readers and call on them to practice as the texts say.<br />
<br />
====2.2Study method====<br />
Manual translations of the three texts are selected from authoritative versions and universally acknowledged. And machine translations of those come from Youdao Translator. And in this thesis I will compare and evaluate the two methods in word diction, sentence structure, word order and redundancy.<br />
<br />
===3.Comparison and analysis of machine translation and manual translation ===<br />
====3.1Informative text ====<br />
（1）English into Chinese<br />
<br />
①Source language:<br />
<br />
Keep the tip of Apple Pencil clean, as dirt and other small particles may cause excessive wear to the tip or damage the screen of i-pad.<br />
<br />
Target language:<br />
<br />
Machine translation: Apple Pencil笔尖应保持清洁，灰尘等小颗粒可能会导致笔尖过度磨损或损坏ipad屏幕。<br />
<br />
Manual translation: 保持Apple Pencil铅笔的笔尖干净，因为灰尘和其他微粒可能会导致笔尖的过度磨损或损坏iPad屏幕。<br />
<br />
Analysis: Here is the instruction of Apple Pencil. And the manual translation is the Chinese version on the instruction.Product instruction tends to be professional, since there are many terms for some concepts. Machine can easily identify these terms and provide related words to translate. The machine version is faithful and expressive to the source language. So it is well-qualified and readable for readers to understand the instruction. So we can use machine to translate informative text.<br />
<br />
②Source language:<br />
<br />
China on Saturday launched a rocket carrying three astronauts-two men and one woman - to the core module of a future space station where they will live and work for six months, the longest orbit for Chinese astronauts.<br />
<br />
Target language:<br />
<br />
Machine translation: 周六，中国发射了一枚运载三名宇航员(两男一女)的火箭，进入未来空间站的核心舱，他们将在那里生活和工作6个月，这是中国宇航员最长的轨道。<br />
<br />
Manual translation: 周六，中国发射了一枚搭载三名宇航员(两男一女)的火箭，进入未来空间站的核心舱，他们将在那里生活和工作6个月，这是中国宇航员最漫长的一次轨道飞行。<br />
<br />
Analysis: This is a news from Reuters, reporting that China has launched a rocket.The meaning of the two translations is almost the same, except for some word diction. But there are some details dealt with different choice. For example, the last sentence of the machine translation is a bit of obscure and direct. There are some ambiguous words and expressions.<br />
<br />
(2)Chinese into English<br />
<br />
Source language:湖南省博物馆是湖南省最大的历史艺术类博物馆，占地面积4.9万平方米，总建筑面积为9.1万平方米，是首批国家一级博物馆，中央地方共建的八个国家级重点博物馆之一、全国文化系统先进集体、文化强省建设有突出贡献先进集体。<br />
<br />
Target language:<br />
Manual translation: As the largest history and art museum in Hunan province, the Hunan Museum covers an area of 49,000㎡, with the building area reaching 91,000㎡. It is one of the first batch of national first-level museums and one of the first eight national museums co-funded by central and local governments.<br />
<br />
Machine translation: Museum in hunan province is one of the largest historical art museum in hunan province, covers an area of 49000 square meters, a total construction area of 91000 square meters, is the first national museum, the central place to build one of the eight national key museum, national cultural system advanced collectives, strong culture began with outstanding contribution of advanced collective.<br />
<br />
Analysis: Machine translation is not faithful enough in content. For instance, “首批国家一级博物馆” is translated into “first national museum”, which is not the meaning of the source language. And there are some obvious grammar mistakes in the machine translation. For example, machine translates it into just one sentence but there are multiple predicates in it. So it is not grammatically permissible. What’s more, the sentence structure of machine translation is confusing and the focus is not specific enough.<br />
<br />
====3.2Expressive text ====<br />
(1)English into Chinese<br />
<br />
Source language:<br />
<br />
An individual human existence should be like a river- small at first, narrowly contained within its banks, and rushing passionately past rocks and over waterfalls. Gradually the river grows wider, the banks recede, the waters flow more quietly, and in the end, without any visible breaks, they become merged in the sea, and painlessly lose their individual being.<br />
<br />
Target language:<br />
<br />
Machine translation: 一个人的存在应该像一条河流——开始很小，被紧紧地夹在两岸中间，然后热情奔放地冲过岩石，飞下瀑布。渐渐地，河面变宽，两岸后退，水流更加平缓，最后，没有任何明显的停顿，它们汇入大海，毫无痛苦地失去了自己的存在。<br />
<br />
Manual translation:人生在世，如若河流；河口初始狭窄，河岸虬曲，而后狂涛击石，飞泻成瀑。河道渐趋开阔，峡岸退去，水流潺缓，终了，一马平川，汇于大海，消逝无影。<br />
<br />
Analysis: Here is a well-known metaphor in the prose How to Grow Old written by Bertrand Russell. The manual translation is written by Tian Rongchang.This is a philosophical prose with graceful language. Literary translation is a most important and difficult branch of translation. Translator should focus on the literal meaning, culture, writing style and so on. It is a combination of beauty and elegance. Therefore, translators find it in a dilemma of beauty and faithfulness, let alone translating machine. Compared with manual translation, machine translation has difficulty in word choice. It is faithful and expressive, but not elegant enough.<br />
<br />
(2)Chinese into English<br />
<br />
Source language:没有一个人将小草叫做“大力士”，但是它的力量之大，的确是世界无比。这种力，是一般人看不见的生命力，只要生命存在，这种力就要显现，上面的石块，丝毫不足以阻挡。因为它是一种“长期抗战”的力，有弹性，能屈能伸的力，有韧性，不达目的不止的力。<br />
<br />
Target language:<br />
<br />
Machine translation: No one calls the little grass "hercules", but its power is truly matchless in the world. This force is invisible life force. As long as there is life, this force will show itself. The stone above is not strong enough to stop it. Because it is a "long-term resistance" of the force, elastic, can bend and extend force, tenacity, not to achieve the purpose of the force.<br />
<br />
Manual translation: Though nobody describes the little grass as a “husky”, yet its herculean strength is unrivalled. It is the force of life invisible to naked eye. It will display itself so long as there is life. The rock is utterly helpless before this force- a force that will forever remain militant, a force that is resilient and can take temporary setbacks calmly, a force that is tenacity itself and will never give up until the goal is reached. (by Zhang Peiji)<br />
<br />
Analysis:This is the excerpt of a well-known Chinese prose written by Xia Yan. It is written during the war of Resistance Against Japan. So the prose holds symbolic meaning, eulogizing the invisible tenacious vitality so as to encourage Chinese to have confidence in the anti-aggression war. Compared with manual translation, machine translation is much more abstract and confusing, especially for the word diction. For example, “大力士” is translated into “hercules” which is a man of exceptional strength and size in Greek and Roman Mythology, making it difficult to understand if readers of target language have no idea of the allusion. What’s worse, the machine version doesn’t reveal the symbolic meaning of the text, which is the core of this prose.<br />
<br />
====3.3Vocative text ====<br />
<br />
(1)English into Chinese<br />
<br />
①Source language:<br />
<br />
iPhone went to film school, so you don’t have to. (Advertisement of iPhone13)<br />
<br />
Target language:<br />
<br />
Machine translation: iPhone上的是电影学院，所以你不用去。<br />
<br />
Manual translation:电影专业课，iPhone同学替你上完了。<br />
<br />
Analysis：Here are advertisements of iPhone on Apple official website. There is a personification in the source language. It is used to stress the advancement and proficiency in camera, which is an appealing selling point to potential buyers. Compared with manual translation, machine translation is plain and not eye-catching enough for customers.<br />
<br />
②Source language: <br />
<br />
5G speed OMGGGGG<br />
<br />
Machine language: 5克的速度 OMGGGGG<br />
<br />
Manual translation:<br />
<br />
iPhone的5G 巨巨巨巨巨5G<br />
<br />
Analysis: The “G” in the source language is the unit of speed, standing for generation. However, it is mistaken as a unit of weight, representing gram in the machine translation. So the meaning is not faithful to the source language at all. As for manual translation, it complies with the source in form. Specifically speaking, five “G”s in the former complies with five characters “巨”in the latter. And the pronunciation of the two is similar. There are two layers of meaning for the 5 “G”s. One exclaims the fast speed of 5 generation network and the other new technology. In the manual version, “巨”can be used to show degree, meaning “quite” or “very”. <br />
<br />
③Source language: <br />
<br />
History, faith and reason show the way, the way of unity. We can see each other not as adversaries but as neighbors. We can treat each other with dignity and respect, we can join forces, stop the shouting and lower the temperature. For without unity, there is no peace, only bitterness and fury."<br />
<br />
Target language:<br />
<br />
Machine translation: 历史、信仰和理性指明了团结的道路。我们可以把彼此视为邻居，而不是对手。我们可以尊严地对待彼此，我们可以联合起来，停止大喊大叫，降低温度。因为没有团结，就没有和平，只有痛苦和愤怒。<br />
<br />
Manual translation:历史、信仰和理性为我们指明道路。那是团结之路。我们可以把彼此视为邻居，而不是对手。我们可以有尊严地相互尊重。我们可以联合起来，停止喊叫，减少愤怒。因为没有团结就没有和平，只有痛苦和愤怒<br />
<br />
Analysis: Speech is a way to propagate some activity in public. It is an art to inspire emotion of the audience. The source language is the excerpt of Joe Biden’s inaugural speech. The speech should be inspiring and logic. The machine translation has some misunderstanding. Taking the translation of “lower the temperature” for example, machine only translates its literal meaning, relating to the temperature itself, without considering the context. What’s more, it is less logic than the manual one. Therefore, it adds difficulty to inspire the audience and infect their emotion.<br />
<br />
===4.Common mistakes in machine translation ===<br />
<br />
====4.1 lexical mistakes ====<br />
<br />
Common lexical mistakes include misunderstandings in word category, lexical meaning and emotive and evaluative meaning. Misunderstanding in word category shows in the classification of word in the source language. As for misunderstanding in lexical meaning, machine has difficulty in precisely reflecting the meaning of the original texts, due to different cultural background and different language system. And for misunderstanding in emotive meaning, machine has no intention and emotion like human-beings. Therefore, it’s impossible for it to know writers’ feelings and their writing purposes. So sometimes, it may translate something negative into something positive.<br />
<br />
====4.2 grammatical mistakes====<br />
<br />
Grammatical analysis plays an important part in translation. Normally speaking, every language has its own unique grammatical rules. So in the process of translation, if translators don’t know the formation rule well, the sentence meaning will be affected. Even though all the lexical meanings are well-known by translators, the lack of consciousness of grammaticality makes it harder to arrange words according to sequential rule. English tends to be hypotactic, while Chinese tends to be paratactic. English sentences are connected through syntactic devices and lexical devices. While Chinese sentences are semantically connected, which means there are limited logical words and connection words in Chinese. So when translating English sentence, we should first analyze its grammaticality and logical structure and then rearrange its sequence. However, online translating machine has troubles in grammatical analysis, which makes its improvement more difficult.<br />
<br />
====4.3 other mistakes====<br />
<br />
The two mistakes above are the internal ones. Apart from mistakes in linguistic system, there are some mistakes in other aspects, such as cultural background.<br />
<br />
===5.Reasons for its common mistakes ===<br />
<br />
====5.1 Difference in two linguistic system====<br />
<br />
With different history, English and Chinese have different ways of expression. Commonly speaking, English is synthetic language which expresses grammatical meaning through inflection such as tense and Chinese is analytic language which expresses grammatical meaning through word order and function word. In addition, English is more compact with full sentences. Subordinate sentence is one of the most important features in modern English. Chinese, on the other hand, is more diffusive with minor sentences.<br />
<br />
====5.2 Difference in thinking patterns and cultural background====<br />
<br />
According to Sapir-Whorf’s Hypothesis, our language helps mould our way of thinking and consequently, different languages may probably express their unique ways of understanding the world. For two different speech communities, the greater their structural differentiations are, the more diverse their conceptualization of the world will be. For example, western culture is more direct and eastern culture more euphemistic. What’s more, English culture tends to be individualism, focusing on detail, through which it reflects the whole, while Chinese culture tends to be collective. Different thinking patterns will add difficulty for machine to translate texts.<br />
<br />
====5.3 Limitation of computer====<br />
<br />
Recently, there are some breakthroughs and innovation in machine translation. However, due to its own limitation, online translation has limitation in some ways. Firstly, compared with machine, human brain is much more complicated, consisting of ten billions of neuron, each of which has different function to affect human’s daily activities and help humans avoid some errors. However, computer can only function according to preset programming has no intention or consciousness. Until now, countless related scholars have invested much time in machine translation. They upload massive language database, which include almost all linguistic rules. But computers still fail to precisely reflect the meaning of source language for many times due to the complexity and flexibility of language. On the other hand, computers can’t take context into consideration. During translation, it is often the case that machine chooses the most-frequently used meaning of one word. So without the correct and exact meaning, readers are easier to feel confused and even misunderstand the meaning of source language.<br />
<br />
===6.Conclusion===<br />
From the analysis above, we can draw a conclusion that machine deals with informative text best, followed by non-literary translation of expressive text. What’s more, machine can be a useful tool to get to know the gist and main idea of a specific topic, for the simple sentence structure and numerous terms. And it can improve translating efficiency with high speed. But machine has difficulty in translating literary works, especially proses and poems.<br />
<br />
Machine translation has mixed future. From the perspective of commercial, machine translation boasts a bright future. With the process of globalization, the demand for translation is increasing accordingly. On one hand, if we only depend on human translator to deal with translating works, the quality and accuracy of translation can be greatly affected. On the other hand, if machine is used properly to do some basic work, human translators only need to make preparation before translating, progress, polish and other advanced work, contributing to highly-qualified translation and high working efficiency.<br />
<br />
However, compared with manual translation, machine translation has a bleak future. It is still impossible for machine to replace interpreter or translator in a short term. With intelligence and initiative, humans are able to learn new knowledge constantly, which machine will never accomplish. Besides, machine is not used to replace translators but to assist them in work. In other words, translators and machine carry out their own duties and they are not incompatible.<br />
<br />
To draw a conclusion, although there are certain limitations of machine translation, it can serve as a catalyst for translating works. Therefore, with the rapid development of artificial intelligence and related technology, there are still many opportunities for machine translation.<br />
<br />
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He Wen 何雯, Wang Xiufeng 王秀峰.信息型文本的在线机器翻译错误研究[J][Research on Errors in Online Machine Translation of Informative text ].Overseas English海外英语,2021(15):188-189.<br />
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Li Hanji 李晗佶. (2021). 人工智能时代翻译技术与译者关系演变与重构 [Evolution and reconstruction of the relationship between translation technology and translators in the era of artificial intelligence]. 西华师范大学学报(哲学社会科学版) Journal of West China Normal University (PHILOSOPHY AND SOCIAL SCIENCES EDITION) (2021-12-04) 1-6.<br />
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Wei Guang魏光. 人工翻译与机器翻译译文编辑比较研究[J][Comparative Study of Translation Editing between Manual Translation and Machine Translation]. Overseas English 海外英语,2021(19):18-19+21.<br />
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=Chapter 11 陈惠妮=Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts=<br />
<br />
机器翻译的译前编辑研究——以医学类文摘为例<br />
<br />
陈惠妮 Chen Huini, Hunan Normal University<br />
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[[Machine_Trans_EN_11]]<br />
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===Abstract===<br />
At present, globalization is accelerating and the market demand for language services is rapidly increasing . Machine translation, as an important translation method, can greatly improve translation efficiency due to its low cost and high speed. However, because of the limitations of machine translation and the differences between Chinese and English language, machine translation is not accurate enough. In order to balance translation efficiency and translation quality, a great number of manual revisions in translation are required for the machine translating texts. Medical papers are specialized, special and purposeful, so it requires accurate,qualified and professional translation. However, the quality of translations by machine is inefficient to meet the high-quality requirements of medical papers translation. Therefore, the introduction of pre-editing can greatly improve the efficiency and quality of machine translation.<br />
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===Key words===<br />
Pre-editing, Machine translation, Medical texts<br />
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===题目===<br />
Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts<br />
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===摘要===<br />
在全球化加速发展的今天，市场对语言服务的需求迅速增加。机器翻译作为一种重要的翻译途径，由于其成本低、速度快，可以大大提高翻译效率。然而，由于机器翻译的局限性以及中英文语言的差异，机器翻译的准确性不高。为了平衡翻译效率和翻译质量，机器翻译文本需要大量的手工修改。<br />
医学论文具有专业性、特殊性和目的性，要求其译文准确、合格、专业。然而，机器翻译的质量较低，无法满足医学论文对翻译的高质量要求。因此，译前编辑的引入可以大大提高机器翻译的效率和质量。<br />
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===关键词===<br />
译前编辑；机器翻译；医学文本<br />
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===1. Introduction===<br />
===1.1 Definition of Machine Translation===<br />
As Cronin(2013) revealed: "Translation is undergoing a revolutionary upheaval. The influence of digital technology and the Internet on translation is continuous, extensive and profound. From the popularity of automatic online translation applications, translation revolution is everywhere (Cronin,2013) However,the concept of machine translation was firstly proposed in the 1930s. Since 1940s, the machine translation technology has been evolving from rule-based machine translation (RBMT) to statistical machine translation (SMT), and to neural machine translation (NMT). Machine translation refers to the automatic translation of source language into target language by using a computer system. That is, machine translation refers to the automatic translation of text from one language into another natural language by computer software or other online translation webs. Machine translation is also defined as the process of “using a computer system to automatically translate text or speech from one natural language to another” according to the definition by ISO (Cui, 2014).O 'Brien (2002) defines it as "the behavior of modifying errors in machine translation to ensure that the target translation meets certain quality requirements". On the basic level, machine translation performs mechanical substitution of words in one language for words in another language, but that rarely produces a good translation, therefore, recognition of the whole phrases and their closest counterparts in the target language is needed. Not all words in one language have equivalents in another language, and many words have more than one meaning. A huge demand for translation is greatly needed in today’s global world, which creates new opportunities for the development of machine translation, attracts more and more attention and becomes one of the current research focuses.<br />
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===1.2 Definition of Pre-editing===<br />
Pre-editing means to adjust and modify the source language to make it fit more with the characteristics of the machine translation software before putting the source language before the into machine translation, so as to improve the quality of the translation machine translation (Wei Changhong, 2008:93-94). Pre-editing is to modify the original text before putting it into machine translation software, in order to improve the recognition rate of machine translation, optimize the output quality of translated text and reduce the workload of post-editing. Because pre-translation editing only needs to be modified in one language, the operation is simpler than post-translation editing, which can realize the double improvement of quality and efficiency. A good pre-editing translation can help machine translation more smoothly, thus improving the machine readability and quality of the output translation. Pre-editing is mostly applied in the following situations: one is when the original text is of poor quality and the machine is difficult to recognize the meaning of the sentence, such as user generated content with poor readability and translatability (Gerlach et al, 2003:45-53); Documents that need to be published in multiple languages; Next is when the original text contains a lot of jargon; The last is the original text has a corresponding translation memory bank. If the original text is edited, it can better match the content of the translation memory bank. Pre-editing is a process of identifying problems. It requires to pre-edit the source texts before putting it into machine translation according to the requirements, listing the expressions or sentences that may have trouble in machine translation and then pre-edit it by human. The purpose is to enable the computer to translate better, improve the translatability of machine translation. (Slype G V & Guinet J F & Seitz F,1984:115)<br />
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===1.3 Machine Translation Mode===<br />
According to the different knowledge acquisition methods, machine translation modes can be classified as follows: one is rule-based machine translation, which is based on bilingual dictionaries and a library of language rules for each language. The quality of translation depends on whether the source language conforms to the existing rules, but the inexhaustible rules are the hinders of this model. The second mode is machine translation based on statistics. This translation model relies on the principles of mathematics and statistics to find various existing translations corresponding to the translation tasks through the employment of corpus, analyzing the frequency of their occurrence and selecting the translation with the highest frequency for output. The disadvantage of this translation model is that it ignores the flexibility of language and the importance of context. The last one is neural network language model. This model is different from previous translation models in that it uses end-to-end neural network to realize automatic translation between natural languages. At present, the quality of its translation is much higher than that of the previous translation models.<br />
===1.4 Source of Study Abstracts===<br />
The core medical journals from domestic are selected in order to make the paper more representative and authoritative, such as National Medical Journal of China, Journal of Peking University (Health Science), and Journal of Third Military Medical University. All of them include basic medical science, biomedical technology, laboratory medical science and other fields. In this way, the pre-editing approaches included are applicable enough to machine translation of medical abstracts.<br />
===1.5 Selection of Translation Software===<br />
Generally, machine translation software includes Google Translation, Youdao Translator and Niu Translation, which has their own special use in translation. Take Google as an example, it is more like neurons in human brain, enabling to learn and collect information to establish connections with its neural machine translator’s neurons. However, it also causes many errors because of the lack of enough information. In this paper, contrastive analysis will be carried on by using Google translation. On the one hand, being a pioneer in the translation of NET, it is inevitably to sue Google as the translation software to translate medical texts in this paper. Comparing the program called “Google brain”, other NMT of translation software are relatively disadvantages. On the other hand, the Google translation enjoys the largest users in the world, with its downloads of more than one billion. The output quality of Google translation is more correct and complete than other machine translation software. With years of development and improvement, Google Translation has been greatly promoted. In this paper, Chinese medical abstract will be automatically translated by Google translation, and then the translation output will be compared with the translation by human and the publication of English abstracts. The main purpose is to prove that the improvement and promotion of quality and accuracy in the medical abstracts will be obtained through the pre-editing approaches.<br />
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===2.Language Characteristics and Error Division===<br />
Since Chinese and English are two different languages, it is quite neccessary to identify their own characteristics so as to better analyze and understand the two languages. Tytler (1978) argued in his Essay on the Principles of Translation that there are three principles of translation that in all the translation should give readers the same feelings as the source text, except for complete transcript of ideas. There actually also exit some mistakes of these two languages. So the following will make some clarrifications of these two languages to ensure more accurate translation.<br />
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===2.1 Language Characteristics of Medical Abstracts===<br />
Chinese and English belong to different language systems, so there are differences in their language structure and the way users think of the languages. When using machine translation from Chinese to English, due to the unequal language levels, there will be many mistakes in the translation process, especially for ESP texts, such as medical papers. <br />
Here, these differences mainly refer to the linguistic characteristics of medical abstracts. In medical abstracts, it usually includes structured and unstructured abstracts. Although in different forms, they both describe the purpose, methods and conclusions of the research.<br />
In the method section of medial abstract, several Chinese sentences can be connected with commas, but each sentence may convey different information. In contrast, English sentences contain a great deal of information, but in order to ensure clarity, some modifiers need to be isolated and then reconstructed. However, in Chinese-English machine translation, a lot of information is put into the sentence because the machine segments the sentence on the basis of the full comma.<br />
In addition, subjectless sentence is used in the objective and method parts of Chinese medical abstracts. The subjectless sentence means the sentence without or free from subject and is usually employed in two contexts. The first is "needless to say". In Chinese sentences, it is common to omit the subject of the sentence. Chinese sentences can convey meanings by using incomplete sentence structures, so Chinese speakers can understand the meanings of the sentences even though the sentence subject is omitted. The second is "emphasis of action". In this context, subjectless sentences are used to describe behaviors, especially in the study of traditional Chinese medicine abstracts. Comparatively speaking, it should be avoided in English medical abstracts. Abstract sentences are more subjective when describing the learning process, while the essential requirement of English medical abstract is objectivity. From this point, sentences without subject should be avoided in English medical abstracts. Another feature is voice. Few words with passive meanings appear in Chinese abstracts. English sentences are more favoured in using passive voice. When translating from Chinese to English, passive voice should be used to make the contents objective, which is also the basic requirement of medical papers.<br />
These are the linguistic features of Chinese medical abstracts. There are great differences in sentence structure and expression between Chinese and English medical abstracts. These differences may reduce the accuracy of machine translation, so it is necessary to introduce pre-editing to edit the source text to ensure that the source text can be accurately recognized by the machine and fully translated into English.<br />
===2.2 Error Division of Machine Translation in Translating Abstracts of Medical Papers from Chinese to English===<br />
In this paper, errors in machine translation are listed out after analyzing. Some are due to the principles, such as statistical-based MT and NMT, employed by machine translation. Based on the original errors, they can be studied from two levels. One is from the macro-level, referring to mistakes caused by objective factors. These are not human factors, but the disadvantage of machine translation and the limitations of language. The limitations of machine translation are derived from the principles and models adopted and manifests itself as a reliance on reference sources. In this case, semantic ambiguity and textual incoherence may occur in the absence of reference sources.<br />
However, for ESP texts such as medical papers, the requirements for terms and sentence patterns are far beyond the existing corpora. For unfamiliar texts (that is, the corresponding texts cannot be found in the corpora). The quality of output translation will be relatively low, which is determined by the principles of machine translation. As mentioned above, machine translation has evolved over the past few decades from rules-based to corp-based statistics to NMT. However, there still exist some limitations in machine translation.<br />
Mistakes on micro-level are mainly caused by the variations and differences of linguistic structure between Chinese and English. Chinese is an implicit language, while English is an explicit one (Lian, 2010) To put it in another way, Chinese expression does not depend on language structures, while English does the opposite. This may result in mismatches between the original and machine translated sentences. This kind of error is mainly divided into two types: component fragment and component missing. For a medical paper, such errors are very serious. As a kind of ESP discourse, medical paper has the characteristics of fixed, objective and accurate language structure. In order to reproduce the characteristics of medical abstracts in translation, it is necessary to avoid errors at the level of words and sentences and pay attention to logic and consistency.<br />
Lexical errors here refer to the inconsistency of fixed expressions in the translation of terms. Although these terms are machine translation based on the corpus, the corpus may not be perfect for ESP texts such as medical papers. Therefore, fixed expressions are not used in term translation. This is also the most common mistake in machine translation (the term here includes but is not limited to nouns). For ESP texts, medical text in particular, the fixed expressions and the accuracy of terms are of great importance.<br />
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===3.Approaches Proposed for Pre-editing ===<br />
Generally speaking, there is a close relationship between pre-editing and post-editing, both of which aim to convey information and ensure high or publishable translation quality. Proper pre-editing can improve the quality of machine translation in terms of adequacy and consistency. <br />
Complexity of natural language and people use language arbitrarily, bringing many difficulties to Chinese-English machine translation, Hu Qingping (2005:24) has proposed "the research of translation software and the development of the controlled language are the two directions to improve the quality of machine translation : the former aims at the difficulty in the natural language processing, the latter overcomes the arbitrariness of natural language". Feng Quangong and Gao Lin (2017: 63-68) put forward: "The writing principles of controlled language can be applied to pre-editing of machine translation. Pre-editing based on controlled language can effectively reduce the complexity and ambiguity of source text, improve the identifiability of machine translation (the translatability of source text itself), and thus reduce (fully) the workload of post-editing".<br />
The central task of pre-editing is to transform human-friendly content into machine-friendly content, so words and sentences need to be repositioned or even changed. Based on the analysis of the language characteristics of Chinese and English, the Chinese ideographic group should be split before the Chinese source text is input into the machine software to translate so that the sentence structure is complete which can be easily recognized by the machine translation software.<br />
===3.1 Extraction and Replacement of Terms in the Source Text===<br />
As a branch of applied English, medical English is the product of the combination of English language knowledge and medical knowledge. The terms in medical papers have the characteristics of fixed and complex language structure. Although Google Translation is based on a corpus, the language structure is not fixed due to the limited size of the corpus. Therefore, the following two steps should be followed before machine translation. The first is to extract terms and create a glossary, and then replace the Chinese terms in the source text with the corresponding English expressions in the glossary. Of course, the terms of extraction should be searched and verified according to the actual situation. All of these words are verified before they can be replaced. This method can not only ensure the accuracy of term translation, but also maintain the consistency of expression, especially when dealing with long texts.<br />
Example1<br />
Source abstract: 口腔白斑病癌变相关缺氧应答基因和微小RNA的芯片及表达验证。<br />
Google translation: Microarray detection/Chip detection and expression verification of hypoxia response genes and microRNAs related to oral leukoplakia canceration.<br />
Published translation:Transcriptome array screening and verification of oral leukoplakia carcinogenesis-related hypoxia-responsive gene and microRNA. <br />
Analysis: The expression “ Affymetrix GeneChip” empolyed in this thesis is a human transcription array for transcriptome array. In the source abstract, “芯片检测“ can be meant “screening” but not detection, so the proper and appropriate translation of these expression should be “transcription array screening”, but the Google translates it into “microarry detection of chip detection”. Therefore, term extraction is essential and inevitable before putting the source abstracts into the machine translation software.<br />
After pre-editing source abstracts: 对 oral leukoplakia carcinogenesis 相关 hypoxia-responsive gene 和微小RNA进行 transcriptome array screening 及表达验证。<br />
After pre-editing translation: Transcriptome array screening and expression- verification of hypoxia-responsive genes and microRNAs related to oral leukoplakia carcinogenesis.<br />
===3.2 Explication of Subordination===<br />
As mentioned above, the subordination of Chinese sentences is judged by certain specific words in machine translation . Chinese is a paratactic language, and sentences are often connected by internal logical relationships; while English depends on sentence structure, so sentences are often closely linked by various language forms. In order to improve the accuracy of machine translation, we can adjust the sentence structure and adjust the position of adjectives and their modifiers. <br />
Example 2<br />
Source abstracts:回顾性分析南京医科大学附属儿童医院中重度HIE患儿49例及同期就诊的无神经系统症状体征的足月新生儿为对照组31例的头颅磁共振成像（MRI）资料。<br />
Google translation: A retrospective analysis that the brain magnetic resonance imaging (MRI) data of 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University and full-term neonates with no neurological symptoms and signs during the same period were included in the control group.<br />
Published translation: A total of 49 children with moderate to severe HIE admitted to the children’s Hospital Affiliated to Nanjing Medical University were retrospectively analyzed. Cranial magnetic resonance imaging (MRI) date of 31 full-term neonates without neurological symptoms and signs who visited the hospital during the same period were recruited as the control group. <br />
Analysis: As the above example shows that “中重度HIE患儿” and “足月新生儿” in the source abstracts are from the Children’s Hospital Affiliated Nanjing Medical University. The Google translation shows that 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University. There is an error due to the misuse of subordination. There are some advice in order to solve the problem. That is, first to segment the sentence and then reconstruct the sentence. For instance, the Chinese expression “南京医科大学附属儿童医院” carries many modifiers, which requires to cut the modifiers and reconstruct the sentence. Therefore, the Chinese expression “南京医科大学附属儿童医院’can be divided into abstractions, leaving two sentences instead of one long sentence with modifiers..<br />
After pre-editing source abstracts: 对49例中重度HIE患儿以及31例无神经系统症状体征的足月新生儿的MRI资料进行回顾性分析。这些患者收治于南京医科大学儿童附属医院。<br />
After pre-editing translation: The MRI data of 49 children with moderate to severe HIE and 31 full-term newborns without neurological symptoms and signs were retrospectively analyzed. These patients were admitted to the Children’s Hospital of Nanjing Medical University.<br />
===3.3 Explication of Subject through Voice Changing===<br />
The extensive use of subjectless sentences are quite common in the Chinese abstracts, while English prefers to employ passive voice in the sentences so as to make them object and accurate. In order to take the characteristics of English sentences into great and careful consideration, the sentences written in passive voice in particular, it will be helpful for machine translation to find out the subject and reconstruct a sentence in passive voice (Wang Yan: 2008). The first is to discover the “doee” in a sentence and then is to reconstruct the sentence structure and put the “doee” before the verb. The last is to explicate the passive relation between the noun and the verb.<br />
Example 3<br />
Source abstract: 回顾性分析2018年1月至2020年12月解放军总医院第七医学中心收治的500例老年髋部骨折患者的资料。<br />
Google translation: A retrospective analysis of the data of 500 elderly patients with hip fractures admitted to the Seventh Medical Center of the PLA General Hospital from January 2018 to December 2020.<br />
Published translation: From January 2018 to December 2020, the data of 500 elderly patients with hip fracture treated in the Seventh Medical Center of PLA General Hospital were analyzed retrospectively.<br />
Analysis: The above example indicates that the “回顾性分析”in the Google Translate is a noun phrase, but the source abstracts actually describes an action or a behavior. The reason for such a mistake is that the machine can’t recognize the Chinese subjetless sentences. To find the subject of the sentence, the source abstracts can be revised and adjusted. Finding the “doee” is the first step, which refers to “500例老年髋部骨折患者的资料”in the source abstracts. And next is to put it in front of the verb, that is to place it in the beginning of the sentence. Last but not least, the passive voice should be explicated in the sentence. <br />
After pre-editing source abstract: 500例老年髋部骨折患者的资料被回顾性分析，他们在2018年1月之2020年12月期间收治于解放军总医院第七医学中心。<br />
After pre-editing translation: The data of 500 elderly hip fracture patients were retrospectively analyzed. They were admitted to the Seventh Medical Center of the PLA General Hospital between January 2018 and December 2020.<br />
===3.4 Relocation of Modifiers===<br />
Modifiers, including noun, adverbial and attributive are constantly employed in the Chinese medical papers in order to add information to subject and object in the sentence. By doing this, complicated sentences can be structured, thus causing obstacles to machine translation for recognizing this complex sentence structures when translating Chinese-English sentences. To make the machine translation successfully recognize the sentence structure, simplifying the sentence structure is quite necessary. The key is to moving the location of the modifiers and thus making the modifiers an independent sentence. In other words, the modifiers ought to be put before or behind the main part of the sentence to satisfy the common use of English. <br />
Usually, the modifiers in Chinese sentence can only be placed in front of the core word, while in English, modifiers are very flexible. It is all right to place the modifiers in front of or behind the major part of the sentences with adjective or a connective noun.<br />
Example 4<br />
Source abstracts: 利用DNA重组技术以pET-28a表达系统在E.coli BL21(DE3) 中重组表达Hepl。<br />
Google Translation: Hepl is recombined in E.coli BL21 (DE3) using DNA recombination technology with pET-28a expression system.<br />
Published translation: The recombinant Hcpl protein was expressed by using DNA recombination technology through pET-28a expression system in E. coli BL21 (De3).<br />
Analysis: The Chinese medical text includes two modifiers, “利用DNA”and “以pET-28a)表达系统”. These two modifiers will be translated by machine, which catches more attention to the two constituents. From the Google translation above, one failure is obvious that it misplaces the location of the two modifiers and presents it in not accurate form. To make it translate correctly by machine translation, dividing the sentences is very important so that the source abstracts can be correctly recognized by machine translation.<br />
After pre-editing source abstract: 利用DNA重组技术，Hcp1重组表达在E.coli BL21 (DE3)中， 通过pET-28a表达系统在E. coli BL21 (DE3)中。<br />
Google translation: Using DNA recombination technology, Hcp1 recombination is expressed in E.coli BL21 (DE3), via pET-28a expression system in E. coli BL21 (DE3).<br />
The second is the independence of modifiers, that is, modifiers can also be reconstructed into clauses to modify core words. Compared with English, There is no subordinate clause in Chinese, so redundant Chinese modifiers need to be reconstructed into subordinate clauses in Chinese-English translation to meet the characteristics of English. English, especially sentences with multiple modifiers. Otherwise, sentence structure may be confused, such as scattered modifiers of core words. To avoid this mistake, it is necessary to separate the modifier from the main part of the sentence. These modifiers should be reconstituted into clauses. Also, keep your sentences simple and easy to understand.<br />
===3.5 Proper Omission and Deletion of Category Words===<br />
Category words are commonly used in Chinese. Category words complement the meaning of words, including problems, positions, situations and jobs. Adding this supplementary word is more in line with Chinese custom. In many cases, it has no real meaning, so it can be omitted in translation. In Chinese, category words are frequently used. However, it is rarely used in English, which is one of the differences between Chinese and English. There are many kinds of category words. Considering objectivity and the fixed structure of language, redundant category words should be deleted in Chinese-English translation. In the general, the most commonly used category of words in the Chinese medical abstracts are "process", "behavior", and "situation". These categories of words hinder the language conversion of Chinese and English, resulting in redundancy. <br />
Example 5<br />
Source abstract: 探讨口腔白斑病癌变进程中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia response genes and associated microRNAs in the process of oral leukoplakia cancer was discussed.<br />
Published translation: To study the hypoxia response gene and microRNA (miRNA)expression profiles in the pathogenesis and progression of oral leukoplakia (OLK).<br />
Analysis: As the above example shows, the Chinese words “进程” belongs to a category word. However, the Chinese expression “癌变”contains the process, so the “进程” expression can be deleted before placing it into the machine translation, because the meaning of it has been overlapping between the expression “癌变”. Therefore, its place can be replaced with preposition “during”.<br />
After pre-editing source abstract: 探讨口腔白斑病癌变中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia responsive genes and miRNA in oral leukoplakia cancer was investigated.<br />
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===Conclusion===<br />
After years of development, machine translation has made great progress. The accuracy of machine translation has been greatly improved in both text recognition and sentence pattern conversion. However, machine translation has its own limitations. In other words, it needs to rely on the parallel corpus as a reference source for improving its accuracy. ESP text, in particular, is harder to get the high quality by machine translation. <br />
As one of the research papers, the characteristics of medical abstracts are fixed language structures, objectivity and accuracy (Qin Yi:2004). Therefore, medical translation must be accurate, object and understandable to follow the specific demands of the medical paper. Being an important field in the human society, medical paper translation is on a great demand, which means that it needs a huge demand for human labor. However, with the machine translation promoting, it will be more efficient to translate medical papers combining the effort by human and machine. The improvement and development of machine translation requires the joint efforts of computer science, information science, statistics, linguistics and other academic circles to achieve more mature human-computer mutual assistance translation (Li Yafei, Zhang Ruihua : 2019).<br />
However, errors can occur during the process of machine translation of Chinese- English, because of the differences of the Chinese and English and the processing of the machine. Errors from the perspective of linguistic or grammar can affect the machine translation a lot. After division and recognition of errors, some pre-editing approaches are put forward to help the machine translation more accurate and readable, that are, extraction and replacement of terms in the source text, relocation of modifiers, explication of subordination, proper omission, deletion of category words and explication of subject through voice changing. <br />
The paper mainly focuses on the pre-editing machine translation by using medical papers as a case study. The errors of machine translation occurring in the translation of medical abstracts and pre-editing approaches for machine translation. The quality of machine translation of medical papers is greatly improved after employing the pre-editing methods. However, machine translation is not as flexible and accurate as human brain, so it is of importance to combine pre-editing and post-editing approaches with machine translation in order to produce more accurate, more object machine translation of medical papers.<br />
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[11] 秦毅(2004),从翻译基本标准议医学英语的翻译[J]. 遵义医学院学报,27 (4): 421-423. <br />
<br />
[12] 王燕 (2008). 医学英语翻译与写作教程[M]. 重庆:重庆大学出版社<br />
<br />
=12 蔡珠凤=(The Mistranslation of C-J Machine Translation of Political Statements)=<br />
[[Machine_Trans_EN_12]]<br />
===Abstract===<br />
Language is the main way of communication between people. With the continuous development of globalization, the scale of cross-border exchanges is also expanding. However, due to cultural differences and diversity, the languages of different countries and regions are very different, which seriously hinders people's communication. The demand for efficient and convenient translation tools is increasing. At the same time, with the development of network technology and artificial intelligence, recognition technology based on deep learning is more and more widely used in English, Japanese and other fields.<br />
<br />
===Key words===<br />
machine translation; political statements; mistranslation of C-J machine translation<br />
===题目===<br />
The Mistranslation of C-J Machine Translation of Political Statements<br />
===摘要===<br />
语言是人与人之间交流的主要方式。随着全球化的不断发展，跨境交流的规模也在不断扩大。然而，由于文化的差异和多样性，不同国家和地区的语言差异很大，这严重阻碍了人们的交流。对高效便捷的翻译工具的需求正在增加。同时，随着网络技术和人工智能的发展，基于深度学习的识别技术在英语、日语等领域的应用越来越广泛。<br />
<br />
===关键词===<br />
机器翻译；政治发言；政治发言中译日的误译<br />
===1. Introduction===<br />
===Introduction to machine translation===<br />
Machine translation, also known as automatic translation, is a process of using computers to convert one natural language (source language) into another natural language (target language). It is a branch of computational linguistics, one of the ultimate goals of artificial intelligence, and has important scientific research value.At the same time, machine translation has important practical value. With the rapid development of economic globalization and the Internet, machine translation technology plays a more and more important role in promoting political, economic and cultural exchanges.The development of machine translation technology has been closely accompanied by the development of computer technology, information theory, linguistics and other disciplines. From the early dictionary matching, to the rule translation of dictionaries combined with linguistic expert knowledge, and then to the statistical machine translation based on corpus, with the improvement of computer computing power and the explosive growth of multilingual information, machine translation technology gradually stepped out of the ivory tower and began to provide real-time and convenient translation services for general users.（Zhang 2019:5-6)<br />
<br />
=== C-J machine translation software===<br />
Today's online machine translation software includes Baidu translation, Tencent translation, Google translation, Youdao translation, Bing translation and so on. Google was the first company to launch the machine translation system, and Baidu was the first company to import the machine translation system in China. In addition, Tencent and Youdao have attracted much attention.Machine translation is the process of using computers to convert one natural language into another. It usually refers to sentence and full-text translation between natural languages. In order to continuously improve the translation quality, R & D personnel have added artificial intelligence technologies such as speech recognition, image processing and deep neural network to machine translation on the basis of traditional machine translation based on rules, statistics and examples.With the increase of using machine translation, the joint cooperation between manual translation and machine translation will also increase significantly in the future. What criteria should be used to evaluate the quality of machine translation? In the evolving field of machine translation, there is an urgent need to clarify the unsolvable questions and solved problems.<br />
<br />
===The history of machine translation===<br />
The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. alchuni put forward the idea of using machines for translation. In 1933, Soviet inventor П.П. Trojansky designed a machine to translate one language into another, and registered his invention on September 5 of the same year; However, due to the low technical level in the 1930s, his translation machine was not made. In 1946, the first modern electronic computer ENIAC was born. Shortly after that, W. weaver, an American scientist and A. D. booth, a British engineer, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947 when discussing the application scope of electronic computer. In 1949, W. Weaver published the translation memorandum, which formally put forward the idea of machine translation. After 60 years of ups and downs, machine translation has experienced a tortuous and long development path. The academic community generally divides it into the following four stages:<br />
<br />
Pioneering period（1947-1964）<br />
<br />
In 1954, with the cooperation of IBM, Georgetown University completed the English Russian machine translation experiment with ibm-701 computer for the first time, showing the feasibility of machine translation to the public and the scientific community, thus opening the prelude to the study of machine translation.<br />
It is not too late for China to start this research. As early as 1956, the state included this research in the national scientific work development plan. The topic name is "machine translation, the construction of natural language translation rules and the mathematical theory of natural language". In 1957, the Institute of language and the Institute of computing technology of the Chinese Academy of Sciences cooperated in the Russian Chinese machine translation experiment, translating 9 different types of more complex sentences.From the 1950s to the first half of the 1960s, machine translation research has been on the rise. The United States and the former Soviet Union, two superpowers, have provided a lot of financial support for machine translation projects for military, political and economic purposes, while European countries have also paid considerable attention to machine translation research due to geopolitical and economic needs, and machine translation has become an upsurge for a time. In this period, although machine translation is just in the pioneering stage, it has entered an optimistic period of prosperity.<br />
<br />
Frustrated period（1964-1975）<br />
<br />
In 1964, in order to evaluate the research progress of machine translation, the American Academy of Sciences established the automatic language processing Advisory Committee (Alpac Committee) and began a two-year comprehensive investigation, analysis and test.In November 1966, the committee published a report entitled "language and machine" (Alpac report for short), which comprehensively denied the feasibility of machine translation and suggested stopping the financial support for machine translation projects. The publication of this report has dealt a blow to the booming machine translation, and the research of machine translation has fallen into a standstill. Coincidentally, during this period, China broke out the "ten-year Cultural Revolution", and basically these studies also stagnated. Machine translation has entered a depression.<br />
<br />
convalescence（1975-1989）<br />
<br />
Since the 1970s, with the development of science and technology and the increasingly frequent exchange of scientific and technological information among countries, the language barriers between countries have become more serious. The traditional manual operation mode has been far from meeting the needs, and there is an urgent need for computers to engage in translation. At the same time, the development of computer science and linguistics, especially the substantial improvement of computer hardware technology and the application of artificial intelligence in natural language processing, have promoted the recovery of machine translation research from the technical level. Machine translation projects have begun to develop again, and various practical and experimental systems have been launched successively, such as weinder system Eurpotra multilingual translation system, taum-meteo system, etc.However, after the end of the "ten-year holocaust", China has perked up again, and machine translation research has been put on the agenda again. The "784" project has paid enough attention to machine translation research. After the mid-1980s, the development of machine translation research in China has further accelerated. Firstly, two English Chinese machine translation systems, ky-1 and MT / ec863, have been successfully developed, indicating that China has made great progress in machine translation technology.<br />
<br />
New period(1990 present)<br />
<br />
With the universal application of the Internet, the acceleration of the process of world economic integration and the increasingly frequent exchanges in the international community, the traditional way of manual operation is far from meeting the rapidly growing needs of translation. People's demand for machine translation has increased unprecedentedly, and machine translation has ushered in a new development opportunity. International conferences on machine translation research have been held frequently, and China has made unprecedented achievements. A series of machine translation software have been launched, such as "Yixing", "Yaxin", "Tongyi", "Huajian", etc. Driven by the market demand, the commercial machine translation system has entered the practical stage, entered the market and came to the users.Since the new century, with the emergence and popularization of the Internet, the amount of data has increased sharply, and statistical methods have been fully applied. Internet companies have set up machine translation research groups and developed machine translation systems based on Internet big data, so as to make machine translation really practical, such as "Baidu translation", "Google translation", etc. In recent years, with the progress of in-depth learning, machine translation technology has further developed, which has promoted the rapid improvement of translation quality, and the translation in oral and other fields is more authentic and fluent.<br />
<br />
===The problem of machine translation at present===<br />
Error is inevitable<br />
Many people have misunderstandings about machine translation. They think that machine translation has great deviation and can't help people solve any problems. In fact, the error is inevitable. The reason is that machine translation uses linguistic principles. The machine automatically recognizes grammar, calls the stored thesaurus and automatically performs corresponding translation. However, errors are inevitable due to changes or irregularities in grammar, morphology and syntax, such as sentences with adverbials after "give me a reason to kill you first" in Dahua journey to the West. After all, a machine is a machine. No one has special feelings for language. How can it feel the lasting charm of "the tenderness of lowering its head, like the shame of a water lotus? After all, the meaning of Chinese is very different due to the changes of morphology, grammar and syntax and the change of context. Even many Chinese people are zhanger monks - they can't touch their heads, let alone machines.<br />
Bottleneck<br />
In fact, no matter which method, the biggest factor affecting the development of machine translation lies in the quality of translation. Judging from the achievements, the quality of machine translation is still far from the ultimate goal.<br />
Chinese mathematician and linguist Zhou Haizhong once pointed out in his paper "fifty years of machine translation": to improve the quality of machine translation, the first thing to solve is the problem of language itself rather than programming; It is certainly impossible to improve the quality of machine translation by relying on several programs alone. At the same time, he also pointed out that it is impossible for machine translation to achieve the degree of "faithfulness, expressiveness and elegance" when human beings have not yet understood how the brain performs fuzzy recognition and logical judgment of language. This view may reveal the bottleneck restricting the quality of translation. <br />
It is worth mentioning that American inventor and futurist ray cozwell predicted in an interview with Huffington Post that the quality of machine translation will reach the level of human translation by 2029. There are still many disputes about this thesis in the academic circles.<br />
<br />
===2.Mistranslation of Chinese Japanese machine translation===<br />
===2.1Vocabulary mistranslation in Chinese Japanese machine translation===<br />
===2.1.1Mistranslation of proper nouns===<br />
In political materials, there are often political dignitaries' names, place names or a large number of proper nouns in the political field. The morphemes of such words are definite and inseparable. Mistranslation will make the source language lose its specific meaning. <br />
<br />
Japanese translation into Chinese Chinese translation into Japanese<br />
<br />
original text translation by Youdao reference translation original text translation by Youdao reference translation<br />
<br />
朱鎔基 朱基 朱镕基 栗战书 栗戰史書 栗戰書<br />
<br />
労安 劳安 劳安 李克强 李克強 李克強 <br />
<br />
筑紫哲也 筑紫哲也 筑紫哲也 习近平 習近平 習近平<br />
<br />
山口百惠 山口百惠 山口百惠 韩正 韓中 韓正<br />
<br />
田中角栄 田中角荣 田中角荣 王沪宁 王上海氏 王滬寧<br />
<br />
東条英機 东条英社 东条英机 汪洋 汪洋 汪洋<br />
<br />
毛沢东 毛泽东 毛泽东 赵乐际 趙樂南 趙樂際<br />
<br />
トウ・ショウヘイ 大酱 邓小平 江泽民 江沢民 江沢民<br />
<br />
周恩来 周恩来 周恩来<br />
<br />
クリントン 克林顿 克林顿<br />
<br />
The above table counts 18 special names in the two texts, and 7 machine translation errors. In terms of mistranslation, there are not only "战书" but also "戰史" and "史書" in Chinese. "沪" is the abbreviation of Shanghai. In other words, since "战书" and "沪" are originally common nouns, this disrupts the choice of target language in machine translation. Except Katakana interference (except for トウシゃウヘイ, most of the mistranslations appear in the new Standing Committee. It can be seen that the machine translation system does not update the thesaurus in time. Different from other words, people's names have particularity. Especially as members of the Standing Committee of the Political Bureau of the CPC Central Committee, the translation of their names is officially unified. In this regard, public figures such as political dignitaries, stars, famous hosts and important. In addition, the machine also translates Japanese surnames such as "タ二モト" (谷本), "アンドウ" (安藤) into "塔尼莫特" and "龙胆". It can be found that the language feature that Japanese will be written together with Chinese characters and Hiragana also directly affects the translation quality of Japanese Chinese machine translation. Japanese people often use Katakana pronunciation. For example, when Japanese people talk with Premier Zhu, they often use "二ーハウ" (Hello). However, the machine only recognizes the two pseudonyms "二" and "ハ" and transliterates them into "尼哈" , ignoring the long sound after "二" and "ハ".<br />
<br />
original text Translation by Youdao reference translation<br />
<br />
日美安全体制 日米の安全体制 日米安保体制<br />
<br />
中国共产党第十九次全国代表大会 中国共産党第19回全国代表大会 中国共産党第19回全国代表大会（第19回党大会）<br />
<br />
十八大 十八大 第18回党大会中国特色社会主义<br />
<br />
中国特色社会主義 中国の特色ある社会主義 第18回党大会<br />
<br />
中国共产党中央委员会 中国共産党中央委員会 中国共産党中央委員会<br />
<br />
中国共産党中央委員会十八届中共中央政治局常委 第18代中国共產党中央政治局常務委員 第18期中共中央政治局常務委員<br />
<br />
十八届中共中央政治局委员 18期の中国共產党中央政治局委員 第18期中共中央政治局委員<br />
<br />
十九届中共中央政治局常委 十九回中国共產党中央政治局常務委員 第19期中央政治局常務委員<br />
<br />
中共十九届一中全会 中国共產党第十九回一中央委員会 第19期中央委員会第1回全体会議<br />
<br />
The above table is a comparison of the original and translated versions of some proper nouns. As shown in the table, the mistranslation problems are mainly reflected in the mismatch of numerals + quantifiers, the wrong addition of case auxiliary word "の", the lack of connectors, the Mistranslation of abbreviations, dead translation, and the writing errors of Chinese characters. The following is a specific analysis one by one.<br />
<br />
"十八届中共中央政治局常委", "十八届中共中央政治局委员", "十九届中共中央政治局常委" and "中共十九届一中全会" all have quantifiers "届", which are translated into "代", "期" and "回" respectively. The meaning is vague and should be uniformly translated into "期"; Among them, the translation of the last three proper nouns lacks the Chinese conjunction "第". The case auxiliary word "の" was added by mistake in the translation of "十八届中共中央政治局委员" and "日美安全体制". The "中国共产党中央委员会" did not write in the form of Japanese Ming Dynasty characters, but directly used simplified Chinese characters.<br />
<br />
The full name of "中共十九届一中全会" is "中国共产党第十九届中央委员会第一次全体会议", in which "一" stands for "第一次", which is an ordinal word rather than a cardinal word. Machine translation does not produce a correct translation. The fundamental reason is that there are no "规则" for translating such words in machine translation. If we can formulate corresponding rules for such words (for example, 中共m'1届m2 中全会→第m1期中央委员会第m2回全体会议), the translation system must be able to translate well no matter how many plenary sessions of the CPC Central Committee.<br />
<br />
===2.1.2Mistranslation of Polysemy===<br />
original text Translation by Youdao reference translation<br />
<br />
スタジオ 摄影棚/工作室 直播现场/演播厅<br />
<br />
日中関係の話 中日关系的故事 就中日关系(话题)<br />
<br />
溝 水沟 鸿沟<br />
<br />
それでは日中の問題について質問のある方。 那么对白天的问题有提问的人。 关于中日问题的话题，举手提问。<br />
<br />
私たちのクラスは20人ちょっとですが、 我们班有20人左右， 我们班二十多人的意见统一很难，<br />
<br />
いろいろな意見が出て、まとめるのは大変です。 但是又各种各样的意见，总结起来很困难。 <br />
<br />
一体どうやって、13億人もの人をまとめているんですか。 到底是怎么处理13亿的人的呢？ 中国是怎样把13亿人凝聚在一起的？<br />
<br />
In the original text, the word "スタジオ" appeared four times and was translated into "摄影棚" or "工作室" respectively, but the context is on-site interview, not the production site of photography or film. "話" has many semantics, such as "说话", "事情", "道理", etc. In accordance with the practice of the interview program, Premier Zhu Rongji was invited to answer five questions on the topic of China Japan relations. Obviously, the meaning of the word "故事" is very abrupt. The inherent Japanese word "日中" means "晌午、白天". At the same time, it is also the abbreviation of the names of the two countries. The machine failed to deal with it correctly according to the context. "溝" refers to the gap between China and Japan, not a "水沟". The former "まとめる" appears in the original text with the word "意见", which is intended to describe that it is difficult to unify everyone's opinions. The latter "まとめている" also refers to unifying the thoughts of 1.3 billion people, not "处理". Therefore, it is more appropriate to use "统一" and "处理" in human translation, rather than "总结意见" or "处理意见".<br />
<br />
Mistranslation of polysemy has always been a difficult problem in machine translation research. The translation of each word is correct, but it is often very different from the original expression in the context. Zhang Zhengzeng said that ambiguity is a common phenomenon in natural language. Its essence is that the same language form may have different meanings, which is also one of the differences between natural language and artificial language. Therefore, one of the difficulties faced by machine translation is language disambiguation (Zhang Zheng, 2005:60). In this regard, we can mark all the meanings of polysemous words and judge which meaning to choose by common collocation with other words. At the same time, strengthen the text recognition ability of the machine to avoid the translation inconsistent with the current context. In this way, we can avoid the mistake of "大酱" in the place where famous figures such as Deng Xiaoping should have appeared in the previous political articles.<br />
<br />
===2.1.3Mistranslation of compound words===<br />
Multi class words refer to a word with two or more parts of speech, also known as the same word and different classes.<br />
<br />
original text Translation by Youdao reference translation<br />
<br />
1998年江泽民主席曾经访问日本, 1998年の江沢民国家主席の日本訪問し、 1998年、江沢民総書記が日本を訪問し、かつ<br />
<br />
同已故小渊首相签署了联合宣言。 かつて同じ故小渕首相が署名した共同宣言 亡くなられた小渕総理と宣言に調印されました<br />
<br />
Chinese "同" has two parts of speech: prepositions and conjunctions: "故" has many parts of speech, such as nouns, verbs, adjectives and conjunctions. This directly affects the judgment of the machine at the source language level. The word "同" in the above table is used as a conjunction to indicate the other party of a common act. "故" is used as a verb with the semantic meaning of "死亡", which is a modifier of "小渊首相". The machine regards "同" as an adjective and "故" as a noun "原因", which leads to confusion in the structure and unclear semantics of the translation. Different from the polysemy problem, multi category words have at least two parts of speech, and there is often not only one meaning under each part of speech. In this regard, software R & D personnel should fully consider the existence of multi category words, so that the translation machine can distinguish the meaning of words on the basis of marking the part of speech, so as to select the translation through the context and the components of the word in the sentence. Of course, the realization of this function is difficult, and we need to give full play to the wisdom of R & D personnel.<br />
<br />
===2.2 Syntactic mistranslation in Chinese Japanese machine translation===<br />
===2.2.1Mistranslation of tenses===<br />
original text：History is written by the people, and all achievements are attributed to the people. <br />
<br />
Translation by Youdao：歴史は人民が書いたものであり、すべての成果は人民のためである。<br />
<br />
reference translation：歴史は人民が綴っていくものであり、すべての成果は人民に帰することとなります。<br />
<br />
The original sentence meaning is "历史是人民书写的历史" or "历史是人民书写的东西". When translated into Japanese, due to the influence of Japanese language habits, the formal noun "もの" should be supplemented accordingly. In this sentence, both the machine and the interpreter have translated correctly. However, the machine's recognition of "的" is biased, resulting in tense translation errors. The past, present and future will become "history", and this is a continuous action. The form translated as "~ ていく" not only reflects that the action is a continuous action, but also conforms to the tense and semantic information of the original text. In addition, the machine's treatment of the preposition "于" is also inappropriate. In the original text, "人民" is the recipient of action, not the target language. As the most obvious feature of isolated language, function words in Chinese play an important role in semantic expression, and their translation should be regarded as a focus of machine translation research.<br />
<br />
original text：李克强同志是十六届中共中央政治局常委,其他五位同志都是十六届中共中央政治局委员。<br />
<br />
Translation by Youdao：李克強総理は第16代中国共産党中央政治局常務委員であり、他の５人の同志はいずれも16期の中国共産党中央政治局委員である。<br />
<br />
reference translation：李克強同志は第16期中国共産党中央政治局常務委員を務め、他の５人は第16期中共中央政治局委員を務めました。<br />
<br />
Judging the verb "是" in the original text plays its most basic positive role, but due to the complexity of Chinese language, "是" often can not be completely transformed into the form of "だ / である" in the process of translation. This sentence is a judgment of what has happened. Compared with the 19th session, the 18th session has become history. This is an implicit temporal information. It is very difficult for machines without human brain to recognize this implicit information. "中共中央政治局常委" is the abbreviation of "中共中央政治局常务委员会委员" and it is a kind of position. In Japanese, often use it with verbs such as "務める","担当する",etc. The translator adopts the past tense of "務める", which conforms to the expression habit of Japanese and deals with the problem of tense at the same time. However, machine translation only mechanically translates this sentence into judgment sentence, and fails to correctly deal with the past information implied by the word "十八届".<br />
<br />
===2.2.2Mistranslation of honorifics===<br />
Honorific language is a language means to show respect to the listener. Different from Japanese, there is no grammatical category of honorifics in Chinese. There is no specific fixed grammatical form to express honorifics, self modesty and politeness. Instead, specific words such as "您", "请" and "劳驾" are used to express all kinds of respect or self modesty. <br />
<br />
Original text translation by Youdao reference translation<br />
<br />
女士们，先生们，同志们，朋友们 さんたち、先生たち、同志たち、お友达さん ご列席の皆さん<br />
<br />
谢谢大家！ ありがとうございます！ ご清聴ありがとうございました。<br />
<br />
これはどうされますか。 这是怎么回事呢? 您将如何解决这一问题？<br />
<br />
こうした問題をどうお考えでしょうか。 我们会如何考虑这些问题呢？ 您如何看待这一问题？<br />
<br />
For example, for the processing of "女士们，先生们，同志们，朋友们", the machine makes the translation correspond to the original one by one based on the principle of unchanged format, but there are errors in semantic communication and pragmatic habits. When speaking on formal occasions, Chinese expression tends to be comprehensive and detailed, as well as the address of the audience. In contrast, Japanese usually uses general terms such as "皆様", "ご臨席の皆さん" or "代表団の方々" as the opening remarks. In terms of Japanese Chinese translation, the machine also failed to recognize the usage of Japanese honorifics such as "さ れ る" and "お考え". It can be seen that Chinese Japanese machine translation has a low ability to deal with honorific expressions. The occasion is more formal. A good translation should not only be fluent in meaning, but also conform to the expression habits of the target language and match the current translation environment.<br />
<br />
===Conclusion===<br />
In the process of discussing the Mistranslation of machine translation, this paper mainly cites the Mistranslation of Youdao translator. When I studied the problem of machine translation mistranslation, I also used a large number of translation software such as Google and Baidu for parallel comparison, and found that these translation software also had similar problems with Youdao translator. For example, the "新世界新未来" in Chinese ABAC structural phrases is translated by Baidu as "新し世界の新しい未来", which, like Youdao, is not translated in the form of parallel phrases; Google online translation translates the word "“全心全意" into a completely wrong "全面的に"; The original sentence "我吃了很多亏" in the Mistranslation of the unique expression in the language is translated by Google online as "私はたくさんの損失を食べました". Because the "吃" and "亏" in the original text are not closely adjacent, the machine can not recognize this echo and mistakenly treats "亏" as a kind of food, so the machine translates the predicate as "食べました"; Japanese "こうした問題をどう考えでしょうか", Google's online translation is "你如何看待这些问题?", "你" tone can not reflect the tone of Japanese honorific, while Baidu translates as "你怎么想这样的问题呢?" Although the meaning can be understood, it is an irregular spoken language. Google and Baidu also made the same mistakes as Youdao in the translation of proper nouns. For example, they translated "十七届(中共中央政治局常委)”" into "第17回..." .In short, similar mistranslations of Youdao are also common in Baidu and Google. Due to space constraints, they will not be listed one by one here.<br />
<br />
Mr. Liu Yongquan, Institute of language, Chinese Academy of Social Sciences (1997) It has been pointed out that machine translation is a linguistic problem in the final analysis. Although corpus based machine translation does not require a lot of linguistic knowledge, language expression is ever-changing. Machine translation based solely on statistical ideas can not avoid the translation quality problems caused by the lack of language rules. The following is based on the analysis of lexical, syntactic and other mistranslations From the perspective of the characteristics of the source language, this paper summarizes some difficulties of Chinese and Japanese in machine translation.<br />
<br />
(1) The difficulties of Chinese in machine translation <br />
<br />
Chinese is a typical isolated language. The relationship between words needs to be reflected by word order and function words. We should pay attention to the transformation of function words such as "的", "在", "向" and "了". Some special verbs, such as "是", "做" and "作", are widely used. How to translate on the basis of conforming to Japanese pragmatic habits and expressions is a difficulty in machine translation research. In terms of word order, Chinese is basically "subject→predicate→object". At the same time, Chinese pays attention to parataxis, and there is no need to be clear in expression with meaningful cohesion, which increases the difficulty of Chinese Japanese machine translation. When there are multiple verbs or modifier components in complex long sentences, machine translation usually can not accurately divide the components of the sentence, resulting in the result that the translation is completely unreadable. <br />
<br />
(2) Difficulties of Japanese in machine translation <br />
<br />
Both Chinese and Japanese languages use Chinese characters, and most machines produce the target language through the corresponding translation of Chinese characters. However, pseudonyms in Japanese play an important role in judging the part of speech and meaning, and can not only recognize Chinese characters and judge the structure and semantics of sentences. Japanese vocabulary is composed of Chinese vocabulary, inherent vocabulary and foreign vocabulary. Among them, the first two have a great impact on Chinese Japanese machine translation. For example "提出" is translated as "提出する" or "打ち出す"; and "重视" is translated as "重視する" or "大切にする". Japanese is an adhesive language, which contains a lot of "けど", "が" and "けれども" Some of them are transitional and progressive structures, but there are also some sequential expressions that do not need translation, and these translation software often can not accurately grasp them.<br />
<br />
===References===<br />
[1] Navroz Kaur Kahlon,(2021(prepublish));Williamjeet Singh.Machine translation from text to sign language: a systematic review[J].Universal Access in the Information Society,1-35.<br />
<br />
[2] Cao Qianyu;Hao Hanmei,(2021);Ahmed Syed Hassan.A Chaotic Neural Network Model for English Machine Translation Based on Big Data Analysis[J].Computational Intelligence and Neuroscience,3274326-3274326.<br />
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[3]Hwang Yongkeun;Kim Yanghoon;Jung Kyomin.(2021)Context-Aware Neural Machine Translation for Korean Honorific Expressions[J].Electronics,10(13):1589-1589.<br />
<br />
[4]Zakaryia Almahasees.(2021)Analysing English-Arabic Machine Translation:Google Translate, Microsoft Translator and Sakhr.<br />
<br />
[5](2021)Machine learning in translation[J].Nature Biomedical Engineering,5(6):485-486.<br />
<br />
[6]Shaimaa Marzouk.(2021(prepublish))An in-depth analysis of the individual impact of controlled language rules on machine translation output: a mixed-methods approach[J].Machine Translation,1-37.<br />
<br />
[7]Welnitzová Katarína;Munková Daša.(2021)Sentence-structure errors of machine translation into Slovak[J].Topics in Linguistics,22(1):78-92.<br />
<br />
[8]Xu Xueyuan.(2021).Machine learning-based prediction of urban soil environment and corpus translation teaching[J].Arabian Journal of Geosciences,14(11). <br />
<br />
[9]Chen Bingchang 陈丙昌(2016).機械翻訳の誤訳分析【D】.Error analysis of mechanical translation.贵州大学.2016(05) <br />
<br />
[10]Lv Yinqiu 呂寅秋(1996).機械翻訳の言語規則と伝統文法との相違点.【D】The language rules of mechanical translation, the traditional grammar, and the points of contradiction.日本学研究.Japanese Studies.1996(00):21-22 <br />
<br />
[11]Liu Jun 刘君(2014).基于语料库的中日同形词词义用法对比及其日中机器翻译研究【D】.A Corpus-based Comparison of the Meanings of Chinese and Japanese Homographs and Research on Japanese-Chinese Machine Translation.广西大学.(03) <br />
<br />
[12]Cun Qianqian 崔倩倩(2019).机器翻译错误与译后编辑策略研究【D】.Research on Machine Translation Errors and Post-Editing Strategies.北京外国语大学.(09) <br />
<br />
[13]Zhang Yi 张义(2019).机器翻译的译文分析【D】.Translation analysis of machine translation.西安外国语大学.(10) <br />
<br />
[14]Zhang Linqian 张琳婧(2019).在线机器翻译中日翻译错误原因及对策【D】.Causes and countermeasures of online machine translation errors in Chinese-Japanese translation.山西大学.(02)<br />
<br />
[15]Wang Dan 王丹(2020).基于机器翻译的专利文本译后编辑对策研究【D】.Research on countermeasures for post-translational editing of patent texts based on machine translation.大连理工大学.(06)<br />
<br />
[16]Yang Xiaokun 杨晓琨(2020).日中机器翻译中的前编辑规则与效果验证【D】.Pre-editing rules and effect verification in Japanese-Chinese machine translation.大连理工大学.(06)<br />
<br />
[17]Zuo Jia 左嘉(2021). 机器翻译日译汉误译研究【D】. Research on Mistranslation of Machine Translation from Japanese to Chinese.北京第二外国语学院.<br />
<br />
[18]Guan Biying 关碧莹(2018).关于政治类发言的汉日机器翻译误译分析【D】.Analysis of Chinese-Japanese Machine Translation Mistranslations of Political Speeches.哈尔滨理工大学.<br />
<br />
[19]Che Tong 车彤(2021).汉译日机器翻译质量评估及译后编辑策略研究【D】.Research on Quality Evaluation of Chinese-Japanese Machine Translation and Post-translation Editing Strategies.北京外国语大学.(09)<br />
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Networking Linking<br />
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http://www.elecfans.com/rengongzhineng/692245.html<br />
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https://baike.baidu.com/item/%E6%9C%BA%E5%99%A8%E7%BF%BB%E8%AF%91/411793<br />
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=13 陈湘琼Chen Xiangqiong（Study on Post-editing from the Perspective of Functional Equivalence Theory ）=<br />
[[Machine_Trans_EN_13]]<br />
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=14 Bi bi Nadia（Machine Translation a Challenge for Human Translators)=<br />
[[Machine_Trans_EN_14]]<br />
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===Abstract===<br />
Machine translation is a big obstacle in the way of Human translators or interpretors although it is quick and less time consuming.people are trying to get translation of their target language using through source language.For this purpose they are using digital apps like Google translation Google translation does not give accurate and exact interpretation.Google translator is translates word to word translate that doesn't clarifies it's true and actual meaning.On the other hand human translators can give exact and accurate translation ,they take care of grammatical errors , diction and sentence structure.They clarify the purpose of target language through using source language.<br />
<br />
===Keywords===<br />
Descriptive translation, academic, interdiscipline, comparative literature, localization,Translatology,school of thought,translation , studies, linguistics, corresponding.<br />
<br />
===Body of article===<br />
Translation is the process of reworking text from one language into another to maintain the original message and communication. But, like everything else, there are different methods of translation, and they vary in form and function. What is translation? The term has become widely used among knowledge transfer researchers and practitioners, especially in the fields of health and health care. In a landmark review, Jonathan Lomas began to argue that 'The tasks… may be defined as to establish and maintain links between researchers and their audience, via the appropriate translation of research findings' (Lomas 1997, p 4). In 2004, the World Health Organization's World Report on Knowledge for Better Health suggested that 'One of the key contributions of research to health systems is the translation of knowledge into actions' (WHO 2004, p 33 and p 100). By 2006, special issues of WHO's Bulletin as well as the journals Evaluation and the Health Professions and the Journal of Continuing Education in the Health Professions were dedicated to translation.But what does 'translation' mean? It may be a new word for an old problem, meaning nothing more than 'transfer'. The rapidly emerging field of 'translational medicine' seems to take translation to mean generally what transfer might have meant, that is the transmission of knowledge and evidence 'from bench to bedside'. As one commentator has observed, "Translational medicine" as a fashionable term is being increasingly used to describe the wish of biomedical researchers to ultimately help patients' (Wehling 2008, abstract). <br />
Semantic uncertainty persists, not least because of the different interests of scientists, clinicians, patients and commercial firms (Littman et al 2007): 'Translational research means different things to different people, but it seems important to almost everyone' (Woolf 2008, p 211).<br />
In related fields, such as public health (Armstrong et al 2006), 'translation' seems to signify dissatisfaction with 'transfer'. It wants to move away from thinking of knowledge transfer as a form of technology transfer or dissemination, rejecting if only by implication its mechanistic assumptions and its model of linear messaging from A to B. But still, what does it signify?<br />
<br />
===Why translation?===<br />
'Translation' indicates a closer attention to the problem of shared meaning and how it might be developed. It seems to represent some new epistemological lubricant, facilitating the dissemination of texts and the application and use of the knowledge and information they in. Simply, translation might be the key to transfer. And yet, when we stop to think, we are more ambivalent. What is translated often seems somehow inferior, not real or original. Note how readily commentators reach for the idea that things might be 'lost in translation'. Knowing at a distance – made in and mediated by translation - makes for incomplete renditions, blurred images, partial truths. So what might 'translation' really mean? The purpose of this paper is to set out, for policy makers and practitioners, the theoretical and conceptual resources translation holds and seems to represent. In doing so, it explores understandings of translation in the fields of literature and linguistics and in the sociology of science and technology. It begins by setting out just why this idea of translation should make immediate, intuitive sense in relation to research, policy and practice.<br />
1translation in research, policy and practice research as translation<br />
Research often entails translation from one language to another: where data is collected from more than one ethnic group, for example, or where the language of the researcher is other than that of the research subject. It may draw on a secondary literature or source documents written in different languages, and may be published and disseminated in languages other than the one in which it is first written up.<br />
2In a different way, to conduct an interview is to ask for an account of experience and its meanings, but it is also to construct and translate that experience in terms defined at least in part by the researcher. In representing what is said, transcripts then select data, usually excluding significant gesture and eye-contact, for example. Often, certain characteristics of speech-acts (such as hesitations) will be edited out. In turn, the format of the transcript shapes the analytic use the researcher may make of it. The basis of research 'findings', then, is an artefact, a transcript or translation, not an original interaction (Ochs 1979, Barnes, Bloor and Henry 1996, Ross 2009).<br />
3In this way, the researcher recasts aspects of his or her problem or topic in new, scientific form: 'All researchers "translate" the experiences of others' (Temple 1997, p 609). Research is invariably conducted in a sort of 'metalanguage' (Hantrais and Ager 1985): the research process can be conceived as one of successive translations (from theoretical formulation to operationalization, transcription, interpretation and dissemination). Theorization is a process of reciprocal back and forth between theory and fact, in which conceptions of each are revised in order that one fit the other (Baldamus 1974).<br />
4 It is a kind of translation: a rereading, re-use, re-application or re-representation of what we know in new terms (Turner 1980). Referencing, too, is an act of translation, a form of appropriation and incorporation of one text by another (Gilbert 1977).policy as translation Each of the fields covered by the paper is diverse and ill-defined, and there is no intention here to provide a comprehensive account of any them. Sources have been chosen for their relevance: main references are cited in the text, and additional sources listed in footnotes.<br />
<br />
2For a brief introduction to the technical issues involved in social research in more than one language, see Birbili (2000). For translation issues in survey research and question design in general, see Ervin and Bower (1952) and Deutscher (1968); on translating survey research instruments (in this instance health-related quality of life measures), Bowden and Fox-Rushby (2003). On the use of translators and interpreters, see Temple (1997) and Jentsch (1998).<br />
3For an interesting discussion of this problem, see Bourdieu (1999), esp pp 621-626, 'The risks of writing'. <br />
===Difference between machine translation and human translators===<br />
Few people disagree on the differences between the two, but many argue over the quality of the translations. How accurate are machine translations? How reliable are human translations? Some say machine translation produces near-perfect translations while others are adamant that translations are incomprehensible and cause more problems than they solve. Results will, of course, vary depending on the source and target languages, the machine translation service used (e.g. Google Translate), and the complexity of the original text.<br />
Machine translation, love it or hate it, is here to stay. In fact, the machine translation market is growing at such a fast pace that it is predicted to reach $980 million by 2022.<br />
===Machine translation: the pros and cons===<br />
The advantages of machine translation generally come down to two factors: it’s faster and cheaper. The downside to this is the standard of translation can be anywhere from inaccurate, to incomprehensible, and potentially dangerous (more on that shortly).<br />
==The advantages of machine translation==<br />
Many free tools are readily available (Google Translate, Skype Translator, etc.)<br />
Quick turnaround time <br />
You can translate between multiple languages using one tool<br />
Translation technology is constantly improving<br />
The disadvantages of machine translation<br />
Level of accuracy can be very low <br />
Accuracy is also very inconsistent across different languages<br />
==Machines can't translate context ==<br />
Mistakes are sometimes costly <br />
Sometimes translation simply doesn’t work<br />
The most important thing to consider with any kind of translation is the cost of potential mistakes. Translating instructions for medical equipment, aviation manuals, legal documents and many other kinds of content require 100% accuracy. In such cases, mistakes can cost lives, huge amounts of money and irreparable damage to your company’s image. So choose carefully!<br />
Human translation: the pros and cons<br />
Human translation essentially switches the table in terms of pros and cons. A higher standard of accuracy comes at the price of longer turnaround times and higher costs. What you have to decide is whether that initial investment outweighs the potential cost of mistakes. Alternatively, whether mistakes simply aren’t an option, like the cases we looked at in the previous section.<br />
<br />
==The advantages of human translation ==<br />
It’s a translator’s job to ensure the highest accuracy<br />
Humans can interpret context and capture the same meaning, rather than simply translating words<br />
Human translators can review their work and provide a quality process<br />
Humans can interpret the creative use of language, e.g. puns, metaphors, slogans, etc.<br />
Professional translators understand the idiomatic differences between their languages<br />
Humans can spot pieces of content where literal translation isn’t possible and find the most suitable alternative<br />
The disadvantages of human translation<br />
Turnaround time is longer <br />
Translators rarely work for free <br />
Unless you use a translation agency, with access to thousands of translators, you’re limited to the languages any one translator can work with<br />
Simply put, human translation is your best option when accuracy is even remotely important. Other considerations to make are the complexity of your source material and the two languages you’re translating between – both of which can render machines pretty useless.<br />
<br />
When to use machine and human translation<br />
The truth is, the debate over machine vs human translation is an unnecessary distraction. What we should really be talking about is when to use these two different types of translation services, because they both serve a very valid purpose.<br />
<br />
Examples of when to use machine translation<br />
When you have a large bulk of content to translate and the general meaning is enough<br />
When your translation never reaches the final audience, e.g. you’re translating a resource as research for another piece of content<br />
Translating documents for internal use within a company, provided 100% accuracy isn’t needed<br />
To partially translate large chunks of content for a human translator to improve upon<br />
Examples of when to use human translation<br />
When accuracy is important<br />
Most cases where your translated content is received by a consumer audience<br />
When you have a duty of care to provide accurate translations (e.g. legal documents, product instructions, medical guidelines or health and safety content)<br />
When translating marketing material or other texts for creative language uses.<br />
<br />
types of machine translation.<br />
<br />
What is Machine Translation? Rule Based Machine Translation vs. Statistical Machine Translation. Machine translation (MT) is automated translation. It is the process by which computer software is used to translate a text from one natural language (such as English) to another (such as Spanish).<br />
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To process any translation, human or automated, the meaning of a text in the original (source) language must be fully restored in the target language, i.e. the translation. While on the surface this seems straightforward, it is far more complex. Translation is not a mere word-for-word substitution. A translator must interpret and analyze all of the elements in the text and know how each word may influence another. This requires extensive expertise in grammar, syntax (sentence structure), semantics (meanings), etc., in the source and target languages, as well as familiarity with each local region.<br />
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Human and machine translation each have their share of challenges. For example, no two individual translators can produce identical translations of the same text in the same language pair, and it may take several rounds of revisions to meet customer satisfaction. But the greater challenge lies in how machine translation can produce publishable quality translations.<br />
<br />
Rule-Based Machine Translation Technology<br />
Rule-based machine translation relies on countless built-in linguistic rules and millions of bilingual dictionaries for each language pair.<br />
The software parses text and creates a transitional representation from which the text in the target language is generated. This process requires extensive lexicons with morphological, syntactic, and semantic information, and large sets of rules. The software uses these complex rule sets and then transfers the grammatical structure of the source language into the target language.<br />
Translations are built on gigantic dictionaries and sophisticated linguistic rules. Users can improve the out-of-the-box translation quality by adding their terminology into the translation process. They create user-defined dictionaries which override the system’s default settings.<br />
In most cases, there are two steps: an initial investment that significantly increases the quality at a limited cost, and an ongoing investment to increase quality incrementally. While rule-based MT brings companies to the quality threshold and beyond, the quality improvement process may be long and expensive.<br />
<br />
Statistical Machine Translation Technology<br />
Statistical machine translation utilizes statistical translation models whose parameters stem from the analysis of monolingual and bilingual corpora. Building statistical translation models is a quick process, but the technology relies heavily on existing multilingual corpora. A minimum of 2 million words for a specific domain and even more for general language are required. Theoretically it is possible to reach the quality threshold but most companies do not have such large amounts of existing multilingual corpora to build the necessary translation models. Additionally, statistical machine translation is CPU intensive and requires an extensive hardware configuration to run translation models for average performance levels.<br />
<br />
Rule-Based MT vs. Statistical MT<br />
Rule-based MT provides good out-of-domain quality and is by nature predictable. Dictionary-based customization guarantees improved quality and compliance with corporate terminology. But translation results may lack the fluency readers expect. In terms of investment, the customization cycle needed to reach the quality threshold can be long and costly. The performance is high even on standard hardware.<br />
<br />
Statistical MT provides good quality when large and qualified corpora are available. The translation is fluent, meaning it reads well and therefore meets user expectations. However, the translation is neither predictable nor consistent. Training from good corpora is automated and cheaper. But training on general language corpora, meaning text other than the specified domain, is poor. Furthermore, statistical MT requires significant hardware to build and manage large translation models.<br />
<br />
Rule-Based MT Statistical MT<br />
+ Consistent and predictable quality – Unpredictable translation quality<br />
+ Out-of-domain translation quality – Poor out-of-domain quality<br />
+ Knows grammatical rules – Does not know grammar <br />
+ High performance and robustness – High CPU and disk space requirements<br />
+ Consistency between versions – Inconsistency between versions <br />
– Lack of fluency + Good fluency<br />
– Hard to handle exceptions to rules + Good for catching exceptions to rules <br />
– High development and customization costs + Rapid and cost-effective development costs provided the required corpus exists<br />
Given the overall requirements, there is a clear need for a third approach through which users would reach better translation quality and high performance (similar to rule-based MT), with less investment (similar to statistical MT).<br />
Post-Edited Machine Translation (PEMT)<br />
Often, PEMT is used to bridge the gap between the speed of machine translation and the quality of human translation, as translators review, edit and improve machine-translated texts. PEMT services cost more than plain machine translations but less than 100% human translation, especially since the post-editors don’t have to be fluently bilingual—they just have to be skilled proofreaders with some experience in the language and target region.<br />
Successful translation is about more than just the words, which is why we advocate for not just human translation by skilled linguists, but for translation by people deeply familiar with the cultures they’re writing for. Life experience, study and the knowledge that only comes from living in a geographic region can make the difference between words that are understandable and language that is capable of having real, positive impact. <br />
<br />
PacTranz<br />
The HUGE list of 51 translation types, methods and techniques<br />
Upper section of infographic of 51 common types of translation classified in 4 broad categoriesThere are a bewildering number of different types of translation.<br />
So we’ve identified the 51 types you’re most likely to come across, and explain exactly what each one means.<br />
This includes all the main translation methods, techniques, strategies, procedures and areas of specialisation.<br />
It’s our way of helping you make sense of the many different kinds of translation – and deciding which ones are right for you.<br />
Don’t miss our free summary pdf download later in the article!<br />
The 51 types of translation we’ve identified fall neatly into four distinct categories.<br />
Translation Category A: 15 types of translation based on the technical field or subject area of the text<br />
Icons representing 15 types of translation categorised by the technical field or subject area of the textTranslation companies often define the various kinds of translation they provide according to the subject area of the text.<br />
This is a useful way of classifying translation types because specialist texts normally require translators with specialist knowledge.<br />
Here are the most common types you’re like to come across in this category.<br />
<br />
1. General Translation<br />
What is it?<br />
The translation of non-specialised text. That is, text that we can all understand without needing specialist knowledge in some area.<br />
The text may still contain some technical terms and jargon, but these will either be widely understood, or easily researched.<br />
What this means<br />
The implication is that you don’t need someone with specialist knowledge for this type of translation – any professional translator can handle them.<br />
Translators who only do this kind of translation (don’t have a specialist field) are sometimes referred to as ‘generalist’ or ‘general purpose’ translators.<br />
Examples<br />
Most business correspondence, website content, company and product/service info, non-technical reports.<br />
Most of the rest of the translation types in this Category do require specialist translators.<br />
Check out our video on 13 types of translation requiring special translator expertise:<br />
<br />
2. Technical Translation<br />
What is it?<br />
We use the term “technical translation” in two different ways:<br />
Broad meaning: any translation where the translator needs specialist knowledge in some domain or area.<br />
This definition would include almost all the translation types described in this section.<br />
Narrow meaning: limited to the translation of engineering (in all its forms), IT and industrial texts.<br />
This narrower meaning would exclude legal, financial and medical translations for example, where these would be included in the broader definition.<br />
What this means<br />
Technical translations require knowledge of the specialist field or domain of the text.<br />
That’s because without it translators won’t completely understand the text and its implications. And this is essential if we want a fully accurate and appropriate translation.Good to know Many technical translation projects also have a typesetting/dtp requirement. Be sure your translation provider can handle this component, and that you’ve allowed for it in your project costings and time frames.<br />
Examples<br />
Manuals, specialist reports, product brochures<br />
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3. Scientific Translation<br />
What is it?<br />
The translation of scientific research or documents relating to it.<br />
What this means<br />
These texts invariably contain domain-specific terminology, and often involve cutting edge research.<br />
So it’s imperative the translator has the necessary knowledge of the field to fully understand the text. That’s why scientific translators are typically either experts in the field who have turned to translation, or professionally qualified translators who also have qualifications and/or experience in that domain.<br />
On occasion the translator may have to consult either with the author or other domain experts to fully comprehend the material and so translate it appropriately.<br />
Examples<br />
Research papers, journal articles, experiment/trial results<br />
<br />
4. Medical Translation<br />
What is it?<br />
The translation of healthcare, medical product, pharmaceutical and biotechnology materials.<br />
Medical translation is a very broad term covering a wide variety of specialist areas and materials – everything from patient information to regulatory, marketing and technical documents.<br />
As a result, this translation type has numerous potential sub-categories – ‘medical device translations’ and ‘clinical trial translations’, for example.<br />
What this means<br />
As with any text, the translators need to fully understand the materials they’re translating. That means sound knowledge of medical terminology and they’ll often also need specific subject-matter expertise.<br />
Good to know<br />
Many countries have specific requirements governing the translation of medical device and pharmaceutical documentation. This includes both your client-facing and product-related materials.<br />
Examples<br />
Medical reports, product instructions, labeling, clinical trial documentation<br />
<br />
5. Financial Translation<br />
What is it?<br />
In broad terms, the translation of banking, stock exchange, forex, financing and financial reporting documents.<br />
However, the term is generally used only for the more technical of these documents that require translators with knowledge of the field.<br />
Any competent translator could translate a bank statement, for example, so that wouldn’t typically be considered a financial translation.<br />
What this means<br />
You need translators with domain expertise to correctly understand and translate the financial terminology in these texts.<br />
Examples<br />
Company accounts, annual reports, fund or product prospectuses, audit reports, IPO documentation<br />
<br />
6. Economic Translations<br />
What is it?<br />
1. Sometimes used as a synonym for financial translations.<br />
2. Other times used somewhat loosely to refer to any area of economic activity – so combining business/commercial, financial and some types of technical translations.<br />
3. More narrowly, the translation of documents relating specifically to the economy and the field of economics.<br />
What this means<br />
As always, you need translators with the relevant expertise and knowledge for this type of translation.<br />
<br />
7. Legal Translation<br />
What is it?<br />
The translation of documents relating to the law and legal process.<br />
What this means<br />
Legal texts require translators with a legal background.<br />
That’s because without it, a translator may not:<br />
– fully understand the legal concepts<br />
– write in legal style<br />
– understand the differences between legal systems, and how best to translate concepts that don’t correspond.<br />
And we need all that to produce professional quality legal translations – translations that are accurate, terminologically correct and stylistically appropriate.<br />
Examples<br />
Contracts, legal reports, court judgments, expert opinions, legislation<br />
<br />
8. Juridical Translation<br />
What is it?<br />
1. Generally used as a synonym for legal translations.<br />
2. Alternatively, can refer to translations requiring some form of legal verification, certification or notarization that is common in many jurisdictions.<br />
<br />
9. Judicial Translation<br />
What is it?<br />
1. Most commonly a synonym for legal translations.<br />
2. Rarely, used to refer specifically to the translation of court proceeding documentation – so judgments, minutes, testimonies, etc. <br />
<br />
10. Patent Translation<br />
What is it?<br />
The translation of intellectual property and patent-related documents.<br />
Key features<br />
Patents have a specific structure, established terminology and a requirement for complete consistency throughout – read more on this here. These are key aspects to patent translations that translators need to get right.<br />
In addition, subject matter can be highly technical.<br />
What this means<br />
You need translators who have been trained in the specific requirements for translating patent documents. And with the domain expertise needed to handle any technical content.<br />
Examples<br />
Patent specifications, prior art documents, oppositions, opinions<br />
<br />
11. Literary Translation<br />
What is it?<br />
The translation of literary works – novels, short stories, plays, essays, poems.<br />
Key features<br />
Literary translation is widely regarded as the most difficult form of translation.<br />
That’s because it involves much more than simply conveying all meaning in an appropriate style. The translator’s challenge is to also reproduce the character, subtlety and impact of the original – the essence of what makes that work unique.<br />
This is a monumental task, and why it’s often said that the translation of a literary work should be a literary work in its own right.<br />
What this means<br />
Literary translators must be talented wordsmiths with exceptional creative writing skills.<br />
Because few translators have this skillset, you should only consider dedicated literary translators for this type of translation.<br />
<br />
<br />
12. Commercial Translation<br />
What is it?<br />
The translation of documents relating to the world of business.<br />
This is a very generic, wide-reaching translation type. It includes other more specialised forms of translation – legal, financial and technical, for example. And all types of more general business documentation.<br />
Also, some documents will require familiarity with business jargon and an ability to write in that style.<br />
What this means<br />
Different translators will be required for different document types – specialists should handle materials involving technical and specialist fields, whereas generalist translators can translate non-specialist materials.<br />
Examples<br />
Business correspondence, reports, marketing and promotional materials, sales proposals<br />
<br />
13. Business Translations<br />
What is it?<br />
A synonym for Commercial Translations.<br />
<br />
14. Administrative Translations<br />
What is it?<br />
The translation of business management and administration documents.<br />
So it’s a subset of business / commercial translations.<br />
What this means<br />
The implication is these documents will include business jargon and ‘management speak’, so require a translator familiar with, and practised at, writing in that style.<br />
Examples<br />
Management reports and proposals<br />
<br />
15. Marketing Translations<br />
What is it?<br />
The translation of advertising, marketing and promotional materials.<br />
This is a subset of business or commercial translations.<br />
Key features<br />
Marketing copy is designed to have a specific impact on the audience – to appeal and persuade.<br />
So the translated copy must do this too.<br />
But a direct translation will seldom achieve this – so translators need to adapt their wording to produce the impact the text is seeking.<br />
And sometimes a completely new message might be needed – see transcreation in our next category of translation types.<br />
What this means<br />
Marketing translations require translators who are skilled writers with a flair for producing persuasive, impactful copy.<br />
As relatively few translators have these skills, engaging the right translator is key.<br />
Good to know<br />
This type of translation often comes with a typesetting or dtp requirement – particularly for adverts, posters, brochures, etc.<br />
Its best for your translation provider to handle this component. That’s because multilingual typesetters understand the design and aesthetic conventions in other languages/cultures. And these are essential to ensure your materials have the desired impact and appeal in your target markets.<br />
Examples<br />
Advertising, brochures, some website/social media text.<br />
Translation Category B: 14 types of translation based on the end product or use of the translation<br />
This category is all about how the translation is going to be used or the end product that’s produced.<br />
Most of these types involve either adapting or processing a completed translation in some way, or converting or incorporating it into another program or format.<br />
You’ll see that some are very specialised, and complex.<br />
It’s another way translation providers refer to the range of services they provide.<br />
Check out our video of the most specialised of these types of translation:<br />
<br />
16. Document Translations<br />
What is it?<br />
The translation of documents of all sorts.<br />
Here the translation itself is the end product and needs no further processing beyond standard formatting and layout.<br />
<br />
17. Text Translations<br />
What is it?<br />
A synonym for document translation.<br />
<br />
18. Certified Translations<br />
What is it?<br />
A translation with some form of certification.<br />
Key features<br />
The certification can take many forms. It can be a statement by the translation company, signed and dated, and optionally with their company seal. Or a similar certification by the translator.<br />
The exact format and wording will depend on what clients and authorities require – here’s an example.<br />
<br />
19. Official Translations<br />
What is it?<br />
1. Generally used as a synonym for certified translations.<br />
2. Can also refer to the translation of ‘official’ documents issued by the authorities in a foreign country. These will almost always need to be certified.<br />
<br />
20. Software Localisation<br />
What is it?<br />
Adapting software for another language/culture.<br />
Key features<br />
The goal of software localisation is not just to make the program or product available in other languages. It’s also about ensuring the user experience in those languages is as natural and effective as possible.<br />
Translating the user interface, messaging, documentation, etc is a major part of the process.<br />
Also key is a customisation process to ensure everything matches the conventions, norms and expectations of the target cultures.<br />
Adjusting time, date and currency formats are examples of simple customisations. Others might involve adapting symbols, graphics, colours and even concepts and ideas.<br />
Localisation is often preceded by internationalisation – a review process to ensure the software is optimally designed to handle other languages.<br />
And it’s almost always followed by thorough testing – to ensure all text is in the correct place and fits the space, and that everything makes sense, functions as intended and is culturally appropriate.<br />
Localisation is often abbreviated to L10N, internationalisation to i18n.<br />
What this means<br />
Software localisation is a specialised kind of translation, and you should always engage a company that specialises in it.<br />
They’ll have the systems, tools, personnel and experience needed to achieve top quality outcomes for your product.<br />
<br />
21. Game Localisation<br />
What is it?<br />
Adapting games for other languages and markets.<br />
<br />
It’s a subset of software localisation.<br />
Key features<br />
The goal of game localisation is to provide an engaging and fun gaming experience for speakers of other languages.<br />
<br />
It involves translating all text and recording any required foreign language audio.<br />
<br />
But also adapting anything that would clash with the target culture’s customs, sensibilities and regulations.<br />
<br />
For example, content involving alcohol, violence or gambling may either be censored or inappropriate in the target market.<br />
<br />
And at a more basic level, anything that makes users feel uncomfortable or awkward will detract from their experience and thus the success of the game in that market.<br />
<br />
So portions of the game may have to be removed, added to or re-worked.<br />
<br />
Game localisation involves at least the steps of translation, adaptation, integrating the translations and adaptations into the game, and testing.<br />
<br />
What this means<br />
Game localisation is a very specialised type of translation best left to those with specific expertise and experience in this area.<br />
<br />
22. Multimedia Localisation<br />
What is it?<br />
Adapting multimedia for other languages and cultures.<br />
<br />
Multimedia refers to any material that combines visual, audio and/or interactive elements. So videos and movies, on-line presentations, e-Learning courses, etc.<br />
Key features<br />
Anything a user can see or hear may need localising.<br />
<br />
That means the audio and any text appearing on screen or in images and animations.<br />
<br />
Plus it can mean reviewing and adapting the visuals and/or script if these aren’t suitable for the target culture.<br />
<br />
The localisation process will typical involve:<br />
– Translation<br />
– Modifying the translation for cultural reasons and/or to meet technical requirements<br />
– Producing the other language versions<br />
<br />
Audio output may be voice-overs, dubbing or subtitling.<br />
<br />
And output for visuals can involve re-creating elements, or supplying the translated text for the designers/engineers to incorporate.<br />
<br />
What this means<br />
Multimedia localisation projects vary hugely, and it’s essential your translation providers have the specific expertise needed for your materials.<br />
<br />
<br />
<br />
23. Script Translations<br />
What is it?<br />
Preparing the text of recorded material for recording in other languages.<br />
Key features<br />
There are several issues with script translation.<br />
<br />
One is that translations typically end up longer than the original script. So voicing the translation would take up more space/time on the video than the original language.<br />
<br />
Sometimes that space will be available and this will be OK.<br />
<br />
But generally it won’t be. So the translation has to be edited back until it can be comfortably voiced within the time available on the video.<br />
<br />
Another challenge is the translation may have to synchronise with specific actions, animations or text on screen.<br />
<br />
Also, some scripts also deal with technical subject areas involving specialist technical terminology.<br />
<br />
Finally, some scripts may be very culture-specific – featuring humour, customs or activities that won’t work well in another language. Here the script, and sometimes also the associated visuals, may need to be adjusted before beginning the translation process.<br />
<br />
It goes without saying that a script translation must be done well. If it’s not, there’ll be problems producing a good foreign language audio, which will compromise the effectiveness of the video.<br />
<br />
Translators typically work from a time-coded transcript. This is the original script marked to show the time available for each section of the translation.<br />
<br />
What this means<br />
There are several potential pitfalls in script translations. So it’s vital your translation provider is practiced at this type of translation and able to handle any technical content.<br />
<br />
<br />
<br />
24. Voice-over and Dubbing Projects<br />
What is it?<br />
Translation and recording of scripts in other languages.<br />
<br />
Voice-overs vs dubbing<br />
There is a technical difference.<br />
A voice-over adds a new track to the production, dubbing replaces an existing one.<br />
<br />
<br />
Key features<br />
These projects involve two parts:<br />
– a script translation (as described above), and<br />
– producing the audio<br />
<br />
So they involve the combined efforts of translators and voice artists.<br />
The task for the voice artist is to produce a high quality read. That’s one that matches the style, tone and richness of the original.<br />
<br />
Often each section of the new audio will need to be the same length as the original.<br />
<br />
But sometimes the segments will need to be shorter – for example where the voice-over lags the original by a second or two. This is common in interviews etc, where the original voice is heard initially then drops out.<br />
<br />
The most difficult form of dubbing is lip-syncing – where the new audio needs to synchronise with the original speaker’s lip movements, gestures and actions.<br />
<br />
Lip-syncing requires an exceptionally skilled voice talent and considerable time spent rehearsing and fine tuning the translation.<br />
<br />
What this means<br />
You need to use experienced professionals every step of the way in this type of project.<br />
<br />
That’s to ensure firstly that your foreign-language scripts are first class, then that the voicing is of high professional standard.<br />
<br />
Anything less will mean your foreign language versions will be way less effective and appealing to your target audience.<br />
<br />
<br />
<br />
25. Subtitle Translations<br />
What is it?<br />
Producing foreign language captions for sub or surtitles.<br />
Key features<br />
The goal with subtitling is to produce captions that viewers can comfortably read in the time available and still follow what’s happening on the video.<br />
<br />
To achieve this, languages have “rules” governing the number of characters per line and the minimum time each subtitle should display.<br />
<br />
Sticking to these guidelines is essential if your subtitles are to be effective.<br />
<br />
But this is no easy task – it requires simple language, short words, and a very succinct style. Translators will spend considerable time mulling over and re-working their translation to get it just right.<br />
<br />
Most subtitle translators use specialised software that will output the captions in the format sound engineers need for incorporation into the video.<br />
<br />
What this means<br />
As with other specialised types of translation, you should only use translators with specific expertise and experience in subtitling.<br />
<br />
<br />
<br />
26. Website Localisation<br />
What is it?<br />
The translation and adapting of relevant content on a website to best suit the target language and culture.<br />
<br />
Note: Many providers use the term website translation as a synonym for localisation. Strictly speaking though, translation is just one part of localisation.<br />
Key features<br />
<br />
Not all pages on a website may need to be localised – clients should review their content to identify what’s relevant for the other language versions.<br />
Some content may need specialist translators – legal and technical pages for example.<br />
There may also be videos, linked documents, and text or captions in graphics to translate.<br />
Adaptation can mean changing date, time, currency and number formats, units of measure, etc.<br />
But also images, colours and even the overall site design and style if these won’t have the desired impact in the target culture.<br />
Translated files can be supplied in a wide range of formats – translators usually coordinate output with the site webmasters.<br />
New language versions are normally thoroughly reviewed and tested before going live to confirm everything is displaying correctly, works as intended and is cultural appropriate.<br />
What this means<br />
The first step should be to review your content and identify what needs to be translated. This might lead you to modify some pages for the foreign language versions.<br />
<br />
In choosing your translation providers be sure they can:<br />
– handle any technical or legal content,<br />
– provide your webmaster with the file types they want.<br />
<br />
And you should always get your translators to systematically review the foreign language versions before going live.<br />
<br />
<br />
<br />
27. Transcreation<br />
What is it?<br />
Adapting a message to elicit the same emotional response in another language and culture.<br />
Translation is all about conveying the message or meaning of a text in another language. But sometimes that message or meaning won’t have the desired effect in the target culture.<br />
<br />
This is where transcreation comes in. Transcreation creates a new message that will get the desired emotional response in that culture, while preserving the style and tone of the original.<br />
<br />
So it’s a sort of creative translation – which is where the word comes from, a combination of ‘translation’ and ‘creation’.<br />
<br />
At one level transcreation may be as simple as choosing an appropriate idiom to convey the same intent in the target language – something translators do all the time.<br />
<br />
But mostly the term is used to refer to adapting key advertising and marketing messaging. Which requires copywriting skills, cultural awareness and an excellent knowledge of the target market.<br />
<br />
Who does it?<br />
Some translation companies have suitably skilled personnel and offer transcreation services.<br />
<br />
Often though it’s done in the target country by specialist copywriters or an advertising or marketing agency – particularly for significant campaigns and to establish a brand in the target marketplace.<br />
<br />
What this means<br />
Most general marketing and promotional texts won’t need transcreation – they can be handled by a translator with excellent creative writing skills.<br />
<br />
But slogans, by-lines, advertising copy and branding statements often do.<br />
<br />
Whether you should opt for a translation company or an in-market agency will depend on the nature and importance of the material, and of course your budget.<br />
<br />
<br />
<br />
28. Audio Translations<br />
What is it?<br />
Broad meaning: the translation of any type of recorded material into another language.<br />
<br />
More commonly: the translation of a foreign language video or audio recording into your own language. So this is where you want to know and document what a recording says.<br />
Key features<br />
The first challenge with audio translations is it’s often impossible to pick up every word that’s said. That’s because audio quality, speech clarity and speaking speed can all vary enormously.<br />
<br />
It’s also a mentally challenging task to listen to an audio and translate it directly into another language. It’s easy to miss a word or an aspect of meaning.<br />
<br />
So best practice is to first transcribe the audio (type up exactly what is said in the language it is spoken in), then translate that transcription.<br />
<br />
However, this is time consuming and therefore costly, and there are other options if lesser precision is acceptable.<br />
<br />
What this means<br />
It’s best to discuss your requirements for this kind of translation with your translation provider. They’ll be able to suggest the best translation process for your needs.<br />
<br />
Examples<br />
Interviews, product videos, police recordings, social media videos.<br />
<br />
<br />
<br />
29. Translations with DTP<br />
What is it?<br />
Translation incorporated into graphic design files.multilingual dtp example in the form of a Rubik's Cube with foreign text on each square<br />
Key features<br />
Graphic design programs are used by professional designers and graphic artists to combine text and images to create brochures, books, posters, packaging, etc.<br />
<br />
Translation plus dtp projects involve 3 steps – translation, typesetting, output.<br />
<br />
The typesetting component requires specific expertise and resources – software and fonts, typesetting know-how, an appreciation of foreign language display conventions and aesthetics.<br />
<br />
What this means<br />
Make sure your translation company has the required multilingual typesetting/desktop publishing expertise whenever you’re translating a document created in a graphic design program.<br />
<br />
Translation Category C: 13 types of translation based on the translation method employed<br />
This category has two sub-groups:<br />
– the practical methods translation providers use to produce their translations, and<br />
– the translation strategies/methods identified and discussed within academia.<br />
<br />
The translation methods translation providers use<br />
There are 4 main methods used in the translation industry today. We have an overview of each below, but for more detail, including when to use each one, see our comprehensive blog article.<br />
<br />
Or watch our video.<br />
<br />
Important: If you’re a client you need to understand these 4 methods – choose the wrong one and the translation you end up with may not meet your needs!<br />
<br />
<br />
30. Machine Translation (MT)<br />
What is it?<br />
A translation produced entirely by a software program with no human intervention.<br />
<br />
A widely used, and free, example is Google Translate. And there are also commercial MT engines, generally tailored to specific domains, languages and/or clients.<br />
<br />
Pros and cons<br />
There are two limitations to MT:<br />
– they make mistakes (incorrect translations), and<br />
– quality of wording is patchy (some parts good, others unnatural or even nonsensical)<br />
<br />
On they positive side they are virtually instantaneous and many are free.<br />
<br />
Best suited for:<br />
Getting the general idea of what a text says.<br />
<br />
This method should never be relied on when high accuracy and/or good quality wording is needed.<br />
<br />
<br />
<br />
31. Machine Translation plus Human Editing (PEMT)<br />
What is it?<br />
A machine translation subsequently edited by a human translator or editor (often called Post-editing Machine Translation = PEMT).<br />
<br />
The editing process is designed to rectify some of the deficiencies of a machine translation.<br />
<br />
This process can take different forms, with different desired outcomes. Probably most common is a ‘light editing’ process where the editor ensures the text is understandable, without trying to fix quality of expression.<br />
<br />
Pros and cons<br />
This method won’t necessarily eliminate all translation mistakes. That’s because the program may have chosen a wrong word (meaning) that wasn’t obvious to the editor.<br />
<br />
And wording won’t generally be as good as a professional human translator would produce.<br />
<br />
Its advantage is it’s generally quicker and a little cheaper than a full translation by a professional translator.<br />
<br />
Best suited for:<br />
Translations for information purposes only.<br />
<br />
Again, this method shouldn’t be used when full accuracy and/or consistent, natural wording is needed.<br />
<br />
<br />
<br />
32. Human Translation<br />
What is it?<br />
Translation by a professional human translator.<br />
Pros and cons<br />
Professional translators should produce translations that are fully accurate and well-worded.<br />
<br />
That said, there is always the possibility of ‘human error’, which is why translation companies like us typically offer an additional review process – see next method.<br />
<br />
This method will take a little longer and likely cost more than the PEMT method.<br />
<br />
Best suited for:<br />
Most if not all translation purposes.<br />
<br />
<br />
<br />
33. Human Translation + Revision<br />
What is it?<br />
A human translation with an additional review by a second translator.<br />
<br />
The review is essentially a safety check – designed to pick up any translation errors and refine wording if need be.<br />
<br />
Pros and cons<br />
This produces the highest level of translation quality.<br />
<br />
It’s also the most expensive of the 4 methods, and takes the longest.<br />
<br />
Best suited for:<br />
All translation purposes.<br />
<br />
Gearwheel with 5 practical translation methods written on the teeth <br />
There’s also one other common term used by practitioners and academics alike to describe a type (method) of translation:<br />
<br />
34. Computer-Assisted Translation (CAT)<br />
What is it?<br />
A human translator using computer tools to aid the translation process.<br />
<br />
Key features<br />
Virtually all translators use such tools these days.<br />
<br />
The most prevalent tool is Translation Memory (TM) software. This creates a database of previous translations that can be accessed for future work.<br />
<br />
TM software is particularly useful when dealing with repeated and closely-matching text, and for ensuring consistency of terminology. For certain projects it can speed up the translation process.<br />
<br />
<br />
<br />
The translation methods described by academia<br />
A great deal has been written within academia analysing how human translators go about their craft.<br />
<br />
Seminal has been the work of Newmark, and the following methods of translation attributed to him are widely discussed in the literature.Gearwheel with Newmark's 8 translation methods written on the teeth <br />
These methods are approaches and strategies for translating the text as a whole, not techniques for handling smaller text units, which we discuss in our final translation category.<br />
<br />
<br />
<br />
35. Word-for-word Translation<br />
This method translates each word into the other language using its most common meaning and keeping the word order of the original language.<br />
<br />
So the translator deliberately ignores context and target language grammar and syntax.<br />
<br />
Its main purpose is to help understand the source language structure and word use.<br />
<br />
Often the translation will be placed below the original text to aid comparison.<br />
<br />
<br />
<br />
36. Literal Translation<br />
Words are again translated independently using their most common meanings and out of context, but word order changed to the closest acceptable target language grammatical structure to the original.<br />
<br />
Its main suggested purpose is to help someone read the original text.<br />
<br />
<br />
<br />
37. Faithful Translation<br />
Faithful translation focuses on the intention of the author and seeks to convey the precise meaning of the original text.<br />
<br />
It uses correct target language structures, but structure is less important than meaning.<br />
<br />
<br />
<br />
38. Semantic Translation<br />
Semantic translation is also author-focused and seeks to convey the exact meaning.<br />
<br />
Where it differs from faithful translation is that it places equal emphasis on aesthetics, ie the ‘sounds’ of the text – repetition, word play, assonance, etc.<br />
<br />
In this method form is as important as meaning as it seeks to “recreate the precise flavour and tone of the original” (Newmark).slide showing definition of semantic translation as a translation method<br />
<br />
39. Communicative Translation<br />
Seeks to communicate the message and meaning of the text in a natural and easily understood way.<br />
<br />
It’s described as reader-focused, seeking to produce the same effect on the reader as the original text.<br />
<br />
A good comparison of Communicative and Semantic translation can be found here.<br />
<br />
40. Free Translation<br />
Here conveying the meaning and effect of the original are all important.<br />
<br />
There are no constraints on grammatical form or word choice to achieve this.<br />
<br />
Often the translation will paraphrase, so may be of markedly different length to the original.<br />
<br />
41. Adaptation<br />
Mainly used for poetry and plays, this method involves re-writing the text where the translation would otherwise lack the same resonance and impact on the audience.<br />
<br />
Themes, storylines and characters will generally be retained, but cultural references, acts and situations adapted to relevant target culture ones.<br />
<br />
So this is effectively a re-creation of the work for the target culture.<br />
<br />
42. Idiomatic Translation<br />
Reproduces the meaning or message of the text using idioms and colloquial expressions and language wherever possible.<br />
<br />
The goal is to produce a translation with language that is as natural as possible.<br />
<br />
Translation Category D: 9 types of translation based on the translation technique used<br />
These translation types are specific strategies, techniques and procedures for dealing with short chunks of text – generally words or phrases.<br />
<br />
They’re often thought of as techniques for solving translation problems.<br />
<br />
They differ from the translation methods of the previous category which deal with the text as a whole.<br />
9 translation techniques as titles of books in a bookcase<br />
<br />
43. Borrowing<br />
What is it?<br />
Using a word or phrase from the original text unchanged in the translation.<br />
<br />
Key features<br />
With this procedure we don’t translate the word or phrase at all – we simply ‘borrow’ it from the source language.<br />
<br />
Borrowing is a very common strategy across languages. Initially, borrowed words seem clearly ‘foreign’, but as they become more familiar, they can lose that ‘foreignness’.<br />
<br />
Translators use this technique:<br />
– when it’s the best word to use – either because it has become the standard, or it’s the most precise term, or<br />
– for stylist effect – borrowings can add a prestigious or scholarly flavour.<br />
<br />
Borrowed words or phrases are often italicised in English.<br />
<br />
Examples of borrowings in English<br />
grand prix, kindergarten, tango, perestroika, barista, sampan, karaoke, tofu<br />
<br />
<br />
44. Transliteration<br />
What is it?<br />
Reproducing the approximate sounds of a name or term from a language with a different writing system.<br />
<br />
Key features<br />
In English we use the Roman (Latin) alphabet in common with many other languages including almost all European languages.<br />
<br />
Other writing systems include Arabic, Cyrillic, Chinese, Japanese, Korean, Thai, and the Indian languages.<br />
<br />
Transliteration from such systems into the Roman alphabet is also called romanisation.<br />
<br />
There are accepted systems for how individual letters/sounds should be romanised from most other languages – there are three common systems for Chinese, for example.<br />
<br />
English borrowings from languages using non-Roman writing systems also require transliteration – perestroika, sampan, karaoke, tofu are examples from the above list.<br />
<br />
Translators mostly use transliteration as a procedure for translating proper names.<br />
<br />
Examples<br />
毛泽东 Mao Tse-tung or Mao Zedong<br />
Владимир Путин Vladimir Putin<br />
서울 Seoul<br />
ភ្នំពេញ Phnom Penh<br />
<br />
45. Calque or Loan Translation<br />
What is it?<br />
A literal translation of a foreign word or phrase to create a new term with the same meaning in the target language.<br />
<br />
So a calque is a borrowing with translation if you like. The new term may be changed slightly to reflect target language structures.<br />
<br />
Examples<br />
German ‘Kindergarten’ has been calqued as детский сад in Russian, literally ‘children garden’ in both languages.<br />
<br />
Chinese 洗腦 ‘wash’ + ‘brain’ is the origin of ‘brainwash’ in English.<br />
<br />
English skyscraper is calqued as gratte-ciel in French and rascacielos in Spanish, literally ‘scratches sky’ in both languages.<br />
<br />
46. Word-for-word translation<br />
What is it?<br />
A literal translation that is natural and correct in the target language.<br />
<br />
Alternative names are ‘literal translation’ or ‘metaphrase’.<br />
<br />
Note: this technique is different to the translation method of the same name, which does not produce correct and natural text and has a different purpose.<br />
<br />
Key features<br />
This translation strategy will only work between languages that have very similar grammatical structures.<br />
<br />
And even then, only sometimes.<br />
<br />
For example, standard word order in Turkish is Subject-Object-Verb whereas in English it’s Subject-Verb-Object. So a literal translation between these two will seldom work:<br />
– Yusuf elmayı yedi is literally ‘Joseph the apple ate’.<br />
<br />
When word-for-word translations don’t produce natural and correct text, translators resort to some of the other techniques described below.<br />
Examples<br />
French ‘Quelle heure est-il?’ works into English as ‘What time is it?’.<br />
<br />
Russian ‘Oн хочет что-нибудь поесть’ is ‘He wants something to eat’.<br />
<br />
47. Transposition<br />
What is it?<br />
Translation with a change of grammatical structure.<br />
<br />
This technique gives the translation more natural wording and/or makes it grammatically correct.<br />
<br />
Examples<br />
A change in word order:<br />
Our Turkish example Yusuf elmayı yedi (literally ‘Joseph the apple ate’) –> Joseph ate the apple.<br />
<br />
Spanish La Casa Blanca (literally ‘The House White’) –> The White House<br />
<br />
A change in grammatical category:<br />
German Er hört gerne Musik (literally ‘he listens gladly [to] music’)<br />
= subject pronoun + verb + adverb + noun<br />
becomes Spanish Le gusta escuchar música (literally ‘[to] him [it] pleases to listen [to] music’)<br />
= indirect object pronoun + verb + infinitive + noun<br />
and English He likes listening to music<br />
= subject pronoun + verb + gerund + noun.<br />
<br />
48. Modulation<br />
What is it?<br />
Translation with a change of focus or point of view in the target language.<br />
<br />
This technique makes the translation more idiomatic – how people would normally say it in the language.<br />
<br />
Examples<br />
English talks of the ‘top floor’ of a building, French the dernier étage = last floor. ‘Last floor’ would be unnatural in English, so too ‘top floor’ in French.<br />
<br />
German uses the term Lebensgefahr (literally ‘danger to life’) where in English we’d be more likely to say ‘risk of death’.<br />
In English we’d say ‘I dropped the key’, in Spanish se me cayó la llave, literally ‘the key fell from me’. The English perspective is that I did something (dropped the key), whereas in Spanish something happened to me – I’m the recipient of the action.<br />
<br />
49. Equivalence or Reformulation<br />
What is it?<br />
Translating the underlying concept or meaning using a totally different expression.<br />
<br />
This technique is widely used when translating idioms and proverbs.<br />
<br />
And it’s common in titles and advertising slogans.<br />
<br />
It’s a common strategy where a direct translation either wouldn’t make sense or wouldn’t resonate in the same way.<br />
<br />
Examples<br />
Here are some equivalents of the English saying “Pigs may fly”, meaning something will never happen, or “you’re being unrealistic” (Source):<br />
– Thai: ชาติหน้าตอนบ่าย ๆ – literally, ‘One afternoon in your next reincarnation’<br />
– French: Quand les poules auront des dents – literally, ‘When hens have teeth’<br />
– Russian, Когда рак на горе свистнет – literally, ‘When a lobster whistles on top of a mountain’<br />
– Dutch, Als de koeien op het ijs dansen – literally, ‘When the cows dance on the ice’<br />
– Chinese: 除非太陽從西邊出來！– literally, ‘Only if the sun rises in the west’<br />
<br />
50. Adaptation<br />
What is it?<br />
A translation that substitutes a culturally-specific reference with something that’s more relevant or meaningful in the target language.<br />
<br />
It’s also known as cultural substitution or cultural equivalence.<br />
<br />
It’s a useful technique when a reference wouldn’t be understood at all, or the associated nuances or connotations would be lost in the target language.<br />
<br />
Note: the translation method of the same name is a similar concept but applied to the text as a whole.<br />
<br />
Examples<br />
Different cultures celebrate different coming of age birthdays – 21 in many cultures, 20, 15 or 16 in others. A translator might consider changing the age to the target culture custom where the coming of age implications were important in the original text.<br />
Animals have different connotations across languages and cultures. Owls for example are associated with wisdom in English, but are a bad omen to Vietnamese. A translator might want to remove or amend an animal reference where this would create a different image in the target language.<br />
<br />
51. Compensation<br />
What is it?<br />
A meaning or nuance that can’t be directly translated is expressed in another way in the text.<br />
Example<br />
Many languages have ways of expressing social status (honorifics) encoded into their grammatical structures.<br />
<br />
So you can convey different levels of respect, politeness, humility, etc simply by choosing different forms of words or grammatical elements.<br />
But these nuances will be lost when translating into languages that don’t have these structures.<br />
Then translating into languages that don’t have these structures<br />
Then translating into languages that don’t have these structures.<br />
<br />
===Conclusion ===<br />
<br />
In the light of above mentioned facts and figures here by we would say that machine translation is challenge for human translators because it can reduces the wokload of translation but can't give accurate and exact translation of the traget language.It can be less reliable than human translation..</div>Chen Huinihttps://bou.de/u/index.php?title=Machine_translation&diff=131818Machine translation2021-12-13T12:15:45Z<p>Chen Huini: /* 11 陈惠妮=(Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts) */</p>
<hr />
<div>'''Machine Translation - A challenge or a chance for human translators?'''<br />
<br />
[[Machine_translation|Overview Page of Machine Translation]]<br />
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30 Chapters（0/30)<br />
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[[Machine_Trans_EN_1]] [[Machine_Trans_EN_2]] [[Machine_Trans_EN_3]] [[Machine_Trans_EN_4]] [[Machine_Trans_EN_5]] [[Machine_Trans_EN_6]] [[Machine_Trans_EN_7]] [[Machine_Trans_EN_8]] [[Machine_Trans_EN_9]] [[Machine_Trans_EN_10]] [[Machine_Trans_EN_11]] [[Machine_Trans_EN_12]] [[Machine_Trans_EN_13]] [[Machine_Trans_EN_14]] [[Machine_Trans_EN_15]] [[Machine_Trans_EN_16]] [[Machine_Trans_EN_17]] [[Machine_Trans_EN_18]] [[Machine_Trans_EN_19]] [[Machine_Trans_EN_20]] [[Machine_Trans_EN_21]] [[Machine_Trans_EN_22]] [[Machine_Trans_EN_23]] [[Machine_Trans_EN_24]] [[Machine_Trans_EN_25]] [[Machine_Trans_EN_26]] [[Machine_Trans_EN_27]] [[Machine_Trans_EN_28]] [[Machine_Trans_EN_29]] [[Machine_Trans_EN_30]] ...<br />
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[[Book_projects|Back to translation project overview]]<br />
<br />
[[DCG-To-Do|To the To Do list]]<br />
<br />
=Chapter 1:A Comparison Between the Quality of Machine Translation and Human Translation——A Case Study of the Application of artificial intelligence in Sports Events=<br />
'''论机器翻译与人工翻译的质量对比——以人工智能在体育赛事领域的应用为例'''<br />
<br />
卫怡雯 Wei Yiwen, Hunan Normal University, China<br />
<br />
[[Machine_Trans_EN_1]]<br />
<br />
=Chapter 3：On the Realm Advantages And Mutual Development of Machine Translation And Huamn Translation)=<br />
"'论机器翻译与人工翻译的领域优势及共同发展'"<br />
<br />
肖毅瑶 Xiao Yiyao, Hunan Normal University, China<br />
[[Machine_Trans_EN_3]]<br />
<br />
=Chapter 4 : A Comparison Between Machine Translation of Netease and Traditional Human Translation—A Case Study of The Economist Articles)= <br />
'''网易有道机器翻译与人工翻译的译文对比——以经济学人语料为例'''<br />
<br />
王李菲 Wang Lifei, Hunan Normal University<br />
<br />
[[Machine_Trans_EN_4]]<br />
<br />
=Chapter 5: Muhammad Saqib Mehran - Problems in translation studies=<br />
[[Machine_Trans_EN_5]]<br />
<br />
=6 徐敏赟(Machine Translation Based on Neural Network --Challenge or Chance?)=<br />
[[Machine_Trans_EN_6]]<br />
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=Chapter 7: The Cultivation of artificial intelligence and translation talents in the Belt and Road Initiative=<br />
[[Machine_Trans_EN_7]]<br />
<br />
一带一路背景下人工智能与翻译人才的培养<br />
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颜莉莉 Yan Lili, Hunan Normal University, China<br />
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=Chapter 8: On Machine Translation Under Lanuguage Intelligence——An Option and Opportunity for Human Translators=<br />
'''论语言智能下的机器翻译——译者的选择与机遇'''<br />
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颜静 Yan Jing, Hunan Normal University, China<br />
<br />
[[Machine_Trans_EN_8]]<br />
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=9 谢佳芬（ Machine Translation and Artificial Translation in the Era of Artificial Intelligence）=<br />
[[Machine_Trans_EN_9]]<br />
<br />
=10 熊敏(Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts)=<br />
[[Machine_Trans_EN_10]]<br />
===Abstract===<br />
The history of machine translation can be traced to 1940s, undergoing germination, recession, revival and flourish. Nowadays, with the rapid development of information technology, machine translation technology emerged and is gradually becoming mature. Whether manual translation can be replaced by machine translation becomes a widely-disputed topic. So in order to explore the ability of machine translation, I adopts two versions of translation, which are manual translation and machine translation(this paper uses Youdao translation) for different types of texts(according to Peter Newmark's types of text：informative text, expressive text and vocative text). The results are quite different in terms of quality and accuracy. The results show that machine translation is more suitable for informative text for its brevity, while the expressive texts especially literary works and vocative texts are difficult to be translated, because there are too many loaded words and cultural images in expressive text and dependence on the readers. To draw a conclusion, the relationship between machine translation and manual translation should be complementary rather than competitive.<br />
<br />
===Key words===<br />
machine translation; manual translation; Newmark's type of texts<br />
<br />
===题目===<br />
Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts<br />
<br />
===摘要===<br />
机器翻译的历史可以追溯到上个世纪40年代，经历了萌芽期、萧条期、复兴期和繁荣期四个阶段。如今，随着信息技术的高速发展，机器翻译技术日益成熟，于是机器翻译能不能替代人工翻译这个话题引起了广泛热议。为了探究机器翻译的能力水平是否足以替代人工翻译，本人根据皮特纽马克的文本类型分类理论（信息类文本，表达文本，呼吁文本），选择了相应的译文类型，并且将其机器翻译的版本以及人工翻译的版本进行对比。结果发现，就质量和准确度而言，译文的水平大相径庭。因为其简洁的特性，机器比较适合翻译信息文本。而就表达文本，尤其是文学作品，因其存在文化负载词及相应的文化现象，所以机器翻译这类文本存在难度。而呼唤文本因其目的性以及对读者的依赖性较强，翻译难度较大。由此得知，机器翻译和人工翻译的关系应该是互补的，而不是竞争的。<br />
<br />
===关键词===<br />
机器翻译；人工翻译；纽马克文本类型<br />
<br />
===1. Introduction===<br />
====1.1.Introduction to machine translation====<br />
Machine translation, also known as computer translation is a technique to translating through machine translator, such as Google translation and Youdao translation. Machine translation is one of the branches of computational linguistics, ranging from computer science, statistics, information science and so on. Machine translation plays an important role in all aspects.<br />
Machine translation can be traced back to 1940s, when British engineer Booth and American engineer Weaver proposed using computer to translate and started to study machines used for translation. And the first machine translating system launched in 1954, used in English and Russian translation. And this means machine translation becomes a reality. However, the arrival of new things always accompanies barriers and setbacks. In the 1960s, reports from ALPAC (Automated Language Processing Advisory Committee) showed studies on machine translation had stagnated for a decade. At that time, due to the high cost of machine, choosing human translators to finish translating works was more economical for most companies. What’s worse, machine had difficulty in understanding semantic and pragmatic meanings. Fortunately, in the 1970s, with the advance and popularity of computer, machine translation was gradually back on track. With the development of science and technology, machine translation has better technological guarantee, such as artificial intelligence and computer technology. In the last decades, from the late 90s till now, machine translation is becoming more and more mature. The initial rule-oriented machine translation has been updated and Corpus-aided machine translation appears. And nowadays, technology in voice recognition has been further developed. This technique is frequently applied in speech translation products. Users only need to speak source language to the online translator and instantly it will speak out the target version. But machine can’t fully provide precise and accurate translation without any grammatical or semantic problems. Machine can understand the literal meaning of texts, but not the meaning in context. For example, there is polysemy in English, which refers to words having multiple meanings. So when translating these words, translators should take contexts into consideration. But machine has troubles in this way. In addition, machine has no feeling or intention. Hence, it is also difficult to convey the feelings from the source language.<br />
<br />
====1.2.Process of machine translation====<br />
The process of manual translation is different from that of machine translation. Here is the process of the former. (1) Understand source language. (2) Use target language to organize language. (3) Generate translation. Unlike manual translation, machine translation tends to analyze and code source language first, then look for related codes in corpus, and work out the code that represents target language, generating translation. But they share a common feature, which is that Lexicon, grammatical rules and syntactic structure are taken into consideration. This is one of the biggest challenges for machine translation.<br />
<br />
===2.Theorectical framework===<br />
====2.1Newmark’s text typology====<br />
Text typology is a theory from linguistics. It undergoes several phrases. Karl Buhler, a German psychologist and linguist defines communicative functions into expressive, information and vocative. Then Roman Jakobson proposes categorization of texts, which are intralingual translation, interlingual translation and intersemiotic translation. Accordingly, he defines six main functions of language, including the referential function, conative function, emotive function, poetic function, metalingual function and the phatic function. Based on the two theories, Peter Newmark, a translation theorist, summarizes the language function into six parts: the informative function, the vocative function, the expressive function, the phatic function, the aesthetic function, and the metalingual function. Later, he further divided texts into informative type, expressive text and vocative type according to the linguistic functions of various texts. <br />
The core of the informative texts is the truth. It is to convey facts, information, knowledge of certain topics, reality and theories. The language style of the text is objective, logical and standardized. Reports, papers, scientific and technological textbooks are all attributed to informative texts. Translators are required to take format into account for different styles.<br />
The core of the expressive text is the sender’s emotions and attitudes. It is to express sender’s preferences, feelings, views and so on. The language style of it is subjective. Imaginative literature, including fictions, poems and dramas, autobiography and authoritative statements belong to expressive text. It requires translators to be faithful to the source language and maintain the style.<br />
The core of the vocative text is readership. It is to call upon readers to act, think, feel and react in the way intended by the text. So it is reader-oriented. Such texts as advertisement, propaganda and notices are of vocative text. Newmark noted that translators need to consider cultural background of the source language and the semantic and pragmatic effect of target language. If readers can comprehend the meaning at once and do as the text says, then the goal of information communication is achieved.<br />
To draw a conclusion, informative texts focus on the information and facts. Expressive texts center on the mind of addressers, including feelings, prejudices and so on. And vocative texts are oriented on readers and call on them to practice as the texts say.<br />
<br />
====2.2Study method====<br />
Manual translations of the three texts are selected from authoritative versions and universally acknowledged. And machine translations of those come from Youdao Translator. And in this thesis I will compare and evaluate the two methods in word diction, sentence structure, word order and redundancy.<br />
<br />
===3.Comparison and analysis of machine translation and manual translation ===<br />
====3.1Informative text ====<br />
（1）English into Chinese<br />
<br />
①Source language:<br />
<br />
Keep the tip of Apple Pencil clean, as dirt and other small particles may cause excessive wear to the tip or damage the screen of i-pad.<br />
<br />
Target language:<br />
<br />
Machine translation: Apple Pencil笔尖应保持清洁，灰尘等小颗粒可能会导致笔尖过度磨损或损坏ipad屏幕。<br />
<br />
Manual translation: 保持Apple Pencil铅笔的笔尖干净，因为灰尘和其他微粒可能会导致笔尖的过度磨损或损坏iPad屏幕。<br />
<br />
Analysis: Here is the instruction of Apple Pencil. And the manual translation is the Chinese version on the instruction.Product instruction tends to be professional, since there are many terms for some concepts. Machine can easily identify these terms and provide related words to translate. The machine version is faithful and expressive to the source language. So it is well-qualified and readable for readers to understand the instruction. So we can use machine to translate informative text.<br />
<br />
②Source language:<br />
<br />
China on Saturday launched a rocket carrying three astronauts-two men and one woman - to the core module of a future space station where they will live and work for six months, the longest orbit for Chinese astronauts.<br />
<br />
Target language:<br />
<br />
Machine translation: 周六，中国发射了一枚运载三名宇航员(两男一女)的火箭，进入未来空间站的核心舱，他们将在那里生活和工作6个月，这是中国宇航员最长的轨道。<br />
<br />
Manual translation: 周六，中国发射了一枚搭载三名宇航员(两男一女)的火箭，进入未来空间站的核心舱，他们将在那里生活和工作6个月，这是中国宇航员最漫长的一次轨道飞行。<br />
<br />
Analysis: This is a news from Reuters, reporting that China has launched a rocket.The meaning of the two translations is almost the same, except for some word diction. But there are some details dealt with different choice. For example, the last sentence of the machine translation is a bit of obscure and direct. There are some ambiguous words and expressions.<br />
<br />
(2)Chinese into English<br />
<br />
Source language:湖南省博物馆是湖南省最大的历史艺术类博物馆，占地面积4.9万平方米，总建筑面积为9.1万平方米，是首批国家一级博物馆，中央地方共建的八个国家级重点博物馆之一、全国文化系统先进集体、文化强省建设有突出贡献先进集体。<br />
<br />
Target language:<br />
Manual translation: As the largest history and art museum in Hunan province, the Hunan Museum covers an area of 49,000㎡, with the building area reaching 91,000㎡. It is one of the first batch of national first-level museums and one of the first eight national museums co-funded by central and local governments.<br />
<br />
Machine translation: Museum in hunan province is one of the largest historical art museum in hunan province, covers an area of 49000 square meters, a total construction area of 91000 square meters, is the first national museum, the central place to build one of the eight national key museum, national cultural system advanced collectives, strong culture began with outstanding contribution of advanced collective.<br />
<br />
Analysis: Machine translation is not faithful enough in content. For instance, “首批国家一级博物馆” is translated into “first national museum”, which is not the meaning of the source language. And there are some obvious grammar mistakes in the machine translation. For example, machine translates it into just one sentence but there are multiple predicates in it. So it is not grammatically permissible. What’s more, the sentence structure of machine translation is confusing and the focus is not specific enough.<br />
<br />
====3.2Expressive text ====<br />
(1)English into Chinese<br />
<br />
Source language:<br />
<br />
An individual human existence should be like a river- small at first, narrowly contained within its banks, and rushing passionately past rocks and over waterfalls. Gradually the river grows wider, the banks recede, the waters flow more quietly, and in the end, without any visible breaks, they become merged in the sea, and painlessly lose their individual being.<br />
<br />
Target language:<br />
<br />
Machine translation: 一个人的存在应该像一条河流——开始很小，被紧紧地夹在两岸中间，然后热情奔放地冲过岩石，飞下瀑布。渐渐地，河面变宽，两岸后退，水流更加平缓，最后，没有任何明显的停顿，它们汇入大海，毫无痛苦地失去了自己的存在。<br />
<br />
Manual translation:人生在世，如若河流；河口初始狭窄，河岸虬曲，而后狂涛击石，飞泻成瀑。河道渐趋开阔，峡岸退去，水流潺缓，终了，一马平川，汇于大海，消逝无影。<br />
<br />
Analysis: Here is a well-known metaphor in the prose How to Grow Old written by Bertrand Russell. The manual translation is written by Tian Rongchang.This is a philosophical prose with graceful language. Literary translation is a most important and difficult branch of translation. Translator should focus on the literal meaning, culture, writing style and so on. It is a combination of beauty and elegance. Therefore, translators find it in a dilemma of beauty and faithfulness, let alone translating machine. Compared with manual translation, machine translation has difficulty in word choice. It is faithful and expressive, but not elegant enough.<br />
<br />
(2)Chinese into English<br />
<br />
Source language:没有一个人将小草叫做“大力士”，但是它的力量之大，的确是世界无比。这种力，是一般人看不见的生命力，只要生命存在，这种力就要显现，上面的石块，丝毫不足以阻挡。因为它是一种“长期抗战”的力，有弹性，能屈能伸的力，有韧性，不达目的不止的力。<br />
<br />
Target language:<br />
<br />
Machine translation: No one calls the little grass "hercules", but its power is truly matchless in the world. This force is invisible life force. As long as there is life, this force will show itself. The stone above is not strong enough to stop it. Because it is a "long-term resistance" of the force, elastic, can bend and extend force, tenacity, not to achieve the purpose of the force.<br />
<br />
Manual translation: Though nobody describes the little grass as a “husky”, yet its herculean strength is unrivalled. It is the force of life invisible to naked eye. It will display itself so long as there is life. The rock is utterly helpless before this force- a force that will forever remain militant, a force that is resilient and can take temporary setbacks calmly, a force that is tenacity itself and will never give up until the goal is reached. (by Zhang Peiji)<br />
<br />
Analysis:This is the excerpt of a well-known Chinese prose written by Xia Yan. It is written during the war of Resistance Against Japan. So the prose holds symbolic meaning, eulogizing the invisible tenacious vitality so as to encourage Chinese to have confidence in the anti-aggression war. Compared with manual translation, machine translation is much more abstract and confusing, especially for the word diction. For example, “大力士” is translated into “hercules” which is a man of exceptional strength and size in Greek and Roman Mythology, making it difficult to understand if readers of target language have no idea of the allusion. What’s worse, the machine version doesn’t reveal the symbolic meaning of the text, which is the core of this prose.<br />
<br />
====3.3Vocative text ====<br />
<br />
(1)English into Chinese<br />
<br />
①Source language:<br />
<br />
iPhone went to film school, so you don’t have to. (Advertisement of iPhone13)<br />
<br />
Target language:<br />
<br />
Machine translation: iPhone上的是电影学院，所以你不用去。<br />
<br />
Manual translation:电影专业课，iPhone同学替你上完了。<br />
<br />
Analysis：Here are advertisements of iPhone on Apple official website. There is a personification in the source language. It is used to stress the advancement and proficiency in camera, which is an appealing selling point to potential buyers. Compared with manual translation, machine translation is plain and not eye-catching enough for customers.<br />
<br />
②Source language: <br />
<br />
5G speed OMGGGGG<br />
<br />
Machine language: 5克的速度 OMGGGGG<br />
<br />
Manual translation:<br />
<br />
iPhone的5G 巨巨巨巨巨5G<br />
<br />
Analysis: The “G” in the source language is the unit of speed, standing for generation. However, it is mistaken as a unit of weight, representing gram in the machine translation. So the meaning is not faithful to the source language at all. As for manual translation, it complies with the source in form. Specifically speaking, five “G”s in the former complies with five characters “巨”in the latter. And the pronunciation of the two is similar. There are two layers of meaning for the 5 “G”s. One exclaims the fast speed of 5 generation network and the other new technology. In the manual version, “巨”can be used to show degree, meaning “quite” or “very”. <br />
<br />
③Source language: <br />
<br />
History, faith and reason show the way, the way of unity. We can see each other not as adversaries but as neighbors. We can treat each other with dignity and respect, we can join forces, stop the shouting and lower the temperature. For without unity, there is no peace, only bitterness and fury."<br />
<br />
Target language:<br />
<br />
Machine translation: 历史、信仰和理性指明了团结的道路。我们可以把彼此视为邻居，而不是对手。我们可以尊严地对待彼此，我们可以联合起来，停止大喊大叫，降低温度。因为没有团结，就没有和平，只有痛苦和愤怒。<br />
<br />
Manual translation:历史、信仰和理性为我们指明道路。那是团结之路。我们可以把彼此视为邻居，而不是对手。我们可以有尊严地相互尊重。我们可以联合起来，停止喊叫，减少愤怒。因为没有团结就没有和平，只有痛苦和愤怒<br />
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Analysis: Speech is a way to propagate some activity in public. It is an art to inspire emotion of the audience. The source language is the excerpt of Joe Biden’s inaugural speech. The speech should be inspiring and logic. The machine translation has some misunderstanding. Taking the translation of “lower the temperature” for example, machine only translates its literal meaning, relating to the temperature itself, without considering the context. What’s more, it is less logic than the manual one. Therefore, it adds difficulty to inspire the audience and infect their emotion.<br />
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===4.Common mistakes in machine translation ===<br />
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====4.1 lexical mistakes ====<br />
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Common lexical mistakes include misunderstandings in word category, lexical meaning and emotive and evaluative meaning. Misunderstanding in word category shows in the classification of word in the source language. As for misunderstanding in lexical meaning, machine has difficulty in precisely reflecting the meaning of the original texts, due to different cultural background and different language system. And for misunderstanding in emotive meaning, machine has no intention and emotion like human-beings. Therefore, it’s impossible for it to know writers’ feelings and their writing purposes. So sometimes, it may translate something negative into something positive.<br />
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====4.2 grammatical mistakes====<br />
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Grammatical analysis plays an important part in translation. Normally speaking, every language has its own unique grammatical rules. So in the process of translation, if translators don’t know the formation rule well, the sentence meaning will be affected. Even though all the lexical meanings are well-known by translators, the lack of consciousness of grammaticality makes it harder to arrange words according to sequential rule. English tends to be hypotactic, while Chinese tends to be paratactic. English sentences are connected through syntactic devices and lexical devices. While Chinese sentences are semantically connected, which means there are limited logical words and connection words in Chinese. So when translating English sentence, we should first analyze its grammaticality and logical structure and then rearrange its sequence. However, online translating machine has troubles in grammatical analysis, which makes its improvement more difficult.<br />
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====4.3 other mistakes====<br />
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The two mistakes above are the internal ones. Apart from mistakes in linguistic system, there are some mistakes in other aspects, such as cultural background.<br />
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===5.Reasons for its common mistakes ===<br />
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====5.1 Difference in two linguistic system====<br />
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With different history, English and Chinese have different ways of expression. Commonly speaking, English is synthetic language which expresses grammatical meaning through inflection such as tense and Chinese is analytic language which expresses grammatical meaning through word order and function word. In addition, English is more compact with full sentences. Subordinate sentence is one of the most important features in modern English. Chinese, on the other hand, is more diffusive with minor sentences.<br />
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====5.2 Difference in thinking patterns and cultural background====<br />
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According to Sapir-Whorf’s Hypothesis, our language helps mould our way of thinking and consequently, different languages may probably express their unique ways of understanding the world. For two different speech communities, the greater their structural differentiations are, the more diverse their conceptualization of the world will be. For example, western culture is more direct and eastern culture more euphemistic. What’s more, English culture tends to be individualism, focusing on detail, through which it reflects the whole, while Chinese culture tends to be collective. Different thinking patterns will add difficulty for machine to translate texts.<br />
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====5.3 Limitation of computer====<br />
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Recently, there are some breakthroughs and innovation in machine translation. However, due to its own limitation, online translation has limitation in some ways. Firstly, compared with machine, human brain is much more complicated, consisting of ten billions of neuron, each of which has different function to affect human’s daily activities and help humans avoid some errors. However, computer can only function according to preset programming has no intention or consciousness. Until now, countless related scholars have invested much time in machine translation. They upload massive language database, which include almost all linguistic rules. But computers still fail to precisely reflect the meaning of source language for many times due to the complexity and flexibility of language. On the other hand, computers can’t take context into consideration. During translation, it is often the case that machine chooses the most-frequently used meaning of one word. So without the correct and exact meaning, readers are easier to feel confused and even misunderstand the meaning of source language.<br />
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===6.Conclusion===<br />
From the analysis above, we can draw a conclusion that machine deals with informative text best, followed by non-literary translation of expressive text. What’s more, machine can be a useful tool to get to know the gist and main idea of a specific topic, for the simple sentence structure and numerous terms. And it can improve translating efficiency with high speed. But machine has difficulty in translating literary works, especially proses and poems.<br />
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Machine translation has mixed future. From the perspective of commercial, machine translation boasts a bright future. With the process of globalization, the demand for translation is increasing accordingly. On one hand, if we only depend on human translator to deal with translating works, the quality and accuracy of translation can be greatly affected. On the other hand, if machine is used properly to do some basic work, human translators only need to make preparation before translating, progress, polish and other advanced work, contributing to highly-qualified translation and high working efficiency.<br />
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However, compared with manual translation, machine translation has a bleak future. It is still impossible for machine to replace interpreter or translator in a short term. With intelligence and initiative, humans are able to learn new knowledge constantly, which machine will never accomplish. Besides, machine is not used to replace translators but to assist them in work. In other words, translators and machine carry out their own duties and they are not incompatible.<br />
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To draw a conclusion, although there are certain limitations of machine translation, it can serve as a catalyst for translating works. Therefore, with the rapid development of artificial intelligence and related technology, there are still many opportunities for machine translation.<br />
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===Reference ===<br />
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Cui Zihan 崔子涵.机器翻译译文质量对比——以谷歌翻译和DeepL为例[J] [Comparison among Machine Translation--Taking Google Translation and Deepl for Example].Overseas English 海外英语,2021(15):182-183.<br />
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Li Deyi 李德毅. (2018). 人工智能导论 [Introduction to Artificial Intelligence]. Beijing: China Science and Technology Press 中国科学技术出版社.<br />
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Qiu Quanju 仇全菊.大数据时代背景下机器翻译及其发展趋势[J][Machine Translation and its Development Trend under the Background of Big Data Era]. English Teachers 英语教师,2021,21(16):60-62.<br />
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Zhuo Jianbin 卓键滨,Liu Wenxian 刘文娴,Peng Zili 彭子莉.机器翻译对各类型文本的德汉翻译能力探究[J][Research on the German Chinese Translation Ability of Machine Translation for Various Types of Texts]. Comparative Study of Cultural innovation 文化创新比较研究,2021,5(28):122-125.<br />
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(英) Peter Newmark A Textbook of Translation[M] Shanghai Foreign Education Press, 2002<br />
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Wang Hongqi 王红旗. (2008). 语言学概论 [Introduction to Linguistics]. Beijing: Peking University Press 北京大学出版社.<br />
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Liu Qin刘琴.功能目的论对于不同文本类型的翻译解读[J][Analysis of Translations in Different Types of Text based on Functionalist Approaches].Overseas Engliosh 海外英语,2021(17):8-9.<br />
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Zhang Peiji 张培基.英译中国现代散文选[M][Selected Modern Chinese Prose Writings]. Shanghai Foreign Languages Education Press 上海外语教育出版社, 2002.<br />
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Chen Cheng陈诚.机器翻译技术的综述[J][Overview of Machine Translation Technology].Electronic Techonology 电子技术,2021,50(11):290-291.<br />
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He Xinyu何馨宇.机器翻译的发展及其对翻译职业化的影响研究[J] [The Development of Machine Translation and its Effect on Professional Transltors].Overseas English 海外英语,2021(20):48-49.<br />
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He Wen 何雯, Wang Xiufeng 王秀峰.信息型文本的在线机器翻译错误研究[J][Research on Errors in Online Machine Translation of Informative text ].Overseas English海外英语,2021(15):188-189.<br />
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Li Hanji 李晗佶. (2021). 人工智能时代翻译技术与译者关系演变与重构 [Evolution and reconstruction of the relationship between translation technology and translators in the era of artificial intelligence]. 西华师范大学学报(哲学社会科学版) Journal of West China Normal University (PHILOSOPHY AND SOCIAL SCIENCES EDITION) (2021-12-04) 1-6.<br />
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Wei Guang魏光. 人工翻译与机器翻译译文编辑比较研究[J][Comparative Study of Translation Editing between Manual Translation and Machine Translation]. Overseas English 海外英语,2021(19):18-19+21.<br />
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=Chapter 11 陈惠妮=Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts 机器翻译的译前编辑研究——以医学类文摘为例=<br />
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陈惠妮 Chen Huini, Hunan Normal University<br />
[[Machine_Trans_EN_11]]<br />
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===Abstract===<br />
At present, globalization is accelerating and the market demand for language services is rapidly increasing . Machine translation, as an important translation method, can greatly improve translation efficiency due to its low cost and high speed. However, because of the limitations of machine translation and the differences between Chinese and English language, machine translation is not accurate enough. In order to balance translation efficiency and translation quality, a great number of manual revisions in translation are required for the machine translating texts. Medical papers are specialized, special and purposeful, so it requires accurate,qualified and professional translation. However, the quality of translations by machine is inefficient to meet the high-quality requirements of medical papers translation. Therefore, the introduction of pre-editing can greatly improve the efficiency and quality of machine translation.<br />
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===Key words===<br />
Pre-editing, Machine translation, Medical texts<br />
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===题目===<br />
Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts<br />
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===摘要===<br />
在全球化加速发展的今天，市场对语言服务的需求迅速增加。机器翻译作为一种重要的翻译途径，由于其成本低、速度快，可以大大提高翻译效率。然而，由于机器翻译的局限性以及中英文语言的差异，机器翻译的准确性不高。为了平衡翻译效率和翻译质量，机器翻译文本需要大量的手工修改。<br />
医学论文具有专业性、特殊性和目的性，要求其译文准确、合格、专业。然而，机器翻译的质量较低，无法满足医学论文对翻译的高质量要求。因此，译前编辑的引入可以大大提高机器翻译的效率和质量。<br />
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===关键词===<br />
译前编辑；机器翻译；医学文本<br />
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===1. Introduction===<br />
===1.1 Definition of Machine Translation===<br />
As Cronin(2013) revealed: "Translation is undergoing a revolutionary upheaval. The influence of digital technology and the Internet on translation is continuous, extensive and profound. From the popularity of automatic online translation applications, translation revolution is everywhere (Cronin,2013) However,the concept of machine translation was firstly proposed in the 1930s. Since 1940s, the machine translation technology has been evolving from rule-based machine translation (RBMT) to statistical machine translation (SMT), and to neural machine translation (NMT). Machine translation refers to the automatic translation of source language into target language by using a computer system. That is, machine translation refers to the automatic translation of text from one language into another natural language by computer software or other online translation webs. Machine translation is also defined as the process of “using a computer system to automatically translate text or speech from one natural language to another” according to the definition by ISO (Cui, 2014).O 'Brien (2002) defines it as "the behavior of modifying errors in machine translation to ensure that the target translation meets certain quality requirements". On the basic level, machine translation performs mechanical substitution of words in one language for words in another language, but that rarely produces a good translation, therefore, recognition of the whole phrases and their closest counterparts in the target language is needed. Not all words in one language have equivalents in another language, and many words have more than one meaning. A huge demand for translation is greatly needed in today’s global world, which creates new opportunities for the development of machine translation, attracts more and more attention and becomes one of the current research focuses.<br />
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===1.2 Definition of Pre-editing===<br />
Pre-editing means to adjust and modify the source language to make it fit more with the characteristics of the machine translation software before putting the source language before the into machine translation, so as to improve the quality of the translation machine translation (Wei Changhong, 2008:93-94). Pre-editing is to modify the original text before putting it into machine translation software, in order to improve the recognition rate of machine translation, optimize the output quality of translated text and reduce the workload of post-editing. Because pre-translation editing only needs to be modified in one language, the operation is simpler than post-translation editing, which can realize the double improvement of quality and efficiency. A good pre-editing translation can help machine translation more smoothly, thus improving the machine readability and quality of the output translation. Pre-editing is mostly applied in the following situations: one is when the original text is of poor quality and the machine is difficult to recognize the meaning of the sentence, such as user generated content with poor readability and translatability (Gerlach et al, 2003:45-53); Documents that need to be published in multiple languages; Next is when the original text contains a lot of jargon; The last is the original text has a corresponding translation memory bank. If the original text is edited, it can better match the content of the translation memory bank. Pre-editing is a process of identifying problems. It requires to pre-edit the source texts before putting it into machine translation according to the requirements, listing the expressions or sentences that may have trouble in machine translation and then pre-edit it by human. The purpose is to enable the computer to translate better, improve the translatability of machine translation. (Slype G V & Guinet J F & Seitz F,1984:115)<br />
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===1.3 Machine Translation Mode===<br />
According to the different knowledge acquisition methods, machine translation modes can be classified as follows: one is rule-based machine translation, which is based on bilingual dictionaries and a library of language rules for each language. The quality of translation depends on whether the source language conforms to the existing rules, but the inexhaustible rules are the hinders of this model. The second mode is machine translation based on statistics. This translation model relies on the principles of mathematics and statistics to find various existing translations corresponding to the translation tasks through the employment of corpus, analyzing the frequency of their occurrence and selecting the translation with the highest frequency for output. The disadvantage of this translation model is that it ignores the flexibility of language and the importance of context. The last one is neural network language model. This model is different from previous translation models in that it uses end-to-end neural network to realize automatic translation between natural languages. At present, the quality of its translation is much higher than that of the previous translation models.<br />
===1.4 Source of Study Abstracts===<br />
The core medical journals from domestic are selected in order to make the paper more representative and authoritative, such as National Medical Journal of China, Journal of Peking University (Health Science), and Journal of Third Military Medical University. All of them include basic medical science, biomedical technology, laboratory medical science and other fields. In this way, the pre-editing approaches included are applicable enough to machine translation of medical abstracts.<br />
===1.5 Selection of Translation Software===<br />
Generally, machine translation software includes Google Translation, Youdao Translator and Niu Translation, which has their own special use in translation. Take Google as an example, it is more like neurons in human brain, enabling to learn and collect information to establish connections with its neural machine translator’s neurons. However, it also causes many errors because of the lack of enough information. In this paper, contrastive analysis will be carried on by using Google translation. On the one hand, being a pioneer in the translation of NET, it is inevitably to sue Google as the translation software to translate medical texts in this paper. Comparing the program called “Google brain”, other NMT of translation software are relatively disadvantages. On the other hand, the Google translation enjoys the largest users in the world, with its downloads of more than one billion. The output quality of Google translation is more correct and complete than other machine translation software. With years of development and improvement, Google Translation has been greatly promoted. In this paper, Chinese medical abstract will be automatically translated by Google translation, and then the translation output will be compared with the translation by human and the publication of English abstracts. The main purpose is to prove that the improvement and promotion of quality and accuracy in the medical abstracts will be obtained through the pre-editing approaches.<br />
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===2.Language Characteristics and Error Division===<br />
Since Chinese and English are two different languages, it is quite neccessary to identify their own characteristics so as to better analyze and understand the two languages. Tytler (1978) argued in his Essay on the Principles of Translation that there are three principles of translation that in all the translation should give readers the same feelings as the source text, except for complete transcript of ideas. There actually also exit some mistakes of these two languages. So the following will make some clarrifications of these two languages to ensure more accurate translation.<br />
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===2.1 Language Characteristics of Medical Abstracts===<br />
Chinese and English belong to different language systems, so there are differences in their language structure and the way users think of the languages. When using machine translation from Chinese to English, due to the unequal language levels, there will be many mistakes in the translation process, especially for ESP texts, such as medical papers. <br />
Here, these differences mainly refer to the linguistic characteristics of medical abstracts. In medical abstracts, it usually includes structured and unstructured abstracts. Although in different forms, they both describe the purpose, methods and conclusions of the research.<br />
In the method section of medial abstract, several Chinese sentences can be connected with commas, but each sentence may convey different information. In contrast, English sentences contain a great deal of information, but in order to ensure clarity, some modifiers need to be isolated and then reconstructed. However, in Chinese-English machine translation, a lot of information is put into the sentence because the machine segments the sentence on the basis of the full comma.<br />
In addition, subjectless sentence is used in the objective and method parts of Chinese medical abstracts. The subjectless sentence means the sentence without or free from subject and is usually employed in two contexts. The first is "needless to say". In Chinese sentences, it is common to omit the subject of the sentence. Chinese sentences can convey meanings by using incomplete sentence structures, so Chinese speakers can understand the meanings of the sentences even though the sentence subject is omitted. The second is "emphasis of action". In this context, subjectless sentences are used to describe behaviors, especially in the study of traditional Chinese medicine abstracts. Comparatively speaking, it should be avoided in English medical abstracts. Abstract sentences are more subjective when describing the learning process, while the essential requirement of English medical abstract is objectivity. From this point, sentences without subject should be avoided in English medical abstracts. Another feature is voice. Few words with passive meanings appear in Chinese abstracts. English sentences are more favoured in using passive voice. When translating from Chinese to English, passive voice should be used to make the contents objective, which is also the basic requirement of medical papers.<br />
These are the linguistic features of Chinese medical abstracts. There are great differences in sentence structure and expression between Chinese and English medical abstracts. These differences may reduce the accuracy of machine translation, so it is necessary to introduce pre-editing to edit the source text to ensure that the source text can be accurately recognized by the machine and fully translated into English.<br />
===2.2 Error Division of Machine Translation in Translating Abstracts of Medical Papers from Chinese to English===<br />
In this paper, errors in machine translation are listed out after analyzing. Some are due to the principles, such as statistical-based MT and NMT, employed by machine translation. Based on the original errors, they can be studied from two levels. One is from the macro-level, referring to mistakes caused by objective factors. These are not human factors, but the disadvantage of machine translation and the limitations of language. The limitations of machine translation are derived from the principles and models adopted and manifests itself as a reliance on reference sources. In this case, semantic ambiguity and textual incoherence may occur in the absence of reference sources.<br />
However, for ESP texts such as medical papers, the requirements for terms and sentence patterns are far beyond the existing corpora. For unfamiliar texts (that is, the corresponding texts cannot be found in the corpora). The quality of output translation will be relatively low, which is determined by the principles of machine translation. As mentioned above, machine translation has evolved over the past few decades from rules-based to corp-based statistics to NMT. However, there still exist some limitations in machine translation.<br />
Mistakes on micro-level are mainly caused by the variations and differences of linguistic structure between Chinese and English. Chinese is an implicit language, while English is an explicit one (Lian, 2010) To put it in another way, Chinese expression does not depend on language structures, while English does the opposite. This may result in mismatches between the original and machine translated sentences. This kind of error is mainly divided into two types: component fragment and component missing. For a medical paper, such errors are very serious. As a kind of ESP discourse, medical paper has the characteristics of fixed, objective and accurate language structure. In order to reproduce the characteristics of medical abstracts in translation, it is necessary to avoid errors at the level of words and sentences and pay attention to logic and consistency.<br />
Lexical errors here refer to the inconsistency of fixed expressions in the translation of terms. Although these terms are machine translation based on the corpus, the corpus may not be perfect for ESP texts such as medical papers. Therefore, fixed expressions are not used in term translation. This is also the most common mistake in machine translation (the term here includes but is not limited to nouns). For ESP texts, medical text in particular, the fixed expressions and the accuracy of terms are of great importance.<br />
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===3.Approaches Proposed for Pre-editing ===<br />
Generally speaking, there is a close relationship between pre-editing and post-editing, both of which aim to convey information and ensure high or publishable translation quality. Proper pre-editing can improve the quality of machine translation in terms of adequacy and consistency. <br />
Complexity of natural language and people use language arbitrarily, bringing many difficulties to Chinese-English machine translation, Hu Qingping (2005:24) has proposed "the research of translation software and the development of the controlled language are the two directions to improve the quality of machine translation : the former aims at the difficulty in the natural language processing, the latter overcomes the arbitrariness of natural language". Feng Quangong and Gao Lin (2017: 63-68) put forward: "The writing principles of controlled language can be applied to pre-editing of machine translation. Pre-editing based on controlled language can effectively reduce the complexity and ambiguity of source text, improve the identifiability of machine translation (the translatability of source text itself), and thus reduce (fully) the workload of post-editing".<br />
The central task of pre-editing is to transform human-friendly content into machine-friendly content, so words and sentences need to be repositioned or even changed. Based on the analysis of the language characteristics of Chinese and English, the Chinese ideographic group should be split before the Chinese source text is input into the machine software to translate so that the sentence structure is complete which can be easily recognized by the machine translation software.<br />
===3.1 Extraction and Replacement of Terms in the Source Text===<br />
As a branch of applied English, medical English is the product of the combination of English language knowledge and medical knowledge. The terms in medical papers have the characteristics of fixed and complex language structure. Although Google Translation is based on a corpus, the language structure is not fixed due to the limited size of the corpus. Therefore, the following two steps should be followed before machine translation. The first is to extract terms and create a glossary, and then replace the Chinese terms in the source text with the corresponding English expressions in the glossary. Of course, the terms of extraction should be searched and verified according to the actual situation. All of these words are verified before they can be replaced. This method can not only ensure the accuracy of term translation, but also maintain the consistency of expression, especially when dealing with long texts.<br />
Example1<br />
Source abstract: 口腔白斑病癌变相关缺氧应答基因和微小RNA的芯片及表达验证。<br />
Google translation: Microarray detection/Chip detection and expression verification of hypoxia response genes and microRNAs related to oral leukoplakia canceration.<br />
Published translation:Transcriptome array screening and verification of oral leukoplakia carcinogenesis-related hypoxia-responsive gene and microRNA. <br />
Analysis: The expression “ Affymetrix GeneChip” empolyed in this thesis is a human transcription array for transcriptome array. In the source abstract, “芯片检测“ can be meant “screening” but not detection, so the proper and appropriate translation of these expression should be “transcription array screening”, but the Google translates it into “microarry detection of chip detection”. Therefore, term extraction is essential and inevitable before putting the source abstracts into the machine translation software.<br />
After pre-editing source abstracts: 对 oral leukoplakia carcinogenesis 相关 hypoxia-responsive gene 和微小RNA进行 transcriptome array screening 及表达验证。<br />
After pre-editing translation: Transcriptome array screening and expression- verification of hypoxia-responsive genes and microRNAs related to oral leukoplakia carcinogenesis.<br />
===3.2 Explication of Subordination===<br />
As mentioned above, the subordination of Chinese sentences is judged by certain specific words in machine translation . Chinese is a paratactic language, and sentences are often connected by internal logical relationships; while English depends on sentence structure, so sentences are often closely linked by various language forms. In order to improve the accuracy of machine translation, we can adjust the sentence structure and adjust the position of adjectives and their modifiers. <br />
Example 2<br />
Source abstracts:回顾性分析南京医科大学附属儿童医院中重度HIE患儿49例及同期就诊的无神经系统症状体征的足月新生儿为对照组31例的头颅磁共振成像（MRI）资料。<br />
Google translation: A retrospective analysis that the brain magnetic resonance imaging (MRI) data of 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University and full-term neonates with no neurological symptoms and signs during the same period were included in the control group.<br />
Published translation: A total of 49 children with moderate to severe HIE admitted to the children’s Hospital Affiliated to Nanjing Medical University were retrospectively analyzed. Cranial magnetic resonance imaging (MRI) date of 31 full-term neonates without neurological symptoms and signs who visited the hospital during the same period were recruited as the control group. <br />
Analysis: As the above example shows that “中重度HIE患儿” and “足月新生儿” in the source abstracts are from the Children’s Hospital Affiliated Nanjing Medical University. The Google translation shows that 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University. There is an error due to the misuse of subordination. There are some advice in order to solve the problem. That is, first to segment the sentence and then reconstruct the sentence. For instance, the Chinese expression “南京医科大学附属儿童医院” carries many modifiers, which requires to cut the modifiers and reconstruct the sentence. Therefore, the Chinese expression “南京医科大学附属儿童医院’can be divided into abstractions, leaving two sentences instead of one long sentence with modifiers..<br />
After pre-editing source abstracts: 对49例中重度HIE患儿以及31例无神经系统症状体征的足月新生儿的MRI资料进行回顾性分析。这些患者收治于南京医科大学儿童附属医院。<br />
After pre-editing translation: The MRI data of 49 children with moderate to severe HIE and 31 full-term newborns without neurological symptoms and signs were retrospectively analyzed. These patients were admitted to the Children’s Hospital of Nanjing Medical University.<br />
===3.3 Explication of Subject through Voice Changing===<br />
The extensive use of subjectless sentences are quite common in the Chinese abstracts, while English prefers to employ passive voice in the sentences so as to make them object and accurate. In order to take the characteristics of English sentences into great and careful consideration, the sentences written in passive voice in particular, it will be helpful for machine translation to find out the subject and reconstruct a sentence in passive voice (Wang Yan: 2008). The first is to discover the “doee” in a sentence and then is to reconstruct the sentence structure and put the “doee” before the verb. The last is to explicate the passive relation between the noun and the verb.<br />
Example 3<br />
Source abstract: 回顾性分析2018年1月至2020年12月解放军总医院第七医学中心收治的500例老年髋部骨折患者的资料。<br />
Google translation: A retrospective analysis of the data of 500 elderly patients with hip fractures admitted to the Seventh Medical Center of the PLA General Hospital from January 2018 to December 2020.<br />
Published translation: From January 2018 to December 2020, the data of 500 elderly patients with hip fracture treated in the Seventh Medical Center of PLA General Hospital were analyzed retrospectively.<br />
Analysis: The above example indicates that the “回顾性分析”in the Google Translate is a noun phrase, but the source abstracts actually describes an action or a behavior. The reason for such a mistake is that the machine can’t recognize the Chinese subjetless sentences. To find the subject of the sentence, the source abstracts can be revised and adjusted. Finding the “doee” is the first step, which refers to “500例老年髋部骨折患者的资料”in the source abstracts. And next is to put it in front of the verb, that is to place it in the beginning of the sentence. Last but not least, the passive voice should be explicated in the sentence. <br />
After pre-editing source abstract: 500例老年髋部骨折患者的资料被回顾性分析，他们在2018年1月之2020年12月期间收治于解放军总医院第七医学中心。<br />
After pre-editing translation: The data of 500 elderly hip fracture patients were retrospectively analyzed. They were admitted to the Seventh Medical Center of the PLA General Hospital between January 2018 and December 2020.<br />
===3.4 Relocation of Modifiers===<br />
Modifiers, including noun, adverbial and attributive are constantly employed in the Chinese medical papers in order to add information to subject and object in the sentence. By doing this, complicated sentences can be structured, thus causing obstacles to machine translation for recognizing this complex sentence structures when translating Chinese-English sentences. To make the machine translation successfully recognize the sentence structure, simplifying the sentence structure is quite necessary. The key is to moving the location of the modifiers and thus making the modifiers an independent sentence. In other words, the modifiers ought to be put before or behind the main part of the sentence to satisfy the common use of English. <br />
Usually, the modifiers in Chinese sentence can only be placed in front of the core word, while in English, modifiers are very flexible. It is all right to place the modifiers in front of or behind the major part of the sentences with adjective or a connective noun.<br />
Example 4<br />
Source abstracts: 利用DNA重组技术以pET-28a表达系统在E.coli BL21(DE3) 中重组表达Hepl。<br />
Google Translation: Hepl is recombined in E.coli BL21 (DE3) using DNA recombination technology with pET-28a expression system.<br />
Published translation: The recombinant Hcpl protein was expressed by using DNA recombination technology through pET-28a expression system in E. coli BL21 (De3).<br />
Analysis: The Chinese medical text includes two modifiers, “利用DNA”and “以pET-28a)表达系统”. These two modifiers will be translated by machine, which catches more attention to the two constituents. From the Google translation above, one failure is obvious that it misplaces the location of the two modifiers and presents it in not accurate form. To make it translate correctly by machine translation, dividing the sentences is very important so that the source abstracts can be correctly recognized by machine translation.<br />
After pre-editing source abstract: 利用DNA重组技术，Hcp1重组表达在E.coli BL21 (DE3)中， 通过pET-28a表达系统在E. coli BL21 (DE3)中。<br />
Google translation: Using DNA recombination technology, Hcp1 recombination is expressed in E.coli BL21 (DE3), via pET-28a expression system in E. coli BL21 (DE3).<br />
The second is the independence of modifiers, that is, modifiers can also be reconstructed into clauses to modify core words. Compared with English, There is no subordinate clause in Chinese, so redundant Chinese modifiers need to be reconstructed into subordinate clauses in Chinese-English translation to meet the characteristics of English. English, especially sentences with multiple modifiers. Otherwise, sentence structure may be confused, such as scattered modifiers of core words. To avoid this mistake, it is necessary to separate the modifier from the main part of the sentence. These modifiers should be reconstituted into clauses. Also, keep your sentences simple and easy to understand.<br />
===3.5 Proper Omission and Deletion of Category Words===<br />
Category words are commonly used in Chinese. Category words complement the meaning of words, including problems, positions, situations and jobs. Adding this supplementary word is more in line with Chinese custom. In many cases, it has no real meaning, so it can be omitted in translation. In Chinese, category words are frequently used. However, it is rarely used in English, which is one of the differences between Chinese and English. There are many kinds of category words. Considering objectivity and the fixed structure of language, redundant category words should be deleted in Chinese-English translation. In the general, the most commonly used category of words in the Chinese medical abstracts are "process", "behavior", and "situation". These categories of words hinder the language conversion of Chinese and English, resulting in redundancy. <br />
Example 5<br />
Source abstract: 探讨口腔白斑病癌变进程中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia response genes and associated microRNAs in the process of oral leukoplakia cancer was discussed.<br />
Published translation: To study the hypoxia response gene and microRNA (miRNA)expression profiles in the pathogenesis and progression of oral leukoplakia (OLK).<br />
Analysis: As the above example shows, the Chinese words “进程” belongs to a category word. However, the Chinese expression “癌变”contains the process, so the “进程” expression can be deleted before placing it into the machine translation, because the meaning of it has been overlapping between the expression “癌变”. Therefore, its place can be replaced with preposition “during”.<br />
After pre-editing source abstract: 探讨口腔白斑病癌变中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia responsive genes and miRNA in oral leukoplakia cancer was investigated.<br />
<br />
===Conclusion===<br />
After years of development, machine translation has made great progress. The accuracy of machine translation has been greatly improved in both text recognition and sentence pattern conversion. However, machine translation has its own limitations. In other words, it needs to rely on the parallel corpus as a reference source for improving its accuracy. ESP text, in particular, is harder to get the high quality by machine translation. <br />
As one of the research papers, the characteristics of medical abstracts are fixed language structures, objectivity and accuracy (Qin Yi:2004). Therefore, medical translation must be accurate, object and understandable to follow the specific demands of the medical paper. Being an important field in the human society, medical paper translation is on a great demand, which means that it needs a huge demand for human labor. However, with the machine translation promoting, it will be more efficient to translate medical papers combining the effort by human and machine. The improvement and development of machine translation requires the joint efforts of computer science, information science, statistics, linguistics and other academic circles to achieve more mature human-computer mutual assistance translation (Li Yafei, Zhang Ruihua : 2019).<br />
However, errors can occur during the process of machine translation of Chinese- English, because of the differences of the Chinese and English and the processing of the machine. Errors from the perspective of linguistic or grammar can affect the machine translation a lot. After division and recognition of errors, some pre-editing approaches are put forward to help the machine translation more accurate and readable, that are, extraction and replacement of terms in the source text, relocation of modifiers, explication of subordination, proper omission, deletion of category words and explication of subject through voice changing. <br />
The paper mainly focuses on the pre-editing machine translation by using medical papers as a case study. The errors of machine translation occurring in the translation of medical abstracts and pre-editing approaches for machine translation. The quality of machine translation of medical papers is greatly improved after employing the pre-editing methods. However, machine translation is not as flexible and accurate as human brain, so it is of importance to combine pre-editing and post-editing approaches with machine translation in order to produce more accurate, more object machine translation of medical papers.<br />
<br />
===References===<br />
[1]. Cronin, Michael (2013). Translation in the Digital Age[M]. New York&London: Routledge.<br />
<br />
[2]. GERLACH J, et al ( 2013). Combining Pre-editing and Post-editing to Improve SMT of User-generated Content[M]// Proceedings of MT Summit XIV Workshop on Post-editing Technology and Practice. 45-53.<br />
<br />
[3]. Slype G V & Guinet J F & Seitz F (1984). Better Translation for Better Communication [M] .Oxford: Pergamon Press Ltd (U.K.), <br />
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[4]. O'Brien S (2002). Teaching Post-editing: A Proposal for Course Con-tent [EB/OL]. http://mt-archive. Info/EAMT-2002-0brien. Pdf.<br />
<br />
[5]. Tytler, A. F. (1978). Essay On The Principles of Translation[M]. Amsterdam: JohnBenjamins Publishing.<br />
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[6] 崔启亮. (2014), 论机器翻译的译后编辑[J], 中国翻译, 035(006):68-73.<br />
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[7] 冯全功,高琳 (2017) 基于受控语言的译前编辑对机器翻译的影响[J]. 当代外语研究,(2): 63-68+87+110.<br />
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[8] 胡清平(2005). 机器翻译中的受控语言[J]. 中国科技翻译, (03): 24-27. <br />
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[9] 连淑能 (2010). 英汉对比研究增订本[M]. 北京:高等教育出版社.<br />
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[10] 黎亚飞,张瑞华 (2019). 机器翻译发展与现状[J]. 中国轻工教育,(5):38-45. <br />
<br />
[11] 秦毅(2004),从翻译基本标准议医学英语的翻译[J]. 遵义医学院学报,27 (4): 421-423. <br />
<br />
[12] 王燕 (2008). 医学英语翻译与写作教程[M]. 重庆:重庆大学出版社<br />
<br />
=12 蔡珠凤=(The Mistranslation of C-J Machine Translation of Political Statements)=<br />
[[Machine_Trans_EN_12]]<br />
===Abstract===<br />
Language is the main way of communication between people. With the continuous development of globalization, the scale of cross-border exchanges is also expanding. However, due to cultural differences and diversity, the languages of different countries and regions are very different, which seriously hinders people's communication. The demand for efficient and convenient translation tools is increasing. At the same time, with the development of network technology and artificial intelligence, recognition technology based on deep learning is more and more widely used in English, Japanese and other fields.<br />
<br />
===Key words===<br />
machine translation; political statements; mistranslation of C-J machine translation<br />
===题目===<br />
The Mistranslation of C-J Machine Translation of Political Statements<br />
===摘要===<br />
语言是人与人之间交流的主要方式。随着全球化的不断发展，跨境交流的规模也在不断扩大。然而，由于文化的差异和多样性，不同国家和地区的语言差异很大，这严重阻碍了人们的交流。对高效便捷的翻译工具的需求正在增加。同时，随着网络技术和人工智能的发展，基于深度学习的识别技术在英语、日语等领域的应用越来越广泛。<br />
<br />
===关键词===<br />
机器翻译；政治发言；政治发言中译日的误译<br />
===1. Introduction===<br />
===Introduction to machine translation===<br />
Machine translation, also known as automatic translation, is a process of using computers to convert one natural language (source language) into another natural language (target language). It is a branch of computational linguistics, one of the ultimate goals of artificial intelligence, and has important scientific research value.At the same time, machine translation has important practical value. With the rapid development of economic globalization and the Internet, machine translation technology plays a more and more important role in promoting political, economic and cultural exchanges.The development of machine translation technology has been closely accompanied by the development of computer technology, information theory, linguistics and other disciplines. From the early dictionary matching, to the rule translation of dictionaries combined with linguistic expert knowledge, and then to the statistical machine translation based on corpus, with the improvement of computer computing power and the explosive growth of multilingual information, machine translation technology gradually stepped out of the ivory tower and began to provide real-time and convenient translation services for general users.（Zhang 2019:5-6)<br />
<br />
=== C-J machine translation software===<br />
Today's online machine translation software includes Baidu translation, Tencent translation, Google translation, Youdao translation, Bing translation and so on. Google was the first company to launch the machine translation system, and Baidu was the first company to import the machine translation system in China. In addition, Tencent and Youdao have attracted much attention.Machine translation is the process of using computers to convert one natural language into another. It usually refers to sentence and full-text translation between natural languages. In order to continuously improve the translation quality, R & D personnel have added artificial intelligence technologies such as speech recognition, image processing and deep neural network to machine translation on the basis of traditional machine translation based on rules, statistics and examples.With the increase of using machine translation, the joint cooperation between manual translation and machine translation will also increase significantly in the future. What criteria should be used to evaluate the quality of machine translation? In the evolving field of machine translation, there is an urgent need to clarify the unsolvable questions and solved problems.<br />
<br />
===The history of machine translation===<br />
The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. alchuni put forward the idea of using machines for translation. In 1933, Soviet inventor П.П. Trojansky designed a machine to translate one language into another, and registered his invention on September 5 of the same year; However, due to the low technical level in the 1930s, his translation machine was not made. In 1946, the first modern electronic computer ENIAC was born. Shortly after that, W. weaver, an American scientist and A. D. booth, a British engineer, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947 when discussing the application scope of electronic computer. In 1949, W. Weaver published the translation memorandum, which formally put forward the idea of machine translation. After 60 years of ups and downs, machine translation has experienced a tortuous and long development path. The academic community generally divides it into the following four stages:<br />
<br />
Pioneering period（1947-1964）<br />
<br />
In 1954, with the cooperation of IBM, Georgetown University completed the English Russian machine translation experiment with ibm-701 computer for the first time, showing the feasibility of machine translation to the public and the scientific community, thus opening the prelude to the study of machine translation.<br />
It is not too late for China to start this research. As early as 1956, the state included this research in the national scientific work development plan. The topic name is "machine translation, the construction of natural language translation rules and the mathematical theory of natural language". In 1957, the Institute of language and the Institute of computing technology of the Chinese Academy of Sciences cooperated in the Russian Chinese machine translation experiment, translating 9 different types of more complex sentences.From the 1950s to the first half of the 1960s, machine translation research has been on the rise. The United States and the former Soviet Union, two superpowers, have provided a lot of financial support for machine translation projects for military, political and economic purposes, while European countries have also paid considerable attention to machine translation research due to geopolitical and economic needs, and machine translation has become an upsurge for a time. In this period, although machine translation is just in the pioneering stage, it has entered an optimistic period of prosperity.<br />
<br />
Frustrated period（1964-1975）<br />
<br />
In 1964, in order to evaluate the research progress of machine translation, the American Academy of Sciences established the automatic language processing Advisory Committee (Alpac Committee) and began a two-year comprehensive investigation, analysis and test.In November 1966, the committee published a report entitled "language and machine" (Alpac report for short), which comprehensively denied the feasibility of machine translation and suggested stopping the financial support for machine translation projects. The publication of this report has dealt a blow to the booming machine translation, and the research of machine translation has fallen into a standstill. Coincidentally, during this period, China broke out the "ten-year Cultural Revolution", and basically these studies also stagnated. Machine translation has entered a depression.<br />
<br />
convalescence（1975-1989）<br />
<br />
Since the 1970s, with the development of science and technology and the increasingly frequent exchange of scientific and technological information among countries, the language barriers between countries have become more serious. The traditional manual operation mode has been far from meeting the needs, and there is an urgent need for computers to engage in translation. At the same time, the development of computer science and linguistics, especially the substantial improvement of computer hardware technology and the application of artificial intelligence in natural language processing, have promoted the recovery of machine translation research from the technical level. Machine translation projects have begun to develop again, and various practical and experimental systems have been launched successively, such as weinder system Eurpotra multilingual translation system, taum-meteo system, etc.However, after the end of the "ten-year holocaust", China has perked up again, and machine translation research has been put on the agenda again. The "784" project has paid enough attention to machine translation research. After the mid-1980s, the development of machine translation research in China has further accelerated. Firstly, two English Chinese machine translation systems, ky-1 and MT / ec863, have been successfully developed, indicating that China has made great progress in machine translation technology.<br />
<br />
New period(1990 present)<br />
<br />
With the universal application of the Internet, the acceleration of the process of world economic integration and the increasingly frequent exchanges in the international community, the traditional way of manual operation is far from meeting the rapidly growing needs of translation. People's demand for machine translation has increased unprecedentedly, and machine translation has ushered in a new development opportunity. International conferences on machine translation research have been held frequently, and China has made unprecedented achievements. A series of machine translation software have been launched, such as "Yixing", "Yaxin", "Tongyi", "Huajian", etc. Driven by the market demand, the commercial machine translation system has entered the practical stage, entered the market and came to the users.Since the new century, with the emergence and popularization of the Internet, the amount of data has increased sharply, and statistical methods have been fully applied. Internet companies have set up machine translation research groups and developed machine translation systems based on Internet big data, so as to make machine translation really practical, such as "Baidu translation", "Google translation", etc. In recent years, with the progress of in-depth learning, machine translation technology has further developed, which has promoted the rapid improvement of translation quality, and the translation in oral and other fields is more authentic and fluent.<br />
<br />
===The problem of machine translation at present===<br />
Error is inevitable<br />
Many people have misunderstandings about machine translation. They think that machine translation has great deviation and can't help people solve any problems. In fact, the error is inevitable. The reason is that machine translation uses linguistic principles. The machine automatically recognizes grammar, calls the stored thesaurus and automatically performs corresponding translation. However, errors are inevitable due to changes or irregularities in grammar, morphology and syntax, such as sentences with adverbials after "give me a reason to kill you first" in Dahua journey to the West. After all, a machine is a machine. No one has special feelings for language. How can it feel the lasting charm of "the tenderness of lowering its head, like the shame of a water lotus? After all, the meaning of Chinese is very different due to the changes of morphology, grammar and syntax and the change of context. Even many Chinese people are zhanger monks - they can't touch their heads, let alone machines.<br />
Bottleneck<br />
In fact, no matter which method, the biggest factor affecting the development of machine translation lies in the quality of translation. Judging from the achievements, the quality of machine translation is still far from the ultimate goal.<br />
Chinese mathematician and linguist Zhou Haizhong once pointed out in his paper "fifty years of machine translation": to improve the quality of machine translation, the first thing to solve is the problem of language itself rather than programming; It is certainly impossible to improve the quality of machine translation by relying on several programs alone. At the same time, he also pointed out that it is impossible for machine translation to achieve the degree of "faithfulness, expressiveness and elegance" when human beings have not yet understood how the brain performs fuzzy recognition and logical judgment of language. This view may reveal the bottleneck restricting the quality of translation. <br />
It is worth mentioning that American inventor and futurist ray cozwell predicted in an interview with Huffington Post that the quality of machine translation will reach the level of human translation by 2029. There are still many disputes about this thesis in the academic circles.<br />
<br />
===2.Mistranslation of Chinese Japanese machine translation===<br />
===2.1Vocabulary mistranslation in Chinese Japanese machine translation===<br />
===2.1.1Mistranslation of proper nouns===<br />
In political materials, there are often political dignitaries' names, place names or a large number of proper nouns in the political field. The morphemes of such words are definite and inseparable. Mistranslation will make the source language lose its specific meaning. <br />
<br />
Japanese translation into Chinese Chinese translation into Japanese<br />
<br />
original text translation by Youdao reference translation original text translation by Youdao reference translation<br />
<br />
朱鎔基 朱基 朱镕基 栗战书 栗戰史書 栗戰書<br />
<br />
労安 劳安 劳安 李克强 李克強 李克強 <br />
<br />
筑紫哲也 筑紫哲也 筑紫哲也 习近平 習近平 習近平<br />
<br />
山口百惠 山口百惠 山口百惠 韩正 韓中 韓正<br />
<br />
田中角栄 田中角荣 田中角荣 王沪宁 王上海氏 王滬寧<br />
<br />
東条英機 东条英社 东条英机 汪洋 汪洋 汪洋<br />
<br />
毛沢东 毛泽东 毛泽东 赵乐际 趙樂南 趙樂際<br />
<br />
トウ・ショウヘイ 大酱 邓小平 江泽民 江沢民 江沢民<br />
<br />
周恩来 周恩来 周恩来<br />
<br />
クリントン 克林顿 克林顿<br />
<br />
The above table counts 18 special names in the two texts, and 7 machine translation errors. In terms of mistranslation, there are not only "战书" but also "戰史" and "史書" in Chinese. "沪" is the abbreviation of Shanghai. In other words, since "战书" and "沪" are originally common nouns, this disrupts the choice of target language in machine translation. Except Katakana interference (except for トウシゃウヘイ, most of the mistranslations appear in the new Standing Committee. It can be seen that the machine translation system does not update the thesaurus in time. Different from other words, people's names have particularity. Especially as members of the Standing Committee of the Political Bureau of the CPC Central Committee, the translation of their names is officially unified. In this regard, public figures such as political dignitaries, stars, famous hosts and important. In addition, the machine also translates Japanese surnames such as "タ二モト" (谷本), "アンドウ" (安藤) into "塔尼莫特" and "龙胆". It can be found that the language feature that Japanese will be written together with Chinese characters and Hiragana also directly affects the translation quality of Japanese Chinese machine translation. Japanese people often use Katakana pronunciation. For example, when Japanese people talk with Premier Zhu, they often use "二ーハウ" (Hello). However, the machine only recognizes the two pseudonyms "二" and "ハ" and transliterates them into "尼哈" , ignoring the long sound after "二" and "ハ".<br />
<br />
original text Translation by Youdao reference translation<br />
<br />
日美安全体制 日米の安全体制 日米安保体制<br />
<br />
中国共产党第十九次全国代表大会 中国共産党第19回全国代表大会 中国共産党第19回全国代表大会（第19回党大会）<br />
<br />
十八大 十八大 第18回党大会中国特色社会主义<br />
<br />
中国特色社会主義 中国の特色ある社会主義 第18回党大会<br />
<br />
中国共产党中央委员会 中国共産党中央委員会 中国共産党中央委員会<br />
<br />
中国共産党中央委員会十八届中共中央政治局常委 第18代中国共產党中央政治局常務委員 第18期中共中央政治局常務委員<br />
<br />
十八届中共中央政治局委员 18期の中国共產党中央政治局委員 第18期中共中央政治局委員<br />
<br />
十九届中共中央政治局常委 十九回中国共產党中央政治局常務委員 第19期中央政治局常務委員<br />
<br />
中共十九届一中全会 中国共產党第十九回一中央委員会 第19期中央委員会第1回全体会議<br />
<br />
The above table is a comparison of the original and translated versions of some proper nouns. As shown in the table, the mistranslation problems are mainly reflected in the mismatch of numerals + quantifiers, the wrong addition of case auxiliary word "の", the lack of connectors, the Mistranslation of abbreviations, dead translation, and the writing errors of Chinese characters. The following is a specific analysis one by one.<br />
<br />
"十八届中共中央政治局常委", "十八届中共中央政治局委员", "十九届中共中央政治局常委" and "中共十九届一中全会" all have quantifiers "届", which are translated into "代", "期" and "回" respectively. The meaning is vague and should be uniformly translated into "期"; Among them, the translation of the last three proper nouns lacks the Chinese conjunction "第". The case auxiliary word "の" was added by mistake in the translation of "十八届中共中央政治局委员" and "日美安全体制". The "中国共产党中央委员会" did not write in the form of Japanese Ming Dynasty characters, but directly used simplified Chinese characters.<br />
<br />
The full name of "中共十九届一中全会" is "中国共产党第十九届中央委员会第一次全体会议", in which "一" stands for "第一次", which is an ordinal word rather than a cardinal word. Machine translation does not produce a correct translation. The fundamental reason is that there are no "规则" for translating such words in machine translation. If we can formulate corresponding rules for such words (for example, 中共m'1届m2 中全会→第m1期中央委员会第m2回全体会议), the translation system must be able to translate well no matter how many plenary sessions of the CPC Central Committee.<br />
<br />
===2.1.2Mistranslation of Polysemy===<br />
original text Translation by Youdao reference translation<br />
<br />
スタジオ 摄影棚/工作室 直播现场/演播厅<br />
<br />
日中関係の話 中日关系的故事 就中日关系(话题)<br />
<br />
溝 水沟 鸿沟<br />
<br />
それでは日中の問題について質問のある方。 那么对白天的问题有提问的人。 关于中日问题的话题，举手提问。<br />
<br />
私たちのクラスは20人ちょっとですが、 我们班有20人左右， 我们班二十多人的意见统一很难，<br />
<br />
いろいろな意見が出て、まとめるのは大変です。 但是又各种各样的意见，总结起来很困难。 <br />
<br />
一体どうやって、13億人もの人をまとめているんですか。 到底是怎么处理13亿的人的呢？ 中国是怎样把13亿人凝聚在一起的？<br />
<br />
In the original text, the word "スタジオ" appeared four times and was translated into "摄影棚" or "工作室" respectively, but the context is on-site interview, not the production site of photography or film. "話" has many semantics, such as "说话", "事情", "道理", etc. In accordance with the practice of the interview program, Premier Zhu Rongji was invited to answer five questions on the topic of China Japan relations. Obviously, the meaning of the word "故事" is very abrupt. The inherent Japanese word "日中" means "晌午、白天". At the same time, it is also the abbreviation of the names of the two countries. The machine failed to deal with it correctly according to the context. "溝" refers to the gap between China and Japan, not a "水沟". The former "まとめる" appears in the original text with the word "意见", which is intended to describe that it is difficult to unify everyone's opinions. The latter "まとめている" also refers to unifying the thoughts of 1.3 billion people, not "处理". Therefore, it is more appropriate to use "统一" and "处理" in human translation, rather than "总结意见" or "处理意见".<br />
<br />
Mistranslation of polysemy has always been a difficult problem in machine translation research. The translation of each word is correct, but it is often very different from the original expression in the context. Zhang Zhengzeng said that ambiguity is a common phenomenon in natural language. Its essence is that the same language form may have different meanings, which is also one of the differences between natural language and artificial language. Therefore, one of the difficulties faced by machine translation is language disambiguation (Zhang Zheng, 2005:60). In this regard, we can mark all the meanings of polysemous words and judge which meaning to choose by common collocation with other words. At the same time, strengthen the text recognition ability of the machine to avoid the translation inconsistent with the current context. In this way, we can avoid the mistake of "大酱" in the place where famous figures such as Deng Xiaoping should have appeared in the previous political articles.<br />
<br />
===2.1.3Mistranslation of compound words===<br />
Multi class words refer to a word with two or more parts of speech, also known as the same word and different classes.<br />
<br />
original text Translation by Youdao reference translation<br />
<br />
1998年江泽民主席曾经访问日本, 1998年の江沢民国家主席の日本訪問し、 1998年、江沢民総書記が日本を訪問し、かつ<br />
<br />
同已故小渊首相签署了联合宣言。 かつて同じ故小渕首相が署名した共同宣言 亡くなられた小渕総理と宣言に調印されました<br />
<br />
Chinese "同" has two parts of speech: prepositions and conjunctions: "故" has many parts of speech, such as nouns, verbs, adjectives and conjunctions. This directly affects the judgment of the machine at the source language level. The word "同" in the above table is used as a conjunction to indicate the other party of a common act. "故" is used as a verb with the semantic meaning of "死亡", which is a modifier of "小渊首相". The machine regards "同" as an adjective and "故" as a noun "原因", which leads to confusion in the structure and unclear semantics of the translation. Different from the polysemy problem, multi category words have at least two parts of speech, and there is often not only one meaning under each part of speech. In this regard, software R & D personnel should fully consider the existence of multi category words, so that the translation machine can distinguish the meaning of words on the basis of marking the part of speech, so as to select the translation through the context and the components of the word in the sentence. Of course, the realization of this function is difficult, and we need to give full play to the wisdom of R & D personnel.<br />
<br />
===2.2 Syntactic mistranslation in Chinese Japanese machine translation===<br />
===2.2.1Mistranslation of tenses===<br />
original text：History is written by the people, and all achievements are attributed to the people. <br />
<br />
Translation by Youdao：歴史は人民が書いたものであり、すべての成果は人民のためである。<br />
<br />
reference translation：歴史は人民が綴っていくものであり、すべての成果は人民に帰することとなります。<br />
<br />
The original sentence meaning is "历史是人民书写的历史" or "历史是人民书写的东西". When translated into Japanese, due to the influence of Japanese language habits, the formal noun "もの" should be supplemented accordingly. In this sentence, both the machine and the interpreter have translated correctly. However, the machine's recognition of "的" is biased, resulting in tense translation errors. The past, present and future will become "history", and this is a continuous action. The form translated as "~ ていく" not only reflects that the action is a continuous action, but also conforms to the tense and semantic information of the original text. In addition, the machine's treatment of the preposition "于" is also inappropriate. In the original text, "人民" is the recipient of action, not the target language. As the most obvious feature of isolated language, function words in Chinese play an important role in semantic expression, and their translation should be regarded as a focus of machine translation research.<br />
<br />
original text：李克强同志是十六届中共中央政治局常委,其他五位同志都是十六届中共中央政治局委员。<br />
<br />
Translation by Youdao：李克強総理は第16代中国共産党中央政治局常務委員であり、他の５人の同志はいずれも16期の中国共産党中央政治局委員である。<br />
<br />
reference translation：李克強同志は第16期中国共産党中央政治局常務委員を務め、他の５人は第16期中共中央政治局委員を務めました。<br />
<br />
Judging the verb "是" in the original text plays its most basic positive role, but due to the complexity of Chinese language, "是" often can not be completely transformed into the form of "だ / である" in the process of translation. This sentence is a judgment of what has happened. Compared with the 19th session, the 18th session has become history. This is an implicit temporal information. It is very difficult for machines without human brain to recognize this implicit information. "中共中央政治局常委" is the abbreviation of "中共中央政治局常务委员会委员" and it is a kind of position. In Japanese, often use it with verbs such as "務める","担当する",etc. The translator adopts the past tense of "務める", which conforms to the expression habit of Japanese and deals with the problem of tense at the same time. However, machine translation only mechanically translates this sentence into judgment sentence, and fails to correctly deal with the past information implied by the word "十八届".<br />
<br />
===2.2.2Mistranslation of honorifics===<br />
Honorific language is a language means to show respect to the listener. Different from Japanese, there is no grammatical category of honorifics in Chinese. There is no specific fixed grammatical form to express honorifics, self modesty and politeness. Instead, specific words such as "您", "请" and "劳驾" are used to express all kinds of respect or self modesty. <br />
<br />
Original text translation by Youdao reference translation<br />
<br />
女士们，先生们，同志们，朋友们 さんたち、先生たち、同志たち、お友达さん ご列席の皆さん<br />
<br />
谢谢大家！ ありがとうございます！ ご清聴ありがとうございました。<br />
<br />
これはどうされますか。 这是怎么回事呢? 您将如何解决这一问题？<br />
<br />
こうした問題をどうお考えでしょうか。 我们会如何考虑这些问题呢？ 您如何看待这一问题？<br />
<br />
For example, for the processing of "女士们，先生们，同志们，朋友们", the machine makes the translation correspond to the original one by one based on the principle of unchanged format, but there are errors in semantic communication and pragmatic habits. When speaking on formal occasions, Chinese expression tends to be comprehensive and detailed, as well as the address of the audience. In contrast, Japanese usually uses general terms such as "皆様", "ご臨席の皆さん" or "代表団の方々" as the opening remarks. In terms of Japanese Chinese translation, the machine also failed to recognize the usage of Japanese honorifics such as "さ れ る" and "お考え". It can be seen that Chinese Japanese machine translation has a low ability to deal with honorific expressions. The occasion is more formal. A good translation should not only be fluent in meaning, but also conform to the expression habits of the target language and match the current translation environment.<br />
<br />
===Conclusion===<br />
In the process of discussing the Mistranslation of machine translation, this paper mainly cites the Mistranslation of Youdao translator. When I studied the problem of machine translation mistranslation, I also used a large number of translation software such as Google and Baidu for parallel comparison, and found that these translation software also had similar problems with Youdao translator. For example, the "新世界新未来" in Chinese ABAC structural phrases is translated by Baidu as "新し世界の新しい未来", which, like Youdao, is not translated in the form of parallel phrases; Google online translation translates the word "“全心全意" into a completely wrong "全面的に"; The original sentence "我吃了很多亏" in the Mistranslation of the unique expression in the language is translated by Google online as "私はたくさんの損失を食べました". Because the "吃" and "亏" in the original text are not closely adjacent, the machine can not recognize this echo and mistakenly treats "亏" as a kind of food, so the machine translates the predicate as "食べました"; Japanese "こうした問題をどう考えでしょうか", Google's online translation is "你如何看待这些问题?", "你" tone can not reflect the tone of Japanese honorific, while Baidu translates as "你怎么想这样的问题呢?" Although the meaning can be understood, it is an irregular spoken language. Google and Baidu also made the same mistakes as Youdao in the translation of proper nouns. For example, they translated "十七届(中共中央政治局常委)”" into "第17回..." .In short, similar mistranslations of Youdao are also common in Baidu and Google. Due to space constraints, they will not be listed one by one here.<br />
<br />
Mr. Liu Yongquan, Institute of language, Chinese Academy of Social Sciences (1997) It has been pointed out that machine translation is a linguistic problem in the final analysis. Although corpus based machine translation does not require a lot of linguistic knowledge, language expression is ever-changing. Machine translation based solely on statistical ideas can not avoid the translation quality problems caused by the lack of language rules. The following is based on the analysis of lexical, syntactic and other mistranslations From the perspective of the characteristics of the source language, this paper summarizes some difficulties of Chinese and Japanese in machine translation.<br />
<br />
(1) The difficulties of Chinese in machine translation <br />
<br />
Chinese is a typical isolated language. The relationship between words needs to be reflected by word order and function words. We should pay attention to the transformation of function words such as "的", "在", "向" and "了". Some special verbs, such as "是", "做" and "作", are widely used. How to translate on the basis of conforming to Japanese pragmatic habits and expressions is a difficulty in machine translation research. In terms of word order, Chinese is basically "subject→predicate→object". At the same time, Chinese pays attention to parataxis, and there is no need to be clear in expression with meaningful cohesion, which increases the difficulty of Chinese Japanese machine translation. When there are multiple verbs or modifier components in complex long sentences, machine translation usually can not accurately divide the components of the sentence, resulting in the result that the translation is completely unreadable. <br />
<br />
(2) Difficulties of Japanese in machine translation <br />
<br />
Both Chinese and Japanese languages use Chinese characters, and most machines produce the target language through the corresponding translation of Chinese characters. However, pseudonyms in Japanese play an important role in judging the part of speech and meaning, and can not only recognize Chinese characters and judge the structure and semantics of sentences. Japanese vocabulary is composed of Chinese vocabulary, inherent vocabulary and foreign vocabulary. Among them, the first two have a great impact on Chinese Japanese machine translation. For example "提出" is translated as "提出する" or "打ち出す"; and "重视" is translated as "重視する" or "大切にする". Japanese is an adhesive language, which contains a lot of "けど", "が" and "けれども" Some of them are transitional and progressive structures, but there are also some sequential expressions that do not need translation, and these translation software often can not accurately grasp them.<br />
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===References===<br />
[1] Navroz Kaur Kahlon;Williamjeet Singh.Machine translation from text to sign language: a systematic review[J].Universal Access in the Information Society,2021(prepublish):1-35.<br />
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[2] Cao Qianyu;Hao Hanmei;Ahmed Syed Hassan.A Chaotic Neural Network Model for English Machine Translation Based on Big Data Analysis[J].Computational Intelligence and Neuroscience,2021,2021:3274326-3274326.<br />
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[3]Hwang Yongkeun;Kim Yanghoon;Jung Kyomin.Context-Aware Neural Machine Translation for Korean Honorific Expressions[J].Electronics,2021,10(13):1589-1589.<br />
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[4]Zakaryia Almahasees.Analysing English-Arabic Machine Translation:Google Translate, Microsoft Translator and Sakhr,2021.<br />
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[5]Machine learning in translation[J].Nature Biomedical Engineering,2021,5(6):485-486.<br />
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[6]Shaimaa Marzouk.An in-depth analysis of the individual impact of controlled language rules on machine translation output: a mixed-methods approach[J].Machine Translation,2021(prepublish):1-37.<br />
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[8]Xu Xueyuan.Machine learning-based prediction of urban soil environment and corpus translation teaching[J].Arabian Journal of Geosciences,2021,14(11). <br />
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[10]呂寅秋.機械翻訳の言語規則と伝統文法との相違点.日本学研究.1996(00):21-22 <br />
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[11]刘君.基于语料库的中日同形词词义用法对比及其日中机器翻译研究【D】.广西大学.2014(03) <br />
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[12]崔倩倩.机器翻译错误与译后编辑策略研究【D】.北京外国语大学.2019(09) <br />
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[13]张义.机器翻译的译文分析【D】.西安外国语大学.2019(10) <br />
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[14]张琳婧.在线机器翻译中日翻译错误原因及对策【D】.山西大学.2019(02)<br />
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[15]王丹.基于机器翻译的专利文本译后编辑对策研究【D】.大连理工大学.2020(06)<br />
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[16]杨晓琨.日中机器翻译中的前编辑规则与效果验证【D】.大连理工大学.2020(06)<br />
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<br />
Networking Linking<br />
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http://www.elecfans.com/rengongzhineng/692245.html<br />
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https://baike.baidu.com/item/%E6%9C%BA%E5%99%A8%E7%BF%BB%E8%AF%91/411793<br />
<br />
=13 陈湘琼Chen Xiangqiong（Study on Post-editing from the Perspective of Functional Equivalence Theory ）=<br />
[[Machine_Trans_EN_13]]<br />
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=14 Bi bi Nadia（Machine Translation a Challenge for Human Translators)=<br />
[[Machine_Trans_EN_14]]<br />
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===Abstract===<br />
Machine translation is a big obstacle in the way of Human translators or interpretors although it is quick and less time consuming.people are trying to get translation of their target language using through source language.For this purpose they are using digital apps like Google translation Google translation does not give accurate and exact interpretation.Google translator is translates word to word translate that doesn't clarifies it's true and actual meaning.On the other hand human translators can give exact and accurate translation ,they take care of grammatical errors , diction and sentence structure.They clarify the purpose of target language through using source language.<br />
<br />
===Keywords===<br />
Descriptive translation, academic, interdiscipline, comparative literature, localization,Translatology,school of thought,translation , studies, linguistics, corresponding.<br />
<br />
===Body of article===<br />
Translation is the process of reworking text from one language into another to maintain the original message and communication. But, like everything else, there are different methods of translation, and they vary in form and function. What is translation? The term has become widely used among knowledge transfer researchers and practitioners, especially in the fields of health and health care. In a landmark review, Jonathan Lomas began to argue that 'The tasks… may be defined as to establish and maintain links between researchers and their audience, via the appropriate translation of research findings' (Lomas 1997, p 4). In 2004, the World Health Organization's World Report on Knowledge for Better Health suggested that 'One of the key contributions of research to health systems is the translation of knowledge into actions' (WHO 2004, p 33 and p 100). By 2006, special issues of WHO's Bulletin as well as the journals Evaluation and the Health Professions and the Journal of Continuing Education in the Health Professions were dedicated to translation.But what does 'translation' mean? It may be a new word for an old problem, meaning nothing more than 'transfer'. The rapidly emerging field of 'translational medicine' seems to take translation to mean generally what transfer might have meant, that is the transmission of knowledge and evidence 'from bench to bedside'. As one commentator has observed, "Translational medicine" as a fashionable term is being increasingly used to describe the wish of biomedical researchers to ultimately help patients' (Wehling 2008, abstract). <br />
Semantic uncertainty persists, not least because of the different interests of scientists, clinicians, patients and commercial firms (Littman et al 2007): 'Translational research means different things to different people, but it seems important to almost everyone' (Woolf 2008, p 211).<br />
In related fields, such as public health (Armstrong et al 2006), 'translation' seems to signify dissatisfaction with 'transfer'. It wants to move away from thinking of knowledge transfer as a form of technology transfer or dissemination, rejecting if only by implication its mechanistic assumptions and its model of linear messaging from A to B. But still, what does it signify?<br />
<br />
===Why translation?===<br />
'Translation' indicates a closer attention to the problem of shared meaning and how it might be developed. It seems to represent some new epistemological lubricant, facilitating the dissemination of texts and the application and use of the knowledge and information they in. Simply, translation might be the key to transfer. And yet, when we stop to think, we are more ambivalent. What is translated often seems somehow inferior, not real or original. Note how readily commentators reach for the idea that things might be 'lost in translation'. Knowing at a distance – made in and mediated by translation - makes for incomplete renditions, blurred images, partial truths. So what might 'translation' really mean? The purpose of this paper is to set out, for policy makers and practitioners, the theoretical and conceptual resources translation holds and seems to represent. In doing so, it explores understandings of translation in the fields of literature and linguistics and in the sociology of science and technology. It begins by setting out just why this idea of translation should make immediate, intuitive sense in relation to research, policy and practice.<br />
1translation in research, policy and practice research as translation<br />
Research often entails translation from one language to another: where data is collected from more than one ethnic group, for example, or where the language of the researcher is other than that of the research subject. It may draw on a secondary literature or source documents written in different languages, and may be published and disseminated in languages other than the one in which it is first written up.<br />
2In a different way, to conduct an interview is to ask for an account of experience and its meanings, but it is also to construct and translate that experience in terms defined at least in part by the researcher. In representing what is said, transcripts then select data, usually excluding significant gesture and eye-contact, for example. Often, certain characteristics of speech-acts (such as hesitations) will be edited out. In turn, the format of the transcript shapes the analytic use the researcher may make of it. The basis of research 'findings', then, is an artefact, a transcript or translation, not an original interaction (Ochs 1979, Barnes, Bloor and Henry 1996, Ross 2009).<br />
3In this way, the researcher recasts aspects of his or her problem or topic in new, scientific form: 'All researchers "translate" the experiences of others' (Temple 1997, p 609). Research is invariably conducted in a sort of 'metalanguage' (Hantrais and Ager 1985): the research process can be conceived as one of successive translations (from theoretical formulation to operationalization, transcription, interpretation and dissemination). Theorization is a process of reciprocal back and forth between theory and fact, in which conceptions of each are revised in order that one fit the other (Baldamus 1974).<br />
4 It is a kind of translation: a rereading, re-use, re-application or re-representation of what we know in new terms (Turner 1980). Referencing, too, is an act of translation, a form of appropriation and incorporation of one text by another (Gilbert 1977).policy as translation Each of the fields covered by the paper is diverse and ill-defined, and there is no intention here to provide a comprehensive account of any them. Sources have been chosen for their relevance: main references are cited in the text, and additional sources listed in footnotes.<br />
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2For a brief introduction to the technical issues involved in social research in more than one language, see Birbili (2000). For translation issues in survey research and question design in general, see Ervin and Bower (1952) and Deutscher (1968); on translating survey research instruments (in this instance health-related quality of life measures), Bowden and Fox-Rushby (2003). On the use of translators and interpreters, see Temple (1997) and Jentsch (1998).<br />
3For an interesting discussion of this problem, see Bourdieu (1999), esp pp 621-626, 'The risks of writing'. <br />
===Difference between machine translation and human translators===<br />
Few people disagree on the differences between the two, but many argue over the quality of the translations. How accurate are machine translations? How reliable are human translations? Some say machine translation produces near-perfect translations while others are adamant that translations are incomprehensible and cause more problems than they solve. Results will, of course, vary depending on the source and target languages, the machine translation service used (e.g. Google Translate), and the complexity of the original text.<br />
Machine translation, love it or hate it, is here to stay. In fact, the machine translation market is growing at such a fast pace that it is predicted to reach $980 million by 2022.<br />
===Machine translation: the pros and cons===<br />
The advantages of machine translation generally come down to two factors: it’s faster and cheaper. The downside to this is the standard of translation can be anywhere from inaccurate, to incomprehensible, and potentially dangerous (more on that shortly).<br />
==The advantages of machine translation==<br />
Many free tools are readily available (Google Translate, Skype Translator, etc.)<br />
Quick turnaround time <br />
You can translate between multiple languages using one tool<br />
Translation technology is constantly improving<br />
The disadvantages of machine translation<br />
Level of accuracy can be very low <br />
Accuracy is also very inconsistent across different languages<br />
==Machines can't translate context ==<br />
Mistakes are sometimes costly <br />
Sometimes translation simply doesn’t work<br />
The most important thing to consider with any kind of translation is the cost of potential mistakes. Translating instructions for medical equipment, aviation manuals, legal documents and many other kinds of content require 100% accuracy. In such cases, mistakes can cost lives, huge amounts of money and irreparable damage to your company’s image. So choose carefully!<br />
Human translation: the pros and cons<br />
Human translation essentially switches the table in terms of pros and cons. A higher standard of accuracy comes at the price of longer turnaround times and higher costs. What you have to decide is whether that initial investment outweighs the potential cost of mistakes. Alternatively, whether mistakes simply aren’t an option, like the cases we looked at in the previous section.<br />
<br />
==The advantages of human translation ==<br />
It’s a translator’s job to ensure the highest accuracy<br />
Humans can interpret context and capture the same meaning, rather than simply translating words<br />
Human translators can review their work and provide a quality process<br />
Humans can interpret the creative use of language, e.g. puns, metaphors, slogans, etc.<br />
Professional translators understand the idiomatic differences between their languages<br />
Humans can spot pieces of content where literal translation isn’t possible and find the most suitable alternative<br />
The disadvantages of human translation<br />
Turnaround time is longer <br />
Translators rarely work for free <br />
Unless you use a translation agency, with access to thousands of translators, you’re limited to the languages any one translator can work with<br />
Simply put, human translation is your best option when accuracy is even remotely important. Other considerations to make are the complexity of your source material and the two languages you’re translating between – both of which can render machines pretty useless.<br />
<br />
When to use machine and human translation<br />
The truth is, the debate over machine vs human translation is an unnecessary distraction. What we should really be talking about is when to use these two different types of translation services, because they both serve a very valid purpose.<br />
<br />
Examples of when to use machine translation<br />
When you have a large bulk of content to translate and the general meaning is enough<br />
When your translation never reaches the final audience, e.g. you’re translating a resource as research for another piece of content<br />
Translating documents for internal use within a company, provided 100% accuracy isn’t needed<br />
To partially translate large chunks of content for a human translator to improve upon<br />
Examples of when to use human translation<br />
When accuracy is important<br />
Most cases where your translated content is received by a consumer audience<br />
When you have a duty of care to provide accurate translations (e.g. legal documents, product instructions, medical guidelines or health and safety content)<br />
When translating marketing material or other texts for creative language uses.<br />
<br />
types of machine translation.<br />
<br />
What is Machine Translation? Rule Based Machine Translation vs. Statistical Machine Translation. Machine translation (MT) is automated translation. It is the process by which computer software is used to translate a text from one natural language (such as English) to another (such as Spanish).<br />
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To process any translation, human or automated, the meaning of a text in the original (source) language must be fully restored in the target language, i.e. the translation. While on the surface this seems straightforward, it is far more complex. Translation is not a mere word-for-word substitution. A translator must interpret and analyze all of the elements in the text and know how each word may influence another. This requires extensive expertise in grammar, syntax (sentence structure), semantics (meanings), etc., in the source and target languages, as well as familiarity with each local region.<br />
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Human and machine translation each have their share of challenges. For example, no two individual translators can produce identical translations of the same text in the same language pair, and it may take several rounds of revisions to meet customer satisfaction. But the greater challenge lies in how machine translation can produce publishable quality translations.<br />
<br />
Rule-Based Machine Translation Technology<br />
Rule-based machine translation relies on countless built-in linguistic rules and millions of bilingual dictionaries for each language pair.<br />
The software parses text and creates a transitional representation from which the text in the target language is generated. This process requires extensive lexicons with morphological, syntactic, and semantic information, and large sets of rules. The software uses these complex rule sets and then transfers the grammatical structure of the source language into the target language.<br />
Translations are built on gigantic dictionaries and sophisticated linguistic rules. Users can improve the out-of-the-box translation quality by adding their terminology into the translation process. They create user-defined dictionaries which override the system’s default settings.<br />
In most cases, there are two steps: an initial investment that significantly increases the quality at a limited cost, and an ongoing investment to increase quality incrementally. While rule-based MT brings companies to the quality threshold and beyond, the quality improvement process may be long and expensive.<br />
<br />
Statistical Machine Translation Technology<br />
Statistical machine translation utilizes statistical translation models whose parameters stem from the analysis of monolingual and bilingual corpora. Building statistical translation models is a quick process, but the technology relies heavily on existing multilingual corpora. A minimum of 2 million words for a specific domain and even more for general language are required. Theoretically it is possible to reach the quality threshold but most companies do not have such large amounts of existing multilingual corpora to build the necessary translation models. Additionally, statistical machine translation is CPU intensive and requires an extensive hardware configuration to run translation models for average performance levels.<br />
<br />
Rule-Based MT vs. Statistical MT<br />
Rule-based MT provides good out-of-domain quality and is by nature predictable. Dictionary-based customization guarantees improved quality and compliance with corporate terminology. But translation results may lack the fluency readers expect. In terms of investment, the customization cycle needed to reach the quality threshold can be long and costly. The performance is high even on standard hardware.<br />
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Statistical MT provides good quality when large and qualified corpora are available. The translation is fluent, meaning it reads well and therefore meets user expectations. However, the translation is neither predictable nor consistent. Training from good corpora is automated and cheaper. But training on general language corpora, meaning text other than the specified domain, is poor. Furthermore, statistical MT requires significant hardware to build and manage large translation models.<br />
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Rule-Based MT Statistical MT<br />
+ Consistent and predictable quality – Unpredictable translation quality<br />
+ Out-of-domain translation quality – Poor out-of-domain quality<br />
+ Knows grammatical rules – Does not know grammar <br />
+ High performance and robustness – High CPU and disk space requirements<br />
+ Consistency between versions – Inconsistency between versions <br />
– Lack of fluency + Good fluency<br />
– Hard to handle exceptions to rules + Good for catching exceptions to rules <br />
– High development and customization costs + Rapid and cost-effective development costs provided the required corpus exists<br />
Given the overall requirements, there is a clear need for a third approach through which users would reach better translation quality and high performance (similar to rule-based MT), with less investment (similar to statistical MT).<br />
Post-Edited Machine Translation (PEMT)<br />
Often, PEMT is used to bridge the gap between the speed of machine translation and the quality of human translation, as translators review, edit and improve machine-translated texts. PEMT services cost more than plain machine translations but less than 100% human translation, especially since the post-editors don’t have to be fluently bilingual—they just have to be skilled proofreaders with some experience in the language and target region.<br />
Successful translation is about more than just the words, which is why we advocate for not just human translation by skilled linguists, but for translation by people deeply familiar with the cultures they’re writing for. Life experience, study and the knowledge that only comes from living in a geographic region can make the difference between words that are understandable and language that is capable of having real, positive impact. <br />
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PacTranz<br />
The HUGE list of 51 translation types, methods and techniques<br />
Upper section of infographic of 51 common types of translation classified in 4 broad categoriesThere are a bewildering number of different types of translation.<br />
So we’ve identified the 51 types you’re most likely to come across, and explain exactly what each one means.<br />
This includes all the main translation methods, techniques, strategies, procedures and areas of specialisation.<br />
It’s our way of helping you make sense of the many different kinds of translation – and deciding which ones are right for you.<br />
Don’t miss our free summary pdf download later in the article!<br />
The 51 types of translation we’ve identified fall neatly into four distinct categories.<br />
Translation Category A: 15 types of translation based on the technical field or subject area of the text<br />
Icons representing 15 types of translation categorised by the technical field or subject area of the textTranslation companies often define the various kinds of translation they provide according to the subject area of the text.<br />
This is a useful way of classifying translation types because specialist texts normally require translators with specialist knowledge.<br />
Here are the most common types you’re like to come across in this category.<br />
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1. General Translation<br />
What is it?<br />
The translation of non-specialised text. That is, text that we can all understand without needing specialist knowledge in some area.<br />
The text may still contain some technical terms and jargon, but these will either be widely understood, or easily researched.<br />
What this means<br />
The implication is that you don’t need someone with specialist knowledge for this type of translation – any professional translator can handle them.<br />
Translators who only do this kind of translation (don’t have a specialist field) are sometimes referred to as ‘generalist’ or ‘general purpose’ translators.<br />
Examples<br />
Most business correspondence, website content, company and product/service info, non-technical reports.<br />
Most of the rest of the translation types in this Category do require specialist translators.<br />
Check out our video on 13 types of translation requiring special translator expertise:<br />
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2. Technical Translation<br />
What is it?<br />
We use the term “technical translation” in two different ways:<br />
Broad meaning: any translation where the translator needs specialist knowledge in some domain or area.<br />
This definition would include almost all the translation types described in this section.<br />
Narrow meaning: limited to the translation of engineering (in all its forms), IT and industrial texts.<br />
This narrower meaning would exclude legal, financial and medical translations for example, where these would be included in the broader definition.<br />
What this means<br />
Technical translations require knowledge of the specialist field or domain of the text.<br />
That’s because without it translators won’t completely understand the text and its implications. And this is essential if we want a fully accurate and appropriate translation.Good to know Many technical translation projects also have a typesetting/dtp requirement. Be sure your translation provider can handle this component, and that you’ve allowed for it in your project costings and time frames.<br />
Examples<br />
Manuals, specialist reports, product brochures<br />
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3. Scientific Translation<br />
What is it?<br />
The translation of scientific research or documents relating to it.<br />
What this means<br />
These texts invariably contain domain-specific terminology, and often involve cutting edge research.<br />
So it’s imperative the translator has the necessary knowledge of the field to fully understand the text. That’s why scientific translators are typically either experts in the field who have turned to translation, or professionally qualified translators who also have qualifications and/or experience in that domain.<br />
On occasion the translator may have to consult either with the author or other domain experts to fully comprehend the material and so translate it appropriately.<br />
Examples<br />
Research papers, journal articles, experiment/trial results<br />
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4. Medical Translation<br />
What is it?<br />
The translation of healthcare, medical product, pharmaceutical and biotechnology materials.<br />
Medical translation is a very broad term covering a wide variety of specialist areas and materials – everything from patient information to regulatory, marketing and technical documents.<br />
As a result, this translation type has numerous potential sub-categories – ‘medical device translations’ and ‘clinical trial translations’, for example.<br />
What this means<br />
As with any text, the translators need to fully understand the materials they’re translating. That means sound knowledge of medical terminology and they’ll often also need specific subject-matter expertise.<br />
Good to know<br />
Many countries have specific requirements governing the translation of medical device and pharmaceutical documentation. This includes both your client-facing and product-related materials.<br />
Examples<br />
Medical reports, product instructions, labeling, clinical trial documentation<br />
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5. Financial Translation<br />
What is it?<br />
In broad terms, the translation of banking, stock exchange, forex, financing and financial reporting documents.<br />
However, the term is generally used only for the more technical of these documents that require translators with knowledge of the field.<br />
Any competent translator could translate a bank statement, for example, so that wouldn’t typically be considered a financial translation.<br />
What this means<br />
You need translators with domain expertise to correctly understand and translate the financial terminology in these texts.<br />
Examples<br />
Company accounts, annual reports, fund or product prospectuses, audit reports, IPO documentation<br />
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6. Economic Translations<br />
What is it?<br />
1. Sometimes used as a synonym for financial translations.<br />
2. Other times used somewhat loosely to refer to any area of economic activity – so combining business/commercial, financial and some types of technical translations.<br />
3. More narrowly, the translation of documents relating specifically to the economy and the field of economics.<br />
What this means<br />
As always, you need translators with the relevant expertise and knowledge for this type of translation.<br />
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7. Legal Translation<br />
What is it?<br />
The translation of documents relating to the law and legal process.<br />
What this means<br />
Legal texts require translators with a legal background.<br />
That’s because without it, a translator may not:<br />
– fully understand the legal concepts<br />
– write in legal style<br />
– understand the differences between legal systems, and how best to translate concepts that don’t correspond.<br />
And we need all that to produce professional quality legal translations – translations that are accurate, terminologically correct and stylistically appropriate.<br />
Examples<br />
Contracts, legal reports, court judgments, expert opinions, legislation<br />
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8. Juridical Translation<br />
What is it?<br />
1. Generally used as a synonym for legal translations.<br />
2. Alternatively, can refer to translations requiring some form of legal verification, certification or notarization that is common in many jurisdictions.<br />
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9. Judicial Translation<br />
What is it?<br />
1. Most commonly a synonym for legal translations.<br />
2. Rarely, used to refer specifically to the translation of court proceeding documentation – so judgments, minutes, testimonies, etc. <br />
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10. Patent Translation<br />
What is it?<br />
The translation of intellectual property and patent-related documents.<br />
Key features<br />
Patents have a specific structure, established terminology and a requirement for complete consistency throughout – read more on this here. These are key aspects to patent translations that translators need to get right.<br />
In addition, subject matter can be highly technical.<br />
What this means<br />
You need translators who have been trained in the specific requirements for translating patent documents. And with the domain expertise needed to handle any technical content.<br />
Examples<br />
Patent specifications, prior art documents, oppositions, opinions<br />
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11. Literary Translation<br />
What is it?<br />
The translation of literary works – novels, short stories, plays, essays, poems.<br />
Key features<br />
Literary translation is widely regarded as the most difficult form of translation.<br />
That’s because it involves much more than simply conveying all meaning in an appropriate style. The translator’s challenge is to also reproduce the character, subtlety and impact of the original – the essence of what makes that work unique.<br />
This is a monumental task, and why it’s often said that the translation of a literary work should be a literary work in its own right.<br />
What this means<br />
Literary translators must be talented wordsmiths with exceptional creative writing skills.<br />
Because few translators have this skillset, you should only consider dedicated literary translators for this type of translation.<br />
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12. Commercial Translation<br />
What is it?<br />
The translation of documents relating to the world of business.<br />
This is a very generic, wide-reaching translation type. It includes other more specialised forms of translation – legal, financial and technical, for example. And all types of more general business documentation.<br />
Also, some documents will require familiarity with business jargon and an ability to write in that style.<br />
What this means<br />
Different translators will be required for different document types – specialists should handle materials involving technical and specialist fields, whereas generalist translators can translate non-specialist materials.<br />
Examples<br />
Business correspondence, reports, marketing and promotional materials, sales proposals<br />
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13. Business Translations<br />
What is it?<br />
A synonym for Commercial Translations.<br />
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14. Administrative Translations<br />
What is it?<br />
The translation of business management and administration documents.<br />
So it’s a subset of business / commercial translations.<br />
What this means<br />
The implication is these documents will include business jargon and ‘management speak’, so require a translator familiar with, and practised at, writing in that style.<br />
Examples<br />
Management reports and proposals<br />
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15. Marketing Translations<br />
What is it?<br />
The translation of advertising, marketing and promotional materials.<br />
This is a subset of business or commercial translations.<br />
Key features<br />
Marketing copy is designed to have a specific impact on the audience – to appeal and persuade.<br />
So the translated copy must do this too.<br />
But a direct translation will seldom achieve this – so translators need to adapt their wording to produce the impact the text is seeking.<br />
And sometimes a completely new message might be needed – see transcreation in our next category of translation types.<br />
What this means<br />
Marketing translations require translators who are skilled writers with a flair for producing persuasive, impactful copy.<br />
As relatively few translators have these skills, engaging the right translator is key.<br />
Good to know<br />
This type of translation often comes with a typesetting or dtp requirement – particularly for adverts, posters, brochures, etc.<br />
Its best for your translation provider to handle this component. That’s because multilingual typesetters understand the design and aesthetic conventions in other languages/cultures. And these are essential to ensure your materials have the desired impact and appeal in your target markets.<br />
Examples<br />
Advertising, brochures, some website/social media text.<br />
Translation Category B: 14 types of translation based on the end product or use of the translation<br />
This category is all about how the translation is going to be used or the end product that’s produced.<br />
Most of these types involve either adapting or processing a completed translation in some way, or converting or incorporating it into another program or format.<br />
You’ll see that some are very specialised, and complex.<br />
It’s another way translation providers refer to the range of services they provide.<br />
Check out our video of the most specialised of these types of translation:<br />
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16. Document Translations<br />
What is it?<br />
The translation of documents of all sorts.<br />
Here the translation itself is the end product and needs no further processing beyond standard formatting and layout.<br />
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17. Text Translations<br />
What is it?<br />
A synonym for document translation.<br />
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18. Certified Translations<br />
What is it?<br />
A translation with some form of certification.<br />
Key features<br />
The certification can take many forms. It can be a statement by the translation company, signed and dated, and optionally with their company seal. Or a similar certification by the translator.<br />
The exact format and wording will depend on what clients and authorities require – here’s an example.<br />
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19. Official Translations<br />
What is it?<br />
1. Generally used as a synonym for certified translations.<br />
2. Can also refer to the translation of ‘official’ documents issued by the authorities in a foreign country. These will almost always need to be certified.<br />
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20. Software Localisation<br />
What is it?<br />
Adapting software for another language/culture.<br />
Key features<br />
The goal of software localisation is not just to make the program or product available in other languages. It’s also about ensuring the user experience in those languages is as natural and effective as possible.<br />
Translating the user interface, messaging, documentation, etc is a major part of the process.<br />
Also key is a customisation process to ensure everything matches the conventions, norms and expectations of the target cultures.<br />
Adjusting time, date and currency formats are examples of simple customisations. Others might involve adapting symbols, graphics, colours and even concepts and ideas.<br />
Localisation is often preceded by internationalisation – a review process to ensure the software is optimally designed to handle other languages.<br />
And it’s almost always followed by thorough testing – to ensure all text is in the correct place and fits the space, and that everything makes sense, functions as intended and is culturally appropriate.<br />
Localisation is often abbreviated to L10N, internationalisation to i18n.<br />
What this means<br />
Software localisation is a specialised kind of translation, and you should always engage a company that specialises in it.<br />
They’ll have the systems, tools, personnel and experience needed to achieve top quality outcomes for your product.<br />
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21. Game Localisation<br />
What is it?<br />
Adapting games for other languages and markets.<br />
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It’s a subset of software localisation.<br />
Key features<br />
The goal of game localisation is to provide an engaging and fun gaming experience for speakers of other languages.<br />
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It involves translating all text and recording any required foreign language audio.<br />
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But also adapting anything that would clash with the target culture’s customs, sensibilities and regulations.<br />
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For example, content involving alcohol, violence or gambling may either be censored or inappropriate in the target market.<br />
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And at a more basic level, anything that makes users feel uncomfortable or awkward will detract from their experience and thus the success of the game in that market.<br />
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So portions of the game may have to be removed, added to or re-worked.<br />
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Game localisation involves at least the steps of translation, adaptation, integrating the translations and adaptations into the game, and testing.<br />
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What this means<br />
Game localisation is a very specialised type of translation best left to those with specific expertise and experience in this area.<br />
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22. Multimedia Localisation<br />
What is it?<br />
Adapting multimedia for other languages and cultures.<br />
<br />
Multimedia refers to any material that combines visual, audio and/or interactive elements. So videos and movies, on-line presentations, e-Learning courses, etc.<br />
Key features<br />
Anything a user can see or hear may need localising.<br />
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That means the audio and any text appearing on screen or in images and animations.<br />
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Plus it can mean reviewing and adapting the visuals and/or script if these aren’t suitable for the target culture.<br />
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The localisation process will typical involve:<br />
– Translation<br />
– Modifying the translation for cultural reasons and/or to meet technical requirements<br />
– Producing the other language versions<br />
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Audio output may be voice-overs, dubbing or subtitling.<br />
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And output for visuals can involve re-creating elements, or supplying the translated text for the designers/engineers to incorporate.<br />
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What this means<br />
Multimedia localisation projects vary hugely, and it’s essential your translation providers have the specific expertise needed for your materials.<br />
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23. Script Translations<br />
What is it?<br />
Preparing the text of recorded material for recording in other languages.<br />
Key features<br />
There are several issues with script translation.<br />
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One is that translations typically end up longer than the original script. So voicing the translation would take up more space/time on the video than the original language.<br />
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Sometimes that space will be available and this will be OK.<br />
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But generally it won’t be. So the translation has to be edited back until it can be comfortably voiced within the time available on the video.<br />
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Another challenge is the translation may have to synchronise with specific actions, animations or text on screen.<br />
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Also, some scripts also deal with technical subject areas involving specialist technical terminology.<br />
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Finally, some scripts may be very culture-specific – featuring humour, customs or activities that won’t work well in another language. Here the script, and sometimes also the associated visuals, may need to be adjusted before beginning the translation process.<br />
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It goes without saying that a script translation must be done well. If it’s not, there’ll be problems producing a good foreign language audio, which will compromise the effectiveness of the video.<br />
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Translators typically work from a time-coded transcript. This is the original script marked to show the time available for each section of the translation.<br />
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What this means<br />
There are several potential pitfalls in script translations. So it’s vital your translation provider is practiced at this type of translation and able to handle any technical content.<br />
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24. Voice-over and Dubbing Projects<br />
What is it?<br />
Translation and recording of scripts in other languages.<br />
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Voice-overs vs dubbing<br />
There is a technical difference.<br />
A voice-over adds a new track to the production, dubbing replaces an existing one.<br />
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<br />
Key features<br />
These projects involve two parts:<br />
– a script translation (as described above), and<br />
– producing the audio<br />
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So they involve the combined efforts of translators and voice artists.<br />
The task for the voice artist is to produce a high quality read. That’s one that matches the style, tone and richness of the original.<br />
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Often each section of the new audio will need to be the same length as the original.<br />
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But sometimes the segments will need to be shorter – for example where the voice-over lags the original by a second or two. This is common in interviews etc, where the original voice is heard initially then drops out.<br />
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The most difficult form of dubbing is lip-syncing – where the new audio needs to synchronise with the original speaker’s lip movements, gestures and actions.<br />
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Lip-syncing requires an exceptionally skilled voice talent and considerable time spent rehearsing and fine tuning the translation.<br />
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What this means<br />
You need to use experienced professionals every step of the way in this type of project.<br />
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That’s to ensure firstly that your foreign-language scripts are first class, then that the voicing is of high professional standard.<br />
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Anything less will mean your foreign language versions will be way less effective and appealing to your target audience.<br />
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25. Subtitle Translations<br />
What is it?<br />
Producing foreign language captions for sub or surtitles.<br />
Key features<br />
The goal with subtitling is to produce captions that viewers can comfortably read in the time available and still follow what’s happening on the video.<br />
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To achieve this, languages have “rules” governing the number of characters per line and the minimum time each subtitle should display.<br />
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Sticking to these guidelines is essential if your subtitles are to be effective.<br />
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But this is no easy task – it requires simple language, short words, and a very succinct style. Translators will spend considerable time mulling over and re-working their translation to get it just right.<br />
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Most subtitle translators use specialised software that will output the captions in the format sound engineers need for incorporation into the video.<br />
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What this means<br />
As with other specialised types of translation, you should only use translators with specific expertise and experience in subtitling.<br />
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26. Website Localisation<br />
What is it?<br />
The translation and adapting of relevant content on a website to best suit the target language and culture.<br />
<br />
Note: Many providers use the term website translation as a synonym for localisation. Strictly speaking though, translation is just one part of localisation.<br />
Key features<br />
<br />
Not all pages on a website may need to be localised – clients should review their content to identify what’s relevant for the other language versions.<br />
Some content may need specialist translators – legal and technical pages for example.<br />
There may also be videos, linked documents, and text or captions in graphics to translate.<br />
Adaptation can mean changing date, time, currency and number formats, units of measure, etc.<br />
But also images, colours and even the overall site design and style if these won’t have the desired impact in the target culture.<br />
Translated files can be supplied in a wide range of formats – translators usually coordinate output with the site webmasters.<br />
New language versions are normally thoroughly reviewed and tested before going live to confirm everything is displaying correctly, works as intended and is cultural appropriate.<br />
What this means<br />
The first step should be to review your content and identify what needs to be translated. This might lead you to modify some pages for the foreign language versions.<br />
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In choosing your translation providers be sure they can:<br />
– handle any technical or legal content,<br />
– provide your webmaster with the file types they want.<br />
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And you should always get your translators to systematically review the foreign language versions before going live.<br />
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27. Transcreation<br />
What is it?<br />
Adapting a message to elicit the same emotional response in another language and culture.<br />
Translation is all about conveying the message or meaning of a text in another language. But sometimes that message or meaning won’t have the desired effect in the target culture.<br />
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This is where transcreation comes in. Transcreation creates a new message that will get the desired emotional response in that culture, while preserving the style and tone of the original.<br />
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So it’s a sort of creative translation – which is where the word comes from, a combination of ‘translation’ and ‘creation’.<br />
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At one level transcreation may be as simple as choosing an appropriate idiom to convey the same intent in the target language – something translators do all the time.<br />
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But mostly the term is used to refer to adapting key advertising and marketing messaging. Which requires copywriting skills, cultural awareness and an excellent knowledge of the target market.<br />
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Who does it?<br />
Some translation companies have suitably skilled personnel and offer transcreation services.<br />
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Often though it’s done in the target country by specialist copywriters or an advertising or marketing agency – particularly for significant campaigns and to establish a brand in the target marketplace.<br />
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What this means<br />
Most general marketing and promotional texts won’t need transcreation – they can be handled by a translator with excellent creative writing skills.<br />
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But slogans, by-lines, advertising copy and branding statements often do.<br />
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Whether you should opt for a translation company or an in-market agency will depend on the nature and importance of the material, and of course your budget.<br />
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28. Audio Translations<br />
What is it?<br />
Broad meaning: the translation of any type of recorded material into another language.<br />
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More commonly: the translation of a foreign language video or audio recording into your own language. So this is where you want to know and document what a recording says.<br />
Key features<br />
The first challenge with audio translations is it’s often impossible to pick up every word that’s said. That’s because audio quality, speech clarity and speaking speed can all vary enormously.<br />
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It’s also a mentally challenging task to listen to an audio and translate it directly into another language. It’s easy to miss a word or an aspect of meaning.<br />
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So best practice is to first transcribe the audio (type up exactly what is said in the language it is spoken in), then translate that transcription.<br />
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However, this is time consuming and therefore costly, and there are other options if lesser precision is acceptable.<br />
<br />
What this means<br />
It’s best to discuss your requirements for this kind of translation with your translation provider. They’ll be able to suggest the best translation process for your needs.<br />
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Examples<br />
Interviews, product videos, police recordings, social media videos.<br />
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29. Translations with DTP<br />
What is it?<br />
Translation incorporated into graphic design files.multilingual dtp example in the form of a Rubik's Cube with foreign text on each square<br />
Key features<br />
Graphic design programs are used by professional designers and graphic artists to combine text and images to create brochures, books, posters, packaging, etc.<br />
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Translation plus dtp projects involve 3 steps – translation, typesetting, output.<br />
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The typesetting component requires specific expertise and resources – software and fonts, typesetting know-how, an appreciation of foreign language display conventions and aesthetics.<br />
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What this means<br />
Make sure your translation company has the required multilingual typesetting/desktop publishing expertise whenever you’re translating a document created in a graphic design program.<br />
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Translation Category C: 13 types of translation based on the translation method employed<br />
This category has two sub-groups:<br />
– the practical methods translation providers use to produce their translations, and<br />
– the translation strategies/methods identified and discussed within academia.<br />
<br />
The translation methods translation providers use<br />
There are 4 main methods used in the translation industry today. We have an overview of each below, but for more detail, including when to use each one, see our comprehensive blog article.<br />
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Or watch our video.<br />
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Important: If you’re a client you need to understand these 4 methods – choose the wrong one and the translation you end up with may not meet your needs!<br />
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30. Machine Translation (MT)<br />
What is it?<br />
A translation produced entirely by a software program with no human intervention.<br />
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A widely used, and free, example is Google Translate. And there are also commercial MT engines, generally tailored to specific domains, languages and/or clients.<br />
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Pros and cons<br />
There are two limitations to MT:<br />
– they make mistakes (incorrect translations), and<br />
– quality of wording is patchy (some parts good, others unnatural or even nonsensical)<br />
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On they positive side they are virtually instantaneous and many are free.<br />
<br />
Best suited for:<br />
Getting the general idea of what a text says.<br />
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This method should never be relied on when high accuracy and/or good quality wording is needed.<br />
<br />
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31. Machine Translation plus Human Editing (PEMT)<br />
What is it?<br />
A machine translation subsequently edited by a human translator or editor (often called Post-editing Machine Translation = PEMT).<br />
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The editing process is designed to rectify some of the deficiencies of a machine translation.<br />
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This process can take different forms, with different desired outcomes. Probably most common is a ‘light editing’ process where the editor ensures the text is understandable, without trying to fix quality of expression.<br />
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Pros and cons<br />
This method won’t necessarily eliminate all translation mistakes. That’s because the program may have chosen a wrong word (meaning) that wasn’t obvious to the editor.<br />
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And wording won’t generally be as good as a professional human translator would produce.<br />
<br />
Its advantage is it’s generally quicker and a little cheaper than a full translation by a professional translator.<br />
<br />
Best suited for:<br />
Translations for information purposes only.<br />
<br />
Again, this method shouldn’t be used when full accuracy and/or consistent, natural wording is needed.<br />
<br />
<br />
<br />
32. Human Translation<br />
What is it?<br />
Translation by a professional human translator.<br />
Pros and cons<br />
Professional translators should produce translations that are fully accurate and well-worded.<br />
<br />
That said, there is always the possibility of ‘human error’, which is why translation companies like us typically offer an additional review process – see next method.<br />
<br />
This method will take a little longer and likely cost more than the PEMT method.<br />
<br />
Best suited for:<br />
Most if not all translation purposes.<br />
<br />
<br />
<br />
33. Human Translation + Revision<br />
What is it?<br />
A human translation with an additional review by a second translator.<br />
<br />
The review is essentially a safety check – designed to pick up any translation errors and refine wording if need be.<br />
<br />
Pros and cons<br />
This produces the highest level of translation quality.<br />
<br />
It’s also the most expensive of the 4 methods, and takes the longest.<br />
<br />
Best suited for:<br />
All translation purposes.<br />
<br />
Gearwheel with 5 practical translation methods written on the teeth <br />
There’s also one other common term used by practitioners and academics alike to describe a type (method) of translation:<br />
<br />
34. Computer-Assisted Translation (CAT)<br />
What is it?<br />
A human translator using computer tools to aid the translation process.<br />
<br />
Key features<br />
Virtually all translators use such tools these days.<br />
<br />
The most prevalent tool is Translation Memory (TM) software. This creates a database of previous translations that can be accessed for future work.<br />
<br />
TM software is particularly useful when dealing with repeated and closely-matching text, and for ensuring consistency of terminology. For certain projects it can speed up the translation process.<br />
<br />
<br />
<br />
The translation methods described by academia<br />
A great deal has been written within academia analysing how human translators go about their craft.<br />
<br />
Seminal has been the work of Newmark, and the following methods of translation attributed to him are widely discussed in the literature.Gearwheel with Newmark's 8 translation methods written on the teeth <br />
These methods are approaches and strategies for translating the text as a whole, not techniques for handling smaller text units, which we discuss in our final translation category.<br />
<br />
<br />
<br />
35. Word-for-word Translation<br />
This method translates each word into the other language using its most common meaning and keeping the word order of the original language.<br />
<br />
So the translator deliberately ignores context and target language grammar and syntax.<br />
<br />
Its main purpose is to help understand the source language structure and word use.<br />
<br />
Often the translation will be placed below the original text to aid comparison.<br />
<br />
<br />
<br />
36. Literal Translation<br />
Words are again translated independently using their most common meanings and out of context, but word order changed to the closest acceptable target language grammatical structure to the original.<br />
<br />
Its main suggested purpose is to help someone read the original text.<br />
<br />
<br />
<br />
37. Faithful Translation<br />
Faithful translation focuses on the intention of the author and seeks to convey the precise meaning of the original text.<br />
<br />
It uses correct target language structures, but structure is less important than meaning.<br />
<br />
<br />
<br />
38. Semantic Translation<br />
Semantic translation is also author-focused and seeks to convey the exact meaning.<br />
<br />
Where it differs from faithful translation is that it places equal emphasis on aesthetics, ie the ‘sounds’ of the text – repetition, word play, assonance, etc.<br />
<br />
In this method form is as important as meaning as it seeks to “recreate the precise flavour and tone of the original” (Newmark).slide showing definition of semantic translation as a translation method<br />
<br />
39. Communicative Translation<br />
Seeks to communicate the message and meaning of the text in a natural and easily understood way.<br />
<br />
It’s described as reader-focused, seeking to produce the same effect on the reader as the original text.<br />
<br />
A good comparison of Communicative and Semantic translation can be found here.<br />
<br />
40. Free Translation<br />
Here conveying the meaning and effect of the original are all important.<br />
<br />
There are no constraints on grammatical form or word choice to achieve this.<br />
<br />
Often the translation will paraphrase, so may be of markedly different length to the original.<br />
<br />
41. Adaptation<br />
Mainly used for poetry and plays, this method involves re-writing the text where the translation would otherwise lack the same resonance and impact on the audience.<br />
<br />
Themes, storylines and characters will generally be retained, but cultural references, acts and situations adapted to relevant target culture ones.<br />
<br />
So this is effectively a re-creation of the work for the target culture.<br />
<br />
42. Idiomatic Translation<br />
Reproduces the meaning or message of the text using idioms and colloquial expressions and language wherever possible.<br />
<br />
The goal is to produce a translation with language that is as natural as possible.<br />
<br />
Translation Category D: 9 types of translation based on the translation technique used<br />
These translation types are specific strategies, techniques and procedures for dealing with short chunks of text – generally words or phrases.<br />
<br />
They’re often thought of as techniques for solving translation problems.<br />
<br />
They differ from the translation methods of the previous category which deal with the text as a whole.<br />
9 translation techniques as titles of books in a bookcase<br />
<br />
43. Borrowing<br />
What is it?<br />
Using a word or phrase from the original text unchanged in the translation.<br />
<br />
Key features<br />
With this procedure we don’t translate the word or phrase at all – we simply ‘borrow’ it from the source language.<br />
<br />
Borrowing is a very common strategy across languages. Initially, borrowed words seem clearly ‘foreign’, but as they become more familiar, they can lose that ‘foreignness’.<br />
<br />
Translators use this technique:<br />
– when it’s the best word to use – either because it has become the standard, or it’s the most precise term, or<br />
– for stylist effect – borrowings can add a prestigious or scholarly flavour.<br />
<br />
Borrowed words or phrases are often italicised in English.<br />
<br />
Examples of borrowings in English<br />
grand prix, kindergarten, tango, perestroika, barista, sampan, karaoke, tofu<br />
<br />
<br />
44. Transliteration<br />
What is it?<br />
Reproducing the approximate sounds of a name or term from a language with a different writing system.<br />
<br />
Key features<br />
In English we use the Roman (Latin) alphabet in common with many other languages including almost all European languages.<br />
<br />
Other writing systems include Arabic, Cyrillic, Chinese, Japanese, Korean, Thai, and the Indian languages.<br />
<br />
Transliteration from such systems into the Roman alphabet is also called romanisation.<br />
<br />
There are accepted systems for how individual letters/sounds should be romanised from most other languages – there are three common systems for Chinese, for example.<br />
<br />
English borrowings from languages using non-Roman writing systems also require transliteration – perestroika, sampan, karaoke, tofu are examples from the above list.<br />
<br />
Translators mostly use transliteration as a procedure for translating proper names.<br />
<br />
Examples<br />
毛泽东 Mao Tse-tung or Mao Zedong<br />
Владимир Путин Vladimir Putin<br />
서울 Seoul<br />
ភ្នំពេញ Phnom Penh<br />
<br />
45. Calque or Loan Translation<br />
What is it?<br />
A literal translation of a foreign word or phrase to create a new term with the same meaning in the target language.<br />
<br />
So a calque is a borrowing with translation if you like. The new term may be changed slightly to reflect target language structures.<br />
<br />
Examples<br />
German ‘Kindergarten’ has been calqued as детский сад in Russian, literally ‘children garden’ in both languages.<br />
<br />
Chinese 洗腦 ‘wash’ + ‘brain’ is the origin of ‘brainwash’ in English.<br />
<br />
English skyscraper is calqued as gratte-ciel in French and rascacielos in Spanish, literally ‘scratches sky’ in both languages.<br />
<br />
46. Word-for-word translation<br />
What is it?<br />
A literal translation that is natural and correct in the target language.<br />
<br />
Alternative names are ‘literal translation’ or ‘metaphrase’.<br />
<br />
Note: this technique is different to the translation method of the same name, which does not produce correct and natural text and has a different purpose.<br />
<br />
Key features<br />
This translation strategy will only work between languages that have very similar grammatical structures.<br />
<br />
And even then, only sometimes.<br />
<br />
For example, standard word order in Turkish is Subject-Object-Verb whereas in English it’s Subject-Verb-Object. So a literal translation between these two will seldom work:<br />
– Yusuf elmayı yedi is literally ‘Joseph the apple ate’.<br />
<br />
When word-for-word translations don’t produce natural and correct text, translators resort to some of the other techniques described below.<br />
Examples<br />
French ‘Quelle heure est-il?’ works into English as ‘What time is it?’.<br />
<br />
Russian ‘Oн хочет что-нибудь поесть’ is ‘He wants something to eat’.<br />
<br />
47. Transposition<br />
What is it?<br />
Translation with a change of grammatical structure.<br />
<br />
This technique gives the translation more natural wording and/or makes it grammatically correct.<br />
<br />
Examples<br />
A change in word order:<br />
Our Turkish example Yusuf elmayı yedi (literally ‘Joseph the apple ate’) –> Joseph ate the apple.<br />
<br />
Spanish La Casa Blanca (literally ‘The House White’) –> The White House<br />
<br />
A change in grammatical category:<br />
German Er hört gerne Musik (literally ‘he listens gladly [to] music’)<br />
= subject pronoun + verb + adverb + noun<br />
becomes Spanish Le gusta escuchar música (literally ‘[to] him [it] pleases to listen [to] music’)<br />
= indirect object pronoun + verb + infinitive + noun<br />
and English He likes listening to music<br />
= subject pronoun + verb + gerund + noun.<br />
<br />
48. Modulation<br />
What is it?<br />
Translation with a change of focus or point of view in the target language.<br />
<br />
This technique makes the translation more idiomatic – how people would normally say it in the language.<br />
<br />
Examples<br />
English talks of the ‘top floor’ of a building, French the dernier étage = last floor. ‘Last floor’ would be unnatural in English, so too ‘top floor’ in French.<br />
<br />
German uses the term Lebensgefahr (literally ‘danger to life’) where in English we’d be more likely to say ‘risk of death’.<br />
In English we’d say ‘I dropped the key’, in Spanish se me cayó la llave, literally ‘the key fell from me’. The English perspective is that I did something (dropped the key), whereas in Spanish something happened to me – I’m the recipient of the action.<br />
<br />
49. Equivalence or Reformulation<br />
What is it?<br />
Translating the underlying concept or meaning using a totally different expression.<br />
<br />
This technique is widely used when translating idioms and proverbs.<br />
<br />
And it’s common in titles and advertising slogans.<br />
<br />
It’s a common strategy where a direct translation either wouldn’t make sense or wouldn’t resonate in the same way.<br />
<br />
Examples<br />
Here are some equivalents of the English saying “Pigs may fly”, meaning something will never happen, or “you’re being unrealistic” (Source):<br />
– Thai: ชาติหน้าตอนบ่าย ๆ – literally, ‘One afternoon in your next reincarnation’<br />
– French: Quand les poules auront des dents – literally, ‘When hens have teeth’<br />
– Russian, Когда рак на горе свистнет – literally, ‘When a lobster whistles on top of a mountain’<br />
– Dutch, Als de koeien op het ijs dansen – literally, ‘When the cows dance on the ice’<br />
– Chinese: 除非太陽從西邊出來！– literally, ‘Only if the sun rises in the west’<br />
<br />
50. Adaptation<br />
What is it?<br />
A translation that substitutes a culturally-specific reference with something that’s more relevant or meaningful in the target language.<br />
<br />
It’s also known as cultural substitution or cultural equivalence.<br />
<br />
It’s a useful technique when a reference wouldn’t be understood at all, or the associated nuances or connotations would be lost in the target language.<br />
<br />
Note: the translation method of the same name is a similar concept but applied to the text as a whole.<br />
<br />
Examples<br />
Different cultures celebrate different coming of age birthdays – 21 in many cultures, 20, 15 or 16 in others. A translator might consider changing the age to the target culture custom where the coming of age implications were important in the original text.<br />
Animals have different connotations across languages and cultures. Owls for example are associated with wisdom in English, but are a bad omen to Vietnamese. A translator might want to remove or amend an animal reference where this would create a different image in the target language.<br />
<br />
51. Compensation<br />
What is it?<br />
A meaning or nuance that can’t be directly translated is expressed in another way in the text.<br />
Example<br />
Many languages have ways of expressing social status (honorifics) encoded into their grammatical structures.<br />
<br />
So you can convey different levels of respect, politeness, humility, etc simply by choosing different forms of words or grammatical elements.<br />
But these nuances will be lost when translating into languages that don’t have these structures.<br />
Then translating into languages that don’t have these structures<br />
Then translating into languages that don’t have these structures.<br />
<br />
===Conclusion ===<br />
<br />
In the light of above mentioned facts and figures here by we would say that machine translation is challenge for human translators because it can reduces the wokload of translation but can't give accurate and exact translation of the traget language.It can be less reliable than human translation..</div>Chen Huinihttps://bou.de/u/index.php?title=Machine_translation&diff=129783Machine translation2021-12-08T01:59:03Z<p>Chen Huini: /* 2.1.1Mistranslation of proper nouns */</p>
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<div>'''Machine Translation - A challenge or a chance for human translators?'''<br />
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[[Machine_translation|Overview Page of Machine Translation]]<br />
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30 Chapters（0/30)<br />
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[[Machine_Trans_EN_1]] [[Machine_Trans_EN_2]] [[Machine_Trans_EN_3]] [[Machine_Trans_EN_4]] [[Machine_Trans_EN_5]] [[Machine_Trans_EN_6]] [[Machine_Trans_EN_7]] [[Machine_Trans_EN_8]] [[Machine_Trans_EN_9]] [[Machine_Trans_EN_10]] [[Machine_Trans_EN_11]] [[Machine_Trans_EN_12]] [[Machine_Trans_EN_13]] [[Machine_Trans_EN_14]] [[Machine_Trans_EN_15]] [[Machine_Trans_EN_16]] [[Machine_Trans_EN_17]] [[Machine_Trans_EN_18]] [[Machine_Trans_EN_19]] [[Machine_Trans_EN_20]] [[Machine_Trans_EN_21]] [[Machine_Trans_EN_22]] [[Machine_Trans_EN_23]] [[Machine_Trans_EN_24]] [[Machine_Trans_EN_25]] [[Machine_Trans_EN_26]] [[Machine_Trans_EN_27]] [[Machine_Trans_EN_28]] [[Machine_Trans_EN_29]] [[Machine_Trans_EN_30]] ...<br />
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=1 卫怡雯(A Comparison Between the Quality of Machine Translation and Human Translation——A Case Study of the Application of artificial intelligence in Sports Events)=<br />
[[Machine_Trans_EN_1]]<br />
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=2 吴映红（The Introduction of Machine Translation)= <br />
[[Machine_Trans_EN_2]]<br />
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=3 肖毅瑶(On the Realm Advantages And Symbiotic Development of Machine Translation And Huamn Translation)=<br />
[[Machine_Trans_EN_3]]<br />
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=4 王李菲 （Comparison Between Neural Machine Translation of Netease and Traditional Human Translation—A Case Study of The Economist Articles)= <br />
[[Machine_Trans_EN_4]]<br />
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=6 徐敏赟(Machine Translation Based on Neural Network --Challenge or Chance?)=<br />
[[Machine_Trans_EN_6]]<br />
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=7 颜莉莉(一带一路背景下人工智能与翻译人才的培养)=<br />
[[Machine_Trans_EN_7]]<br />
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===Abstract===<br />
In the era of artificial intelligence, artificial intelligence has been applied to various fields. In the field of translation, traditional translation models can no longer meet the rapid development and updating of the information age. The development of machine translation has brought structural changes to the language service industry, which poses challenges to the cultivation of translation talents. Under the background of "The Belt and Road initiative", translation talents have higher and higher requirements on translation literacy. Artificial intelligence and translation technology are used to reform the training mode of translation talents, so as to better serve the development of The Times. This paper mainly explores the cultivation of artificial intelligence and translation talents under the background of the Belt and Road Initiative. The cultivation of translation talents is moving towards comprehensive cultivation of talents. On the contrary, artificial intelligence and machine translation can also be used to improve the teaching mode and teaching content, so as to win together in cooperation.<br />
===Key words===<br />
Artificial intelligence,Machine translation,cultivation of translation talents,"The Belt and Road initiative"<br />
===题目===<br />
一带一路背景下人工智能与翻译人才的培养<br />
<br />
===摘要===<br />
进入人工智能时代，人工智能被应用于各个领域。在翻译领域，传统的翻译模式已无法满足信息化时代的飞速发展和更新，机器翻译的发展给语言服务行业带来了结构性改变，这对翻译人才的培养提出了挑战。“一带一路”背景下，对翻译人才的翻译素养要求越来越高，利用人工智能和翻译技术对翻译人才培养模式进行革新，更好为时代发展服务。本文主要探究在一带一路背景下人工智能和翻译人才培养，翻译人才的培养过程中正向对人才的综合性培养，反之也可以利用人工智能和机器翻译完善教学模式和教学内容，在合作中共赢。<br />
===关键词===<br />
人工智能；机器翻译；翻译人才培养；一带一路<br />
<br />
===1. Introduction===<br />
With the development of science and technology in China, artificial intelligence has also been greatly improved, and related technologies have been applied to various fields, such as the use of intelligent robots to deliver food to quarantined people during the epidemic, which has made people's lives more convenient. The most controversial and widely discussed issue is machine translation. Before the emergence of machine translation, translation was generally dominated by human translation, including translation and interpretation, which was divided into simultaneous interpretation and hand transmission, etc. It takes a lot of time and energy to cultivate a translation talent. However, nowadays, the era is developing rapidly and information is updated rapidly. As a translation talent, it is necessary to constantly update its knowledge reserve to keep up with the pace of The Times. The emergence of machine translation has also posed challenges to translation talents and the training of translation talents. Although machine translation had some problems in the early stage, it is now constantly improving its functions. In the context of the belt and Road Initiative, both machine translation and human translation are facing difficulties. Regardless of whether human translation is still needed, what is more important at present is how to train translators to adapt to difficulties and promote the cooperation between human translation and machine translation.<br />
<br />
===2.Development status of machine translation in the era of artificial intelligence ===<br />
With the development of AI technology, machine translation has made great progress and has been applied to people's lives. For example, more and more tourists choose to download translation software when traveling abroad, which makes machine translation take an absolute advantage in daily email reply and other translation activities that do not require high accuracy. The translation software commonly used by netizens include Google Translation, Baidu Translation, Youdao Translation, IFly.com Translation, etc. Even wechat and other chat software can also carry out instant Translation into English. Some companies have also launched translation pens, translation machines and other equipment, which enables even native speakers to rely on machine translation to carry out basic communication with other Chinese people.<br />
But so far, machine translation still faces huge problems. Although machine translation has made great progress, it is highly dependent on corpus and other big data matching. It does not reach the thinking level of human brain, and cannot deal with the problem of translation differences caused by culture and religion. In addition, many minor languages cannot be translated by machine due to lack of corpus.<br />
<br />
What's more, most of the corpus is about developed countries such as Britain and France, and most of the corpus is about diplomacy, politics, science and technology, etc., while there are very few about nationality, culture, religion, etc.<br />
<br />
In addition, machine translation can only be used for daily communication at present. If it involves important occasions such as large conferences and international affairs, it is impossible to risk using machine translation for translation work. Professional translators are required to carry out translation work. So machine translation still has a long way to go.<br />
<br />
===3.Challenges in the training of translation talents in universities===<br />
The cultivation of translators is targeted at the market. Professors Zhu Yifan and Guan Xinchao from the School of Foreign Languages at Shanghai Jiao Tong University believe that the cultivation of translators can be divided into four types: high-end translators and interpreters, senior translators and researchers, compound translators and applied translators.<br />
<br />
From their names, it can be seen that high-end translators and interpreters and senior translators and researchers talents have high requirements on the knowledge and quality of interpreters, because they have to face the changing international situation, and have to deal with all kinds of sensitive relations and political related content, they should have flexible cross-cultural communication skills. In addition, for literature, sociology and humanities academic works, it is not only necessary to translate their content, but also to understand their essence. Therefore, translators should not only have humanistic feelings, but also need to have a deep understanding of Chinese and western culture.<br />
<br />
However, there is not much demand for this kind of translation in the society. Such high-level translation requirements are not needed in daily life and work. The greatest demand is for compound translators, which means that they should master knowledge in a specific field while mastering a foreign language. For example, compound translators in the financial field should not only be good at foreign languages, but also master financial knowledge, including professional terms, special expressions and sentence patterns.<br />
<br />
Now we say that machine translation can replace human translation should refer to the field of compound translation talents. Although AI technology has enabled machine translation to participate in creation, it does not mean that compound translation talents will be replaced by machines. The complexity of language and the flexible cross-cultural awareness required in communication make it impossible for machine translation to completely replace human translation.<br />
<br />
The last type of applied translation talents are mostly involved in the general text without too much technical content and few professional terms, so it is easy to be replaced by machine translation.<br />
<br />
Therefore, the author thinks that what universities are facing at present is not only how to train translation talents to cope with the development of machine translation, but to consider the application of machine translation in the process of training translation talents to achieve human-machine integration, so as to better complete the translation work.<br />
<br />
===4.The Language environment and opportunities and challenges of the Belt and Road initiative===<br />
During visits to Central and Southeast Asian countries in September and October 2013, Chinese President Xi Jinping put forward the major initiative of jointly building the Silk Road Economic Belt and the 21st Century Maritime Silk Road. And began to be abbreviated as the Belt and Road Initiative.<br />
<br />
According to the Vision and Actions for Jointly Building silk Road Economic Belt and 21st Century Maritime Silk Road, the Silk Road Economic Belt focuses on connecting China, Central Asia, Russia and Europe (the Baltic Sea). From China to the Persian Gulf and the Mediterranean Sea via Central and West Asia; China to Southeast Asia, South Asia, Indian Ocean. The focus of the 21st Century Maritime Silk Road is to stretch from China's coastal ports to Europe, through the South China Sea and the Indian Ocean. From China's coastal ports across the South China Sea to the South Pacific.<br />
<br />
The Belt and Road "construction is comply with the world multi-polarization and economic globalization, cultural diversity, the initiative of social informatization tide, drive along the countries achieve economic policy coordination, to carry out a wider range, higher level, the deeper regional cooperation and jointly create open, inclusive and balanced, pratt &whitney regional economic cooperation framework.<br />
<br />
====4.1The language environment of the Belt and Road====<br />
The "Belt and Road" involves a wide range of countries and regions, and their languages and cultures are very complex. How to make good use of language, do a good job in translation services, actively spread Chinese culture to the world, strengthen the ability of discourse, and tell Chinese stories well, the first thing to do is to understand the language situation of the countries along the "Belt and Road".<br />
<br />
=====4.1.1The most common language in countries along the "Belt and Road" =====<br />
<br />
There are a wide variety of languages spoken in 65 countries along the Belt and Road, involving nine language families. However, The status of English as the first language in the world is undeniable. Most of the countries participating in the Belt and Road are developing countries, and many of them speak English as their first foreign language. Especially in southeast Asian and South Asian countries, English plays an important role in foreign communication, whether as the official language or the first foreign language. Besides English, more than 100 million people speak Russian, Hindi, Bengali, Arabic and other major languages in the "Belt and Road" countries. It can also be seen that a common feature of languages in countries along the "Belt and Road" is the popularization of English education. English is widely used in international politics, economy, culture, education, science and technology, playing the role of the most important language in the world.<br />
<br />
=====4.1.2The complex language conditions of countries along the "Belt and Road" =====<br />
<br />
The languages spoken in countries along the Belt and Road involve nine major language families and almost all the world's religious types. Differences in religious beliefs also result in differences in culture, customs and social values behind languages. The languages of some countries along the belt and Road have also been influenced by historical and realistic factors, such as colonization, internal division and immigration. <br />
<br />
India, for example, has no national language, but more than 20 official languages. India is a multi-ethnic country, a total of more than 100 people, one of the most obvious difference between nation and nation is the language problem. Therefore, according to the difference of language, India divides different ethnic groups into different states, big and small. Ethnic groups that use the same language are divided into one state. If there are two languages in a state, the state is divided into two parts. And Indian languages differ not only in word order but also in the way they are written. In India, for example, Hindi is spoken by the largest number of people in the north, with about 700 million speakers and 530 million as their first language. It is written in The Hindu language and belongs to the Indo-European language family. Telugu in the east is spoken by about 95 million people and 81.13 million as their first language. It is written in Telugu, which belongs to the Dravidian language family and is quite different from Hindi. As a result, a parliamentary session in India requires dozens of interpreters. <br />
<br />
These factors cannot be ignored in the process of translation, from language communication to cultural understanding, from text to thought exchange, through the bridge of language to truly connect the people, so as to avoid misreading and misunderstanding caused by differences in language and national conditions.<br />
<br />
====4.2 Opportunities and challenges of the "Belt and Road" ====<br />
With the promotion of the Belt and Road Initiative, there has been an unprecedented boom in translation. In the previous translation boom in China, most of the foreign languages were translated into Chinese, and most of the foreign cultures were imported into China. However, this time, in the context of the "Belt and Road" initiative, translating Chinese into foreign languages has become an important task for translators. As is known to all, there are many different kinds of "One Belt And One Road" along the national language and culture is complex, the service "area" construction has become a factor in Chinese translation talents training mode reform, one of the foreign language universities have action, many colleges and universities to establish the "area" all the way along the country's small language major, as a result, "One Belt And One Road" initiative to promote, It has brought unprecedented opportunities for human translation. The cultivation of diversified translation talents and the cultivation of translation talents in small languages is an urgent problem to be solved in China. The cultivation of translation talents cannot be completed overnight, and the state needs to reform the training mode of translation talents from the perspective of language strategic development. Only in this way can we meet the new demand for human translation under the new situation of the belt and Road Initiative.<br />
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For a long time, the traditional orientation of translation curriculum and training goal in colleges and universities is to train translation teachers and translators in need of society through translation theory and practice and literary translation practice, which cannot meet the needs of society. Since 2007, in order to meet the needs of the socialist market economy for application-oriented high-level professionals, the Academic Degrees Committee of The State Council approved the establishment of Master of Translation and Interpreting (MTI for short). After joining the pilot program of MTI, more and more universities are reforming the curriculum and training mode of master of Translation in order to cultivate translators who meet the needs of the society.<br />
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Language is an important carrier of culture, and translation is an important link for exporting culture. The quality of translation output also reflects the cultural soft power of a country. With the rise of China, more and more people are interested in Chinese culture, and the number of Chinese learners keeps increasing. Under the background of "One Belt and One Road", excellent translators are urgently needed to spread Chinese culture. With the promotion of "One Belt and One Road" Initiative, the number of other countries learning mutual learning and cultural exchanges with China has increased unprecedeningly, bringing vigorous opportunities for the spread of Chinese culture. Translation talents who understand small languages and multi-lingual translators are needed. They should not only use language to convey information, but also use language as a lubricant for communication.<br />
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===5.Training translation talents from the perspective of machine translation===<br />
Under the prevailing environment of machine translation, it poses a great challenge to the cultivation of translation talents. According to the current situation, translation needs and the shortage of translation talents, colleges and universities should reform and innovate the existing training programs for translation talents in terms of the quality of translation talents, the reform of training mode and the use of artificial intelligence. Based on the obtained data and literature, the author discusses how to train translation talents in the perspective of machine translation from the following aspects.<br />
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====5.1 Quality requirements for translation talents ====<br />
Zhong Weihe and Murray made a more detailed and profound discussion on translator's literacy, believing that "translators should not only be proficient in two languages, but also have extensive cultural and encyclopedic knowledge and relevant professional knowledge; Master a variety of translation skills, a lot of translation practice; Have a clear translator role awareness, good professional ethics, practical and enterprising style of work, conscious team spirit and calm psychological quality ". According to the collected data, the author will elaborate the requirements for translation talents from four aspects: language literacy, humanistic literacy, translation ability and innovation ability.<br />
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The first is language literacy, which is the most basic and important requirement. MAO Dun pointed out that "mastery of mother tongue and target language are the foundation of translation". A solid foundation of bilingual skills is the basic skills of translators. Poor language proficiency seems to be a common problem among students majoring in translation and interpreting. Many translation diseases are caused by poor Chinese foundation. As part of going global, the belt and Road initiative is to tell Chinese culture and Chinese stories, which requires translators to be able to use both languages flexibly. Therefore, the first problem that colleges and universities face to solve is to improve the language level of foreign language learners.<br />
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The second is humanistic literacy. Humanistic literacy is mainly manifested by a good command of politics, economy, history, literature and other knowledge, which is particularly important for interpreters. In addition, cross-cultural communication cannot be ignored. In the process of communicating with foreigners or translating, translators often encounter the first cross-cultural contradiction. Cross-culture refers to having a full and correct understanding of cultural phenomena, customs and habits that differ or conflict with the national culture, and accepting and adapting to them in an inclusive manner on this basis. So the interpreter can first fully understand and master the national conditions and culture of the target country, which is particularly important in the "Belt and Road". There are more than 60 countries along the "Belt and Road", and it takes a lot of energy to master their national conditions and culture.<br />
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The third is translation ability. We should distinguish between translation ability and language ability. Translation ability is actually a system of knowledge and skills necessary for translation, the core of which is conversion ability. First of all, it reflects the ability to use tools to assist translation, such as computer application, translation technology and so on. In addition, interpreters should have enough healthy psychological quality and good professional quality. In terms of translation ability, the current training model of translation talents is inadequate.<br />
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The last one is innovation. The cultivation of learners' thinking ability is the key to translation teaching and the cultivation of thoughtful translators should be the connotation of translation teaching. Therefore, the interpreter is not only a translation tool, which is no different from machine translation. More importantly, it is necessary to explore translation with thoughts, have a sense of lifelong learning and innovation consciousness. Translators must constantly innovate themselves, learn new knowledge, and strive to seek reform and innovation. Many colleges and universities should also consciously cultivate students' innovation ability and broaden their thinking and vision.<br />
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====5.2 The reform of college curriculum setting====<br />
First, we will further reform the curriculum of colleges and universities. Add economics, law and engineering to the curriculum, these contents in the "belt and Road".<br />
"One Road" is very important in the construction. According to the author's personal experience, the most typical problem of foreign language majors in colleges and universities is the single learning of foreign languages. More professional foreign language colleges and universities will add some literature courses and national conditions courses of the language target countries. Obviously, whether foreign language graduates are engaged in translation work or not, these knowledge is not enough. Of course, great reforms have been carried out in foreign language teaching, such as combining foreign language with finance, law, diplomacy and so on, and taking the way of minor training foreign language majors.<br />
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Domestic enterprises with a high degree of internationalization attach great importance to translation. Their translation research includes cutting-edge theoretical and applied research, involving machine translation, natural language processing and AI theory, algorithm and model. With such a foundation, enterprises can solve problems by themselves, such as embedding automatic translation functions in mobile phones. International enterprises not only do technical translation, but also deal with all forms of translation and localization in society. At present, translation teaching in most colleges and universities is still in the early mode, and it is an objective fact that it is divorced from the workplace and has a gap with the needs of enterprises.<br />
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Second, we should adjust and strengthen the construction of second foreign language teaching for foreign language majors. In the 1980s, our country was in urgent need of Russian translation. At that time, students majoring in English could translate microelectronic product manuals and related business documents in English and Russian at the same time after learning Russian for half a year. The mutual conversion between English and Russian played a great role in practice. According to the author, in the Graduate Institute of Interpretation and Translation of Beijing Foreign Studies University a very few students majored in multiple languages at the graduate level, that is, they majored in minor languages at the undergraduate level and were admitted to the Graduate Institute of Interpretation and Translation in English. Their training mode is to study English in the Graduate Institute of Interpretation and Translation for two years and the third year in the corresponding department of the undergraduate major. Such training mode in my opinion is a bigger model, cost It's more difficult for students. <br />
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In addition, there is a great disparity in the development of second foreign language teaching in colleges and universities, and the overall level is not high enough. Part of the second foreign language university foreign language professional may still be too much focus in languages such as German, French and Japanese, should as far as possible, considering the need of the construction of the "region", like Croatia, Serbia, Turkish, Hungarian, Italian, Indonesian, Albanian, these are the countries along the "area" the language of the two countries, Colleges and universities should encourage the teaching of a second foreign language.<br />
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Third, the teaching of translation technology should be strengthened. Traditional translation teaching teaches translation skills, such as the translation of words, sentences, texts and figures of speech. Translation technology refers to a series of practical workplace technologies with computer-aided translation software and translation project management as the core, which can greatly improve translation efficiency. However, due to the relative lack of translation technology teachers and equipment in colleges and universities, there is a disconnect between talent training and the requirements of translation technology in the translation field.<br />
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====5.3 Application of artificial intelligence to translation teaching practice====<br />
In order to improve the teaching quality and train students' English translation ability, it is necessary to realize the effective integration of ARTIFICIAL intelligence and translation activity courses, which should not only reflect the effectiveness of artificial intelligence translation technology, but also help students establish a healthy concept of English communication. Through the application of artificial intelligence technology, students can strengthen their flexible translation skills through close communication with "AI program" during the learning stage of English translation activity class. For example, teachers can ask students to translate directly against the translation content provided on the translation screen of the ARTIFICIAL intelligence system. After that, the system can collect the translation answers with the help of speech recognition function, and then judge the accuracy of the translation content, thus providing important feedback to students.<br />
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China has used such artificial intelligence technology in the Putonghua test to ensure that every student can find a suitable translation method in practical communication. The so-called artificial intelligence technology is a new kind of technology modeled after the characteristics of human neural network thinking, can combine the human mind to respond. If it can be integrated into English translation activity teaching, it can not only improve the teaching efficiency, but also enhance students' enthusiasm in learning the course.<br />
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At the same time, during the training of translation talents, teachers also need to take into account the importance of influencing education factors, so that students can form a higher disciplinary quality in translation, so as to fit the concept of quality education in the new era. Only when artificial intelligence translation content is fully integrated into college English translation activity courses can the overall translation ability of college students be maximized.<br />
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====5.4The improvement of translator's technical ability====<br />
In the previous part, the author roughly mentioned that translation teaching should be improved, which will be elaborated here. At present, only a few universities can make full use of the advantages of translation technology in translation teaching and focus on cultivating professional translation talents. Most universities still cannot get rid of the traditional teaching mode of "language + relevant professional knowledge" in translation teaching, and generally lack a correct understanding of COMPUTER-aided translation teaching.<br />
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According to Wang Huashu et al., the courses that can be offered around the composition of translators' technical literacy include computer-assisted translation, translation and corpus, machine translation and post-translation editing, localization and internationalization, film and television translation (subtitle), technical communication and technical writing, and computer programming. The course modules involved are: Fundamentals of COMPUTER-aided Translation, CAT tool application, corpus alignment and processing, term management, QA technology for translation quality assurance, OFFICE fundamentals, translation management technology, basic computer knowledge, desktop typesetting, localization and internationalization, project management system and content management system, technical writing, basic knowledge of computer programming, basic knowledge of web code, etc.<br />
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===6针对一带一路的机器翻译与翻译人才的合作===<br />
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===Conclusion===<br />
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===References===<br />
<br />
=8 颜静(On Machine Translation Under Lanuguage Intelligence——An Option and Opportunity for Human Translators)=<br />
[[Machine_Trans_EN_8]]<br />
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=9 谢佳芬（人工智能时代下的机器翻译与人工翻译）=<br />
[[Machine_Trans_EN_9]]<br />
===Abstract===<br />
With the continuous development of information technology, many industries are facing the competitive pressure of artificial intelligence, and so is the field of translation. Artificial intelligence technology has developed rapidly and combined with the field of translation，which has brought great impact and changes to traditional translation, but artificial intelligence translation and artificial translation have their own advantages and disadvantages. Artificial translation is in the leading position in adapting to human language logical habits and understanding characteristics, but in terms of translation threshold and economic value, the efficiency of artificial intelligence translation is even better. In a word, we need to know that machine translation and human translation are complementary rather than antagonistic.<br />
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===Key Words===<br />
Machine Translation; Artificial Translation; Artificial Intelligence<br />
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===题目===<br />
人工智能时代下的机器翻译与人工翻译<br />
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===摘要===<br />
伴随着信息技术的不断发展，多个行业面临着人工智能的竞争压力，翻译领域也是如此。人工智能技术快速发展并与翻译领域结合，人工智能翻译给传统翻译带来了巨大的冲击和变革，但人工智能翻译与人工翻译存在着各自的优劣特点和发展空间，在适应人类语言逻辑习惯和理解特点的翻译效果上，人工翻译处于领先地位，但在翻译门槛和经济价值上，人工智能翻译的效率则更胜一筹。总的来说，我们要知道机器翻译与人工翻译是互补而非对立的关系。<br />
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===关键词===<br />
机器翻译;人工翻译;人工智能<br />
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===1. Introduction===<br />
====1.1 The History of Machine Translation Aborad====<br />
The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. Alchuni put forward the idea of using machines for translation. In 1933, the Soviet inventor Troyansky designed a machine to translate one language into another. [1]In 1946, the world's first modern electronic computer ENIAC was born. Soon after, American scientist Warren Weaver, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947. In 1949, Warren Weaver published a memorandum entitled Translation, which formally raised the issue of machine translation. In 1954, Georgetown University, with the cooperation of IBM, completed the English-Russian machine translation experiment with IBM-701 computer for the first time, which opened the prelude of machine translation research. [2] In 2006, Google translation was officially released as a free service software, bringing a big upsurge of statistical machine translation research. It was Franz Och who joined Google in 2004 and led Google translation. What’s more, it is precisely because of the unremitting efforts of generations of scientists that science fiction has been brought into reality step by step.<br />
====1.2 The History of Machine Translation in China====<br />
In 1956, the research and development of machine translation has been named in the scientific and technological work and made little achievements in China. On the eve of the tenth anniversary of the National Day in 1959, our country successfully carried out experiments, which translated nine different types of complicated sentences on large general-purpose electronic computers. The dictionary includes 2030 entries, and the grammar rule system consists of 29 circuit diagrams. [3]. After a period of stagnation, China's machine translation ushered in a high-speed development stage after the 1980s in the wave of the third scientific and technological revolution. With the rapid development of economy and science and technology, China has made a qualitative leap in the field of machine translation research with the pace of reform and opening up. In 1978, Institute of Scientific and Technological Information of China, Institute of Computing Technology and Institute of Linguistics carried out an English-Chinese translation experiment with 20 Metallurgical Title examples as the objects and achieved satisfactory results. Subsequently, they developed a JYE-I machine translation system, which based on 200 sentences from metallurgical documents. Its principles and methods were also widely used in the machine translation system developed in the future. In addition, the research achievements of machine translation in China during the 1980s and 1990s also include that Institute of Post and Telecommunication Sciences developed a machine translation system, C Retrieval and automatic typesetting system with good performance and strong practicability in October 1986; In 1988, ISTC launched the ISTIC-I English-Chinese Title System for the translation of applied literature of metallurgy, Information Research Institute of Railway developed an English-Chinese Title Recording machine translation system for railway documents; the Language Institute of the Academy of Social Sciences developed "Tianyu" English-Chinese machine translation system and Matr English-Chinese machine translation system developed by the computer department of National University of Defense Technology. After many explorations and studies, machine translation in China has gradually moved towards application, popularization and commercialization. China Software Technology Corporation launched "Yixing I" in 1988, marking China's machine translation system officially going to the market. After "Yixing", a series of machine translation systems such as Gaoli system in Beijing, Tongyi system in Tianjin and Langwei system in Shaanxi have also entered the public. In the 21st century, the development of a series of apps such as Kingsoft Powerword, Youdao translation and Baidu translation has greatly met the needs of ordinary users for translation. According to the working principle, machine translation has roughly experienced three stages: rule-based machine translation, statistics-based machine translation and deep learning based neural machine translation. [4] These three stages witnessed a leap in the quality of machine translation. Machine translation is more and more used in daily life and even the translation of some texts is almost comparable to artificial translation. In addition to text translation, voice translation, photo translation and other functions have also been listed, which provides great convenience for people's life. It is undeniable that machine translation has become the development trend of translation in the future.<br />
====1.3 The Status Quo of Machine Translation====<br />
In this big data era of information explosion, the prospect of machine translation is also bright. At present, the circular neural network system launched by Google has supported universal translation in more than 60 languages. Many Internet companies such as Microsoft Bing, Sogou, Tencent, Baidu and NetEase Youdao have also launched their own Internet free machine translation systems. [5] Users can obtain translation results free of charge by logging in to the corresponding websites. At present, the circular neural network translation system launched by Google can support real-time translation of more than 60 languages, and the domestic Baidu online machine translation system can also support real-time translation of 28 languages. These Internet online machine translation systems are suitable for a variety of terminal platforms such as mobile phone, PC, tablet and web and its functions are also quite diverse, supporting many translation forms, such as screen word selection, text scanning translation, photo translation, offline translation, web page translation and so on. Although its translation quality needs to be improved, it has been outstanding in the fields of daily dialogue, news translation and so on.<br />
===2. Advantages and Disadvantages of Machine Translation===<br />
Generally speaking, machine translation has the characteristics of high efficiency, low cost, accurate term translation and great development potential and etc. Machine translation is fast and efficient, this is something that artificial translation can’t catch up with. In addition, with the continuous emergence of all kinds of translation software in the market, compared with artificial translation, machine translation is cheap and sometimes even free, which greatly saves the economic cost and time for users with low translation quality requirements. What's more, compared with artificial translation, machine translation has a huge corpus, which makes the translation of some terms, especially the latest scientific and technological terms, more rapid and accurate. The accurate translation of these terms requires the translator to constantly learn, but learning needs a process, which has a certain test on the translator's learning ability and learning speed. In this regard, artificial translation has uncertainty and hysteretic nature. At the same time, with the progress of science and technology and the development of society, the function of machine translation will be more perfect and the quality of translation will be better.Today's machine translation tools and software are easy to carry, all you need to do is just to use the software and electronic dictionary in the mobile phone. There is no need to carry paper dictionaries and books for translation, which saves time and space. At the same time, machine translation covers many fields and is suitable for translation practice in different situations, such as academic, education, commercial trade, social networking, tourism, production technology, etc, it is also easy to deal with various professional terms. However, due to the limitation of translators' own knowledge, artificial translation is often limited to one or a few fields or industries. For example, it is difficult for an interpreter specializing in medical English to translate legal English.<br />
At the same time, machine translation also has its limitations. At first, machine can only operate word to word translation, which only plays the function and role of dictionary. Then, the application of syntax enables the process of sentence translation and it can be solved by using the direct translation method. When the original text and the target language are highly similar, it can be translated directly. For example, the original text "他是个老师." The target language is "he is a teacher ". With the increase of the structural complexity of the original text, the effect of machine translation is greatly reduced. Therefore, at the syntactic level, machine translation still stays in sentences with relatively simple structure. Meanwhile, the original text and the results of machine translation cannot be interchanged equally, indicating that English-Chinese translation has strong randomness, and is not rigorous and scientific enough. <br />
Nowadays, machine translation is highly dependent on parallel corpora, but the construction of parallel corpora is not perfect. At present, the resources of some mainstream languages such as Chinese and English are relatively rich, while the data collection of many small languages is not satisfactory. Moreover, the current corpus is mainly concentrated in the fields of government literature, science and technology, current affairs and news, while there is a serious lack of data in other fields, which can’t reflect the advantages of machine translation. At the same time, corpus construction lags behind. Some informative texts introducing the latest cutting-edge research results often spread the latest academic knowledge and use a large number of new professional terms, such as academic papers and teaching materials while the corpus often lacks the corresponding words of the target language, which makes machine translation powerless<br />
Besides, machine translation is not culturally sensitive. Human may never be able to program machines to understand and experience a particular culture. Different cultures have unique and different language systems, and machines do not have complexity to understand or recognize slang, jargon, puns and idioms. Therefore, their translation may not conform to cultural values and specific norms. This is also one of the challenges that the machine needs to overcome.[6] Artificial intelligence may have human abstract thinking ability in the future, but it is difficult to have image thinking ability including imagination and emotion. [7] Therefore, machine translation is often used in news, science and technology, patents, specifications and other text fields with the purpose of fact description, knowledge and information transmission. These words rarely involve emotional and cultural background. When translating expressive texts, the limitations of machine translation are exposed. The so-called expressive text refers to the text that pays attention to emotional expression and is full of imagination. Its main characteristics are subjectivity, emotion and imagination, such as novels, poetry, prose, art and so on. This kind of text attaches importance to the emotional expression of the author or character image, and uses a lot of metaphors, symbols and other expressions. Machine translation is difficult to catch up with artificial translation in this kind of text, it can only translate the main idea, lack of connotation and literary grace and it cannot have subjective feelings and rational analysis like human beings. In fact, it is not difficult to simulate the human brain, the difficulty is that it is impossible to learn from the rich social experience and life experience of excellent translators. In other words, machine translation lacks the personalization and creativity of human translation. It is this personalization and creativity that promote the development and evolution of language, and what machine translation can only output is mechanical "machine language".<br />
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===3.The Irreplaceability of Artificial Translation ===<br />
====3.1 Translation is Constrained by Context====<br />
At present, machine translation can help people deal with language communication in people's daily life and work, such as clothing, food, housing and transportation, but there is a big gap from the "faithfulness, expressiveness and elegance" emphasized by high-level translation. Language itself is art，which pays more attention to artistry than functionality, and the discipline of art is difficult to quantify and unify. Sometimes it is regular, rigorous, logical and clear, and sometimes it is random, free and logical. If it is translated by machine, it is difficult to grasp this degree. Sometimes, machine translation cannot connect words with contextual meaning. In many languages, the same word may have multiple completely unrelated meanings. In this case, context will have a great impact on word meaning, and the understanding of word meaning depends largely on the meaning read from context. Only human beings can combine words with context, determine their true meaning, and creatively adjust and modify the language to obtain a complete and accurate translation. This is undoubtedly very difficult for machine translation. Artificial translation can get rid of the constraints of the source language and translate the translation in line with the grammar, sentence patterns and word habits of the target language. In the process of translation, translators can use their own knowledge reserves to analyze the differences between the source language and the target language in thinking mode, cultural characteristics, social background, customs and habits, so as to translate a more accurate translation. Artificial translation can also add, delete, domesticate, modify and polish the translation according to the style, make up for the lack of culture, try to maintain the thought, artistic conception and charm of the original text and the style of the source language. In addition, translators can also judge and consider the words with multiple meanings or easy to produce ambiguity according to the context, so as to make the translation more clear and more accurate and improve the quality of the translation.<br />
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===4. Discussion on the Relationship Between Machine Translation and Artificial Translation ===<br />
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===5. Suggestions on the Combined Development of Machine Translation and Artificial Translation===<br />
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===6. ===<br />
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===7. ===<br />
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===Conclusion===<br />
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===References===<br />
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=10 熊敏(Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts)=<br />
[[Machine_Trans_EN_10]]<br />
===Abstract===<br />
The history of machine translation can be traced to 1940s, undergoing germination, recession, revival and flourish. Nowadays, with the rapid development of information technology, machine translation technology emerged and is gradually becoming mature. Whether manual translation can be replaced by machine translation becomes a widely-disputed topic. So in order to explore the ability of machine translation, I adopts two versions of translation, which are manual translation and machine translation(this paper uses Youdao translation) for different types of texts(according to Peter Newmark's types of text：informative text, expressive text and vocative text). The results are quite different in terms of quality and accuracy. The results show that machine translation is more suitable for informative text for its brevity, while the expressive texts especially literary works and vocative texts are difficult to be translated, because there are too many loaded words and cultural images in expressive text and dependence on the readers. To draw a conclusion, the relationship between machine translation and manual translation should be complementary rather than competitive.<br />
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===Key words===<br />
machine translation; manual translation; Newmark's type of texts<br />
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===题目===<br />
Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts<br />
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===摘要===<br />
机器翻译的历史可以追溯到上个世纪40年代，经历了萌芽期、萧条期、复兴期和繁荣期四个阶段。如今，随着信息技术的高速发展，机器翻译技术日益成熟，于是机器翻译能不能替代人工翻译这个话题引起了广泛热议。为了探究机器翻译的能力水平是否足以替代人工翻译，本人根据皮特纽马克的文本类型分类理论（信息类文本，表达文本，呼吁文本），选择了相应的译文类型，并且将其机器翻译的版本以及人工翻译的版本进行对比。结果发现，就质量和准确度而言，译文的水平大相径庭。因为其简洁的特性，机器比较适合翻译信息文本。而就表达文本，尤其是文学作品，因其存在文化负载词及相应的文化现象，所以机器翻译这类文本存在难度。而呼唤文本因其目的性以及对读者的依赖性较强，翻译难度较大。由此得知，机器翻译和人工翻译的关系应该是互补的，而不是竞争的。<br />
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===关键词===<br />
机器翻译；人工翻译；纽马克文本类型<br />
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===1. Introduction===<br />
====1.1.Introduction to machine translation====<br />
Machine translation, also known as computer translation is a technique to translating through machine translator, such as Google translation and Youdao translation. Machine translation is one of the branches of computational linguistics, ranging from computer science, statistics, information science and so on. Machine translation plays an important role in all aspects.<br />
Machine translation can be traced back to 1940s, when British engineer Booth and American engineer Weaver proposed using computer to translate and started to study machines used for translation. And the first machine translating system launched in 1954, used in English and Russian translation. And this means machine translation becomes a reality. However, the arrival of new things always accompanies barriers and setbacks. In the 1960s, reports from ALPAC (Automated Language Processing Advisory Committee) showed studies on machine translation had stagnated for a decade. At that time, due to the high cost of machine, choosing human translators to finish translating works was more economical for most companies. What’s worse, machine had difficulty in understanding semantic and pragmatic meanings. Fortunately, in the 1970s, with the advance and popularity of computer, machine translation was gradually back on track. With the development of science and technology, machine translation has better technological guarantee, such as artificial intelligence and computer technology. In the last decades, from the late 90s till now, machine translation is becoming more and more mature. The initial rule-oriented machine translation has been updated and Corpus-aided machine translation appears. And nowadays, technology in voice recognition has been further developed. This technique is frequently applied in speech translation products. Users only need to speak source language to the online translator and instantly it will speak out the target version. But machine can’t fully provide precise and accurate translation without any grammatical or semantic problems. Machine can understand the literal meaning of texts, but not the meaning in context. For example, there is polysemy in English, which refers to words having multiple meanings. So when translating these words, translators should take contexts into consideration. But machine has troubles in this way. In addition, machine has no feeling or intention. Hence, it is also difficult to convey the feelings from the source language.<br />
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====1.2.Process of machine translation====<br />
The process of manual translation is different from that of machine translation. Here is the process of the former. (1) Understand source language. (2) Use target language to organize language. (3) Generate translation. Unlike manual translation, machine translation tends to analyze and code source language first, then look for related codes in corpus, and work out the code that represents target language, generating translation. But they share a common feature, which is that Lexicon, grammatical rules and syntactic structure are taken into consideration. This is one of the biggest challenges for machine translation.<br />
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===2.Theorectical framework===<br />
====2.1Newmark’s text typology====<br />
Text typology is a theory from linguistics. It undergoes several phrases. Karl Buhler, a German psychologist and linguist defines communicative functions into expressive, information and vocative. Then Roman Jakobson proposes categorization of texts, which are intralingual translation, interlingual translation and intersemiotic translation. Accordingly, he defines six main functions of language, including the referential function, conative function, emotive function, poetic function, metalingual function and the phatic function. Based on the two theories, Peter Newmark, a translation theorist, summarizes the language function into six parts: the informative function, the vocative function, the expressive function, the phatic function, the aesthetic function, and the metalingual function. Later, he further divided texts into informative type, expressive text and vocative type according to the linguistic functions of various texts. <br />
The core of the informative texts is the truth. It is to convey facts, information, knowledge of certain topics, reality and theories. The language style of the text is objective, logical and standardized. Reports, papers, scientific and technological textbooks are all attributed to informative texts. Translators are required to take format into account for different styles.<br />
The core of the expressive text is the sender’s emotions and attitudes. It is to express sender’s preferences, feelings, views and so on. The language style of it is subjective. Imaginative literature, including fictions, poems and dramas, autobiography and authoritative statements belong to expressive text. It requires translators to be faithful to the source language and maintain the style.<br />
The core of the vocative text is readership. It is to call upon readers to act, think, feel and react in the way intended by the text. So it is reader-oriented. Such texts as advertisement, propaganda and notices are of vocative text. Newmark noted that translators need to consider cultural background of the source language and the semantic and pragmatic effect of target language. If readers can comprehend the meaning at once and do as the text says, then the goal of information communication is achieved.<br />
To draw a conclusion, informative texts focus on the information and facts. Expressive texts center on the mind of addressers, including feelings, prejudices and so on. And vocative texts are oriented on readers and call on them to practice as the texts say.<br />
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====2.2Study method====<br />
Manual translations of the three texts are selected from authoritative versions and universally acknowledged. And machine translations of those come from Youdao Translator. And in this thesis I will compare and evaluate the two methods in word diction, sentence structure, word order and redundancy.<br />
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===3.Comparison and analysis of machine translation and manual translation ===<br />
====3.1Informative text ====<br />
（1）English into Chinese<br />
<br />
①Source language:<br />
<br />
Keep the tip of Apple Pencil clean, as dirt and other small particles may cause excessive wear to the tip or damage the screen of i-pad.<br />
<br />
Target language:<br />
<br />
Machine translation: Apple Pencil笔尖应保持清洁，灰尘等小颗粒可能会导致笔尖过度磨损或损坏ipad屏幕。<br />
<br />
Manual translation: 保持Apple Pencil铅笔的笔尖干净，因为灰尘和其他微粒可能会导致笔尖的过度磨损或损坏iPad屏幕。<br />
<br />
Analysis: Here is the instruction of Apple Pencil. And the manual translation is the Chinese version on the instruction.Product instruction tends to be professional, since there are many terms for some concepts. Machine can easily identify these terms and provide related words to translate. The machine version is faithful and expressive to the source language. So it is well-qualified and readable for readers to understand the instruction. So we can use machine to translate informative text.<br />
<br />
②Source language:<br />
<br />
China on Saturday launched a rocket carrying three astronauts-two men and one woman - to the core module of a future space station where they will live and work for six months, the longest orbit for Chinese astronauts.<br />
<br />
Target language:<br />
<br />
Machine translation: 周六，中国发射了一枚运载三名宇航员(两男一女)的火箭，进入未来空间站的核心舱，他们将在那里生活和工作6个月，这是中国宇航员最长的轨道。<br />
<br />
Manual translation: 周六，中国发射了一枚搭载三名宇航员(两男一女)的火箭，进入未来空间站的核心舱，他们将在那里生活和工作6个月，这是中国宇航员最漫长的一次轨道飞行。<br />
<br />
Analysis: This is a news from Reuters, reporting that China has launched a rocket.The meaning of the two translations is almost the same, except for some word diction. But there are some details dealt with different choice. For example, the last sentence of the machine translation is a bit of obscure and direct. There are some ambiguous words and expressions.<br />
<br />
(2)Chinese into English<br />
<br />
Source language:湖南省博物馆是湖南省最大的历史艺术类博物馆，占地面积4.9万平方米，总建筑面积为9.1万平方米，是首批国家一级博物馆，中央地方共建的八个国家级重点博物馆之一、全国文化系统先进集体、文化强省建设有突出贡献先进集体。<br />
<br />
Target language:<br />
Manual translation: As the largest history and art museum in Hunan province, the Hunan Museum covers an area of 49,000㎡, with the building area reaching 91,000㎡. It is one of the first batch of national first-level museums and one of the first eight national museums co-funded by central and local governments.<br />
<br />
Machine translation: Museum in hunan province is one of the largest historical art museum in hunan province, covers an area of 49000 square meters, a total construction area of 91000 square meters, is the first national museum, the central place to build one of the eight national key museum, national cultural system advanced collectives, strong culture began with outstanding contribution of advanced collective.<br />
<br />
Analysis: Machine translation is not faithful enough in content. For instance, “首批国家一级博物馆” is translated into “first national museum”, which is not the meaning of the source language. And there are some obvious grammar mistakes in the machine translation. For example, machine translates it into just one sentence but there are multiple predicates in it. So it is not grammatically permissible. What’s more, the sentence structure of machine translation is confusing and the focus is not specific enough.<br />
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===4. ===<br />
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===5. ===<br />
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===6. ===<br />
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===7. ===<br />
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===Conclusion===<br />
<br />
===References===<br />
<br />
=11 陈惠妮=(Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts)=<br />
[[Machine_Trans_EN_11]]<br />
===Abstract===<br />
At present, globalization is accelerating and the market demand for language services is rapidly increasing . Machine translation, as an important translation method, can greatly improve translation efficiency due to its low cost and high speed. However, because of the limitations of machine translation and the differences between Chinese and English language, machine translation is not accurate enough. In order to balance translation efficiency and translation quality, a great number of manual revisions in translation are required for the machine translating texts. Medical papers are specialized, special and purposeful, so it requires accurate,qualified and professional translation. However, the quality of translations by machine is inefficient to meet the high-quality requirements of medical papers translation. Therefore, the introduction of pre-editing can greatly improve the efficiency and quality of machine translation.<br />
<br />
===Key words===<br />
Pre-editing, Machine translation, Medical texts<br />
<br />
===题目===<br />
Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts<br />
<br />
===摘要===<br />
在全球化加速发展的今天，市场对语言服务的需求迅速增加。机器翻译作为一种重要的翻译途径，由于其成本低、速度快，可以大大提高翻译效率。然而，由于机器翻译的局限性以及中英文语言的差异，机器翻译的准确性不高。为了平衡翻译效率和翻译质量，机器翻译文本需要大量的手工修改。<br />
医学论文具有专业性、特殊性和目的性，要求其译文准确、合格、专业。然而，机器翻译的质量较低，无法满足医学论文对翻译的高质量要求。因此，译前编辑的引入可以大大提高机器翻译的效率和质量。<br />
<br />
===关键词===<br />
译前编辑；机器翻译；医学文本<br />
<br />
===1. Introduction===<br />
===1.1 Definition of Machine Translation===<br />
As Cronin(2013) revealed: "Translation is undergoing a revolutionary upheaval. The influence of digital technology and the Internet on translation is continuous, extensive and profound. From the popularity of automatic online translation applications, translation revolution is everywhere (Cronin,2013) However,the concept of machine translation was firstly proposed in the 1930s. Since 1940s, the machine translation technology has been evolving from rule-based machine translation (RBMT) to statistical machine translation (SMT), and to neural machine translation (NMT). Machine translation refers to the automatic translation of source language into target language by using a computer system. That is, machine translation refers to the automatic translation of text from one language into another natural language by computer software or other online translation webs. Machine translation is also defined as the process of “using a computer system to automatically translate text or speech from one natural language to another” according to the definition by ISO (Cui, 2014).O 'Brien (2002) defines it as "the behavior of modifying errors in machine translation to ensure that the target translation meets certain quality requirements". On the basic level, machine translation performs mechanical substitution of words in one language for words in another language, but that rarely produces a good translation, therefore, recognition of the whole phrases and their closest counterparts in the target language is needed. Not all words in one language have equivalents in another language, and many words have more than one meaning. A huge demand for translation is greatly needed in today’s global world, which creates new opportunities for the development of machine translation, attracts more and more attention and becomes one of the current research focuses.<br />
<br />
===1.2 Definition of Pre-editing===<br />
Pre-editing means to adjust and modify the source language to make it fit more with the characteristics of the machine translation software before putting the source language before the into machine translation, so as to improve the quality of the translation machine translation (Wei Changhong, 2008:93-94). Pre-editing is to modify the original text before putting it into machine translation software, in order to improve the recognition rate of machine translation, optimize the output quality of translated text and reduce the workload of post-editing. Because pre-translation editing only needs to be modified in one language, the operation is simpler than post-translation editing, which can realize the double improvement of quality and efficiency. A good pre-editing translation can help machine translation more smoothly, thus improving the machine readability and quality of the output translation. Pre-editing is mostly applied in the following situations: one is when the original text is of poor quality and the machine is difficult to recognize the meaning of the sentence, such as user generated content with poor readability and translatability (Gerlach et al, 2003:45-53); Documents that need to be published in multiple languages; Next is when the original text contains a lot of jargon; The last is the original text has a corresponding translation memory bank. If the original text is edited, it can better match the content of the translation memory bank. Pre-editing is a process of identifying problems. It requires to pre-edit the source texts before putting it into machine translation according to the requirements, listing the expressions or sentences that may have trouble in machine translation and then pre-edit it by human. The purpose is to enable the computer to translate better, improve the translatability of machine translation. (Slype G V & Guinet J F & Seitz F,1984:115)<br />
<br />
===1.3 Machine Translation Mode===<br />
According to the different knowledge acquisition methods, machine translation modes can be classified as follows: one is rule-based machine translation, which is based on bilingual dictionaries and a library of language rules for each language. The quality of translation depends on whether the source language conforms to the existing rules, but the inexhaustible rules are the hinders of this model. The second mode is machine translation based on statistics. This translation model relies on the principles of mathematics and statistics to find various existing translations corresponding to the translation tasks through the employment of corpus, analyzing the frequency of their occurrence and selecting the translation with the highest frequency for output. The disadvantage of this translation model is that it ignores the flexibility of language and the importance of context. The last one is neural network language model. This model is different from previous translation models in that it uses end-to-end neural network to realize automatic translation between natural languages. At present, the quality of its translation is much higher than that of the previous translation models.<br />
===1.4 Source of Study Abstracts===<br />
The core medical journals from domestic are selected in order to make the paper more representative and authoritative, such as National Medical Journal of China, Journal of Peking University (Health Science), and Journal of Third Military Medical University. All of them include basic medical science, biomedical technology, laboratory medical science and other fields. In this way, the pre-editing approaches included are applicable enough to machine translation of medical abstracts.<br />
===1.5 Selection of Translation Software===<br />
Generally, machine translation software includes Google Translation, Youdao Translator and Niu Translation, which has their own special use in translation. Take Google as an example, it is more like neurons in human brain, enabling to learn and collect information to establish connections with its neural machine translator’s neurons. However, it also causes many errors because of the lack of enough information. In this paper, contrastive analysis will be carried on by using Google translation. On the one hand, being a pioneer in the translation of NET, it is inevitably to sue Google as the translation software to translate medical texts in this paper. Comparing the program called “Google brain”, other NMT of translation software are relatively disadvantages. On the other hand, the Google translation enjoys the largest users in the world, with its downloads of more than one billion. The output quality of Google translation is more correct and complete than other machine translation software. With years of development and improvement, Google Translation has been greatly promoted. In this paper, Chinese medical abstract will be automatically translated by Google translation, and then the translation output will be compared with the translation by human and the publication of English abstracts. The main purpose is to prove that the improvement and promotion of quality and accuracy in the medical abstracts will be obtained through the pre-editing approaches.<br />
<br />
===2.Language Characteristics and Error Division===<br />
Since Chinese and English are two different languages, it is quite neccessary to identify their own characteristics so as to better analyze and understand the two languages. Tytler (1978) argued in his Essay on the Principles of Translation that there are three principles of translation that in all the translation should give readers the same feelings as the source text, except for complete transcript of ideas. There actually also exit some mistakes of these two languages. So the following will make some clarrifications of these two languages to ensure more accurate translation.<br />
<br />
===2.1 Language Characteristics of Medical Abstracts===<br />
Chinese and English belong to different language systems, so there are differences in their language structure and the way users think of the languages. When using machine translation from Chinese to English, due to the unequal language levels, there will be many mistakes in the translation process, especially for ESP texts, such as medical papers. <br />
Here, these differences mainly refer to the linguistic characteristics of medical abstracts. In medical abstracts, it usually includes structured and unstructured abstracts. Although in different forms, they both describe the purpose, methods and conclusions of the research.<br />
In the method section of medial abstract, several Chinese sentences can be connected with commas, but each sentence may convey different information. In contrast, English sentences contain a great deal of information, but in order to ensure clarity, some modifiers need to be isolated and then reconstructed. However, in Chinese-English machine translation, a lot of information is put into the sentence because the machine segments the sentence on the basis of the full comma.<br />
In addition, subjectless sentence is used in the objective and method parts of Chinese medical abstracts. The subjectless sentence means the sentence without or free from subject and is usually employed in two contexts. The first is "needless to say". In Chinese sentences, it is common to omit the subject of the sentence. Chinese sentences can convey meanings by using incomplete sentence structures, so Chinese speakers can understand the meanings of the sentences even though the sentence subject is omitted. The second is "emphasis of action". In this context, subjectless sentences are used to describe behaviors, especially in the study of traditional Chinese medicine abstracts. Comparatively speaking, it should be avoided in English medical abstracts. Abstract sentences are more subjective when describing the learning process, while the essential requirement of English medical abstract is objectivity. From this point, sentences without subject should be avoided in English medical abstracts. Another feature is voice. Few words with passive meanings appear in Chinese abstracts. English sentences are more favoured in using passive voice. When translating from Chinese to English, passive voice should be used to make the contents objective, which is also the basic requirement of medical papers.<br />
These are the linguistic features of Chinese medical abstracts. There are great differences in sentence structure and expression between Chinese and English medical abstracts. These differences may reduce the accuracy of machine translation, so it is necessary to introduce pre-editing to edit the source text to ensure that the source text can be accurately recognized by the machine and fully translated into English.<br />
===2.2 Error Division of Machine Translation in Translating Abstracts of Medical Papers from Chinese to English===<br />
In this paper, errors in machine translation are listed out after analyzing. Some are due to the principles, such as statistical-based MT and NMT, employed by machine translation. Based on the original errors, they can be studied from two levels. One is from the macro-level, referring to mistakes caused by objective factors. These are not human factors, but the disadvantage of machine translation and the limitations of language. The limitations of machine translation are derived from the principles and models adopted and manifests itself as a reliance on reference sources. In this case, semantic ambiguity and textual incoherence may occur in the absence of reference sources.<br />
However, for ESP texts such as medical papers, the requirements for terms and sentence patterns are far beyond the existing corpora. For unfamiliar texts (that is, the corresponding texts cannot be found in the corpora). The quality of output translation will be relatively low, which is determined by the principles of machine translation. As mentioned above, machine translation has evolved over the past few decades from rules-based to corp-based statistics to NMT. However, there still exist some limitations in machine translation.<br />
Mistakes on micro-level are mainly caused by the variations and differences of linguistic structure between Chinese and English. Chinese is an implicit language, while English is an explicit one (Lian, 2010) To put it in another way, Chinese expression does not depend on language structures, while English does the opposite. This may result in mismatches between the original and machine translated sentences. This kind of error is mainly divided into two types: component fragment and component missing. For a medical paper, such errors are very serious. As a kind of ESP discourse, medical paper has the characteristics of fixed, objective and accurate language structure. In order to reproduce the characteristics of medical abstracts in translation, it is necessary to avoid errors at the level of words and sentences and pay attention to logic and consistency.<br />
Lexical errors here refer to the inconsistency of fixed expressions in the translation of terms. Although these terms are machine translation based on the corpus, the corpus may not be perfect for ESP texts such as medical papers. Therefore, fixed expressions are not used in term translation. This is also the most common mistake in machine translation (the term here includes but is not limited to nouns). For ESP texts, medical text in particular, the fixed expressions and the accuracy of terms are of great importance.<br />
<br />
===3.Approaches Proposed for Pre-editing ===<br />
Generally speaking, there is a close relationship between pre-editing and post-editing, both of which aim to convey information and ensure high or publishable translation quality. Proper pre-editing can improve the quality of machine translation in terms of adequacy and consistency. <br />
Complexity of natural language and people use language arbitrarily, bringing many difficulties to Chinese-English machine translation, Hu Qingping (2005:24) has proposed "the research of translation software and the development of the controlled language are the two directions to improve the quality of machine translation : the former aims at the difficulty in the natural language processing, the latter overcomes the arbitrariness of natural language". Feng Quangong and Gao Lin (2017: 63-68) put forward: "The writing principles of controlled language can be applied to pre-editing of machine translation. Pre-editing based on controlled language can effectively reduce the complexity and ambiguity of source text, improve the identifiability of machine translation (the translatability of source text itself), and thus reduce (fully) the workload of post-editing".<br />
The central task of pre-editing is to transform human-friendly content into machine-friendly content, so words and sentences need to be repositioned or even changed. Based on the analysis of the language characteristics of Chinese and English, the Chinese ideographic group should be split before the Chinese source text is input into the machine software to translate so that the sentence structure is complete which can be easily recognized by the machine translation software.<br />
===3.1 Extraction and Replacement of Terms in the Source Text===<br />
As a branch of applied English, medical English is the product of the combination of English language knowledge and medical knowledge. The terms in medical papers have the characteristics of fixed and complex language structure. Although Google Translation is based on a corpus, the language structure is not fixed due to the limited size of the corpus. Therefore, the following two steps should be followed before machine translation. The first is to extract terms and create a glossary, and then replace the Chinese terms in the source text with the corresponding English expressions in the glossary. Of course, the terms of extraction should be searched and verified according to the actual situation. All of these words are verified before they can be replaced. This method can not only ensure the accuracy of term translation, but also maintain the consistency of expression, especially when dealing with long texts.<br />
Example1<br />
Source abstract: 口腔白斑病癌变相关缺氧应答基因和微小RNA的芯片及表达验证。<br />
Google translation: Microarray detection/Chip detection and expression verification of hypoxia response genes and microRNAs related to oral leukoplakia canceration.<br />
Published translation:Transcriptome array screening and verification of oral leukoplakia carcinogenesis-related hypoxia-responsive gene and microRNA. <br />
Analysis: The expression “ Affymetrix GeneChip” empolyed in this thesis is a human transcription array for transcriptome array. In the source abstract, “芯片检测“ can be meant “screening” but not detection, so the proper and appropriate translation of these expression should be “transcription array screening”, but the Google translates it into “microarry detection of chip detection”. Therefore, term extraction is essential and inevitable before putting the source abstracts into the machine translation software.<br />
After pre-editing source abstracts: 对 oral leukoplakia carcinogenesis 相关 hypoxia-responsive gene 和微小RNA进行 transcriptome array screening 及表达验证。<br />
After pre-editing translation: Transcriptome array screening and expression- verification of hypoxia-responsive genes and microRNAs related to oral leukoplakia carcinogenesis.<br />
===3.2 Explication of Subordination===<br />
As mentioned above, the subordination of Chinese sentences is judged by certain specific words in machine translation . Chinese is a paratactic language, and sentences are often connected by internal logical relationships; while English depends on sentence structure, so sentences are often closely linked by various language forms. In order to improve the accuracy of machine translation, we can adjust the sentence structure and adjust the position of adjectives and their modifiers. <br />
Example 2<br />
Source abstracts:回顾性分析南京医科大学附属儿童医院中重度HIE患儿49例及同期就诊的无神经系统症状体征的足月新生儿为对照组31例的头颅磁共振成像（MRI）资料。<br />
Google translation: A retrospective analysis that the brain magnetic resonance imaging (MRI) data of 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University and full-term neonates with no neurological symptoms and signs during the same period were included in the control group.<br />
Published translation: A total of 49 children with moderate to severe HIE admitted to the children’s Hospital Affiliated to Nanjing Medical University were retrospectively analyzed. Cranial magnetic resonance imaging (MRI) date of 31 full-term neonates without neurological symptoms and signs who visited the hospital during the same period were recruited as the control group. <br />
Analysis: As the above example shows that “中重度HIE患儿” and “足月新生儿” in the source abstracts are from the Children’s Hospital Affiliated Nanjing Medical University. The Google translation shows that 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University. There is an error due to the misuse of subordination. There are some advice in order to solve the problem. That is, first to segment the sentence and then reconstruct the sentence. For instance, the Chinese expression “南京医科大学附属儿童医院” carries many modifiers, which requires to cut the modifiers and reconstruct the sentence. Therefore, the Chinese expression “南京医科大学附属儿童医院’can be divided into abstractions, leaving two sentences instead of one long sentence with modifiers..<br />
After pre-editing source abstracts: 对49例中重度HIE患儿以及31例无神经系统症状体征的足月新生儿的MRI资料进行回顾性分析。这些患者收治于南京医科大学儿童附属医院。<br />
After pre-editing translation: The MRI data of 49 children with moderate to severe HIE and 31 full-term newborns without neurological symptoms and signs were retrospectively analyzed. These patients were admitted to the Children’s Hospital of Nanjing Medical University.<br />
===3.3 Explication of Subject through Voice Changing===<br />
The extensive use of subjectless sentences are quite common in the Chinese abstracts, while English prefers to employ passive voice in the sentences so as to make them object and accurate. In order to take the characteristics of English sentences into great and careful consideration, the sentences written in passive voice in particular, it will be helpful for machine translation to find out the subject and reconstruct a sentence in passive voice (Wang Yan: 2008). The first is to discover the “doee” in a sentence and then is to reconstruct the sentence structure and put the “doee” before the verb. The last is to explicate the passive relation between the noun and the verb.<br />
Example 3<br />
Source abstract: 回顾性分析2018年1月至2020年12月解放军总医院第七医学中心收治的500例老年髋部骨折患者的资料。<br />
Google translation: A retrospective analysis of the data of 500 elderly patients with hip fractures admitted to the Seventh Medical Center of the PLA General Hospital from January 2018 to December 2020.<br />
Published translation: From January 2018 to December 2020, the data of 500 elderly patients with hip fracture treated in the Seventh Medical Center of PLA General Hospital were analyzed retrospectively.<br />
Analysis: The above example indicates that the “回顾性分析”in the Google Translate is a noun phrase, but the source abstracts actually describes an action or a behavior. The reason for such a mistake is that the machine can’t recognize the Chinese subjetless sentences. To find the subject of the sentence, the source abstracts can be revised and adjusted. Finding the “doee” is the first step, which refers to “500例老年髋部骨折患者的资料”in the source abstracts. And next is to put it in front of the verb, that is to place it in the beginning of the sentence. Last but not least, the passive voice should be explicated in the sentence. <br />
After pre-editing source abstract: 500例老年髋部骨折患者的资料被回顾性分析，他们在2018年1月之2020年12月期间收治于解放军总医院第七医学中心。<br />
After pre-editing translation: The data of 500 elderly hip fracture patients were retrospectively analyzed. They were admitted to the Seventh Medical Center of the PLA General Hospital between January 2018 and December 2020.<br />
===3.4 Relocation of Modifiers===<br />
Modifiers, including noun, adverbial and attributive are constantly employed in the Chinese medical papers in order to add information to subject and object in the sentence. By doing this, complicated sentences can be structured, thus causing obstacles to machine translation for recognizing this complex sentence structures when translating Chinese-English sentences. To make the machine translation successfully recognize the sentence structure, simplifying the sentence structure is quite necessary. The key is to moving the location of the modifiers and thus making the modifiers an independent sentence. In other words, the modifiers ought to be put before or behind the main part of the sentence to satisfy the common use of English. <br />
Usually, the modifiers in Chinese sentence can only be placed in front of the core word, while in English, modifiers are very flexible. It is all right to place the modifiers in front of or behind the major part of the sentences with adjective or a connective noun.<br />
Example 4<br />
Source abstracts: 利用DNA重组技术以pET-28a表达系统在E.coli BL21(DE3) 中重组表达Hepl。<br />
Google Translation: Hepl is recombined in E.coli BL21 (DE3) using DNA recombination technology with pET-28a expression system.<br />
Published translation: The recombinant Hcpl protein was expressed by using DNA recombination technology through pET-28a expression system in E. coli BL21 (De3).<br />
Analysis: The Chinese medical text includes two modifiers, “利用DNA”and “以pET-28a)表达系统”. These two modifiers will be translated by machine, which catches more attention to the two constituents. From the Google translation above, one failure is obvious that it misplaces the location of the two modifiers and presents it in not accurate form. To make it translate correctly by machine translation, dividing the sentences is very important so that the source abstracts can be correctly recognized by machine translation.<br />
After pre-editing source abstract: 利用DNA重组技术，Hcp1重组表达在E.coli BL21 (DE3)中， 通过pET-28a表达系统在E. coli BL21 (DE3)中。<br />
Google translation: Using DNA recombination technology, Hcp1 recombination is expressed in E.coli BL21 (DE3), via pET-28a expression system in E. coli BL21 (DE3).<br />
The second is the independence of modifiers, that is, modifiers can also be reconstructed into clauses to modify core words. Compared with English, There is no subordinate clause in Chinese, so redundant Chinese modifiers need to be reconstructed into subordinate clauses in Chinese-English translation to meet the characteristics of English. English, especially sentences with multiple modifiers. Otherwise, sentence structure may be confused, such as scattered modifiers of core words. To avoid this mistake, it is necessary to separate the modifier from the main part of the sentence. These modifiers should be reconstituted into clauses. Also, keep your sentences simple and easy to understand.<br />
===3.5 Proper Omission and Deletion of Category Words===<br />
Category words are commonly used in Chinese. Category words complement the meaning of words, including problems, positions, situations and jobs. Adding this supplementary word is more in line with Chinese custom. In many cases, it has no real meaning, so it can be omitted in translation. In Chinese, category words are frequently used. However, it is rarely used in English, which is one of the differences between Chinese and English. There are many kinds of category words. Considering objectivity and the fixed structure of language, redundant category words should be deleted in Chinese-English translation. In the general, the most commonly used category of words in the Chinese medical abstracts are "process", "behavior", and "situation". These categories of words hinder the language conversion of Chinese and English, resulting in redundancy. <br />
Example 5<br />
Source abstract: 探讨口腔白斑病癌变进程中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia response genes and associated microRNAs in the process of oral leukoplakia cancer was discussed.<br />
Published translation: To study the hypoxia response gene and microRNA (miRNA)expression profiles in the pathogenesis and progression of oral leukoplakia (OLK).<br />
Analysis: As the above example shows, the Chinese words “进程” belongs to a category word. However, the Chinese expression “癌变”contains the process, so the “进程” expression can be deleted before placing it into the machine translation, because the meaning of it has been overlapping between the expression “癌变”. Therefore, its place can be replaced with preposition “during”.<br />
After pre-editing source abstract: 探讨口腔白斑病癌变中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia responsive genes and miRNA in oral leukoplakia cancer was investigated.<br />
<br />
===Conclusion===<br />
After years of development, machine translation has made great progress. The accuracy of machine translation has been greatly improved in both text recognition and sentence pattern conversion. However, machine translation has its own limitations. In other words, it needs to rely on the parallel corpus as a reference source for improving its accuracy. ESP text, in particular, is harder to get the high quality by machine translation. <br />
As one of the research papers, the characteristics of medical abstracts are fixed language structures, objectivity and accuracy (Qin Yi:2004). Therefore, medical translation must be accurate, object and understandable to follow the specific demands of the medical paper. Being an important field in the human society, medical paper translation is on a great demand, which means that it needs a huge demand for human labor. However, with the machine translation promoting, it will be more efficient to translate medical papers combining the effort by human and machine. The improvement and development of machine translation requires the joint efforts of computer science, information science, statistics, linguistics and other academic circles to achieve more mature human-computer mutual assistance translation (Li Yafei, Zhang Ruihua : 2019).<br />
However, errors can occur during the process of machine translation of Chinese- English, because of the differences of the Chinese and English and the processing of the machine. Errors from the perspective of linguistic or grammar can affect the machine translation a lot. After division and recognition of errors, some pre-editing approaches are put forward to help the machine translation more accurate and readable, that are, extraction and replacement of terms in the source text, relocation of modifiers, explication of subordination, proper omission, deletion of category words and explication of subject through voice changing. <br />
The paper mainly focuses on the pre-editing machine translation by using medical papers as a case study. The errors of machine translation occurring in the translation of medical abstracts and pre-editing approaches for machine translation. The quality of machine translation of medical papers is greatly improved after employing the pre-editing methods. However, machine translation is not as flexible and accurate as human brain, so it is of importance to combine pre-editing and post-editing approaches with machine translation in order to produce more accurate, more object machine translation of medical papers.<br />
<br />
===References===<br />
[1]. Cronin, Michael (2013). Translation in the Digital Age[M]. New York&London: Routledge.<br />
<br />
[2]. GERLACH J, et al ( 2013). Combining Pre-editing and Post-editing to Improve SMT of User-generated Content[M]// Proceedings of MT Summit XIV Workshop on Post-editing Technology and Practice. 45-53.<br />
<br />
[3]. Slype G V & Guinet J F & Seitz F (1984). Better Translation for Better Communication [M] .Oxford: Pergamon Press Ltd (U.K.), <br />
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[4]. O'Brien S (2002). Teaching Post-editing: A Proposal for Course Con-tent [EB/OL]. http://mt-archive. Info/EAMT-2002-0brien. Pdf.<br />
<br />
[5]. Tytler, A. F. (1978). Essay On The Principles of Translation[M]. Amsterdam: JohnBenjamins Publishing.<br />
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[6] 崔启亮. (2014), 论机器翻译的译后编辑[J], 中国翻译, 035(006):68-73.<br />
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[7] 冯全功,高琳 (2017) 基于受控语言的译前编辑对机器翻译的影响[J]. 当代外语研究,(2): 63-68+87+110.<br />
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[8] 胡清平(2005). 机器翻译中的受控语言[J]. 中国科技翻译, (03): 24-27. <br />
<br />
[9] 连淑能 (2010). 英汉对比研究增订本[M]. 北京:高等教育出版社.<br />
<br />
[10] 黎亚飞,张瑞华 (2019). 机器翻译发展与现状[J]. 中国轻工教育,(5):38-45. <br />
<br />
[11] 秦毅(2004),从翻译基本标准议医学英语的翻译[J]. 遵义医学院学报,27 (4): 421-423. <br />
<br />
[12] 王燕 (2008). 医学英语翻译与写作教程[M]. 重庆:重庆大学出版社<br />
<br />
=12 蔡珠凤=(The Mistranslation of C-J Machine Translation of Political Statements)=<br />
[[Machine_Trans_EN_12]]<br />
===Abstract===<br />
Language is the main way of communication between people. With the continuous development of globalization, the scale of cross-border exchanges is also expanding. However, due to cultural differences and diversity, the languages of different countries and regions are very different, which seriously hinders people's communication. The demand for efficient and convenient translation tools is increasing. At the same time, with the development of network technology and artificial intelligence, recognition technology based on deep learning is more and more widely used in English, Japanese and other fields.<br />
<br />
===Key words===<br />
machine translation; political statements; mistranslation of C-J machine translation<br />
===题目===<br />
The Mistranslation of C-J Machine Translation of Political Statements<br />
===摘要===<br />
语言是人与人之间交流的主要方式。随着全球化的不断发展，跨境交流的规模也在不断扩大。然而，由于文化的差异和多样性，不同国家和地区的语言差异很大，这严重阻碍了人们的交流。对高效便捷的翻译工具的需求正在增加。同时，随着网络技术和人工智能的发展，基于深度学习的识别技术在英语、日语等领域的应用越来越广泛。<br />
<br />
===关键词===<br />
机器翻译；政治发言；政治发言中译日的误译<br />
===1. Introduction===<br />
===Introduction to machine translation===<br />
Machine translation, also known as automatic translation, is a process of using computers to convert one natural language (source language) into another natural language (target language). It is a branch of computational linguistics, one of the ultimate goals of artificial intelligence, and has important scientific research value.At the same time, machine translation has important practical value. With the rapid development of economic globalization and the Internet, machine translation technology plays a more and more important role in promoting political, economic and cultural exchanges.The development of machine translation technology has been closely accompanied by the development of computer technology, information theory, linguistics and other disciplines. From the early dictionary matching, to the rule translation of dictionaries combined with linguistic expert knowledge, and then to the statistical machine translation based on corpus, with the improvement of computer computing power and the explosive growth of multilingual information, machine translation technology gradually stepped out of the ivory tower and began to provide real-time and convenient translation services for general users.<br />
<br />
=== C-J machine translation software===<br />
Today's online machine translation software includes Baidu translation, Tencent translation, Google translation, Youdao translation, Bing translation and so on. Google was the first company to launch the machine translation system, and Baidu was the first company to import the machine translation system in China. In addition, Tencent and Youdao have attracted much attention.Machine translation is the process of using computers to convert one natural language into another. It usually refers to sentence and full-text translation between natural languages. In order to continuously improve the translation quality, R & D personnel have added artificial intelligence technologies such as speech recognition, image processing and deep neural network to machine translation on the basis of traditional machine translation based on rules, statistics and examples.With the increase of using machine translation, the joint cooperation between manual translation and machine translation will also increase significantly in the future. What criteria should be used to evaluate the quality of machine translation? In the evolving field of machine translation, there is an urgent need to clarify the unsolvable questions and solved problems.<br />
<br />
===The history of machine translation===<br />
The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. alchuni put forward the idea of using machines for translation. In 1933, Soviet inventor П.П. Trojansky designed a machine to translate one language into another, and registered his invention on September 5 of the same year; However, due to the low technical level in the 1930s, his translation machine was not made. In 1946, the first modern electronic computer ENIAC was born. Shortly after that, W. weaver, an American scientist and A. D. booth, a British engineer, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947 when discussing the application scope of electronic computer. In 1949, W. Weaver published the translation memorandum, which formally put forward the idea of machine translation. After 60 years of ups and downs, machine translation has experienced a tortuous and long development path. The academic community generally divides it into the following four stages:<br />
Pioneering period（1947-1964）<br />
In 1954, with the cooperation of IBM, Georgetown University completed the English Russian machine translation experiment with ibm-701 computer for the first time, showing the feasibility of machine translation to the public and the scientific community, thus opening the prelude to the study of machine translation.<br />
It is not too late for China to start this research. As early as 1956, the state included this research in the national scientific work development plan. The topic name is "machine translation, the construction of natural language translation rules and the mathematical theory of natural language". In 1957, the Institute of language and the Institute of computing technology of the Chinese Academy of Sciences cooperated in the Russian Chinese machine translation experiment, translating 9 different types of more complex sentences.<br />
From the 1950s to the first half of the 1960s, machine translation research has been on the rise. The United States and the former Soviet Union, two superpowers, have provided a lot of financial support for machine translation projects for military, political and economic purposes, while European countries have also paid considerable attention to machine translation research due to geopolitical and economic needs, and machine translation has become an upsurge for a time. In this period, although machine translation is just in the pioneering stage, it has entered an optimistic period of prosperity.<br />
Frustrated period（1964-1975）<br />
In 1964, in order to evaluate the research progress of machine translation, the American Academy of Sciences established the automatic language processing Advisory Committee (Alpac Committee) and began a two-year comprehensive investigation, analysis and test.<br />
In November 1966, the committee published a report entitled "language and machine" (Alpac report for short), which comprehensively denied the feasibility of machine translation and suggested stopping the financial support for machine translation projects. The publication of this report has dealt a blow to the booming machine translation, and the research of machine translation has fallen into a standstill. Coincidentally, during this period, China broke out the "ten-year Cultural Revolution", and basically these studies also stagnated. Machine translation has entered a depression.<br />
convalescence（1975-1989）<br />
Since the 1970s, with the development of science and technology and the increasingly frequent exchange of scientific and technological information among countries, the language barriers between countries have become more serious. The traditional manual operation mode has been far from meeting the needs, and there is an urgent need for computers to engage in translation. At the same time, the development of computer science and linguistics, especially the substantial improvement of computer hardware technology and the application of artificial intelligence in natural language processing, have promoted the recovery of machine translation research from the technical level. Machine translation projects have begun to develop again, and various practical and experimental systems have been launched successively, such as weinder system Eurpotra multilingual translation system, taum-meteo system, etc.<br />
However, after the end of the "ten-year holocaust", China has perked up again, and machine translation research has been put on the agenda again. The "784" project has paid enough attention to machine translation research. After the mid-1980s, the development of machine translation research in China has further accelerated. Firstly, two English Chinese machine translation systems, ky-1 and MT / ec863, have been successfully developed, indicating that China has made great progress in machine translation technology.<br />
New period(1990 present)<br />
With the universal application of the Internet, the acceleration of the process of world economic integration and the increasingly frequent exchanges in the international community, the traditional way of manual operation is far from meeting the rapidly growing needs of translation. People's demand for machine translation has increased unprecedentedly, and machine translation has ushered in a new development opportunity. International conferences on machine translation research have been held frequently, and China has made unprecedented achievements. A series of machine translation software have been launched, such as "Yixing", "Yaxin", "Tongyi", "Huajian", etc. Driven by the market demand, the commercial machine translation system has entered the practical stage, entered the market and came to the users.<br />
Since the new century, with the emergence and popularization of the Internet, the amount of data has increased sharply, and statistical methods have been fully applied. Internet companies have set up machine translation research groups and developed machine translation systems based on Internet big data, so as to make machine translation really practical, such as "Baidu translation", "Google translation", etc. In recent years, with the progress of in-depth learning, machine translation technology has further developed, which has promoted the rapid improvement of translation quality, and the translation in oral and other fields is more authentic and fluent.<br />
<br />
===The problem of machine translation at present===<br />
Error is inevitable<br />
Many people have misunderstandings about machine translation. They think that machine translation has great deviation and can't help people solve any problems. In fact, the error is inevitable. The reason is that machine translation uses linguistic principles. The machine automatically recognizes grammar, calls the stored thesaurus and automatically performs corresponding translation. However, errors are inevitable due to changes or irregularities in grammar, morphology and syntax, such as sentences with adverbials after "give me a reason to kill you first" in Dahua journey to the West. After all, a machine is a machine. No one has special feelings for language. How can it feel the lasting charm of "the tenderness of lowering its head, like the shame of a water lotus? After all, the meaning of Chinese is very different due to the changes of morphology, grammar and syntax and the change of context. Even many Chinese people are zhanger monks - they can't touch their heads, let alone machines.<br />
Bottleneck<br />
In fact, no matter which method, the biggest factor affecting the development of machine translation lies in the quality of translation. Judging from the achievements, the quality of machine translation is still far from the ultimate goal.<br />
Chinese mathematician and linguist Zhou Haizhong once pointed out in his paper "fifty years of machine translation": to improve the quality of machine translation, the first thing to solve is the problem of language itself rather than programming; It is certainly impossible to improve the quality of machine translation by relying on several programs alone. At the same time, he also pointed out that it is impossible for machine translation to achieve the degree of "faithfulness, expressiveness and elegance" when human beings have not yet understood how the brain performs fuzzy recognition and logical judgment of language. This view may reveal the bottleneck restricting the quality of translation. <br />
It is worth mentioning that American inventor and futurist ray cozwell predicted in an interview with Huffington Post that the quality of machine translation will reach the level of human translation by 2029. There are still many disputes about this thesis in the academic circles.<br />
<br />
===2.Mistranslation of Chinese Japanese machine translation===<br />
===2.1Vocabulary mistranslation in Chinese Japanese machine translation===<br />
===2.1.1Mistranslation of proper nouns===<br />
In political materials, there are often political dignitaries' names, place names or a large number of proper nouns in the political field. The morphemes of such words are definite and inseparable. Mistranslation will make the source language lose its specific meaning. <br />
<br />
Chinese translation into Japanese Japanese translation into Chinese<br />
<br />
original text translation by Youdao reference translation original text translation by Youdao reference translation<br />
栗战书 栗戰史書 栗戰書 労安 劳安 劳安<br />
李克强 李克強 李克強 朱鎔基 朱基 朱镕基<br />
习近平 習近平 習近平 筑紫哲也 筑紫哲也 筑紫哲也<br />
韩正 韓中 韓正 山口百惠 山口百惠 山口百惠<br />
王沪宁 王上海氏 王滬寧 田中角栄 田中角荣 田中角荣<br />
汪洋 汪洋 汪洋 東条英機 东条英社 东条英机<br />
赵乐际 趙樂南 趙樂際 毛沢东 毛泽东 毛泽东<br />
江泽民 江沢民 江沢民 トウ・ショウヘイ 大酱 邓小平<br />
周恩来 周恩来 周恩来<br />
クリントン 克林顿 克林顿<br />
<br />
The above table counts 18 special names in the two texts, and 7 machine translation errors. In terms of mistranslation, there are not only "战书" but also "戰史" and "史書" in Chinese. "沪" is the abbreviation of Shanghai. In other words, since "战书" and "沪" are originally common nouns, this disrupts the choice of target language in machine translation. Except Katakana interference (except for トウシゃウヘイ, most of the mistranslations appear in the new Standing Committee. It can be seen that the machine translation system does not update the thesaurus in time. Different from other words, people's names have particularity. Especially as members of the Standing Committee of the Political Bureau of the CPC Central Committee, the translation of their names is officially unified. In this regard, public figures such as political dignitaries, stars, famous hosts and important. In addition, the machine also translates Japanese surnames such as "タ二モト" (谷本), "アンドウ" (安藤) into "塔尼莫特" and "龙胆". It can be found that the language feature that Japanese will be written together with Chinese characters and Hiragana also directly affects the translation quality of Japanese Chinese machine translation. Japanese people often use Katakana pronunciation. For example, when Japanese people talk with Premier Zhu, they often use "二ーハウ" (Hello). However, the machine only recognizes the two pseudonyms "二" and "ハ" and transliterates them into "尼哈" , ignoring the long sound after "二" and "ハ".<br />
<br />
original text Translation by Youdao reference translation<br />
日美安全体制 日米の安全体制 日米安保体制<br />
中国共产党第十九次全国代表大会 中国共産党第19回全国代表大会 中国共産党第19回全国代表大会（第19回党大会）<br />
十八大 十八大 第18回党大会中国特色社会主义 中国特色社会主義 中国の特色ある社会主義<br />
中国共产党中央委员会 中国共産党中央委員会 中国共産党中央委員会十八届中共中央政治局常委 第18代中国共產党中央政治局常務委員 第18期中共中央政治局常務委員<br />
十八届中共中央政治局委员 18期の中国共產党中央政治局委員 第18期中共中央政治局委員<br />
十九届中共中央政治局常委 十九回中国共產党中央政治局常務委員 第19期中央政治局常務委員<br />
中共十九届一中全会 中国共產党第十九回一中央委員会 第19期中央委員会第1回全体会議<br />
<br />
The above table is a comparison of the original and translated versions of some proper nouns. As shown in the table, the mistranslation problems are mainly reflected in the mismatch of numerals + quantifiers, the wrong addition of case auxiliary word "の", the lack of connectors, the Mistranslation of abbreviations, dead translation, and the writing errors of Chinese characters. The following is a specific analysis one by one.<br />
"十八届中共中央政治局常委", "十八届中共中央政治局委员", "十九届中共中央政治局常委" and "中共十九届一中全会" all have quantifiers "届", which are translated into "代", "期" and "回" respectively. The meaning is vague and should be uniformly translated into "期"; Among them, the translation of the last three proper nouns lacks the Chinese conjunction "第". The case auxiliary word "の" was added by mistake in the translation of "十八届中共中央政治局委员" and "日美安全体制". The "中国共产党中央委员会" did not write in the form of Japanese Ming Dynasty characters, but directly used simplified Chinese characters.<br />
The full name of "中共十九届一中全会" is "中国共产党第十九届中央委员会第一次全体会议", in which "一" stands for "第一次", which is an ordinal word rather than a cardinal word. Machine translation does not produce a correct translation. The fundamental reason is that there are no "规则" for translating such words in machine translation. If we can formulate corresponding rules for such words (for example, 中共m'1届m2 中全会→第m1期中央委员会第m2回全体会议), the translation system must be able to translate well no matter how many plenary sessions of the CPC Central Committee.<br />
<br />
===2.1.2Mistranslation of Polysemy===<br />
<br />
original text Translation by Youdao reference translation<br />
スタジオ 摄影棚/工作室 直播现场/演播厅<br />
日中関係の話 中日关系的故事 就中日关系(话题)<br />
溝 水沟 鸿沟<br />
それでは日中の問題について質問のある方。 那么对白天的问题有提问的人。 关于中日问题的话题，举手提问。<br />
私たちのクラスは20人ちょっとですが、 我们班有20人左右， 我们班二十多人的意见统一很难，<br />
いろいろな意見が出て、まとめるのは大変です。 但是又各种各样的意见，总结起来很困难。 中国是怎样把13亿人凝聚在一起的？<br />
一体どうやって、13億人もの人をまとめているんですか。 到底是怎么处理13亿的人的呢？ <br />
In the original text, the word "スタジオ" appeared four times and was translated into "摄影棚" or "工作室" respectively, but the context is on-site interview, not the production site of photography or film. "話" has many semantics, such as "说话", "事情", "道理", etc. In accordance with the practice of the interview program, Premier Zhu Rongji was invited to answer five questions on the topic of China Japan relations. Obviously, the meaning of the word "故事" is very abrupt. The inherent Japanese word "日中" means "晌午、白天". At the same time, it is also the abbreviation of the names of the two countries. The machine failed to deal with it correctly according to the context. "溝" refers to the gap between China and Japan, not a "水沟". The former "まとめる" appears in the original text with the word "意见", which is intended to describe that it is difficult to unify everyone's opinions. The latter "まとめている" also refers to unifying the thoughts of 1.3 billion people, not "处理". Therefore, it is more appropriate to use "统一" and "处理" in human translation, rather than "总结意见" or "处理意见".<br />
Mistranslation of polysemy has always been a difficult problem in machine translation research. The translation of each word is correct, but it is often very different from the original expression in the context. Zhang Zhengzeng said that ambiguity is a common phenomenon in natural language. Its essence is that the same language form may have different meanings, which is also one of the differences between natural language and artificial language. Therefore, one of the difficulties faced by machine translation is language disambiguation (Zhang Zheng, 2005:60). In this regard, we can mark all the meanings of polysemous words and judge which meaning to choose by common collocation with other words. At the same time, strengthen the text recognition ability of the machine to avoid the translation inconsistent with the current context. In this way, we can avoid the mistake of "大酱" in the place where famous figures such as Deng Xiaoping should have appeared in the previous political articles.<br />
<br />
===2.1.3Mistranslation of compound words===<br />
Multi class words refer to a word with two or more parts of speech, also known as the same word and different classes.<br />
original text Translation by Youdao reference translation<br />
1998年江泽民主席曾经访问日本, 1998年の江沢民国家主席の日本訪問し、 1998年、江沢民総書記が日本を訪問し、かつ、<br />
同已故小渊首相签署了联合宣言。 かつて同じ故小渕首相が署名した共同宣言 亡くなられた小渕総理と宣言に調印されました<br />
<br />
Chinese "同" has two parts of speech: prepositions and conjunctions: "故" has many parts of speech, such as nouns, verbs, adjectives and conjunctions. This directly affects the judgment of the machine at the source language level. The word "同" in the above table is used as a conjunction to indicate the other party of a common act. "故" is used as a verb with the semantic meaning of "死亡", which is a modifier of "小渊首相". The machine regards "同" as an adjective and "故" as a noun "原因", which leads to confusion in the structure and unclear semantics of the translation. Different from the polysemy problem, multi category words have at least two parts of speech, and there is often not only one meaning under each part of speech. In this regard, software R & D personnel should fully consider the existence of multi category words, so that the translation machine can distinguish the meaning of words on the basis of marking the part of speech, so as to select the translation through the context and the components of the word in the sentence. Of course, the realization of this function is difficult, and we need to give full play to the wisdom of R & D personnel.<br />
<br />
===2.2 Syntactic mistranslation in Chinese Japanese machine translation===<br />
===2.2.1Mistranslation of tenses===<br />
original text：History is written by the people, and all achievements are attributed to the people. <br />
Translation by Youdao：歴史は人民が書いたものであり、すべての成果は人民のためである。<br />
reference translation：歴史は人民が綴っていくものであり、すべての成果は人民に帰することとなります。<br />
The original sentence meaning is "历史是人民书写的历史" or "历史是人民书写的东西". When translated into Japanese, due to the influence of Japanese language habits, the formal noun "もの" should be supplemented accordingly. In this sentence, both the machine and the interpreter have translated correctly. However, the machine's recognition of "的" is biased, resulting in tense translation errors. The past, present and future will become "history", and this is a continuous action. The form translated as "~ ていく" not only reflects that the action is a continuous action, but also conforms to the tense and semantic information of the original text. In addition, the machine's treatment of the preposition "于" is also inappropriate. In the original text, "人民" is the recipient of action, not the target language. As the most obvious feature of isolated language, function words in Chinese play an important role in semantic expression, and their translation should be regarded as a focus of machine translation research.<br />
<br />
original text：李克强同志是十六届中共中央政治局常委,其他五位同志都是十六届中共中央政治局委员。<br />
Translation by Youdao：李克強総理は第16代中国共産党中央政治局常務委員であり、他の５人の同志はいずれも16期の中国共産党中央政治局委員である。<br />
reference translation：李克強同志は第16期中国共産党中央政治局常務委員を務め、他の５人は第16期中共中央政治局委員を務めました。<br />
Judging the verb "是" in the original text plays its most basic positive role, but due to the complexity of Chinese language, "是" often can not be completely transformed into the form of "だ / である" in the process of translation. This sentence is a judgment of what has happened. Compared with the 19th session, the 18th session has become history. This is an implicit temporal information. It is very difficult for machines without human brain to recognize this implicit information. "中共中央政治局常委" is the abbreviation of "中共中央政治局常务委员会委员" and it is a kind of position. In Japanese, often use it with verbs such as "務める","担当する",etc. The translator adopts the past tense of "務める", which conforms to the expression habit of Japanese and deals with the problem of tense at the same time. However, machine translation only mechanically translates this sentence into judgment sentence, and fails to correctly deal with the past information implied by the word "十八届".<br />
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===2.2.2Mistranslation of honorifics===<br />
Honorific language is a language means to show respect to the listener. Different from Japanese, there is no grammatical category of honorifics in Chinese. There is no specific fixed grammatical form to express honorifics, self modesty and politeness. Instead, specific words such as "您", "请" and "劳驾" are used to express all kinds of respect or self modesty. <br />
Original text translation by Youdao reference translation<br />
女士们，先生们，同志们，朋友们 さんたち、先生たち、同志たち、お友达さん ご列席の皆さん<br />
谢谢大家！ ありがとうございます！ ご清聴ありがとうございました。<br />
これはどうされますか。 这是怎么回事呢? 您将如何解决这一问题？<br />
こうした問題をどうお考えでしょうか。 我们会如何考虑这些问题呢？ 您如何看待这一问题？ <br />
For example, for the processing of "女士们，先生们，同志们，朋友们", the machine makes the translation correspond to the original one by one based on the principle of unchanged format, but there are errors in semantic communication and pragmatic habits. When speaking on formal occasions, Chinese expression tends to be comprehensive and detailed, as well as the address of the audience. In contrast, Japanese usually uses general terms such as "皆様", "ご臨席の皆さん" or "代表団の方々" as the opening remarks. In terms of Japanese Chinese translation, the machine also failed to recognize the usage of Japanese honorifics such as "さ れ る" and "お考え". It can be seen that Chinese Japanese machine translation has a low ability to deal with honorific expressions. The occasion is more formal. A good translation should not only be fluent in meaning, but also conform to the expression habits of the target language and match the current translation environment.<br />
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===Conclusion===<br />
In the process of discussing the Mistranslation of machine translation, this paper mainly cites the Mistranslation of Youdao translator. When I studied the problem of machine translation mistranslation, I also used a large number of translation software such as Google and Baidu for parallel comparison, and found that these translation software also had similar problems with Youdao translator. For example, the "新世界新未来" in Chinese ABAC structural phrases is translated by Baidu as "新し世界の新しい未来", which, like Youdao, is not translated in the form of parallel phrases; Google online translation translates the word "“全心全意" into a completely wrong "全面的に"; The original sentence "我吃了很多亏" in the Mistranslation of the unique expression in the language is translated by Google online as "私はたくさんの損失を食べました". Because the "吃" and "亏" in the original text are not closely adjacent, the machine can not recognize this echo and mistakenly treats "亏" as a kind of food, so the machine translates the predicate as "食べました"; Japanese "こうした問題をどう考えでしょうか", Google's online translation is "你如何看待这些问题?", "你" tone can not reflect the tone of Japanese honorific, while Baidu translates as "你怎么想这样的问题呢?" Although the meaning can be understood, it is an irregular spoken language. Google and Baidu also made the same mistakes as Youdao in the translation of proper nouns. For example, they translated "十七届(中共中央政治局常委)”" into "第17回..." .In short, similar mistranslations of Youdao are also common in Baidu and Google. Due to space constraints, they will not be listed one by one here. <br />
Mr. Liu Yongquan, Institute of language, Chinese Academy of Social Sciences (1997) It has been pointed out that machine translation is a linguistic problem in the final analysis. Although corpus based machine translation does not require a lot of linguistic knowledge, language expression is ever-changing. Machine translation based solely on statistical ideas can not avoid the translation quality problems caused by the lack of language rules. The following is based on the analysis of lexical, syntactic and other mistranslations From the perspective of the characteristics of the source language, this paper summarizes some difficulties of Chinese and Japanese in machine translation. <br />
(1) The difficulties of Chinese in machine translation <br />
Chinese is a typical isolated language. The relationship between words needs to be reflected by word order and function words. We should pay attention to the transformation of function words such as "的", "在", "向" and "了". Some special verbs, such as "是", "做" and "作", are widely used. How to translate on the basis of conforming to Japanese pragmatic habits and expressions is a difficulty in machine translation research. In terms of word order, Chinese is basically "subject→predicate→object". At the same time, Chinese pays attention to parataxis, and there is no need to be clear in expression with meaningful cohesion, which increases the difficulty of Chinese Japanese machine translation. When there are multiple verbs or modifier components in complex long sentences, machine translation usually can not accurately divide the components of the sentence, resulting in the result that the translation is completely unreadable. <br />
(2) Difficulties of Japanese in machine translation <br />
Both Chinese and Japanese languages use Chinese characters, and most machines produce the target language through the corresponding translation of Chinese characters. However, pseudonyms in Japanese play an important role in judging the part of speech and meaning, and can not only recognize Chinese characters and judge the structure and semantics of sentences. Japanese vocabulary is composed of Chinese vocabulary, inherent vocabulary and foreign vocabulary. Among them, the first two have a great impact on Chinese Japanese machine translation. For example "提出" is translated as "提出する" or "打ち出す"; and "重视" is translated as "重視する" or "大切にする". Japanese is an adhesive language, which contains a lot of "けど", "が" and "けれども" Some of them are transitional and progressive structures, but there are also some sequential expressions that do not need translation, and these translation software often can not accurately grasp them.<br />
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===References===<br />
[1] Navroz Kaur Kahlon;Williamjeet Singh.Machine translation from text to sign language: a systematic review[J].Universal Access in the Information Society,2021(prepublish):1-35.<br />
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[2] Cao Qianyu;Hao Hanmei;Ahmed Syed Hassan.A Chaotic Neural Network Model for English Machine Translation Based on Big Data Analysis[J].Computational Intelligence and Neuroscience,2021,2021:3274326-3274326.<br />
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[3]Hwang Yongkeun;Kim Yanghoon;Jung Kyomin.Context-Aware Neural Machine Translation for Korean Honorific Expressions[J].Electronics,2021,10(13):1589-1589.<br />
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[4]Zakaryia Almahasees.Analysing English-Arabic Machine Translation:Google Translate, Microsoft Translator and Sakhr,2021.<br />
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[5]Machine learning in translation[J].Nature Biomedical Engineering,2021,5(6):485-486.<br />
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[6]Shaimaa Marzouk.An in-depth analysis of the individual impact of controlled language rules on machine translation output: a mixed-methods approach[J].Machine Translation,2021(prepublish):1-37.<br />
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[7]Welnitzová Katarína;Munková Daša.Sentence-structure errors of machine translation into Slovak[J].Topics in Linguistics,2021,22(1):78-92.<br />
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[8]Xu Xueyuan.Machine learning-based prediction of urban soil environment and corpus translation teaching[J].Arabian Journal of Geosciences,2021,14(11). <br />
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[9]陈丙昌.機械翻訳の誤訳分析【D】.贵州大学.2016(05) <br />
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[10]呂寅秋.機械翻訳の言語規則と伝統文法との相違点.日本学研究.1996(00):21-22 <br />
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[11]刘君.基于语料库的中日同形词词义用法对比及其日中机器翻译研究【D】.广西大学.2014(03) <br />
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[12]崔倩倩.机器翻译错误与译后编辑策略研究【D】.北京外国语大学.2019(09) <br />
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[13]张义.机器翻译的译文分析【D】.西安外国语大学.2019(10) <br />
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[14]张琳婧.在线机器翻译中日翻译错误原因及对策【D】.山西大学.2019(02)<br />
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[15]王丹.基于机器翻译的专利文本译后编辑对策研究【D】.大连理工大学.2020(06)<br />
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[16]杨晓琨.日中机器翻译中的前编辑规则与效果验证【D】.大连理工大学.2020(06)<br />
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[17]左嘉. 机器翻译日译汉误译研究[D]. 北京第二外国语学院, 2021.<br />
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[18]关碧莹.关于政治类发言的汉日机器翻译误译分析[D].哈尔滨理工大学, 2018.<br />
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[19]车彤.汉译日机器翻译质量评估及译后编辑策略研究【D】.北京外国语大学.2021(09)<br />
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Networking Linking<br />
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http://www.elecfans.com/rengongzhineng/692245.html<br />
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https://baike.baidu.com/item/%E6%9C%BA%E5%99%A8%E7%BF%BB%E8%AF%91/411793<br />
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=13 陈湘琼Chen Xiangqiong（Study on Post-editing from the Perspective of Functional Equivalence Theory ）=<br />
[[Machine_Trans_EN_13]]<br />
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=14 Bi bi Nadia（Machine Translation a Challenge for Human Translators)=<br />
[[Machine_Trans_EN_14]]<br />
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===Abstract===<br />
Machine translation is a big obstacle in the way of Human translators or interpretors although it is quick and less time consuming.people are trying to get translation of their target language using through source language.For this purpose they are using digital apps like Google translation Google translation does not give accurate and exact interpretation.Google translator is translates word to word translate that doesn't clarifies it's true and actual meaning.On the other hand human translators can give exact and accurate translation ,they take care of grammatical errors , diction and sentence structure.They clarify the purpose of target language through using source language.<br />
===Keywords===<br />
Descriptive translation, academic, interdiscipline, comparative literature, localization,Translatology,school of thought, translation , studies, linguistics, corresponding<br />
===Body of article===<br />
Translation is the process of reworking text from one language into another to maintain the original message and communication. But, like everything else, there are different methods of translation, and they vary in form and function. What is translation? The term has become widely used among knowledge transfer researchers and practitioners, especially in the fields of health and health care. In a landmark review, Jonathan Lomas began to argue that 'The tasks… may be defined as to establish and maintain links between researchers and their audience, via the appropriate translation of research findings' (Lomas 1997, p 4). In 2004, the World Health Organization's World Report on Knowledge for Better Health suggested that 'One of the key contributions of research to health systems is the translation of knowledge into actions' (WHO 2004, p 33 and p 100). By 2006, special issues of WHO's Bulletin as well as the journals Evaluation and the Health Professions and the Journal of Continuing Education in the Health Professions were dedicated to translation.But what does 'translation' mean? It may be a new word for an old problem, meaning nothing more than 'transfer'. The rapidly emerging field of 'translational medicine' seems to take translation to mean generally what transfer might have meant, that is the transmission of knowledge and evidence 'from bench to bedside'. As one commentator has observed, "Translational medicine" as a fashionable term is being increasingly used to describe the wish of biomedical researchers to ultimately help patients' (Wehling 2008, abstract). <br />
Semantic uncertainty persists, not least because of the different interests of scientists, clinicians, patients and commercial firms (Littman et al 2007): 'Translational research means different things to different people, but it seems important to almost everyone' (Woolf 2008, p 211).<br />
In related fields, such as public health (Armstrong et al 2006), 'translation' seems to signify dissatisfaction with 'transfer'. It wants to move away from thinking of knowledge transfer as a form of technology transfer or dissemination, rejecting if only by implication its mechanistic assumptions and its model of linear messaging from A to B. But still, what does it signify?<br />
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===Why translation?===<br />
'Translation' indicates a closer attention to the problem of shared meaning and how it might be developed. It seems to represent some new epistemological lubricant, facilitating the dissemination of texts and the application and use of the knowledge and information they in. Simply, translation might be the key to transfer. And yet, when we stop to think, we are more ambivalent. What is translated often seems somehow inferior, not real or original. Note how readily commentators reach for the idea that things might be 'lost in translation'. Knowing at a distance – made in and mediated by translation - makes for incomplete renditions, blurred images, partial truths. So what might 'translation' really mean? The purpose of this paper is to set out, for policy makers and practitioners, the theoretical and conceptual resources translation holds and seems to represent. In doing so, it explores understandings of translation in the fields of literature and linguistics and in the sociology of science and technology. It begins by setting out just why this idea of translation should make immediate, intuitive sense in relation to research, policy and practice.<br />
1translation in research, policy and practice research as translation<br />
Research often entails translation from one language to another: where data is collected from more than one ethnic group, for example, or where the language of the researcher is other than that of the research subject. It may draw on a secondary literature or source documents written in different languages, and may be published and disseminated in languages other than the one in which it is first written up.<br />
2In a different way, to conduct an interview is to ask for an account of experience and its meanings, but it is also to construct and translate that experience in terms defined at least in part by the researcher. In representing what is said, transcripts then select data, usually excluding significant gesture and eye-contact, for example. Often, certain characteristics of speech-acts (such as hesitations) will be edited out. In turn, the format of the transcript shapes the analytic use the researcher may make of it. The basis of research 'findings', then, is an artefact, a transcript or translation, not an original interaction (Ochs 1979, Barnes, Bloor and Henry 1996, Ross 2009).<br />
3In this way, the researcher recasts aspects of his or her problem or topic in new, scientific form: 'All researchers "translate" the experiences of others' (Temple 1997, p 609). Research is invariably conducted in a sort of 'metalanguage' (Hantrais and Ager 1985): the research process can be conceived as one of successive translations (from theoretical formulation to operationalization, transcription, interpretation and dissemination). Theorization is a process of reciprocal back and forth between theory and fact, in which conceptions of each are revised in order that one fit the other (Baldamus 1974).<br />
4 It is a kind of translation: a rereading, re-use, re-application or re-representation of what we know in new terms (Turner 1980). Referencing, too, is an act of translation, a form of appropriation and incorporation of one text by another (Gilbert 1977).policy as translation Each of the fields covered by the paper is diverse and ill-defined, and there is no intention here to provide a comprehensive account of any them. Sources have been chosen for their relevance: main references are cited in the text, and additional sources listed in footnotes.<br />
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2For a brief introduction to the technical issues involved in social research in more than one language, see Birbili (2000). For translation issues in survey research and question design in general, see Ervin and Bower (1952) and Deutscher (1968); on translating survey research instruments (in this instance health-related quality of life measures), Bowden and Fox-Rushby (2003). On the use of translators and interpreters, see Temple (1997) and Jentsch (1998).<br />
3For an interesting discussion of this problem, see Bourdieu (1999), esp pp 621-626, 'The risks of writing'. <br />
===Difference between machine translation and human translators===<br />
Few people disagree on the differences between the two, but many argue over the quality of the translations. How accurate are machine translations? How reliable are human translations? Some say machine translation produces near-perfect translations while others are adamant that translations are incomprehensible and cause more problems than they solve. Results will, of course, vary depending on the source and target languages, the machine translation service used (e.g. Google Translate), and the complexity of the original text.<br />
Machine translation, love it or hate it, is here to stay. In fact, the machine translation market is growing at such a fast pace that it is predicted to reach $980 million by 2022.<br />
===Machine translation: the pros and cons===<br />
The advantages of machine translation generally come down to two factors: it’s faster and cheaper. The downside to this is the standard of translation can be anywhere from inaccurate, to incomprehensible, and potentially dangerous (more on that shortly).<br />
==The advantages of machine translation==<br />
Many free tools are readily available (Google Translate, Skype Translator, etc.)<br />
Quick turnaround time <br />
You can translate between multiple languages using one tool<br />
Translation technology is constantly improving<br />
The disadvantages of machine translation<br />
Level of accuracy can be very low <br />
Accuracy is also very inconsistent across different languages<br />
==Machines can't translate context ==<br />
Mistakes are sometimes costly <br />
Sometimes translation simply doesn’t work<br />
The most important thing to consider with any kind of translation is the cost of potential mistakes. Translating instructions for medical equipment, aviation manuals, legal documents and many other kinds of content require 100% accuracy. In such cases, mistakes can cost lives, huge amounts of money and irreparable damage to your company’s image. So choose carefully!<br />
Human translation: the pros and cons<br />
Human translation essentially switches the table in terms of pros and cons. A higher standard of accuracy comes at the price of longer turnaround times and higher costs. What you have to decide is whether that initial investment outweighs the potential cost of mistakes. Alternatively, whether mistakes simply aren’t an option, like the cases we looked at in the previous section.<br />
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==The advantages of human translation ==<br />
It’s a translator’s job to ensure the highest accuracy<br />
Humans can interpret context and capture the same meaning, rather than simply translating words<br />
Human translators can review their work and provide a quality process<br />
Humans can interpret the creative use of language, e.g. puns, metaphors, slogans, etc.<br />
Professional translators understand the idiomatic differences between their languages<br />
Humans can spot pieces of content where literal translation isn’t possible and find the most suitable alternative<br />
The disadvantages of human translation<br />
Turnaround time is longer <br />
Translators rarely work for free <br />
Unless you use a translation agency, with access to thousands of translators, you’re limited to the languages any one translator can work with<br />
Simply put, human translation is your best option when accuracy is even remotely important. Other considerations to make are the complexity of your source material and the two languages you’re translating between – both of which can render machines pretty useless.<br />
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When to use machine and human translation<br />
The truth is, the debate over machine vs human translation is an unnecessary distraction. What we should really be talking about is when to use these two different types of translation services, because they both serve a very valid purpose.<br />
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Examples of when to use machine translation<br />
When you have a large bulk of content to translate and the general meaning is enough<br />
When your translation never reaches the final audience, e.g. you’re translating a resource as research for another piece of content<br />
Translating documents for internal use within a company, provided 100% accuracy isn’t needed<br />
To partially translate large chunks of content for a human translator to improve upon<br />
Examples of when to use human translation<br />
When accuracy is important<br />
Most cases where your translated content is received by a consumer audience<br />
When you have a duty of care to provide accurate translations (e.g. legal documents, product instructions, medical guidelines or health and safety content)<br />
When translating marketing material or other texts for creative language uses.<br />
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===Conclusion ===<br />
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In the light of above mentioned facts and figures here by we would say that machine translation is challenge for human translators because it can reduces the wokload of translation but can't give accurate and exact translation of the traget language.It can be less reliable than human translation..</div>Chen Huinihttps://bou.de/u/index.php?title=Machine_translation&diff=129775Machine translation2021-12-08T01:50:00Z<p>Chen Huini: /* 2.1.1Mistranslation of proper nouns */</p>
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<div>'''Machine Translation - A challenge or a chance for human translators?'''<br />
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[[Machine_translation|Overview Page of Machine Translation]]<br />
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[[Machine_Trans_EN_1]] [[Machine_Trans_EN_2]] [[Machine_Trans_EN_3]] [[Machine_Trans_EN_4]] [[Machine_Trans_EN_5]] [[Machine_Trans_EN_6]] [[Machine_Trans_EN_7]] [[Machine_Trans_EN_8]] [[Machine_Trans_EN_9]] [[Machine_Trans_EN_10]] [[Machine_Trans_EN_11]] [[Machine_Trans_EN_12]] [[Machine_Trans_EN_13]] [[Machine_Trans_EN_14]] [[Machine_Trans_EN_15]] [[Machine_Trans_EN_16]] [[Machine_Trans_EN_17]] [[Machine_Trans_EN_18]] [[Machine_Trans_EN_19]] [[Machine_Trans_EN_20]] [[Machine_Trans_EN_21]] [[Machine_Trans_EN_22]] [[Machine_Trans_EN_23]] [[Machine_Trans_EN_24]] [[Machine_Trans_EN_25]] [[Machine_Trans_EN_26]] [[Machine_Trans_EN_27]] [[Machine_Trans_EN_28]] [[Machine_Trans_EN_29]] [[Machine_Trans_EN_30]] ...<br />
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[[Book_projects|Back to translation project overview]]<br />
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[[DCG-To-Do|To the To Do list]]<br />
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=1 卫怡雯(A Comparison Between the Quality of Machine Translation and Human Translation——A Case Study of the Application of artificial intelligence in Sports Events)=<br />
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=2 吴映红（The Introduction of Machine Translation)= <br />
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=3 肖毅瑶(On the Realm Advantages And Symbiotic Development of Machine Translation And Huamn Translation)=<br />
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=4 王李菲 （Comparison Between Neural Machine Translation of Netease and Traditional Human Translation—A Case Study of The Economist Articles)= <br />
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=6 徐敏赟(Machine Translation Based on Neural Network --Challenge or Chance?)=<br />
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=7 颜莉莉(一带一路背景下人工智能与翻译人才的培养)=<br />
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===Abstract===<br />
In the era of artificial intelligence, artificial intelligence has been applied to various fields. In the field of translation, traditional translation models can no longer meet the rapid development and updating of the information age. The development of machine translation has brought structural changes to the language service industry, which poses challenges to the cultivation of translation talents. Under the background of "The Belt and Road initiative", translation talents have higher and higher requirements on translation literacy. Artificial intelligence and translation technology are used to reform the training mode of translation talents, so as to better serve the development of The Times. This paper mainly explores the cultivation of artificial intelligence and translation talents under the background of the Belt and Road Initiative. The cultivation of translation talents is moving towards comprehensive cultivation of talents. On the contrary, artificial intelligence and machine translation can also be used to improve the teaching mode and teaching content, so as to win together in cooperation.<br />
===Key words===<br />
Artificial intelligence,Machine translation,cultivation of translation talents,"The Belt and Road initiative"<br />
===题目===<br />
一带一路背景下人工智能与翻译人才的培养<br />
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===摘要===<br />
进入人工智能时代，人工智能被应用于各个领域。在翻译领域，传统的翻译模式已无法满足信息化时代的飞速发展和更新，机器翻译的发展给语言服务行业带来了结构性改变，这对翻译人才的培养提出了挑战。“一带一路”背景下，对翻译人才的翻译素养要求越来越高，利用人工智能和翻译技术对翻译人才培养模式进行革新，更好为时代发展服务。本文主要探究在一带一路背景下人工智能和翻译人才培养，翻译人才的培养过程中正向对人才的综合性培养，反之也可以利用人工智能和机器翻译完善教学模式和教学内容，在合作中共赢。<br />
===关键词===<br />
人工智能；机器翻译；翻译人才培养；一带一路<br />
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===1. Introduction===<br />
With the development of science and technology in China, artificial intelligence has also been greatly improved, and related technologies have been applied to various fields, such as the use of intelligent robots to deliver food to quarantined people during the epidemic, which has made people's lives more convenient. The most controversial and widely discussed issue is machine translation. Before the emergence of machine translation, translation was generally dominated by human translation, including translation and interpretation, which was divided into simultaneous interpretation and hand transmission, etc. It takes a lot of time and energy to cultivate a translation talent. However, nowadays, the era is developing rapidly and information is updated rapidly. As a translation talent, it is necessary to constantly update its knowledge reserve to keep up with the pace of The Times. The emergence of machine translation has also posed challenges to translation talents and the training of translation talents. Although machine translation had some problems in the early stage, it is now constantly improving its functions. In the context of the belt and Road Initiative, both machine translation and human translation are facing difficulties. Regardless of whether human translation is still needed, what is more important at present is how to train translators to adapt to difficulties and promote the cooperation between human translation and machine translation.<br />
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===2.Development status of machine translation in the era of artificial intelligence ===<br />
With the development of AI technology, machine translation has made great progress and has been applied to people's lives. For example, more and more tourists choose to download translation software when traveling abroad, which makes machine translation take an absolute advantage in daily email reply and other translation activities that do not require high accuracy. The translation software commonly used by netizens include Google Translation, Baidu Translation, Youdao Translation, IFly.com Translation, etc. Even wechat and other chat software can also carry out instant Translation into English. Some companies have also launched translation pens, translation machines and other equipment, which enables even native speakers to rely on machine translation to carry out basic communication with other Chinese people.<br />
But so far, machine translation still faces huge problems. Although machine translation has made great progress, it is highly dependent on corpus and other big data matching. It does not reach the thinking level of human brain, and cannot deal with the problem of translation differences caused by culture and religion. In addition, many minor languages cannot be translated by machine due to lack of corpus.<br />
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What's more, most of the corpus is about developed countries such as Britain and France, and most of the corpus is about diplomacy, politics, science and technology, etc., while there are very few about nationality, culture, religion, etc.<br />
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In addition, machine translation can only be used for daily communication at present. If it involves important occasions such as large conferences and international affairs, it is impossible to risk using machine translation for translation work. Professional translators are required to carry out translation work. So machine translation still has a long way to go.<br />
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===3.Challenges in the training of translation talents in universities===<br />
The cultivation of translators is targeted at the market. Professors Zhu Yifan and Guan Xinchao from the School of Foreign Languages at Shanghai Jiao Tong University believe that the cultivation of translators can be divided into four types: high-end translators and interpreters, senior translators and researchers, compound translators and applied translators.<br />
<br />
From their names, it can be seen that high-end translators and interpreters and senior translators and researchers talents have high requirements on the knowledge and quality of interpreters, because they have to face the changing international situation, and have to deal with all kinds of sensitive relations and political related content, they should have flexible cross-cultural communication skills. In addition, for literature, sociology and humanities academic works, it is not only necessary to translate their content, but also to understand their essence. Therefore, translators should not only have humanistic feelings, but also need to have a deep understanding of Chinese and western culture.<br />
<br />
However, there is not much demand for this kind of translation in the society. Such high-level translation requirements are not needed in daily life and work. The greatest demand is for compound translators, which means that they should master knowledge in a specific field while mastering a foreign language. For example, compound translators in the financial field should not only be good at foreign languages, but also master financial knowledge, including professional terms, special expressions and sentence patterns.<br />
<br />
Now we say that machine translation can replace human translation should refer to the field of compound translation talents. Although AI technology has enabled machine translation to participate in creation, it does not mean that compound translation talents will be replaced by machines. The complexity of language and the flexible cross-cultural awareness required in communication make it impossible for machine translation to completely replace human translation.<br />
<br />
The last type of applied translation talents are mostly involved in the general text without too much technical content and few professional terms, so it is easy to be replaced by machine translation.<br />
<br />
Therefore, the author thinks that what universities are facing at present is not only how to train translation talents to cope with the development of machine translation, but to consider the application of machine translation in the process of training translation talents to achieve human-machine integration, so as to better complete the translation work.<br />
<br />
===4.The Language environment and opportunities and challenges of the Belt and Road initiative===<br />
During visits to Central and Southeast Asian countries in September and October 2013, Chinese President Xi Jinping put forward the major initiative of jointly building the Silk Road Economic Belt and the 21st Century Maritime Silk Road. And began to be abbreviated as the Belt and Road Initiative.<br />
<br />
According to the Vision and Actions for Jointly Building silk Road Economic Belt and 21st Century Maritime Silk Road, the Silk Road Economic Belt focuses on connecting China, Central Asia, Russia and Europe (the Baltic Sea). From China to the Persian Gulf and the Mediterranean Sea via Central and West Asia; China to Southeast Asia, South Asia, Indian Ocean. The focus of the 21st Century Maritime Silk Road is to stretch from China's coastal ports to Europe, through the South China Sea and the Indian Ocean. From China's coastal ports across the South China Sea to the South Pacific.<br />
<br />
The Belt and Road "construction is comply with the world multi-polarization and economic globalization, cultural diversity, the initiative of social informatization tide, drive along the countries achieve economic policy coordination, to carry out a wider range, higher level, the deeper regional cooperation and jointly create open, inclusive and balanced, pratt &whitney regional economic cooperation framework.<br />
<br />
====4.1The language environment of the Belt and Road====<br />
The "Belt and Road" involves a wide range of countries and regions, and their languages and cultures are very complex. How to make good use of language, do a good job in translation services, actively spread Chinese culture to the world, strengthen the ability of discourse, and tell Chinese stories well, the first thing to do is to understand the language situation of the countries along the "Belt and Road".<br />
<br />
=====4.1.1The most common language in countries along the "Belt and Road" =====<br />
<br />
There are a wide variety of languages spoken in 65 countries along the Belt and Road, involving nine language families. However, The status of English as the first language in the world is undeniable. Most of the countries participating in the Belt and Road are developing countries, and many of them speak English as their first foreign language. Especially in southeast Asian and South Asian countries, English plays an important role in foreign communication, whether as the official language or the first foreign language. Besides English, more than 100 million people speak Russian, Hindi, Bengali, Arabic and other major languages in the "Belt and Road" countries. It can also be seen that a common feature of languages in countries along the "Belt and Road" is the popularization of English education. English is widely used in international politics, economy, culture, education, science and technology, playing the role of the most important language in the world.<br />
<br />
=====4.1.2The complex language conditions of countries along the "Belt and Road" =====<br />
<br />
The languages spoken in countries along the Belt and Road involve nine major language families and almost all the world's religious types. Differences in religious beliefs also result in differences in culture, customs and social values behind languages. The languages of some countries along the belt and Road have also been influenced by historical and realistic factors, such as colonization, internal division and immigration. <br />
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India, for example, has no national language, but more than 20 official languages. India is a multi-ethnic country, a total of more than 100 people, one of the most obvious difference between nation and nation is the language problem. Therefore, according to the difference of language, India divides different ethnic groups into different states, big and small. Ethnic groups that use the same language are divided into one state. If there are two languages in a state, the state is divided into two parts. And Indian languages differ not only in word order but also in the way they are written. In India, for example, Hindi is spoken by the largest number of people in the north, with about 700 million speakers and 530 million as their first language. It is written in The Hindu language and belongs to the Indo-European language family. Telugu in the east is spoken by about 95 million people and 81.13 million as their first language. It is written in Telugu, which belongs to the Dravidian language family and is quite different from Hindi. As a result, a parliamentary session in India requires dozens of interpreters. <br />
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These factors cannot be ignored in the process of translation, from language communication to cultural understanding, from text to thought exchange, through the bridge of language to truly connect the people, so as to avoid misreading and misunderstanding caused by differences in language and national conditions.<br />
<br />
====4.2 Opportunities and challenges of the "Belt and Road" ====<br />
With the promotion of the Belt and Road Initiative, there has been an unprecedented boom in translation. In the previous translation boom in China, most of the foreign languages were translated into Chinese, and most of the foreign cultures were imported into China. However, this time, in the context of the "Belt and Road" initiative, translating Chinese into foreign languages has become an important task for translators. As is known to all, there are many different kinds of "One Belt And One Road" along the national language and culture is complex, the service "area" construction has become a factor in Chinese translation talents training mode reform, one of the foreign language universities have action, many colleges and universities to establish the "area" all the way along the country's small language major, as a result, "One Belt And One Road" initiative to promote, It has brought unprecedented opportunities for human translation. The cultivation of diversified translation talents and the cultivation of translation talents in small languages is an urgent problem to be solved in China. The cultivation of translation talents cannot be completed overnight, and the state needs to reform the training mode of translation talents from the perspective of language strategic development. Only in this way can we meet the new demand for human translation under the new situation of the belt and Road Initiative.<br />
<br />
For a long time, the traditional orientation of translation curriculum and training goal in colleges and universities is to train translation teachers and translators in need of society through translation theory and practice and literary translation practice, which cannot meet the needs of society. Since 2007, in order to meet the needs of the socialist market economy for application-oriented high-level professionals, the Academic Degrees Committee of The State Council approved the establishment of Master of Translation and Interpreting (MTI for short). After joining the pilot program of MTI, more and more universities are reforming the curriculum and training mode of master of Translation in order to cultivate translators who meet the needs of the society.<br />
<br />
Language is an important carrier of culture, and translation is an important link for exporting culture. The quality of translation output also reflects the cultural soft power of a country. With the rise of China, more and more people are interested in Chinese culture, and the number of Chinese learners keeps increasing. Under the background of "One Belt and One Road", excellent translators are urgently needed to spread Chinese culture. With the promotion of "One Belt and One Road" Initiative, the number of other countries learning mutual learning and cultural exchanges with China has increased unprecedeningly, bringing vigorous opportunities for the spread of Chinese culture. Translation talents who understand small languages and multi-lingual translators are needed. They should not only use language to convey information, but also use language as a lubricant for communication.<br />
<br />
===5.Training translation talents from the perspective of machine translation===<br />
Under the prevailing environment of machine translation, it poses a great challenge to the cultivation of translation talents. According to the current situation, translation needs and the shortage of translation talents, colleges and universities should reform and innovate the existing training programs for translation talents in terms of the quality of translation talents, the reform of training mode and the use of artificial intelligence. Based on the obtained data and literature, the author discusses how to train translation talents in the perspective of machine translation from the following aspects.<br />
<br />
====5.1 Quality requirements for translation talents ====<br />
Zhong Weihe and Murray made a more detailed and profound discussion on translator's literacy, believing that "translators should not only be proficient in two languages, but also have extensive cultural and encyclopedic knowledge and relevant professional knowledge; Master a variety of translation skills, a lot of translation practice; Have a clear translator role awareness, good professional ethics, practical and enterprising style of work, conscious team spirit and calm psychological quality ". According to the collected data, the author will elaborate the requirements for translation talents from four aspects: language literacy, humanistic literacy, translation ability and innovation ability.<br />
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The first is language literacy, which is the most basic and important requirement. MAO Dun pointed out that "mastery of mother tongue and target language are the foundation of translation". A solid foundation of bilingual skills is the basic skills of translators. Poor language proficiency seems to be a common problem among students majoring in translation and interpreting. Many translation diseases are caused by poor Chinese foundation. As part of going global, the belt and Road initiative is to tell Chinese culture and Chinese stories, which requires translators to be able to use both languages flexibly. Therefore, the first problem that colleges and universities face to solve is to improve the language level of foreign language learners.<br />
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The second is humanistic literacy. Humanistic literacy is mainly manifested by a good command of politics, economy, history, literature and other knowledge, which is particularly important for interpreters. In addition, cross-cultural communication cannot be ignored. In the process of communicating with foreigners or translating, translators often encounter the first cross-cultural contradiction. Cross-culture refers to having a full and correct understanding of cultural phenomena, customs and habits that differ or conflict with the national culture, and accepting and adapting to them in an inclusive manner on this basis. So the interpreter can first fully understand and master the national conditions and culture of the target country, which is particularly important in the "Belt and Road". There are more than 60 countries along the "Belt and Road", and it takes a lot of energy to master their national conditions and culture.<br />
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The third is translation ability. We should distinguish between translation ability and language ability. Translation ability is actually a system of knowledge and skills necessary for translation, the core of which is conversion ability. First of all, it reflects the ability to use tools to assist translation, such as computer application, translation technology and so on. In addition, interpreters should have enough healthy psychological quality and good professional quality. In terms of translation ability, the current training model of translation talents is inadequate.<br />
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The last one is innovation. The cultivation of learners' thinking ability is the key to translation teaching and the cultivation of thoughtful translators should be the connotation of translation teaching. Therefore, the interpreter is not only a translation tool, which is no different from machine translation. More importantly, it is necessary to explore translation with thoughts, have a sense of lifelong learning and innovation consciousness. Translators must constantly innovate themselves, learn new knowledge, and strive to seek reform and innovation. Many colleges and universities should also consciously cultivate students' innovation ability and broaden their thinking and vision.<br />
<br />
====5.2 The reform of college curriculum setting====<br />
First, we will further reform the curriculum of colleges and universities. Add economics, law and engineering to the curriculum, these contents in the "belt and Road".<br />
"One Road" is very important in the construction. According to the author's personal experience, the most typical problem of foreign language majors in colleges and universities is the single learning of foreign languages. More professional foreign language colleges and universities will add some literature courses and national conditions courses of the language target countries. Obviously, whether foreign language graduates are engaged in translation work or not, these knowledge is not enough. Of course, great reforms have been carried out in foreign language teaching, such as combining foreign language with finance, law, diplomacy and so on, and taking the way of minor training foreign language majors.<br />
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Domestic enterprises with a high degree of internationalization attach great importance to translation. Their translation research includes cutting-edge theoretical and applied research, involving machine translation, natural language processing and AI theory, algorithm and model. With such a foundation, enterprises can solve problems by themselves, such as embedding automatic translation functions in mobile phones. International enterprises not only do technical translation, but also deal with all forms of translation and localization in society. At present, translation teaching in most colleges and universities is still in the early mode, and it is an objective fact that it is divorced from the workplace and has a gap with the needs of enterprises.<br />
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Second, we should adjust and strengthen the construction of second foreign language teaching for foreign language majors. In the 1980s, our country was in urgent need of Russian translation. At that time, students majoring in English could translate microelectronic product manuals and related business documents in English and Russian at the same time after learning Russian for half a year. The mutual conversion between English and Russian played a great role in practice. According to the author, in the Graduate Institute of Interpretation and Translation of Beijing Foreign Studies University a very few students majored in multiple languages at the graduate level, that is, they majored in minor languages at the undergraduate level and were admitted to the Graduate Institute of Interpretation and Translation in English. Their training mode is to study English in the Graduate Institute of Interpretation and Translation for two years and the third year in the corresponding department of the undergraduate major. Such training mode in my opinion is a bigger model, cost It's more difficult for students. <br />
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In addition, there is a great disparity in the development of second foreign language teaching in colleges and universities, and the overall level is not high enough. Part of the second foreign language university foreign language professional may still be too much focus in languages such as German, French and Japanese, should as far as possible, considering the need of the construction of the "region", like Croatia, Serbia, Turkish, Hungarian, Italian, Indonesian, Albanian, these are the countries along the "area" the language of the two countries, Colleges and universities should encourage the teaching of a second foreign language.<br />
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Third, the teaching of translation technology should be strengthened. Traditional translation teaching teaches translation skills, such as the translation of words, sentences, texts and figures of speech. Translation technology refers to a series of practical workplace technologies with computer-aided translation software and translation project management as the core, which can greatly improve translation efficiency. However, due to the relative lack of translation technology teachers and equipment in colleges and universities, there is a disconnect between talent training and the requirements of translation technology in the translation field.<br />
<br />
====5.3 Application of artificial intelligence to translation teaching practice====<br />
In order to improve the teaching quality and train students' English translation ability, it is necessary to realize the effective integration of ARTIFICIAL intelligence and translation activity courses, which should not only reflect the effectiveness of artificial intelligence translation technology, but also help students establish a healthy concept of English communication. Through the application of artificial intelligence technology, students can strengthen their flexible translation skills through close communication with "AI program" during the learning stage of English translation activity class. For example, teachers can ask students to translate directly against the translation content provided on the translation screen of the ARTIFICIAL intelligence system. After that, the system can collect the translation answers with the help of speech recognition function, and then judge the accuracy of the translation content, thus providing important feedback to students.<br />
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China has used such artificial intelligence technology in the Putonghua test to ensure that every student can find a suitable translation method in practical communication. The so-called artificial intelligence technology is a new kind of technology modeled after the characteristics of human neural network thinking, can combine the human mind to respond. If it can be integrated into English translation activity teaching, it can not only improve the teaching efficiency, but also enhance students' enthusiasm in learning the course.<br />
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At the same time, during the training of translation talents, teachers also need to take into account the importance of influencing education factors, so that students can form a higher disciplinary quality in translation, so as to fit the concept of quality education in the new era. Only when artificial intelligence translation content is fully integrated into college English translation activity courses can the overall translation ability of college students be maximized.<br />
<br />
====5.4The improvement of translator's technical ability====<br />
In the previous part, the author roughly mentioned that translation teaching should be improved, which will be elaborated here. At present, only a few universities can make full use of the advantages of translation technology in translation teaching and focus on cultivating professional translation talents. Most universities still cannot get rid of the traditional teaching mode of "language + relevant professional knowledge" in translation teaching, and generally lack a correct understanding of COMPUTER-aided translation teaching.<br />
<br />
According to Wang Huashu et al., the courses that can be offered around the composition of translators' technical literacy include computer-assisted translation, translation and corpus, machine translation and post-translation editing, localization and internationalization, film and television translation (subtitle), technical communication and technical writing, and computer programming. The course modules involved are: Fundamentals of COMPUTER-aided Translation, CAT tool application, corpus alignment and processing, term management, QA technology for translation quality assurance, OFFICE fundamentals, translation management technology, basic computer knowledge, desktop typesetting, localization and internationalization, project management system and content management system, technical writing, basic knowledge of computer programming, basic knowledge of web code, etc.<br />
<br />
<br />
===6针对一带一路的机器翻译与翻译人才的合作===<br />
<br />
===Conclusion===<br />
<br />
===References===<br />
<br />
=8 颜静(On Machine Translation Under Lanuguage Intelligence——An Option and Opportunity for Human Translators)=<br />
[[Machine_Trans_EN_8]]<br />
<br />
=9 谢佳芬（人工智能时代下的机器翻译与人工翻译）=<br />
[[Machine_Trans_EN_9]]<br />
===Abstract===<br />
With the continuous development of information technology, many industries are facing the competitive pressure of artificial intelligence, and so is the field of translation. Artificial intelligence technology has developed rapidly and combined with the field of translation，which has brought great impact and changes to traditional translation, but artificial intelligence translation and artificial translation have their own advantages and disadvantages. Artificial translation is in the leading position in adapting to human language logical habits and understanding characteristics, but in terms of translation threshold and economic value, the efficiency of artificial intelligence translation is even better. In a word, we need to know that machine translation and human translation are complementary rather than antagonistic.<br />
<br />
===Key Words===<br />
Machine Translation; Artificial Translation; Artificial Intelligence<br />
<br />
===题目===<br />
人工智能时代下的机器翻译与人工翻译<br />
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===摘要===<br />
伴随着信息技术的不断发展，多个行业面临着人工智能的竞争压力，翻译领域也是如此。人工智能技术快速发展并与翻译领域结合，人工智能翻译给传统翻译带来了巨大的冲击和变革，但人工智能翻译与人工翻译存在着各自的优劣特点和发展空间，在适应人类语言逻辑习惯和理解特点的翻译效果上，人工翻译处于领先地位，但在翻译门槛和经济价值上，人工智能翻译的效率则更胜一筹。总的来说，我们要知道机器翻译与人工翻译是互补而非对立的关系。<br />
<br />
===关键词===<br />
机器翻译;人工翻译;人工智能<br />
<br />
===1. Introduction===<br />
====1.1 The History of Machine Translation Aborad====<br />
The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. Alchuni put forward the idea of using machines for translation. In 1933, the Soviet inventor Troyansky designed a machine to translate one language into another. [1]In 1946, the world's first modern electronic computer ENIAC was born. Soon after, American scientist Warren Weaver, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947. In 1949, Warren Weaver published a memorandum entitled Translation, which formally raised the issue of machine translation. In 1954, Georgetown University, with the cooperation of IBM, completed the English-Russian machine translation experiment with IBM-701 computer for the first time, which opened the prelude of machine translation research. [2] In 2006, Google translation was officially released as a free service software, bringing a big upsurge of statistical machine translation research. It was Franz Och who joined Google in 2004 and led Google translation. What’s more, it is precisely because of the unremitting efforts of generations of scientists that science fiction has been brought into reality step by step.<br />
====1.2 The History of Machine Translation in China====<br />
In 1956, the research and development of machine translation has been named in the scientific and technological work and made little achievements in China. On the eve of the tenth anniversary of the National Day in 1959, our country successfully carried out experiments, which translated nine different types of complicated sentences on large general-purpose electronic computers. The dictionary includes 2030 entries, and the grammar rule system consists of 29 circuit diagrams. [3]. After a period of stagnation, China's machine translation ushered in a high-speed development stage after the 1980s in the wave of the third scientific and technological revolution. With the rapid development of economy and science and technology, China has made a qualitative leap in the field of machine translation research with the pace of reform and opening up. In 1978, Institute of Scientific and Technological Information of China, Institute of Computing Technology and Institute of Linguistics carried out an English-Chinese translation experiment with 20 Metallurgical Title examples as the objects and achieved satisfactory results. Subsequently, they developed a JYE-I machine translation system, which based on 200 sentences from metallurgical documents. Its principles and methods were also widely used in the machine translation system developed in the future. In addition, the research achievements of machine translation in China during the 1980s and 1990s also include that Institute of Post and Telecommunication Sciences developed a machine translation system, C Retrieval and automatic typesetting system with good performance and strong practicability in October 1986; In 1988, ISTC launched the ISTIC-I English-Chinese Title System for the translation of applied literature of metallurgy, Information Research Institute of Railway developed an English-Chinese Title Recording machine translation system for railway documents; the Language Institute of the Academy of Social Sciences developed "Tianyu" English-Chinese machine translation system and Matr English-Chinese machine translation system developed by the computer department of National University of Defense Technology. After many explorations and studies, machine translation in China has gradually moved towards application, popularization and commercialization. China Software Technology Corporation launched "Yixing I" in 1988, marking China's machine translation system officially going to the market. After "Yixing", a series of machine translation systems such as Gaoli system in Beijing, Tongyi system in Tianjin and Langwei system in Shaanxi have also entered the public. In the 21st century, the development of a series of apps such as Kingsoft Powerword, Youdao translation and Baidu translation has greatly met the needs of ordinary users for translation. According to the working principle, machine translation has roughly experienced three stages: rule-based machine translation, statistics-based machine translation and deep learning based neural machine translation. [4] These three stages witnessed a leap in the quality of machine translation. Machine translation is more and more used in daily life and even the translation of some texts is almost comparable to artificial translation. In addition to text translation, voice translation, photo translation and other functions have also been listed, which provides great convenience for people's life. It is undeniable that machine translation has become the development trend of translation in the future.<br />
====1.3 The Status Quo of Machine Translation====<br />
In this big data era of information explosion, the prospect of machine translation is also bright. At present, the circular neural network system launched by Google has supported universal translation in more than 60 languages. Many Internet companies such as Microsoft Bing, Sogou, Tencent, Baidu and NetEase Youdao have also launched their own Internet free machine translation systems. [5] Users can obtain translation results free of charge by logging in to the corresponding websites. At present, the circular neural network translation system launched by Google can support real-time translation of more than 60 languages, and the domestic Baidu online machine translation system can also support real-time translation of 28 languages. These Internet online machine translation systems are suitable for a variety of terminal platforms such as mobile phone, PC, tablet and web and its functions are also quite diverse, supporting many translation forms, such as screen word selection, text scanning translation, photo translation, offline translation, web page translation and so on. Although its translation quality needs to be improved, it has been outstanding in the fields of daily dialogue, news translation and so on.<br />
===2. Advantages and Disadvantages of Machine Translation===<br />
Generally speaking, machine translation has the characteristics of high efficiency, low cost, accurate term translation and great development potential and etc. Machine translation is fast and efficient, this is something that artificial translation can’t catch up with. In addition, with the continuous emergence of all kinds of translation software in the market, compared with artificial translation, machine translation is cheap and sometimes even free, which greatly saves the economic cost and time for users with low translation quality requirements. What's more, compared with artificial translation, machine translation has a huge corpus, which makes the translation of some terms, especially the latest scientific and technological terms, more rapid and accurate. The accurate translation of these terms requires the translator to constantly learn, but learning needs a process, which has a certain test on the translator's learning ability and learning speed. In this regard, artificial translation has uncertainty and hysteretic nature. At the same time, with the progress of science and technology and the development of society, the function of machine translation will be more perfect and the quality of translation will be better.Today's machine translation tools and software are easy to carry, all you need to do is just to use the software and electronic dictionary in the mobile phone. There is no need to carry paper dictionaries and books for translation, which saves time and space. At the same time, machine translation covers many fields and is suitable for translation practice in different situations, such as academic, education, commercial trade, social networking, tourism, production technology, etc, it is also easy to deal with various professional terms. However, due to the limitation of translators' own knowledge, artificial translation is often limited to one or a few fields or industries. For example, it is difficult for an interpreter specializing in medical English to translate legal English.<br />
At the same time, machine translation also has its limitations. At first, machine can only operate word to word translation, which only plays the function and role of dictionary. Then, the application of syntax enables the process of sentence translation and it can be solved by using the direct translation method. When the original text and the target language are highly similar, it can be translated directly. For example, the original text "他是个老师." The target language is "he is a teacher ". With the increase of the structural complexity of the original text, the effect of machine translation is greatly reduced. Therefore, at the syntactic level, machine translation still stays in sentences with relatively simple structure. Meanwhile, the original text and the results of machine translation cannot be interchanged equally, indicating that English-Chinese translation has strong randomness, and is not rigorous and scientific enough. <br />
Nowadays, machine translation is highly dependent on parallel corpora, but the construction of parallel corpora is not perfect. At present, the resources of some mainstream languages such as Chinese and English are relatively rich, while the data collection of many small languages is not satisfactory. Moreover, the current corpus is mainly concentrated in the fields of government literature, science and technology, current affairs and news, while there is a serious lack of data in other fields, which can’t reflect the advantages of machine translation. At the same time, corpus construction lags behind. Some informative texts introducing the latest cutting-edge research results often spread the latest academic knowledge and use a large number of new professional terms, such as academic papers and teaching materials while the corpus often lacks the corresponding words of the target language, which makes machine translation powerless<br />
Besides, machine translation is not culturally sensitive. Human may never be able to program machines to understand and experience a particular culture. Different cultures have unique and different language systems, and machines do not have complexity to understand or recognize slang, jargon, puns and idioms. Therefore, their translation may not conform to cultural values and specific norms. This is also one of the challenges that the machine needs to overcome.[6] Artificial intelligence may have human abstract thinking ability in the future, but it is difficult to have image thinking ability including imagination and emotion. [7] Therefore, machine translation is often used in news, science and technology, patents, specifications and other text fields with the purpose of fact description, knowledge and information transmission. These words rarely involve emotional and cultural background. When translating expressive texts, the limitations of machine translation are exposed. The so-called expressive text refers to the text that pays attention to emotional expression and is full of imagination. Its main characteristics are subjectivity, emotion and imagination, such as novels, poetry, prose, art and so on. This kind of text attaches importance to the emotional expression of the author or character image, and uses a lot of metaphors, symbols and other expressions. Machine translation is difficult to catch up with artificial translation in this kind of text, it can only translate the main idea, lack of connotation and literary grace and it cannot have subjective feelings and rational analysis like human beings. In fact, it is not difficult to simulate the human brain, the difficulty is that it is impossible to learn from the rich social experience and life experience of excellent translators. In other words, machine translation lacks the personalization and creativity of human translation. It is this personalization and creativity that promote the development and evolution of language, and what machine translation can only output is mechanical "machine language".<br />
<br />
===3.The Irreplaceability of Artificial Translation ===<br />
====3.1 Translation is Constrained by Context====<br />
At present, machine translation can help people deal with language communication in people's daily life and work, such as clothing, food, housing and transportation, but there is a big gap from the "faithfulness, expressiveness and elegance" emphasized by high-level translation. Language itself is art，which pays more attention to artistry than functionality, and the discipline of art is difficult to quantify and unify. Sometimes it is regular, rigorous, logical and clear, and sometimes it is random, free and logical. If it is translated by machine, it is difficult to grasp this degree. Sometimes, machine translation cannot connect words with contextual meaning. In many languages, the same word may have multiple completely unrelated meanings. In this case, context will have a great impact on word meaning, and the understanding of word meaning depends largely on the meaning read from context. Only human beings can combine words with context, determine their true meaning, and creatively adjust and modify the language to obtain a complete and accurate translation. This is undoubtedly very difficult for machine translation. Artificial translation can get rid of the constraints of the source language and translate the translation in line with the grammar, sentence patterns and word habits of the target language. In the process of translation, translators can use their own knowledge reserves to analyze the differences between the source language and the target language in thinking mode, cultural characteristics, social background, customs and habits, so as to translate a more accurate translation. Artificial translation can also add, delete, domesticate, modify and polish the translation according to the style, make up for the lack of culture, try to maintain the thought, artistic conception and charm of the original text and the style of the source language. In addition, translators can also judge and consider the words with multiple meanings or easy to produce ambiguity according to the context, so as to make the translation more clear and more accurate and improve the quality of the translation.<br />
<br />
<br />
===4. Discussion on the Relationship Between Machine Translation and Artificial Translation ===<br />
<br />
===5. Suggestions on the Combined Development of Machine Translation and Artificial Translation===<br />
<br />
===6. ===<br />
<br />
===7. ===<br />
<br />
===Conclusion===<br />
<br />
===References===<br />
<br />
=10 熊敏(Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts)=<br />
[[Machine_Trans_EN_10]]<br />
===Abstract===<br />
The history of machine translation can be traced to 1940s, undergoing germination, recession, revival and flourish. Nowadays, with the rapid development of information technology, machine translation technology emerged and is gradually becoming mature. Whether manual translation can be replaced by machine translation becomes a widely-disputed topic. So in order to explore the ability of machine translation, I adopts two versions of translation, which are manual translation and machine translation(this paper uses Youdao translation) for different types of texts(according to Peter Newmark's types of text：informative text, expressive text and vocative text). The results are quite different in terms of quality and accuracy. The results show that machine translation is more suitable for informative text for its brevity, while the expressive texts especially literary works and vocative texts are difficult to be translated, because there are too many loaded words and cultural images in expressive text and dependence on the readers. To draw a conclusion, the relationship between machine translation and manual translation should be complementary rather than competitive.<br />
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===Key words===<br />
machine translation; manual translation; Newmark's type of texts<br />
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===题目===<br />
Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts<br />
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===摘要===<br />
机器翻译的历史可以追溯到上个世纪40年代，经历了萌芽期、萧条期、复兴期和繁荣期四个阶段。如今，随着信息技术的高速发展，机器翻译技术日益成熟，于是机器翻译能不能替代人工翻译这个话题引起了广泛热议。为了探究机器翻译的能力水平是否足以替代人工翻译，本人根据皮特纽马克的文本类型分类理论（信息类文本，表达文本，呼吁文本），选择了相应的译文类型，并且将其机器翻译的版本以及人工翻译的版本进行对比。结果发现，就质量和准确度而言，译文的水平大相径庭。因为其简洁的特性，机器比较适合翻译信息文本。而就表达文本，尤其是文学作品，因其存在文化负载词及相应的文化现象，所以机器翻译这类文本存在难度。而呼唤文本因其目的性以及对读者的依赖性较强，翻译难度较大。由此得知，机器翻译和人工翻译的关系应该是互补的，而不是竞争的。<br />
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===关键词===<br />
机器翻译；人工翻译；纽马克文本类型<br />
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===1. Introduction===<br />
====1.1.Introduction to machine translation====<br />
Machine translation, also known as computer translation is a technique to translating through machine translator, such as Google translation and Youdao translation. Machine translation is one of the branches of computational linguistics, ranging from computer science, statistics, information science and so on. Machine translation plays an important role in all aspects.<br />
Machine translation can be traced back to 1940s, when British engineer Booth and American engineer Weaver proposed using computer to translate and started to study machines used for translation. And the first machine translating system launched in 1954, used in English and Russian translation. And this means machine translation becomes a reality. However, the arrival of new things always accompanies barriers and setbacks. In the 1960s, reports from ALPAC (Automated Language Processing Advisory Committee) showed studies on machine translation had stagnated for a decade. At that time, due to the high cost of machine, choosing human translators to finish translating works was more economical for most companies. What’s worse, machine had difficulty in understanding semantic and pragmatic meanings. Fortunately, in the 1970s, with the advance and popularity of computer, machine translation was gradually back on track. With the development of science and technology, machine translation has better technological guarantee, such as artificial intelligence and computer technology. In the last decades, from the late 90s till now, machine translation is becoming more and more mature. The initial rule-oriented machine translation has been updated and Corpus-aided machine translation appears. And nowadays, technology in voice recognition has been further developed. This technique is frequently applied in speech translation products. Users only need to speak source language to the online translator and instantly it will speak out the target version. But machine can’t fully provide precise and accurate translation without any grammatical or semantic problems. Machine can understand the literal meaning of texts, but not the meaning in context. For example, there is polysemy in English, which refers to words having multiple meanings. So when translating these words, translators should take contexts into consideration. But machine has troubles in this way. In addition, machine has no feeling or intention. Hence, it is also difficult to convey the feelings from the source language.<br />
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====1.2.Process of machine translation====<br />
The process of manual translation is different from that of machine translation. Here is the process of the former. (1) Understand source language. (2) Use target language to organize language. (3) Generate translation. Unlike manual translation, machine translation tends to analyze and code source language first, then look for related codes in corpus, and work out the code that represents target language, generating translation. But they share a common feature, which is that Lexicon, grammatical rules and syntactic structure are taken into consideration. This is one of the biggest challenges for machine translation.<br />
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===2.Theorectical framework===<br />
====2.1Newmark’s text typology====<br />
Text typology is a theory from linguistics. It undergoes several phrases. Karl Buhler, a German psychologist and linguist defines communicative functions into expressive, information and vocative. Then Roman Jakobson proposes categorization of texts, which are intralingual translation, interlingual translation and intersemiotic translation. Accordingly, he defines six main functions of language, including the referential function, conative function, emotive function, poetic function, metalingual function and the phatic function. Based on the two theories, Peter Newmark, a translation theorist, summarizes the language function into six parts: the informative function, the vocative function, the expressive function, the phatic function, the aesthetic function, and the metalingual function. Later, he further divided texts into informative type, expressive text and vocative type according to the linguistic functions of various texts. <br />
The core of the informative texts is the truth. It is to convey facts, information, knowledge of certain topics, reality and theories. The language style of the text is objective, logical and standardized. Reports, papers, scientific and technological textbooks are all attributed to informative texts. Translators are required to take format into account for different styles.<br />
The core of the expressive text is the sender’s emotions and attitudes. It is to express sender’s preferences, feelings, views and so on. The language style of it is subjective. Imaginative literature, including fictions, poems and dramas, autobiography and authoritative statements belong to expressive text. It requires translators to be faithful to the source language and maintain the style.<br />
The core of the vocative text is readership. It is to call upon readers to act, think, feel and react in the way intended by the text. So it is reader-oriented. Such texts as advertisement, propaganda and notices are of vocative text. Newmark noted that translators need to consider cultural background of the source language and the semantic and pragmatic effect of target language. If readers can comprehend the meaning at once and do as the text says, then the goal of information communication is achieved.<br />
To draw a conclusion, informative texts focus on the information and facts. Expressive texts center on the mind of addressers, including feelings, prejudices and so on. And vocative texts are oriented on readers and call on them to practice as the texts say.<br />
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====2.2Study method====<br />
Manual translations of the three texts are selected from authoritative versions and universally acknowledged. And machine translations of those come from Youdao Translator. And in this thesis I will compare and evaluate the two methods in word diction, sentence structure, word order and redundancy.<br />
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===Conclusion===<br />
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===References===<br />
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=11 陈惠妮=(Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts)=<br />
[[Machine_Trans_EN_11]]<br />
===Abstract===<br />
At present, globalization is accelerating and the market demand for language services is rapidly increasing . Machine translation, as an important translation method, can greatly improve translation efficiency due to its low cost and high speed. However, because of the limitations of machine translation and the differences between Chinese and English language, machine translation is not accurate enough. In order to balance translation efficiency and translation quality, a great number of manual revisions in translation are required for the machine translating texts. Medical papers are specialized, special and purposeful, so it requires accurate,qualified and professional translation. However, the quality of translations by machine is inefficient to meet the high-quality requirements of medical papers translation. Therefore, the introduction of pre-editing can greatly improve the efficiency and quality of machine translation.<br />
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===Key words===<br />
Pre-editing, Machine translation, Medical texts<br />
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===题目===<br />
Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts<br />
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===摘要===<br />
在全球化加速发展的今天，市场对语言服务的需求迅速增加。机器翻译作为一种重要的翻译途径，由于其成本低、速度快，可以大大提高翻译效率。然而，由于机器翻译的局限性以及中英文语言的差异，机器翻译的准确性不高。为了平衡翻译效率和翻译质量，机器翻译文本需要大量的手工修改。<br />
医学论文具有专业性、特殊性和目的性，要求其译文准确、合格、专业。然而，机器翻译的质量较低，无法满足医学论文对翻译的高质量要求。因此，译前编辑的引入可以大大提高机器翻译的效率和质量。<br />
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===关键词===<br />
译前编辑；机器翻译；医学文本<br />
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===1. Introduction===<br />
===1.1 Definition of Machine Translation===<br />
As Cronin(2013) revealed: "Translation is undergoing a revolutionary upheaval. The influence of digital technology and the Internet on translation is continuous, extensive and profound. From the popularity of automatic online translation applications, translation revolution is everywhere (Cronin,2013) However,the concept of machine translation was firstly proposed in the 1930s. Since 1940s, the machine translation technology has been evolving from rule-based machine translation (RBMT) to statistical machine translation (SMT), and to neural machine translation (NMT). Machine translation refers to the automatic translation of source language into target language by using a computer system. That is, machine translation refers to the automatic translation of text from one language into another natural language by computer software or other online translation webs. Machine translation is also defined as the process of “using a computer system to automatically translate text or speech from one natural language to another” according to the definition by ISO (Cui, 2014).O 'Brien (2002) defines it as "the behavior of modifying errors in machine translation to ensure that the target translation meets certain quality requirements". On the basic level, machine translation performs mechanical substitution of words in one language for words in another language, but that rarely produces a good translation, therefore, recognition of the whole phrases and their closest counterparts in the target language is needed. Not all words in one language have equivalents in another language, and many words have more than one meaning. A huge demand for translation is greatly needed in today’s global world, which creates new opportunities for the development of machine translation, attracts more and more attention and becomes one of the current research focuses.<br />
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===1.2 Definition of Pre-editing===<br />
Pre-editing means to adjust and modify the source language to make it fit more with the characteristics of the machine translation software before putting the source language before the into machine translation, so as to improve the quality of the translation machine translation (Wei Changhong, 2008:93-94). Pre-editing is to modify the original text before putting it into machine translation software, in order to improve the recognition rate of machine translation, optimize the output quality of translated text and reduce the workload of post-editing. Because pre-translation editing only needs to be modified in one language, the operation is simpler than post-translation editing, which can realize the double improvement of quality and efficiency. A good pre-editing translation can help machine translation more smoothly, thus improving the machine readability and quality of the output translation. Pre-editing is mostly applied in the following situations: one is when the original text is of poor quality and the machine is difficult to recognize the meaning of the sentence, such as user generated content with poor readability and translatability (Gerlach et al, 2003:45-53); Documents that need to be published in multiple languages; Next is when the original text contains a lot of jargon; The last is the original text has a corresponding translation memory bank. If the original text is edited, it can better match the content of the translation memory bank. Pre-editing is a process of identifying problems. It requires to pre-edit the source texts before putting it into machine translation according to the requirements, listing the expressions or sentences that may have trouble in machine translation and then pre-edit it by human. The purpose is to enable the computer to translate better, improve the translatability of machine translation. (Slype G V & Guinet J F & Seitz F,1984:115)<br />
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===1.3 Machine Translation Mode===<br />
According to the different knowledge acquisition methods, machine translation modes can be classified as follows: one is rule-based machine translation, which is based on bilingual dictionaries and a library of language rules for each language. The quality of translation depends on whether the source language conforms to the existing rules, but the inexhaustible rules are the hinders of this model. The second mode is machine translation based on statistics. This translation model relies on the principles of mathematics and statistics to find various existing translations corresponding to the translation tasks through the employment of corpus, analyzing the frequency of their occurrence and selecting the translation with the highest frequency for output. The disadvantage of this translation model is that it ignores the flexibility of language and the importance of context. The last one is neural network language model. This model is different from previous translation models in that it uses end-to-end neural network to realize automatic translation between natural languages. At present, the quality of its translation is much higher than that of the previous translation models.<br />
===1.4 Source of Study Abstracts===<br />
The core medical journals from domestic are selected in order to make the paper more representative and authoritative, such as National Medical Journal of China, Journal of Peking University (Health Science), and Journal of Third Military Medical University. All of them include basic medical science, biomedical technology, laboratory medical science and other fields. In this way, the pre-editing approaches included are applicable enough to machine translation of medical abstracts.<br />
===1.5 Selection of Translation Software===<br />
Generally, machine translation software includes Google Translation, Youdao Translator and Niu Translation, which has their own special use in translation. Take Google as an example, it is more like neurons in human brain, enabling to learn and collect information to establish connections with its neural machine translator’s neurons. However, it also causes many errors because of the lack of enough information. In this paper, contrastive analysis will be carried on by using Google translation. On the one hand, being a pioneer in the translation of NET, it is inevitably to sue Google as the translation software to translate medical texts in this paper. Comparing the program called “Google brain”, other NMT of translation software are relatively disadvantages. On the other hand, the Google translation enjoys the largest users in the world, with its downloads of more than one billion. The output quality of Google translation is more correct and complete than other machine translation software. With years of development and improvement, Google Translation has been greatly promoted. In this paper, Chinese medical abstract will be automatically translated by Google translation, and then the translation output will be compared with the translation by human and the publication of English abstracts. The main purpose is to prove that the improvement and promotion of quality and accuracy in the medical abstracts will be obtained through the pre-editing approaches.<br />
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===2.Language Characteristics and Error Division===<br />
Since Chinese and English are two different languages, it is quite neccessary to identify their own characteristics so as to better analyze and understand the two languages. Tytler (1978) argued in his Essay on the Principles of Translation that there are three principles of translation that in all the translation should give readers the same feelings as the source text, except for complete transcript of ideas. There actually also exit some mistakes of these two languages. So the following will make some clarrifications of these two languages to ensure more accurate translation.<br />
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===2.1 Language Characteristics of Medical Abstracts===<br />
Chinese and English belong to different language systems, so there are differences in their language structure and the way users think of the languages. When using machine translation from Chinese to English, due to the unequal language levels, there will be many mistakes in the translation process, especially for ESP texts, such as medical papers. <br />
Here, these differences mainly refer to the linguistic characteristics of medical abstracts. In medical abstracts, it usually includes structured and unstructured abstracts. Although in different forms, they both describe the purpose, methods and conclusions of the research.<br />
In the method section of medial abstract, several Chinese sentences can be connected with commas, but each sentence may convey different information. In contrast, English sentences contain a great deal of information, but in order to ensure clarity, some modifiers need to be isolated and then reconstructed. However, in Chinese-English machine translation, a lot of information is put into the sentence because the machine segments the sentence on the basis of the full comma.<br />
In addition, subjectless sentence is used in the objective and method parts of Chinese medical abstracts. The subjectless sentence means the sentence without or free from subject and is usually employed in two contexts. The first is "needless to say". In Chinese sentences, it is common to omit the subject of the sentence. Chinese sentences can convey meanings by using incomplete sentence structures, so Chinese speakers can understand the meanings of the sentences even though the sentence subject is omitted. The second is "emphasis of action". In this context, subjectless sentences are used to describe behaviors, especially in the study of traditional Chinese medicine abstracts. Comparatively speaking, it should be avoided in English medical abstracts. Abstract sentences are more subjective when describing the learning process, while the essential requirement of English medical abstract is objectivity. From this point, sentences without subject should be avoided in English medical abstracts. Another feature is voice. Few words with passive meanings appear in Chinese abstracts. English sentences are more favoured in using passive voice. When translating from Chinese to English, passive voice should be used to make the contents objective, which is also the basic requirement of medical papers.<br />
These are the linguistic features of Chinese medical abstracts. There are great differences in sentence structure and expression between Chinese and English medical abstracts. These differences may reduce the accuracy of machine translation, so it is necessary to introduce pre-editing to edit the source text to ensure that the source text can be accurately recognized by the machine and fully translated into English.<br />
===2.2 Error Division of Machine Translation in Translating Abstracts of Medical Papers from Chinese to English===<br />
In this paper, errors in machine translation are listed out after analyzing. Some are due to the principles, such as statistical-based MT and NMT, employed by machine translation. Based on the original errors, they can be studied from two levels. One is from the macro-level, referring to mistakes caused by objective factors. These are not human factors, but the disadvantage of machine translation and the limitations of language. The limitations of machine translation are derived from the principles and models adopted and manifests itself as a reliance on reference sources. In this case, semantic ambiguity and textual incoherence may occur in the absence of reference sources.<br />
However, for ESP texts such as medical papers, the requirements for terms and sentence patterns are far beyond the existing corpora. For unfamiliar texts (that is, the corresponding texts cannot be found in the corpora). The quality of output translation will be relatively low, which is determined by the principles of machine translation. As mentioned above, machine translation has evolved over the past few decades from rules-based to corp-based statistics to NMT. However, there still exist some limitations in machine translation.<br />
Mistakes on micro-level are mainly caused by the variations and differences of linguistic structure between Chinese and English. Chinese is an implicit language, while English is an explicit one (Lian, 2010) To put it in another way, Chinese expression does not depend on language structures, while English does the opposite. This may result in mismatches between the original and machine translated sentences. This kind of error is mainly divided into two types: component fragment and component missing. For a medical paper, such errors are very serious. As a kind of ESP discourse, medical paper has the characteristics of fixed, objective and accurate language structure. In order to reproduce the characteristics of medical abstracts in translation, it is necessary to avoid errors at the level of words and sentences and pay attention to logic and consistency.<br />
Lexical errors here refer to the inconsistency of fixed expressions in the translation of terms. Although these terms are machine translation based on the corpus, the corpus may not be perfect for ESP texts such as medical papers. Therefore, fixed expressions are not used in term translation. This is also the most common mistake in machine translation (the term here includes but is not limited to nouns). For ESP texts, medical text in particular, the fixed expressions and the accuracy of terms are of great importance.<br />
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===3.Approaches Proposed for Pre-editing ===<br />
Generally speaking, there is a close relationship between pre-editing and post-editing, both of which aim to convey information and ensure high or publishable translation quality. Proper pre-editing can improve the quality of machine translation in terms of adequacy and consistency. <br />
Complexity of natural language and people use language arbitrarily, bringing many difficulties to Chinese-English machine translation, Hu Qingping (2005:24) has proposed "the research of translation software and the development of the controlled language are the two directions to improve the quality of machine translation : the former aims at the difficulty in the natural language processing, the latter overcomes the arbitrariness of natural language". Feng Quangong and Gao Lin (2017: 63-68) put forward: "The writing principles of controlled language can be applied to pre-editing of machine translation. Pre-editing based on controlled language can effectively reduce the complexity and ambiguity of source text, improve the identifiability of machine translation (the translatability of source text itself), and thus reduce (fully) the workload of post-editing".<br />
The central task of pre-editing is to transform human-friendly content into machine-friendly content, so words and sentences need to be repositioned or even changed. Based on the analysis of the language characteristics of Chinese and English, the Chinese ideographic group should be split before the Chinese source text is input into the machine software to translate so that the sentence structure is complete which can be easily recognized by the machine translation software.<br />
===3.1 Extraction and Replacement of Terms in the Source Text===<br />
As a branch of applied English, medical English is the product of the combination of English language knowledge and medical knowledge. The terms in medical papers have the characteristics of fixed and complex language structure. Although Google Translation is based on a corpus, the language structure is not fixed due to the limited size of the corpus. Therefore, the following two steps should be followed before machine translation. The first is to extract terms and create a glossary, and then replace the Chinese terms in the source text with the corresponding English expressions in the glossary. Of course, the terms of extraction should be searched and verified according to the actual situation. All of these words are verified before they can be replaced. This method can not only ensure the accuracy of term translation, but also maintain the consistency of expression, especially when dealing with long texts.<br />
Example1<br />
Source abstract: 口腔白斑病癌变相关缺氧应答基因和微小RNA的芯片及表达验证。<br />
Google translation: Microarray detection/Chip detection and expression verification of hypoxia response genes and microRNAs related to oral leukoplakia canceration.<br />
Published translation:Transcriptome array screening and verification of oral leukoplakia carcinogenesis-related hypoxia-responsive gene and microRNA. <br />
Analysis: The expression “ Affymetrix GeneChip” empolyed in this thesis is a human transcription array for transcriptome array. In the source abstract, “芯片检测“ can be meant “screening” but not detection, so the proper and appropriate translation of these expression should be “transcription array screening”, but the Google translates it into “microarry detection of chip detection”. Therefore, term extraction is essential and inevitable before putting the source abstracts into the machine translation software.<br />
After pre-editing source abstracts: 对 oral leukoplakia carcinogenesis 相关 hypoxia-responsive gene 和微小RNA进行 transcriptome array screening 及表达验证。<br />
After pre-editing translation: Transcriptome array screening and expression- verification of hypoxia-responsive genes and microRNAs related to oral leukoplakia carcinogenesis.<br />
===3.2 Explication of Subordination===<br />
As mentioned above, the subordination of Chinese sentences is judged by certain specific words in machine translation . Chinese is a paratactic language, and sentences are often connected by internal logical relationships; while English depends on sentence structure, so sentences are often closely linked by various language forms. In order to improve the accuracy of machine translation, we can adjust the sentence structure and adjust the position of adjectives and their modifiers. <br />
Example 2<br />
Source abstracts:回顾性分析南京医科大学附属儿童医院中重度HIE患儿49例及同期就诊的无神经系统症状体征的足月新生儿为对照组31例的头颅磁共振成像（MRI）资料。<br />
Google translation: A retrospective analysis that the brain magnetic resonance imaging (MRI) data of 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University and full-term neonates with no neurological symptoms and signs during the same period were included in the control group.<br />
Published translation: A total of 49 children with moderate to severe HIE admitted to the children’s Hospital Affiliated to Nanjing Medical University were retrospectively analyzed. Cranial magnetic resonance imaging (MRI) date of 31 full-term neonates without neurological symptoms and signs who visited the hospital during the same period were recruited as the control group. <br />
Analysis: As the above example shows that “中重度HIE患儿” and “足月新生儿” in the source abstracts are from the Children’s Hospital Affiliated Nanjing Medical University. The Google translation shows that 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University. There is an error due to the misuse of subordination. There are some advice in order to solve the problem. That is, first to segment the sentence and then reconstruct the sentence. For instance, the Chinese expression “南京医科大学附属儿童医院” carries many modifiers, which requires to cut the modifiers and reconstruct the sentence. Therefore, the Chinese expression “南京医科大学附属儿童医院’can be divided into abstractions, leaving two sentences instead of one long sentence with modifiers..<br />
After pre-editing source abstracts: 对49例中重度HIE患儿以及31例无神经系统症状体征的足月新生儿的MRI资料进行回顾性分析。这些患者收治于南京医科大学儿童附属医院。<br />
After pre-editing translation: The MRI data of 49 children with moderate to severe HIE and 31 full-term newborns without neurological symptoms and signs were retrospectively analyzed. These patients were admitted to the Children’s Hospital of Nanjing Medical University.<br />
===3.3 Explication of Subject through Voice Changing===<br />
The extensive use of subjectless sentences are quite common in the Chinese abstracts, while English prefers to employ passive voice in the sentences so as to make them object and accurate. In order to take the characteristics of English sentences into great and careful consideration, the sentences written in passive voice in particular, it will be helpful for machine translation to find out the subject and reconstruct a sentence in passive voice (Wang Yan: 2008). The first is to discover the “doee” in a sentence and then is to reconstruct the sentence structure and put the “doee” before the verb. The last is to explicate the passive relation between the noun and the verb.<br />
Example 3<br />
Source abstract: 回顾性分析2018年1月至2020年12月解放军总医院第七医学中心收治的500例老年髋部骨折患者的资料。<br />
Google translation: A retrospective analysis of the data of 500 elderly patients with hip fractures admitted to the Seventh Medical Center of the PLA General Hospital from January 2018 to December 2020.<br />
Published translation: From January 2018 to December 2020, the data of 500 elderly patients with hip fracture treated in the Seventh Medical Center of PLA General Hospital were analyzed retrospectively.<br />
Analysis: The above example indicates that the “回顾性分析”in the Google Translate is a noun phrase, but the source abstracts actually describes an action or a behavior. The reason for such a mistake is that the machine can’t recognize the Chinese subjetless sentences. To find the subject of the sentence, the source abstracts can be revised and adjusted. Finding the “doee” is the first step, which refers to “500例老年髋部骨折患者的资料”in the source abstracts. And next is to put it in front of the verb, that is to place it in the beginning of the sentence. Last but not least, the passive voice should be explicated in the sentence. <br />
After pre-editing source abstract: 500例老年髋部骨折患者的资料被回顾性分析，他们在2018年1月之2020年12月期间收治于解放军总医院第七医学中心。<br />
After pre-editing translation: The data of 500 elderly hip fracture patients were retrospectively analyzed. They were admitted to the Seventh Medical Center of the PLA General Hospital between January 2018 and December 2020.<br />
===3.4 Relocation of Modifiers===<br />
Modifiers, including noun, adverbial and attributive are constantly employed in the Chinese medical papers in order to add information to subject and object in the sentence. By doing this, complicated sentences can be structured, thus causing obstacles to machine translation for recognizing this complex sentence structures when translating Chinese-English sentences. To make the machine translation successfully recognize the sentence structure, simplifying the sentence structure is quite necessary. The key is to moving the location of the modifiers and thus making the modifiers an independent sentence. In other words, the modifiers ought to be put before or behind the main part of the sentence to satisfy the common use of English. <br />
Usually, the modifiers in Chinese sentence can only be placed in front of the core word, while in English, modifiers are very flexible. It is all right to place the modifiers in front of or behind the major part of the sentences with adjective or a connective noun.<br />
Example 4<br />
Source abstracts: 利用DNA重组技术以pET-28a表达系统在E.coli BL21(DE3) 中重组表达Hepl。<br />
Google Translation: Hepl is recombined in E.coli BL21 (DE3) using DNA recombination technology with pET-28a expression system.<br />
Published translation: The recombinant Hcpl protein was expressed by using DNA recombination technology through pET-28a expression system in E. coli BL21 (De3).<br />
Analysis: The Chinese medical text includes two modifiers, “利用DNA”and “以pET-28a)表达系统”. These two modifiers will be translated by machine, which catches more attention to the two constituents. From the Google translation above, one failure is obvious that it misplaces the location of the two modifiers and presents it in not accurate form. To make it translate correctly by machine translation, dividing the sentences is very important so that the source abstracts can be correctly recognized by machine translation.<br />
After pre-editing source abstract: 利用DNA重组技术，Hcp1重组表达在E.coli BL21 (DE3)中， 通过pET-28a表达系统在E. coli BL21 (DE3)中。<br />
Google translation: Using DNA recombination technology, Hcp1 recombination is expressed in E.coli BL21 (DE3), via pET-28a expression system in E. coli BL21 (DE3).<br />
The second is the independence of modifiers, that is, modifiers can also be reconstructed into clauses to modify core words. Compared with English, There is no subordinate clause in Chinese, so redundant Chinese modifiers need to be reconstructed into subordinate clauses in Chinese-English translation to meet the characteristics of English. English, especially sentences with multiple modifiers. Otherwise, sentence structure may be confused, such as scattered modifiers of core words. To avoid this mistake, it is necessary to separate the modifier from the main part of the sentence. These modifiers should be reconstituted into clauses. Also, keep your sentences simple and easy to understand.<br />
===3.5 Proper Omission and Deletion of Category Words===<br />
Category words are commonly used in Chinese. Category words complement the meaning of words, including problems, positions, situations and jobs. Adding this supplementary word is more in line with Chinese custom. In many cases, it has no real meaning, so it can be omitted in translation. In Chinese, category words are frequently used. However, it is rarely used in English, which is one of the differences between Chinese and English. There are many kinds of category words. Considering objectivity and the fixed structure of language, redundant category words should be deleted in Chinese-English translation. In the general, the most commonly used category of words in the Chinese medical abstracts are "process", "behavior", and "situation". These categories of words hinder the language conversion of Chinese and English, resulting in redundancy. <br />
Example 5<br />
Source abstract: 探讨口腔白斑病癌变进程中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia response genes and associated microRNAs in the process of oral leukoplakia cancer was discussed.<br />
Published translation: To study the hypoxia response gene and microRNA (miRNA)expression profiles in the pathogenesis and progression of oral leukoplakia (OLK).<br />
Analysis: As the above example shows, the Chinese words “进程” belongs to a category word. However, the Chinese expression “癌变”contains the process, so the “进程” expression can be deleted before placing it into the machine translation, because the meaning of it has been overlapping between the expression “癌变”. Therefore, its place can be replaced with preposition “during”.<br />
After pre-editing source abstract: 探讨口腔白斑病癌变中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia responsive genes and miRNA in oral leukoplakia cancer was investigated.<br />
<br />
===Conclusion===<br />
After years of development, machine translation has made great progress. The accuracy of machine translation has been greatly improved in both text recognition and sentence pattern conversion. However, machine translation has its own limitations. In other words, it needs to rely on the parallel corpus as a reference source for improving its accuracy. ESP text, in particular, is harder to get the high quality by machine translation. <br />
As one of the research papers, the characteristics of medical abstracts are fixed language structures, objectivity and accuracy (Qin Yi:2004). Therefore, medical translation must be accurate, object and understandable to follow the specific demands of the medical paper. Being an important field in the human society, medical paper translation is on a great demand, which means that it needs a huge demand for human labor. However, with the machine translation promoting, it will be more efficient to translate medical papers combining the effort by human and machine. The improvement and development of machine translation requires the joint efforts of computer science, information science, statistics, linguistics and other academic circles to achieve more mature human-computer mutual assistance translation (Li Yafei, Zhang Ruihua : 2019).<br />
However, errors can occur during the process of machine translation of Chinese- English, because of the differences of the Chinese and English and the processing of the machine. Errors from the perspective of linguistic or grammar can affect the machine translation a lot. After division and recognition of errors, some pre-editing approaches are put forward to help the machine translation more accurate and readable, that are, extraction and replacement of terms in the source text, relocation of modifiers, explication of subordination, proper omission, deletion of category words and explication of subject through voice changing. <br />
The paper mainly focuses on the pre-editing machine translation by using medical papers as a case study. The errors of machine translation occurring in the translation of medical abstracts and pre-editing approaches for machine translation. The quality of machine translation of medical papers is greatly improved after employing the pre-editing methods. However, machine translation is not as flexible and accurate as human brain, so it is of importance to combine pre-editing and post-editing approaches with machine translation in order to produce more accurate, more object machine translation of medical papers.<br />
<br />
===References===<br />
[1]. Cronin, Michael (2013). Translation in the Digital Age[M]. New York&London: Routledge.<br />
<br />
[2]. GERLACH J, et al ( 2013). Combining Pre-editing and Post-editing to Improve SMT of User-generated Content[M]// Proceedings of MT Summit XIV Workshop on Post-editing Technology and Practice. 45-53.<br />
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[3]. Slype G V & Guinet J F & Seitz F (1984). Better Translation for Better Communication [M] .Oxford: Pergamon Press Ltd (U.K.), <br />
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[4]. O'Brien S (2002). Teaching Post-editing: A Proposal for Course Con-tent [EB/OL]. http://mt-archive. Info/EAMT-2002-0brien. Pdf.<br />
<br />
[5]. Tytler, A. F. (1978). Essay On The Principles of Translation[M]. Amsterdam: JohnBenjamins Publishing.<br />
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[6] 崔启亮. (2014), 论机器翻译的译后编辑[J], 中国翻译, 035(006):68-73.<br />
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[7] 冯全功,高琳 (2017) 基于受控语言的译前编辑对机器翻译的影响[J]. 当代外语研究,(2): 63-68+87+110.<br />
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[8] 胡清平(2005). 机器翻译中的受控语言[J]. 中国科技翻译, (03): 24-27. <br />
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[9] 连淑能 (2010). 英汉对比研究增订本[M]. 北京:高等教育出版社.<br />
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[10] 黎亚飞,张瑞华 (2019). 机器翻译发展与现状[J]. 中国轻工教育,(5):38-45. <br />
<br />
[11] 秦毅(2004),从翻译基本标准议医学英语的翻译[J]. 遵义医学院学报,27 (4): 421-423. <br />
<br />
[12] 王燕 (2008). 医学英语翻译与写作教程[M]. 重庆:重庆大学出版社<br />
<br />
=12 蔡珠凤=(The Mistranslation of C-J Machine Translation of Political Statements)=<br />
[[Machine_Trans_EN_12]]<br />
===Abstract===<br />
Language is the main way of communication between people. With the continuous development of globalization, the scale of cross-border exchanges is also expanding. However, due to cultural differences and diversity, the languages of different countries and regions are very different, which seriously hinders people's communication. The demand for efficient and convenient translation tools is increasing. At the same time, with the development of network technology and artificial intelligence, recognition technology based on deep learning is more and more widely used in English, Japanese and other fields.<br />
<br />
===Key words===<br />
machine translation; political statements; mistranslation of C-J machine translation<br />
===题目===<br />
The Mistranslation of C-J Machine Translation of Political Statements<br />
===摘要===<br />
语言是人与人之间交流的主要方式。随着全球化的不断发展，跨境交流的规模也在不断扩大。然而，由于文化的差异和多样性，不同国家和地区的语言差异很大，这严重阻碍了人们的交流。对高效便捷的翻译工具的需求正在增加。同时，随着网络技术和人工智能的发展，基于深度学习的识别技术在英语、日语等领域的应用越来越广泛。<br />
<br />
===关键词===<br />
机器翻译；政治发言；政治发言中译日的误译<br />
===1. Introduction===<br />
===Introduction to machine translation===<br />
Machine translation, also known as automatic translation, is a process of using computers to convert one natural language (source language) into another natural language (target language). It is a branch of computational linguistics, one of the ultimate goals of artificial intelligence, and has important scientific research value.At the same time, machine translation has important practical value. With the rapid development of economic globalization and the Internet, machine translation technology plays a more and more important role in promoting political, economic and cultural exchanges.The development of machine translation technology has been closely accompanied by the development of computer technology, information theory, linguistics and other disciplines. From the early dictionary matching, to the rule translation of dictionaries combined with linguistic expert knowledge, and then to the statistical machine translation based on corpus, with the improvement of computer computing power and the explosive growth of multilingual information, machine translation technology gradually stepped out of the ivory tower and began to provide real-time and convenient translation services for general users.<br />
<br />
=== C-J machine translation software===<br />
Today's online machine translation software includes Baidu translation, Tencent translation, Google translation, Youdao translation, Bing translation and so on. Google was the first company to launch the machine translation system, and Baidu was the first company to import the machine translation system in China. In addition, Tencent and Youdao have attracted much attention.Machine translation is the process of using computers to convert one natural language into another. It usually refers to sentence and full-text translation between natural languages. In order to continuously improve the translation quality, R & D personnel have added artificial intelligence technologies such as speech recognition, image processing and deep neural network to machine translation on the basis of traditional machine translation based on rules, statistics and examples.With the increase of using machine translation, the joint cooperation between manual translation and machine translation will also increase significantly in the future. What criteria should be used to evaluate the quality of machine translation? In the evolving field of machine translation, there is an urgent need to clarify the unsolvable questions and solved problems.<br />
<br />
===The history of machine translation===<br />
The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. alchuni put forward the idea of using machines for translation. In 1933, Soviet inventor П.П. Trojansky designed a machine to translate one language into another, and registered his invention on September 5 of the same year; However, due to the low technical level in the 1930s, his translation machine was not made. In 1946, the first modern electronic computer ENIAC was born. Shortly after that, W. weaver, an American scientist and A. D. booth, a British engineer, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947 when discussing the application scope of electronic computer. In 1949, W. Weaver published the translation memorandum, which formally put forward the idea of machine translation. After 60 years of ups and downs, machine translation has experienced a tortuous and long development path. The academic community generally divides it into the following four stages:<br />
Pioneering period（1947-1964）<br />
In 1954, with the cooperation of IBM, Georgetown University completed the English Russian machine translation experiment with ibm-701 computer for the first time, showing the feasibility of machine translation to the public and the scientific community, thus opening the prelude to the study of machine translation.<br />
It is not too late for China to start this research. As early as 1956, the state included this research in the national scientific work development plan. The topic name is "machine translation, the construction of natural language translation rules and the mathematical theory of natural language". In 1957, the Institute of language and the Institute of computing technology of the Chinese Academy of Sciences cooperated in the Russian Chinese machine translation experiment, translating 9 different types of more complex sentences.<br />
From the 1950s to the first half of the 1960s, machine translation research has been on the rise. The United States and the former Soviet Union, two superpowers, have provided a lot of financial support for machine translation projects for military, political and economic purposes, while European countries have also paid considerable attention to machine translation research due to geopolitical and economic needs, and machine translation has become an upsurge for a time. In this period, although machine translation is just in the pioneering stage, it has entered an optimistic period of prosperity.<br />
Frustrated period（1964-1975）<br />
In 1964, in order to evaluate the research progress of machine translation, the American Academy of Sciences established the automatic language processing Advisory Committee (Alpac Committee) and began a two-year comprehensive investigation, analysis and test.<br />
In November 1966, the committee published a report entitled "language and machine" (Alpac report for short), which comprehensively denied the feasibility of machine translation and suggested stopping the financial support for machine translation projects. The publication of this report has dealt a blow to the booming machine translation, and the research of machine translation has fallen into a standstill. Coincidentally, during this period, China broke out the "ten-year Cultural Revolution", and basically these studies also stagnated. Machine translation has entered a depression.<br />
convalescence（1975-1989）<br />
Since the 1970s, with the development of science and technology and the increasingly frequent exchange of scientific and technological information among countries, the language barriers between countries have become more serious. The traditional manual operation mode has been far from meeting the needs, and there is an urgent need for computers to engage in translation. At the same time, the development of computer science and linguistics, especially the substantial improvement of computer hardware technology and the application of artificial intelligence in natural language processing, have promoted the recovery of machine translation research from the technical level. Machine translation projects have begun to develop again, and various practical and experimental systems have been launched successively, such as weinder system Eurpotra multilingual translation system, taum-meteo system, etc.<br />
However, after the end of the "ten-year holocaust", China has perked up again, and machine translation research has been put on the agenda again. The "784" project has paid enough attention to machine translation research. After the mid-1980s, the development of machine translation research in China has further accelerated. Firstly, two English Chinese machine translation systems, ky-1 and MT / ec863, have been successfully developed, indicating that China has made great progress in machine translation technology.<br />
New period(1990 present)<br />
With the universal application of the Internet, the acceleration of the process of world economic integration and the increasingly frequent exchanges in the international community, the traditional way of manual operation is far from meeting the rapidly growing needs of translation. People's demand for machine translation has increased unprecedentedly, and machine translation has ushered in a new development opportunity. International conferences on machine translation research have been held frequently, and China has made unprecedented achievements. A series of machine translation software have been launched, such as "Yixing", "Yaxin", "Tongyi", "Huajian", etc. Driven by the market demand, the commercial machine translation system has entered the practical stage, entered the market and came to the users.<br />
Since the new century, with the emergence and popularization of the Internet, the amount of data has increased sharply, and statistical methods have been fully applied. Internet companies have set up machine translation research groups and developed machine translation systems based on Internet big data, so as to make machine translation really practical, such as "Baidu translation", "Google translation", etc. In recent years, with the progress of in-depth learning, machine translation technology has further developed, which has promoted the rapid improvement of translation quality, and the translation in oral and other fields is more authentic and fluent.<br />
<br />
===The problem of machine translation at present===<br />
Error is inevitable<br />
Many people have misunderstandings about machine translation. They think that machine translation has great deviation and can't help people solve any problems. In fact, the error is inevitable. The reason is that machine translation uses linguistic principles. The machine automatically recognizes grammar, calls the stored thesaurus and automatically performs corresponding translation. However, errors are inevitable due to changes or irregularities in grammar, morphology and syntax, such as sentences with adverbials after "give me a reason to kill you first" in Dahua journey to the West. After all, a machine is a machine. No one has special feelings for language. How can it feel the lasting charm of "the tenderness of lowering its head, like the shame of a water lotus? After all, the meaning of Chinese is very different due to the changes of morphology, grammar and syntax and the change of context. Even many Chinese people are zhanger monks - they can't touch their heads, let alone machines.<br />
Bottleneck<br />
In fact, no matter which method, the biggest factor affecting the development of machine translation lies in the quality of translation. Judging from the achievements, the quality of machine translation is still far from the ultimate goal.<br />
Chinese mathematician and linguist Zhou Haizhong once pointed out in his paper "fifty years of machine translation": to improve the quality of machine translation, the first thing to solve is the problem of language itself rather than programming; It is certainly impossible to improve the quality of machine translation by relying on several programs alone. At the same time, he also pointed out that it is impossible for machine translation to achieve the degree of "faithfulness, expressiveness and elegance" when human beings have not yet understood how the brain performs fuzzy recognition and logical judgment of language. This view may reveal the bottleneck restricting the quality of translation. <br />
It is worth mentioning that American inventor and futurist ray cozwell predicted in an interview with Huffington Post that the quality of machine translation will reach the level of human translation by 2029. There are still many disputes about this thesis in the academic circles.<br />
<br />
===2.Mistranslation of Chinese Japanese machine translation===<br />
===2.1Vocabulary mistranslation in Chinese Japanese machine translation===<br />
===2.1.1Mistranslation of proper nouns===<br />
In political materials, there are often political dignitaries' names, place names or a large number of proper nouns in the political field. The morphemes of such words are definite and inseparable. Mistranslation will make the source language lose its specific meaning. <br />
<br />
Chinese translation into Japanese Japanese translation into Chinese<br />
<br />
original text translation by Youdao reference translation original text translation by Youdao reference translation<br />
栗战书 栗戰史書 栗戰書 労安 劳安 劳安<br />
李克强 李克強 李克強 朱鎔基 朱基 朱镕基<br />
习近平 習近平 習近平 筑紫哲也 筑紫哲也 筑紫哲也<br />
韩正 韓中 韓正 山口百惠 山口百惠 山口百惠<br />
王沪宁 王上海氏 王滬寧 田中角栄 田中角荣 田中角荣<br />
汪洋 汪洋 汪洋 東条英機 东条英社 东条英机<br />
赵乐际 趙樂南 趙樂際 毛沢东 毛泽东 毛泽东<br />
江泽民 江沢民 江沢民 トウ・ショウヘイ 大酱 邓小平<br />
周恩来 周恩来 周恩来<br />
クリントン 克林顿 克林顿<br />
<br />
The above table counts 18 special names in the two texts, and 7 machine translation errors. In terms of mistranslation, there are not only "战书" but also "戰史" and "史書" in Chinese. "沪" is the abbreviation of Shanghai. In other words, since "战书" and "沪" are originally common nouns, this disrupts the choice of target language in machine translation. Except Katakana interference (except for トウシゃウヘイ, most of the mistranslations appear in the new Standing Committee. It can be seen that the machine translation system does not update the thesaurus in time. Different from other words, people's names have particularity. Especially as members of the Standing Committee of the Political Bureau of the CPC Central Committee, the translation of their names is officially unified. In this regard, public figures such as political dignitaries, stars, famous hosts and important. In addition, the machine also translates Japanese surnames such as "タ二モト" (谷本), "アンドウ" (安藤) into "塔尼莫特" and "龙胆". It can be found that the language feature that Japanese will be written together with Chinese characters and Hiragana also directly affects the translation quality of Japanese Chinese machine translation. Japanese people often use Katakana pronunciation. For example, when Japanese people talk with Premier Zhu, they often use "二ーハウ" (Hello). However, the machine only recognizes the two pseudonyms "二" and "ハ" and transliterates them into "尼哈" , ignoring the long sound after "二" and "ハ".<br />
<br />
original text Translation by Youdao reference translation<br />
日美安全体制 日米の安全体制 日米安保体制<br />
中国共产党第十九次全国代表大会 中国共産党第19回全国代表大会 中国共産党第19回全国代表大会（第19回党大会）<br />
十八大 十八大 第18回党大会<br />
中国特色社会主义 中国特色社会主義 中国の特色ある社会主義<br />
中国共产党中央委员会 中国共産党中央委員会 中国共産党中央委員会<br />
十八届中共中央政治局常委 第18代中国共產党中央政治局常務委員 第18期中共中央政治局常務委員<br />
十八届中共中央政治局委员 18期の中国共產党中央政治局委員 第18期中共中央政治局委員<br />
十九届中共中央政治局常委 十九回中国共產党中央政治局常務委員 第19期中央政治局常務委員<br />
中共十九届一中全会 中国共產党第十九回一中央委員会 第19期中央委員会第1回全体会議<br />
The above table is a comparison of the original and translated versions of some proper nouns. As shown in the table, the mistranslation problems are mainly reflected in the mismatch of numerals + quantifiers, the wrong addition of case auxiliary word "の", the lack of connectors, the Mistranslation of abbreviations, dead translation, and the writing errors of Chinese characters. The following is a specific analysis one by one.<br />
"十八届中共中央政治局常委", "十八届中共中央政治局委员", "十九届中共中央政治局常委" and "中共十九届一中全会" all have quantifiers "届", which are translated into "代", "期" and "回" respectively. The meaning is vague and should be uniformly translated into "期"; Among them, the translation of the last three proper nouns lacks the Chinese conjunction "第". The case auxiliary word "の" was added by mistake in the translation of "十八届中共中央政治局委员" and "日美安全体制". The "中国共产党中央委员会" did not write in the form of Japanese Ming Dynasty characters, but directly used simplified Chinese characters.<br />
The full name of "中共十九届一中全会" is "中国共产党第十九届中央委员会第一次全体会议", in which "一" stands for "第一次", which is an ordinal word rather than a cardinal word. Machine translation does not produce a correct translation. The fundamental reason is that there are no "规则" for translating such words in machine translation. If we can formulate corresponding rules for such words (for example, 中共m'1届m2 中全会→第m1期中央委员会第m2回全体会议), the translation system must be able to translate well no matter how many plenary sessions of the CPC Central Committee.<br />
<br />
===2.1.2Mistranslation of Polysemy===<br />
<br />
original text Translation by Youdao reference translation<br />
スタジオ 摄影棚/工作室 直播现场/演播厅<br />
日中関係の話 中日关系的故事 就中日关系(话题)<br />
溝 水沟 鸿沟<br />
それでは日中の問題について質問のある方。 那么对白天的问题有提问的人。 关于中日问题的话题，举手提问。<br />
私たちのクラスは20人ちょっとですが、 我们班有20人左右， 我们班二十多人的意见统一很难，<br />
いろいろな意見が出て、まとめるのは大変です。 但是又各种各样的意见，总结起来很困难。 中国是怎样把13亿人凝聚在一起的？<br />
一体どうやって、13億人もの人をまとめているんですか。 到底是怎么处理13亿的人的呢？ <br />
In the original text, the word "スタジオ" appeared four times and was translated into "摄影棚" or "工作室" respectively, but the context is on-site interview, not the production site of photography or film. "話" has many semantics, such as "说话", "事情", "道理", etc. In accordance with the practice of the interview program, Premier Zhu Rongji was invited to answer five questions on the topic of China Japan relations. Obviously, the meaning of the word "故事" is very abrupt. The inherent Japanese word "日中" means "晌午、白天". At the same time, it is also the abbreviation of the names of the two countries. The machine failed to deal with it correctly according to the context. "溝" refers to the gap between China and Japan, not a "水沟". The former "まとめる" appears in the original text with the word "意见", which is intended to describe that it is difficult to unify everyone's opinions. The latter "まとめている" also refers to unifying the thoughts of 1.3 billion people, not "处理". Therefore, it is more appropriate to use "统一" and "处理" in human translation, rather than "总结意见" or "处理意见".<br />
Mistranslation of polysemy has always been a difficult problem in machine translation research. The translation of each word is correct, but it is often very different from the original expression in the context. Zhang Zhengzeng said that ambiguity is a common phenomenon in natural language. Its essence is that the same language form may have different meanings, which is also one of the differences between natural language and artificial language. Therefore, one of the difficulties faced by machine translation is language disambiguation (Zhang Zheng, 2005:60). In this regard, we can mark all the meanings of polysemous words and judge which meaning to choose by common collocation with other words. At the same time, strengthen the text recognition ability of the machine to avoid the translation inconsistent with the current context. In this way, we can avoid the mistake of "大酱" in the place where famous figures such as Deng Xiaoping should have appeared in the previous political articles.<br />
<br />
===2.1.3Mistranslation of compound words===<br />
Multi class words refer to a word with two or more parts of speech, also known as the same word and different classes.<br />
original text Translation by Youdao reference translation<br />
1998年江泽民主席曾经访问日本, 1998年の江沢民国家主席の日本訪問し、 1998年、江沢民総書記が日本を訪問し、かつ、<br />
同已故小渊首相签署了联合宣言。 かつて同じ故小渕首相が署名した共同宣言 亡くなられた小渕総理と宣言に調印されました<br />
<br />
Chinese "同" has two parts of speech: prepositions and conjunctions: "故" has many parts of speech, such as nouns, verbs, adjectives and conjunctions. This directly affects the judgment of the machine at the source language level. The word "同" in the above table is used as a conjunction to indicate the other party of a common act. "故" is used as a verb with the semantic meaning of "死亡", which is a modifier of "小渊首相". The machine regards "同" as an adjective and "故" as a noun "原因", which leads to confusion in the structure and unclear semantics of the translation. Different from the polysemy problem, multi category words have at least two parts of speech, and there is often not only one meaning under each part of speech. In this regard, software R & D personnel should fully consider the existence of multi category words, so that the translation machine can distinguish the meaning of words on the basis of marking the part of speech, so as to select the translation through the context and the components of the word in the sentence. Of course, the realization of this function is difficult, and we need to give full play to the wisdom of R & D personnel.<br />
<br />
===2.2 Syntactic mistranslation in Chinese Japanese machine translation===<br />
===2.2.1Mistranslation of tenses===<br />
original text：History is written by the people, and all achievements are attributed to the people. <br />
Translation by Youdao：歴史は人民が書いたものであり、すべての成果は人民のためである。<br />
reference translation：歴史は人民が綴っていくものであり、すべての成果は人民に帰することとなります。<br />
The original sentence meaning is "历史是人民书写的历史" or "历史是人民书写的东西". When translated into Japanese, due to the influence of Japanese language habits, the formal noun "もの" should be supplemented accordingly. In this sentence, both the machine and the interpreter have translated correctly. However, the machine's recognition of "的" is biased, resulting in tense translation errors. The past, present and future will become "history", and this is a continuous action. The form translated as "~ ていく" not only reflects that the action is a continuous action, but also conforms to the tense and semantic information of the original text. In addition, the machine's treatment of the preposition "于" is also inappropriate. In the original text, "人民" is the recipient of action, not the target language. As the most obvious feature of isolated language, function words in Chinese play an important role in semantic expression, and their translation should be regarded as a focus of machine translation research.<br />
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original text：李克强同志是十六届中共中央政治局常委,其他五位同志都是十六届中共中央政治局委员。<br />
Translation by Youdao：李克強総理は第16代中国共産党中央政治局常務委員であり、他の５人の同志はいずれも16期の中国共産党中央政治局委員である。<br />
reference translation：李克強同志は第16期中国共産党中央政治局常務委員を務め、他の５人は第16期中共中央政治局委員を務めました。<br />
Judging the verb "是" in the original text plays its most basic positive role, but due to the complexity of Chinese language, "是" often can not be completely transformed into the form of "だ / である" in the process of translation. This sentence is a judgment of what has happened. Compared with the 19th session, the 18th session has become history. This is an implicit temporal information. It is very difficult for machines without human brain to recognize this implicit information. "中共中央政治局常委" is the abbreviation of "中共中央政治局常务委员会委员" and it is a kind of position. In Japanese, often use it with verbs such as "務める","担当する",etc. The translator adopts the past tense of "務める", which conforms to the expression habit of Japanese and deals with the problem of tense at the same time. However, machine translation only mechanically translates this sentence into judgment sentence, and fails to correctly deal with the past information implied by the word "十八届".<br />
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===2.2.2Mistranslation of honorifics===<br />
Honorific language is a language means to show respect to the listener. Different from Japanese, there is no grammatical category of honorifics in Chinese. There is no specific fixed grammatical form to express honorifics, self modesty and politeness. Instead, specific words such as "您", "请" and "劳驾" are used to express all kinds of respect or self modesty. <br />
Original text translation by Youdao reference translation<br />
女士们，先生们，同志们，朋友们 さんたち、先生たち、同志たち、お友达さん ご列席の皆さん<br />
谢谢大家！ ありがとうございます！ ご清聴ありがとうございました。<br />
これはどうされますか。 这是怎么回事呢? 您将如何解决这一问题？<br />
こうした問題をどうお考えでしょうか。 我们会如何考虑这些问题呢？ 您如何看待这一问题？ <br />
For example, for the processing of "女士们，先生们，同志们，朋友们", the machine makes the translation correspond to the original one by one based on the principle of unchanged format, but there are errors in semantic communication and pragmatic habits. When speaking on formal occasions, Chinese expression tends to be comprehensive and detailed, as well as the address of the audience. In contrast, Japanese usually uses general terms such as "皆様", "ご臨席の皆さん" or "代表団の方々" as the opening remarks. In terms of Japanese Chinese translation, the machine also failed to recognize the usage of Japanese honorifics such as "さ れ る" and "お考え". It can be seen that Chinese Japanese machine translation has a low ability to deal with honorific expressions. The occasion is more formal. A good translation should not only be fluent in meaning, but also conform to the expression habits of the target language and match the current translation environment.<br />
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===Conclusion===<br />
In the process of discussing the Mistranslation of machine translation, this paper mainly cites the Mistranslation of Youdao translator. When I studied the problem of machine translation mistranslation, I also used a large number of translation software such as Google and Baidu for parallel comparison, and found that these translation software also had similar problems with Youdao translator. For example, the "新世界新未来" in Chinese ABAC structural phrases is translated by Baidu as "新し世界の新しい未来", which, like Youdao, is not translated in the form of parallel phrases; Google online translation translates the word "“全心全意" into a completely wrong "全面的に"; The original sentence "我吃了很多亏" in the Mistranslation of the unique expression in the language is translated by Google online as "私はたくさんの損失を食べました". Because the "吃" and "亏" in the original text are not closely adjacent, the machine can not recognize this echo and mistakenly treats "亏" as a kind of food, so the machine translates the predicate as "食べました"; Japanese "こうした問題をどう考えでしょうか", Google's online translation is "你如何看待这些问题?", "你" tone can not reflect the tone of Japanese honorific, while Baidu translates as "你怎么想这样的问题呢?" Although the meaning can be understood, it is an irregular spoken language. Google and Baidu also made the same mistakes as Youdao in the translation of proper nouns. For example, they translated "十七届(中共中央政治局常委)”" into "第17回..." .In short, similar mistranslations of Youdao are also common in Baidu and Google. Due to space constraints, they will not be listed one by one here. <br />
Mr. Liu Yongquan, Institute of language, Chinese Academy of Social Sciences (1997) It has been pointed out that machine translation is a linguistic problem in the final analysis. Although corpus based machine translation does not require a lot of linguistic knowledge, language expression is ever-changing. Machine translation based solely on statistical ideas can not avoid the translation quality problems caused by the lack of language rules. The following is based on the analysis of lexical, syntactic and other mistranslations From the perspective of the characteristics of the source language, this paper summarizes some difficulties of Chinese and Japanese in machine translation. <br />
(1) The difficulties of Chinese in machine translation <br />
Chinese is a typical isolated language. The relationship between words needs to be reflected by word order and function words. We should pay attention to the transformation of function words such as "的", "在", "向" and "了". Some special verbs, such as "是", "做" and "作", are widely used. How to translate on the basis of conforming to Japanese pragmatic habits and expressions is a difficulty in machine translation research. In terms of word order, Chinese is basically "subject→predicate→object". At the same time, Chinese pays attention to parataxis, and there is no need to be clear in expression with meaningful cohesion, which increases the difficulty of Chinese Japanese machine translation. When there are multiple verbs or modifier components in complex long sentences, machine translation usually can not accurately divide the components of the sentence, resulting in the result that the translation is completely unreadable. <br />
(2) Difficulties of Japanese in machine translation <br />
Both Chinese and Japanese languages use Chinese characters, and most machines produce the target language through the corresponding translation of Chinese characters. However, pseudonyms in Japanese play an important role in judging the part of speech and meaning, and can not only recognize Chinese characters and judge the structure and semantics of sentences. Japanese vocabulary is composed of Chinese vocabulary, inherent vocabulary and foreign vocabulary. Among them, the first two have a great impact on Chinese Japanese machine translation. For example "提出" is translated as "提出する" or "打ち出す"; and "重视" is translated as "重視する" or "大切にする". Japanese is an adhesive language, which contains a lot of "けど", "が" and "けれども" Some of them are transitional and progressive structures, but there are also some sequential expressions that do not need translation, and these translation software often can not accurately grasp them.<br />
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===References===<br />
[1] Navroz Kaur Kahlon;Williamjeet Singh.Machine translation from text to sign language: a systematic review[J].Universal Access in the Information Society,2021(prepublish):1-35.<br />
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[2] Cao Qianyu;Hao Hanmei;Ahmed Syed Hassan.A Chaotic Neural Network Model for English Machine Translation Based on Big Data Analysis[J].Computational Intelligence and Neuroscience,2021,2021:3274326-3274326.<br />
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[3]Hwang Yongkeun;Kim Yanghoon;Jung Kyomin.Context-Aware Neural Machine Translation for Korean Honorific Expressions[J].Electronics,2021,10(13):1589-1589.<br />
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[4]Zakaryia Almahasees.Analysing English-Arabic Machine Translation:Google Translate, Microsoft Translator and Sakhr,2021.<br />
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[5]Machine learning in translation[J].Nature Biomedical Engineering,2021,5(6):485-486.<br />
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[6]Shaimaa Marzouk.An in-depth analysis of the individual impact of controlled language rules on machine translation output: a mixed-methods approach[J].Machine Translation,2021(prepublish):1-37.<br />
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[7]Welnitzová Katarína;Munková Daša.Sentence-structure errors of machine translation into Slovak[J].Topics in Linguistics,2021,22(1):78-92.<br />
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[8]Xu Xueyuan.Machine learning-based prediction of urban soil environment and corpus translation teaching[J].Arabian Journal of Geosciences,2021,14(11). <br />
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[9]陈丙昌.機械翻訳の誤訳分析【D】.贵州大学.2016(05) <br />
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[10]呂寅秋.機械翻訳の言語規則と伝統文法との相違点.日本学研究.1996(00):21-22 <br />
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[11]刘君.基于语料库的中日同形词词义用法对比及其日中机器翻译研究【D】.广西大学.2014(03) <br />
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[12]崔倩倩.机器翻译错误与译后编辑策略研究【D】.北京外国语大学.2019(09) <br />
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[13]张义.机器翻译的译文分析【D】.西安外国语大学.2019(10) <br />
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[14]张琳婧.在线机器翻译中日翻译错误原因及对策【D】.山西大学.2019(02)<br />
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[15]王丹.基于机器翻译的专利文本译后编辑对策研究【D】.大连理工大学.2020(06)<br />
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[16]杨晓琨.日中机器翻译中的前编辑规则与效果验证【D】.大连理工大学.2020(06)<br />
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[17]左嘉. 机器翻译日译汉误译研究[D]. 北京第二外国语学院, 2021.<br />
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[18]关碧莹.关于政治类发言的汉日机器翻译误译分析[D].哈尔滨理工大学, 2018.<br />
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[19]车彤.汉译日机器翻译质量评估及译后编辑策略研究【D】.北京外国语大学.2021(09)<br />
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Networking Linking<br />
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http://www.elecfans.com/rengongzhineng/692245.html<br />
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https://baike.baidu.com/item/%E6%9C%BA%E5%99%A8%E7%BF%BB%E8%AF%91/411793<br />
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=13 陈湘琼Chen Xiangqiong（Study on Post-editing from the Perspective of Functional Equivalence Theory ）=<br />
[[Machine_Trans_EN_13]]<br />
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=14 Bi bi Nadia（Machine Translation a Challenge for Human Translators)=<br />
[[Machine_Trans_EN_14]]<br />
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===Abstract===<br />
Machine translation is a big obstacle in the way of Human translators or interpretors although it is quick and less time consuming.people are trying to get translation of their target language using through source language.For this purpose they are using digital apps like Google translation Google translation does not give accurate and exact interpretation.Google translator is translates word to word translate that doesn't clarifies it's true and actual meaning.On the other hand human translators can give exact and accurate translation ,they take care of grammatical errors , diction and sentence structure.They clarify the purpose of target language through using source language.<br />
===Keywords===<br />
Descriptive translation, academic, interdiscipline, comparative literature, localization,Translatology,school of thought, translation , studies, linguistics, corresponding<br />
===Body of article===<br />
Translation is the process of reworking text from one language into another to maintain the original message and communication. But, like everything else, there are different methods of translation, and they vary in form and function. What is translation? The term has become widely used among knowledge transfer researchers and practitioners, especially in the fields of health and health care. In a landmark review, Jonathan Lomas began to argue that 'The tasks… may be defined as to establish and maintain links between researchers and their audience, via the appropriate translation of research findings' (Lomas 1997, p 4). In 2004, the World Health Organization's World Report on Knowledge for Better Health suggested that 'One of the key contributions of research to health systems is the translation of knowledge into actions' (WHO 2004, p 33 and p 100). By 2006, special issues of WHO's Bulletin as well as the journals Evaluation and the Health Professions and the Journal of Continuing Education in the Health Professions were dedicated to translation.But what does 'translation' mean? It may be a new word for an old problem, meaning nothing more than 'transfer'. The rapidly emerging field of 'translational medicine' seems to take translation to mean generally what transfer might have meant, that is the transmission of knowledge and evidence 'from bench to bedside'. As one commentator has observed, "Translational medicine" as a fashionable term is being increasingly used to describe the wish of biomedical researchers to ultimately help patients' (Wehling 2008, abstract). <br />
Semantic uncertainty persists, not least because of the different interests of scientists, clinicians, patients and commercial firms (Littman et al 2007): 'Translational research means different things to different people, but it seems important to almost everyone' (Woolf 2008, p 211).<br />
In related fields, such as public health (Armstrong et al 2006), 'translation' seems to signify dissatisfaction with 'transfer'. It wants to move away from thinking of knowledge transfer as a form of technology transfer or dissemination, rejecting if only by implication its mechanistic assumptions and its model of linear messaging from A to B. But still, what does it signify?<br />
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===Why translation?===<br />
'Translation' indicates a closer attention to the problem of shared meaning and how it might be developed. It seems to represent some new epistemological lubricant, facilitating the dissemination of texts and the application and use of the knowledge and information they in. Simply, translation might be the key to transfer. And yet, when we stop to think, we are more ambivalent. What is translated often seems somehow inferior, not real or original. Note how readily commentators reach for the idea that things might be 'lost in translation'. Knowing at a distance – made in and mediated by translation - makes for incomplete renditions, blurred images, partial truths. So what might 'translation' really mean? The purpose of this paper is to set out, for policy makers and practitioners, the theoretical and conceptual resources translation holds and seems to represent. In doing so, it explores understandings of translation in the fields of literature and linguistics and in the sociology of science and technology. It begins by setting out just why this idea of translation should make immediate, intuitive sense in relation to research, policy and practice.<br />
1translation in research, policy and practice research as translation<br />
Research often entails translation from one language to another: where data is collected from more than one ethnic group, for example, or where the language of the researcher is other than that of the research subject. It may draw on a secondary literature or source documents written in different languages, and may be published and disseminated in languages other than the one in which it is first written up.<br />
2In a different way, to conduct an interview is to ask for an account of experience and its meanings, but it is also to construct and translate that experience in terms defined at least in part by the researcher. In representing what is said, transcripts then select data, usually excluding significant gesture and eye-contact, for example. Often, certain characteristics of speech-acts (such as hesitations) will be edited out. In turn, the format of the transcript shapes the analytic use the researcher may make of it. The basis of research 'findings', then, is an artefact, a transcript or translation, not an original interaction (Ochs 1979, Barnes, Bloor and Henry 1996, Ross 2009).<br />
3In this way, the researcher recasts aspects of his or her problem or topic in new, scientific form: 'All researchers "translate" the experiences of others' (Temple 1997, p 609). Research is invariably conducted in a sort of 'metalanguage' (Hantrais and Ager 1985): the research process can be conceived as one of successive translations (from theoretical formulation to operationalization, transcription, interpretation and dissemination). Theorization is a process of reciprocal back and forth between theory and fact, in which conceptions of each are revised in order that one fit the other (Baldamus 1974).<br />
4 It is a kind of translation: a rereading, re-use, re-application or re-representation of what we know in new terms (Turner 1980). Referencing, too, is an act of translation, a form of appropriation and incorporation of one text by another (Gilbert 1977).policy as translation Each of the fields covered by the paper is diverse and ill-defined, and there is no intention here to provide a comprehensive account of any them. Sources have been chosen for their relevance: main references are cited in the text, and additional sources listed in footnotes.<br />
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2For a brief introduction to the technical issues involved in social research in more than one language, see Birbili (2000). For translation issues in survey research and question design in general, see Ervin and Bower (1952) and Deutscher (1968); on translating survey research instruments (in this instance health-related quality of life measures), Bowden and Fox-Rushby (2003). On the use of translators and interpreters, see Temple (1997) and Jentsch (1998).<br />
3For an interesting discussion of this problem, see Bourdieu (1999), esp pp 621-626, 'The risks of writing'. <br />
===Difference between machine translation and human translators===<br />
Few people disagree on the differences between the two, but many argue over the quality of the translations. How accurate are machine translations? How reliable are human translations? Some say machine translation produces near-perfect translations while others are adamant that translations are incomprehensible and cause more problems than they solve. Results will, of course, vary depending on the source and target languages, the machine translation service used (e.g. Google Translate), and the complexity of the original text.<br />
Machine translation, love it or hate it, is here to stay. In fact, the machine translation market is growing at such a fast pace that it is predicted to reach $980 million by 2022.<br />
===Machine translation: the pros and cons===<br />
The advantages of machine translation generally come down to two factors: it’s faster and cheaper. The downside to this is the standard of translation can be anywhere from inaccurate, to incomprehensible, and potentially dangerous (more on that shortly).<br />
==The advantages of machine translation==<br />
Many free tools are readily available (Google Translate, Skype Translator, etc.)<br />
Quick turnaround time <br />
You can translate between multiple languages using one tool<br />
Translation technology is constantly improving<br />
The disadvantages of machine translation<br />
Level of accuracy can be very low <br />
Accuracy is also very inconsistent across different languages<br />
==Machines can't translate context ==<br />
Mistakes are sometimes costly <br />
Sometimes translation simply doesn’t work<br />
The most important thing to consider with any kind of translation is the cost of potential mistakes. Translating instructions for medical equipment, aviation manuals, legal documents and many other kinds of content require 100% accuracy. In such cases, mistakes can cost lives, huge amounts of money and irreparable damage to your company’s image. So choose carefully!<br />
Human translation: the pros and cons<br />
Human translation essentially switches the table in terms of pros and cons. A higher standard of accuracy comes at the price of longer turnaround times and higher costs. What you have to decide is whether that initial investment outweighs the potential cost of mistakes. Alternatively, whether mistakes simply aren’t an option, like the cases we looked at in the previous section.<br />
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==The advantages of human translation ==<br />
It’s a translator’s job to ensure the highest accuracy<br />
Humans can interpret context and capture the same meaning, rather than simply translating words<br />
Human translators can review their work and provide a quality process<br />
Humans can interpret the creative use of language, e.g. puns, metaphors, slogans, etc.<br />
Professional translators understand the idiomatic differences between their languages<br />
Humans can spot pieces of content where literal translation isn’t possible and find the most suitable alternative<br />
The disadvantages of human translation<br />
Turnaround time is longer <br />
Translators rarely work for free <br />
Unless you use a translation agency, with access to thousands of translators, you’re limited to the languages any one translator can work with<br />
Simply put, human translation is your best option when accuracy is even remotely important. Other considerations to make are the complexity of your source material and the two languages you’re translating between – both of which can render machines pretty useless.<br />
<br />
When to use machine and human translation<br />
The truth is, the debate over machine vs human translation is an unnecessary distraction. What we should really be talking about is when to use these two different types of translation services, because they both serve a very valid purpose.<br />
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Examples of when to use machine translation<br />
When you have a large bulk of content to translate and the general meaning is enough<br />
When your translation never reaches the final audience, e.g. you’re translating a resource as research for another piece of content<br />
Translating documents for internal use within a company, provided 100% accuracy isn’t needed<br />
To partially translate large chunks of content for a human translator to improve upon<br />
Examples of when to use human translation<br />
When accuracy is important<br />
Most cases where your translated content is received by a consumer audience<br />
When you have a duty of care to provide accurate translations (e.g. legal documents, product instructions, medical guidelines or health and safety content)<br />
When translating marketing material or other texts for creative language uses.<br />
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===Conclusion ===<br />
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In the light of above mentioned facts and figures here by we would say that machine translation is challenge for human translators because it can reduces the wokload of translation but can't give accurate and exact translation of the traget language.It can be less reliable than human translation..</div>Chen Huinihttps://bou.de/u/index.php?title=Machine_translation&diff=129768Machine translation2021-12-08T01:38:17Z<p>Chen Huini: /* References */</p>
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<div>'''Machine Translation - A challenge or a chance for human translators?'''<br />
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[[Machine_translation|Overview Page of Machine Translation]]<br />
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[[Machine_Trans_EN_1]] [[Machine_Trans_EN_2]] [[Machine_Trans_EN_3]] [[Machine_Trans_EN_4]] [[Machine_Trans_EN_5]] [[Machine_Trans_EN_6]] [[Machine_Trans_EN_7]] [[Machine_Trans_EN_8]] [[Machine_Trans_EN_9]] [[Machine_Trans_EN_10]] [[Machine_Trans_EN_11]] [[Machine_Trans_EN_12]] [[Machine_Trans_EN_13]] [[Machine_Trans_EN_14]] [[Machine_Trans_EN_15]] [[Machine_Trans_EN_16]] [[Machine_Trans_EN_17]] [[Machine_Trans_EN_18]] [[Machine_Trans_EN_19]] [[Machine_Trans_EN_20]] [[Machine_Trans_EN_21]] [[Machine_Trans_EN_22]] [[Machine_Trans_EN_23]] [[Machine_Trans_EN_24]] [[Machine_Trans_EN_25]] [[Machine_Trans_EN_26]] [[Machine_Trans_EN_27]] [[Machine_Trans_EN_28]] [[Machine_Trans_EN_29]] [[Machine_Trans_EN_30]] ...<br />
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[[Book_projects|Back to translation project overview]]<br />
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[[DCG-To-Do|To the To Do list]]<br />
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=1 卫怡雯(A Comparison Between the Quality of Machine Translation and Human Translation——A Case Study of the Application of artificial intelligence in Sports Events)=<br />
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=2 吴映红（The Introduction of Machine Translation)= <br />
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=3 肖毅瑶(On the Realm Advantages And Symbiotic Development of Machine Translation And Huamn Translation)=<br />
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=4 王李菲 （Comparison Between Neural Machine Translation of Netease and Traditional Human Translation—A Case Study of The Economist Articles)= <br />
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=6 徐敏赟(Machine Translation Based on Neural Network --Challenge or Chance?)=<br />
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=7 颜莉莉(一带一路背景下人工智能与翻译人才的培养)=<br />
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===Abstract===<br />
In the era of artificial intelligence, artificial intelligence has been applied to various fields. In the field of translation, traditional translation models can no longer meet the rapid development and updating of the information age. The development of machine translation has brought structural changes to the language service industry, which poses challenges to the cultivation of translation talents. Under the background of "The Belt and Road initiative", translation talents have higher and higher requirements on translation literacy. Artificial intelligence and translation technology are used to reform the training mode of translation talents, so as to better serve the development of The Times. This paper mainly explores the cultivation of artificial intelligence and translation talents under the background of the Belt and Road Initiative. The cultivation of translation talents is moving towards comprehensive cultivation of talents. On the contrary, artificial intelligence and machine translation can also be used to improve the teaching mode and teaching content, so as to win together in cooperation.<br />
===Key words===<br />
Artificial intelligence,Machine translation,cultivation of translation talents,"The Belt and Road initiative"<br />
===题目===<br />
一带一路背景下人工智能与翻译人才的培养<br />
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===摘要===<br />
进入人工智能时代，人工智能被应用于各个领域。在翻译领域，传统的翻译模式已无法满足信息化时代的飞速发展和更新，机器翻译的发展给语言服务行业带来了结构性改变，这对翻译人才的培养提出了挑战。“一带一路”背景下，对翻译人才的翻译素养要求越来越高，利用人工智能和翻译技术对翻译人才培养模式进行革新，更好为时代发展服务。本文主要探究在一带一路背景下人工智能和翻译人才培养，翻译人才的培养过程中正向对人才的综合性培养，反之也可以利用人工智能和机器翻译完善教学模式和教学内容，在合作中共赢。<br />
===关键词===<br />
人工智能；机器翻译；翻译人才培养；一带一路<br />
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===1. Introduction===<br />
With the development of science and technology in China, artificial intelligence has also been greatly improved, and related technologies have been applied to various fields, such as the use of intelligent robots to deliver food to quarantined people during the epidemic, which has made people's lives more convenient. The most controversial and widely discussed issue is machine translation. Before the emergence of machine translation, translation was generally dominated by human translation, including translation and interpretation, which was divided into simultaneous interpretation and hand transmission, etc. It takes a lot of time and energy to cultivate a translation talent. However, nowadays, the era is developing rapidly and information is updated rapidly. As a translation talent, it is necessary to constantly update its knowledge reserve to keep up with the pace of The Times. The emergence of machine translation has also posed challenges to translation talents and the training of translation talents. Although machine translation had some problems in the early stage, it is now constantly improving its functions. In the context of the belt and Road Initiative, both machine translation and human translation are facing difficulties. Regardless of whether human translation is still needed, what is more important at present is how to train translators to adapt to difficulties and promote the cooperation between human translation and machine translation.<br />
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===2.Development status of machine translation in the era of artificial intelligence ===<br />
With the development of AI technology, machine translation has made great progress and has been applied to people's lives. For example, more and more tourists choose to download translation software when traveling abroad, which makes machine translation take an absolute advantage in daily email reply and other translation activities that do not require high accuracy. The translation software commonly used by netizens include Google Translation, Baidu Translation, Youdao Translation, IFly.com Translation, etc. Even wechat and other chat software can also carry out instant Translation into English. Some companies have also launched translation pens, translation machines and other equipment, which enables even native speakers to rely on machine translation to carry out basic communication with other Chinese people.<br />
But so far, machine translation still faces huge problems. Although machine translation has made great progress, it is highly dependent on corpus and other big data matching. It does not reach the thinking level of human brain, and cannot deal with the problem of translation differences caused by culture and religion. In addition, many minor languages cannot be translated by machine due to lack of corpus.<br />
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What's more, most of the corpus is about developed countries such as Britain and France, and most of the corpus is about diplomacy, politics, science and technology, etc., while there are very few about nationality, culture, religion, etc.<br />
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In addition, machine translation can only be used for daily communication at present. If it involves important occasions such as large conferences and international affairs, it is impossible to risk using machine translation for translation work. Professional translators are required to carry out translation work. So machine translation still has a long way to go.<br />
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===3.Challenges in the training of translation talents in universities===<br />
The cultivation of translators is targeted at the market. Professors Zhu Yifan and Guan Xinchao from the School of Foreign Languages at Shanghai Jiao Tong University believe that the cultivation of translators can be divided into four types: high-end translators and interpreters, senior translators and researchers, compound translators and applied translators.<br />
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From their names, it can be seen that high-end translators and interpreters and senior translators and researchers talents have high requirements on the knowledge and quality of interpreters, because they have to face the changing international situation, and have to deal with all kinds of sensitive relations and political related content, they should have flexible cross-cultural communication skills. In addition, for literature, sociology and humanities academic works, it is not only necessary to translate their content, but also to understand their essence. Therefore, translators should not only have humanistic feelings, but also need to have a deep understanding of Chinese and western culture.<br />
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However, there is not much demand for this kind of translation in the society. Such high-level translation requirements are not needed in daily life and work. The greatest demand is for compound translators, which means that they should master knowledge in a specific field while mastering a foreign language. For example, compound translators in the financial field should not only be good at foreign languages, but also master financial knowledge, including professional terms, special expressions and sentence patterns.<br />
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Now we say that machine translation can replace human translation should refer to the field of compound translation talents. Although AI technology has enabled machine translation to participate in creation, it does not mean that compound translation talents will be replaced by machines. The complexity of language and the flexible cross-cultural awareness required in communication make it impossible for machine translation to completely replace human translation.<br />
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The last type of applied translation talents are mostly involved in the general text without too much technical content and few professional terms, so it is easy to be replaced by machine translation.<br />
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Therefore, the author thinks that what universities are facing at present is not only how to train translation talents to cope with the development of machine translation, but to consider the application of machine translation in the process of training translation talents to achieve human-machine integration, so as to better complete the translation work.<br />
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===4.The Language environment and opportunities and challenges of the Belt and Road initiative===<br />
During visits to Central and Southeast Asian countries in September and October 2013, Chinese President Xi Jinping put forward the major initiative of jointly building the Silk Road Economic Belt and the 21st Century Maritime Silk Road. And began to be abbreviated as the Belt and Road Initiative.<br />
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According to the Vision and Actions for Jointly Building silk Road Economic Belt and 21st Century Maritime Silk Road, the Silk Road Economic Belt focuses on connecting China, Central Asia, Russia and Europe (the Baltic Sea). From China to the Persian Gulf and the Mediterranean Sea via Central and West Asia; China to Southeast Asia, South Asia, Indian Ocean. The focus of the 21st Century Maritime Silk Road is to stretch from China's coastal ports to Europe, through the South China Sea and the Indian Ocean. From China's coastal ports across the South China Sea to the South Pacific.<br />
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The Belt and Road "construction is comply with the world multi-polarization and economic globalization, cultural diversity, the initiative of social informatization tide, drive along the countries achieve economic policy coordination, to carry out a wider range, higher level, the deeper regional cooperation and jointly create open, inclusive and balanced, pratt &whitney regional economic cooperation framework.<br />
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====4.1The language environment of the Belt and Road====<br />
The "Belt and Road" involves a wide range of countries and regions, and their languages and cultures are very complex. How to make good use of language, do a good job in translation services, actively spread Chinese culture to the world, strengthen the ability of discourse, and tell Chinese stories well, the first thing to do is to understand the language situation of the countries along the "Belt and Road".<br />
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=====4.1.1The most common language in countries along the "Belt and Road" =====<br />
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There are a wide variety of languages spoken in 65 countries along the Belt and Road, involving nine language families. However, The status of English as the first language in the world is undeniable. Most of the countries participating in the Belt and Road are developing countries, and many of them speak English as their first foreign language. Especially in southeast Asian and South Asian countries, English plays an important role in foreign communication, whether as the official language or the first foreign language. Besides English, more than 100 million people speak Russian, Hindi, Bengali, Arabic and other major languages in the "Belt and Road" countries. It can also be seen that a common feature of languages in countries along the "Belt and Road" is the popularization of English education. English is widely used in international politics, economy, culture, education, science and technology, playing the role of the most important language in the world.<br />
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=====4.1.2The complex language conditions of countries along the "Belt and Road" =====<br />
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The languages spoken in countries along the Belt and Road involve nine major language families and almost all the world's religious types. Differences in religious beliefs also result in differences in culture, customs and social values behind languages. The languages of some countries along the belt and Road have also been influenced by historical and realistic factors, such as colonization, internal division and immigration. <br />
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India, for example, has no national language, but more than 20 official languages. India is a multi-ethnic country, a total of more than 100 people, one of the most obvious difference between nation and nation is the language problem. Therefore, according to the difference of language, India divides different ethnic groups into different states, big and small. Ethnic groups that use the same language are divided into one state. If there are two languages in a state, the state is divided into two parts. And Indian languages differ not only in word order but also in the way they are written. In India, for example, Hindi is spoken by the largest number of people in the north, with about 700 million speakers and 530 million as their first language. It is written in The Hindu language and belongs to the Indo-European language family. Telugu in the east is spoken by about 95 million people and 81.13 million as their first language. It is written in Telugu, which belongs to the Dravidian language family and is quite different from Hindi. As a result, a parliamentary session in India requires dozens of interpreters. <br />
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These factors cannot be ignored in the process of translation, from language communication to cultural understanding, from text to thought exchange, through the bridge of language to truly connect the people, so as to avoid misreading and misunderstanding caused by differences in language and national conditions.<br />
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====4.2 Opportunities and challenges of the "Belt and Road" ====<br />
With the promotion of the Belt and Road Initiative, there has been an unprecedented boom in translation. In the previous translation boom in China, most of the foreign languages were translated into Chinese, and most of the foreign cultures were imported into China. However, this time, in the context of the "Belt and Road" initiative, translating Chinese into foreign languages has become an important task for translators. As is known to all, there are many different kinds of "One Belt And One Road" along the national language and culture is complex, the service "area" construction has become a factor in Chinese translation talents training mode reform, one of the foreign language universities have action, many colleges and universities to establish the "area" all the way along the country's small language major, as a result, "One Belt And One Road" initiative to promote, It has brought unprecedented opportunities for human translation. The cultivation of diversified translation talents and the cultivation of translation talents in small languages is an urgent problem to be solved in China. The cultivation of translation talents cannot be completed overnight, and the state needs to reform the training mode of translation talents from the perspective of language strategic development. Only in this way can we meet the new demand for human translation under the new situation of the belt and Road Initiative.<br />
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For a long time, the traditional orientation of translation curriculum and training goal in colleges and universities is to train translation teachers and translators in need of society through translation theory and practice and literary translation practice, which cannot meet the needs of society. Since 2007, in order to meet the needs of the socialist market economy for application-oriented high-level professionals, the Academic Degrees Committee of The State Council approved the establishment of Master of Translation and Interpreting (MTI for short). After joining the pilot program of MTI, more and more universities are reforming the curriculum and training mode of master of Translation in order to cultivate translators who meet the needs of the society.<br />
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Language is an important carrier of culture, and translation is an important link for exporting culture. The quality of translation output also reflects the cultural soft power of a country. With the rise of China, more and more people are interested in Chinese culture, and the number of Chinese learners keeps increasing. Under the background of "One Belt and One Road", excellent translators are urgently needed to spread Chinese culture. With the promotion of "One Belt and One Road" Initiative, the number of other countries learning mutual learning and cultural exchanges with China has increased unprecedeningly, bringing vigorous opportunities for the spread of Chinese culture. Translation talents who understand small languages and multi-lingual translators are needed. They should not only use language to convey information, but also use language as a lubricant for communication.<br />
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===5.Training translation talents from the perspective of machine translation===<br />
Under the prevailing environment of machine translation, it poses a great challenge to the cultivation of translation talents. According to the current situation, translation needs and the shortage of translation talents, colleges and universities should reform and innovate the existing training programs for translation talents in terms of the quality of translation talents, the reform of training mode and the use of artificial intelligence. Based on the obtained data and literature, the author discusses how to train translation talents in the perspective of machine translation from the following aspects.<br />
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====5.1 Quality requirements for translation talents ====<br />
Zhong Weihe and Murray made a more detailed and profound discussion on translator's literacy, believing that "translators should not only be proficient in two languages, but also have extensive cultural and encyclopedic knowledge and relevant professional knowledge; Master a variety of translation skills, a lot of translation practice; Have a clear translator role awareness, good professional ethics, practical and enterprising style of work, conscious team spirit and calm psychological quality ". According to the collected data, the author will elaborate the requirements for translation talents from four aspects: language literacy, humanistic literacy, translation ability and innovation ability.<br />
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The first is language literacy, which is the most basic and important requirement. MAO Dun pointed out that "mastery of mother tongue and target language are the foundation of translation". A solid foundation of bilingual skills is the basic skills of translators. Poor language proficiency seems to be a common problem among students majoring in translation and interpreting. Many translation diseases are caused by poor Chinese foundation. As part of going global, the belt and Road initiative is to tell Chinese culture and Chinese stories, which requires translators to be able to use both languages flexibly. Therefore, the first problem that colleges and universities face to solve is to improve the language level of foreign language learners.<br />
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The second is humanistic literacy. Humanistic literacy is mainly manifested by a good command of politics, economy, history, literature and other knowledge, which is particularly important for interpreters. In addition, cross-cultural communication cannot be ignored. In the process of communicating with foreigners or translating, translators often encounter the first cross-cultural contradiction. Cross-culture refers to having a full and correct understanding of cultural phenomena, customs and habits that differ or conflict with the national culture, and accepting and adapting to them in an inclusive manner on this basis. So the interpreter can first fully understand and master the national conditions and culture of the target country, which is particularly important in the "Belt and Road". There are more than 60 countries along the "Belt and Road", and it takes a lot of energy to master their national conditions and culture.<br />
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The third is translation ability. We should distinguish between translation ability and language ability. Translation ability is actually a system of knowledge and skills necessary for translation, the core of which is conversion ability. First of all, it reflects the ability to use tools to assist translation, such as computer application, translation technology and so on. In addition, interpreters should have enough healthy psychological quality and good professional quality. In terms of translation ability, the current training model of translation talents is inadequate.<br />
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The last one is innovation. The cultivation of learners' thinking ability is the key to translation teaching and the cultivation of thoughtful translators should be the connotation of translation teaching. Therefore, the interpreter is not only a translation tool, which is no different from machine translation. More importantly, it is necessary to explore translation with thoughts, have a sense of lifelong learning and innovation consciousness. Translators must constantly innovate themselves, learn new knowledge, and strive to seek reform and innovation. Many colleges and universities should also consciously cultivate students' innovation ability and broaden their thinking and vision.<br />
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====5.2 The reform of college curriculum setting====<br />
First, we will further reform the curriculum of colleges and universities. Add economics, law and engineering to the curriculum, these contents in the "belt and Road".<br />
"One Road" is very important in the construction. According to the author's personal experience, the most typical problem of foreign language majors in colleges and universities is the single learning of foreign languages. More professional foreign language colleges and universities will add some literature courses and national conditions courses of the language target countries. Obviously, whether foreign language graduates are engaged in translation work or not, these knowledge is not enough. Of course, great reforms have been carried out in foreign language teaching, such as combining foreign language with finance, law, diplomacy and so on, and taking the way of minor training foreign language majors.<br />
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Domestic enterprises with a high degree of internationalization attach great importance to translation. Their translation research includes cutting-edge theoretical and applied research, involving machine translation, natural language processing and AI theory, algorithm and model. With such a foundation, enterprises can solve problems by themselves, such as embedding automatic translation functions in mobile phones. International enterprises not only do technical translation, but also deal with all forms of translation and localization in society. At present, translation teaching in most colleges and universities is still in the early mode, and it is an objective fact that it is divorced from the workplace and has a gap with the needs of enterprises.<br />
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Second, we should adjust and strengthen the construction of second foreign language teaching for foreign language majors. In the 1980s, our country was in urgent need of Russian translation. At that time, students majoring in English could translate microelectronic product manuals and related business documents in English and Russian at the same time after learning Russian for half a year. The mutual conversion between English and Russian played a great role in practice. According to the author, in the Graduate Institute of Interpretation and Translation of Beijing Foreign Studies University a very few students majored in multiple languages at the graduate level, that is, they majored in minor languages at the undergraduate level and were admitted to the Graduate Institute of Interpretation and Translation in English. Their training mode is to study English in the Graduate Institute of Interpretation and Translation for two years and the third year in the corresponding department of the undergraduate major. Such training mode in my opinion is a bigger model, cost It's more difficult for students. <br />
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In addition, there is a great disparity in the development of second foreign language teaching in colleges and universities, and the overall level is not high enough. Part of the second foreign language university foreign language professional may still be too much focus in languages such as German, French and Japanese, should as far as possible, considering the need of the construction of the "region", like Croatia, Serbia, Turkish, Hungarian, Italian, Indonesian, Albanian, these are the countries along the "area" the language of the two countries, Colleges and universities should encourage the teaching of a second foreign language.<br />
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Third, the teaching of translation technology should be strengthened. Traditional translation teaching teaches translation skills, such as the translation of words, sentences, texts and figures of speech. Translation technology refers to a series of practical workplace technologies with computer-aided translation software and translation project management as the core, which can greatly improve translation efficiency. However, due to the relative lack of translation technology teachers and equipment in colleges and universities, there is a disconnect between talent training and the requirements of translation technology in the translation field.<br />
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====5.3 Application of artificial intelligence to translation teaching practice====<br />
In order to improve the teaching quality and train students' English translation ability, it is necessary to realize the effective integration of ARTIFICIAL intelligence and translation activity courses, which should not only reflect the effectiveness of artificial intelligence translation technology, but also help students establish a healthy concept of English communication. Through the application of artificial intelligence technology, students can strengthen their flexible translation skills through close communication with "AI program" during the learning stage of English translation activity class. For example, teachers can ask students to translate directly against the translation content provided on the translation screen of the ARTIFICIAL intelligence system. After that, the system can collect the translation answers with the help of speech recognition function, and then judge the accuracy of the translation content, thus providing important feedback to students.<br />
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China has used such artificial intelligence technology in the Putonghua test to ensure that every student can find a suitable translation method in practical communication. The so-called artificial intelligence technology is a new kind of technology modeled after the characteristics of human neural network thinking, can combine the human mind to respond. If it can be integrated into English translation activity teaching, it can not only improve the teaching efficiency, but also enhance students' enthusiasm in learning the course.<br />
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At the same time, during the training of translation talents, teachers also need to take into account the importance of influencing education factors, so that students can form a higher disciplinary quality in translation, so as to fit the concept of quality education in the new era. Only when artificial intelligence translation content is fully integrated into college English translation activity courses can the overall translation ability of college students be maximized.<br />
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====5.4The improvement of translator's technical ability====<br />
In the previous part, the author roughly mentioned that translation teaching should be improved, which will be elaborated here. At present, only a few universities can make full use of the advantages of translation technology in translation teaching and focus on cultivating professional translation talents. Most universities still cannot get rid of the traditional teaching mode of "language + relevant professional knowledge" in translation teaching, and generally lack a correct understanding of COMPUTER-aided translation teaching.<br />
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According to Wang Huashu et al., the courses that can be offered around the composition of translators' technical literacy include computer-assisted translation, translation and corpus, machine translation and post-translation editing, localization and internationalization, film and television translation (subtitle), technical communication and technical writing, and computer programming. The course modules involved are: Fundamentals of COMPUTER-aided Translation, CAT tool application, corpus alignment and processing, term management, QA technology for translation quality assurance, OFFICE fundamentals, translation management technology, basic computer knowledge, desktop typesetting, localization and internationalization, project management system and content management system, technical writing, basic knowledge of computer programming, basic knowledge of web code, etc.<br />
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===6针对一带一路的机器翻译与翻译人才的合作===<br />
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===Conclusion===<br />
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===References===<br />
<br />
=8 颜静(On Machine Translation Under Lanuguage Intelligence——An Option and Opportunity for Human Translators)=<br />
[[Machine_Trans_EN_8]]<br />
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=9 谢佳芬（人工智能时代下的机器翻译与人工翻译）=<br />
[[Machine_Trans_EN_9]]<br />
===Abstract===<br />
With the continuous development of information technology, many industries are facing the competitive pressure of artificial intelligence, and so is the field of translation. Artificial intelligence technology has developed rapidly and combined with the field of translation，which has brought great impact and changes to traditional translation, but artificial intelligence translation and artificial translation have their own advantages and disadvantages. Artificial translation is in the leading position in adapting to human language logical habits and understanding characteristics, but in terms of translation threshold and economic value, the efficiency of artificial intelligence translation is even better. In a word, we need to know that machine translation and human translation are complementary rather than antagonistic.<br />
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===Key Words===<br />
Machine Translation; Artificial Translation; Artificial Intelligence<br />
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===题目===<br />
人工智能时代下的机器翻译与人工翻译<br />
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===摘要===<br />
伴随着信息技术的不断发展，多个行业面临着人工智能的竞争压力，翻译领域也是如此。人工智能技术快速发展并与翻译领域结合，人工智能翻译给传统翻译带来了巨大的冲击和变革，但人工智能翻译与人工翻译存在着各自的优劣特点和发展空间，在适应人类语言逻辑习惯和理解特点的翻译效果上，人工翻译处于领先地位，但在翻译门槛和经济价值上，人工智能翻译的效率则更胜一筹。总的来说，我们要知道机器翻译与人工翻译是互补而非对立的关系。<br />
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===关键词===<br />
机器翻译;人工翻译;人工智能<br />
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===1. Introduction===<br />
====1.1 The History of Machine Translation Aborad====<br />
The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. Alchuni put forward the idea of using machines for translation. In 1933, the Soviet inventor Troyansky designed a machine to translate one language into another. [1]In 1946, the world's first modern electronic computer ENIAC was born. Soon after, American scientist Warren Weaver, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947. In 1949, Warren Weaver published a memorandum entitled Translation, which formally raised the issue of machine translation. In 1954, Georgetown University, with the cooperation of IBM, completed the English-Russian machine translation experiment with IBM-701 computer for the first time, which opened the prelude of machine translation research. [2] In 2006, Google translation was officially released as a free service software, bringing a big upsurge of statistical machine translation research. It was Franz Och who joined Google in 2004 and led Google translation. What’s more, it is precisely because of the unremitting efforts of generations of scientists that science fiction has been brought into reality step by step.<br />
====1.2 The History of Machine Translation in China====<br />
In 1956, the research and development of machine translation has been named in the scientific and technological work and made little achievements in China. On the eve of the tenth anniversary of the National Day in 1959, our country successfully carried out experiments, which translated nine different types of complicated sentences on large general-purpose electronic computers. The dictionary includes 2030 entries, and the grammar rule system consists of 29 circuit diagrams. [3]. After a period of stagnation, China's machine translation ushered in a high-speed development stage after the 1980s in the wave of the third scientific and technological revolution. With the rapid development of economy and science and technology, China has made a qualitative leap in the field of machine translation research with the pace of reform and opening up. In 1978, Institute of Scientific and Technological Information of China, Institute of Computing Technology and Institute of Linguistics carried out an English-Chinese translation experiment with 20 Metallurgical Title examples as the objects and achieved satisfactory results. Subsequently, they developed a JYE-I machine translation system, which based on 200 sentences from metallurgical documents. Its principles and methods were also widely used in the machine translation system developed in the future. In addition, the research achievements of machine translation in China during the 1980s and 1990s also include that Institute of Post and Telecommunication Sciences developed a machine translation system, C Retrieval and automatic typesetting system with good performance and strong practicability in October 1986; In 1988, ISTC launched the ISTIC-I English-Chinese Title System for the translation of applied literature of metallurgy, Information Research Institute of Railway developed an English-Chinese Title Recording machine translation system for railway documents; the Language Institute of the Academy of Social Sciences developed "Tianyu" English-Chinese machine translation system and Matr English-Chinese machine translation system developed by the computer department of National University of Defense Technology. After many explorations and studies, machine translation in China has gradually moved towards application, popularization and commercialization. China Software Technology Corporation launched "Yixing I" in 1988, marking China's machine translation system officially going to the market. After "Yixing", a series of machine translation systems such as Gaoli system in Beijing, Tongyi system in Tianjin and Langwei system in Shaanxi have also entered the public. In the 21st century, the development of a series of apps such as Kingsoft Powerword, Youdao translation and Baidu translation has greatly met the needs of ordinary users for translation. According to the working principle, machine translation has roughly experienced three stages: rule-based machine translation, statistics-based machine translation and deep learning based neural machine translation. [4] These three stages witnessed a leap in the quality of machine translation. Machine translation is more and more used in daily life and even the translation of some texts is almost comparable to artificial translation. In addition to text translation, voice translation, photo translation and other functions have also been listed, which provides great convenience for people's life. It is undeniable that machine translation has become the development trend of translation in the future.<br />
====1.3 The Status Quo of Machine Translation====<br />
In this big data era of information explosion, the prospect of machine translation is also bright. At present, the circular neural network system launched by Google has supported universal translation in more than 60 languages. Many Internet companies such as Microsoft Bing, Sogou, Tencent, Baidu and NetEase Youdao have also launched their own Internet free machine translation systems. [5] Users can obtain translation results free of charge by logging in to the corresponding websites. At present, the circular neural network translation system launched by Google can support real-time translation of more than 60 languages, and the domestic Baidu online machine translation system can also support real-time translation of 28 languages. These Internet online machine translation systems are suitable for a variety of terminal platforms such as mobile phone, PC, tablet and web and its functions are also quite diverse, supporting many translation forms, such as screen word selection, text scanning translation, photo translation, offline translation, web page translation and so on. Although its translation quality needs to be improved, it has been outstanding in the fields of daily dialogue, news translation and so on.<br />
===2. Advantages and Disadvantages of Machine Translation===<br />
Generally speaking, machine translation has the characteristics of high efficiency, low cost, accurate term translation and great development potential and etc. Machine translation is fast and efficient, this is something that artificial translation can’t catch up with. In addition, with the continuous emergence of all kinds of translation software in the market, compared with artificial translation, machine translation is cheap and sometimes even free, which greatly saves the economic cost and time for users with low translation quality requirements. What's more, compared with artificial translation, machine translation has a huge corpus, which makes the translation of some terms, especially the latest scientific and technological terms, more rapid and accurate. The accurate translation of these terms requires the translator to constantly learn, but learning needs a process, which has a certain test on the translator's learning ability and learning speed. In this regard, artificial translation has uncertainty and hysteretic nature. At the same time, with the progress of science and technology and the development of society, the function of machine translation will be more perfect and the quality of translation will be better.Today's machine translation tools and software are easy to carry, all you need to do is just to use the software and electronic dictionary in the mobile phone. There is no need to carry paper dictionaries and books for translation, which saves time and space. At the same time, machine translation covers many fields and is suitable for translation practice in different situations, such as academic, education, commercial trade, social networking, tourism, production technology, etc, it is also easy to deal with various professional terms. However, due to the limitation of translators' own knowledge, artificial translation is often limited to one or a few fields or industries. For example, it is difficult for an interpreter specializing in medical English to translate legal English.<br />
At the same time, machine translation also has its limitations. At first, machine can only operate word to word translation, which only plays the function and role of dictionary. Then, the application of syntax enables the process of sentence translation and it can be solved by using the direct translation method. When the original text and the target language are highly similar, it can be translated directly. For example, the original text "他是个老师." The target language is "he is a teacher ". With the increase of the structural complexity of the original text, the effect of machine translation is greatly reduced. Therefore, at the syntactic level, machine translation still stays in sentences with relatively simple structure. Meanwhile, the original text and the results of machine translation cannot be interchanged equally, indicating that English-Chinese translation has strong randomness, and is not rigorous and scientific enough. <br />
Nowadays, machine translation is highly dependent on parallel corpora, but the construction of parallel corpora is not perfect. At present, the resources of some mainstream languages such as Chinese and English are relatively rich, while the data collection of many small languages is not satisfactory. Moreover, the current corpus is mainly concentrated in the fields of government literature, science and technology, current affairs and news, while there is a serious lack of data in other fields, which can’t reflect the advantages of machine translation. At the same time, corpus construction lags behind. Some informative texts introducing the latest cutting-edge research results often spread the latest academic knowledge and use a large number of new professional terms, such as academic papers and teaching materials while the corpus often lacks the corresponding words of the target language, which makes machine translation powerless<br />
Besides, machine translation is not culturally sensitive. Human may never be able to program machines to understand and experience a particular culture. Different cultures have unique and different language systems, and machines do not have complexity to understand or recognize slang, jargon, puns and idioms. Therefore, their translation may not conform to cultural values and specific norms. This is also one of the challenges that the machine needs to overcome.[6] Artificial intelligence may have human abstract thinking ability in the future, but it is difficult to have image thinking ability including imagination and emotion. [7] Therefore, machine translation is often used in news, science and technology, patents, specifications and other text fields with the purpose of fact description, knowledge and information transmission. These words rarely involve emotional and cultural background. When translating expressive texts, the limitations of machine translation are exposed. The so-called expressive text refers to the text that pays attention to emotional expression and is full of imagination. Its main characteristics are subjectivity, emotion and imagination, such as novels, poetry, prose, art and so on. This kind of text attaches importance to the emotional expression of the author or character image, and uses a lot of metaphors, symbols and other expressions. Machine translation is difficult to catch up with artificial translation in this kind of text, it can only translate the main idea, lack of connotation and literary grace and it cannot have subjective feelings and rational analysis like human beings. In fact, it is not difficult to simulate the human brain, the difficulty is that it is impossible to learn from the rich social experience and life experience of excellent translators. In other words, machine translation lacks the personalization and creativity of human translation. It is this personalization and creativity that promote the development and evolution of language, and what machine translation can only output is mechanical "machine language".<br />
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===3.The Irreplaceability of Artificial Translation ===<br />
====3.1 Translation is Constrained by Context====<br />
At present, machine translation can help people deal with language communication in people's daily life and work, such as clothing, food, housing and transportation, but there is a big gap from the "faithfulness, expressiveness and elegance" emphasized by high-level translation. Language itself is art，which pays more attention to artistry than functionality, and the discipline of art is difficult to quantify and unify. Sometimes it is regular, rigorous, logical and clear, and sometimes it is random, free and logical. If it is translated by machine, it is difficult to grasp this degree. Sometimes, machine translation cannot connect words with contextual meaning. In many languages, the same word may have multiple completely unrelated meanings. In this case, context will have a great impact on word meaning, and the understanding of word meaning depends largely on the meaning read from context. Only human beings can combine words with context, determine their true meaning, and creatively adjust and modify the language to obtain a complete and accurate translation. This is undoubtedly very difficult for machine translation. Artificial translation can get rid of the constraints of the source language and translate the translation in line with the grammar, sentence patterns and word habits of the target language. In the process of translation, translators can use their own knowledge reserves to analyze the differences between the source language and the target language in thinking mode, cultural characteristics, social background, customs and habits, so as to translate a more accurate translation. Artificial translation can also add, delete, domesticate, modify and polish the translation according to the style, make up for the lack of culture, try to maintain the thought, artistic conception and charm of the original text and the style of the source language. In addition, translators can also judge and consider the words with multiple meanings or easy to produce ambiguity according to the context, so as to make the translation more clear and more accurate and improve the quality of the translation.<br />
<br />
<br />
===4. Discussion on the Relationship Between Machine Translation and Artificial Translation ===<br />
<br />
===5. Suggestions on the Combined Development of Machine Translation and Artificial Translation===<br />
<br />
===6. ===<br />
<br />
===7. ===<br />
<br />
===Conclusion===<br />
<br />
===References===<br />
<br />
=10 熊敏(Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts)=<br />
[[Machine_Trans_EN_10]]<br />
===Abstract===<br />
The history of machine translation can be traced to 1940s, undergoing germination, recession, revival and flourish. Nowadays, with the rapid development of information technology, machine translation technology emerged and is gradually becoming mature. Whether manual translation can be replaced by machine translation becomes a widely-disputed topic. So in order to explore the ability of machine translation, I adopts two versions of translation, which are manual translation and machine translation(this paper uses Youdao translation) for different types of texts(according to Peter Newmark's types of text：informative text, expressive text and vocative text). The results are quite different in terms of quality and accuracy. The results show that machine translation is more suitable for informative text for its brevity, while the expressive texts especially literary works and vocative texts are difficult to be translated, because there are too many loaded words and cultural images in expressive text and dependence on the readers. To draw a conclusion, the relationship between machine translation and manual translation should be complementary rather than competitive.<br />
<br />
===Key words===<br />
machine translation; manual translation; Newmark's type of texts<br />
<br />
===题目===<br />
Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts<br />
<br />
===摘要===<br />
机器翻译的历史可以追溯到上个世纪40年代，经历了萌芽期、萧条期、复兴期和繁荣期四个阶段。如今，随着信息技术的高速发展，机器翻译技术日益成熟，于是机器翻译能不能替代人工翻译这个话题引起了广泛热议。为了探究机器翻译的能力水平是否足以替代人工翻译，本人根据皮特纽马克的文本类型分类理论（信息类文本，表达文本，呼吁文本），选择了相应的译文类型，并且将其机器翻译的版本以及人工翻译的版本进行对比。结果发现，就质量和准确度而言，译文的水平大相径庭。因为其简洁的特性，机器比较适合翻译信息文本。而就表达文本，尤其是文学作品，因其存在文化负载词及相应的文化现象，所以机器翻译这类文本存在难度。而呼唤文本因其目的性以及对读者的依赖性较强，翻译难度较大。由此得知，机器翻译和人工翻译的关系应该是互补的，而不是竞争的。<br />
<br />
===关键词===<br />
机器翻译；人工翻译；纽马克文本类型<br />
<br />
===1. Introduction===<br />
====1.1.Introduction to machine translation====<br />
Machine translation, also known as computer translation is a technique to translating through machine translator, such as Google translation and Youdao translation. Machine translation is one of the branches of computational linguistics, ranging from computer science, statistics, information science and so on. Machine translation plays an important role in all aspects.<br />
Machine translation can be traced back to 1940s, when British engineer Booth and American engineer Weaver proposed using computer to translate and started to study machines used for translation. And the first machine translating system launched in 1954, used in English and Russian translation. And this means machine translation becomes a reality. However, the arrival of new things always accompanies barriers and setbacks. In the 1960s, reports from ALPAC (Automated Language Processing Advisory Committee) showed studies on machine translation had stagnated for a decade. At that time, due to the high cost of machine, choosing human translators to finish translating works was more economical for most companies. What’s worse, machine had difficulty in understanding semantic and pragmatic meanings. Fortunately, in the 1970s, with the advance and popularity of computer, machine translation was gradually back on track. With the development of science and technology, machine translation has better technological guarantee, such as artificial intelligence and computer technology. In the last decades, from the late 90s till now, machine translation is becoming more and more mature. The initial rule-oriented machine translation has been updated and Corpus-aided machine translation appears. And nowadays, technology in voice recognition has been further developed. This technique is frequently applied in speech translation products. Users only need to speak source language to the online translator and instantly it will speak out the target version. But machine can’t fully provide precise and accurate translation without any grammatical or semantic problems. Machine can understand the literal meaning of texts, but not the meaning in context. For example, there is polysemy in English, which refers to words having multiple meanings. So when translating these words, translators should take contexts into consideration. But machine has troubles in this way. In addition, machine has no feeling or intention. Hence, it is also difficult to convey the feelings from the source language.<br />
<br />
====1.2.Process of machine translation====<br />
The process of manual translation is different from that of machine translation. Here is the process of the former. (1) Understand source language. (2) Use target language to organize language. (3) Generate translation. Unlike manual translation, machine translation tends to analyze and code source language first, then look for related codes in corpus, and work out the code that represents target language, generating translation. But they share a common feature, which is that Lexicon, grammatical rules and syntactic structure are taken into consideration. This is one of the biggest challenges for machine translation.<br />
<br />
===2.Theorectical framework===<br />
====2.1Newmark’s text typology====<br />
Text typology is a theory from linguistics. It undergoes several phrases. Karl Buhler, a German psychologist and linguist defines communicative functions into expressive, information and vocative. Then Roman Jakobson proposes categorization of texts, which are intralingual translation, interlingual translation and intersemiotic translation. Accordingly, he defines six main functions of language, including the referential function, conative function, emotive function, poetic function, metalingual function and the phatic function. Based on the two theories, Peter Newmark, a translation theorist, summarizes the language function into six parts: the informative function, the vocative function, the expressive function, the phatic function, the aesthetic function, and the metalingual function. Later, he further divided texts into informative type, expressive text and vocative type according to the linguistic functions of various texts. <br />
The core of the informative texts is the truth. It is to convey facts, information, knowledge of certain topics, reality and theories. The language style of the text is objective, logical and standardized. Reports, papers, scientific and technological textbooks are all attributed to informative texts. Translators are required to take format into account for different styles.<br />
The core of the expressive text is the sender’s emotions and attitudes. It is to express sender’s preferences, feelings, views and so on. The language style of it is subjective. Imaginative literature, including fictions, poems and dramas, autobiography and authoritative statements belong to expressive text. It requires translators to be faithful to the source language and maintain the style.<br />
The core of the vocative text is readership. It is to call upon readers to act, think, feel and react in the way intended by the text. So it is reader-oriented. Such texts as advertisement, propaganda and notices are of vocative text. Newmark noted that translators need to consider cultural background of the source language and the semantic and pragmatic effect of target language. If readers can comprehend the meaning at once and do as the text says, then the goal of information communication is achieved.<br />
To draw a conclusion, informative texts focus on the information and facts. Expressive texts center on the mind of addressers, including feelings, prejudices and so on. And vocative texts are oriented on readers and call on them to practice as the texts say.<br />
<br />
====2.2Study method====<br />
Manual translations of the three texts are selected from authoritative versions and universally acknowledged. And machine translations of those come from Youdao Translator. And in this thesis I will compare and evaluate the two methods in word diction, sentence structure, word order and redundancy.<br />
<br />
===3. ===<br />
<br />
===4. ===<br />
<br />
===5. ===<br />
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===6. ===<br />
<br />
===7. ===<br />
<br />
===Conclusion===<br />
<br />
===References===<br />
<br />
=11 陈惠妮=(Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts)=<br />
[[Machine_Trans_EN_11]]<br />
===Abstract===<br />
At present, globalization is accelerating and the market demand for language services is rapidly increasing . Machine translation, as an important translation method, can greatly improve translation efficiency due to its low cost and high speed. However, because of the limitations of machine translation and the differences between Chinese and English language, machine translation is not accurate enough. In order to balance translation efficiency and translation quality, a great number of manual revisions in translation are required for the machine translating texts. Medical papers are specialized, special and purposeful, so it requires accurate,qualified and professional translation. However, the quality of translations by machine is inefficient to meet the high-quality requirements of medical papers translation. Therefore, the introduction of pre-editing can greatly improve the efficiency and quality of machine translation.<br />
<br />
===Key words===<br />
Pre-editing, Machine translation, Medical texts<br />
<br />
===题目===<br />
Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts<br />
<br />
===摘要===<br />
在全球化加速发展的今天，市场对语言服务的需求迅速增加。机器翻译作为一种重要的翻译途径，由于其成本低、速度快，可以大大提高翻译效率。然而，由于机器翻译的局限性以及中英文语言的差异，机器翻译的准确性不高。为了平衡翻译效率和翻译质量，机器翻译文本需要大量的手工修改。<br />
医学论文具有专业性、特殊性和目的性，要求其译文准确、合格、专业。然而，机器翻译的质量较低，无法满足医学论文对翻译的高质量要求。因此，译前编辑的引入可以大大提高机器翻译的效率和质量。<br />
<br />
===关键词===<br />
译前编辑；机器翻译；医学文本<br />
<br />
===1. Introduction===<br />
===1.1 Definition of Machine Translation===<br />
As Cronin(2013) revealed: "Translation is undergoing a revolutionary upheaval. The influence of digital technology and the Internet on translation is continuous, extensive and profound. From the popularity of automatic online translation applications, translation revolution is everywhere (Cronin,2013) However,the concept of machine translation was firstly proposed in the 1930s. Since 1940s, the machine translation technology has been evolving from rule-based machine translation (RBMT) to statistical machine translation (SMT), and to neural machine translation (NMT). Machine translation refers to the automatic translation of source language into target language by using a computer system. That is, machine translation refers to the automatic translation of text from one language into another natural language by computer software or other online translation webs. Machine translation is also defined as the process of “using a computer system to automatically translate text or speech from one natural language to another” according to the definition by ISO (Cui, 2014).O 'Brien (2002) defines it as "the behavior of modifying errors in machine translation to ensure that the target translation meets certain quality requirements". On the basic level, machine translation performs mechanical substitution of words in one language for words in another language, but that rarely produces a good translation, therefore, recognition of the whole phrases and their closest counterparts in the target language is needed. Not all words in one language have equivalents in another language, and many words have more than one meaning. A huge demand for translation is greatly needed in today’s global world, which creates new opportunities for the development of machine translation, attracts more and more attention and becomes one of the current research focuses.<br />
<br />
===1.2 Definition of Pre-editing===<br />
Pre-editing means to adjust and modify the source language to make it fit more with the characteristics of the machine translation software before putting the source language before the into machine translation, so as to improve the quality of the translation machine translation (Wei Changhong, 2008:93-94). Pre-editing is to modify the original text before putting it into machine translation software, in order to improve the recognition rate of machine translation, optimize the output quality of translated text and reduce the workload of post-editing. Because pre-translation editing only needs to be modified in one language, the operation is simpler than post-translation editing, which can realize the double improvement of quality and efficiency. A good pre-editing translation can help machine translation more smoothly, thus improving the machine readability and quality of the output translation. Pre-editing is mostly applied in the following situations: one is when the original text is of poor quality and the machine is difficult to recognize the meaning of the sentence, such as user generated content with poor readability and translatability (Gerlach et al, 2003:45-53); Documents that need to be published in multiple languages; Next is when the original text contains a lot of jargon; The last is the original text has a corresponding translation memory bank. If the original text is edited, it can better match the content of the translation memory bank. Pre-editing is a process of identifying problems. It requires to pre-edit the source texts before putting it into machine translation according to the requirements, listing the expressions or sentences that may have trouble in machine translation and then pre-edit it by human. The purpose is to enable the computer to translate better, improve the translatability of machine translation. (Slype G V & Guinet J F & Seitz F,1984:115)<br />
<br />
===1.3 Machine Translation Mode===<br />
According to the different knowledge acquisition methods, machine translation modes can be classified as follows: one is rule-based machine translation, which is based on bilingual dictionaries and a library of language rules for each language. The quality of translation depends on whether the source language conforms to the existing rules, but the inexhaustible rules are the hinders of this model. The second mode is machine translation based on statistics. This translation model relies on the principles of mathematics and statistics to find various existing translations corresponding to the translation tasks through the employment of corpus, analyzing the frequency of their occurrence and selecting the translation with the highest frequency for output. The disadvantage of this translation model is that it ignores the flexibility of language and the importance of context. The last one is neural network language model. This model is different from previous translation models in that it uses end-to-end neural network to realize automatic translation between natural languages. At present, the quality of its translation is much higher than that of the previous translation models.<br />
===1.4 Source of Study Abstracts===<br />
The core medical journals from domestic are selected in order to make the paper more representative and authoritative, such as National Medical Journal of China, Journal of Peking University (Health Science), and Journal of Third Military Medical University. All of them include basic medical science, biomedical technology, laboratory medical science and other fields. In this way, the pre-editing approaches included are applicable enough to machine translation of medical abstracts.<br />
===1.5 Selection of Translation Software===<br />
Generally, machine translation software includes Google Translation, Youdao Translator and Niu Translation, which has their own special use in translation. Take Google as an example, it is more like neurons in human brain, enabling to learn and collect information to establish connections with its neural machine translator’s neurons. However, it also causes many errors because of the lack of enough information. In this paper, contrastive analysis will be carried on by using Google translation. On the one hand, being a pioneer in the translation of NET, it is inevitably to sue Google as the translation software to translate medical texts in this paper. Comparing the program called “Google brain”, other NMT of translation software are relatively disadvantages. On the other hand, the Google translation enjoys the largest users in the world, with its downloads of more than one billion. The output quality of Google translation is more correct and complete than other machine translation software. With years of development and improvement, Google Translation has been greatly promoted. In this paper, Chinese medical abstract will be automatically translated by Google translation, and then the translation output will be compared with the translation by human and the publication of English abstracts. The main purpose is to prove that the improvement and promotion of quality and accuracy in the medical abstracts will be obtained through the pre-editing approaches.<br />
<br />
===2.Language Characteristics and Error Division===<br />
Since Chinese and English are two different languages, it is quite neccessary to identify their own characteristics so as to better analyze and understand the two languages. Tytler (1978) argued in his Essay on the Principles of Translation that there are three principles of translation that in all the translation should give readers the same feelings as the source text, except for complete transcript of ideas. There actually also exit some mistakes of these two languages. So the following will make some clarrifications of these two languages to ensure more accurate translation.<br />
<br />
===2.1 Language Characteristics of Medical Abstracts===<br />
Chinese and English belong to different language systems, so there are differences in their language structure and the way users think of the languages. When using machine translation from Chinese to English, due to the unequal language levels, there will be many mistakes in the translation process, especially for ESP texts, such as medical papers. <br />
Here, these differences mainly refer to the linguistic characteristics of medical abstracts. In medical abstracts, it usually includes structured and unstructured abstracts. Although in different forms, they both describe the purpose, methods and conclusions of the research.<br />
In the method section of medial abstract, several Chinese sentences can be connected with commas, but each sentence may convey different information. In contrast, English sentences contain a great deal of information, but in order to ensure clarity, some modifiers need to be isolated and then reconstructed. However, in Chinese-English machine translation, a lot of information is put into the sentence because the machine segments the sentence on the basis of the full comma.<br />
In addition, subjectless sentence is used in the objective and method parts of Chinese medical abstracts. The subjectless sentence means the sentence without or free from subject and is usually employed in two contexts. The first is "needless to say". In Chinese sentences, it is common to omit the subject of the sentence. Chinese sentences can convey meanings by using incomplete sentence structures, so Chinese speakers can understand the meanings of the sentences even though the sentence subject is omitted. The second is "emphasis of action". In this context, subjectless sentences are used to describe behaviors, especially in the study of traditional Chinese medicine abstracts. Comparatively speaking, it should be avoided in English medical abstracts. Abstract sentences are more subjective when describing the learning process, while the essential requirement of English medical abstract is objectivity. From this point, sentences without subject should be avoided in English medical abstracts. Another feature is voice. Few words with passive meanings appear in Chinese abstracts. English sentences are more favoured in using passive voice. When translating from Chinese to English, passive voice should be used to make the contents objective, which is also the basic requirement of medical papers.<br />
These are the linguistic features of Chinese medical abstracts. There are great differences in sentence structure and expression between Chinese and English medical abstracts. These differences may reduce the accuracy of machine translation, so it is necessary to introduce pre-editing to edit the source text to ensure that the source text can be accurately recognized by the machine and fully translated into English.<br />
===2.2 Error Division of Machine Translation in Translating Abstracts of Medical Papers from Chinese to English===<br />
In this paper, errors in machine translation are listed out after analyzing. Some are due to the principles, such as statistical-based MT and NMT, employed by machine translation. Based on the original errors, they can be studied from two levels. One is from the macro-level, referring to mistakes caused by objective factors. These are not human factors, but the disadvantage of machine translation and the limitations of language. The limitations of machine translation are derived from the principles and models adopted and manifests itself as a reliance on reference sources. In this case, semantic ambiguity and textual incoherence may occur in the absence of reference sources.<br />
However, for ESP texts such as medical papers, the requirements for terms and sentence patterns are far beyond the existing corpora. For unfamiliar texts (that is, the corresponding texts cannot be found in the corpora). The quality of output translation will be relatively low, which is determined by the principles of machine translation. As mentioned above, machine translation has evolved over the past few decades from rules-based to corp-based statistics to NMT. However, there still exist some limitations in machine translation.<br />
Mistakes on micro-level are mainly caused by the variations and differences of linguistic structure between Chinese and English. Chinese is an implicit language, while English is an explicit one (Lian, 2010) To put it in another way, Chinese expression does not depend on language structures, while English does the opposite. This may result in mismatches between the original and machine translated sentences. This kind of error is mainly divided into two types: component fragment and component missing. For a medical paper, such errors are very serious. As a kind of ESP discourse, medical paper has the characteristics of fixed, objective and accurate language structure. In order to reproduce the characteristics of medical abstracts in translation, it is necessary to avoid errors at the level of words and sentences and pay attention to logic and consistency.<br />
Lexical errors here refer to the inconsistency of fixed expressions in the translation of terms. Although these terms are machine translation based on the corpus, the corpus may not be perfect for ESP texts such as medical papers. Therefore, fixed expressions are not used in term translation. This is also the most common mistake in machine translation (the term here includes but is not limited to nouns). For ESP texts, medical text in particular, the fixed expressions and the accuracy of terms are of great importance.<br />
<br />
===3.Approaches Proposed for Pre-editing ===<br />
Generally speaking, there is a close relationship between pre-editing and post-editing, both of which aim to convey information and ensure high or publishable translation quality. Proper pre-editing can improve the quality of machine translation in terms of adequacy and consistency. <br />
Complexity of natural language and people use language arbitrarily, bringing many difficulties to Chinese-English machine translation, Hu Qingping (2005:24) has proposed "the research of translation software and the development of the controlled language are the two directions to improve the quality of machine translation : the former aims at the difficulty in the natural language processing, the latter overcomes the arbitrariness of natural language". Feng Quangong and Gao Lin (2017: 63-68) put forward: "The writing principles of controlled language can be applied to pre-editing of machine translation. Pre-editing based on controlled language can effectively reduce the complexity and ambiguity of source text, improve the identifiability of machine translation (the translatability of source text itself), and thus reduce (fully) the workload of post-editing".<br />
The central task of pre-editing is to transform human-friendly content into machine-friendly content, so words and sentences need to be repositioned or even changed. Based on the analysis of the language characteristics of Chinese and English, the Chinese ideographic group should be split before the Chinese source text is input into the machine software to translate so that the sentence structure is complete which can be easily recognized by the machine translation software.<br />
===3.1 Extraction and Replacement of Terms in the Source Text===<br />
As a branch of applied English, medical English is the product of the combination of English language knowledge and medical knowledge. The terms in medical papers have the characteristics of fixed and complex language structure. Although Google Translation is based on a corpus, the language structure is not fixed due to the limited size of the corpus. Therefore, the following two steps should be followed before machine translation. The first is to extract terms and create a glossary, and then replace the Chinese terms in the source text with the corresponding English expressions in the glossary. Of course, the terms of extraction should be searched and verified according to the actual situation. All of these words are verified before they can be replaced. This method can not only ensure the accuracy of term translation, but also maintain the consistency of expression, especially when dealing with long texts.<br />
Example1<br />
Source abstract: 口腔白斑病癌变相关缺氧应答基因和微小RNA的芯片及表达验证。<br />
Google translation: Microarray detection/Chip detection and expression verification of hypoxia response genes and microRNAs related to oral leukoplakia canceration.<br />
Published translation:Transcriptome array screening and verification of oral leukoplakia carcinogenesis-related hypoxia-responsive gene and microRNA. <br />
Analysis: The expression “ Affymetrix GeneChip” empolyed in this thesis is a human transcription array for transcriptome array. In the source abstract, “芯片检测“ can be meant “screening” but not detection, so the proper and appropriate translation of these expression should be “transcription array screening”, but the Google translates it into “microarry detection of chip detection”. Therefore, term extraction is essential and inevitable before putting the source abstracts into the machine translation software.<br />
After pre-editing source abstracts: 对 oral leukoplakia carcinogenesis 相关 hypoxia-responsive gene 和微小RNA进行 transcriptome array screening 及表达验证。<br />
After pre-editing translation: Transcriptome array screening and expression- verification of hypoxia-responsive genes and microRNAs related to oral leukoplakia carcinogenesis.<br />
===3.2 Explication of Subordination===<br />
As mentioned above, the subordination of Chinese sentences is judged by certain specific words in machine translation . Chinese is a paratactic language, and sentences are often connected by internal logical relationships; while English depends on sentence structure, so sentences are often closely linked by various language forms. In order to improve the accuracy of machine translation, we can adjust the sentence structure and adjust the position of adjectives and their modifiers. <br />
Example 2<br />
Source abstracts:回顾性分析南京医科大学附属儿童医院中重度HIE患儿49例及同期就诊的无神经系统症状体征的足月新生儿为对照组31例的头颅磁共振成像（MRI）资料。<br />
Google translation: A retrospective analysis that the brain magnetic resonance imaging (MRI) data of 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University and full-term neonates with no neurological symptoms and signs during the same period were included in the control group.<br />
Published translation: A total of 49 children with moderate to severe HIE admitted to the children’s Hospital Affiliated to Nanjing Medical University were retrospectively analyzed. Cranial magnetic resonance imaging (MRI) date of 31 full-term neonates without neurological symptoms and signs who visited the hospital during the same period were recruited as the control group. <br />
Analysis: As the above example shows that “中重度HIE患儿” and “足月新生儿” in the source abstracts are from the Children’s Hospital Affiliated Nanjing Medical University. The Google translation shows that 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University. There is an error due to the misuse of subordination. There are some advice in order to solve the problem. That is, first to segment the sentence and then reconstruct the sentence. For instance, the Chinese expression “南京医科大学附属儿童医院” carries many modifiers, which requires to cut the modifiers and reconstruct the sentence. Therefore, the Chinese expression “南京医科大学附属儿童医院’can be divided into abstractions, leaving two sentences instead of one long sentence with modifiers..<br />
After pre-editing source abstracts: 对49例中重度HIE患儿以及31例无神经系统症状体征的足月新生儿的MRI资料进行回顾性分析。这些患者收治于南京医科大学儿童附属医院。<br />
After pre-editing translation: The MRI data of 49 children with moderate to severe HIE and 31 full-term newborns without neurological symptoms and signs were retrospectively analyzed. These patients were admitted to the Children’s Hospital of Nanjing Medical University.<br />
===3.3 Explication of Subject through Voice Changing===<br />
The extensive use of subjectless sentences are quite common in the Chinese abstracts, while English prefers to employ passive voice in the sentences so as to make them object and accurate. In order to take the characteristics of English sentences into great and careful consideration, the sentences written in passive voice in particular, it will be helpful for machine translation to find out the subject and reconstruct a sentence in passive voice (Wang Yan: 2008). The first is to discover the “doee” in a sentence and then is to reconstruct the sentence structure and put the “doee” before the verb. The last is to explicate the passive relation between the noun and the verb.<br />
Example 3<br />
Source abstract: 回顾性分析2018年1月至2020年12月解放军总医院第七医学中心收治的500例老年髋部骨折患者的资料。<br />
Google translation: A retrospective analysis of the data of 500 elderly patients with hip fractures admitted to the Seventh Medical Center of the PLA General Hospital from January 2018 to December 2020.<br />
Published translation: From January 2018 to December 2020, the data of 500 elderly patients with hip fracture treated in the Seventh Medical Center of PLA General Hospital were analyzed retrospectively.<br />
Analysis: The above example indicates that the “回顾性分析”in the Google Translate is a noun phrase, but the source abstracts actually describes an action or a behavior. The reason for such a mistake is that the machine can’t recognize the Chinese subjetless sentences. To find the subject of the sentence, the source abstracts can be revised and adjusted. Finding the “doee” is the first step, which refers to “500例老年髋部骨折患者的资料”in the source abstracts. And next is to put it in front of the verb, that is to place it in the beginning of the sentence. Last but not least, the passive voice should be explicated in the sentence. <br />
After pre-editing source abstract: 500例老年髋部骨折患者的资料被回顾性分析，他们在2018年1月之2020年12月期间收治于解放军总医院第七医学中心。<br />
After pre-editing translation: The data of 500 elderly hip fracture patients were retrospectively analyzed. They were admitted to the Seventh Medical Center of the PLA General Hospital between January 2018 and December 2020.<br />
===3.4 Relocation of Modifiers===<br />
Modifiers, including noun, adverbial and attributive are constantly employed in the Chinese medical papers in order to add information to subject and object in the sentence. By doing this, complicated sentences can be structured, thus causing obstacles to machine translation for recognizing this complex sentence structures when translating Chinese-English sentences. To make the machine translation successfully recognize the sentence structure, simplifying the sentence structure is quite necessary. The key is to moving the location of the modifiers and thus making the modifiers an independent sentence. In other words, the modifiers ought to be put before or behind the main part of the sentence to satisfy the common use of English. <br />
Usually, the modifiers in Chinese sentence can only be placed in front of the core word, while in English, modifiers are very flexible. It is all right to place the modifiers in front of or behind the major part of the sentences with adjective or a connective noun.<br />
Example 4<br />
Source abstracts: 利用DNA重组技术以pET-28a表达系统在E.coli BL21(DE3) 中重组表达Hepl。<br />
Google Translation: Hepl is recombined in E.coli BL21 (DE3) using DNA recombination technology with pET-28a expression system.<br />
Published translation: The recombinant Hcpl protein was expressed by using DNA recombination technology through pET-28a expression system in E. coli BL21 (De3).<br />
Analysis: The Chinese medical text includes two modifiers, “利用DNA”and “以pET-28a)表达系统”. These two modifiers will be translated by machine, which catches more attention to the two constituents. From the Google translation above, one failure is obvious that it misplaces the location of the two modifiers and presents it in not accurate form. To make it translate correctly by machine translation, dividing the sentences is very important so that the source abstracts can be correctly recognized by machine translation.<br />
After pre-editing source abstract: 利用DNA重组技术，Hcp1重组表达在E.coli BL21 (DE3)中， 通过pET-28a表达系统在E. coli BL21 (DE3)中。<br />
Google translation: Using DNA recombination technology, Hcp1 recombination is expressed in E.coli BL21 (DE3), via pET-28a expression system in E. coli BL21 (DE3).<br />
The second is the independence of modifiers, that is, modifiers can also be reconstructed into clauses to modify core words. Compared with English, There is no subordinate clause in Chinese, so redundant Chinese modifiers need to be reconstructed into subordinate clauses in Chinese-English translation to meet the characteristics of English. English, especially sentences with multiple modifiers. Otherwise, sentence structure may be confused, such as scattered modifiers of core words. To avoid this mistake, it is necessary to separate the modifier from the main part of the sentence. These modifiers should be reconstituted into clauses. Also, keep your sentences simple and easy to understand.<br />
===3.5 Proper Omission and Deletion of Category Words===<br />
Category words are commonly used in Chinese. Category words complement the meaning of words, including problems, positions, situations and jobs. Adding this supplementary word is more in line with Chinese custom. In many cases, it has no real meaning, so it can be omitted in translation. In Chinese, category words are frequently used. However, it is rarely used in English, which is one of the differences between Chinese and English. There are many kinds of category words. Considering objectivity and the fixed structure of language, redundant category words should be deleted in Chinese-English translation. In the general, the most commonly used category of words in the Chinese medical abstracts are "process", "behavior", and "situation". These categories of words hinder the language conversion of Chinese and English, resulting in redundancy. <br />
Example 5<br />
Source abstract: 探讨口腔白斑病癌变进程中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia response genes and associated microRNAs in the process of oral leukoplakia cancer was discussed.<br />
Published translation: To study the hypoxia response gene and microRNA (miRNA)expression profiles in the pathogenesis and progression of oral leukoplakia (OLK).<br />
Analysis: As the above example shows, the Chinese words “进程” belongs to a category word. However, the Chinese expression “癌变”contains the process, so the “进程” expression can be deleted before placing it into the machine translation, because the meaning of it has been overlapping between the expression “癌变”. Therefore, its place can be replaced with preposition “during”.<br />
After pre-editing source abstract: 探讨口腔白斑病癌变中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia responsive genes and miRNA in oral leukoplakia cancer was investigated.<br />
<br />
===Conclusion===<br />
After years of development, machine translation has made great progress. The accuracy of machine translation has been greatly improved in both text recognition and sentence pattern conversion. However, machine translation has its own limitations. In other words, it needs to rely on the parallel corpus as a reference source for improving its accuracy. ESP text, in particular, is harder to get the high quality by machine translation. <br />
As one of the research papers, the characteristics of medical abstracts are fixed language structures, objectivity and accuracy (Qin Yi:2004). Therefore, medical translation must be accurate, object and understandable to follow the specific demands of the medical paper. Being an important field in the human society, medical paper translation is on a great demand, which means that it needs a huge demand for human labor. However, with the machine translation promoting, it will be more efficient to translate medical papers combining the effort by human and machine. The improvement and development of machine translation requires the joint efforts of computer science, information science, statistics, linguistics and other academic circles to achieve more mature human-computer mutual assistance translation (Li Yafei, Zhang Ruihua : 2019).<br />
However, errors can occur during the process of machine translation of Chinese- English, because of the differences of the Chinese and English and the processing of the machine. Errors from the perspective of linguistic or grammar can affect the machine translation a lot. After division and recognition of errors, some pre-editing approaches are put forward to help the machine translation more accurate and readable, that are, extraction and replacement of terms in the source text, relocation of modifiers, explication of subordination, proper omission, deletion of category words and explication of subject through voice changing. <br />
The paper mainly focuses on the pre-editing machine translation by using medical papers as a case study. The errors of machine translation occurring in the translation of medical abstracts and pre-editing approaches for machine translation. The quality of machine translation of medical papers is greatly improved after employing the pre-editing methods. However, machine translation is not as flexible and accurate as human brain, so it is of importance to combine pre-editing and post-editing approaches with machine translation in order to produce more accurate, more object machine translation of medical papers.<br />
<br />
===References===<br />
[1]. Cronin, Michael (2013). Translation in the Digital Age[M]. New York&London: Routledge.<br />
<br />
[2]. GERLACH J, et al ( 2013). Combining Pre-editing and Post-editing to Improve SMT of User-generated Content[M]// Proceedings of MT Summit XIV Workshop on Post-editing Technology and Practice. 45-53.<br />
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[3]. Slype G V & Guinet J F & Seitz F (1984). Better Translation for Better Communication [M] .Oxford: Pergamon Press Ltd (U.K.), <br />
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[4]. O'Brien S (2002). Teaching Post-editing: A Proposal for Course Con-tent [EB/OL]. http://mt-archive. Info/EAMT-2002-0brien. Pdf.<br />
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[5]. Tytler, A. F. (1978). Essay On The Principles of Translation[M]. Amsterdam: JohnBenjamins Publishing.<br />
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[6] 崔启亮. (2014), 论机器翻译的译后编辑[J], 中国翻译, 035(006):68-73.<br />
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[7] 冯全功,高琳 (2017) 基于受控语言的译前编辑对机器翻译的影响[J]. 当代外语研究,(2): 63-68+87+110.<br />
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[8] 胡清平(2005). 机器翻译中的受控语言[J]. 中国科技翻译, (03): 24-27. <br />
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[9] 连淑能 (2010). 英汉对比研究增订本[M]. 北京:高等教育出版社.<br />
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[10] 黎亚飞,张瑞华 (2019). 机器翻译发展与现状[J]. 中国轻工教育,(5):38-45. <br />
<br />
[11] 秦毅(2004),从翻译基本标准议医学英语的翻译[J]. 遵义医学院学报,27 (4): 421-423. <br />
<br />
[12] 王燕 (2008). 医学英语翻译与写作教程[M]. 重庆:重庆大学出版社<br />
<br />
=12 蔡珠凤=(The Mistranslation of C-J Machine Translation of Political Statements)=<br />
[[Machine_Trans_EN_12]]<br />
===Abstract===<br />
Language is the main way of communication between people. With the continuous development of globalization, the scale of cross-border exchanges is also expanding. However, due to cultural differences and diversity, the languages of different countries and regions are very different, which seriously hinders people's communication. The demand for efficient and convenient translation tools is increasing. At the same time, with the development of network technology and artificial intelligence, recognition technology based on deep learning is more and more widely used in English, Japanese and other fields.<br />
<br />
===Key words===<br />
machine translation; political statements; mistranslation of C-J machine translation<br />
===题目===<br />
The Mistranslation of C-J Machine Translation of Political Statements<br />
===摘要===<br />
语言是人与人之间交流的主要方式。随着全球化的不断发展，跨境交流的规模也在不断扩大。然而，由于文化的差异和多样性，不同国家和地区的语言差异很大，这严重阻碍了人们的交流。对高效便捷的翻译工具的需求正在增加。同时，随着网络技术和人工智能的发展，基于深度学习的识别技术在英语、日语等领域的应用越来越广泛。<br />
<br />
===关键词===<br />
机器翻译；政治发言；政治发言中译日的误译<br />
===1. Introduction===<br />
===Introduction to machine translation===<br />
Machine translation, also known as automatic translation, is a process of using computers to convert one natural language (source language) into another natural language (target language). It is a branch of computational linguistics, one of the ultimate goals of artificial intelligence, and has important scientific research value.At the same time, machine translation has important practical value. With the rapid development of economic globalization and the Internet, machine translation technology plays a more and more important role in promoting political, economic and cultural exchanges.The development of machine translation technology has been closely accompanied by the development of computer technology, information theory, linguistics and other disciplines. From the early dictionary matching, to the rule translation of dictionaries combined with linguistic expert knowledge, and then to the statistical machine translation based on corpus, with the improvement of computer computing power and the explosive growth of multilingual information, machine translation technology gradually stepped out of the ivory tower and began to provide real-time and convenient translation services for general users.<br />
<br />
=== C-J machine translation software===<br />
Today's online machine translation software includes Baidu translation, Tencent translation, Google translation, Youdao translation, Bing translation and so on. Google was the first company to launch the machine translation system, and Baidu was the first company to import the machine translation system in China. In addition, Tencent and Youdao have attracted much attention.Machine translation is the process of using computers to convert one natural language into another. It usually refers to sentence and full-text translation between natural languages. In order to continuously improve the translation quality, R & D personnel have added artificial intelligence technologies such as speech recognition, image processing and deep neural network to machine translation on the basis of traditional machine translation based on rules, statistics and examples.With the increase of using machine translation, the joint cooperation between manual translation and machine translation will also increase significantly in the future. What criteria should be used to evaluate the quality of machine translation? In the evolving field of machine translation, there is an urgent need to clarify the unsolvable questions and solved problems.<br />
<br />
===The history of machine translation===<br />
The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. alchuni put forward the idea of using machines for translation. In 1933, Soviet inventor П.П. Trojansky designed a machine to translate one language into another, and registered his invention on September 5 of the same year; However, due to the low technical level in the 1930s, his translation machine was not made. In 1946, the first modern electronic computer ENIAC was born. Shortly after that, W. weaver, an American scientist and A. D. booth, a British engineer, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947 when discussing the application scope of electronic computer. In 1949, W. Weaver published the translation memorandum, which formally put forward the idea of machine translation. After 60 years of ups and downs, machine translation has experienced a tortuous and long development path. The academic community generally divides it into the following four stages:<br />
Pioneering period（1947-1964）<br />
In 1954, with the cooperation of IBM, Georgetown University completed the English Russian machine translation experiment with ibm-701 computer for the first time, showing the feasibility of machine translation to the public and the scientific community, thus opening the prelude to the study of machine translation.<br />
It is not too late for China to start this research. As early as 1956, the state included this research in the national scientific work development plan. The topic name is "machine translation, the construction of natural language translation rules and the mathematical theory of natural language". In 1957, the Institute of language and the Institute of computing technology of the Chinese Academy of Sciences cooperated in the Russian Chinese machine translation experiment, translating 9 different types of more complex sentences.<br />
From the 1950s to the first half of the 1960s, machine translation research has been on the rise. The United States and the former Soviet Union, two superpowers, have provided a lot of financial support for machine translation projects for military, political and economic purposes, while European countries have also paid considerable attention to machine translation research due to geopolitical and economic needs, and machine translation has become an upsurge for a time. In this period, although machine translation is just in the pioneering stage, it has entered an optimistic period of prosperity.<br />
Frustrated period（1964-1975）<br />
In 1964, in order to evaluate the research progress of machine translation, the American Academy of Sciences established the automatic language processing Advisory Committee (Alpac Committee) and began a two-year comprehensive investigation, analysis and test.<br />
In November 1966, the committee published a report entitled "language and machine" (Alpac report for short), which comprehensively denied the feasibility of machine translation and suggested stopping the financial support for machine translation projects. The publication of this report has dealt a blow to the booming machine translation, and the research of machine translation has fallen into a standstill. Coincidentally, during this period, China broke out the "ten-year Cultural Revolution", and basically these studies also stagnated. Machine translation has entered a depression.<br />
convalescence（1975-1989）<br />
Since the 1970s, with the development of science and technology and the increasingly frequent exchange of scientific and technological information among countries, the language barriers between countries have become more serious. The traditional manual operation mode has been far from meeting the needs, and there is an urgent need for computers to engage in translation. At the same time, the development of computer science and linguistics, especially the substantial improvement of computer hardware technology and the application of artificial intelligence in natural language processing, have promoted the recovery of machine translation research from the technical level. Machine translation projects have begun to develop again, and various practical and experimental systems have been launched successively, such as weinder system Eurpotra multilingual translation system, taum-meteo system, etc.<br />
However, after the end of the "ten-year holocaust", China has perked up again, and machine translation research has been put on the agenda again. The "784" project has paid enough attention to machine translation research. After the mid-1980s, the development of machine translation research in China has further accelerated. Firstly, two English Chinese machine translation systems, ky-1 and MT / ec863, have been successfully developed, indicating that China has made great progress in machine translation technology.<br />
New period(1990 present)<br />
With the universal application of the Internet, the acceleration of the process of world economic integration and the increasingly frequent exchanges in the international community, the traditional way of manual operation is far from meeting the rapidly growing needs of translation. People's demand for machine translation has increased unprecedentedly, and machine translation has ushered in a new development opportunity. International conferences on machine translation research have been held frequently, and China has made unprecedented achievements. A series of machine translation software have been launched, such as "Yixing", "Yaxin", "Tongyi", "Huajian", etc. Driven by the market demand, the commercial machine translation system has entered the practical stage, entered the market and came to the users.<br />
Since the new century, with the emergence and popularization of the Internet, the amount of data has increased sharply, and statistical methods have been fully applied. Internet companies have set up machine translation research groups and developed machine translation systems based on Internet big data, so as to make machine translation really practical, such as "Baidu translation", "Google translation", etc. In recent years, with the progress of in-depth learning, machine translation technology has further developed, which has promoted the rapid improvement of translation quality, and the translation in oral and other fields is more authentic and fluent.<br />
<br />
===The problem of machine translation at present===<br />
Error is inevitable<br />
Many people have misunderstandings about machine translation. They think that machine translation has great deviation and can't help people solve any problems. In fact, the error is inevitable. The reason is that machine translation uses linguistic principles. The machine automatically recognizes grammar, calls the stored thesaurus and automatically performs corresponding translation. However, errors are inevitable due to changes or irregularities in grammar, morphology and syntax, such as sentences with adverbials after "give me a reason to kill you first" in Dahua journey to the West. After all, a machine is a machine. No one has special feelings for language. How can it feel the lasting charm of "the tenderness of lowering its head, like the shame of a water lotus? After all, the meaning of Chinese is very different due to the changes of morphology, grammar and syntax and the change of context. Even many Chinese people are zhanger monks - they can't touch their heads, let alone machines.<br />
Bottleneck<br />
In fact, no matter which method, the biggest factor affecting the development of machine translation lies in the quality of translation. Judging from the achievements, the quality of machine translation is still far from the ultimate goal.<br />
Chinese mathematician and linguist Zhou Haizhong once pointed out in his paper "fifty years of machine translation": to improve the quality of machine translation, the first thing to solve is the problem of language itself rather than programming; It is certainly impossible to improve the quality of machine translation by relying on several programs alone. At the same time, he also pointed out that it is impossible for machine translation to achieve the degree of "faithfulness, expressiveness and elegance" when human beings have not yet understood how the brain performs fuzzy recognition and logical judgment of language. This view may reveal the bottleneck restricting the quality of translation. <br />
It is worth mentioning that American inventor and futurist ray cozwell predicted in an interview with Huffington Post that the quality of machine translation will reach the level of human translation by 2029. There are still many disputes about this thesis in the academic circles.<br />
<br />
===2.Mistranslation of Chinese Japanese machine translation===<br />
===2.1Vocabulary mistranslation in Chinese Japanese machine translation===<br />
===2.1.1Mistranslation of proper nouns===<br />
In political materials, there are often political dignitaries' names, place names or a large number of proper nouns in the political field. The morphemes of such words are definite and inseparable. Mistranslation will make the source language lose its specific meaning. <br />
<br />
Chinese translation into Japanese Japanese translation into Chinese<br />
<br />
original text translation by Youdao reference translation original text translation by Youdao reference translation<br />
栗战书 栗戰史書 栗戰書 労安 劳安 劳安<br />
李克强 李克強 李克強 朱鎔基 朱基 朱镕基<br />
习近平 習近平 習近平 筑紫哲也 筑紫哲也 筑紫哲也<br />
韩正 韓中 韓正 山口百惠 山口百惠 山口百惠<br />
王沪宁 王上海氏 王滬寧 田中角栄 田中角荣 田中角荣<br />
汪洋 汪洋 汪洋 東条英機 东条英社 东条英机<br />
赵乐际 趙樂南 趙樂際 毛沢东 毛泽东 毛泽东<br />
江泽民 江沢民 江沢民 トウ・ショウヘイ 大酱 邓小平<br />
周恩来 周恩来 周恩来<br />
クリントン 克林顿 克林顿<br />
<br />
The above table counts 18 special names in the two texts, and 7 machine translation errors. In terms of mistranslation, there are not only "战书" but also "戰史" and "史書" in Chinese. "沪" is the abbreviation of Shanghai. In other words, since "战书" and "沪" are originally common nouns, this disrupts the choice of target language in machine translation. Except Katakana interference (except for トウシゃウヘイ, most of the mistranslations appear in the new Standing Committee. It can be seen that the machine translation system does not update the thesaurus in time. Different from other words, people's names have particularity. Especially as members of the Standing Committee of the Political Bureau of the CPC Central Committee, the translation of their names is officially unified. In this regard, public figures such as political dignitaries, stars, famous hosts and important. In addition, the machine also translates Japanese surnames such as "タ二モト" (谷本), "アンドウ" (安藤) into "塔尼莫特" and "龙胆". It can be found that the language feature that Japanese will be written together with Chinese characters and Hiragana also directly affects the translation quality of Japanese Chinese machine translation. Japanese people often use Katakana pronunciation. For example, when Japanese people talk with Premier Zhu, they often use "二ーハウ" (Hello). However, the machine only recognizes the two pseudonyms "二" and "ハ" and transliterates them into "尼哈" , ignoring the long sound after "二" and "ハ".<br />
<br />
original text Translation by Youdao reference translation<br />
日美安全体制 日米の安全体制 日米安保体制<br />
中国共产党第十九次全国代表大会 中国共産党第19回全国代表大会 中国共産党第19回全国代表大会（第19回党大会）<br />
十八大 十八大 第18回党大会<br />
中国特色社会主义 中国特色社会主義 中国の特色ある社会主義<br />
中国共产党中央委员会 中国共産党中央委員会 中国共産党中央委員会<br />
十八届中共中央政治局常委 第18代中国共產党中央政治局常務委員 第18期中共中央政治局常務委員<br />
十八届中共中央政治局委员 18期の中国共產党中央政治局委員 第18期中共中央政治局委員<br />
十九届中共中央政治局常委 十九回中国共產党中央政治局常務委員 第19期中央政治局常務委員<br />
中共十九届一中全会 中国共產党第十九回一中央委員会 第19期中央委員会第1回全体会議<br />
The above table is a comparison of the original and translated versions of some proper nouns. As shown in the table, the mistranslation problems are mainly reflected in the mismatch of numerals + quantifiers, the wrong addition of case auxiliary word "の", the lack of connectors, the Mistranslation of abbreviations, dead translation, and the writing errors of Chinese characters. The following is a specific analysis one by one.<br />
"十八届中共中央政治局常委", "十八届中共中央政治局委员", "十九届中共中央政治局常委" and "中共十九届一中全会" all have quantifiers "届", which are translated into "代", "期" and "回" respectively. The meaning is vague and should be uniformly translated into "期"; Among them, the translation of the last three proper nouns lacks the Chinese conjunction "第". The case auxiliary word "の" was added by mistake in the translation of "十八届中共中央政治局委员" and "日美安全体制". The "中国共产党中央委员会" did not write in the form of Japanese Ming Dynasty characters, but directly used simplified Chinese characters.<br />
The full name of "中共十九届一中全会" is "中国共产党第十九届中央委员会第一次全体会议", in which "一" stands for "第一次", which is an ordinal word rather than a cardinal word. Machine translation does not produce a correct translation. The fundamental reason is that there are no "规则" for translating such words in machine translation. If we can formulate corresponding rules for such words (for example, 中共m'1届m2 中全会→第m1期中央委员会第m2回全体会议), the translation system must be able to translate well no matter how many plenary sessions of the CPC Central Committee.<br />
<br />
===2.1.2Mistranslation of Polysemy===<br />
<br />
original text Translation by Youdao reference translation<br />
スタジオ 摄影棚/工作室 直播现场/演播厅<br />
日中関係の話 中日关系的故事 就中日关系(话题)<br />
溝 水沟 鸿沟<br />
それでは日中の問題について質問のある方。 那么对白天的问题有提问的人。 关于中日问题的话题，举手提问。<br />
私たちのクラスは20人ちょっとですが、 我们班有20人左右， 我们班二十多人的意见统一很难，<br />
いろいろな意見が出て、まとめるのは大変です。 但是又各种各样的意见，总结起来很困难。 中国是怎样把13亿人凝聚在一起的？<br />
一体どうやって、13億人もの人をまとめているんですか。 到底是怎么处理13亿的人的呢？ <br />
In the original text, the word "スタジオ" appeared four times and was translated into "摄影棚" or "工作室" respectively, but the context is on-site interview, not the production site of photography or film. "話" has many semantics, such as "说话", "事情", "道理", etc. In accordance with the practice of the interview program, Premier Zhu Rongji was invited to answer five questions on the topic of China Japan relations. Obviously, the meaning of the word "故事" is very abrupt. The inherent Japanese word "日中" means "晌午、白天". At the same time, it is also the abbreviation of the names of the two countries. The machine failed to deal with it correctly according to the context. "溝" refers to the gap between China and Japan, not a "水沟". The former "まとめる" appears in the original text with the word "意见", which is intended to describe that it is difficult to unify everyone's opinions. The latter "まとめている" also refers to unifying the thoughts of 1.3 billion people, not "处理". Therefore, it is more appropriate to use "统一" and "处理" in human translation, rather than "总结意见" or "处理意见".<br />
Mistranslation of polysemy has always been a difficult problem in machine translation research. The translation of each word is correct, but it is often very different from the original expression in the context. Zhang Zhengzeng said that ambiguity is a common phenomenon in natural language. Its essence is that the same language form may have different meanings, which is also one of the differences between natural language and artificial language. Therefore, one of the difficulties faced by machine translation is language disambiguation (Zhang Zheng, 2005:60). In this regard, we can mark all the meanings of polysemous words and judge which meaning to choose by common collocation with other words. At the same time, strengthen the text recognition ability of the machine to avoid the translation inconsistent with the current context. In this way, we can avoid the mistake of "大酱" in the place where famous figures such as Deng Xiaoping should have appeared in the previous political articles.<br />
<br />
===2.1.3Mistranslation of compound words===<br />
Multi class words refer to a word with two or more parts of speech, also known as the same word and different classes.<br />
original text Translation by Youdao reference translation<br />
1998年江泽民主席曾经访问日本, 1998年の江沢民国家主席の日本訪問し、 1998年、江沢民総書記が日本を訪問し、かつ、<br />
同已故小渊首相签署了联合宣言。 かつて同じ故小渕首相が署名した共同宣言 亡くなられた小渕総理と宣言に調印されました<br />
<br />
Chinese "同" has two parts of speech: prepositions and conjunctions: "故" has many parts of speech, such as nouns, verbs, adjectives and conjunctions. This directly affects the judgment of the machine at the source language level. The word "同" in the above table is used as a conjunction to indicate the other party of a common act. "故" is used as a verb with the semantic meaning of "死亡", which is a modifier of "小渊首相". The machine regards "同" as an adjective and "故" as a noun "原因", which leads to confusion in the structure and unclear semantics of the translation. Different from the polysemy problem, multi category words have at least two parts of speech, and there is often not only one meaning under each part of speech. In this regard, software R & D personnel should fully consider the existence of multi category words, so that the translation machine can distinguish the meaning of words on the basis of marking the part of speech, so as to select the translation through the context and the components of the word in the sentence. Of course, the realization of this function is difficult, and we need to give full play to the wisdom of R & D personnel.<br />
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===2.2 Syntactic mistranslation in Chinese Japanese machine translation===<br />
===2.2.1Mistranslation of tenses===<br />
original text：History is written by the people, and all achievements are attributed to the people. <br />
Translation by Youdao：歴史は人民が書いたものであり、すべての成果は人民のためである。<br />
reference translation：歴史は人民が綴っていくものであり、すべての成果は人民に帰することとなります。<br />
The original sentence meaning is "历史是人民书写的历史" or "历史是人民书写的东西". When translated into Japanese, due to the influence of Japanese language habits, the formal noun "もの" should be supplemented accordingly. In this sentence, both the machine and the interpreter have translated correctly. However, the machine's recognition of "的" is biased, resulting in tense translation errors. The past, present and future will become "history", and this is a continuous action. The form translated as "~ ていく" not only reflects that the action is a continuous action, but also conforms to the tense and semantic information of the original text. In addition, the machine's treatment of the preposition "于" is also inappropriate. In the original text, "人民" is the recipient of action, not the target language. As the most obvious feature of isolated language, function words in Chinese play an important role in semantic expression, and their translation should be regarded as a focus of machine translation research.<br />
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original text：李克强同志是十六届中共中央政治局常委,其他五位同志都是十六届中共中央政治局委员。<br />
Translation by Youdao：李克強総理は第16代中国共産党中央政治局常務委員であり、他の５人の同志はいずれも16期の中国共産党中央政治局委員である。<br />
reference translation：李克強同志は第16期中国共産党中央政治局常務委員を務め、他の５人は第16期中共中央政治局委員を務めました。<br />
Judging the verb "是" in the original text plays its most basic positive role, but due to the complexity of Chinese language, "是" often can not be completely transformed into the form of "だ / である" in the process of translation. This sentence is a judgment of what has happened. Compared with the 19th session, the 18th session has become history. This is an implicit temporal information. It is very difficult for machines without human brain to recognize this implicit information. "中共中央政治局常委" is the abbreviation of "中共中央政治局常务委员会委员" and it is a kind of position. In Japanese, often use it with verbs such as "務める","担当する",etc. The translator adopts the past tense of "務める", which conforms to the expression habit of Japanese and deals with the problem of tense at the same time. However, machine translation only mechanically translates this sentence into judgment sentence, and fails to correctly deal with the past information implied by the word "十八届".<br />
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===2.2.2Mistranslation of honorifics===<br />
Honorific language is a language means to show respect to the listener. Different from Japanese, there is no grammatical category of honorifics in Chinese. There is no specific fixed grammatical form to express honorifics, self modesty and politeness. Instead, specific words such as "您", "请" and "劳驾" are used to express all kinds of respect or self modesty. <br />
Original text translation by Youdao reference translation<br />
女士们，先生们，同志们，朋友们 さんたち、先生たち、同志たち、お友达さん ご列席の皆さん<br />
谢谢大家！ ありがとうございます！ ご清聴ありがとうございました。<br />
これはどうされますか。 这是怎么回事呢? 您将如何解决这一问题？<br />
こうした問題をどうお考えでしょうか。 我们会如何考虑这些问题呢？ 您如何看待这一问题？ <br />
For example, for the processing of "女士们，先生们，同志们，朋友们", the machine makes the translation correspond to the original one by one based on the principle of unchanged format, but there are errors in semantic communication and pragmatic habits. When speaking on formal occasions, Chinese expression tends to be comprehensive and detailed, as well as the address of the audience. In contrast, Japanese usually uses general terms such as "皆様", "ご臨席の皆さん" or "代表団の方々" as the opening remarks. In terms of Japanese Chinese translation, the machine also failed to recognize the usage of Japanese honorifics such as "さ れ る" and "お考え". It can be seen that Chinese Japanese machine translation has a low ability to deal with honorific expressions. The occasion is more formal. A good translation should not only be fluent in meaning, but also conform to the expression habits of the target language and match the current translation environment.<br />
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===Conclusion===<br />
In the process of discussing the Mistranslation of machine translation, this paper mainly cites the Mistranslation of Youdao translator. When I studied the problem of machine translation mistranslation, I also used a large number of translation software such as Google and Baidu for parallel comparison, and found that these translation software also had similar problems with Youdao translator. For example, the "新世界新未来" in Chinese ABAC structural phrases is translated by Baidu as "新し世界の新しい未来", which, like Youdao, is not translated in the form of parallel phrases; Google online translation translates the word "“全心全意" into a completely wrong "全面的に"; The original sentence "我吃了很多亏" in the Mistranslation of the unique expression in the language is translated by Google online as "私はたくさんの損失を食べました". Because the "吃" and "亏" in the original text are not closely adjacent, the machine can not recognize this echo and mistakenly treats "亏" as a kind of food, so the machine translates the predicate as "食べました"; Japanese "こうした問題をどう考えでしょうか", Google's online translation is "你如何看待这些问题?", "你" tone can not reflect the tone of Japanese honorific, while Baidu translates as "你怎么想这样的问题呢?" Although the meaning can be understood, it is an irregular spoken language. Google and Baidu also made the same mistakes as Youdao in the translation of proper nouns. For example, they translated "十七届(中共中央政治局常委)”" into "第17回..." .In short, similar mistranslations of Youdao are also common in Baidu and Google. Due to space constraints, they will not be listed one by one here. <br />
Mr. Liu Yongquan, Institute of language, Chinese Academy of Social Sciences (1997) It has been pointed out that machine translation is a linguistic problem in the final analysis. Although corpus based machine translation does not require a lot of linguistic knowledge, language expression is ever-changing. Machine translation based solely on statistical ideas can not avoid the translation quality problems caused by the lack of language rules. The following is based on the analysis of lexical, syntactic and other mistranslations From the perspective of the characteristics of the source language, this paper summarizes some difficulties of Chinese and Japanese in machine translation. <br />
(1) The difficulties of Chinese in machine translation <br />
Chinese is a typical isolated language. The relationship between words needs to be reflected by word order and function words. We should pay attention to the transformation of function words such as "的", "在", "向" and "了". Some special verbs, such as "是", "做" and "作", are widely used. How to translate on the basis of conforming to Japanese pragmatic habits and expressions is a difficulty in machine translation research. In terms of word order, Chinese is basically "subject→predicate→object". At the same time, Chinese pays attention to parataxis, and there is no need to be clear in expression with meaningful cohesion, which increases the difficulty of Chinese Japanese machine translation. When there are multiple verbs or modifier components in complex long sentences, machine translation usually can not accurately divide the components of the sentence, resulting in the result that the translation is completely unreadable. <br />
(2) Difficulties of Japanese in machine translation <br />
Both Chinese and Japanese languages use Chinese characters, and most machines produce the target language through the corresponding translation of Chinese characters. However, pseudonyms in Japanese play an important role in judging the part of speech and meaning, and can not only recognize Chinese characters and judge the structure and semantics of sentences. Japanese vocabulary is composed of Chinese vocabulary, inherent vocabulary and foreign vocabulary. Among them, the first two have a great impact on Chinese Japanese machine translation. For example "提出" is translated as "提出する" or "打ち出す"; and "重视" is translated as "重視する" or "大切にする". Japanese is an adhesive language, which contains a lot of "けど", "が" and "けれども" Some of them are transitional and progressive structures, but there are also some sequential expressions that do not need translation, and these translation software often can not accurately grasp them.<br />
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===References===<br />
[1] Navroz Kaur Kahlon;Williamjeet Singh.Machine translation from text to sign language: a systematic review[J].Universal Access in the Information Society,2021(prepublish):1-35.<br />
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[2] Cao Qianyu;Hao Hanmei;Ahmed Syed Hassan.A Chaotic Neural Network Model for English Machine Translation Based on Big Data Analysis[J].Computational Intelligence and Neuroscience,2021,2021:3274326-3274326.<br />
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[3]Hwang Yongkeun;Kim Yanghoon;Jung Kyomin.Context-Aware Neural Machine Translation for Korean Honorific Expressions[J].Electronics,2021,10(13):1589-1589.<br />
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[4]Zakaryia Almahasees.Analysing English-Arabic Machine Translation:Google Translate, Microsoft Translator and Sakhr,2021.<br />
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[5]Machine learning in translation[J].Nature Biomedical Engineering,2021,5(6):485-486.<br />
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[6]Shaimaa Marzouk.An in-depth analysis of the individual impact of controlled language rules on machine translation output: a mixed-methods approach[J].Machine Translation,2021(prepublish):1-37.<br />
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[7]Welnitzová Katarína;Munková Daša.Sentence-structure errors of machine translation into Slovak[J].Topics in Linguistics,2021,22(1):78-92.<br />
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[8]Xu Xueyuan.Machine learning-based prediction of urban soil environment and corpus translation teaching[J].Arabian Journal of Geosciences,2021,14(11). <br />
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[9]陈丙昌.機械翻訳の誤訳分析【D】.贵州大学.2016(05) <br />
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[10]呂寅秋.機械翻訳の言語規則と伝統文法との相違点.日本学研究.1996(00):21-22 <br />
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[11]刘君.基于语料库的中日同形词词义用法对比及其日中机器翻译研究【D】.广西大学.2014(03) <br />
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[12]崔倩倩.机器翻译错误与译后编辑策略研究【D】.北京外国语大学.2019(09) <br />
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[13]张义.机器翻译的译文分析【D】.西安外国语大学.2019(10) <br />
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[14]张琳婧.在线机器翻译中日翻译错误原因及对策【D】.山西大学.2019(02)<br />
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[15]王丹.基于机器翻译的专利文本译后编辑对策研究【D】.大连理工大学.2020(06)<br />
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[16]杨晓琨.日中机器翻译中的前编辑规则与效果验证【D】.大连理工大学.2020(06)<br />
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[17]左嘉. 机器翻译日译汉误译研究[D]. 北京第二外国语学院, 2021.<br />
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[18]关碧莹.关于政治类发言的汉日机器翻译误译分析[D].哈尔滨理工大学, 2018.<br />
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[19]车彤.汉译日机器翻译质量评估及译后编辑策略研究【D】.北京外国语大学.2021(09)<br />
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Networking Linking<br />
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http://www.elecfans.com/rengongzhineng/692245.html<br />
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https://baike.baidu.com/item/%E6%9C%BA%E5%99%A8%E7%BF%BB%E8%AF%91/411793<br />
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=13 陈湘琼Chen Xiangqiong（Study on Post-editing from the Perspective of Functional Equivalence Theory ）=<br />
[[Machine_Trans_EN_13]]<br />
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=14 Bi bi Nadia（Machine Translation a Challenge for Human Translators)=<br />
[[Machine_Trans_EN_14]]<br />
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===Abstract===<br />
Machine translation is a big obstacle in the way of Human translators or interpretors although it is quick and less time consuming.people are trying to get translation of their target language using through source language.For this purpose they are using digital apps like Google translation Google translation does not give accurate and exact interpretation.Google translator is translates word to word translate that doesn't clarifies it's true and actual meaning.On the other hand human translators can give exact and accurate translation ,they take care of grammatical errors , diction and sentence structure.They clarify the purpose of target language through using source language.<br />
===Keywords===<br />
Descriptive translation, academic, interdiscipline, comparative literature, localization,Translatology,school of thought, translation , studies, linguistics, corresponding<br />
===Body of article===<br />
Translation is the process of reworking text from one language into another to maintain the original message and communication. But, like everything else, there are different methods of translation, and they vary in form and function. What is translation? The term has become widely used among knowledge transfer researchers and practitioners, especially in the fields of health and health care. In a landmark review, Jonathan Lomas began to argue that 'The tasks… may be defined as to establish and maintain links between researchers and their audience, via the appropriate translation of research findings' (Lomas 1997, p 4). In 2004, the World Health Organization's World Report on Knowledge for Better Health suggested that 'One of the key contributions of research to health systems is the translation of knowledge into actions' (WHO 2004, p 33 and p 100). By 2006, special issues of WHO's Bulletin as well as the journals Evaluation and the Health Professions and the Journal of Continuing Education in the Health Professions were dedicated to translation.But what does 'translation' mean? It may be a new word for an old problem, meaning nothing more than 'transfer'. The rapidly emerging field of 'translational medicine' seems to take translation to mean generally what transfer might have meant, that is the transmission of knowledge and evidence 'from bench to bedside'. As one commentator has observed, "Translational medicine" as a fashionable term is being increasingly used to describe the wish of biomedical researchers to ultimately help patients' (Wehling 2008, abstract). <br />
Semantic uncertainty persists, not least because of the different interests of scientists, clinicians, patients and commercial firms (Littman et al 2007): 'Translational research means different things to different people, but it seems important to almost everyone' (Woolf 2008, p 211).<br />
In related fields, such as public health (Armstrong et al 2006), 'translation' seems to signify dissatisfaction with 'transfer'. It wants to move away from thinking of knowledge transfer as a form of technology transfer or dissemination, rejecting if only by implication its mechanistic assumptions and its model of linear messaging from A to B. But still, what does it signify?<br />
<br />
===Why translation?===<br />
'Translation' indicates a closer attention to the problem of shared meaning and how it might be developed. It seems to represent some new epistemological lubricant, facilitating the dissemination of texts and the application and use of the knowledge and information they in. Simply, translation might be the key to transfer. And yet, when we stop to think, we are more ambivalent. What is translated often seems somehow inferior, not real or original. Note how readily commentators reach for the idea that things might be 'lost in translation'. Knowing at a distance – made in and mediated by translation - makes for incomplete renditions, blurred images, partial truths. So what might 'translation' really mean? The purpose of this paper is to set out, for policy makers and practitioners, the theoretical and conceptual resources translation holds and seems to represent. In doing so, it explores understandings of translation in the fields of literature and linguistics and in the sociology of science and technology. It begins by setting out just why this idea of translation should make immediate, intuitive sense in relation to research, policy and practice.<br />
1translation in research, policy and practice research as translation<br />
Research often entails translation from one language to another: where data is collected from more than one ethnic group, for example, or where the language of the researcher is other than that of the research subject. It may draw on a secondary literature or source documents written in different languages, and may be published and disseminated in languages other than the one in which it is first written up.<br />
2In a different way, to conduct an interview is to ask for an account of experience and its meanings, but it is also to construct and translate that experience in terms defined at least in part by the researcher. In representing what is said, transcripts then select data, usually excluding significant gesture and eye-contact, for example. Often, certain characteristics of speech-acts (such as hesitations) will be edited out. In turn, the format of the transcript shapes the analytic use the researcher may make of it. The basis of research 'findings', then, is an artefact, a transcript or translation, not an original interaction (Ochs 1979, Barnes, Bloor and Henry 1996, Ross 2009).<br />
3In this way, the researcher recasts aspects of his or her problem or topic in new, scientific form: 'All researchers "translate" the experiences of others' (Temple 1997, p 609). Research is invariably conducted in a sort of 'metalanguage' (Hantrais and Ager 1985): the research process can be conceived as one of successive translations (from theoretical formulation to operationalization, transcription, interpretation and dissemination). Theorization is a process of reciprocal back and forth between theory and fact, in which conceptions of each are revised in order that one fit the other (Baldamus 1974).<br />
4 It is a kind of translation: a rereading, re-use, re-application or re-representation of what we know in new terms (Turner 1980). Referencing, too, is an act of translation, a form of appropriation and incorporation of one text by another (Gilbert 1977).policy as translation Each of the fields covered by the paper is diverse and ill-defined, and there is no intention here to provide a comprehensive account of any them. Sources have been chosen for their relevance: main references are cited in the text, and additional sources listed in footnotes.<br />
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2For a brief introduction to the technical issues involved in social research in more than one language, see Birbili (2000). For translation issues in survey research and question design in general, see Ervin and Bower (1952) and Deutscher (1968); on translating survey research instruments (in this instance health-related quality of life measures), Bowden and Fox-Rushby (2003). On the use of translators and interpreters, see Temple (1997) and Jentsch (1998).<br />
3For an interesting discussion of this problem, see Bourdieu (1999), esp pp 621-626, 'The risks of writing'. <br />
===Difference between machine translation and human translators===<br />
Few people disagree on the differences between the two, but many argue over the quality of the translations. How accurate are machine translations? How reliable are human translations? Some say machine translation produces near-perfect translations while others are adamant that translations are incomprehensible and cause more problems than they solve. Results will, of course, vary depending on the source and target languages, the machine translation service used (e.g. Google Translate), and the complexity of the original text.<br />
Machine translation, love it or hate it, is here to stay. In fact, the machine translation market is growing at such a fast pace that it is predicted to reach $980 million by 2022.<br />
===Machine translation: the pros and cons===<br />
The advantages of machine translation generally come down to two factors: it’s faster and cheaper. The downside to this is the standard of translation can be anywhere from inaccurate, to incomprehensible, and potentially dangerous (more on that shortly).<br />
==The advantages of machine translation==<br />
Many free tools are readily available (Google Translate, Skype Translator, etc.)<br />
Quick turnaround time <br />
You can translate between multiple languages using one tool<br />
Translation technology is constantly improving<br />
The disadvantages of machine translation<br />
Level of accuracy can be very low <br />
Accuracy is also very inconsistent across different languages<br />
==Machines can't translate context ==<br />
Mistakes are sometimes costly <br />
Sometimes translation simply doesn’t work<br />
The most important thing to consider with any kind of translation is the cost of potential mistakes. Translating instructions for medical equipment, aviation manuals, legal documents and many other kinds of content require 100% accuracy. In such cases, mistakes can cost lives, huge amounts of money and irreparable damage to your company’s image. So choose carefully!<br />
Human translation: the pros and cons<br />
Human translation essentially switches the table in terms of pros and cons. A higher standard of accuracy comes at the price of longer turnaround times and higher costs. What you have to decide is whether that initial investment outweighs the potential cost of mistakes. Alternatively, whether mistakes simply aren’t an option, like the cases we looked at in the previous section.<br />
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==The advantages of human translation ==<br />
It’s a translator’s job to ensure the highest accuracy<br />
Humans can interpret context and capture the same meaning, rather than simply translating words<br />
Human translators can review their work and provide a quality process<br />
Humans can interpret the creative use of language, e.g. puns, metaphors, slogans, etc.<br />
Professional translators understand the idiomatic differences between their languages<br />
Humans can spot pieces of content where literal translation isn’t possible and find the most suitable alternative<br />
The disadvantages of human translation<br />
Turnaround time is longer <br />
Translators rarely work for free <br />
Unless you use a translation agency, with access to thousands of translators, you’re limited to the languages any one translator can work with<br />
Simply put, human translation is your best option when accuracy is even remotely important. Other considerations to make are the complexity of your source material and the two languages you’re translating between – both of which can render machines pretty useless.<br />
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When to use machine and human translation<br />
The truth is, the debate over machine vs human translation is an unnecessary distraction. What we should really be talking about is when to use these two different types of translation services, because they both serve a very valid purpose.<br />
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Examples of when to use machine translation<br />
When you have a large bulk of content to translate and the general meaning is enough<br />
When your translation never reaches the final audience, e.g. you’re translating a resource as research for another piece of content<br />
Translating documents for internal use within a company, provided 100% accuracy isn’t needed<br />
To partially translate large chunks of content for a human translator to improve upon<br />
Examples of when to use human translation<br />
When accuracy is important<br />
Most cases where your translated content is received by a consumer audience<br />
When you have a duty of care to provide accurate translations (e.g. legal documents, product instructions, medical guidelines or health and safety content)<br />
When translating marketing material or other texts for creative language uses.<br />
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===Conclusion ===<br />
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In the light of above mentioned facts and figures here by we would say that machine translation is challenge for human translators because it can reduces the wokload of translation but can't give accurate and exact translation of the traget language.It can be less reliable than human translation..</div>Chen Huinihttps://bou.de/u/index.php?title=Machine_translation&diff=129766Machine translation2021-12-08T01:34:34Z<p>Chen Huini: /* References */</p>
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<div>'''Machine Translation - A challenge or a chance for human translators?'''<br />
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=1 卫怡雯(A Comparison Between the Quality of Machine Translation and Human Translation——A Case Study of the Application of artificial intelligence in Sports Events)=<br />
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=2 吴映红（The Introduction of Machine Translation)= <br />
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=3 肖毅瑶(On the Realm Advantages And Symbiotic Development of Machine Translation And Huamn Translation)=<br />
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=4 王李菲 （Comparison Between Neural Machine Translation of Netease and Traditional Human Translation—A Case Study of The Economist Articles)= <br />
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=6 徐敏赟(Machine Translation Based on Neural Network --Challenge or Chance?)=<br />
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=7 颜莉莉(一带一路背景下人工智能与翻译人才的培养)=<br />
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===Abstract===<br />
In the era of artificial intelligence, artificial intelligence has been applied to various fields. In the field of translation, traditional translation models can no longer meet the rapid development and updating of the information age. The development of machine translation has brought structural changes to the language service industry, which poses challenges to the cultivation of translation talents. Under the background of "The Belt and Road initiative", translation talents have higher and higher requirements on translation literacy. Artificial intelligence and translation technology are used to reform the training mode of translation talents, so as to better serve the development of The Times. This paper mainly explores the cultivation of artificial intelligence and translation talents under the background of the Belt and Road Initiative. The cultivation of translation talents is moving towards comprehensive cultivation of talents. On the contrary, artificial intelligence and machine translation can also be used to improve the teaching mode and teaching content, so as to win together in cooperation.<br />
===Key words===<br />
Artificial intelligence,Machine translation,cultivation of translation talents,"The Belt and Road initiative"<br />
===题目===<br />
一带一路背景下人工智能与翻译人才的培养<br />
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===摘要===<br />
进入人工智能时代，人工智能被应用于各个领域。在翻译领域，传统的翻译模式已无法满足信息化时代的飞速发展和更新，机器翻译的发展给语言服务行业带来了结构性改变，这对翻译人才的培养提出了挑战。“一带一路”背景下，对翻译人才的翻译素养要求越来越高，利用人工智能和翻译技术对翻译人才培养模式进行革新，更好为时代发展服务。本文主要探究在一带一路背景下人工智能和翻译人才培养，翻译人才的培养过程中正向对人才的综合性培养，反之也可以利用人工智能和机器翻译完善教学模式和教学内容，在合作中共赢。<br />
===关键词===<br />
人工智能；机器翻译；翻译人才培养；一带一路<br />
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===1. Introduction===<br />
With the development of science and technology in China, artificial intelligence has also been greatly improved, and related technologies have been applied to various fields, such as the use of intelligent robots to deliver food to quarantined people during the epidemic, which has made people's lives more convenient. The most controversial and widely discussed issue is machine translation. Before the emergence of machine translation, translation was generally dominated by human translation, including translation and interpretation, which was divided into simultaneous interpretation and hand transmission, etc. It takes a lot of time and energy to cultivate a translation talent. However, nowadays, the era is developing rapidly and information is updated rapidly. As a translation talent, it is necessary to constantly update its knowledge reserve to keep up with the pace of The Times. The emergence of machine translation has also posed challenges to translation talents and the training of translation talents. Although machine translation had some problems in the early stage, it is now constantly improving its functions. In the context of the belt and Road Initiative, both machine translation and human translation are facing difficulties. Regardless of whether human translation is still needed, what is more important at present is how to train translators to adapt to difficulties and promote the cooperation between human translation and machine translation.<br />
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===2.Development status of machine translation in the era of artificial intelligence ===<br />
With the development of AI technology, machine translation has made great progress and has been applied to people's lives. For example, more and more tourists choose to download translation software when traveling abroad, which makes machine translation take an absolute advantage in daily email reply and other translation activities that do not require high accuracy. The translation software commonly used by netizens include Google Translation, Baidu Translation, Youdao Translation, IFly.com Translation, etc. Even wechat and other chat software can also carry out instant Translation into English. Some companies have also launched translation pens, translation machines and other equipment, which enables even native speakers to rely on machine translation to carry out basic communication with other Chinese people.<br />
But so far, machine translation still faces huge problems. Although machine translation has made great progress, it is highly dependent on corpus and other big data matching. It does not reach the thinking level of human brain, and cannot deal with the problem of translation differences caused by culture and religion. In addition, many minor languages cannot be translated by machine due to lack of corpus.<br />
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What's more, most of the corpus is about developed countries such as Britain and France, and most of the corpus is about diplomacy, politics, science and technology, etc., while there are very few about nationality, culture, religion, etc.<br />
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In addition, machine translation can only be used for daily communication at present. If it involves important occasions such as large conferences and international affairs, it is impossible to risk using machine translation for translation work. Professional translators are required to carry out translation work. So machine translation still has a long way to go.<br />
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===3.Challenges in the training of translation talents in universities===<br />
The cultivation of translators is targeted at the market. Professors Zhu Yifan and Guan Xinchao from the School of Foreign Languages at Shanghai Jiao Tong University believe that the cultivation of translators can be divided into four types: high-end translators and interpreters, senior translators and researchers, compound translators and applied translators.<br />
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From their names, it can be seen that high-end translators and interpreters and senior translators and researchers talents have high requirements on the knowledge and quality of interpreters, because they have to face the changing international situation, and have to deal with all kinds of sensitive relations and political related content, they should have flexible cross-cultural communication skills. In addition, for literature, sociology and humanities academic works, it is not only necessary to translate their content, but also to understand their essence. Therefore, translators should not only have humanistic feelings, but also need to have a deep understanding of Chinese and western culture.<br />
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However, there is not much demand for this kind of translation in the society. Such high-level translation requirements are not needed in daily life and work. The greatest demand is for compound translators, which means that they should master knowledge in a specific field while mastering a foreign language. For example, compound translators in the financial field should not only be good at foreign languages, but also master financial knowledge, including professional terms, special expressions and sentence patterns.<br />
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Now we say that machine translation can replace human translation should refer to the field of compound translation talents. Although AI technology has enabled machine translation to participate in creation, it does not mean that compound translation talents will be replaced by machines. The complexity of language and the flexible cross-cultural awareness required in communication make it impossible for machine translation to completely replace human translation.<br />
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The last type of applied translation talents are mostly involved in the general text without too much technical content and few professional terms, so it is easy to be replaced by machine translation.<br />
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Therefore, the author thinks that what universities are facing at present is not only how to train translation talents to cope with the development of machine translation, but to consider the application of machine translation in the process of training translation talents to achieve human-machine integration, so as to better complete the translation work.<br />
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===4.The Language environment and opportunities and challenges of the Belt and Road initiative===<br />
During visits to Central and Southeast Asian countries in September and October 2013, Chinese President Xi Jinping put forward the major initiative of jointly building the Silk Road Economic Belt and the 21st Century Maritime Silk Road. And began to be abbreviated as the Belt and Road Initiative.<br />
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According to the Vision and Actions for Jointly Building silk Road Economic Belt and 21st Century Maritime Silk Road, the Silk Road Economic Belt focuses on connecting China, Central Asia, Russia and Europe (the Baltic Sea). From China to the Persian Gulf and the Mediterranean Sea via Central and West Asia; China to Southeast Asia, South Asia, Indian Ocean. The focus of the 21st Century Maritime Silk Road is to stretch from China's coastal ports to Europe, through the South China Sea and the Indian Ocean. From China's coastal ports across the South China Sea to the South Pacific.<br />
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The Belt and Road "construction is comply with the world multi-polarization and economic globalization, cultural diversity, the initiative of social informatization tide, drive along the countries achieve economic policy coordination, to carry out a wider range, higher level, the deeper regional cooperation and jointly create open, inclusive and balanced, pratt &whitney regional economic cooperation framework.<br />
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====4.1The language environment of the Belt and Road====<br />
The "Belt and Road" involves a wide range of countries and regions, and their languages and cultures are very complex. How to make good use of language, do a good job in translation services, actively spread Chinese culture to the world, strengthen the ability of discourse, and tell Chinese stories well, the first thing to do is to understand the language situation of the countries along the "Belt and Road".<br />
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=====4.1.1The most common language in countries along the "Belt and Road" =====<br />
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There are a wide variety of languages spoken in 65 countries along the Belt and Road, involving nine language families. However, The status of English as the first language in the world is undeniable. Most of the countries participating in the Belt and Road are developing countries, and many of them speak English as their first foreign language. Especially in southeast Asian and South Asian countries, English plays an important role in foreign communication, whether as the official language or the first foreign language. Besides English, more than 100 million people speak Russian, Hindi, Bengali, Arabic and other major languages in the "Belt and Road" countries. It can also be seen that a common feature of languages in countries along the "Belt and Road" is the popularization of English education. English is widely used in international politics, economy, culture, education, science and technology, playing the role of the most important language in the world.<br />
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=====4.1.2The complex language conditions of countries along the "Belt and Road" =====<br />
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The languages spoken in countries along the Belt and Road involve nine major language families and almost all the world's religious types. Differences in religious beliefs also result in differences in culture, customs and social values behind languages. The languages of some countries along the belt and Road have also been influenced by historical and realistic factors, such as colonization, internal division and immigration. <br />
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India, for example, has no national language, but more than 20 official languages. India is a multi-ethnic country, a total of more than 100 people, one of the most obvious difference between nation and nation is the language problem. Therefore, according to the difference of language, India divides different ethnic groups into different states, big and small. Ethnic groups that use the same language are divided into one state. If there are two languages in a state, the state is divided into two parts. And Indian languages differ not only in word order but also in the way they are written. In India, for example, Hindi is spoken by the largest number of people in the north, with about 700 million speakers and 530 million as their first language. It is written in The Hindu language and belongs to the Indo-European language family. Telugu in the east is spoken by about 95 million people and 81.13 million as their first language. It is written in Telugu, which belongs to the Dravidian language family and is quite different from Hindi. As a result, a parliamentary session in India requires dozens of interpreters. <br />
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These factors cannot be ignored in the process of translation, from language communication to cultural understanding, from text to thought exchange, through the bridge of language to truly connect the people, so as to avoid misreading and misunderstanding caused by differences in language and national conditions.<br />
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====4.2 Opportunities and challenges of the "Belt and Road" ====<br />
With the promotion of the Belt and Road Initiative, there has been an unprecedented boom in translation. In the previous translation boom in China, most of the foreign languages were translated into Chinese, and most of the foreign cultures were imported into China. However, this time, in the context of the "Belt and Road" initiative, translating Chinese into foreign languages has become an important task for translators. As is known to all, there are many different kinds of "One Belt And One Road" along the national language and culture is complex, the service "area" construction has become a factor in Chinese translation talents training mode reform, one of the foreign language universities have action, many colleges and universities to establish the "area" all the way along the country's small language major, as a result, "One Belt And One Road" initiative to promote, It has brought unprecedented opportunities for human translation. The cultivation of diversified translation talents and the cultivation of translation talents in small languages is an urgent problem to be solved in China. The cultivation of translation talents cannot be completed overnight, and the state needs to reform the training mode of translation talents from the perspective of language strategic development. Only in this way can we meet the new demand for human translation under the new situation of the belt and Road Initiative.<br />
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For a long time, the traditional orientation of translation curriculum and training goal in colleges and universities is to train translation teachers and translators in need of society through translation theory and practice and literary translation practice, which cannot meet the needs of society. Since 2007, in order to meet the needs of the socialist market economy for application-oriented high-level professionals, the Academic Degrees Committee of The State Council approved the establishment of Master of Translation and Interpreting (MTI for short). After joining the pilot program of MTI, more and more universities are reforming the curriculum and training mode of master of Translation in order to cultivate translators who meet the needs of the society.<br />
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Language is an important carrier of culture, and translation is an important link for exporting culture. The quality of translation output also reflects the cultural soft power of a country. With the rise of China, more and more people are interested in Chinese culture, and the number of Chinese learners keeps increasing. Under the background of "One Belt and One Road", excellent translators are urgently needed to spread Chinese culture. With the promotion of "One Belt and One Road" Initiative, the number of other countries learning mutual learning and cultural exchanges with China has increased unprecedeningly, bringing vigorous opportunities for the spread of Chinese culture. Translation talents who understand small languages and multi-lingual translators are needed. They should not only use language to convey information, but also use language as a lubricant for communication.<br />
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===5.Training translation talents from the perspective of machine translation===<br />
Under the prevailing environment of machine translation, it poses a great challenge to the cultivation of translation talents. According to the current situation, translation needs and the shortage of translation talents, colleges and universities should reform and innovate the existing training programs for translation talents in terms of the quality of translation talents, the reform of training mode and the use of artificial intelligence. Based on the obtained data and literature, the author discusses how to train translation talents in the perspective of machine translation from the following aspects.<br />
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====5.1 Quality requirements for translation talents ====<br />
Zhong Weihe and Murray made a more detailed and profound discussion on translator's literacy, believing that "translators should not only be proficient in two languages, but also have extensive cultural and encyclopedic knowledge and relevant professional knowledge; Master a variety of translation skills, a lot of translation practice; Have a clear translator role awareness, good professional ethics, practical and enterprising style of work, conscious team spirit and calm psychological quality ". According to the collected data, the author will elaborate the requirements for translation talents from four aspects: language literacy, humanistic literacy, translation ability and innovation ability.<br />
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The first is language literacy, which is the most basic and important requirement. MAO Dun pointed out that "mastery of mother tongue and target language are the foundation of translation". A solid foundation of bilingual skills is the basic skills of translators. Poor language proficiency seems to be a common problem among students majoring in translation and interpreting. Many translation diseases are caused by poor Chinese foundation. As part of going global, the belt and Road initiative is to tell Chinese culture and Chinese stories, which requires translators to be able to use both languages flexibly. Therefore, the first problem that colleges and universities face to solve is to improve the language level of foreign language learners.<br />
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The second is humanistic literacy. Humanistic literacy is mainly manifested by a good command of politics, economy, history, literature and other knowledge, which is particularly important for interpreters. In addition, cross-cultural communication cannot be ignored. In the process of communicating with foreigners or translating, translators often encounter the first cross-cultural contradiction. Cross-culture refers to having a full and correct understanding of cultural phenomena, customs and habits that differ or conflict with the national culture, and accepting and adapting to them in an inclusive manner on this basis. So the interpreter can first fully understand and master the national conditions and culture of the target country, which is particularly important in the "Belt and Road". There are more than 60 countries along the "Belt and Road", and it takes a lot of energy to master their national conditions and culture.<br />
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The third is translation ability. We should distinguish between translation ability and language ability. Translation ability is actually a system of knowledge and skills necessary for translation, the core of which is conversion ability. First of all, it reflects the ability to use tools to assist translation, such as computer application, translation technology and so on. In addition, interpreters should have enough healthy psychological quality and good professional quality. In terms of translation ability, the current training model of translation talents is inadequate.<br />
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The last one is innovation. The cultivation of learners' thinking ability is the key to translation teaching and the cultivation of thoughtful translators should be the connotation of translation teaching. Therefore, the interpreter is not only a translation tool, which is no different from machine translation. More importantly, it is necessary to explore translation with thoughts, have a sense of lifelong learning and innovation consciousness. Translators must constantly innovate themselves, learn new knowledge, and strive to seek reform and innovation. Many colleges and universities should also consciously cultivate students' innovation ability and broaden their thinking and vision.<br />
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====5.2 The reform of college curriculum setting====<br />
First, we will further reform the curriculum of colleges and universities. Add economics, law and engineering to the curriculum, these contents in the "belt and Road".<br />
"One Road" is very important in the construction. According to the author's personal experience, the most typical problem of foreign language majors in colleges and universities is the single learning of foreign languages. More professional foreign language colleges and universities will add some literature courses and national conditions courses of the language target countries. Obviously, whether foreign language graduates are engaged in translation work or not, these knowledge is not enough. Of course, great reforms have been carried out in foreign language teaching, such as combining foreign language with finance, law, diplomacy and so on, and taking the way of minor training foreign language majors.<br />
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Domestic enterprises with a high degree of internationalization attach great importance to translation. Their translation research includes cutting-edge theoretical and applied research, involving machine translation, natural language processing and AI theory, algorithm and model. With such a foundation, enterprises can solve problems by themselves, such as embedding automatic translation functions in mobile phones. International enterprises not only do technical translation, but also deal with all forms of translation and localization in society. At present, translation teaching in most colleges and universities is still in the early mode, and it is an objective fact that it is divorced from the workplace and has a gap with the needs of enterprises.<br />
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Second, we should adjust and strengthen the construction of second foreign language teaching for foreign language majors. In the 1980s, our country was in urgent need of Russian translation. At that time, students majoring in English could translate microelectronic product manuals and related business documents in English and Russian at the same time after learning Russian for half a year. The mutual conversion between English and Russian played a great role in practice. According to the author, in the Graduate Institute of Interpretation and Translation of Beijing Foreign Studies University a very few students majored in multiple languages at the graduate level, that is, they majored in minor languages at the undergraduate level and were admitted to the Graduate Institute of Interpretation and Translation in English. Their training mode is to study English in the Graduate Institute of Interpretation and Translation for two years and the third year in the corresponding department of the undergraduate major. Such training mode in my opinion is a bigger model, cost It's more difficult for students. <br />
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In addition, there is a great disparity in the development of second foreign language teaching in colleges and universities, and the overall level is not high enough. Part of the second foreign language university foreign language professional may still be too much focus in languages such as German, French and Japanese, should as far as possible, considering the need of the construction of the "region", like Croatia, Serbia, Turkish, Hungarian, Italian, Indonesian, Albanian, these are the countries along the "area" the language of the two countries, Colleges and universities should encourage the teaching of a second foreign language.<br />
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Third, the teaching of translation technology should be strengthened. Traditional translation teaching teaches translation skills, such as the translation of words, sentences, texts and figures of speech. Translation technology refers to a series of practical workplace technologies with computer-aided translation software and translation project management as the core, which can greatly improve translation efficiency. However, due to the relative lack of translation technology teachers and equipment in colleges and universities, there is a disconnect between talent training and the requirements of translation technology in the translation field.<br />
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====5.3 Application of artificial intelligence to translation teaching practice====<br />
In order to improve the teaching quality and train students' English translation ability, it is necessary to realize the effective integration of ARTIFICIAL intelligence and translation activity courses, which should not only reflect the effectiveness of artificial intelligence translation technology, but also help students establish a healthy concept of English communication. Through the application of artificial intelligence technology, students can strengthen their flexible translation skills through close communication with "AI program" during the learning stage of English translation activity class. For example, teachers can ask students to translate directly against the translation content provided on the translation screen of the ARTIFICIAL intelligence system. After that, the system can collect the translation answers with the help of speech recognition function, and then judge the accuracy of the translation content, thus providing important feedback to students.<br />
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China has used such artificial intelligence technology in the Putonghua test to ensure that every student can find a suitable translation method in practical communication. The so-called artificial intelligence technology is a new kind of technology modeled after the characteristics of human neural network thinking, can combine the human mind to respond. If it can be integrated into English translation activity teaching, it can not only improve the teaching efficiency, but also enhance students' enthusiasm in learning the course.<br />
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At the same time, during the training of translation talents, teachers also need to take into account the importance of influencing education factors, so that students can form a higher disciplinary quality in translation, so as to fit the concept of quality education in the new era. Only when artificial intelligence translation content is fully integrated into college English translation activity courses can the overall translation ability of college students be maximized.<br />
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====5.4The improvement of translator's technical ability====<br />
In the previous part, the author roughly mentioned that translation teaching should be improved, which will be elaborated here. At present, only a few universities can make full use of the advantages of translation technology in translation teaching and focus on cultivating professional translation talents. Most universities still cannot get rid of the traditional teaching mode of "language + relevant professional knowledge" in translation teaching, and generally lack a correct understanding of COMPUTER-aided translation teaching.<br />
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According to Wang Huashu et al., the courses that can be offered around the composition of translators' technical literacy include computer-assisted translation, translation and corpus, machine translation and post-translation editing, localization and internationalization, film and television translation (subtitle), technical communication and technical writing, and computer programming. The course modules involved are: Fundamentals of COMPUTER-aided Translation, CAT tool application, corpus alignment and processing, term management, QA technology for translation quality assurance, OFFICE fundamentals, translation management technology, basic computer knowledge, desktop typesetting, localization and internationalization, project management system and content management system, technical writing, basic knowledge of computer programming, basic knowledge of web code, etc.<br />
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===6针对一带一路的机器翻译与翻译人才的合作===<br />
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===Conclusion===<br />
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===References===<br />
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=8 颜静(On Machine Translation Under Lanuguage Intelligence——An Option and Opportunity for Human Translators)=<br />
[[Machine_Trans_EN_8]]<br />
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=9 谢佳芬（人工智能时代下的机器翻译与人工翻译）=<br />
[[Machine_Trans_EN_9]]<br />
===Abstract===<br />
With the continuous development of information technology, many industries are facing the competitive pressure of artificial intelligence, and so is the field of translation. Artificial intelligence technology has developed rapidly and combined with the field of translation，which has brought great impact and changes to traditional translation, but artificial intelligence translation and artificial translation have their own advantages and disadvantages. Artificial translation is in the leading position in adapting to human language logical habits and understanding characteristics, but in terms of translation threshold and economic value, the efficiency of artificial intelligence translation is even better. In a word, we need to know that machine translation and human translation are complementary rather than antagonistic.<br />
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===Key Words===<br />
Machine Translation; Artificial Translation; Artificial Intelligence<br />
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===题目===<br />
人工智能时代下的机器翻译与人工翻译<br />
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===摘要===<br />
伴随着信息技术的不断发展，多个行业面临着人工智能的竞争压力，翻译领域也是如此。人工智能技术快速发展并与翻译领域结合，人工智能翻译给传统翻译带来了巨大的冲击和变革，但人工智能翻译与人工翻译存在着各自的优劣特点和发展空间，在适应人类语言逻辑习惯和理解特点的翻译效果上，人工翻译处于领先地位，但在翻译门槛和经济价值上，人工智能翻译的效率则更胜一筹。总的来说，我们要知道机器翻译与人工翻译是互补而非对立的关系。<br />
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===关键词===<br />
机器翻译;人工翻译;人工智能<br />
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===1. Introduction===<br />
====1.1 The History of Machine Translation Aborad====<br />
The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. Alchuni put forward the idea of using machines for translation. In 1933, the Soviet inventor Troyansky designed a machine to translate one language into another. [1]In 1946, the world's first modern electronic computer ENIAC was born. Soon after, American scientist Warren Weaver, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947. In 1949, Warren Weaver published a memorandum entitled Translation, which formally raised the issue of machine translation. In 1954, Georgetown University, with the cooperation of IBM, completed the English-Russian machine translation experiment with IBM-701 computer for the first time, which opened the prelude of machine translation research. [2] In 2006, Google translation was officially released as a free service software, bringing a big upsurge of statistical machine translation research. It was Franz Och who joined Google in 2004 and led Google translation. What’s more, it is precisely because of the unremitting efforts of generations of scientists that science fiction has been brought into reality step by step.<br />
====1.2 The History of Machine Translation in China====<br />
In 1956, the research and development of machine translation has been named in the scientific and technological work and made little achievements in China. On the eve of the tenth anniversary of the National Day in 1959, our country successfully carried out experiments, which translated nine different types of complicated sentences on large general-purpose electronic computers. The dictionary includes 2030 entries, and the grammar rule system consists of 29 circuit diagrams. [3]. After a period of stagnation, China's machine translation ushered in a high-speed development stage after the 1980s in the wave of the third scientific and technological revolution. With the rapid development of economy and science and technology, China has made a qualitative leap in the field of machine translation research with the pace of reform and opening up. In 1978, Institute of Scientific and Technological Information of China, Institute of Computing Technology and Institute of Linguistics carried out an English-Chinese translation experiment with 20 Metallurgical Title examples as the objects and achieved satisfactory results. Subsequently, they developed a JYE-I machine translation system, which based on 200 sentences from metallurgical documents. Its principles and methods were also widely used in the machine translation system developed in the future. In addition, the research achievements of machine translation in China during the 1980s and 1990s also include that Institute of Post and Telecommunication Sciences developed a machine translation system, C Retrieval and automatic typesetting system with good performance and strong practicability in October 1986; In 1988, ISTC launched the ISTIC-I English-Chinese Title System for the translation of applied literature of metallurgy, Information Research Institute of Railway developed an English-Chinese Title Recording machine translation system for railway documents; the Language Institute of the Academy of Social Sciences developed "Tianyu" English-Chinese machine translation system and Matr English-Chinese machine translation system developed by the computer department of National University of Defense Technology. After many explorations and studies, machine translation in China has gradually moved towards application, popularization and commercialization. China Software Technology Corporation launched "Yixing I" in 1988, marking China's machine translation system officially going to the market. After "Yixing", a series of machine translation systems such as Gaoli system in Beijing, Tongyi system in Tianjin and Langwei system in Shaanxi have also entered the public. In the 21st century, the development of a series of apps such as Kingsoft Powerword, Youdao translation and Baidu translation has greatly met the needs of ordinary users for translation. According to the working principle, machine translation has roughly experienced three stages: rule-based machine translation, statistics-based machine translation and deep learning based neural machine translation. [4] These three stages witnessed a leap in the quality of machine translation. Machine translation is more and more used in daily life and even the translation of some texts is almost comparable to artificial translation. In addition to text translation, voice translation, photo translation and other functions have also been listed, which provides great convenience for people's life. It is undeniable that machine translation has become the development trend of translation in the future.<br />
====1.3 The Status Quo of Machine Translation====<br />
In this big data era of information explosion, the prospect of machine translation is also bright. At present, the circular neural network system launched by Google has supported universal translation in more than 60 languages. Many Internet companies such as Microsoft Bing, Sogou, Tencent, Baidu and NetEase Youdao have also launched their own Internet free machine translation systems. [5] Users can obtain translation results free of charge by logging in to the corresponding websites. At present, the circular neural network translation system launched by Google can support real-time translation of more than 60 languages, and the domestic Baidu online machine translation system can also support real-time translation of 28 languages. These Internet online machine translation systems are suitable for a variety of terminal platforms such as mobile phone, PC, tablet and web and its functions are also quite diverse, supporting many translation forms, such as screen word selection, text scanning translation, photo translation, offline translation, web page translation and so on. Although its translation quality needs to be improved, it has been outstanding in the fields of daily dialogue, news translation and so on.<br />
===2. Advantages and Disadvantages of Machine Translation===<br />
Generally speaking, machine translation has the characteristics of high efficiency, low cost, accurate term translation and great development potential and etc. Machine translation is fast and efficient, this is something that artificial translation can’t catch up with. In addition, with the continuous emergence of all kinds of translation software in the market, compared with artificial translation, machine translation is cheap and sometimes even free, which greatly saves the economic cost and time for users with low translation quality requirements. What's more, compared with artificial translation, machine translation has a huge corpus, which makes the translation of some terms, especially the latest scientific and technological terms, more rapid and accurate. The accurate translation of these terms requires the translator to constantly learn, but learning needs a process, which has a certain test on the translator's learning ability and learning speed. In this regard, artificial translation has uncertainty and hysteretic nature. At the same time, with the progress of science and technology and the development of society, the function of machine translation will be more perfect and the quality of translation will be better.Today's machine translation tools and software are easy to carry, all you need to do is just to use the software and electronic dictionary in the mobile phone. There is no need to carry paper dictionaries and books for translation, which saves time and space. At the same time, machine translation covers many fields and is suitable for translation practice in different situations, such as academic, education, commercial trade, social networking, tourism, production technology, etc, it is also easy to deal with various professional terms. However, due to the limitation of translators' own knowledge, artificial translation is often limited to one or a few fields or industries. For example, it is difficult for an interpreter specializing in medical English to translate legal English.<br />
At the same time, machine translation also has its limitations. At first, machine can only operate word to word translation, which only plays the function and role of dictionary. Then, the application of syntax enables the process of sentence translation and it can be solved by using the direct translation method. When the original text and the target language are highly similar, it can be translated directly. For example, the original text "他是个老师." The target language is "he is a teacher ". With the increase of the structural complexity of the original text, the effect of machine translation is greatly reduced. Therefore, at the syntactic level, machine translation still stays in sentences with relatively simple structure. Meanwhile, the original text and the results of machine translation cannot be interchanged equally, indicating that English-Chinese translation has strong randomness, and is not rigorous and scientific enough. <br />
Nowadays, machine translation is highly dependent on parallel corpora, but the construction of parallel corpora is not perfect. At present, the resources of some mainstream languages such as Chinese and English are relatively rich, while the data collection of many small languages is not satisfactory. Moreover, the current corpus is mainly concentrated in the fields of government literature, science and technology, current affairs and news, while there is a serious lack of data in other fields, which can’t reflect the advantages of machine translation. At the same time, corpus construction lags behind. Some informative texts introducing the latest cutting-edge research results often spread the latest academic knowledge and use a large number of new professional terms, such as academic papers and teaching materials while the corpus often lacks the corresponding words of the target language, which makes machine translation powerless<br />
Besides, machine translation is not culturally sensitive. Human may never be able to program machines to understand and experience a particular culture. Different cultures have unique and different language systems, and machines do not have complexity to understand or recognize slang, jargon, puns and idioms. Therefore, their translation may not conform to cultural values and specific norms. This is also one of the challenges that the machine needs to overcome.[6] Artificial intelligence may have human abstract thinking ability in the future, but it is difficult to have image thinking ability including imagination and emotion. [7] Therefore, machine translation is often used in news, science and technology, patents, specifications and other text fields with the purpose of fact description, knowledge and information transmission. These words rarely involve emotional and cultural background. When translating expressive texts, the limitations of machine translation are exposed. The so-called expressive text refers to the text that pays attention to emotional expression and is full of imagination. Its main characteristics are subjectivity, emotion and imagination, such as novels, poetry, prose, art and so on. This kind of text attaches importance to the emotional expression of the author or character image, and uses a lot of metaphors, symbols and other expressions. Machine translation is difficult to catch up with artificial translation in this kind of text, it can only translate the main idea, lack of connotation and literary grace and it cannot have subjective feelings and rational analysis like human beings. In fact, it is not difficult to simulate the human brain, the difficulty is that it is impossible to learn from the rich social experience and life experience of excellent translators. In other words, machine translation lacks the personalization and creativity of human translation. It is this personalization and creativity that promote the development and evolution of language, and what machine translation can only output is mechanical "machine language".<br />
<br />
===3.The Irreplaceability of Artificial Translation ===<br />
====3.1 Translation is Constrained by Context====<br />
At present, machine translation can help people deal with language communication in people's daily life and work, such as clothing, food, housing and transportation, but there is a big gap from the "faithfulness, expressiveness and elegance" emphasized by high-level translation. Language itself is art，which pays more attention to artistry than functionality, and the discipline of art is difficult to quantify and unify. Sometimes it is regular, rigorous, logical and clear, and sometimes it is random, free and logical. If it is translated by machine, it is difficult to grasp this degree. Sometimes, machine translation cannot connect words with contextual meaning. In many languages, the same word may have multiple completely unrelated meanings. In this case, context will have a great impact on word meaning, and the understanding of word meaning depends largely on the meaning read from context. Only human beings can combine words with context, determine their true meaning, and creatively adjust and modify the language to obtain a complete and accurate translation. This is undoubtedly very difficult for machine translation. Artificial translation can get rid of the constraints of the source language and translate the translation in line with the grammar, sentence patterns and word habits of the target language. In the process of translation, translators can use their own knowledge reserves to analyze the differences between the source language and the target language in thinking mode, cultural characteristics, social background, customs and habits, so as to translate a more accurate translation. Artificial translation can also add, delete, domesticate, modify and polish the translation according to the style, make up for the lack of culture, try to maintain the thought, artistic conception and charm of the original text and the style of the source language. In addition, translators can also judge and consider the words with multiple meanings or easy to produce ambiguity according to the context, so as to make the translation more clear and more accurate and improve the quality of the translation.<br />
<br />
<br />
===4. Discussion on the Relationship Between Machine Translation and Artificial Translation ===<br />
<br />
===5. Suggestions on the Combined Development of Machine Translation and Artificial Translation===<br />
<br />
===6. ===<br />
<br />
===7. ===<br />
<br />
===Conclusion===<br />
<br />
===References===<br />
<br />
=10 熊敏(Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts)=<br />
[[Machine_Trans_EN_10]]<br />
===Abstract===<br />
The history of machine translation can be traced to 1940s, undergoing germination, recession, revival and flourish. Nowadays, with the rapid development of information technology, machine translation technology emerged and is gradually becoming mature. Whether manual translation can be replaced by machine translation becomes a widely-disputed topic. So in order to explore the ability of machine translation, I adopts two versions of translation, which are manual translation and machine translation(this paper uses Youdao translation) for different types of texts(according to Peter Newmark's types of text：informative text, expressive text and vocative text). The results are quite different in terms of quality and accuracy. The results show that machine translation is more suitable for informative text for its brevity, while the expressive texts especially literary works and vocative texts are difficult to be translated, because there are too many loaded words and cultural images in expressive text and dependence on the readers. To draw a conclusion, the relationship between machine translation and manual translation should be complementary rather than competitive.<br />
<br />
===Key words===<br />
machine translation; manual translation; Newmark's type of texts<br />
<br />
===题目===<br />
Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts<br />
<br />
===摘要===<br />
机器翻译的历史可以追溯到上个世纪40年代，经历了萌芽期、萧条期、复兴期和繁荣期四个阶段。如今，随着信息技术的高速发展，机器翻译技术日益成熟，于是机器翻译能不能替代人工翻译这个话题引起了广泛热议。为了探究机器翻译的能力水平是否足以替代人工翻译，本人根据皮特纽马克的文本类型分类理论（信息类文本，表达文本，呼吁文本），选择了相应的译文类型，并且将其机器翻译的版本以及人工翻译的版本进行对比。结果发现，就质量和准确度而言，译文的水平大相径庭。因为其简洁的特性，机器比较适合翻译信息文本。而就表达文本，尤其是文学作品，因其存在文化负载词及相应的文化现象，所以机器翻译这类文本存在难度。而呼唤文本因其目的性以及对读者的依赖性较强，翻译难度较大。由此得知，机器翻译和人工翻译的关系应该是互补的，而不是竞争的。<br />
<br />
===关键词===<br />
机器翻译；人工翻译；纽马克文本类型<br />
<br />
===1. Introduction===<br />
====1.1.Introduction to machine translation====<br />
Machine translation, also known as computer translation is a technique to translating through machine translator, such as Google translation and Youdao translation. Machine translation is one of the branches of computational linguistics, ranging from computer science, statistics, information science and so on. Machine translation plays an important role in all aspects.<br />
Machine translation can be traced back to 1940s, when British engineer Booth and American engineer Weaver proposed using computer to translate and started to study machines used for translation. And the first machine translating system launched in 1954, used in English and Russian translation. And this means machine translation becomes a reality. However, the arrival of new things always accompanies barriers and setbacks. In the 1960s, reports from ALPAC (Automated Language Processing Advisory Committee) showed studies on machine translation had stagnated for a decade. At that time, due to the high cost of machine, choosing human translators to finish translating works was more economical for most companies. What’s worse, machine had difficulty in understanding semantic and pragmatic meanings. Fortunately, in the 1970s, with the advance and popularity of computer, machine translation was gradually back on track. With the development of science and technology, machine translation has better technological guarantee, such as artificial intelligence and computer technology. In the last decades, from the late 90s till now, machine translation is becoming more and more mature. The initial rule-oriented machine translation has been updated and Corpus-aided machine translation appears. And nowadays, technology in voice recognition has been further developed. This technique is frequently applied in speech translation products. Users only need to speak source language to the online translator and instantly it will speak out the target version. But machine can’t fully provide precise and accurate translation without any grammatical or semantic problems. Machine can understand the literal meaning of texts, but not the meaning in context. For example, there is polysemy in English, which refers to words having multiple meanings. So when translating these words, translators should take contexts into consideration. But machine has troubles in this way. In addition, machine has no feeling or intention. Hence, it is also difficult to convey the feelings from the source language.<br />
<br />
====1.2.Process of machine translation====<br />
The process of manual translation is different from that of machine translation. Here is the process of the former. (1) Understand source language. (2) Use target language to organize language. (3) Generate translation. Unlike manual translation, machine translation tends to analyze and code source language first, then look for related codes in corpus, and work out the code that represents target language, generating translation. But they share a common feature, which is that Lexicon, grammatical rules and syntactic structure are taken into consideration. This is one of the biggest challenges for machine translation.<br />
<br />
===2.Theorectical framework===<br />
====2.1Newmark’s text typology====<br />
Text typology is a theory from linguistics. It undergoes several phrases. Karl Buhler, a German psychologist and linguist defines communicative functions into expressive, information and vocative. Then Roman Jakobson proposes categorization of texts, which are intralingual translation, interlingual translation and intersemiotic translation. Accordingly, he defines six main functions of language, including the referential function, conative function, emotive function, poetic function, metalingual function and the phatic function. Based on the two theories, Peter Newmark, a translation theorist, summarizes the language function into six parts: the informative function, the vocative function, the expressive function, the phatic function, the aesthetic function, and the metalingual function. Later, he further divided texts into informative type, expressive text and vocative type according to the linguistic functions of various texts. <br />
The core of the informative texts is the truth. It is to convey facts, information, knowledge of certain topics, reality and theories. The language style of the text is objective, logical and standardized. Reports, papers, scientific and technological textbooks are all attributed to informative texts. Translators are required to take format into account for different styles.<br />
The core of the expressive text is the sender’s emotions and attitudes. It is to express sender’s preferences, feelings, views and so on. The language style of it is subjective. Imaginative literature, including fictions, poems and dramas, autobiography and authoritative statements belong to expressive text. It requires translators to be faithful to the source language and maintain the style.<br />
The core of the vocative text is readership. It is to call upon readers to act, think, feel and react in the way intended by the text. So it is reader-oriented. Such texts as advertisement, propaganda and notices are of vocative text. Newmark noted that translators need to consider cultural background of the source language and the semantic and pragmatic effect of target language. If readers can comprehend the meaning at once and do as the text says, then the goal of information communication is achieved.<br />
To draw a conclusion, informative texts focus on the information and facts. Expressive texts center on the mind of addressers, including feelings, prejudices and so on. And vocative texts are oriented on readers and call on them to practice as the texts say.<br />
<br />
====2.2Study method====<br />
Manual translations of the three texts are selected from authoritative versions and universally acknowledged. And machine translations of those come from Youdao Translator. And in this thesis I will compare and evaluate the two methods in word diction, sentence structure, word order and redundancy.<br />
<br />
===3. ===<br />
<br />
===4. ===<br />
<br />
===5. ===<br />
<br />
===6. ===<br />
<br />
===7. ===<br />
<br />
===Conclusion===<br />
<br />
===References===<br />
<br />
=11 陈惠妮=(Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts)=<br />
[[Machine_Trans_EN_11]]<br />
===Abstract===<br />
At present, globalization is accelerating and the market demand for language services is rapidly increasing . Machine translation, as an important translation method, can greatly improve translation efficiency due to its low cost and high speed. However, because of the limitations of machine translation and the differences between Chinese and English language, machine translation is not accurate enough. In order to balance translation efficiency and translation quality, a great number of manual revisions in translation are required for the machine translating texts. Medical papers are specialized, special and purposeful, so it requires accurate,qualified and professional translation. However, the quality of translations by machine is inefficient to meet the high-quality requirements of medical papers translation. Therefore, the introduction of pre-editing can greatly improve the efficiency and quality of machine translation.<br />
<br />
===Key words===<br />
Pre-editing, Machine translation, Medical texts<br />
<br />
===题目===<br />
Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts<br />
<br />
===摘要===<br />
在全球化加速发展的今天，市场对语言服务的需求迅速增加。机器翻译作为一种重要的翻译途径，由于其成本低、速度快，可以大大提高翻译效率。然而，由于机器翻译的局限性以及中英文语言的差异，机器翻译的准确性不高。为了平衡翻译效率和翻译质量，机器翻译文本需要大量的手工修改。<br />
医学论文具有专业性、特殊性和目的性，要求其译文准确、合格、专业。然而，机器翻译的质量较低，无法满足医学论文对翻译的高质量要求。因此，译前编辑的引入可以大大提高机器翻译的效率和质量。<br />
<br />
===关键词===<br />
译前编辑；机器翻译；医学文本<br />
<br />
===1. Introduction===<br />
===1.1 Definition of Machine Translation===<br />
As Cronin(2013) revealed: "Translation is undergoing a revolutionary upheaval. The influence of digital technology and the Internet on translation is continuous, extensive and profound. From the popularity of automatic online translation applications, translation revolution is everywhere (Cronin,2013) However,the concept of machine translation was firstly proposed in the 1930s. Since 1940s, the machine translation technology has been evolving from rule-based machine translation (RBMT) to statistical machine translation (SMT), and to neural machine translation (NMT). Machine translation refers to the automatic translation of source language into target language by using a computer system. That is, machine translation refers to the automatic translation of text from one language into another natural language by computer software or other online translation webs. Machine translation is also defined as the process of “using a computer system to automatically translate text or speech from one natural language to another” according to the definition by ISO (Cui, 2014).O 'Brien (2002) defines it as "the behavior of modifying errors in machine translation to ensure that the target translation meets certain quality requirements". On the basic level, machine translation performs mechanical substitution of words in one language for words in another language, but that rarely produces a good translation, therefore, recognition of the whole phrases and their closest counterparts in the target language is needed. Not all words in one language have equivalents in another language, and many words have more than one meaning. A huge demand for translation is greatly needed in today’s global world, which creates new opportunities for the development of machine translation, attracts more and more attention and becomes one of the current research focuses.<br />
<br />
===1.2 Definition of Pre-editing===<br />
Pre-editing means to adjust and modify the source language to make it fit more with the characteristics of the machine translation software before putting the source language before the into machine translation, so as to improve the quality of the translation machine translation (Wei Changhong, 2008:93-94). Pre-editing is to modify the original text before putting it into machine translation software, in order to improve the recognition rate of machine translation, optimize the output quality of translated text and reduce the workload of post-editing. Because pre-translation editing only needs to be modified in one language, the operation is simpler than post-translation editing, which can realize the double improvement of quality and efficiency. A good pre-editing translation can help machine translation more smoothly, thus improving the machine readability and quality of the output translation. Pre-editing is mostly applied in the following situations: one is when the original text is of poor quality and the machine is difficult to recognize the meaning of the sentence, such as user generated content with poor readability and translatability (Gerlach et al, 2003:45-53); Documents that need to be published in multiple languages; Next is when the original text contains a lot of jargon; The last is the original text has a corresponding translation memory bank. If the original text is edited, it can better match the content of the translation memory bank. Pre-editing is a process of identifying problems. It requires to pre-edit the source texts before putting it into machine translation according to the requirements, listing the expressions or sentences that may have trouble in machine translation and then pre-edit it by human. The purpose is to enable the computer to translate better, improve the translatability of machine translation. (Slype G V & Guinet J F & Seitz F,1984:115)<br />
<br />
===1.3 Machine Translation Mode===<br />
According to the different knowledge acquisition methods, machine translation modes can be classified as follows: one is rule-based machine translation, which is based on bilingual dictionaries and a library of language rules for each language. The quality of translation depends on whether the source language conforms to the existing rules, but the inexhaustible rules are the hinders of this model. The second mode is machine translation based on statistics. This translation model relies on the principles of mathematics and statistics to find various existing translations corresponding to the translation tasks through the employment of corpus, analyzing the frequency of their occurrence and selecting the translation with the highest frequency for output. The disadvantage of this translation model is that it ignores the flexibility of language and the importance of context. The last one is neural network language model. This model is different from previous translation models in that it uses end-to-end neural network to realize automatic translation between natural languages. At present, the quality of its translation is much higher than that of the previous translation models.<br />
===1.4 Source of Study Abstracts===<br />
The core medical journals from domestic are selected in order to make the paper more representative and authoritative, such as National Medical Journal of China, Journal of Peking University (Health Science), and Journal of Third Military Medical University. All of them include basic medical science, biomedical technology, laboratory medical science and other fields. In this way, the pre-editing approaches included are applicable enough to machine translation of medical abstracts.<br />
===1.5 Selection of Translation Software===<br />
Generally, machine translation software includes Google Translation, Youdao Translator and Niu Translation, which has their own special use in translation. Take Google as an example, it is more like neurons in human brain, enabling to learn and collect information to establish connections with its neural machine translator’s neurons. However, it also causes many errors because of the lack of enough information. In this paper, contrastive analysis will be carried on by using Google translation. On the one hand, being a pioneer in the translation of NET, it is inevitably to sue Google as the translation software to translate medical texts in this paper. Comparing the program called “Google brain”, other NMT of translation software are relatively disadvantages. On the other hand, the Google translation enjoys the largest users in the world, with its downloads of more than one billion. The output quality of Google translation is more correct and complete than other machine translation software. With years of development and improvement, Google Translation has been greatly promoted. In this paper, Chinese medical abstract will be automatically translated by Google translation, and then the translation output will be compared with the translation by human and the publication of English abstracts. The main purpose is to prove that the improvement and promotion of quality and accuracy in the medical abstracts will be obtained through the pre-editing approaches.<br />
<br />
===2.Language Characteristics and Error Division===<br />
Since Chinese and English are two different languages, it is quite neccessary to identify their own characteristics so as to better analyze and understand the two languages. Tytler (1978) argued in his Essay on the Principles of Translation that there are three principles of translation that in all the translation should give readers the same feelings as the source text, except for complete transcript of ideas. There actually also exit some mistakes of these two languages. So the following will make some clarrifications of these two languages to ensure more accurate translation.<br />
<br />
===2.1 Language Characteristics of Medical Abstracts===<br />
Chinese and English belong to different language systems, so there are differences in their language structure and the way users think of the languages. When using machine translation from Chinese to English, due to the unequal language levels, there will be many mistakes in the translation process, especially for ESP texts, such as medical papers. <br />
Here, these differences mainly refer to the linguistic characteristics of medical abstracts. In medical abstracts, it usually includes structured and unstructured abstracts. Although in different forms, they both describe the purpose, methods and conclusions of the research.<br />
In the method section of medial abstract, several Chinese sentences can be connected with commas, but each sentence may convey different information. In contrast, English sentences contain a great deal of information, but in order to ensure clarity, some modifiers need to be isolated and then reconstructed. However, in Chinese-English machine translation, a lot of information is put into the sentence because the machine segments the sentence on the basis of the full comma.<br />
In addition, subjectless sentence is used in the objective and method parts of Chinese medical abstracts. The subjectless sentence means the sentence without or free from subject and is usually employed in two contexts. The first is "needless to say". In Chinese sentences, it is common to omit the subject of the sentence. Chinese sentences can convey meanings by using incomplete sentence structures, so Chinese speakers can understand the meanings of the sentences even though the sentence subject is omitted. The second is "emphasis of action". In this context, subjectless sentences are used to describe behaviors, especially in the study of traditional Chinese medicine abstracts. Comparatively speaking, it should be avoided in English medical abstracts. Abstract sentences are more subjective when describing the learning process, while the essential requirement of English medical abstract is objectivity. From this point, sentences without subject should be avoided in English medical abstracts. Another feature is voice. Few words with passive meanings appear in Chinese abstracts. English sentences are more favoured in using passive voice. When translating from Chinese to English, passive voice should be used to make the contents objective, which is also the basic requirement of medical papers.<br />
These are the linguistic features of Chinese medical abstracts. There are great differences in sentence structure and expression between Chinese and English medical abstracts. These differences may reduce the accuracy of machine translation, so it is necessary to introduce pre-editing to edit the source text to ensure that the source text can be accurately recognized by the machine and fully translated into English.<br />
===2.2 Error Division of Machine Translation in Translating Abstracts of Medical Papers from Chinese to English===<br />
In this paper, errors in machine translation are listed out after analyzing. Some are due to the principles, such as statistical-based MT and NMT, employed by machine translation. Based on the original errors, they can be studied from two levels. One is from the macro-level, referring to mistakes caused by objective factors. These are not human factors, but the disadvantage of machine translation and the limitations of language. The limitations of machine translation are derived from the principles and models adopted and manifests itself as a reliance on reference sources. In this case, semantic ambiguity and textual incoherence may occur in the absence of reference sources.<br />
However, for ESP texts such as medical papers, the requirements for terms and sentence patterns are far beyond the existing corpora. For unfamiliar texts (that is, the corresponding texts cannot be found in the corpora). The quality of output translation will be relatively low, which is determined by the principles of machine translation. As mentioned above, machine translation has evolved over the past few decades from rules-based to corp-based statistics to NMT. However, there still exist some limitations in machine translation.<br />
Mistakes on micro-level are mainly caused by the variations and differences of linguistic structure between Chinese and English. Chinese is an implicit language, while English is an explicit one (Lian, 2010) To put it in another way, Chinese expression does not depend on language structures, while English does the opposite. This may result in mismatches between the original and machine translated sentences. This kind of error is mainly divided into two types: component fragment and component missing. For a medical paper, such errors are very serious. As a kind of ESP discourse, medical paper has the characteristics of fixed, objective and accurate language structure. In order to reproduce the characteristics of medical abstracts in translation, it is necessary to avoid errors at the level of words and sentences and pay attention to logic and consistency.<br />
Lexical errors here refer to the inconsistency of fixed expressions in the translation of terms. Although these terms are machine translation based on the corpus, the corpus may not be perfect for ESP texts such as medical papers. Therefore, fixed expressions are not used in term translation. This is also the most common mistake in machine translation (the term here includes but is not limited to nouns). For ESP texts, medical text in particular, the fixed expressions and the accuracy of terms are of great importance.<br />
<br />
===3.Approaches Proposed for Pre-editing ===<br />
Generally speaking, there is a close relationship between pre-editing and post-editing, both of which aim to convey information and ensure high or publishable translation quality. Proper pre-editing can improve the quality of machine translation in terms of adequacy and consistency. <br />
Complexity of natural language and people use language arbitrarily, bringing many difficulties to Chinese-English machine translation, Hu Qingping (2005:24) has proposed "the research of translation software and the development of the controlled language are the two directions to improve the quality of machine translation : the former aims at the difficulty in the natural language processing, the latter overcomes the arbitrariness of natural language". Feng Quangong and Gao Lin (2017: 63-68) put forward: "The writing principles of controlled language can be applied to pre-editing of machine translation. Pre-editing based on controlled language can effectively reduce the complexity and ambiguity of source text, improve the identifiability of machine translation (the translatability of source text itself), and thus reduce (fully) the workload of post-editing".<br />
The central task of pre-editing is to transform human-friendly content into machine-friendly content, so words and sentences need to be repositioned or even changed. Based on the analysis of the language characteristics of Chinese and English, the Chinese ideographic group should be split before the Chinese source text is input into the machine software to translate so that the sentence structure is complete which can be easily recognized by the machine translation software.<br />
===3.1 Extraction and Replacement of Terms in the Source Text===<br />
As a branch of applied English, medical English is the product of the combination of English language knowledge and medical knowledge. The terms in medical papers have the characteristics of fixed and complex language structure. Although Google Translation is based on a corpus, the language structure is not fixed due to the limited size of the corpus. Therefore, the following two steps should be followed before machine translation. The first is to extract terms and create a glossary, and then replace the Chinese terms in the source text with the corresponding English expressions in the glossary. Of course, the terms of extraction should be searched and verified according to the actual situation. All of these words are verified before they can be replaced. This method can not only ensure the accuracy of term translation, but also maintain the consistency of expression, especially when dealing with long texts.<br />
Example1<br />
Source abstract: 口腔白斑病癌变相关缺氧应答基因和微小RNA的芯片及表达验证。<br />
Google translation: Microarray detection/Chip detection and expression verification of hypoxia response genes and microRNAs related to oral leukoplakia canceration.<br />
Published translation:Transcriptome array screening and verification of oral leukoplakia carcinogenesis-related hypoxia-responsive gene and microRNA. <br />
Analysis: The expression “ Affymetrix GeneChip” empolyed in this thesis is a human transcription array for transcriptome array. In the source abstract, “芯片检测“ can be meant “screening” but not detection, so the proper and appropriate translation of these expression should be “transcription array screening”, but the Google translates it into “microarry detection of chip detection”. Therefore, term extraction is essential and inevitable before putting the source abstracts into the machine translation software.<br />
After pre-editing source abstracts: 对 oral leukoplakia carcinogenesis 相关 hypoxia-responsive gene 和微小RNA进行 transcriptome array screening 及表达验证。<br />
After pre-editing translation: Transcriptome array screening and expression- verification of hypoxia-responsive genes and microRNAs related to oral leukoplakia carcinogenesis.<br />
===3.2 Explication of Subordination===<br />
As mentioned above, the subordination of Chinese sentences is judged by certain specific words in machine translation . Chinese is a paratactic language, and sentences are often connected by internal logical relationships; while English depends on sentence structure, so sentences are often closely linked by various language forms. In order to improve the accuracy of machine translation, we can adjust the sentence structure and adjust the position of adjectives and their modifiers. <br />
Example 2<br />
Source abstracts:回顾性分析南京医科大学附属儿童医院中重度HIE患儿49例及同期就诊的无神经系统症状体征的足月新生儿为对照组31例的头颅磁共振成像（MRI）资料。<br />
Google translation: A retrospective analysis that the brain magnetic resonance imaging (MRI) data of 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University and full-term neonates with no neurological symptoms and signs during the same period were included in the control group.<br />
Published translation: A total of 49 children with moderate to severe HIE admitted to the children’s Hospital Affiliated to Nanjing Medical University were retrospectively analyzed. Cranial magnetic resonance imaging (MRI) date of 31 full-term neonates without neurological symptoms and signs who visited the hospital during the same period were recruited as the control group. <br />
Analysis: As the above example shows that “中重度HIE患儿” and “足月新生儿” in the source abstracts are from the Children’s Hospital Affiliated Nanjing Medical University. The Google translation shows that 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University. There is an error due to the misuse of subordination. There are some advice in order to solve the problem. That is, first to segment the sentence and then reconstruct the sentence. For instance, the Chinese expression “南京医科大学附属儿童医院” carries many modifiers, which requires to cut the modifiers and reconstruct the sentence. Therefore, the Chinese expression “南京医科大学附属儿童医院’can be divided into abstractions, leaving two sentences instead of one long sentence with modifiers..<br />
After pre-editing source abstracts: 对49例中重度HIE患儿以及31例无神经系统症状体征的足月新生儿的MRI资料进行回顾性分析。这些患者收治于南京医科大学儿童附属医院。<br />
After pre-editing translation: The MRI data of 49 children with moderate to severe HIE and 31 full-term newborns without neurological symptoms and signs were retrospectively analyzed. These patients were admitted to the Children’s Hospital of Nanjing Medical University.<br />
===3.3 Explication of Subject through Voice Changing===<br />
The extensive use of subjectless sentences are quite common in the Chinese abstracts, while English prefers to employ passive voice in the sentences so as to make them object and accurate. In order to take the characteristics of English sentences into great and careful consideration, the sentences written in passive voice in particular, it will be helpful for machine translation to find out the subject and reconstruct a sentence in passive voice (Wang Yan: 2008). The first is to discover the “doee” in a sentence and then is to reconstruct the sentence structure and put the “doee” before the verb. The last is to explicate the passive relation between the noun and the verb.<br />
Example 3<br />
Source abstract: 回顾性分析2018年1月至2020年12月解放军总医院第七医学中心收治的500例老年髋部骨折患者的资料。<br />
Google translation: A retrospective analysis of the data of 500 elderly patients with hip fractures admitted to the Seventh Medical Center of the PLA General Hospital from January 2018 to December 2020.<br />
Published translation: From January 2018 to December 2020, the data of 500 elderly patients with hip fracture treated in the Seventh Medical Center of PLA General Hospital were analyzed retrospectively.<br />
Analysis: The above example indicates that the “回顾性分析”in the Google Translate is a noun phrase, but the source abstracts actually describes an action or a behavior. The reason for such a mistake is that the machine can’t recognize the Chinese subjetless sentences. To find the subject of the sentence, the source abstracts can be revised and adjusted. Finding the “doee” is the first step, which refers to “500例老年髋部骨折患者的资料”in the source abstracts. And next is to put it in front of the verb, that is to place it in the beginning of the sentence. Last but not least, the passive voice should be explicated in the sentence. <br />
After pre-editing source abstract: 500例老年髋部骨折患者的资料被回顾性分析，他们在2018年1月之2020年12月期间收治于解放军总医院第七医学中心。<br />
After pre-editing translation: The data of 500 elderly hip fracture patients were retrospectively analyzed. They were admitted to the Seventh Medical Center of the PLA General Hospital between January 2018 and December 2020.<br />
===3.4 Relocation of Modifiers===<br />
Modifiers, including noun, adverbial and attributive are constantly employed in the Chinese medical papers in order to add information to subject and object in the sentence. By doing this, complicated sentences can be structured, thus causing obstacles to machine translation for recognizing this complex sentence structures when translating Chinese-English sentences. To make the machine translation successfully recognize the sentence structure, simplifying the sentence structure is quite necessary. The key is to moving the location of the modifiers and thus making the modifiers an independent sentence. In other words, the modifiers ought to be put before or behind the main part of the sentence to satisfy the common use of English. <br />
Usually, the modifiers in Chinese sentence can only be placed in front of the core word, while in English, modifiers are very flexible. It is all right to place the modifiers in front of or behind the major part of the sentences with adjective or a connective noun.<br />
Example 4<br />
Source abstracts: 利用DNA重组技术以pET-28a表达系统在E.coli BL21(DE3) 中重组表达Hepl。<br />
Google Translation: Hepl is recombined in E.coli BL21 (DE3) using DNA recombination technology with pET-28a expression system.<br />
Published translation: The recombinant Hcpl protein was expressed by using DNA recombination technology through pET-28a expression system in E. coli BL21 (De3).<br />
Analysis: The Chinese medical text includes two modifiers, “利用DNA”and “以pET-28a)表达系统”. These two modifiers will be translated by machine, which catches more attention to the two constituents. From the Google translation above, one failure is obvious that it misplaces the location of the two modifiers and presents it in not accurate form. To make it translate correctly by machine translation, dividing the sentences is very important so that the source abstracts can be correctly recognized by machine translation.<br />
After pre-editing source abstract: 利用DNA重组技术，Hcp1重组表达在E.coli BL21 (DE3)中， 通过pET-28a表达系统在E. coli BL21 (DE3)中。<br />
Google translation: Using DNA recombination technology, Hcp1 recombination is expressed in E.coli BL21 (DE3), via pET-28a expression system in E. coli BL21 (DE3).<br />
The second is the independence of modifiers, that is, modifiers can also be reconstructed into clauses to modify core words. Compared with English, There is no subordinate clause in Chinese, so redundant Chinese modifiers need to be reconstructed into subordinate clauses in Chinese-English translation to meet the characteristics of English. English, especially sentences with multiple modifiers. Otherwise, sentence structure may be confused, such as scattered modifiers of core words. To avoid this mistake, it is necessary to separate the modifier from the main part of the sentence. These modifiers should be reconstituted into clauses. Also, keep your sentences simple and easy to understand.<br />
===3.5 Proper Omission and Deletion of Category Words===<br />
Category words are commonly used in Chinese. Category words complement the meaning of words, including problems, positions, situations and jobs. Adding this supplementary word is more in line with Chinese custom. In many cases, it has no real meaning, so it can be omitted in translation. In Chinese, category words are frequently used. However, it is rarely used in English, which is one of the differences between Chinese and English. There are many kinds of category words. Considering objectivity and the fixed structure of language, redundant category words should be deleted in Chinese-English translation. In the general, the most commonly used category of words in the Chinese medical abstracts are "process", "behavior", and "situation". These categories of words hinder the language conversion of Chinese and English, resulting in redundancy. <br />
Example 5<br />
Source abstract: 探讨口腔白斑病癌变进程中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia response genes and associated microRNAs in the process of oral leukoplakia cancer was discussed.<br />
Published translation: To study the hypoxia response gene and microRNA (miRNA)expression profiles in the pathogenesis and progression of oral leukoplakia (OLK).<br />
Analysis: As the above example shows, the Chinese words “进程” belongs to a category word. However, the Chinese expression “癌变”contains the process, so the “进程” expression can be deleted before placing it into the machine translation, because the meaning of it has been overlapping between the expression “癌变”. Therefore, its place can be replaced with preposition “during”.<br />
After pre-editing source abstract: 探讨口腔白斑病癌变中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia responsive genes and miRNA in oral leukoplakia cancer was investigated.<br />
<br />
===Conclusion===<br />
After years of development, machine translation has made great progress. The accuracy of machine translation has been greatly improved in both text recognition and sentence pattern conversion. However, machine translation has its own limitations. In other words, it needs to rely on the parallel corpus as a reference source for improving its accuracy. ESP text, in particular, is harder to get the high quality by machine translation. <br />
As one of the research papers, the characteristics of medical abstracts are fixed language structures, objectivity and accuracy (Qin Yi:2004). Therefore, medical translation must be accurate, object and understandable to follow the specific demands of the medical paper. Being an important field in the human society, medical paper translation is on a great demand, which means that it needs a huge demand for human labor. However, with the machine translation promoting, it will be more efficient to translate medical papers combining the effort by human and machine. The improvement and development of machine translation requires the joint efforts of computer science, information science, statistics, linguistics and other academic circles to achieve more mature human-computer mutual assistance translation (Li Yafei, Zhang Ruihua : 2019).<br />
However, errors can occur during the process of machine translation of Chinese- English, because of the differences of the Chinese and English and the processing of the machine. Errors from the perspective of linguistic or grammar can affect the machine translation a lot. After division and recognition of errors, some pre-editing approaches are put forward to help the machine translation more accurate and readable, that are, extraction and replacement of terms in the source text, relocation of modifiers, explication of subordination, proper omission, deletion of category words and explication of subject through voice changing. <br />
The paper mainly focuses on the pre-editing machine translation by using medical papers as a case study. The errors of machine translation occurring in the translation of medical abstracts and pre-editing approaches for machine translation. The quality of machine translation of medical papers is greatly improved after employing the pre-editing methods. However, machine translation is not as flexible and accurate as human brain, so it is of importance to combine pre-editing and post-editing approaches with machine translation in order to produce more accurate, more object machine translation of medical papers.<br />
<br />
===References===<br />
[1]. Cronin, Michael (2013). Translation in the Digital Age[M]. New York&London: Routledge.<br />
[2]. GERLACH J, et al ( 2013). Combining Pre-editing and Post-editing to Improve SMT of User-generated Content[M]// Proceedings of MT Summit XIV Workshop on Post-editing Technology and Practice. 45-53<br />
[3]. Slype G V & Guinet J F & Seitz F (1984). Better Translation for Better Communication [M] .Oxford: Pergamon Press Ltd (U.K.), <br />
[4]. O'Brien S (2002). Teaching Post-editing: A Proposal for Course Con-tent [EB/OL]. http://mt-archive. Info/EAMT-2002-0brien. Pdf.<br />
[5]. Tytler, A. F. (1978). Essay On The Principles of Translation[M]. Amsterdam: JohnBenjamins Publishing.<br />
[6] 崔启亮. (2014), 论机器翻译的译后编辑[J], 中国翻译, 035(006):68-73<br />
[7] 冯全功,高琳 (2017) 基于受控语言的译前编辑对机器翻译的影响[J]. 当代外语研究,(2): 63-68+87+110.<br />
[8] 胡清平(2005). 机器翻译中的受控语言[J]. 中国科技翻译, (03): 24-27. <br />
[9] 连淑能 (2010). 英汉对比研究增订本[M]. 北京:高等教育出版社.<br />
[10] 黎亚飞,张瑞华 (2019). 机器翻译发展与现状[J]. 中国轻工教育,(5):38-45. <br />
[11] 秦毅(2004),从翻译基本标准议医学英语的翻译[J]. 遵义医学院学报,27 (4): 421-423. <br />
[12] 王燕 (2008). 医学英语翻译与写作教程[M]. 重庆:重庆大学出版社<br />
<br />
=12 蔡珠凤=(The Mistranslation of C-J Machine Translation of Political Statements)=<br />
[[Machine_Trans_EN_12]]<br />
===Abstract===<br />
Language is the main way of communication between people. With the continuous development of globalization, the scale of cross-border exchanges is also expanding. However, due to cultural differences and diversity, the languages of different countries and regions are very different, which seriously hinders people's communication. The demand for efficient and convenient translation tools is increasing. At the same time, with the development of network technology and artificial intelligence, recognition technology based on deep learning is more and more widely used in English, Japanese and other fields.<br />
<br />
===Key words===<br />
machine translation; political statements; mistranslation of C-J machine translation<br />
===题目===<br />
The Mistranslation of C-J Machine Translation of Political Statements<br />
===摘要===<br />
语言是人与人之间交流的主要方式。随着全球化的不断发展，跨境交流的规模也在不断扩大。然而，由于文化的差异和多样性，不同国家和地区的语言差异很大，这严重阻碍了人们的交流。对高效便捷的翻译工具的需求正在增加。同时，随着网络技术和人工智能的发展，基于深度学习的识别技术在英语、日语等领域的应用越来越广泛。<br />
<br />
===关键词===<br />
机器翻译；政治发言；政治发言中译日的误译<br />
===1. Introduction===<br />
===Introduction to machine translation===<br />
Machine translation, also known as automatic translation, is a process of using computers to convert one natural language (source language) into another natural language (target language). It is a branch of computational linguistics, one of the ultimate goals of artificial intelligence, and has important scientific research value.At the same time, machine translation has important practical value. With the rapid development of economic globalization and the Internet, machine translation technology plays a more and more important role in promoting political, economic and cultural exchanges.The development of machine translation technology has been closely accompanied by the development of computer technology, information theory, linguistics and other disciplines. From the early dictionary matching, to the rule translation of dictionaries combined with linguistic expert knowledge, and then to the statistical machine translation based on corpus, with the improvement of computer computing power and the explosive growth of multilingual information, machine translation technology gradually stepped out of the ivory tower and began to provide real-time and convenient translation services for general users.<br />
<br />
=== C-J machine translation software===<br />
Today's online machine translation software includes Baidu translation, Tencent translation, Google translation, Youdao translation, Bing translation and so on. Google was the first company to launch the machine translation system, and Baidu was the first company to import the machine translation system in China. In addition, Tencent and Youdao have attracted much attention.Machine translation is the process of using computers to convert one natural language into another. It usually refers to sentence and full-text translation between natural languages. In order to continuously improve the translation quality, R & D personnel have added artificial intelligence technologies such as speech recognition, image processing and deep neural network to machine translation on the basis of traditional machine translation based on rules, statistics and examples.With the increase of using machine translation, the joint cooperation between manual translation and machine translation will also increase significantly in the future. What criteria should be used to evaluate the quality of machine translation? In the evolving field of machine translation, there is an urgent need to clarify the unsolvable questions and solved problems.<br />
<br />
===The history of machine translation===<br />
The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. alchuni put forward the idea of using machines for translation. In 1933, Soviet inventor П.П. Trojansky designed a machine to translate one language into another, and registered his invention on September 5 of the same year; However, due to the low technical level in the 1930s, his translation machine was not made. In 1946, the first modern electronic computer ENIAC was born. Shortly after that, W. weaver, an American scientist and A. D. booth, a British engineer, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947 when discussing the application scope of electronic computer. In 1949, W. Weaver published the translation memorandum, which formally put forward the idea of machine translation. After 60 years of ups and downs, machine translation has experienced a tortuous and long development path. The academic community generally divides it into the following four stages:<br />
Pioneering period（1947-1964）<br />
In 1954, with the cooperation of IBM, Georgetown University completed the English Russian machine translation experiment with ibm-701 computer for the first time, showing the feasibility of machine translation to the public and the scientific community, thus opening the prelude to the study of machine translation.<br />
It is not too late for China to start this research. As early as 1956, the state included this research in the national scientific work development plan. The topic name is "machine translation, the construction of natural language translation rules and the mathematical theory of natural language". In 1957, the Institute of language and the Institute of computing technology of the Chinese Academy of Sciences cooperated in the Russian Chinese machine translation experiment, translating 9 different types of more complex sentences.<br />
From the 1950s to the first half of the 1960s, machine translation research has been on the rise. The United States and the former Soviet Union, two superpowers, have provided a lot of financial support for machine translation projects for military, political and economic purposes, while European countries have also paid considerable attention to machine translation research due to geopolitical and economic needs, and machine translation has become an upsurge for a time. In this period, although machine translation is just in the pioneering stage, it has entered an optimistic period of prosperity.<br />
Frustrated period（1964-1975）<br />
In 1964, in order to evaluate the research progress of machine translation, the American Academy of Sciences established the automatic language processing Advisory Committee (Alpac Committee) and began a two-year comprehensive investigation, analysis and test.<br />
In November 1966, the committee published a report entitled "language and machine" (Alpac report for short), which comprehensively denied the feasibility of machine translation and suggested stopping the financial support for machine translation projects. The publication of this report has dealt a blow to the booming machine translation, and the research of machine translation has fallen into a standstill. Coincidentally, during this period, China broke out the "ten-year Cultural Revolution", and basically these studies also stagnated. Machine translation has entered a depression.<br />
convalescence（1975-1989）<br />
Since the 1970s, with the development of science and technology and the increasingly frequent exchange of scientific and technological information among countries, the language barriers between countries have become more serious. The traditional manual operation mode has been far from meeting the needs, and there is an urgent need for computers to engage in translation. At the same time, the development of computer science and linguistics, especially the substantial improvement of computer hardware technology and the application of artificial intelligence in natural language processing, have promoted the recovery of machine translation research from the technical level. Machine translation projects have begun to develop again, and various practical and experimental systems have been launched successively, such as weinder system Eurpotra multilingual translation system, taum-meteo system, etc.<br />
However, after the end of the "ten-year holocaust", China has perked up again, and machine translation research has been put on the agenda again. The "784" project has paid enough attention to machine translation research. After the mid-1980s, the development of machine translation research in China has further accelerated. Firstly, two English Chinese machine translation systems, ky-1 and MT / ec863, have been successfully developed, indicating that China has made great progress in machine translation technology.<br />
New period(1990 present)<br />
With the universal application of the Internet, the acceleration of the process of world economic integration and the increasingly frequent exchanges in the international community, the traditional way of manual operation is far from meeting the rapidly growing needs of translation. People's demand for machine translation has increased unprecedentedly, and machine translation has ushered in a new development opportunity. International conferences on machine translation research have been held frequently, and China has made unprecedented achievements. A series of machine translation software have been launched, such as "Yixing", "Yaxin", "Tongyi", "Huajian", etc. Driven by the market demand, the commercial machine translation system has entered the practical stage, entered the market and came to the users.<br />
Since the new century, with the emergence and popularization of the Internet, the amount of data has increased sharply, and statistical methods have been fully applied. Internet companies have set up machine translation research groups and developed machine translation systems based on Internet big data, so as to make machine translation really practical, such as "Baidu translation", "Google translation", etc. In recent years, with the progress of in-depth learning, machine translation technology has further developed, which has promoted the rapid improvement of translation quality, and the translation in oral and other fields is more authentic and fluent.<br />
<br />
===The problem of machine translation at present===<br />
Error is inevitable<br />
Many people have misunderstandings about machine translation. They think that machine translation has great deviation and can't help people solve any problems. In fact, the error is inevitable. The reason is that machine translation uses linguistic principles. The machine automatically recognizes grammar, calls the stored thesaurus and automatically performs corresponding translation. However, errors are inevitable due to changes or irregularities in grammar, morphology and syntax, such as sentences with adverbials after "give me a reason to kill you first" in Dahua journey to the West. After all, a machine is a machine. No one has special feelings for language. How can it feel the lasting charm of "the tenderness of lowering its head, like the shame of a water lotus? After all, the meaning of Chinese is very different due to the changes of morphology, grammar and syntax and the change of context. Even many Chinese people are zhanger monks - they can't touch their heads, let alone machines.<br />
Bottleneck<br />
In fact, no matter which method, the biggest factor affecting the development of machine translation lies in the quality of translation. Judging from the achievements, the quality of machine translation is still far from the ultimate goal.<br />
Chinese mathematician and linguist Zhou Haizhong once pointed out in his paper "fifty years of machine translation": to improve the quality of machine translation, the first thing to solve is the problem of language itself rather than programming; It is certainly impossible to improve the quality of machine translation by relying on several programs alone. At the same time, he also pointed out that it is impossible for machine translation to achieve the degree of "faithfulness, expressiveness and elegance" when human beings have not yet understood how the brain performs fuzzy recognition and logical judgment of language. This view may reveal the bottleneck restricting the quality of translation. <br />
It is worth mentioning that American inventor and futurist ray cozwell predicted in an interview with Huffington Post that the quality of machine translation will reach the level of human translation by 2029. There are still many disputes about this thesis in the academic circles.<br />
<br />
===2.Mistranslation of Chinese Japanese machine translation===<br />
===2.1Vocabulary mistranslation in Chinese Japanese machine translation===<br />
===2.1.1Mistranslation of proper nouns===<br />
In political materials, there are often political dignitaries' names, place names or a large number of proper nouns in the political field. The morphemes of such words are definite and inseparable. Mistranslation will make the source language lose its specific meaning. <br />
<br />
Chinese translation into Japanese Japanese translation into Chinese<br />
<br />
original text translation by Youdao reference translation original text translation by Youdao reference translation<br />
栗战书 栗戰史書 栗戰書 労安 劳安 劳安<br />
李克强 李克強 李克強 朱鎔基 朱基 朱镕基<br />
习近平 習近平 習近平 筑紫哲也 筑紫哲也 筑紫哲也<br />
韩正 韓中 韓正 山口百惠 山口百惠 山口百惠<br />
王沪宁 王上海氏 王滬寧 田中角栄 田中角荣 田中角荣<br />
汪洋 汪洋 汪洋 東条英機 东条英社 东条英机<br />
赵乐际 趙樂南 趙樂際 毛沢东 毛泽东 毛泽东<br />
江泽民 江沢民 江沢民 トウ・ショウヘイ 大酱 邓小平<br />
周恩来 周恩来 周恩来<br />
クリントン 克林顿 克林顿<br />
<br />
The above table counts 18 special names in the two texts, and 7 machine translation errors. In terms of mistranslation, there are not only "战书" but also "戰史" and "史書" in Chinese. "沪" is the abbreviation of Shanghai. In other words, since "战书" and "沪" are originally common nouns, this disrupts the choice of target language in machine translation. Except Katakana interference (except for トウシゃウヘイ, most of the mistranslations appear in the new Standing Committee. It can be seen that the machine translation system does not update the thesaurus in time. Different from other words, people's names have particularity. Especially as members of the Standing Committee of the Political Bureau of the CPC Central Committee, the translation of their names is officially unified. In this regard, public figures such as political dignitaries, stars, famous hosts and important. In addition, the machine also translates Japanese surnames such as "タ二モト" (谷本), "アンドウ" (安藤) into "塔尼莫特" and "龙胆". It can be found that the language feature that Japanese will be written together with Chinese characters and Hiragana also directly affects the translation quality of Japanese Chinese machine translation. Japanese people often use Katakana pronunciation. For example, when Japanese people talk with Premier Zhu, they often use "二ーハウ" (Hello). However, the machine only recognizes the two pseudonyms "二" and "ハ" and transliterates them into "尼哈" , ignoring the long sound after "二" and "ハ".<br />
<br />
original text Translation by Youdao reference translation<br />
日美安全体制 日米の安全体制 日米安保体制<br />
中国共产党第十九次全国代表大会 中国共産党第19回全国代表大会 中国共産党第19回全国代表大会（第19回党大会）<br />
十八大 十八大 第18回党大会<br />
中国特色社会主义 中国特色社会主義 中国の特色ある社会主義<br />
中国共产党中央委员会 中国共産党中央委員会 中国共産党中央委員会<br />
十八届中共中央政治局常委 第18代中国共產党中央政治局常務委員 第18期中共中央政治局常務委員<br />
十八届中共中央政治局委员 18期の中国共產党中央政治局委員 第18期中共中央政治局委員<br />
十九届中共中央政治局常委 十九回中国共產党中央政治局常務委員 第19期中央政治局常務委員<br />
中共十九届一中全会 中国共產党第十九回一中央委員会 第19期中央委員会第1回全体会議<br />
The above table is a comparison of the original and translated versions of some proper nouns. As shown in the table, the mistranslation problems are mainly reflected in the mismatch of numerals + quantifiers, the wrong addition of case auxiliary word "の", the lack of connectors, the Mistranslation of abbreviations, dead translation, and the writing errors of Chinese characters. The following is a specific analysis one by one.<br />
"十八届中共中央政治局常委", "十八届中共中央政治局委员", "十九届中共中央政治局常委" and "中共十九届一中全会" all have quantifiers "届", which are translated into "代", "期" and "回" respectively. The meaning is vague and should be uniformly translated into "期"; Among them, the translation of the last three proper nouns lacks the Chinese conjunction "第". The case auxiliary word "の" was added by mistake in the translation of "十八届中共中央政治局委员" and "日美安全体制". The "中国共产党中央委员会" did not write in the form of Japanese Ming Dynasty characters, but directly used simplified Chinese characters.<br />
The full name of "中共十九届一中全会" is "中国共产党第十九届中央委员会第一次全体会议", in which "一" stands for "第一次", which is an ordinal word rather than a cardinal word. Machine translation does not produce a correct translation. The fundamental reason is that there are no "规则" for translating such words in machine translation. If we can formulate corresponding rules for such words (for example, 中共m'1届m2 中全会→第m1期中央委员会第m2回全体会议), the translation system must be able to translate well no matter how many plenary sessions of the CPC Central Committee.<br />
<br />
===2.1.2Mistranslation of Polysemy===<br />
<br />
original text Translation by Youdao reference translation<br />
スタジオ 摄影棚/工作室 直播现场/演播厅<br />
日中関係の話 中日关系的故事 就中日关系(话题)<br />
溝 水沟 鸿沟<br />
それでは日中の問題について質問のある方。 那么对白天的问题有提问的人。 关于中日问题的话题，举手提问。<br />
私たちのクラスは20人ちょっとですが、 我们班有20人左右， 我们班二十多人的意见统一很难，<br />
いろいろな意見が出て、まとめるのは大変です。 但是又各种各样的意见，总结起来很困难。 中国是怎样把13亿人凝聚在一起的？<br />
一体どうやって、13億人もの人をまとめているんですか。 到底是怎么处理13亿的人的呢？ <br />
In the original text, the word "スタジオ" appeared four times and was translated into "摄影棚" or "工作室" respectively, but the context is on-site interview, not the production site of photography or film. "話" has many semantics, such as "说话", "事情", "道理", etc. In accordance with the practice of the interview program, Premier Zhu Rongji was invited to answer five questions on the topic of China Japan relations. Obviously, the meaning of the word "故事" is very abrupt. The inherent Japanese word "日中" means "晌午、白天". At the same time, it is also the abbreviation of the names of the two countries. The machine failed to deal with it correctly according to the context. "溝" refers to the gap between China and Japan, not a "水沟". The former "まとめる" appears in the original text with the word "意见", which is intended to describe that it is difficult to unify everyone's opinions. The latter "まとめている" also refers to unifying the thoughts of 1.3 billion people, not "处理". Therefore, it is more appropriate to use "统一" and "处理" in human translation, rather than "总结意见" or "处理意见".<br />
Mistranslation of polysemy has always been a difficult problem in machine translation research. The translation of each word is correct, but it is often very different from the original expression in the context. Zhang Zhengzeng said that ambiguity is a common phenomenon in natural language. Its essence is that the same language form may have different meanings, which is also one of the differences between natural language and artificial language. Therefore, one of the difficulties faced by machine translation is language disambiguation (Zhang Zheng, 2005:60). In this regard, we can mark all the meanings of polysemous words and judge which meaning to choose by common collocation with other words. At the same time, strengthen the text recognition ability of the machine to avoid the translation inconsistent with the current context. In this way, we can avoid the mistake of "大酱" in the place where famous figures such as Deng Xiaoping should have appeared in the previous political articles.<br />
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===2.1.3Mistranslation of compound words===<br />
Multi class words refer to a word with two or more parts of speech, also known as the same word and different classes.<br />
original text Translation by Youdao reference translation<br />
1998年江泽民主席曾经访问日本, 1998年の江沢民国家主席の日本訪問し、 1998年、江沢民総書記が日本を訪問し、かつ、<br />
同已故小渊首相签署了联合宣言。 かつて同じ故小渕首相が署名した共同宣言 亡くなられた小渕総理と宣言に調印されました<br />
<br />
Chinese "同" has two parts of speech: prepositions and conjunctions: "故" has many parts of speech, such as nouns, verbs, adjectives and conjunctions. This directly affects the judgment of the machine at the source language level. The word "同" in the above table is used as a conjunction to indicate the other party of a common act. "故" is used as a verb with the semantic meaning of "死亡", which is a modifier of "小渊首相". The machine regards "同" as an adjective and "故" as a noun "原因", which leads to confusion in the structure and unclear semantics of the translation. Different from the polysemy problem, multi category words have at least two parts of speech, and there is often not only one meaning under each part of speech. In this regard, software R & D personnel should fully consider the existence of multi category words, so that the translation machine can distinguish the meaning of words on the basis of marking the part of speech, so as to select the translation through the context and the components of the word in the sentence. Of course, the realization of this function is difficult, and we need to give full play to the wisdom of R & D personnel.<br />
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===2.2 Syntactic mistranslation in Chinese Japanese machine translation===<br />
===2.2.1Mistranslation of tenses===<br />
original text：History is written by the people, and all achievements are attributed to the people. <br />
Translation by Youdao：歴史は人民が書いたものであり、すべての成果は人民のためである。<br />
reference translation：歴史は人民が綴っていくものであり、すべての成果は人民に帰することとなります。<br />
The original sentence meaning is "历史是人民书写的历史" or "历史是人民书写的东西". When translated into Japanese, due to the influence of Japanese language habits, the formal noun "もの" should be supplemented accordingly. In this sentence, both the machine and the interpreter have translated correctly. However, the machine's recognition of "的" is biased, resulting in tense translation errors. The past, present and future will become "history", and this is a continuous action. The form translated as "~ ていく" not only reflects that the action is a continuous action, but also conforms to the tense and semantic information of the original text. In addition, the machine's treatment of the preposition "于" is also inappropriate. In the original text, "人民" is the recipient of action, not the target language. As the most obvious feature of isolated language, function words in Chinese play an important role in semantic expression, and their translation should be regarded as a focus of machine translation research.<br />
<br />
original text：李克强同志是十六届中共中央政治局常委,其他五位同志都是十六届中共中央政治局委员。<br />
Translation by Youdao：李克強総理は第16代中国共産党中央政治局常務委員であり、他の５人の同志はいずれも16期の中国共産党中央政治局委員である。<br />
reference translation：李克強同志は第16期中国共産党中央政治局常務委員を務め、他の５人は第16期中共中央政治局委員を務めました。<br />
Judging the verb "是" in the original text plays its most basic positive role, but due to the complexity of Chinese language, "是" often can not be completely transformed into the form of "だ / である" in the process of translation. This sentence is a judgment of what has happened. Compared with the 19th session, the 18th session has become history. This is an implicit temporal information. It is very difficult for machines without human brain to recognize this implicit information. "中共中央政治局常委" is the abbreviation of "中共中央政治局常务委员会委员" and it is a kind of position. In Japanese, often use it with verbs such as "務める","担当する",etc. The translator adopts the past tense of "務める", which conforms to the expression habit of Japanese and deals with the problem of tense at the same time. However, machine translation only mechanically translates this sentence into judgment sentence, and fails to correctly deal with the past information implied by the word "十八届".<br />
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===2.2.2Mistranslation of honorifics===<br />
Honorific language is a language means to show respect to the listener. Different from Japanese, there is no grammatical category of honorifics in Chinese. There is no specific fixed grammatical form to express honorifics, self modesty and politeness. Instead, specific words such as "您", "请" and "劳驾" are used to express all kinds of respect or self modesty. <br />
Original text translation by Youdao reference translation<br />
女士们，先生们，同志们，朋友们 さんたち、先生たち、同志たち、お友达さん ご列席の皆さん<br />
谢谢大家！ ありがとうございます！ ご清聴ありがとうございました。<br />
これはどうされますか。 这是怎么回事呢? 您将如何解决这一问题？<br />
こうした問題をどうお考えでしょうか。 我们会如何考虑这些问题呢？ 您如何看待这一问题？ <br />
For example, for the processing of "女士们，先生们，同志们，朋友们", the machine makes the translation correspond to the original one by one based on the principle of unchanged format, but there are errors in semantic communication and pragmatic habits. When speaking on formal occasions, Chinese expression tends to be comprehensive and detailed, as well as the address of the audience. In contrast, Japanese usually uses general terms such as "皆様", "ご臨席の皆さん" or "代表団の方々" as the opening remarks. In terms of Japanese Chinese translation, the machine also failed to recognize the usage of Japanese honorifics such as "さ れ る" and "お考え". It can be seen that Chinese Japanese machine translation has a low ability to deal with honorific expressions. The occasion is more formal. A good translation should not only be fluent in meaning, but also conform to the expression habits of the target language and match the current translation environment.<br />
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===Conclusion===<br />
In the process of discussing the Mistranslation of machine translation, this paper mainly cites the Mistranslation of Youdao translator. When I studied the problem of machine translation mistranslation, I also used a large number of translation software such as Google and Baidu for parallel comparison, and found that these translation software also had similar problems with Youdao translator. For example, the "新世界新未来" in Chinese ABAC structural phrases is translated by Baidu as "新し世界の新しい未来", which, like Youdao, is not translated in the form of parallel phrases; Google online translation translates the word "“全心全意" into a completely wrong "全面的に"; The original sentence "我吃了很多亏" in the Mistranslation of the unique expression in the language is translated by Google online as "私はたくさんの損失を食べました". Because the "吃" and "亏" in the original text are not closely adjacent, the machine can not recognize this echo and mistakenly treats "亏" as a kind of food, so the machine translates the predicate as "食べました"; Japanese "こうした問題をどう考えでしょうか", Google's online translation is "你如何看待这些问题?", "你" tone can not reflect the tone of Japanese honorific, while Baidu translates as "你怎么想这样的问题呢?" Although the meaning can be understood, it is an irregular spoken language. Google and Baidu also made the same mistakes as Youdao in the translation of proper nouns. For example, they translated "十七届(中共中央政治局常委)”" into "第17回..." .In short, similar mistranslations of Youdao are also common in Baidu and Google. Due to space constraints, they will not be listed one by one here. <br />
Mr. Liu Yongquan, Institute of language, Chinese Academy of Social Sciences (1997) It has been pointed out that machine translation is a linguistic problem in the final analysis. Although corpus based machine translation does not require a lot of linguistic knowledge, language expression is ever-changing. Machine translation based solely on statistical ideas can not avoid the translation quality problems caused by the lack of language rules. The following is based on the analysis of lexical, syntactic and other mistranslations From the perspective of the characteristics of the source language, this paper summarizes some difficulties of Chinese and Japanese in machine translation. <br />
(1) The difficulties of Chinese in machine translation <br />
Chinese is a typical isolated language. The relationship between words needs to be reflected by word order and function words. We should pay attention to the transformation of function words such as "的", "在", "向" and "了". Some special verbs, such as "是", "做" and "作", are widely used. How to translate on the basis of conforming to Japanese pragmatic habits and expressions is a difficulty in machine translation research. In terms of word order, Chinese is basically "subject→predicate→object". At the same time, Chinese pays attention to parataxis, and there is no need to be clear in expression with meaningful cohesion, which increases the difficulty of Chinese Japanese machine translation. When there are multiple verbs or modifier components in complex long sentences, machine translation usually can not accurately divide the components of the sentence, resulting in the result that the translation is completely unreadable. <br />
(2) Difficulties of Japanese in machine translation <br />
Both Chinese and Japanese languages use Chinese characters, and most machines produce the target language through the corresponding translation of Chinese characters. However, pseudonyms in Japanese play an important role in judging the part of speech and meaning, and can not only recognize Chinese characters and judge the structure and semantics of sentences. Japanese vocabulary is composed of Chinese vocabulary, inherent vocabulary and foreign vocabulary. Among them, the first two have a great impact on Chinese Japanese machine translation. For example "提出" is translated as "提出する" or "打ち出す"; and "重视" is translated as "重視する" or "大切にする". Japanese is an adhesive language, which contains a lot of "けど", "が" and "けれども" Some of them are transitional and progressive structures, but there are also some sequential expressions that do not need translation, and these translation software often can not accurately grasp them.<br />
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===References===<br />
[1] Navroz Kaur Kahlon;Williamjeet Singh.Machine translation from text to sign language: a systematic review[J].Universal Access in the Information Society,2021(prepublish):1-35.<br />
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[2] Cao Qianyu;Hao Hanmei;Ahmed Syed Hassan.A Chaotic Neural Network Model for English Machine Translation Based on Big Data Analysis[J].Computational Intelligence and Neuroscience,2021,2021:3274326-3274326.<br />
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[3]Hwang Yongkeun;Kim Yanghoon;Jung Kyomin.Context-Aware Neural Machine Translation for Korean Honorific Expressions[J].Electronics,2021,10(13):1589-1589.<br />
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[4]Zakaryia Almahasees.Analysing English-Arabic Machine Translation:Google Translate, Microsoft Translator and Sakhr,2021.<br />
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[5]Machine learning in translation[J].Nature Biomedical Engineering,2021,5(6):485-486.<br />
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[6]Shaimaa Marzouk.An in-depth analysis of the individual impact of controlled language rules on machine translation output: a mixed-methods approach[J].Machine Translation,2021(prepublish):1-37.<br />
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[7]Welnitzová Katarína;Munková Daša.Sentence-structure errors of machine translation into Slovak[J].Topics in Linguistics,2021,22(1):78-92.<br />
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[8]Xu Xueyuan.Machine learning-based prediction of urban soil environment and corpus translation teaching[J].Arabian Journal of Geosciences,2021,14(11). <br />
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[9]陈丙昌.機械翻訳の誤訳分析【D】.贵州大学.2016(05) <br />
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[10]呂寅秋.機械翻訳の言語規則と伝統文法との相違点.日本学研究.1996(00):21-22 <br />
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[11]刘君.基于语料库的中日同形词词义用法对比及其日中机器翻译研究【D】.广西大学.2014(03) <br />
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[12]崔倩倩.机器翻译错误与译后编辑策略研究【D】.北京外国语大学.2019(09) <br />
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[13]张义.机器翻译的译文分析【D】.西安外国语大学.2019(10) <br />
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[14]张琳婧.在线机器翻译中日翻译错误原因及对策【D】.山西大学.2019(02)<br />
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[15]王丹.基于机器翻译的专利文本译后编辑对策研究【D】.大连理工大学.2020(06)<br />
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[16]杨晓琨.日中机器翻译中的前编辑规则与效果验证【D】.大连理工大学.2020(06)<br />
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[17]左嘉. 机器翻译日译汉误译研究[D]. 北京第二外国语学院, 2021.<br />
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[19]车彤.汉译日机器翻译质量评估及译后编辑策略研究【D】.北京外国语大学.2021(09)<br />
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Networking Linking<br />
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http://www.elecfans.com/rengongzhineng/692245.html<br />
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https://baike.baidu.com/item/%E6%9C%BA%E5%99%A8%E7%BF%BB%E8%AF%91/411793<br />
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=13 陈湘琼Chen Xiangqiong（Study on Post-editing from the Perspective of Functional Equivalence Theory ）=<br />
[[Machine_Trans_EN_13]]<br />
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=14 Bi bi Nadia（Machine Translation a Challenge for Human Translators)=<br />
[[Machine_Trans_EN_14]]<br />
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===Abstract===<br />
Machine translation is a big obstacle in the way of Human translators or interpretors although it is quick and less time consuming.people are trying to get translation of their target language using through source language.For this purpose they are using digital apps like Google translation Google translation does not give accurate and exact interpretation.Google translator is translates word to word translate that doesn't clarifies it's true and actual meaning.On the other hand human translators can give exact and accurate translation ,they take care of grammatical errors , diction and sentence structure.They clarify the purpose of target language through using source language.<br />
===Keywords===<br />
Descriptive translation, academic, interdiscipline, comparative literature, localization,Translatology,school of thought, translation , studies, linguistics, corresponding<br />
===Body of article===<br />
Translation is the process of reworking text from one language into another to maintain the original message and communication. But, like everything else, there are different methods of translation, and they vary in form and function. What is translation? The term has become widely used among knowledge transfer researchers and practitioners, especially in the fields of health and health care. In a landmark review, Jonathan Lomas began to argue that 'The tasks… may be defined as to establish and maintain links between researchers and their audience, via the appropriate translation of research findings' (Lomas 1997, p 4). In 2004, the World Health Organization's World Report on Knowledge for Better Health suggested that 'One of the key contributions of research to health systems is the translation of knowledge into actions' (WHO 2004, p 33 and p 100). By 2006, special issues of WHO's Bulletin as well as the journals Evaluation and the Health Professions and the Journal of Continuing Education in the Health Professions were dedicated to translation.But what does 'translation' mean? It may be a new word for an old problem, meaning nothing more than 'transfer'. The rapidly emerging field of 'translational medicine' seems to take translation to mean generally what transfer might have meant, that is the transmission of knowledge and evidence 'from bench to bedside'. As one commentator has observed, "Translational medicine" as a fashionable term is being increasingly used to describe the wish of biomedical researchers to ultimately help patients' (Wehling 2008, abstract). <br />
Semantic uncertainty persists, not least because of the different interests of scientists, clinicians, patients and commercial firms (Littman et al 2007): 'Translational research means different things to different people, but it seems important to almost everyone' (Woolf 2008, p 211).<br />
In related fields, such as public health (Armstrong et al 2006), 'translation' seems to signify dissatisfaction with 'transfer'. It wants to move away from thinking of knowledge transfer as a form of technology transfer or dissemination, rejecting if only by implication its mechanistic assumptions and its model of linear messaging from A to B. But still, what does it signify?<br />
<br />
===Why translation?===<br />
'Translation' indicates a closer attention to the problem of shared meaning and how it might be developed. It seems to represent some new epistemological lubricant, facilitating the dissemination of texts and the application and use of the knowledge and information they in. Simply, translation might be the key to transfer. And yet, when we stop to think, we are more ambivalent. What is translated often seems somehow inferior, not real or original. Note how readily commentators reach for the idea that things might be 'lost in translation'. Knowing at a distance – made in and mediated by translation - makes for incomplete renditions, blurred images, partial truths. So what might 'translation' really mean? The purpose of this paper is to set out, for policy makers and practitioners, the theoretical and conceptual resources translation holds and seems to represent. In doing so, it explores understandings of translation in the fields of literature and linguistics and in the sociology of science and technology. It begins by setting out just why this idea of translation should make immediate, intuitive sense in relation to research, policy and practice.<br />
1translation in research, policy and practice research as translation<br />
Research often entails translation from one language to another: where data is collected from more than one ethnic group, for example, or where the language of the researcher is other than that of the research subject. It may draw on a secondary literature or source documents written in different languages, and may be published and disseminated in languages other than the one in which it is first written up.<br />
2In a different way, to conduct an interview is to ask for an account of experience and its meanings, but it is also to construct and translate that experience in terms defined at least in part by the researcher. In representing what is said, transcripts then select data, usually excluding significant gesture and eye-contact, for example. Often, certain characteristics of speech-acts (such as hesitations) will be edited out. In turn, the format of the transcript shapes the analytic use the researcher may make of it. The basis of research 'findings', then, is an artefact, a transcript or translation, not an original interaction (Ochs 1979, Barnes, Bloor and Henry 1996, Ross 2009).<br />
3In this way, the researcher recasts aspects of his or her problem or topic in new, scientific form: 'All researchers "translate" the experiences of others' (Temple 1997, p 609). Research is invariably conducted in a sort of 'metalanguage' (Hantrais and Ager 1985): the research process can be conceived as one of successive translations (from theoretical formulation to operationalization, transcription, interpretation and dissemination). Theorization is a process of reciprocal back and forth between theory and fact, in which conceptions of each are revised in order that one fit the other (Baldamus 1974).<br />
4 It is a kind of translation: a rereading, re-use, re-application or re-representation of what we know in new terms (Turner 1980). Referencing, too, is an act of translation, a form of appropriation and incorporation of one text by another (Gilbert 1977).policy as translation Each of the fields covered by the paper is diverse and ill-defined, and there is no intention here to provide a comprehensive account of any them. Sources have been chosen for their relevance: main references are cited in the text, and additional sources listed in footnotes.<br />
<br />
2For a brief introduction to the technical issues involved in social research in more than one language, see Birbili (2000). For translation issues in survey research and question design in general, see Ervin and Bower (1952) and Deutscher (1968); on translating survey research instruments (in this instance health-related quality of life measures), Bowden and Fox-Rushby (2003). On the use of translators and interpreters, see Temple (1997) and Jentsch (1998).<br />
3For an interesting discussion of this problem, see Bourdieu (1999), esp pp 621-626, 'The risks of writing'. <br />
===Difference between machine translation and human translators===<br />
Few people disagree on the differences between the two, but many argue over the quality of the translations. How accurate are machine translations? How reliable are human translations? Some say machine translation produces near-perfect translations while others are adamant that translations are incomprehensible and cause more problems than they solve. Results will, of course, vary depending on the source and target languages, the machine translation service used (e.g. Google Translate), and the complexity of the original text.<br />
Machine translation, love it or hate it, is here to stay. In fact, the machine translation market is growing at such a fast pace that it is predicted to reach $980 million by 2022.<br />
===Machine translation: the pros and cons===<br />
The advantages of machine translation generally come down to two factors: it’s faster and cheaper. The downside to this is the standard of translation can be anywhere from inaccurate, to incomprehensible, and potentially dangerous (more on that shortly).<br />
==The advantages of machine translation==<br />
Many free tools are readily available (Google Translate, Skype Translator, etc.)<br />
Quick turnaround time <br />
You can translate between multiple languages using one tool<br />
Translation technology is constantly improving<br />
The disadvantages of machine translation<br />
Level of accuracy can be very low <br />
Accuracy is also very inconsistent across different languages<br />
==Machines can't translate context ==<br />
Mistakes are sometimes costly <br />
Sometimes translation simply doesn’t work<br />
The most important thing to consider with any kind of translation is the cost of potential mistakes. Translating instructions for medical equipment, aviation manuals, legal documents and many other kinds of content require 100% accuracy. In such cases, mistakes can cost lives, huge amounts of money and irreparable damage to your company’s image. So choose carefully!<br />
Human translation: the pros and cons<br />
Human translation essentially switches the table in terms of pros and cons. A higher standard of accuracy comes at the price of longer turnaround times and higher costs. What you have to decide is whether that initial investment outweighs the potential cost of mistakes. Alternatively, whether mistakes simply aren’t an option, like the cases we looked at in the previous section.<br />
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==The advantages of human translation ==<br />
It’s a translator’s job to ensure the highest accuracy<br />
Humans can interpret context and capture the same meaning, rather than simply translating words<br />
Human translators can review their work and provide a quality process<br />
Humans can interpret the creative use of language, e.g. puns, metaphors, slogans, etc.<br />
Professional translators understand the idiomatic differences between their languages<br />
Humans can spot pieces of content where literal translation isn’t possible and find the most suitable alternative<br />
The disadvantages of human translation<br />
Turnaround time is longer <br />
Translators rarely work for free <br />
Unless you use a translation agency, with access to thousands of translators, you’re limited to the languages any one translator can work with<br />
Simply put, human translation is your best option when accuracy is even remotely important. Other considerations to make are the complexity of your source material and the two languages you’re translating between – both of which can render machines pretty useless.<br />
<br />
When to use machine and human translation<br />
The truth is, the debate over machine vs human translation is an unnecessary distraction. What we should really be talking about is when to use these two different types of translation services, because they both serve a very valid purpose.<br />
<br />
Examples of when to use machine translation<br />
When you have a large bulk of content to translate and the general meaning is enough<br />
When your translation never reaches the final audience, e.g. you’re translating a resource as research for another piece of content<br />
Translating documents for internal use within a company, provided 100% accuracy isn’t needed<br />
To partially translate large chunks of content for a human translator to improve upon<br />
Examples of when to use human translation<br />
When accuracy is important<br />
Most cases where your translated content is received by a consumer audience<br />
When you have a duty of care to provide accurate translations (e.g. legal documents, product instructions, medical guidelines or health and safety content)<br />
When translating marketing material or other texts for creative language uses.<br />
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===Conclusion ===<br />
<br />
In the light of above mentioned facts and figures here by we would say that machine translation is challenge for human translators because it can reduces the wokload of translation but can't give accurate and exact translation of the traget language.It can be less reliable than human translation..</div>Chen Huinihttps://bou.de/u/index.php?title=Machine_translation&diff=129761Machine translation2021-12-08T01:33:04Z<p>Chen Huini: /* Conclusion */</p>
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=1 卫怡雯(A Comparison Between the Quality of Machine Translation and Human Translation——A Case Study of the Application of artificial intelligence in Sports Events)=<br />
[[Machine_Trans_EN_1]]<br />
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=2 吴映红（The Introduction of Machine Translation)= <br />
[[Machine_Trans_EN_2]]<br />
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=3 肖毅瑶(On the Realm Advantages And Symbiotic Development of Machine Translation And Huamn Translation)=<br />
[[Machine_Trans_EN_3]]<br />
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=4 王李菲 （Comparison Between Neural Machine Translation of Netease and Traditional Human Translation—A Case Study of The Economist Articles)= <br />
[[Machine_Trans_EN_4]]<br />
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=6 徐敏赟(Machine Translation Based on Neural Network --Challenge or Chance?)=<br />
[[Machine_Trans_EN_6]]<br />
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=7 颜莉莉(一带一路背景下人工智能与翻译人才的培养)=<br />
[[Machine_Trans_EN_7]]<br />
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===Abstract===<br />
In the era of artificial intelligence, artificial intelligence has been applied to various fields. In the field of translation, traditional translation models can no longer meet the rapid development and updating of the information age. The development of machine translation has brought structural changes to the language service industry, which poses challenges to the cultivation of translation talents. Under the background of "The Belt and Road initiative", translation talents have higher and higher requirements on translation literacy. Artificial intelligence and translation technology are used to reform the training mode of translation talents, so as to better serve the development of The Times. This paper mainly explores the cultivation of artificial intelligence and translation talents under the background of the Belt and Road Initiative. The cultivation of translation talents is moving towards comprehensive cultivation of talents. On the contrary, artificial intelligence and machine translation can also be used to improve the teaching mode and teaching content, so as to win together in cooperation.<br />
===Key words===<br />
Artificial intelligence,Machine translation,cultivation of translation talents,"The Belt and Road initiative"<br />
===题目===<br />
一带一路背景下人工智能与翻译人才的培养<br />
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===摘要===<br />
进入人工智能时代，人工智能被应用于各个领域。在翻译领域，传统的翻译模式已无法满足信息化时代的飞速发展和更新，机器翻译的发展给语言服务行业带来了结构性改变，这对翻译人才的培养提出了挑战。“一带一路”背景下，对翻译人才的翻译素养要求越来越高，利用人工智能和翻译技术对翻译人才培养模式进行革新，更好为时代发展服务。本文主要探究在一带一路背景下人工智能和翻译人才培养，翻译人才的培养过程中正向对人才的综合性培养，反之也可以利用人工智能和机器翻译完善教学模式和教学内容，在合作中共赢。<br />
===关键词===<br />
人工智能；机器翻译；翻译人才培养；一带一路<br />
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===1. Introduction===<br />
With the development of science and technology in China, artificial intelligence has also been greatly improved, and related technologies have been applied to various fields, such as the use of intelligent robots to deliver food to quarantined people during the epidemic, which has made people's lives more convenient. The most controversial and widely discussed issue is machine translation. Before the emergence of machine translation, translation was generally dominated by human translation, including translation and interpretation, which was divided into simultaneous interpretation and hand transmission, etc. It takes a lot of time and energy to cultivate a translation talent. However, nowadays, the era is developing rapidly and information is updated rapidly. As a translation talent, it is necessary to constantly update its knowledge reserve to keep up with the pace of The Times. The emergence of machine translation has also posed challenges to translation talents and the training of translation talents. Although machine translation had some problems in the early stage, it is now constantly improving its functions. In the context of the belt and Road Initiative, both machine translation and human translation are facing difficulties. Regardless of whether human translation is still needed, what is more important at present is how to train translators to adapt to difficulties and promote the cooperation between human translation and machine translation.<br />
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===2.Development status of machine translation in the era of artificial intelligence ===<br />
With the development of AI technology, machine translation has made great progress and has been applied to people's lives. For example, more and more tourists choose to download translation software when traveling abroad, which makes machine translation take an absolute advantage in daily email reply and other translation activities that do not require high accuracy. The translation software commonly used by netizens include Google Translation, Baidu Translation, Youdao Translation, IFly.com Translation, etc. Even wechat and other chat software can also carry out instant Translation into English. Some companies have also launched translation pens, translation machines and other equipment, which enables even native speakers to rely on machine translation to carry out basic communication with other Chinese people.<br />
But so far, machine translation still faces huge problems. Although machine translation has made great progress, it is highly dependent on corpus and other big data matching. It does not reach the thinking level of human brain, and cannot deal with the problem of translation differences caused by culture and religion. In addition, many minor languages cannot be translated by machine due to lack of corpus.<br />
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What's more, most of the corpus is about developed countries such as Britain and France, and most of the corpus is about diplomacy, politics, science and technology, etc., while there are very few about nationality, culture, religion, etc.<br />
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In addition, machine translation can only be used for daily communication at present. If it involves important occasions such as large conferences and international affairs, it is impossible to risk using machine translation for translation work. Professional translators are required to carry out translation work. So machine translation still has a long way to go.<br />
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===3.Challenges in the training of translation talents in universities===<br />
The cultivation of translators is targeted at the market. Professors Zhu Yifan and Guan Xinchao from the School of Foreign Languages at Shanghai Jiao Tong University believe that the cultivation of translators can be divided into four types: high-end translators and interpreters, senior translators and researchers, compound translators and applied translators.<br />
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From their names, it can be seen that high-end translators and interpreters and senior translators and researchers talents have high requirements on the knowledge and quality of interpreters, because they have to face the changing international situation, and have to deal with all kinds of sensitive relations and political related content, they should have flexible cross-cultural communication skills. In addition, for literature, sociology and humanities academic works, it is not only necessary to translate their content, but also to understand their essence. Therefore, translators should not only have humanistic feelings, but also need to have a deep understanding of Chinese and western culture.<br />
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However, there is not much demand for this kind of translation in the society. Such high-level translation requirements are not needed in daily life and work. The greatest demand is for compound translators, which means that they should master knowledge in a specific field while mastering a foreign language. For example, compound translators in the financial field should not only be good at foreign languages, but also master financial knowledge, including professional terms, special expressions and sentence patterns.<br />
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Now we say that machine translation can replace human translation should refer to the field of compound translation talents. Although AI technology has enabled machine translation to participate in creation, it does not mean that compound translation talents will be replaced by machines. The complexity of language and the flexible cross-cultural awareness required in communication make it impossible for machine translation to completely replace human translation.<br />
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The last type of applied translation talents are mostly involved in the general text without too much technical content and few professional terms, so it is easy to be replaced by machine translation.<br />
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Therefore, the author thinks that what universities are facing at present is not only how to train translation talents to cope with the development of machine translation, but to consider the application of machine translation in the process of training translation talents to achieve human-machine integration, so as to better complete the translation work.<br />
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===4.The Language environment and opportunities and challenges of the Belt and Road initiative===<br />
During visits to Central and Southeast Asian countries in September and October 2013, Chinese President Xi Jinping put forward the major initiative of jointly building the Silk Road Economic Belt and the 21st Century Maritime Silk Road. And began to be abbreviated as the Belt and Road Initiative.<br />
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According to the Vision and Actions for Jointly Building silk Road Economic Belt and 21st Century Maritime Silk Road, the Silk Road Economic Belt focuses on connecting China, Central Asia, Russia and Europe (the Baltic Sea). From China to the Persian Gulf and the Mediterranean Sea via Central and West Asia; China to Southeast Asia, South Asia, Indian Ocean. The focus of the 21st Century Maritime Silk Road is to stretch from China's coastal ports to Europe, through the South China Sea and the Indian Ocean. From China's coastal ports across the South China Sea to the South Pacific.<br />
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The Belt and Road "construction is comply with the world multi-polarization and economic globalization, cultural diversity, the initiative of social informatization tide, drive along the countries achieve economic policy coordination, to carry out a wider range, higher level, the deeper regional cooperation and jointly create open, inclusive and balanced, pratt &whitney regional economic cooperation framework.<br />
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====4.1The language environment of the Belt and Road====<br />
The "Belt and Road" involves a wide range of countries and regions, and their languages and cultures are very complex. How to make good use of language, do a good job in translation services, actively spread Chinese culture to the world, strengthen the ability of discourse, and tell Chinese stories well, the first thing to do is to understand the language situation of the countries along the "Belt and Road".<br />
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=====4.1.1The most common language in countries along the "Belt and Road" =====<br />
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There are a wide variety of languages spoken in 65 countries along the Belt and Road, involving nine language families. However, The status of English as the first language in the world is undeniable. Most of the countries participating in the Belt and Road are developing countries, and many of them speak English as their first foreign language. Especially in southeast Asian and South Asian countries, English plays an important role in foreign communication, whether as the official language or the first foreign language. Besides English, more than 100 million people speak Russian, Hindi, Bengali, Arabic and other major languages in the "Belt and Road" countries. It can also be seen that a common feature of languages in countries along the "Belt and Road" is the popularization of English education. English is widely used in international politics, economy, culture, education, science and technology, playing the role of the most important language in the world.<br />
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=====4.1.2The complex language conditions of countries along the "Belt and Road" =====<br />
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The languages spoken in countries along the Belt and Road involve nine major language families and almost all the world's religious types. Differences in religious beliefs also result in differences in culture, customs and social values behind languages. The languages of some countries along the belt and Road have also been influenced by historical and realistic factors, such as colonization, internal division and immigration. <br />
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India, for example, has no national language, but more than 20 official languages. India is a multi-ethnic country, a total of more than 100 people, one of the most obvious difference between nation and nation is the language problem. Therefore, according to the difference of language, India divides different ethnic groups into different states, big and small. Ethnic groups that use the same language are divided into one state. If there are two languages in a state, the state is divided into two parts. And Indian languages differ not only in word order but also in the way they are written. In India, for example, Hindi is spoken by the largest number of people in the north, with about 700 million speakers and 530 million as their first language. It is written in The Hindu language and belongs to the Indo-European language family. Telugu in the east is spoken by about 95 million people and 81.13 million as their first language. It is written in Telugu, which belongs to the Dravidian language family and is quite different from Hindi. As a result, a parliamentary session in India requires dozens of interpreters. <br />
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These factors cannot be ignored in the process of translation, from language communication to cultural understanding, from text to thought exchange, through the bridge of language to truly connect the people, so as to avoid misreading and misunderstanding caused by differences in language and national conditions.<br />
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====4.2 Opportunities and challenges of the "Belt and Road" ====<br />
With the promotion of the Belt and Road Initiative, there has been an unprecedented boom in translation. In the previous translation boom in China, most of the foreign languages were translated into Chinese, and most of the foreign cultures were imported into China. However, this time, in the context of the "Belt and Road" initiative, translating Chinese into foreign languages has become an important task for translators. As is known to all, there are many different kinds of "One Belt And One Road" along the national language and culture is complex, the service "area" construction has become a factor in Chinese translation talents training mode reform, one of the foreign language universities have action, many colleges and universities to establish the "area" all the way along the country's small language major, as a result, "One Belt And One Road" initiative to promote, It has brought unprecedented opportunities for human translation. The cultivation of diversified translation talents and the cultivation of translation talents in small languages is an urgent problem to be solved in China. The cultivation of translation talents cannot be completed overnight, and the state needs to reform the training mode of translation talents from the perspective of language strategic development. Only in this way can we meet the new demand for human translation under the new situation of the belt and Road Initiative.<br />
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For a long time, the traditional orientation of translation curriculum and training goal in colleges and universities is to train translation teachers and translators in need of society through translation theory and practice and literary translation practice, which cannot meet the needs of society. Since 2007, in order to meet the needs of the socialist market economy for application-oriented high-level professionals, the Academic Degrees Committee of The State Council approved the establishment of Master of Translation and Interpreting (MTI for short). After joining the pilot program of MTI, more and more universities are reforming the curriculum and training mode of master of Translation in order to cultivate translators who meet the needs of the society.<br />
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Language is an important carrier of culture, and translation is an important link for exporting culture. The quality of translation output also reflects the cultural soft power of a country. With the rise of China, more and more people are interested in Chinese culture, and the number of Chinese learners keeps increasing. Under the background of "One Belt and One Road", excellent translators are urgently needed to spread Chinese culture. With the promotion of "One Belt and One Road" Initiative, the number of other countries learning mutual learning and cultural exchanges with China has increased unprecedeningly, bringing vigorous opportunities for the spread of Chinese culture. Translation talents who understand small languages and multi-lingual translators are needed. They should not only use language to convey information, but also use language as a lubricant for communication.<br />
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===5.Training translation talents from the perspective of machine translation===<br />
Under the prevailing environment of machine translation, it poses a great challenge to the cultivation of translation talents. According to the current situation, translation needs and the shortage of translation talents, colleges and universities should reform and innovate the existing training programs for translation talents in terms of the quality of translation talents, the reform of training mode and the use of artificial intelligence. Based on the obtained data and literature, the author discusses how to train translation talents in the perspective of machine translation from the following aspects.<br />
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====5.1 Quality requirements for translation talents ====<br />
Zhong Weihe and Murray made a more detailed and profound discussion on translator's literacy, believing that "translators should not only be proficient in two languages, but also have extensive cultural and encyclopedic knowledge and relevant professional knowledge; Master a variety of translation skills, a lot of translation practice; Have a clear translator role awareness, good professional ethics, practical and enterprising style of work, conscious team spirit and calm psychological quality ". According to the collected data, the author will elaborate the requirements for translation talents from four aspects: language literacy, humanistic literacy, translation ability and innovation ability.<br />
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The first is language literacy, which is the most basic and important requirement. MAO Dun pointed out that "mastery of mother tongue and target language are the foundation of translation". A solid foundation of bilingual skills is the basic skills of translators. Poor language proficiency seems to be a common problem among students majoring in translation and interpreting. Many translation diseases are caused by poor Chinese foundation. As part of going global, the belt and Road initiative is to tell Chinese culture and Chinese stories, which requires translators to be able to use both languages flexibly. Therefore, the first problem that colleges and universities face to solve is to improve the language level of foreign language learners.<br />
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The second is humanistic literacy. Humanistic literacy is mainly manifested by a good command of politics, economy, history, literature and other knowledge, which is particularly important for interpreters. In addition, cross-cultural communication cannot be ignored. In the process of communicating with foreigners or translating, translators often encounter the first cross-cultural contradiction. Cross-culture refers to having a full and correct understanding of cultural phenomena, customs and habits that differ or conflict with the national culture, and accepting and adapting to them in an inclusive manner on this basis. So the interpreter can first fully understand and master the national conditions and culture of the target country, which is particularly important in the "Belt and Road". There are more than 60 countries along the "Belt and Road", and it takes a lot of energy to master their national conditions and culture.<br />
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The third is translation ability. We should distinguish between translation ability and language ability. Translation ability is actually a system of knowledge and skills necessary for translation, the core of which is conversion ability. First of all, it reflects the ability to use tools to assist translation, such as computer application, translation technology and so on. In addition, interpreters should have enough healthy psychological quality and good professional quality. In terms of translation ability, the current training model of translation talents is inadequate.<br />
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The last one is innovation. The cultivation of learners' thinking ability is the key to translation teaching and the cultivation of thoughtful translators should be the connotation of translation teaching. Therefore, the interpreter is not only a translation tool, which is no different from machine translation. More importantly, it is necessary to explore translation with thoughts, have a sense of lifelong learning and innovation consciousness. Translators must constantly innovate themselves, learn new knowledge, and strive to seek reform and innovation. Many colleges and universities should also consciously cultivate students' innovation ability and broaden their thinking and vision.<br />
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====5.2 The reform of college curriculum setting====<br />
First, we will further reform the curriculum of colleges and universities. Add economics, law and engineering to the curriculum, these contents in the "belt and Road".<br />
"One Road" is very important in the construction. According to the author's personal experience, the most typical problem of foreign language majors in colleges and universities is the single learning of foreign languages. More professional foreign language colleges and universities will add some literature courses and national conditions courses of the language target countries. Obviously, whether foreign language graduates are engaged in translation work or not, these knowledge is not enough. Of course, great reforms have been carried out in foreign language teaching, such as combining foreign language with finance, law, diplomacy and so on, and taking the way of minor training foreign language majors.<br />
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Domestic enterprises with a high degree of internationalization attach great importance to translation. Their translation research includes cutting-edge theoretical and applied research, involving machine translation, natural language processing and AI theory, algorithm and model. With such a foundation, enterprises can solve problems by themselves, such as embedding automatic translation functions in mobile phones. International enterprises not only do technical translation, but also deal with all forms of translation and localization in society. At present, translation teaching in most colleges and universities is still in the early mode, and it is an objective fact that it is divorced from the workplace and has a gap with the needs of enterprises.<br />
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Second, we should adjust and strengthen the construction of second foreign language teaching for foreign language majors. In the 1980s, our country was in urgent need of Russian translation. At that time, students majoring in English could translate microelectronic product manuals and related business documents in English and Russian at the same time after learning Russian for half a year. The mutual conversion between English and Russian played a great role in practice. According to the author, in the Graduate Institute of Interpretation and Translation of Beijing Foreign Studies University a very few students majored in multiple languages at the graduate level, that is, they majored in minor languages at the undergraduate level and were admitted to the Graduate Institute of Interpretation and Translation in English. Their training mode is to study English in the Graduate Institute of Interpretation and Translation for two years and the third year in the corresponding department of the undergraduate major. Such training mode in my opinion is a bigger model, cost It's more difficult for students. <br />
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In addition, there is a great disparity in the development of second foreign language teaching in colleges and universities, and the overall level is not high enough. Part of the second foreign language university foreign language professional may still be too much focus in languages such as German, French and Japanese, should as far as possible, considering the need of the construction of the "region", like Croatia, Serbia, Turkish, Hungarian, Italian, Indonesian, Albanian, these are the countries along the "area" the language of the two countries, Colleges and universities should encourage the teaching of a second foreign language.<br />
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Third, the teaching of translation technology should be strengthened. Traditional translation teaching teaches translation skills, such as the translation of words, sentences, texts and figures of speech. Translation technology refers to a series of practical workplace technologies with computer-aided translation software and translation project management as the core, which can greatly improve translation efficiency. However, due to the relative lack of translation technology teachers and equipment in colleges and universities, there is a disconnect between talent training and the requirements of translation technology in the translation field.<br />
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====5.3 Application of artificial intelligence to translation teaching practice====<br />
In order to improve the teaching quality and train students' English translation ability, it is necessary to realize the effective integration of ARTIFICIAL intelligence and translation activity courses, which should not only reflect the effectiveness of artificial intelligence translation technology, but also help students establish a healthy concept of English communication. Through the application of artificial intelligence technology, students can strengthen their flexible translation skills through close communication with "AI program" during the learning stage of English translation activity class. For example, teachers can ask students to translate directly against the translation content provided on the translation screen of the ARTIFICIAL intelligence system. After that, the system can collect the translation answers with the help of speech recognition function, and then judge the accuracy of the translation content, thus providing important feedback to students.<br />
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China has used such artificial intelligence technology in the Putonghua test to ensure that every student can find a suitable translation method in practical communication. The so-called artificial intelligence technology is a new kind of technology modeled after the characteristics of human neural network thinking, can combine the human mind to respond. If it can be integrated into English translation activity teaching, it can not only improve the teaching efficiency, but also enhance students' enthusiasm in learning the course.<br />
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At the same time, during the training of translation talents, teachers also need to take into account the importance of influencing education factors, so that students can form a higher disciplinary quality in translation, so as to fit the concept of quality education in the new era. Only when artificial intelligence translation content is fully integrated into college English translation activity courses can the overall translation ability of college students be maximized.<br />
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====5.4The improvement of translator's technical ability====<br />
In the previous part, the author roughly mentioned that translation teaching should be improved, which will be elaborated here. At present, only a few universities can make full use of the advantages of translation technology in translation teaching and focus on cultivating professional translation talents. Most universities still cannot get rid of the traditional teaching mode of "language + relevant professional knowledge" in translation teaching, and generally lack a correct understanding of COMPUTER-aided translation teaching.<br />
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According to Wang Huashu et al., the courses that can be offered around the composition of translators' technical literacy include computer-assisted translation, translation and corpus, machine translation and post-translation editing, localization and internationalization, film and television translation (subtitle), technical communication and technical writing, and computer programming. The course modules involved are: Fundamentals of COMPUTER-aided Translation, CAT tool application, corpus alignment and processing, term management, QA technology for translation quality assurance, OFFICE fundamentals, translation management technology, basic computer knowledge, desktop typesetting, localization and internationalization, project management system and content management system, technical writing, basic knowledge of computer programming, basic knowledge of web code, etc.<br />
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===6针对一带一路的机器翻译与翻译人才的合作===<br />
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===Conclusion===<br />
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===References===<br />
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=8 颜静(On Machine Translation Under Lanuguage Intelligence——An Option and Opportunity for Human Translators)=<br />
[[Machine_Trans_EN_8]]<br />
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=9 谢佳芬（人工智能时代下的机器翻译与人工翻译）=<br />
[[Machine_Trans_EN_9]]<br />
===Abstract===<br />
With the continuous development of information technology, many industries are facing the competitive pressure of artificial intelligence, and so is the field of translation. Artificial intelligence technology has developed rapidly and combined with the field of translation，which has brought great impact and changes to traditional translation, but artificial intelligence translation and artificial translation have their own advantages and disadvantages. Artificial translation is in the leading position in adapting to human language logical habits and understanding characteristics, but in terms of translation threshold and economic value, the efficiency of artificial intelligence translation is even better. In a word, we need to know that machine translation and human translation are complementary rather than antagonistic.<br />
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===Key Words===<br />
Machine Translation; Artificial Translation; Artificial Intelligence<br />
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===题目===<br />
人工智能时代下的机器翻译与人工翻译<br />
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===摘要===<br />
伴随着信息技术的不断发展，多个行业面临着人工智能的竞争压力，翻译领域也是如此。人工智能技术快速发展并与翻译领域结合，人工智能翻译给传统翻译带来了巨大的冲击和变革，但人工智能翻译与人工翻译存在着各自的优劣特点和发展空间，在适应人类语言逻辑习惯和理解特点的翻译效果上，人工翻译处于领先地位，但在翻译门槛和经济价值上，人工智能翻译的效率则更胜一筹。总的来说，我们要知道机器翻译与人工翻译是互补而非对立的关系。<br />
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===关键词===<br />
机器翻译;人工翻译;人工智能<br />
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===1. Introduction===<br />
====1.1 The History of Machine Translation Aborad====<br />
The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. Alchuni put forward the idea of using machines for translation. In 1933, the Soviet inventor Troyansky designed a machine to translate one language into another. [1]In 1946, the world's first modern electronic computer ENIAC was born. Soon after, American scientist Warren Weaver, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947. In 1949, Warren Weaver published a memorandum entitled Translation, which formally raised the issue of machine translation. In 1954, Georgetown University, with the cooperation of IBM, completed the English-Russian machine translation experiment with IBM-701 computer for the first time, which opened the prelude of machine translation research. [2] In 2006, Google translation was officially released as a free service software, bringing a big upsurge of statistical machine translation research. It was Franz Och who joined Google in 2004 and led Google translation. What’s more, it is precisely because of the unremitting efforts of generations of scientists that science fiction has been brought into reality step by step.<br />
====1.2 The History of Machine Translation in China====<br />
In 1956, the research and development of machine translation has been named in the scientific and technological work and made little achievements in China. On the eve of the tenth anniversary of the National Day in 1959, our country successfully carried out experiments, which translated nine different types of complicated sentences on large general-purpose electronic computers. The dictionary includes 2030 entries, and the grammar rule system consists of 29 circuit diagrams. [3]. After a period of stagnation, China's machine translation ushered in a high-speed development stage after the 1980s in the wave of the third scientific and technological revolution. With the rapid development of economy and science and technology, China has made a qualitative leap in the field of machine translation research with the pace of reform and opening up. In 1978, Institute of Scientific and Technological Information of China, Institute of Computing Technology and Institute of Linguistics carried out an English-Chinese translation experiment with 20 Metallurgical Title examples as the objects and achieved satisfactory results. Subsequently, they developed a JYE-I machine translation system, which based on 200 sentences from metallurgical documents. Its principles and methods were also widely used in the machine translation system developed in the future. In addition, the research achievements of machine translation in China during the 1980s and 1990s also include that Institute of Post and Telecommunication Sciences developed a machine translation system, C Retrieval and automatic typesetting system with good performance and strong practicability in October 1986; In 1988, ISTC launched the ISTIC-I English-Chinese Title System for the translation of applied literature of metallurgy, Information Research Institute of Railway developed an English-Chinese Title Recording machine translation system for railway documents; the Language Institute of the Academy of Social Sciences developed "Tianyu" English-Chinese machine translation system and Matr English-Chinese machine translation system developed by the computer department of National University of Defense Technology. After many explorations and studies, machine translation in China has gradually moved towards application, popularization and commercialization. China Software Technology Corporation launched "Yixing I" in 1988, marking China's machine translation system officially going to the market. After "Yixing", a series of machine translation systems such as Gaoli system in Beijing, Tongyi system in Tianjin and Langwei system in Shaanxi have also entered the public. In the 21st century, the development of a series of apps such as Kingsoft Powerword, Youdao translation and Baidu translation has greatly met the needs of ordinary users for translation. According to the working principle, machine translation has roughly experienced three stages: rule-based machine translation, statistics-based machine translation and deep learning based neural machine translation. [4] These three stages witnessed a leap in the quality of machine translation. Machine translation is more and more used in daily life and even the translation of some texts is almost comparable to artificial translation. In addition to text translation, voice translation, photo translation and other functions have also been listed, which provides great convenience for people's life. It is undeniable that machine translation has become the development trend of translation in the future.<br />
====1.3 The Status Quo of Machine Translation====<br />
In this big data era of information explosion, the prospect of machine translation is also bright. At present, the circular neural network system launched by Google has supported universal translation in more than 60 languages. Many Internet companies such as Microsoft Bing, Sogou, Tencent, Baidu and NetEase Youdao have also launched their own Internet free machine translation systems. [5] Users can obtain translation results free of charge by logging in to the corresponding websites. At present, the circular neural network translation system launched by Google can support real-time translation of more than 60 languages, and the domestic Baidu online machine translation system can also support real-time translation of 28 languages. These Internet online machine translation systems are suitable for a variety of terminal platforms such as mobile phone, PC, tablet and web and its functions are also quite diverse, supporting many translation forms, such as screen word selection, text scanning translation, photo translation, offline translation, web page translation and so on. Although its translation quality needs to be improved, it has been outstanding in the fields of daily dialogue, news translation and so on.<br />
===2. Advantages and Disadvantages of Machine Translation===<br />
Generally speaking, machine translation has the characteristics of high efficiency, low cost, accurate term translation and great development potential and etc. Machine translation is fast and efficient, this is something that artificial translation can’t catch up with. In addition, with the continuous emergence of all kinds of translation software in the market, compared with artificial translation, machine translation is cheap and sometimes even free, which greatly saves the economic cost and time for users with low translation quality requirements. What's more, compared with artificial translation, machine translation has a huge corpus, which makes the translation of some terms, especially the latest scientific and technological terms, more rapid and accurate. The accurate translation of these terms requires the translator to constantly learn, but learning needs a process, which has a certain test on the translator's learning ability and learning speed. In this regard, artificial translation has uncertainty and hysteretic nature. At the same time, with the progress of science and technology and the development of society, the function of machine translation will be more perfect and the quality of translation will be better.Today's machine translation tools and software are easy to carry, all you need to do is just to use the software and electronic dictionary in the mobile phone. There is no need to carry paper dictionaries and books for translation, which saves time and space. At the same time, machine translation covers many fields and is suitable for translation practice in different situations, such as academic, education, commercial trade, social networking, tourism, production technology, etc, it is also easy to deal with various professional terms. However, due to the limitation of translators' own knowledge, artificial translation is often limited to one or a few fields or industries. For example, it is difficult for an interpreter specializing in medical English to translate legal English.<br />
At the same time, machine translation also has its limitations. At first, machine can only operate word to word translation, which only plays the function and role of dictionary. Then, the application of syntax enables the process of sentence translation and it can be solved by using the direct translation method. When the original text and the target language are highly similar, it can be translated directly. For example, the original text "他是个老师." The target language is "he is a teacher ". With the increase of the structural complexity of the original text, the effect of machine translation is greatly reduced. Therefore, at the syntactic level, machine translation still stays in sentences with relatively simple structure. Meanwhile, the original text and the results of machine translation cannot be interchanged equally, indicating that English-Chinese translation has strong randomness, and is not rigorous and scientific enough. <br />
Nowadays, machine translation is highly dependent on parallel corpora, but the construction of parallel corpora is not perfect. At present, the resources of some mainstream languages such as Chinese and English are relatively rich, while the data collection of many small languages is not satisfactory. Moreover, the current corpus is mainly concentrated in the fields of government literature, science and technology, current affairs and news, while there is a serious lack of data in other fields, which can’t reflect the advantages of machine translation. At the same time, corpus construction lags behind. Some informative texts introducing the latest cutting-edge research results often spread the latest academic knowledge and use a large number of new professional terms, such as academic papers and teaching materials while the corpus often lacks the corresponding words of the target language, which makes machine translation powerless<br />
Besides, machine translation is not culturally sensitive. Human may never be able to program machines to understand and experience a particular culture. Different cultures have unique and different language systems, and machines do not have complexity to understand or recognize slang, jargon, puns and idioms. Therefore, their translation may not conform to cultural values and specific norms. This is also one of the challenges that the machine needs to overcome.[6] Artificial intelligence may have human abstract thinking ability in the future, but it is difficult to have image thinking ability including imagination and emotion. [7] Therefore, machine translation is often used in news, science and technology, patents, specifications and other text fields with the purpose of fact description, knowledge and information transmission. These words rarely involve emotional and cultural background. When translating expressive texts, the limitations of machine translation are exposed. The so-called expressive text refers to the text that pays attention to emotional expression and is full of imagination. Its main characteristics are subjectivity, emotion and imagination, such as novels, poetry, prose, art and so on. This kind of text attaches importance to the emotional expression of the author or character image, and uses a lot of metaphors, symbols and other expressions. Machine translation is difficult to catch up with artificial translation in this kind of text, it can only translate the main idea, lack of connotation and literary grace and it cannot have subjective feelings and rational analysis like human beings. In fact, it is not difficult to simulate the human brain, the difficulty is that it is impossible to learn from the rich social experience and life experience of excellent translators. In other words, machine translation lacks the personalization and creativity of human translation. It is this personalization and creativity that promote the development and evolution of language, and what machine translation can only output is mechanical "machine language".<br />
<br />
===3.The Irreplaceability of Artificial Translation ===<br />
====3.1 Translation is Constrained by Context====<br />
At present, machine translation can help people deal with language communication in people's daily life and work, such as clothing, food, housing and transportation, but there is a big gap from the "faithfulness, expressiveness and elegance" emphasized by high-level translation. Language itself is art，which pays more attention to artistry than functionality, and the discipline of art is difficult to quantify and unify. Sometimes it is regular, rigorous, logical and clear, and sometimes it is random, free and logical. If it is translated by machine, it is difficult to grasp this degree. Sometimes, machine translation cannot connect words with contextual meaning. In many languages, the same word may have multiple completely unrelated meanings. In this case, context will have a great impact on word meaning, and the understanding of word meaning depends largely on the meaning read from context. Only human beings can combine words with context, determine their true meaning, and creatively adjust and modify the language to obtain a complete and accurate translation. This is undoubtedly very difficult for machine translation. Artificial translation can get rid of the constraints of the source language and translate the translation in line with the grammar, sentence patterns and word habits of the target language. In the process of translation, translators can use their own knowledge reserves to analyze the differences between the source language and the target language in thinking mode, cultural characteristics, social background, customs and habits, so as to translate a more accurate translation. Artificial translation can also add, delete, domesticate, modify and polish the translation according to the style, make up for the lack of culture, try to maintain the thought, artistic conception and charm of the original text and the style of the source language. In addition, translators can also judge and consider the words with multiple meanings or easy to produce ambiguity according to the context, so as to make the translation more clear and more accurate and improve the quality of the translation.<br />
<br />
<br />
===4. Discussion on the Relationship Between Machine Translation and Artificial Translation ===<br />
<br />
===5. Suggestions on the Combined Development of Machine Translation and Artificial Translation===<br />
<br />
===6. ===<br />
<br />
===7. ===<br />
<br />
===Conclusion===<br />
<br />
===References===<br />
<br />
=10 熊敏(Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts)=<br />
[[Machine_Trans_EN_10]]<br />
===Abstract===<br />
The history of machine translation can be traced to 1940s, undergoing germination, recession, revival and flourish. Nowadays, with the rapid development of information technology, machine translation technology emerged and is gradually becoming mature. Whether manual translation can be replaced by machine translation becomes a widely-disputed topic. So in order to explore the ability of machine translation, I adopts two versions of translation, which are manual translation and machine translation(this paper uses Youdao translation) for different types of texts(according to Peter Newmark's types of text：informative text, expressive text and vocative text). The results are quite different in terms of quality and accuracy. The results show that machine translation is more suitable for informative text for its brevity, while the expressive texts especially literary works and vocative texts are difficult to be translated, because there are too many loaded words and cultural images in expressive text and dependence on the readers. To draw a conclusion, the relationship between machine translation and manual translation should be complementary rather than competitive.<br />
<br />
===Key words===<br />
machine translation; manual translation; Newmark's type of texts<br />
<br />
===题目===<br />
Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts<br />
<br />
===摘要===<br />
机器翻译的历史可以追溯到上个世纪40年代，经历了萌芽期、萧条期、复兴期和繁荣期四个阶段。如今，随着信息技术的高速发展，机器翻译技术日益成熟，于是机器翻译能不能替代人工翻译这个话题引起了广泛热议。为了探究机器翻译的能力水平是否足以替代人工翻译，本人根据皮特纽马克的文本类型分类理论（信息类文本，表达文本，呼吁文本），选择了相应的译文类型，并且将其机器翻译的版本以及人工翻译的版本进行对比。结果发现，就质量和准确度而言，译文的水平大相径庭。因为其简洁的特性，机器比较适合翻译信息文本。而就表达文本，尤其是文学作品，因其存在文化负载词及相应的文化现象，所以机器翻译这类文本存在难度。而呼唤文本因其目的性以及对读者的依赖性较强，翻译难度较大。由此得知，机器翻译和人工翻译的关系应该是互补的，而不是竞争的。<br />
<br />
===关键词===<br />
机器翻译；人工翻译；纽马克文本类型<br />
<br />
===1. Introduction===<br />
====1.1.Introduction to machine translation====<br />
Machine translation, also known as computer translation is a technique to translating through machine translator, such as Google translation and Youdao translation. Machine translation is one of the branches of computational linguistics, ranging from computer science, statistics, information science and so on. Machine translation plays an important role in all aspects.<br />
Machine translation can be traced back to 1940s, when British engineer Booth and American engineer Weaver proposed using computer to translate and started to study machines used for translation. And the first machine translating system launched in 1954, used in English and Russian translation. And this means machine translation becomes a reality. However, the arrival of new things always accompanies barriers and setbacks. In the 1960s, reports from ALPAC (Automated Language Processing Advisory Committee) showed studies on machine translation had stagnated for a decade. At that time, due to the high cost of machine, choosing human translators to finish translating works was more economical for most companies. What’s worse, machine had difficulty in understanding semantic and pragmatic meanings. Fortunately, in the 1970s, with the advance and popularity of computer, machine translation was gradually back on track. With the development of science and technology, machine translation has better technological guarantee, such as artificial intelligence and computer technology. In the last decades, from the late 90s till now, machine translation is becoming more and more mature. The initial rule-oriented machine translation has been updated and Corpus-aided machine translation appears. And nowadays, technology in voice recognition has been further developed. This technique is frequently applied in speech translation products. Users only need to speak source language to the online translator and instantly it will speak out the target version. But machine can’t fully provide precise and accurate translation without any grammatical or semantic problems. Machine can understand the literal meaning of texts, but not the meaning in context. For example, there is polysemy in English, which refers to words having multiple meanings. So when translating these words, translators should take contexts into consideration. But machine has troubles in this way. In addition, machine has no feeling or intention. Hence, it is also difficult to convey the feelings from the source language.<br />
<br />
====1.2.Process of machine translation====<br />
The process of manual translation is different from that of machine translation. Here is the process of the former. (1) Understand source language. (2) Use target language to organize language. (3) Generate translation. Unlike manual translation, machine translation tends to analyze and code source language first, then look for related codes in corpus, and work out the code that represents target language, generating translation. But they share a common feature, which is that Lexicon, grammatical rules and syntactic structure are taken into consideration. This is one of the biggest challenges for machine translation.<br />
<br />
===2.Theorectical framework===<br />
====2.1Newmark’s text typology====<br />
Text typology is a theory from linguistics. It undergoes several phrases. Karl Buhler, a German psychologist and linguist defines communicative functions into expressive, information and vocative. Then Roman Jakobson proposes categorization of texts, which are intralingual translation, interlingual translation and intersemiotic translation. Accordingly, he defines six main functions of language, including the referential function, conative function, emotive function, poetic function, metalingual function and the phatic function. Based on the two theories, Peter Newmark, a translation theorist, summarizes the language function into six parts: the informative function, the vocative function, the expressive function, the phatic function, the aesthetic function, and the metalingual function. Later, he further divided texts into informative type, expressive text and vocative type according to the linguistic functions of various texts. <br />
The core of the informative texts is the truth. It is to convey facts, information, knowledge of certain topics, reality and theories. The language style of the text is objective, logical and standardized. Reports, papers, scientific and technological textbooks are all attributed to informative texts. Translators are required to take format into account for different styles.<br />
The core of the expressive text is the sender’s emotions and attitudes. It is to express sender’s preferences, feelings, views and so on. The language style of it is subjective. Imaginative literature, including fictions, poems and dramas, autobiography and authoritative statements belong to expressive text. It requires translators to be faithful to the source language and maintain the style.<br />
The core of the vocative text is readership. It is to call upon readers to act, think, feel and react in the way intended by the text. So it is reader-oriented. Such texts as advertisement, propaganda and notices are of vocative text. Newmark noted that translators need to consider cultural background of the source language and the semantic and pragmatic effect of target language. If readers can comprehend the meaning at once and do as the text says, then the goal of information communication is achieved.<br />
To draw a conclusion, informative texts focus on the information and facts. Expressive texts center on the mind of addressers, including feelings, prejudices and so on. And vocative texts are oriented on readers and call on them to practice as the texts say.<br />
<br />
====2.2Study method====<br />
Manual translations of the three texts are selected from authoritative versions and universally acknowledged. And machine translations of those come from Youdao Translator. And in this thesis I will compare and evaluate the two methods in word diction, sentence structure, word order and redundancy.<br />
<br />
===3. ===<br />
<br />
===4. ===<br />
<br />
===5. ===<br />
<br />
===6. ===<br />
<br />
===7. ===<br />
<br />
===Conclusion===<br />
<br />
===References===<br />
<br />
=11 陈惠妮=(Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts)=<br />
[[Machine_Trans_EN_11]]<br />
===Abstract===<br />
At present, globalization is accelerating and the market demand for language services is rapidly increasing . Machine translation, as an important translation method, can greatly improve translation efficiency due to its low cost and high speed. However, because of the limitations of machine translation and the differences between Chinese and English language, machine translation is not accurate enough. In order to balance translation efficiency and translation quality, a great number of manual revisions in translation are required for the machine translating texts. Medical papers are specialized, special and purposeful, so it requires accurate,qualified and professional translation. However, the quality of translations by machine is inefficient to meet the high-quality requirements of medical papers translation. Therefore, the introduction of pre-editing can greatly improve the efficiency and quality of machine translation.<br />
<br />
===Key words===<br />
Pre-editing, Machine translation, Medical texts<br />
<br />
===题目===<br />
Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts<br />
<br />
===摘要===<br />
在全球化加速发展的今天，市场对语言服务的需求迅速增加。机器翻译作为一种重要的翻译途径，由于其成本低、速度快，可以大大提高翻译效率。然而，由于机器翻译的局限性以及中英文语言的差异，机器翻译的准确性不高。为了平衡翻译效率和翻译质量，机器翻译文本需要大量的手工修改。<br />
医学论文具有专业性、特殊性和目的性，要求其译文准确、合格、专业。然而，机器翻译的质量较低，无法满足医学论文对翻译的高质量要求。因此，译前编辑的引入可以大大提高机器翻译的效率和质量。<br />
<br />
===关键词===<br />
译前编辑；机器翻译；医学文本<br />
<br />
===1. Introduction===<br />
===1.1 Definition of Machine Translation===<br />
As Cronin(2013) revealed: "Translation is undergoing a revolutionary upheaval. The influence of digital technology and the Internet on translation is continuous, extensive and profound. From the popularity of automatic online translation applications, translation revolution is everywhere (Cronin,2013) However,the concept of machine translation was firstly proposed in the 1930s. Since 1940s, the machine translation technology has been evolving from rule-based machine translation (RBMT) to statistical machine translation (SMT), and to neural machine translation (NMT). Machine translation refers to the automatic translation of source language into target language by using a computer system. That is, machine translation refers to the automatic translation of text from one language into another natural language by computer software or other online translation webs. Machine translation is also defined as the process of “using a computer system to automatically translate text or speech from one natural language to another” according to the definition by ISO (Cui, 2014).O 'Brien (2002) defines it as "the behavior of modifying errors in machine translation to ensure that the target translation meets certain quality requirements". On the basic level, machine translation performs mechanical substitution of words in one language for words in another language, but that rarely produces a good translation, therefore, recognition of the whole phrases and their closest counterparts in the target language is needed. Not all words in one language have equivalents in another language, and many words have more than one meaning. A huge demand for translation is greatly needed in today’s global world, which creates new opportunities for the development of machine translation, attracts more and more attention and becomes one of the current research focuses.<br />
<br />
===1.2 Definition of Pre-editing===<br />
Pre-editing means to adjust and modify the source language to make it fit more with the characteristics of the machine translation software before putting the source language before the into machine translation, so as to improve the quality of the translation machine translation (Wei Changhong, 2008:93-94). Pre-editing is to modify the original text before putting it into machine translation software, in order to improve the recognition rate of machine translation, optimize the output quality of translated text and reduce the workload of post-editing. Because pre-translation editing only needs to be modified in one language, the operation is simpler than post-translation editing, which can realize the double improvement of quality and efficiency. A good pre-editing translation can help machine translation more smoothly, thus improving the machine readability and quality of the output translation. Pre-editing is mostly applied in the following situations: one is when the original text is of poor quality and the machine is difficult to recognize the meaning of the sentence, such as user generated content with poor readability and translatability (Gerlach et al, 2003:45-53); Documents that need to be published in multiple languages; Next is when the original text contains a lot of jargon; The last is the original text has a corresponding translation memory bank. If the original text is edited, it can better match the content of the translation memory bank. Pre-editing is a process of identifying problems. It requires to pre-edit the source texts before putting it into machine translation according to the requirements, listing the expressions or sentences that may have trouble in machine translation and then pre-edit it by human. The purpose is to enable the computer to translate better, improve the translatability of machine translation. (Slype G V & Guinet J F & Seitz F,1984:115)<br />
<br />
===1.3 Machine Translation Mode===<br />
According to the different knowledge acquisition methods, machine translation modes can be classified as follows: one is rule-based machine translation, which is based on bilingual dictionaries and a library of language rules for each language. The quality of translation depends on whether the source language conforms to the existing rules, but the inexhaustible rules are the hinders of this model. The second mode is machine translation based on statistics. This translation model relies on the principles of mathematics and statistics to find various existing translations corresponding to the translation tasks through the employment of corpus, analyzing the frequency of their occurrence and selecting the translation with the highest frequency for output. The disadvantage of this translation model is that it ignores the flexibility of language and the importance of context. The last one is neural network language model. This model is different from previous translation models in that it uses end-to-end neural network to realize automatic translation between natural languages. At present, the quality of its translation is much higher than that of the previous translation models.<br />
===1.4 Source of Study Abstracts===<br />
The core medical journals from domestic are selected in order to make the paper more representative and authoritative, such as National Medical Journal of China, Journal of Peking University (Health Science), and Journal of Third Military Medical University. All of them include basic medical science, biomedical technology, laboratory medical science and other fields. In this way, the pre-editing approaches included are applicable enough to machine translation of medical abstracts.<br />
===1.5 Selection of Translation Software===<br />
Generally, machine translation software includes Google Translation, Youdao Translator and Niu Translation, which has their own special use in translation. Take Google as an example, it is more like neurons in human brain, enabling to learn and collect information to establish connections with its neural machine translator’s neurons. However, it also causes many errors because of the lack of enough information. In this paper, contrastive analysis will be carried on by using Google translation. On the one hand, being a pioneer in the translation of NET, it is inevitably to sue Google as the translation software to translate medical texts in this paper. Comparing the program called “Google brain”, other NMT of translation software are relatively disadvantages. On the other hand, the Google translation enjoys the largest users in the world, with its downloads of more than one billion. The output quality of Google translation is more correct and complete than other machine translation software. With years of development and improvement, Google Translation has been greatly promoted. In this paper, Chinese medical abstract will be automatically translated by Google translation, and then the translation output will be compared with the translation by human and the publication of English abstracts. The main purpose is to prove that the improvement and promotion of quality and accuracy in the medical abstracts will be obtained through the pre-editing approaches.<br />
<br />
===2.Language Characteristics and Error Division===<br />
Since Chinese and English are two different languages, it is quite neccessary to identify their own characteristics so as to better analyze and understand the two languages. Tytler (1978) argued in his Essay on the Principles of Translation that there are three principles of translation that in all the translation should give readers the same feelings as the source text, except for complete transcript of ideas. There actually also exit some mistakes of these two languages. So the following will make some clarrifications of these two languages to ensure more accurate translation.<br />
<br />
===2.1 Language Characteristics of Medical Abstracts===<br />
Chinese and English belong to different language systems, so there are differences in their language structure and the way users think of the languages. When using machine translation from Chinese to English, due to the unequal language levels, there will be many mistakes in the translation process, especially for ESP texts, such as medical papers. <br />
Here, these differences mainly refer to the linguistic characteristics of medical abstracts. In medical abstracts, it usually includes structured and unstructured abstracts. Although in different forms, they both describe the purpose, methods and conclusions of the research.<br />
In the method section of medial abstract, several Chinese sentences can be connected with commas, but each sentence may convey different information. In contrast, English sentences contain a great deal of information, but in order to ensure clarity, some modifiers need to be isolated and then reconstructed. However, in Chinese-English machine translation, a lot of information is put into the sentence because the machine segments the sentence on the basis of the full comma.<br />
In addition, subjectless sentence is used in the objective and method parts of Chinese medical abstracts. The subjectless sentence means the sentence without or free from subject and is usually employed in two contexts. The first is "needless to say". In Chinese sentences, it is common to omit the subject of the sentence. Chinese sentences can convey meanings by using incomplete sentence structures, so Chinese speakers can understand the meanings of the sentences even though the sentence subject is omitted. The second is "emphasis of action". In this context, subjectless sentences are used to describe behaviors, especially in the study of traditional Chinese medicine abstracts. Comparatively speaking, it should be avoided in English medical abstracts. Abstract sentences are more subjective when describing the learning process, while the essential requirement of English medical abstract is objectivity. From this point, sentences without subject should be avoided in English medical abstracts. Another feature is voice. Few words with passive meanings appear in Chinese abstracts. English sentences are more favoured in using passive voice. When translating from Chinese to English, passive voice should be used to make the contents objective, which is also the basic requirement of medical papers.<br />
These are the linguistic features of Chinese medical abstracts. There are great differences in sentence structure and expression between Chinese and English medical abstracts. These differences may reduce the accuracy of machine translation, so it is necessary to introduce pre-editing to edit the source text to ensure that the source text can be accurately recognized by the machine and fully translated into English.<br />
===2.2 Error Division of Machine Translation in Translating Abstracts of Medical Papers from Chinese to English===<br />
In this paper, errors in machine translation are listed out after analyzing. Some are due to the principles, such as statistical-based MT and NMT, employed by machine translation. Based on the original errors, they can be studied from two levels. One is from the macro-level, referring to mistakes caused by objective factors. These are not human factors, but the disadvantage of machine translation and the limitations of language. The limitations of machine translation are derived from the principles and models adopted and manifests itself as a reliance on reference sources. In this case, semantic ambiguity and textual incoherence may occur in the absence of reference sources.<br />
However, for ESP texts such as medical papers, the requirements for terms and sentence patterns are far beyond the existing corpora. For unfamiliar texts (that is, the corresponding texts cannot be found in the corpora). The quality of output translation will be relatively low, which is determined by the principles of machine translation. As mentioned above, machine translation has evolved over the past few decades from rules-based to corp-based statistics to NMT. However, there still exist some limitations in machine translation.<br />
Mistakes on micro-level are mainly caused by the variations and differences of linguistic structure between Chinese and English. Chinese is an implicit language, while English is an explicit one (Lian, 2010) To put it in another way, Chinese expression does not depend on language structures, while English does the opposite. This may result in mismatches between the original and machine translated sentences. This kind of error is mainly divided into two types: component fragment and component missing. For a medical paper, such errors are very serious. As a kind of ESP discourse, medical paper has the characteristics of fixed, objective and accurate language structure. In order to reproduce the characteristics of medical abstracts in translation, it is necessary to avoid errors at the level of words and sentences and pay attention to logic and consistency.<br />
Lexical errors here refer to the inconsistency of fixed expressions in the translation of terms. Although these terms are machine translation based on the corpus, the corpus may not be perfect for ESP texts such as medical papers. Therefore, fixed expressions are not used in term translation. This is also the most common mistake in machine translation (the term here includes but is not limited to nouns). For ESP texts, medical text in particular, the fixed expressions and the accuracy of terms are of great importance.<br />
<br />
===3.Approaches Proposed for Pre-editing ===<br />
Generally speaking, there is a close relationship between pre-editing and post-editing, both of which aim to convey information and ensure high or publishable translation quality. Proper pre-editing can improve the quality of machine translation in terms of adequacy and consistency. <br />
Complexity of natural language and people use language arbitrarily, bringing many difficulties to Chinese-English machine translation, Hu Qingping (2005:24) has proposed "the research of translation software and the development of the controlled language are the two directions to improve the quality of machine translation : the former aims at the difficulty in the natural language processing, the latter overcomes the arbitrariness of natural language". Feng Quangong and Gao Lin (2017: 63-68) put forward: "The writing principles of controlled language can be applied to pre-editing of machine translation. Pre-editing based on controlled language can effectively reduce the complexity and ambiguity of source text, improve the identifiability of machine translation (the translatability of source text itself), and thus reduce (fully) the workload of post-editing".<br />
The central task of pre-editing is to transform human-friendly content into machine-friendly content, so words and sentences need to be repositioned or even changed. Based on the analysis of the language characteristics of Chinese and English, the Chinese ideographic group should be split before the Chinese source text is input into the machine software to translate so that the sentence structure is complete which can be easily recognized by the machine translation software.<br />
===3.1 Extraction and Replacement of Terms in the Source Text===<br />
As a branch of applied English, medical English is the product of the combination of English language knowledge and medical knowledge. The terms in medical papers have the characteristics of fixed and complex language structure. Although Google Translation is based on a corpus, the language structure is not fixed due to the limited size of the corpus. Therefore, the following two steps should be followed before machine translation. The first is to extract terms and create a glossary, and then replace the Chinese terms in the source text with the corresponding English expressions in the glossary. Of course, the terms of extraction should be searched and verified according to the actual situation. All of these words are verified before they can be replaced. This method can not only ensure the accuracy of term translation, but also maintain the consistency of expression, especially when dealing with long texts.<br />
Example1<br />
Source abstract: 口腔白斑病癌变相关缺氧应答基因和微小RNA的芯片及表达验证。<br />
Google translation: Microarray detection/Chip detection and expression verification of hypoxia response genes and microRNAs related to oral leukoplakia canceration.<br />
Published translation:Transcriptome array screening and verification of oral leukoplakia carcinogenesis-related hypoxia-responsive gene and microRNA. <br />
Analysis: The expression “ Affymetrix GeneChip” empolyed in this thesis is a human transcription array for transcriptome array. In the source abstract, “芯片检测“ can be meant “screening” but not detection, so the proper and appropriate translation of these expression should be “transcription array screening”, but the Google translates it into “microarry detection of chip detection”. Therefore, term extraction is essential and inevitable before putting the source abstracts into the machine translation software.<br />
After pre-editing source abstracts: 对 oral leukoplakia carcinogenesis 相关 hypoxia-responsive gene 和微小RNA进行 transcriptome array screening 及表达验证。<br />
After pre-editing translation: Transcriptome array screening and expression- verification of hypoxia-responsive genes and microRNAs related to oral leukoplakia carcinogenesis.<br />
===3.2 Explication of Subordination===<br />
As mentioned above, the subordination of Chinese sentences is judged by certain specific words in machine translation . Chinese is a paratactic language, and sentences are often connected by internal logical relationships; while English depends on sentence structure, so sentences are often closely linked by various language forms. In order to improve the accuracy of machine translation, we can adjust the sentence structure and adjust the position of adjectives and their modifiers. <br />
Example 2<br />
Source abstracts:回顾性分析南京医科大学附属儿童医院中重度HIE患儿49例及同期就诊的无神经系统症状体征的足月新生儿为对照组31例的头颅磁共振成像（MRI）资料。<br />
Google translation: A retrospective analysis that the brain magnetic resonance imaging (MRI) data of 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University and full-term neonates with no neurological symptoms and signs during the same period were included in the control group.<br />
Published translation: A total of 49 children with moderate to severe HIE admitted to the children’s Hospital Affiliated to Nanjing Medical University were retrospectively analyzed. Cranial magnetic resonance imaging (MRI) date of 31 full-term neonates without neurological symptoms and signs who visited the hospital during the same period were recruited as the control group. <br />
Analysis: As the above example shows that “中重度HIE患儿” and “足月新生儿” in the source abstracts are from the Children’s Hospital Affiliated Nanjing Medical University. The Google translation shows that 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University. There is an error due to the misuse of subordination. There are some advice in order to solve the problem. That is, first to segment the sentence and then reconstruct the sentence. For instance, the Chinese expression “南京医科大学附属儿童医院” carries many modifiers, which requires to cut the modifiers and reconstruct the sentence. Therefore, the Chinese expression “南京医科大学附属儿童医院’can be divided into abstractions, leaving two sentences instead of one long sentence with modifiers..<br />
After pre-editing source abstracts: 对49例中重度HIE患儿以及31例无神经系统症状体征的足月新生儿的MRI资料进行回顾性分析。这些患者收治于南京医科大学儿童附属医院。<br />
After pre-editing translation: The MRI data of 49 children with moderate to severe HIE and 31 full-term newborns without neurological symptoms and signs were retrospectively analyzed. These patients were admitted to the Children’s Hospital of Nanjing Medical University.<br />
===3.3 Explication of Subject through Voice Changing===<br />
The extensive use of subjectless sentences are quite common in the Chinese abstracts, while English prefers to employ passive voice in the sentences so as to make them object and accurate. In order to take the characteristics of English sentences into great and careful consideration, the sentences written in passive voice in particular, it will be helpful for machine translation to find out the subject and reconstruct a sentence in passive voice (Wang Yan: 2008). The first is to discover the “doee” in a sentence and then is to reconstruct the sentence structure and put the “doee” before the verb. The last is to explicate the passive relation between the noun and the verb.<br />
Example 3<br />
Source abstract: 回顾性分析2018年1月至2020年12月解放军总医院第七医学中心收治的500例老年髋部骨折患者的资料。<br />
Google translation: A retrospective analysis of the data of 500 elderly patients with hip fractures admitted to the Seventh Medical Center of the PLA General Hospital from January 2018 to December 2020.<br />
Published translation: From January 2018 to December 2020, the data of 500 elderly patients with hip fracture treated in the Seventh Medical Center of PLA General Hospital were analyzed retrospectively.<br />
Analysis: The above example indicates that the “回顾性分析”in the Google Translate is a noun phrase, but the source abstracts actually describes an action or a behavior. The reason for such a mistake is that the machine can’t recognize the Chinese subjetless sentences. To find the subject of the sentence, the source abstracts can be revised and adjusted. Finding the “doee” is the first step, which refers to “500例老年髋部骨折患者的资料”in the source abstracts. And next is to put it in front of the verb, that is to place it in the beginning of the sentence. Last but not least, the passive voice should be explicated in the sentence. <br />
After pre-editing source abstract: 500例老年髋部骨折患者的资料被回顾性分析，他们在2018年1月之2020年12月期间收治于解放军总医院第七医学中心。<br />
After pre-editing translation: The data of 500 elderly hip fracture patients were retrospectively analyzed. They were admitted to the Seventh Medical Center of the PLA General Hospital between January 2018 and December 2020.<br />
===3.4 Relocation of Modifiers===<br />
Modifiers, including noun, adverbial and attributive are constantly employed in the Chinese medical papers in order to add information to subject and object in the sentence. By doing this, complicated sentences can be structured, thus causing obstacles to machine translation for recognizing this complex sentence structures when translating Chinese-English sentences. To make the machine translation successfully recognize the sentence structure, simplifying the sentence structure is quite necessary. The key is to moving the location of the modifiers and thus making the modifiers an independent sentence. In other words, the modifiers ought to be put before or behind the main part of the sentence to satisfy the common use of English. <br />
Usually, the modifiers in Chinese sentence can only be placed in front of the core word, while in English, modifiers are very flexible. It is all right to place the modifiers in front of or behind the major part of the sentences with adjective or a connective noun.<br />
Example 4<br />
Source abstracts: 利用DNA重组技术以pET-28a表达系统在E.coli BL21(DE3) 中重组表达Hepl。<br />
Google Translation: Hepl is recombined in E.coli BL21 (DE3) using DNA recombination technology with pET-28a expression system.<br />
Published translation: The recombinant Hcpl protein was expressed by using DNA recombination technology through pET-28a expression system in E. coli BL21 (De3).<br />
Analysis: The Chinese medical text includes two modifiers, “利用DNA”and “以pET-28a)表达系统”. These two modifiers will be translated by machine, which catches more attention to the two constituents. From the Google translation above, one failure is obvious that it misplaces the location of the two modifiers and presents it in not accurate form. To make it translate correctly by machine translation, dividing the sentences is very important so that the source abstracts can be correctly recognized by machine translation.<br />
After pre-editing source abstract: 利用DNA重组技术，Hcp1重组表达在E.coli BL21 (DE3)中， 通过pET-28a表达系统在E. coli BL21 (DE3)中。<br />
Google translation: Using DNA recombination technology, Hcp1 recombination is expressed in E.coli BL21 (DE3), via pET-28a expression system in E. coli BL21 (DE3).<br />
The second is the independence of modifiers, that is, modifiers can also be reconstructed into clauses to modify core words. Compared with English, There is no subordinate clause in Chinese, so redundant Chinese modifiers need to be reconstructed into subordinate clauses in Chinese-English translation to meet the characteristics of English. English, especially sentences with multiple modifiers. Otherwise, sentence structure may be confused, such as scattered modifiers of core words. To avoid this mistake, it is necessary to separate the modifier from the main part of the sentence. These modifiers should be reconstituted into clauses. Also, keep your sentences simple and easy to understand.<br />
===3.5 Proper Omission and Deletion of Category Words===<br />
Category words are commonly used in Chinese. Category words complement the meaning of words, including problems, positions, situations and jobs. Adding this supplementary word is more in line with Chinese custom. In many cases, it has no real meaning, so it can be omitted in translation. In Chinese, category words are frequently used. However, it is rarely used in English, which is one of the differences between Chinese and English. There are many kinds of category words. Considering objectivity and the fixed structure of language, redundant category words should be deleted in Chinese-English translation. In the general, the most commonly used category of words in the Chinese medical abstracts are "process", "behavior", and "situation". These categories of words hinder the language conversion of Chinese and English, resulting in redundancy. <br />
Example 5<br />
Source abstract: 探讨口腔白斑病癌变进程中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia response genes and associated microRNAs in the process of oral leukoplakia cancer was discussed.<br />
Published translation: To study the hypoxia response gene and microRNA (miRNA)expression profiles in the pathogenesis and progression of oral leukoplakia (OLK).<br />
Analysis: As the above example shows, the Chinese words “进程” belongs to a category word. However, the Chinese expression “癌变”contains the process, so the “进程” expression can be deleted before placing it into the machine translation, because the meaning of it has been overlapping between the expression “癌变”. Therefore, its place can be replaced with preposition “during”.<br />
After pre-editing source abstract: 探讨口腔白斑病癌变中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia responsive genes and miRNA in oral leukoplakia cancer was investigated.<br />
<br />
===Conclusion===<br />
After years of development, machine translation has made great progress. The accuracy of machine translation has been greatly improved in both text recognition and sentence pattern conversion. However, machine translation has its own limitations. In other words, it needs to rely on the parallel corpus as a reference source for improving its accuracy. ESP text, in particular, is harder to get the high quality by machine translation. <br />
As one of the research papers, the characteristics of medical abstracts are fixed language structures, objectivity and accuracy (Qin Yi:2004). Therefore, medical translation must be accurate, object and understandable to follow the specific demands of the medical paper. Being an important field in the human society, medical paper translation is on a great demand, which means that it needs a huge demand for human labor. However, with the machine translation promoting, it will be more efficient to translate medical papers combining the effort by human and machine. The improvement and development of machine translation requires the joint efforts of computer science, information science, statistics, linguistics and other academic circles to achieve more mature human-computer mutual assistance translation (Li Yafei, Zhang Ruihua : 2019).<br />
However, errors can occur during the process of machine translation of Chinese- English, because of the differences of the Chinese and English and the processing of the machine. Errors from the perspective of linguistic or grammar can affect the machine translation a lot. After division and recognition of errors, some pre-editing approaches are put forward to help the machine translation more accurate and readable, that are, extraction and replacement of terms in the source text, relocation of modifiers, explication of subordination, proper omission, deletion of category words and explication of subject through voice changing. <br />
The paper mainly focuses on the pre-editing machine translation by using medical papers as a case study. The errors of machine translation occurring in the translation of medical abstracts and pre-editing approaches for machine translation. The quality of machine translation of medical papers is greatly improved after employing the pre-editing methods. However, machine translation is not as flexible and accurate as human brain, so it is of importance to combine pre-editing and post-editing approaches with machine translation in order to produce more accurate, more object machine translation of medical papers.<br />
<br />
===References===<br />
<br />
=12 蔡珠凤=(The Mistranslation of C-J Machine Translation of Political Statements)=<br />
[[Machine_Trans_EN_12]]<br />
===Abstract===<br />
Language is the main way of communication between people. With the continuous development of globalization, the scale of cross-border exchanges is also expanding. However, due to cultural differences and diversity, the languages of different countries and regions are very different, which seriously hinders people's communication. The demand for efficient and convenient translation tools is increasing. At the same time, with the development of network technology and artificial intelligence, recognition technology based on deep learning is more and more widely used in English, Japanese and other fields.<br />
<br />
===Key words===<br />
machine translation; political statements; mistranslation of C-J machine translation<br />
===题目===<br />
The Mistranslation of C-J Machine Translation of Political Statements<br />
===摘要===<br />
语言是人与人之间交流的主要方式。随着全球化的不断发展，跨境交流的规模也在不断扩大。然而，由于文化的差异和多样性，不同国家和地区的语言差异很大，这严重阻碍了人们的交流。对高效便捷的翻译工具的需求正在增加。同时，随着网络技术和人工智能的发展，基于深度学习的识别技术在英语、日语等领域的应用越来越广泛。<br />
<br />
===关键词===<br />
机器翻译；政治发言；政治发言中译日的误译<br />
===1. Introduction===<br />
===Introduction to machine translation===<br />
Machine translation, also known as automatic translation, is a process of using computers to convert one natural language (source language) into another natural language (target language). It is a branch of computational linguistics, one of the ultimate goals of artificial intelligence, and has important scientific research value.At the same time, machine translation has important practical value. With the rapid development of economic globalization and the Internet, machine translation technology plays a more and more important role in promoting political, economic and cultural exchanges.The development of machine translation technology has been closely accompanied by the development of computer technology, information theory, linguistics and other disciplines. From the early dictionary matching, to the rule translation of dictionaries combined with linguistic expert knowledge, and then to the statistical machine translation based on corpus, with the improvement of computer computing power and the explosive growth of multilingual information, machine translation technology gradually stepped out of the ivory tower and began to provide real-time and convenient translation services for general users.<br />
<br />
=== C-J machine translation software===<br />
Today's online machine translation software includes Baidu translation, Tencent translation, Google translation, Youdao translation, Bing translation and so on. Google was the first company to launch the machine translation system, and Baidu was the first company to import the machine translation system in China. In addition, Tencent and Youdao have attracted much attention.Machine translation is the process of using computers to convert one natural language into another. It usually refers to sentence and full-text translation between natural languages. In order to continuously improve the translation quality, R & D personnel have added artificial intelligence technologies such as speech recognition, image processing and deep neural network to machine translation on the basis of traditional machine translation based on rules, statistics and examples.With the increase of using machine translation, the joint cooperation between manual translation and machine translation will also increase significantly in the future. What criteria should be used to evaluate the quality of machine translation? In the evolving field of machine translation, there is an urgent need to clarify the unsolvable questions and solved problems.<br />
<br />
===The history of machine translation===<br />
The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. alchuni put forward the idea of using machines for translation. In 1933, Soviet inventor П.П. Trojansky designed a machine to translate one language into another, and registered his invention on September 5 of the same year; However, due to the low technical level in the 1930s, his translation machine was not made. In 1946, the first modern electronic computer ENIAC was born. Shortly after that, W. weaver, an American scientist and A. D. booth, a British engineer, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947 when discussing the application scope of electronic computer. In 1949, W. Weaver published the translation memorandum, which formally put forward the idea of machine translation. After 60 years of ups and downs, machine translation has experienced a tortuous and long development path. The academic community generally divides it into the following four stages:<br />
Pioneering period（1947-1964）<br />
In 1954, with the cooperation of IBM, Georgetown University completed the English Russian machine translation experiment with ibm-701 computer for the first time, showing the feasibility of machine translation to the public and the scientific community, thus opening the prelude to the study of machine translation.<br />
It is not too late for China to start this research. As early as 1956, the state included this research in the national scientific work development plan. The topic name is "machine translation, the construction of natural language translation rules and the mathematical theory of natural language". In 1957, the Institute of language and the Institute of computing technology of the Chinese Academy of Sciences cooperated in the Russian Chinese machine translation experiment, translating 9 different types of more complex sentences.<br />
From the 1950s to the first half of the 1960s, machine translation research has been on the rise. The United States and the former Soviet Union, two superpowers, have provided a lot of financial support for machine translation projects for military, political and economic purposes, while European countries have also paid considerable attention to machine translation research due to geopolitical and economic needs, and machine translation has become an upsurge for a time. In this period, although machine translation is just in the pioneering stage, it has entered an optimistic period of prosperity.<br />
Frustrated period（1964-1975）<br />
In 1964, in order to evaluate the research progress of machine translation, the American Academy of Sciences established the automatic language processing Advisory Committee (Alpac Committee) and began a two-year comprehensive investigation, analysis and test.<br />
In November 1966, the committee published a report entitled "language and machine" (Alpac report for short), which comprehensively denied the feasibility of machine translation and suggested stopping the financial support for machine translation projects. The publication of this report has dealt a blow to the booming machine translation, and the research of machine translation has fallen into a standstill. Coincidentally, during this period, China broke out the "ten-year Cultural Revolution", and basically these studies also stagnated. Machine translation has entered a depression.<br />
convalescence（1975-1989）<br />
Since the 1970s, with the development of science and technology and the increasingly frequent exchange of scientific and technological information among countries, the language barriers between countries have become more serious. The traditional manual operation mode has been far from meeting the needs, and there is an urgent need for computers to engage in translation. At the same time, the development of computer science and linguistics, especially the substantial improvement of computer hardware technology and the application of artificial intelligence in natural language processing, have promoted the recovery of machine translation research from the technical level. Machine translation projects have begun to develop again, and various practical and experimental systems have been launched successively, such as weinder system Eurpotra multilingual translation system, taum-meteo system, etc.<br />
However, after the end of the "ten-year holocaust", China has perked up again, and machine translation research has been put on the agenda again. The "784" project has paid enough attention to machine translation research. After the mid-1980s, the development of machine translation research in China has further accelerated. Firstly, two English Chinese machine translation systems, ky-1 and MT / ec863, have been successfully developed, indicating that China has made great progress in machine translation technology.<br />
New period(1990 present)<br />
With the universal application of the Internet, the acceleration of the process of world economic integration and the increasingly frequent exchanges in the international community, the traditional way of manual operation is far from meeting the rapidly growing needs of translation. People's demand for machine translation has increased unprecedentedly, and machine translation has ushered in a new development opportunity. International conferences on machine translation research have been held frequently, and China has made unprecedented achievements. A series of machine translation software have been launched, such as "Yixing", "Yaxin", "Tongyi", "Huajian", etc. Driven by the market demand, the commercial machine translation system has entered the practical stage, entered the market and came to the users.<br />
Since the new century, with the emergence and popularization of the Internet, the amount of data has increased sharply, and statistical methods have been fully applied. Internet companies have set up machine translation research groups and developed machine translation systems based on Internet big data, so as to make machine translation really practical, such as "Baidu translation", "Google translation", etc. In recent years, with the progress of in-depth learning, machine translation technology has further developed, which has promoted the rapid improvement of translation quality, and the translation in oral and other fields is more authentic and fluent.<br />
<br />
===The problem of machine translation at present===<br />
Error is inevitable<br />
Many people have misunderstandings about machine translation. They think that machine translation has great deviation and can't help people solve any problems. In fact, the error is inevitable. The reason is that machine translation uses linguistic principles. The machine automatically recognizes grammar, calls the stored thesaurus and automatically performs corresponding translation. However, errors are inevitable due to changes or irregularities in grammar, morphology and syntax, such as sentences with adverbials after "give me a reason to kill you first" in Dahua journey to the West. After all, a machine is a machine. No one has special feelings for language. How can it feel the lasting charm of "the tenderness of lowering its head, like the shame of a water lotus? After all, the meaning of Chinese is very different due to the changes of morphology, grammar and syntax and the change of context. Even many Chinese people are zhanger monks - they can't touch their heads, let alone machines.<br />
Bottleneck<br />
In fact, no matter which method, the biggest factor affecting the development of machine translation lies in the quality of translation. Judging from the achievements, the quality of machine translation is still far from the ultimate goal.<br />
Chinese mathematician and linguist Zhou Haizhong once pointed out in his paper "fifty years of machine translation": to improve the quality of machine translation, the first thing to solve is the problem of language itself rather than programming; It is certainly impossible to improve the quality of machine translation by relying on several programs alone. At the same time, he also pointed out that it is impossible for machine translation to achieve the degree of "faithfulness, expressiveness and elegance" when human beings have not yet understood how the brain performs fuzzy recognition and logical judgment of language. This view may reveal the bottleneck restricting the quality of translation. <br />
It is worth mentioning that American inventor and futurist ray cozwell predicted in an interview with Huffington Post that the quality of machine translation will reach the level of human translation by 2029. There are still many disputes about this thesis in the academic circles.<br />
<br />
===2.Mistranslation of Chinese Japanese machine translation===<br />
===2.1Vocabulary mistranslation in Chinese Japanese machine translation===<br />
===2.1.1Mistranslation of proper nouns===<br />
In political materials, there are often political dignitaries' names, place names or a large number of proper nouns in the political field. The morphemes of such words are definite and inseparable. Mistranslation will make the source language lose its specific meaning. <br />
<br />
Chinese translation into Japanese Japanese translation into Chinese<br />
<br />
original text translation by Youdao reference translation original text translation by Youdao reference translation<br />
栗战书 栗戰史書 栗戰書 労安 劳安 劳安<br />
李克强 李克強 李克強 朱鎔基 朱基 朱镕基<br />
习近平 習近平 習近平 筑紫哲也 筑紫哲也 筑紫哲也<br />
韩正 韓中 韓正 山口百惠 山口百惠 山口百惠<br />
王沪宁 王上海氏 王滬寧 田中角栄 田中角荣 田中角荣<br />
汪洋 汪洋 汪洋 東条英機 东条英社 东条英机<br />
赵乐际 趙樂南 趙樂際 毛沢东 毛泽东 毛泽东<br />
江泽民 江沢民 江沢民 トウ・ショウヘイ 大酱 邓小平<br />
周恩来 周恩来 周恩来<br />
クリントン 克林顿 克林顿<br />
<br />
The above table counts 18 special names in the two texts, and 7 machine translation errors. In terms of mistranslation, there are not only "战书" but also "戰史" and "史書" in Chinese. "沪" is the abbreviation of Shanghai. In other words, since "战书" and "沪" are originally common nouns, this disrupts the choice of target language in machine translation. Except Katakana interference (except for トウシゃウヘイ, most of the mistranslations appear in the new Standing Committee. It can be seen that the machine translation system does not update the thesaurus in time. Different from other words, people's names have particularity. Especially as members of the Standing Committee of the Political Bureau of the CPC Central Committee, the translation of their names is officially unified. In this regard, public figures such as political dignitaries, stars, famous hosts and important. In addition, the machine also translates Japanese surnames such as "タ二モト" (谷本), "アンドウ" (安藤) into "塔尼莫特" and "龙胆". It can be found that the language feature that Japanese will be written together with Chinese characters and Hiragana also directly affects the translation quality of Japanese Chinese machine translation. Japanese people often use Katakana pronunciation. For example, when Japanese people talk with Premier Zhu, they often use "二ーハウ" (Hello). However, the machine only recognizes the two pseudonyms "二" and "ハ" and transliterates them into "尼哈" , ignoring the long sound after "二" and "ハ".<br />
<br />
original text Translation by Youdao reference translation<br />
日美安全体制 日米の安全体制 日米安保体制<br />
中国共产党第十九次全国代表大会 中国共産党第19回全国代表大会 中国共産党第19回全国代表大会（第19回党大会）<br />
十八大 十八大 第18回党大会<br />
中国特色社会主义 中国特色社会主義 中国の特色ある社会主義<br />
中国共产党中央委员会 中国共産党中央委員会 中国共産党中央委員会<br />
十八届中共中央政治局常委 第18代中国共產党中央政治局常務委員 第18期中共中央政治局常務委員<br />
十八届中共中央政治局委员 18期の中国共產党中央政治局委員 第18期中共中央政治局委員<br />
十九届中共中央政治局常委 十九回中国共產党中央政治局常務委員 第19期中央政治局常務委員<br />
中共十九届一中全会 中国共產党第十九回一中央委員会 第19期中央委員会第1回全体会議<br />
The above table is a comparison of the original and translated versions of some proper nouns. As shown in the table, the mistranslation problems are mainly reflected in the mismatch of numerals + quantifiers, the wrong addition of case auxiliary word "の", the lack of connectors, the Mistranslation of abbreviations, dead translation, and the writing errors of Chinese characters. The following is a specific analysis one by one.<br />
"十八届中共中央政治局常委", "十八届中共中央政治局委员", "十九届中共中央政治局常委" and "中共十九届一中全会" all have quantifiers "届", which are translated into "代", "期" and "回" respectively. The meaning is vague and should be uniformly translated into "期"; Among them, the translation of the last three proper nouns lacks the Chinese conjunction "第". The case auxiliary word "の" was added by mistake in the translation of "十八届中共中央政治局委员" and "日美安全体制". The "中国共产党中央委员会" did not write in the form of Japanese Ming Dynasty characters, but directly used simplified Chinese characters.<br />
The full name of "中共十九届一中全会" is "中国共产党第十九届中央委员会第一次全体会议", in which "一" stands for "第一次", which is an ordinal word rather than a cardinal word. Machine translation does not produce a correct translation. The fundamental reason is that there are no "规则" for translating such words in machine translation. If we can formulate corresponding rules for such words (for example, 中共m'1届m2 中全会→第m1期中央委员会第m2回全体会议), the translation system must be able to translate well no matter how many plenary sessions of the CPC Central Committee.<br />
<br />
===2.1.2Mistranslation of Polysemy===<br />
<br />
original text Translation by Youdao reference translation<br />
スタジオ 摄影棚/工作室 直播现场/演播厅<br />
日中関係の話 中日关系的故事 就中日关系(话题)<br />
溝 水沟 鸿沟<br />
それでは日中の問題について質問のある方。 那么对白天的问题有提问的人。 关于中日问题的话题，举手提问。<br />
私たちのクラスは20人ちょっとですが、 我们班有20人左右， 我们班二十多人的意见统一很难，<br />
いろいろな意見が出て、まとめるのは大変です。 但是又各种各样的意见，总结起来很困难。 中国是怎样把13亿人凝聚在一起的？<br />
一体どうやって、13億人もの人をまとめているんですか。 到底是怎么处理13亿的人的呢？ <br />
In the original text, the word "スタジオ" appeared four times and was translated into "摄影棚" or "工作室" respectively, but the context is on-site interview, not the production site of photography or film. "話" has many semantics, such as "说话", "事情", "道理", etc. In accordance with the practice of the interview program, Premier Zhu Rongji was invited to answer five questions on the topic of China Japan relations. Obviously, the meaning of the word "故事" is very abrupt. The inherent Japanese word "日中" means "晌午、白天". At the same time, it is also the abbreviation of the names of the two countries. The machine failed to deal with it correctly according to the context. "溝" refers to the gap between China and Japan, not a "水沟". The former "まとめる" appears in the original text with the word "意见", which is intended to describe that it is difficult to unify everyone's opinions. The latter "まとめている" also refers to unifying the thoughts of 1.3 billion people, not "处理". Therefore, it is more appropriate to use "统一" and "处理" in human translation, rather than "总结意见" or "处理意见".<br />
Mistranslation of polysemy has always been a difficult problem in machine translation research. The translation of each word is correct, but it is often very different from the original expression in the context. Zhang Zhengzeng said that ambiguity is a common phenomenon in natural language. Its essence is that the same language form may have different meanings, which is also one of the differences between natural language and artificial language. Therefore, one of the difficulties faced by machine translation is language disambiguation (Zhang Zheng, 2005:60). In this regard, we can mark all the meanings of polysemous words and judge which meaning to choose by common collocation with other words. At the same time, strengthen the text recognition ability of the machine to avoid the translation inconsistent with the current context. In this way, we can avoid the mistake of "大酱" in the place where famous figures such as Deng Xiaoping should have appeared in the previous political articles.<br />
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===2.1.3Mistranslation of compound words===<br />
Multi class words refer to a word with two or more parts of speech, also known as the same word and different classes.<br />
original text Translation by Youdao reference translation<br />
1998年江泽民主席曾经访问日本, 1998年の江沢民国家主席の日本訪問し、 1998年、江沢民総書記が日本を訪問し、かつ、<br />
同已故小渊首相签署了联合宣言。 かつて同じ故小渕首相が署名した共同宣言 亡くなられた小渕総理と宣言に調印されました<br />
<br />
Chinese "同" has two parts of speech: prepositions and conjunctions: "故" has many parts of speech, such as nouns, verbs, adjectives and conjunctions. This directly affects the judgment of the machine at the source language level. The word "同" in the above table is used as a conjunction to indicate the other party of a common act. "故" is used as a verb with the semantic meaning of "死亡", which is a modifier of "小渊首相". The machine regards "同" as an adjective and "故" as a noun "原因", which leads to confusion in the structure and unclear semantics of the translation. Different from the polysemy problem, multi category words have at least two parts of speech, and there is often not only one meaning under each part of speech. In this regard, software R & D personnel should fully consider the existence of multi category words, so that the translation machine can distinguish the meaning of words on the basis of marking the part of speech, so as to select the translation through the context and the components of the word in the sentence. Of course, the realization of this function is difficult, and we need to give full play to the wisdom of R & D personnel.<br />
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===2.2 Syntactic mistranslation in Chinese Japanese machine translation===<br />
===2.2.1Mistranslation of tenses===<br />
original text：History is written by the people, and all achievements are attributed to the people. <br />
Translation by Youdao：歴史は人民が書いたものであり、すべての成果は人民のためである。<br />
reference translation：歴史は人民が綴っていくものであり、すべての成果は人民に帰することとなります。<br />
The original sentence meaning is "历史是人民书写的历史" or "历史是人民书写的东西". When translated into Japanese, due to the influence of Japanese language habits, the formal noun "もの" should be supplemented accordingly. In this sentence, both the machine and the interpreter have translated correctly. However, the machine's recognition of "的" is biased, resulting in tense translation errors. The past, present and future will become "history", and this is a continuous action. The form translated as "~ ていく" not only reflects that the action is a continuous action, but also conforms to the tense and semantic information of the original text. In addition, the machine's treatment of the preposition "于" is also inappropriate. In the original text, "人民" is the recipient of action, not the target language. As the most obvious feature of isolated language, function words in Chinese play an important role in semantic expression, and their translation should be regarded as a focus of machine translation research.<br />
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original text：李克强同志是十六届中共中央政治局常委,其他五位同志都是十六届中共中央政治局委员。<br />
Translation by Youdao：李克強総理は第16代中国共産党中央政治局常務委員であり、他の５人の同志はいずれも16期の中国共産党中央政治局委員である。<br />
reference translation：李克強同志は第16期中国共産党中央政治局常務委員を務め、他の５人は第16期中共中央政治局委員を務めました。<br />
Judging the verb "是" in the original text plays its most basic positive role, but due to the complexity of Chinese language, "是" often can not be completely transformed into the form of "だ / である" in the process of translation. This sentence is a judgment of what has happened. Compared with the 19th session, the 18th session has become history. This is an implicit temporal information. It is very difficult for machines without human brain to recognize this implicit information. "中共中央政治局常委" is the abbreviation of "中共中央政治局常务委员会委员" and it is a kind of position. In Japanese, often use it with verbs such as "務める","担当する",etc. The translator adopts the past tense of "務める", which conforms to the expression habit of Japanese and deals with the problem of tense at the same time. However, machine translation only mechanically translates this sentence into judgment sentence, and fails to correctly deal with the past information implied by the word "十八届".<br />
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===2.2.2Mistranslation of honorifics===<br />
Honorific language is a language means to show respect to the listener. Different from Japanese, there is no grammatical category of honorifics in Chinese. There is no specific fixed grammatical form to express honorifics, self modesty and politeness. Instead, specific words such as "您", "请" and "劳驾" are used to express all kinds of respect or self modesty. <br />
Original text translation by Youdao reference translation<br />
女士们，先生们，同志们，朋友们 さんたち、先生たち、同志たち、お友达さん ご列席の皆さん<br />
谢谢大家！ ありがとうございます！ ご清聴ありがとうございました。<br />
これはどうされますか。 这是怎么回事呢? 您将如何解决这一问题？<br />
こうした問題をどうお考えでしょうか。 我们会如何考虑这些问题呢？ 您如何看待这一问题？ <br />
For example, for the processing of "女士们，先生们，同志们，朋友们", the machine makes the translation correspond to the original one by one based on the principle of unchanged format, but there are errors in semantic communication and pragmatic habits. When speaking on formal occasions, Chinese expression tends to be comprehensive and detailed, as well as the address of the audience. In contrast, Japanese usually uses general terms such as "皆様", "ご臨席の皆さん" or "代表団の方々" as the opening remarks. In terms of Japanese Chinese translation, the machine also failed to recognize the usage of Japanese honorifics such as "さ れ る" and "お考え". It can be seen that Chinese Japanese machine translation has a low ability to deal with honorific expressions. The occasion is more formal. A good translation should not only be fluent in meaning, but also conform to the expression habits of the target language and match the current translation environment.<br />
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===Conclusion===<br />
In the process of discussing the Mistranslation of machine translation, this paper mainly cites the Mistranslation of Youdao translator. When I studied the problem of machine translation mistranslation, I also used a large number of translation software such as Google and Baidu for parallel comparison, and found that these translation software also had similar problems with Youdao translator. For example, the "新世界新未来" in Chinese ABAC structural phrases is translated by Baidu as "新し世界の新しい未来", which, like Youdao, is not translated in the form of parallel phrases; Google online translation translates the word "“全心全意" into a completely wrong "全面的に"; The original sentence "我吃了很多亏" in the Mistranslation of the unique expression in the language is translated by Google online as "私はたくさんの損失を食べました". Because the "吃" and "亏" in the original text are not closely adjacent, the machine can not recognize this echo and mistakenly treats "亏" as a kind of food, so the machine translates the predicate as "食べました"; Japanese "こうした問題をどう考えでしょうか", Google's online translation is "你如何看待这些问题?", "你" tone can not reflect the tone of Japanese honorific, while Baidu translates as "你怎么想这样的问题呢?" Although the meaning can be understood, it is an irregular spoken language. Google and Baidu also made the same mistakes as Youdao in the translation of proper nouns. For example, they translated "十七届(中共中央政治局常委)”" into "第17回..." .In short, similar mistranslations of Youdao are also common in Baidu and Google. Due to space constraints, they will not be listed one by one here. <br />
Mr. Liu Yongquan, Institute of language, Chinese Academy of Social Sciences (1997) It has been pointed out that machine translation is a linguistic problem in the final analysis. Although corpus based machine translation does not require a lot of linguistic knowledge, language expression is ever-changing. Machine translation based solely on statistical ideas can not avoid the translation quality problems caused by the lack of language rules. The following is based on the analysis of lexical, syntactic and other mistranslations From the perspective of the characteristics of the source language, this paper summarizes some difficulties of Chinese and Japanese in machine translation. <br />
(1) The difficulties of Chinese in machine translation <br />
Chinese is a typical isolated language. The relationship between words needs to be reflected by word order and function words. We should pay attention to the transformation of function words such as "的", "在", "向" and "了". Some special verbs, such as "是", "做" and "作", are widely used. How to translate on the basis of conforming to Japanese pragmatic habits and expressions is a difficulty in machine translation research. In terms of word order, Chinese is basically "subject→predicate→object". At the same time, Chinese pays attention to parataxis, and there is no need to be clear in expression with meaningful cohesion, which increases the difficulty of Chinese Japanese machine translation. When there are multiple verbs or modifier components in complex long sentences, machine translation usually can not accurately divide the components of the sentence, resulting in the result that the translation is completely unreadable. <br />
(2) Difficulties of Japanese in machine translation <br />
Both Chinese and Japanese languages use Chinese characters, and most machines produce the target language through the corresponding translation of Chinese characters. However, pseudonyms in Japanese play an important role in judging the part of speech and meaning, and can not only recognize Chinese characters and judge the structure and semantics of sentences. Japanese vocabulary is composed of Chinese vocabulary, inherent vocabulary and foreign vocabulary. Among them, the first two have a great impact on Chinese Japanese machine translation. For example "提出" is translated as "提出する" or "打ち出す"; and "重视" is translated as "重視する" or "大切にする". Japanese is an adhesive language, which contains a lot of "けど", "が" and "けれども" Some of them are transitional and progressive structures, but there are also some sequential expressions that do not need translation, and these translation software often can not accurately grasp them.<br />
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===References===<br />
[1] Navroz Kaur Kahlon;Williamjeet Singh.Machine translation from text to sign language: a systematic review[J].Universal Access in the Information Society,2021(prepublish):1-35.<br />
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[2] Cao Qianyu;Hao Hanmei;Ahmed Syed Hassan.A Chaotic Neural Network Model for English Machine Translation Based on Big Data Analysis[J].Computational Intelligence and Neuroscience,2021,2021:3274326-3274326.<br />
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[3]Hwang Yongkeun;Kim Yanghoon;Jung Kyomin.Context-Aware Neural Machine Translation for Korean Honorific Expressions[J].Electronics,2021,10(13):1589-1589.<br />
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[4]Zakaryia Almahasees.Analysing English-Arabic Machine Translation:Google Translate, Microsoft Translator and Sakhr,2021.<br />
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[5]Machine learning in translation[J].Nature Biomedical Engineering,2021,5(6):485-486.<br />
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[6]Shaimaa Marzouk.An in-depth analysis of the individual impact of controlled language rules on machine translation output: a mixed-methods approach[J].Machine Translation,2021(prepublish):1-37.<br />
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[7]Welnitzová Katarína;Munková Daša.Sentence-structure errors of machine translation into Slovak[J].Topics in Linguistics,2021,22(1):78-92.<br />
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[8]Xu Xueyuan.Machine learning-based prediction of urban soil environment and corpus translation teaching[J].Arabian Journal of Geosciences,2021,14(11). <br />
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[9]陈丙昌.機械翻訳の誤訳分析【D】.贵州大学.2016(05) <br />
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[10]呂寅秋.機械翻訳の言語規則と伝統文法との相違点.日本学研究.1996(00):21-22 <br />
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[11]刘君.基于语料库的中日同形词词义用法对比及其日中机器翻译研究【D】.广西大学.2014(03) <br />
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[12]崔倩倩.机器翻译错误与译后编辑策略研究【D】.北京外国语大学.2019(09) <br />
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[13]张义.机器翻译的译文分析【D】.西安外国语大学.2019(10) <br />
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[14]张琳婧.在线机器翻译中日翻译错误原因及对策【D】.山西大学.2019(02)<br />
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[15]王丹.基于机器翻译的专利文本译后编辑对策研究【D】.大连理工大学.2020(06)<br />
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[16]杨晓琨.日中机器翻译中的前编辑规则与效果验证【D】.大连理工大学.2020(06)<br />
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[17]左嘉. 机器翻译日译汉误译研究[D]. 北京第二外国语学院, 2021.<br />
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Networking Linking<br />
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http://www.elecfans.com/rengongzhineng/692245.html<br />
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https://baike.baidu.com/item/%E6%9C%BA%E5%99%A8%E7%BF%BB%E8%AF%91/411793<br />
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=13 陈湘琼Chen Xiangqiong（Study on Post-editing from the Perspective of Functional Equivalence Theory ）=<br />
[[Machine_Trans_EN_13]]<br />
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=14 Bi bi Nadia（Machine Translation a Challenge for Human Translators)=<br />
[[Machine_Trans_EN_14]]<br />
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===Abstract===<br />
Machine translation is a big obstacle in the way of Human translators or interpretors although it is quick and less time consuming.people are trying to get translation of their target language using through source language.For this purpose they are using digital apps like Google translation Google translation does not give accurate and exact interpretation.Google translator is translates word to word translate that doesn't clarifies it's true and actual meaning.On the other hand human translators can give exact and accurate translation ,they take care of grammatical errors , diction and sentence structure.They clarify the purpose of target language through using source language.<br />
===Keywords===<br />
Descriptive translation, academic, interdiscipline, comparative literature, localization,Translatology,school of thought, translation , studies, linguistics, corresponding<br />
===Body of article===<br />
Translation is the process of reworking text from one language into another to maintain the original message and communication. But, like everything else, there are different methods of translation, and they vary in form and function. What is translation? The term has become widely used among knowledge transfer researchers and practitioners, especially in the fields of health and health care. In a landmark review, Jonathan Lomas began to argue that 'The tasks… may be defined as to establish and maintain links between researchers and their audience, via the appropriate translation of research findings' (Lomas 1997, p 4). In 2004, the World Health Organization's World Report on Knowledge for Better Health suggested that 'One of the key contributions of research to health systems is the translation of knowledge into actions' (WHO 2004, p 33 and p 100). By 2006, special issues of WHO's Bulletin as well as the journals Evaluation and the Health Professions and the Journal of Continuing Education in the Health Professions were dedicated to translation.But what does 'translation' mean? It may be a new word for an old problem, meaning nothing more than 'transfer'. The rapidly emerging field of 'translational medicine' seems to take translation to mean generally what transfer might have meant, that is the transmission of knowledge and evidence 'from bench to bedside'. As one commentator has observed, "Translational medicine" as a fashionable term is being increasingly used to describe the wish of biomedical researchers to ultimately help patients' (Wehling 2008, abstract). <br />
Semantic uncertainty persists, not least because of the different interests of scientists, clinicians, patients and commercial firms (Littman et al 2007): 'Translational research means different things to different people, but it seems important to almost everyone' (Woolf 2008, p 211).<br />
In related fields, such as public health (Armstrong et al 2006), 'translation' seems to signify dissatisfaction with 'transfer'. It wants to move away from thinking of knowledge transfer as a form of technology transfer or dissemination, rejecting if only by implication its mechanistic assumptions and its model of linear messaging from A to B. But still, what does it signify?<br />
<br />
===Why translation?===<br />
'Translation' indicates a closer attention to the problem of shared meaning and how it might be developed. It seems to represent some new epistemological lubricant, facilitating the dissemination of texts and the application and use of the knowledge and information they in. Simply, translation might be the key to transfer. And yet, when we stop to think, we are more ambivalent. What is translated often seems somehow inferior, not real or original. Note how readily commentators reach for the idea that things might be 'lost in translation'. Knowing at a distance – made in and mediated by translation - makes for incomplete renditions, blurred images, partial truths. So what might 'translation' really mean? The purpose of this paper is to set out, for policy makers and practitioners, the theoretical and conceptual resources translation holds and seems to represent. In doing so, it explores understandings of translation in the fields of literature and linguistics and in the sociology of science and technology. It begins by setting out just why this idea of translation should make immediate, intuitive sense in relation to research, policy and practice.<br />
1translation in research, policy and practice research as translation<br />
Research often entails translation from one language to another: where data is collected from more than one ethnic group, for example, or where the language of the researcher is other than that of the research subject. It may draw on a secondary literature or source documents written in different languages, and may be published and disseminated in languages other than the one in which it is first written up.<br />
2In a different way, to conduct an interview is to ask for an account of experience and its meanings, but it is also to construct and translate that experience in terms defined at least in part by the researcher. In representing what is said, transcripts then select data, usually excluding significant gesture and eye-contact, for example. Often, certain characteristics of speech-acts (such as hesitations) will be edited out. In turn, the format of the transcript shapes the analytic use the researcher may make of it. The basis of research 'findings', then, is an artefact, a transcript or translation, not an original interaction (Ochs 1979, Barnes, Bloor and Henry 1996, Ross 2009).<br />
3In this way, the researcher recasts aspects of his or her problem or topic in new, scientific form: 'All researchers "translate" the experiences of others' (Temple 1997, p 609). Research is invariably conducted in a sort of 'metalanguage' (Hantrais and Ager 1985): the research process can be conceived as one of successive translations (from theoretical formulation to operationalization, transcription, interpretation and dissemination). Theorization is a process of reciprocal back and forth between theory and fact, in which conceptions of each are revised in order that one fit the other (Baldamus 1974).<br />
4 It is a kind of translation: a rereading, re-use, re-application or re-representation of what we know in new terms (Turner 1980). Referencing, too, is an act of translation, a form of appropriation and incorporation of one text by another (Gilbert 1977).policy as translation Each of the fields covered by the paper is diverse and ill-defined, and there is no intention here to provide a comprehensive account of any them. Sources have been chosen for their relevance: main references are cited in the text, and additional sources listed in footnotes.<br />
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2For a brief introduction to the technical issues involved in social research in more than one language, see Birbili (2000). For translation issues in survey research and question design in general, see Ervin and Bower (1952) and Deutscher (1968); on translating survey research instruments (in this instance health-related quality of life measures), Bowden and Fox-Rushby (2003). On the use of translators and interpreters, see Temple (1997) and Jentsch (1998).<br />
3For an interesting discussion of this problem, see Bourdieu (1999), esp pp 621-626, 'The risks of writing'. <br />
===Difference between machine translation and human translators===<br />
Few people disagree on the differences between the two, but many argue over the quality of the translations. How accurate are machine translations? How reliable are human translations? Some say machine translation produces near-perfect translations while others are adamant that translations are incomprehensible and cause more problems than they solve. Results will, of course, vary depending on the source and target languages, the machine translation service used (e.g. Google Translate), and the complexity of the original text.<br />
Machine translation, love it or hate it, is here to stay. In fact, the machine translation market is growing at such a fast pace that it is predicted to reach $980 million by 2022.<br />
===Machine translation: the pros and cons===<br />
The advantages of machine translation generally come down to two factors: it’s faster and cheaper. The downside to this is the standard of translation can be anywhere from inaccurate, to incomprehensible, and potentially dangerous (more on that shortly).<br />
==The advantages of machine translation==<br />
Many free tools are readily available (Google Translate, Skype Translator, etc.)<br />
Quick turnaround time <br />
You can translate between multiple languages using one tool<br />
Translation technology is constantly improving<br />
The disadvantages of machine translation<br />
Level of accuracy can be very low <br />
Accuracy is also very inconsistent across different languages<br />
==Machines can't translate context ==<br />
Mistakes are sometimes costly <br />
Sometimes translation simply doesn’t work<br />
The most important thing to consider with any kind of translation is the cost of potential mistakes. Translating instructions for medical equipment, aviation manuals, legal documents and many other kinds of content require 100% accuracy. In such cases, mistakes can cost lives, huge amounts of money and irreparable damage to your company’s image. So choose carefully!<br />
Human translation: the pros and cons<br />
Human translation essentially switches the table in terms of pros and cons. A higher standard of accuracy comes at the price of longer turnaround times and higher costs. What you have to decide is whether that initial investment outweighs the potential cost of mistakes. Alternatively, whether mistakes simply aren’t an option, like the cases we looked at in the previous section.<br />
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==The advantages of human translation ==<br />
It’s a translator’s job to ensure the highest accuracy<br />
Humans can interpret context and capture the same meaning, rather than simply translating words<br />
Human translators can review their work and provide a quality process<br />
Humans can interpret the creative use of language, e.g. puns, metaphors, slogans, etc.<br />
Professional translators understand the idiomatic differences between their languages<br />
Humans can spot pieces of content where literal translation isn’t possible and find the most suitable alternative<br />
The disadvantages of human translation<br />
Turnaround time is longer <br />
Translators rarely work for free <br />
Unless you use a translation agency, with access to thousands of translators, you’re limited to the languages any one translator can work with<br />
Simply put, human translation is your best option when accuracy is even remotely important. Other considerations to make are the complexity of your source material and the two languages you’re translating between – both of which can render machines pretty useless.<br />
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When to use machine and human translation<br />
The truth is, the debate over machine vs human translation is an unnecessary distraction. What we should really be talking about is when to use these two different types of translation services, because they both serve a very valid purpose.<br />
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Examples of when to use machine translation<br />
When you have a large bulk of content to translate and the general meaning is enough<br />
When your translation never reaches the final audience, e.g. you’re translating a resource as research for another piece of content<br />
Translating documents for internal use within a company, provided 100% accuracy isn’t needed<br />
To partially translate large chunks of content for a human translator to improve upon<br />
Examples of when to use human translation<br />
When accuracy is important<br />
Most cases where your translated content is received by a consumer audience<br />
When you have a duty of care to provide accurate translations (e.g. legal documents, product instructions, medical guidelines or health and safety content)<br />
When translating marketing material or other texts for creative language uses.<br />
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===Conclusion ===<br />
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In the light of above mentioned facts and figures here by we would say that machine translation is challenge for human translators because it can reduces the wokload of translation but can't give accurate and exact translation of the traget language.It can be less reliable than human translation..</div>Chen Huinihttps://bou.de/u/index.php?title=Machine_translation&diff=129760Machine translation2021-12-08T01:32:12Z<p>Chen Huini: /* 4. */</p>
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=1 卫怡雯(A Comparison Between the Quality of Machine Translation and Human Translation——A Case Study of the Application of artificial intelligence in Sports Events)=<br />
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=2 吴映红（The Introduction of Machine Translation)= <br />
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=3 肖毅瑶(On the Realm Advantages And Symbiotic Development of Machine Translation And Huamn Translation)=<br />
[[Machine_Trans_EN_3]]<br />
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=4 王李菲 （Comparison Between Neural Machine Translation of Netease and Traditional Human Translation—A Case Study of The Economist Articles)= <br />
[[Machine_Trans_EN_4]]<br />
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=6 徐敏赟(Machine Translation Based on Neural Network --Challenge or Chance?)=<br />
[[Machine_Trans_EN_6]]<br />
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=7 颜莉莉(一带一路背景下人工智能与翻译人才的培养)=<br />
[[Machine_Trans_EN_7]]<br />
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===Abstract===<br />
In the era of artificial intelligence, artificial intelligence has been applied to various fields. In the field of translation, traditional translation models can no longer meet the rapid development and updating of the information age. The development of machine translation has brought structural changes to the language service industry, which poses challenges to the cultivation of translation talents. Under the background of "The Belt and Road initiative", translation talents have higher and higher requirements on translation literacy. Artificial intelligence and translation technology are used to reform the training mode of translation talents, so as to better serve the development of The Times. This paper mainly explores the cultivation of artificial intelligence and translation talents under the background of the Belt and Road Initiative. The cultivation of translation talents is moving towards comprehensive cultivation of talents. On the contrary, artificial intelligence and machine translation can also be used to improve the teaching mode and teaching content, so as to win together in cooperation.<br />
===Key words===<br />
Artificial intelligence,Machine translation,cultivation of translation talents,"The Belt and Road initiative"<br />
===题目===<br />
一带一路背景下人工智能与翻译人才的培养<br />
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===摘要===<br />
进入人工智能时代，人工智能被应用于各个领域。在翻译领域，传统的翻译模式已无法满足信息化时代的飞速发展和更新，机器翻译的发展给语言服务行业带来了结构性改变，这对翻译人才的培养提出了挑战。“一带一路”背景下，对翻译人才的翻译素养要求越来越高，利用人工智能和翻译技术对翻译人才培养模式进行革新，更好为时代发展服务。本文主要探究在一带一路背景下人工智能和翻译人才培养，翻译人才的培养过程中正向对人才的综合性培养，反之也可以利用人工智能和机器翻译完善教学模式和教学内容，在合作中共赢。<br />
===关键词===<br />
人工智能；机器翻译；翻译人才培养；一带一路<br />
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===1. Introduction===<br />
With the development of science and technology in China, artificial intelligence has also been greatly improved, and related technologies have been applied to various fields, such as the use of intelligent robots to deliver food to quarantined people during the epidemic, which has made people's lives more convenient. The most controversial and widely discussed issue is machine translation. Before the emergence of machine translation, translation was generally dominated by human translation, including translation and interpretation, which was divided into simultaneous interpretation and hand transmission, etc. It takes a lot of time and energy to cultivate a translation talent. However, nowadays, the era is developing rapidly and information is updated rapidly. As a translation talent, it is necessary to constantly update its knowledge reserve to keep up with the pace of The Times. The emergence of machine translation has also posed challenges to translation talents and the training of translation talents. Although machine translation had some problems in the early stage, it is now constantly improving its functions. In the context of the belt and Road Initiative, both machine translation and human translation are facing difficulties. Regardless of whether human translation is still needed, what is more important at present is how to train translators to adapt to difficulties and promote the cooperation between human translation and machine translation.<br />
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===2.Development status of machine translation in the era of artificial intelligence ===<br />
With the development of AI technology, machine translation has made great progress and has been applied to people's lives. For example, more and more tourists choose to download translation software when traveling abroad, which makes machine translation take an absolute advantage in daily email reply and other translation activities that do not require high accuracy. The translation software commonly used by netizens include Google Translation, Baidu Translation, Youdao Translation, IFly.com Translation, etc. Even wechat and other chat software can also carry out instant Translation into English. Some companies have also launched translation pens, translation machines and other equipment, which enables even native speakers to rely on machine translation to carry out basic communication with other Chinese people.<br />
But so far, machine translation still faces huge problems. Although machine translation has made great progress, it is highly dependent on corpus and other big data matching. It does not reach the thinking level of human brain, and cannot deal with the problem of translation differences caused by culture and religion. In addition, many minor languages cannot be translated by machine due to lack of corpus.<br />
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What's more, most of the corpus is about developed countries such as Britain and France, and most of the corpus is about diplomacy, politics, science and technology, etc., while there are very few about nationality, culture, religion, etc.<br />
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In addition, machine translation can only be used for daily communication at present. If it involves important occasions such as large conferences and international affairs, it is impossible to risk using machine translation for translation work. Professional translators are required to carry out translation work. So machine translation still has a long way to go.<br />
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===3.Challenges in the training of translation talents in universities===<br />
The cultivation of translators is targeted at the market. Professors Zhu Yifan and Guan Xinchao from the School of Foreign Languages at Shanghai Jiao Tong University believe that the cultivation of translators can be divided into four types: high-end translators and interpreters, senior translators and researchers, compound translators and applied translators.<br />
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From their names, it can be seen that high-end translators and interpreters and senior translators and researchers talents have high requirements on the knowledge and quality of interpreters, because they have to face the changing international situation, and have to deal with all kinds of sensitive relations and political related content, they should have flexible cross-cultural communication skills. In addition, for literature, sociology and humanities academic works, it is not only necessary to translate their content, but also to understand their essence. Therefore, translators should not only have humanistic feelings, but also need to have a deep understanding of Chinese and western culture.<br />
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However, there is not much demand for this kind of translation in the society. Such high-level translation requirements are not needed in daily life and work. The greatest demand is for compound translators, which means that they should master knowledge in a specific field while mastering a foreign language. For example, compound translators in the financial field should not only be good at foreign languages, but also master financial knowledge, including professional terms, special expressions and sentence patterns.<br />
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Now we say that machine translation can replace human translation should refer to the field of compound translation talents. Although AI technology has enabled machine translation to participate in creation, it does not mean that compound translation talents will be replaced by machines. The complexity of language and the flexible cross-cultural awareness required in communication make it impossible for machine translation to completely replace human translation.<br />
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The last type of applied translation talents are mostly involved in the general text without too much technical content and few professional terms, so it is easy to be replaced by machine translation.<br />
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Therefore, the author thinks that what universities are facing at present is not only how to train translation talents to cope with the development of machine translation, but to consider the application of machine translation in the process of training translation talents to achieve human-machine integration, so as to better complete the translation work.<br />
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===4.The Language environment and opportunities and challenges of the Belt and Road initiative===<br />
During visits to Central and Southeast Asian countries in September and October 2013, Chinese President Xi Jinping put forward the major initiative of jointly building the Silk Road Economic Belt and the 21st Century Maritime Silk Road. And began to be abbreviated as the Belt and Road Initiative.<br />
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According to the Vision and Actions for Jointly Building silk Road Economic Belt and 21st Century Maritime Silk Road, the Silk Road Economic Belt focuses on connecting China, Central Asia, Russia and Europe (the Baltic Sea). From China to the Persian Gulf and the Mediterranean Sea via Central and West Asia; China to Southeast Asia, South Asia, Indian Ocean. The focus of the 21st Century Maritime Silk Road is to stretch from China's coastal ports to Europe, through the South China Sea and the Indian Ocean. From China's coastal ports across the South China Sea to the South Pacific.<br />
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The Belt and Road "construction is comply with the world multi-polarization and economic globalization, cultural diversity, the initiative of social informatization tide, drive along the countries achieve economic policy coordination, to carry out a wider range, higher level, the deeper regional cooperation and jointly create open, inclusive and balanced, pratt &whitney regional economic cooperation framework.<br />
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====4.1The language environment of the Belt and Road====<br />
The "Belt and Road" involves a wide range of countries and regions, and their languages and cultures are very complex. How to make good use of language, do a good job in translation services, actively spread Chinese culture to the world, strengthen the ability of discourse, and tell Chinese stories well, the first thing to do is to understand the language situation of the countries along the "Belt and Road".<br />
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=====4.1.1The most common language in countries along the "Belt and Road" =====<br />
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There are a wide variety of languages spoken in 65 countries along the Belt and Road, involving nine language families. However, The status of English as the first language in the world is undeniable. Most of the countries participating in the Belt and Road are developing countries, and many of them speak English as their first foreign language. Especially in southeast Asian and South Asian countries, English plays an important role in foreign communication, whether as the official language or the first foreign language. Besides English, more than 100 million people speak Russian, Hindi, Bengali, Arabic and other major languages in the "Belt and Road" countries. It can also be seen that a common feature of languages in countries along the "Belt and Road" is the popularization of English education. English is widely used in international politics, economy, culture, education, science and technology, playing the role of the most important language in the world.<br />
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=====4.1.2The complex language conditions of countries along the "Belt and Road" =====<br />
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The languages spoken in countries along the Belt and Road involve nine major language families and almost all the world's religious types. Differences in religious beliefs also result in differences in culture, customs and social values behind languages. The languages of some countries along the belt and Road have also been influenced by historical and realistic factors, such as colonization, internal division and immigration. <br />
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India, for example, has no national language, but more than 20 official languages. India is a multi-ethnic country, a total of more than 100 people, one of the most obvious difference between nation and nation is the language problem. Therefore, according to the difference of language, India divides different ethnic groups into different states, big and small. Ethnic groups that use the same language are divided into one state. If there are two languages in a state, the state is divided into two parts. And Indian languages differ not only in word order but also in the way they are written. In India, for example, Hindi is spoken by the largest number of people in the north, with about 700 million speakers and 530 million as their first language. It is written in The Hindu language and belongs to the Indo-European language family. Telugu in the east is spoken by about 95 million people and 81.13 million as their first language. It is written in Telugu, which belongs to the Dravidian language family and is quite different from Hindi. As a result, a parliamentary session in India requires dozens of interpreters. <br />
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These factors cannot be ignored in the process of translation, from language communication to cultural understanding, from text to thought exchange, through the bridge of language to truly connect the people, so as to avoid misreading and misunderstanding caused by differences in language and national conditions.<br />
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====4.2 Opportunities and challenges of the "Belt and Road" ====<br />
With the promotion of the Belt and Road Initiative, there has been an unprecedented boom in translation. In the previous translation boom in China, most of the foreign languages were translated into Chinese, and most of the foreign cultures were imported into China. However, this time, in the context of the "Belt and Road" initiative, translating Chinese into foreign languages has become an important task for translators. As is known to all, there are many different kinds of "One Belt And One Road" along the national language and culture is complex, the service "area" construction has become a factor in Chinese translation talents training mode reform, one of the foreign language universities have action, many colleges and universities to establish the "area" all the way along the country's small language major, as a result, "One Belt And One Road" initiative to promote, It has brought unprecedented opportunities for human translation. The cultivation of diversified translation talents and the cultivation of translation talents in small languages is an urgent problem to be solved in China. The cultivation of translation talents cannot be completed overnight, and the state needs to reform the training mode of translation talents from the perspective of language strategic development. Only in this way can we meet the new demand for human translation under the new situation of the belt and Road Initiative.<br />
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For a long time, the traditional orientation of translation curriculum and training goal in colleges and universities is to train translation teachers and translators in need of society through translation theory and practice and literary translation practice, which cannot meet the needs of society. Since 2007, in order to meet the needs of the socialist market economy for application-oriented high-level professionals, the Academic Degrees Committee of The State Council approved the establishment of Master of Translation and Interpreting (MTI for short). After joining the pilot program of MTI, more and more universities are reforming the curriculum and training mode of master of Translation in order to cultivate translators who meet the needs of the society.<br />
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Language is an important carrier of culture, and translation is an important link for exporting culture. The quality of translation output also reflects the cultural soft power of a country. With the rise of China, more and more people are interested in Chinese culture, and the number of Chinese learners keeps increasing. Under the background of "One Belt and One Road", excellent translators are urgently needed to spread Chinese culture. With the promotion of "One Belt and One Road" Initiative, the number of other countries learning mutual learning and cultural exchanges with China has increased unprecedeningly, bringing vigorous opportunities for the spread of Chinese culture. Translation talents who understand small languages and multi-lingual translators are needed. They should not only use language to convey information, but also use language as a lubricant for communication.<br />
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===5.Training translation talents from the perspective of machine translation===<br />
Under the prevailing environment of machine translation, it poses a great challenge to the cultivation of translation talents. According to the current situation, translation needs and the shortage of translation talents, colleges and universities should reform and innovate the existing training programs for translation talents in terms of the quality of translation talents, the reform of training mode and the use of artificial intelligence. Based on the obtained data and literature, the author discusses how to train translation talents in the perspective of machine translation from the following aspects.<br />
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====5.1 Quality requirements for translation talents ====<br />
Zhong Weihe and Murray made a more detailed and profound discussion on translator's literacy, believing that "translators should not only be proficient in two languages, but also have extensive cultural and encyclopedic knowledge and relevant professional knowledge; Master a variety of translation skills, a lot of translation practice; Have a clear translator role awareness, good professional ethics, practical and enterprising style of work, conscious team spirit and calm psychological quality ". According to the collected data, the author will elaborate the requirements for translation talents from four aspects: language literacy, humanistic literacy, translation ability and innovation ability.<br />
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The first is language literacy, which is the most basic and important requirement. MAO Dun pointed out that "mastery of mother tongue and target language are the foundation of translation". A solid foundation of bilingual skills is the basic skills of translators. Poor language proficiency seems to be a common problem among students majoring in translation and interpreting. Many translation diseases are caused by poor Chinese foundation. As part of going global, the belt and Road initiative is to tell Chinese culture and Chinese stories, which requires translators to be able to use both languages flexibly. Therefore, the first problem that colleges and universities face to solve is to improve the language level of foreign language learners.<br />
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The second is humanistic literacy. Humanistic literacy is mainly manifested by a good command of politics, economy, history, literature and other knowledge, which is particularly important for interpreters. In addition, cross-cultural communication cannot be ignored. In the process of communicating with foreigners or translating, translators often encounter the first cross-cultural contradiction. Cross-culture refers to having a full and correct understanding of cultural phenomena, customs and habits that differ or conflict with the national culture, and accepting and adapting to them in an inclusive manner on this basis. So the interpreter can first fully understand and master the national conditions and culture of the target country, which is particularly important in the "Belt and Road". There are more than 60 countries along the "Belt and Road", and it takes a lot of energy to master their national conditions and culture.<br />
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The third is translation ability. We should distinguish between translation ability and language ability. Translation ability is actually a system of knowledge and skills necessary for translation, the core of which is conversion ability. First of all, it reflects the ability to use tools to assist translation, such as computer application, translation technology and so on. In addition, interpreters should have enough healthy psychological quality and good professional quality. In terms of translation ability, the current training model of translation talents is inadequate.<br />
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The last one is innovation. The cultivation of learners' thinking ability is the key to translation teaching and the cultivation of thoughtful translators should be the connotation of translation teaching. Therefore, the interpreter is not only a translation tool, which is no different from machine translation. More importantly, it is necessary to explore translation with thoughts, have a sense of lifelong learning and innovation consciousness. Translators must constantly innovate themselves, learn new knowledge, and strive to seek reform and innovation. Many colleges and universities should also consciously cultivate students' innovation ability and broaden their thinking and vision.<br />
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====5.2 The reform of college curriculum setting====<br />
First, we will further reform the curriculum of colleges and universities. Add economics, law and engineering to the curriculum, these contents in the "belt and Road".<br />
"One Road" is very important in the construction. According to the author's personal experience, the most typical problem of foreign language majors in colleges and universities is the single learning of foreign languages. More professional foreign language colleges and universities will add some literature courses and national conditions courses of the language target countries. Obviously, whether foreign language graduates are engaged in translation work or not, these knowledge is not enough. Of course, great reforms have been carried out in foreign language teaching, such as combining foreign language with finance, law, diplomacy and so on, and taking the way of minor training foreign language majors.<br />
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Domestic enterprises with a high degree of internationalization attach great importance to translation. Their translation research includes cutting-edge theoretical and applied research, involving machine translation, natural language processing and AI theory, algorithm and model. With such a foundation, enterprises can solve problems by themselves, such as embedding automatic translation functions in mobile phones. International enterprises not only do technical translation, but also deal with all forms of translation and localization in society. At present, translation teaching in most colleges and universities is still in the early mode, and it is an objective fact that it is divorced from the workplace and has a gap with the needs of enterprises.<br />
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Second, we should adjust and strengthen the construction of second foreign language teaching for foreign language majors. In the 1980s, our country was in urgent need of Russian translation. At that time, students majoring in English could translate microelectronic product manuals and related business documents in English and Russian at the same time after learning Russian for half a year. The mutual conversion between English and Russian played a great role in practice. According to the author, in the Graduate Institute of Interpretation and Translation of Beijing Foreign Studies University a very few students majored in multiple languages at the graduate level, that is, they majored in minor languages at the undergraduate level and were admitted to the Graduate Institute of Interpretation and Translation in English. Their training mode is to study English in the Graduate Institute of Interpretation and Translation for two years and the third year in the corresponding department of the undergraduate major. Such training mode in my opinion is a bigger model, cost It's more difficult for students. <br />
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In addition, there is a great disparity in the development of second foreign language teaching in colleges and universities, and the overall level is not high enough. Part of the second foreign language university foreign language professional may still be too much focus in languages such as German, French and Japanese, should as far as possible, considering the need of the construction of the "region", like Croatia, Serbia, Turkish, Hungarian, Italian, Indonesian, Albanian, these are the countries along the "area" the language of the two countries, Colleges and universities should encourage the teaching of a second foreign language.<br />
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Third, the teaching of translation technology should be strengthened. Traditional translation teaching teaches translation skills, such as the translation of words, sentences, texts and figures of speech. Translation technology refers to a series of practical workplace technologies with computer-aided translation software and translation project management as the core, which can greatly improve translation efficiency. However, due to the relative lack of translation technology teachers and equipment in colleges and universities, there is a disconnect between talent training and the requirements of translation technology in the translation field.<br />
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====5.3 Application of artificial intelligence to translation teaching practice====<br />
In order to improve the teaching quality and train students' English translation ability, it is necessary to realize the effective integration of ARTIFICIAL intelligence and translation activity courses, which should not only reflect the effectiveness of artificial intelligence translation technology, but also help students establish a healthy concept of English communication. Through the application of artificial intelligence technology, students can strengthen their flexible translation skills through close communication with "AI program" during the learning stage of English translation activity class. For example, teachers can ask students to translate directly against the translation content provided on the translation screen of the ARTIFICIAL intelligence system. After that, the system can collect the translation answers with the help of speech recognition function, and then judge the accuracy of the translation content, thus providing important feedback to students.<br />
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China has used such artificial intelligence technology in the Putonghua test to ensure that every student can find a suitable translation method in practical communication. The so-called artificial intelligence technology is a new kind of technology modeled after the characteristics of human neural network thinking, can combine the human mind to respond. If it can be integrated into English translation activity teaching, it can not only improve the teaching efficiency, but also enhance students' enthusiasm in learning the course.<br />
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At the same time, during the training of translation talents, teachers also need to take into account the importance of influencing education factors, so that students can form a higher disciplinary quality in translation, so as to fit the concept of quality education in the new era. Only when artificial intelligence translation content is fully integrated into college English translation activity courses can the overall translation ability of college students be maximized.<br />
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====5.4The improvement of translator's technical ability====<br />
In the previous part, the author roughly mentioned that translation teaching should be improved, which will be elaborated here. At present, only a few universities can make full use of the advantages of translation technology in translation teaching and focus on cultivating professional translation talents. Most universities still cannot get rid of the traditional teaching mode of "language + relevant professional knowledge" in translation teaching, and generally lack a correct understanding of COMPUTER-aided translation teaching.<br />
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According to Wang Huashu et al., the courses that can be offered around the composition of translators' technical literacy include computer-assisted translation, translation and corpus, machine translation and post-translation editing, localization and internationalization, film and television translation (subtitle), technical communication and technical writing, and computer programming. The course modules involved are: Fundamentals of COMPUTER-aided Translation, CAT tool application, corpus alignment and processing, term management, QA technology for translation quality assurance, OFFICE fundamentals, translation management technology, basic computer knowledge, desktop typesetting, localization and internationalization, project management system and content management system, technical writing, basic knowledge of computer programming, basic knowledge of web code, etc.<br />
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===6针对一带一路的机器翻译与翻译人才的合作===<br />
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===Conclusion===<br />
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===References===<br />
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=8 颜静(On Machine Translation Under Lanuguage Intelligence——An Option and Opportunity for Human Translators)=<br />
[[Machine_Trans_EN_8]]<br />
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=9 谢佳芬（人工智能时代下的机器翻译与人工翻译）=<br />
[[Machine_Trans_EN_9]]<br />
===Abstract===<br />
With the continuous development of information technology, many industries are facing the competitive pressure of artificial intelligence, and so is the field of translation. Artificial intelligence technology has developed rapidly and combined with the field of translation，which has brought great impact and changes to traditional translation, but artificial intelligence translation and artificial translation have their own advantages and disadvantages. Artificial translation is in the leading position in adapting to human language logical habits and understanding characteristics, but in terms of translation threshold and economic value, the efficiency of artificial intelligence translation is even better. In a word, we need to know that machine translation and human translation are complementary rather than antagonistic.<br />
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===Key Words===<br />
Machine Translation; Artificial Translation; Artificial Intelligence<br />
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===题目===<br />
人工智能时代下的机器翻译与人工翻译<br />
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===摘要===<br />
伴随着信息技术的不断发展，多个行业面临着人工智能的竞争压力，翻译领域也是如此。人工智能技术快速发展并与翻译领域结合，人工智能翻译给传统翻译带来了巨大的冲击和变革，但人工智能翻译与人工翻译存在着各自的优劣特点和发展空间，在适应人类语言逻辑习惯和理解特点的翻译效果上，人工翻译处于领先地位，但在翻译门槛和经济价值上，人工智能翻译的效率则更胜一筹。总的来说，我们要知道机器翻译与人工翻译是互补而非对立的关系。<br />
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===关键词===<br />
机器翻译;人工翻译;人工智能<br />
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===1. Introduction===<br />
====1.1 The History of Machine Translation Aborad====<br />
The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. Alchuni put forward the idea of using machines for translation. In 1933, the Soviet inventor Troyansky designed a machine to translate one language into another. [1]In 1946, the world's first modern electronic computer ENIAC was born. Soon after, American scientist Warren Weaver, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947. In 1949, Warren Weaver published a memorandum entitled Translation, which formally raised the issue of machine translation. In 1954, Georgetown University, with the cooperation of IBM, completed the English-Russian machine translation experiment with IBM-701 computer for the first time, which opened the prelude of machine translation research. [2] In 2006, Google translation was officially released as a free service software, bringing a big upsurge of statistical machine translation research. It was Franz Och who joined Google in 2004 and led Google translation. What’s more, it is precisely because of the unremitting efforts of generations of scientists that science fiction has been brought into reality step by step.<br />
====1.2 The History of Machine Translation in China====<br />
In 1956, the research and development of machine translation has been named in the scientific and technological work and made little achievements in China. On the eve of the tenth anniversary of the National Day in 1959, our country successfully carried out experiments, which translated nine different types of complicated sentences on large general-purpose electronic computers. The dictionary includes 2030 entries, and the grammar rule system consists of 29 circuit diagrams. [3]. After a period of stagnation, China's machine translation ushered in a high-speed development stage after the 1980s in the wave of the third scientific and technological revolution. With the rapid development of economy and science and technology, China has made a qualitative leap in the field of machine translation research with the pace of reform and opening up. In 1978, Institute of Scientific and Technological Information of China, Institute of Computing Technology and Institute of Linguistics carried out an English-Chinese translation experiment with 20 Metallurgical Title examples as the objects and achieved satisfactory results. Subsequently, they developed a JYE-I machine translation system, which based on 200 sentences from metallurgical documents. Its principles and methods were also widely used in the machine translation system developed in the future. In addition, the research achievements of machine translation in China during the 1980s and 1990s also include that Institute of Post and Telecommunication Sciences developed a machine translation system, C Retrieval and automatic typesetting system with good performance and strong practicability in October 1986; In 1988, ISTC launched the ISTIC-I English-Chinese Title System for the translation of applied literature of metallurgy, Information Research Institute of Railway developed an English-Chinese Title Recording machine translation system for railway documents; the Language Institute of the Academy of Social Sciences developed "Tianyu" English-Chinese machine translation system and Matr English-Chinese machine translation system developed by the computer department of National University of Defense Technology. After many explorations and studies, machine translation in China has gradually moved towards application, popularization and commercialization. China Software Technology Corporation launched "Yixing I" in 1988, marking China's machine translation system officially going to the market. After "Yixing", a series of machine translation systems such as Gaoli system in Beijing, Tongyi system in Tianjin and Langwei system in Shaanxi have also entered the public. In the 21st century, the development of a series of apps such as Kingsoft Powerword, Youdao translation and Baidu translation has greatly met the needs of ordinary users for translation. According to the working principle, machine translation has roughly experienced three stages: rule-based machine translation, statistics-based machine translation and deep learning based neural machine translation. [4] These three stages witnessed a leap in the quality of machine translation. Machine translation is more and more used in daily life and even the translation of some texts is almost comparable to artificial translation. In addition to text translation, voice translation, photo translation and other functions have also been listed, which provides great convenience for people's life. It is undeniable that machine translation has become the development trend of translation in the future.<br />
====1.3 The Status Quo of Machine Translation====<br />
In this big data era of information explosion, the prospect of machine translation is also bright. At present, the circular neural network system launched by Google has supported universal translation in more than 60 languages. Many Internet companies such as Microsoft Bing, Sogou, Tencent, Baidu and NetEase Youdao have also launched their own Internet free machine translation systems. [5] Users can obtain translation results free of charge by logging in to the corresponding websites. At present, the circular neural network translation system launched by Google can support real-time translation of more than 60 languages, and the domestic Baidu online machine translation system can also support real-time translation of 28 languages. These Internet online machine translation systems are suitable for a variety of terminal platforms such as mobile phone, PC, tablet and web and its functions are also quite diverse, supporting many translation forms, such as screen word selection, text scanning translation, photo translation, offline translation, web page translation and so on. Although its translation quality needs to be improved, it has been outstanding in the fields of daily dialogue, news translation and so on.<br />
===2. Advantages and Disadvantages of Machine Translation===<br />
Generally speaking, machine translation has the characteristics of high efficiency, low cost, accurate term translation and great development potential and etc. Machine translation is fast and efficient, this is something that artificial translation can’t catch up with. In addition, with the continuous emergence of all kinds of translation software in the market, compared with artificial translation, machine translation is cheap and sometimes even free, which greatly saves the economic cost and time for users with low translation quality requirements. What's more, compared with artificial translation, machine translation has a huge corpus, which makes the translation of some terms, especially the latest scientific and technological terms, more rapid and accurate. The accurate translation of these terms requires the translator to constantly learn, but learning needs a process, which has a certain test on the translator's learning ability and learning speed. In this regard, artificial translation has uncertainty and hysteretic nature. At the same time, with the progress of science and technology and the development of society, the function of machine translation will be more perfect and the quality of translation will be better.Today's machine translation tools and software are easy to carry, all you need to do is just to use the software and electronic dictionary in the mobile phone. There is no need to carry paper dictionaries and books for translation, which saves time and space. At the same time, machine translation covers many fields and is suitable for translation practice in different situations, such as academic, education, commercial trade, social networking, tourism, production technology, etc, it is also easy to deal with various professional terms. However, due to the limitation of translators' own knowledge, artificial translation is often limited to one or a few fields or industries. For example, it is difficult for an interpreter specializing in medical English to translate legal English.<br />
At the same time, machine translation also has its limitations. At first, machine can only operate word to word translation, which only plays the function and role of dictionary. Then, the application of syntax enables the process of sentence translation and it can be solved by using the direct translation method. When the original text and the target language are highly similar, it can be translated directly. For example, the original text "他是个老师." The target language is "he is a teacher ". With the increase of the structural complexity of the original text, the effect of machine translation is greatly reduced. Therefore, at the syntactic level, machine translation still stays in sentences with relatively simple structure. Meanwhile, the original text and the results of machine translation cannot be interchanged equally, indicating that English-Chinese translation has strong randomness, and is not rigorous and scientific enough. <br />
Nowadays, machine translation is highly dependent on parallel corpora, but the construction of parallel corpora is not perfect. At present, the resources of some mainstream languages such as Chinese and English are relatively rich, while the data collection of many small languages is not satisfactory. Moreover, the current corpus is mainly concentrated in the fields of government literature, science and technology, current affairs and news, while there is a serious lack of data in other fields, which can’t reflect the advantages of machine translation. At the same time, corpus construction lags behind. Some informative texts introducing the latest cutting-edge research results often spread the latest academic knowledge and use a large number of new professional terms, such as academic papers and teaching materials while the corpus often lacks the corresponding words of the target language, which makes machine translation powerless<br />
Besides, machine translation is not culturally sensitive. Human may never be able to program machines to understand and experience a particular culture. Different cultures have unique and different language systems, and machines do not have complexity to understand or recognize slang, jargon, puns and idioms. Therefore, their translation may not conform to cultural values and specific norms. This is also one of the challenges that the machine needs to overcome.[6] Artificial intelligence may have human abstract thinking ability in the future, but it is difficult to have image thinking ability including imagination and emotion. [7] Therefore, machine translation is often used in news, science and technology, patents, specifications and other text fields with the purpose of fact description, knowledge and information transmission. These words rarely involve emotional and cultural background. When translating expressive texts, the limitations of machine translation are exposed. The so-called expressive text refers to the text that pays attention to emotional expression and is full of imagination. Its main characteristics are subjectivity, emotion and imagination, such as novels, poetry, prose, art and so on. This kind of text attaches importance to the emotional expression of the author or character image, and uses a lot of metaphors, symbols and other expressions. Machine translation is difficult to catch up with artificial translation in this kind of text, it can only translate the main idea, lack of connotation and literary grace and it cannot have subjective feelings and rational analysis like human beings. In fact, it is not difficult to simulate the human brain, the difficulty is that it is impossible to learn from the rich social experience and life experience of excellent translators. In other words, machine translation lacks the personalization and creativity of human translation. It is this personalization and creativity that promote the development and evolution of language, and what machine translation can only output is mechanical "machine language".<br />
<br />
===3.The Irreplaceability of Artificial Translation ===<br />
====3.1 Translation is Constrained by Context====<br />
At present, machine translation can help people deal with language communication in people's daily life and work, such as clothing, food, housing and transportation, but there is a big gap from the "faithfulness, expressiveness and elegance" emphasized by high-level translation. Language itself is art，which pays more attention to artistry than functionality, and the discipline of art is difficult to quantify and unify. Sometimes it is regular, rigorous, logical and clear, and sometimes it is random, free and logical. If it is translated by machine, it is difficult to grasp this degree. Sometimes, machine translation cannot connect words with contextual meaning. In many languages, the same word may have multiple completely unrelated meanings. In this case, context will have a great impact on word meaning, and the understanding of word meaning depends largely on the meaning read from context. Only human beings can combine words with context, determine their true meaning, and creatively adjust and modify the language to obtain a complete and accurate translation. This is undoubtedly very difficult for machine translation. Artificial translation can get rid of the constraints of the source language and translate the translation in line with the grammar, sentence patterns and word habits of the target language. In the process of translation, translators can use their own knowledge reserves to analyze the differences between the source language and the target language in thinking mode, cultural characteristics, social background, customs and habits, so as to translate a more accurate translation. Artificial translation can also add, delete, domesticate, modify and polish the translation according to the style, make up for the lack of culture, try to maintain the thought, artistic conception and charm of the original text and the style of the source language. In addition, translators can also judge and consider the words with multiple meanings or easy to produce ambiguity according to the context, so as to make the translation more clear and more accurate and improve the quality of the translation.<br />
<br />
<br />
===4. Discussion on the Relationship Between Machine Translation and Artificial Translation ===<br />
<br />
===5. Suggestions on the Combined Development of Machine Translation and Artificial Translation===<br />
<br />
===6. ===<br />
<br />
===7. ===<br />
<br />
===Conclusion===<br />
<br />
===References===<br />
<br />
=10 熊敏(Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts)=<br />
[[Machine_Trans_EN_10]]<br />
===Abstract===<br />
The history of machine translation can be traced to 1940s, undergoing germination, recession, revival and flourish. Nowadays, with the rapid development of information technology, machine translation technology emerged and is gradually becoming mature. Whether manual translation can be replaced by machine translation becomes a widely-disputed topic. So in order to explore the ability of machine translation, I adopts two versions of translation, which are manual translation and machine translation(this paper uses Youdao translation) for different types of texts(according to Peter Newmark's types of text：informative text, expressive text and vocative text). The results are quite different in terms of quality and accuracy. The results show that machine translation is more suitable for informative text for its brevity, while the expressive texts especially literary works and vocative texts are difficult to be translated, because there are too many loaded words and cultural images in expressive text and dependence on the readers. To draw a conclusion, the relationship between machine translation and manual translation should be complementary rather than competitive.<br />
<br />
===Key words===<br />
machine translation; manual translation; Newmark's type of texts<br />
<br />
===题目===<br />
Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts<br />
<br />
===摘要===<br />
机器翻译的历史可以追溯到上个世纪40年代，经历了萌芽期、萧条期、复兴期和繁荣期四个阶段。如今，随着信息技术的高速发展，机器翻译技术日益成熟，于是机器翻译能不能替代人工翻译这个话题引起了广泛热议。为了探究机器翻译的能力水平是否足以替代人工翻译，本人根据皮特纽马克的文本类型分类理论（信息类文本，表达文本，呼吁文本），选择了相应的译文类型，并且将其机器翻译的版本以及人工翻译的版本进行对比。结果发现，就质量和准确度而言，译文的水平大相径庭。因为其简洁的特性，机器比较适合翻译信息文本。而就表达文本，尤其是文学作品，因其存在文化负载词及相应的文化现象，所以机器翻译这类文本存在难度。而呼唤文本因其目的性以及对读者的依赖性较强，翻译难度较大。由此得知，机器翻译和人工翻译的关系应该是互补的，而不是竞争的。<br />
<br />
===关键词===<br />
机器翻译；人工翻译；纽马克文本类型<br />
<br />
===1. Introduction===<br />
====1.1.Introduction to machine translation====<br />
Machine translation, also known as computer translation is a technique to translating through machine translator, such as Google translation and Youdao translation. Machine translation is one of the branches of computational linguistics, ranging from computer science, statistics, information science and so on. Machine translation plays an important role in all aspects.<br />
Machine translation can be traced back to 1940s, when British engineer Booth and American engineer Weaver proposed using computer to translate and started to study machines used for translation. And the first machine translating system launched in 1954, used in English and Russian translation. And this means machine translation becomes a reality. However, the arrival of new things always accompanies barriers and setbacks. In the 1960s, reports from ALPAC (Automated Language Processing Advisory Committee) showed studies on machine translation had stagnated for a decade. At that time, due to the high cost of machine, choosing human translators to finish translating works was more economical for most companies. What’s worse, machine had difficulty in understanding semantic and pragmatic meanings. Fortunately, in the 1970s, with the advance and popularity of computer, machine translation was gradually back on track. With the development of science and technology, machine translation has better technological guarantee, such as artificial intelligence and computer technology. In the last decades, from the late 90s till now, machine translation is becoming more and more mature. The initial rule-oriented machine translation has been updated and Corpus-aided machine translation appears. And nowadays, technology in voice recognition has been further developed. This technique is frequently applied in speech translation products. Users only need to speak source language to the online translator and instantly it will speak out the target version. But machine can’t fully provide precise and accurate translation without any grammatical or semantic problems. Machine can understand the literal meaning of texts, but not the meaning in context. For example, there is polysemy in English, which refers to words having multiple meanings. So when translating these words, translators should take contexts into consideration. But machine has troubles in this way. In addition, machine has no feeling or intention. Hence, it is also difficult to convey the feelings from the source language.<br />
<br />
====1.2.Process of machine translation====<br />
The process of manual translation is different from that of machine translation. Here is the process of the former. (1) Understand source language. (2) Use target language to organize language. (3) Generate translation. Unlike manual translation, machine translation tends to analyze and code source language first, then look for related codes in corpus, and work out the code that represents target language, generating translation. But they share a common feature, which is that Lexicon, grammatical rules and syntactic structure are taken into consideration. This is one of the biggest challenges for machine translation.<br />
<br />
===2.Theorectical framework===<br />
====2.1Newmark’s text typology====<br />
Text typology is a theory from linguistics. It undergoes several phrases. Karl Buhler, a German psychologist and linguist defines communicative functions into expressive, information and vocative. Then Roman Jakobson proposes categorization of texts, which are intralingual translation, interlingual translation and intersemiotic translation. Accordingly, he defines six main functions of language, including the referential function, conative function, emotive function, poetic function, metalingual function and the phatic function. Based on the two theories, Peter Newmark, a translation theorist, summarizes the language function into six parts: the informative function, the vocative function, the expressive function, the phatic function, the aesthetic function, and the metalingual function. Later, he further divided texts into informative type, expressive text and vocative type according to the linguistic functions of various texts. <br />
The core of the informative texts is the truth. It is to convey facts, information, knowledge of certain topics, reality and theories. The language style of the text is objective, logical and standardized. Reports, papers, scientific and technological textbooks are all attributed to informative texts. Translators are required to take format into account for different styles.<br />
The core of the expressive text is the sender’s emotions and attitudes. It is to express sender’s preferences, feelings, views and so on. The language style of it is subjective. Imaginative literature, including fictions, poems and dramas, autobiography and authoritative statements belong to expressive text. It requires translators to be faithful to the source language and maintain the style.<br />
The core of the vocative text is readership. It is to call upon readers to act, think, feel and react in the way intended by the text. So it is reader-oriented. Such texts as advertisement, propaganda and notices are of vocative text. Newmark noted that translators need to consider cultural background of the source language and the semantic and pragmatic effect of target language. If readers can comprehend the meaning at once and do as the text says, then the goal of information communication is achieved.<br />
To draw a conclusion, informative texts focus on the information and facts. Expressive texts center on the mind of addressers, including feelings, prejudices and so on. And vocative texts are oriented on readers and call on them to practice as the texts say.<br />
<br />
====2.2Study method====<br />
Manual translations of the three texts are selected from authoritative versions and universally acknowledged. And machine translations of those come from Youdao Translator. And in this thesis I will compare and evaluate the two methods in word diction, sentence structure, word order and redundancy.<br />
<br />
===3. ===<br />
<br />
===4. ===<br />
<br />
===5. ===<br />
<br />
===6. ===<br />
<br />
===7. ===<br />
<br />
===Conclusion===<br />
<br />
===References===<br />
<br />
=11 陈惠妮=(Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts)=<br />
[[Machine_Trans_EN_11]]<br />
===Abstract===<br />
At present, globalization is accelerating and the market demand for language services is rapidly increasing . Machine translation, as an important translation method, can greatly improve translation efficiency due to its low cost and high speed. However, because of the limitations of machine translation and the differences between Chinese and English language, machine translation is not accurate enough. In order to balance translation efficiency and translation quality, a great number of manual revisions in translation are required for the machine translating texts. Medical papers are specialized, special and purposeful, so it requires accurate,qualified and professional translation. However, the quality of translations by machine is inefficient to meet the high-quality requirements of medical papers translation. Therefore, the introduction of pre-editing can greatly improve the efficiency and quality of machine translation.<br />
<br />
===Key words===<br />
Pre-editing, Machine translation, Medical texts<br />
<br />
===题目===<br />
Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts<br />
<br />
===摘要===<br />
在全球化加速发展的今天，市场对语言服务的需求迅速增加。机器翻译作为一种重要的翻译途径，由于其成本低、速度快，可以大大提高翻译效率。然而，由于机器翻译的局限性以及中英文语言的差异，机器翻译的准确性不高。为了平衡翻译效率和翻译质量，机器翻译文本需要大量的手工修改。<br />
医学论文具有专业性、特殊性和目的性，要求其译文准确、合格、专业。然而，机器翻译的质量较低，无法满足医学论文对翻译的高质量要求。因此，译前编辑的引入可以大大提高机器翻译的效率和质量。<br />
<br />
===关键词===<br />
译前编辑；机器翻译；医学文本<br />
<br />
===1. Introduction===<br />
===1.1 Definition of Machine Translation===<br />
As Cronin(2013) revealed: "Translation is undergoing a revolutionary upheaval. The influence of digital technology and the Internet on translation is continuous, extensive and profound. From the popularity of automatic online translation applications, translation revolution is everywhere (Cronin,2013) However,the concept of machine translation was firstly proposed in the 1930s. Since 1940s, the machine translation technology has been evolving from rule-based machine translation (RBMT) to statistical machine translation (SMT), and to neural machine translation (NMT). Machine translation refers to the automatic translation of source language into target language by using a computer system. That is, machine translation refers to the automatic translation of text from one language into another natural language by computer software or other online translation webs. Machine translation is also defined as the process of “using a computer system to automatically translate text or speech from one natural language to another” according to the definition by ISO (Cui, 2014).O 'Brien (2002) defines it as "the behavior of modifying errors in machine translation to ensure that the target translation meets certain quality requirements". On the basic level, machine translation performs mechanical substitution of words in one language for words in another language, but that rarely produces a good translation, therefore, recognition of the whole phrases and their closest counterparts in the target language is needed. Not all words in one language have equivalents in another language, and many words have more than one meaning. A huge demand for translation is greatly needed in today’s global world, which creates new opportunities for the development of machine translation, attracts more and more attention and becomes one of the current research focuses.<br />
<br />
===1.2 Definition of Pre-editing===<br />
Pre-editing means to adjust and modify the source language to make it fit more with the characteristics of the machine translation software before putting the source language before the into machine translation, so as to improve the quality of the translation machine translation (Wei Changhong, 2008:93-94). Pre-editing is to modify the original text before putting it into machine translation software, in order to improve the recognition rate of machine translation, optimize the output quality of translated text and reduce the workload of post-editing. Because pre-translation editing only needs to be modified in one language, the operation is simpler than post-translation editing, which can realize the double improvement of quality and efficiency. A good pre-editing translation can help machine translation more smoothly, thus improving the machine readability and quality of the output translation. Pre-editing is mostly applied in the following situations: one is when the original text is of poor quality and the machine is difficult to recognize the meaning of the sentence, such as user generated content with poor readability and translatability (Gerlach et al, 2003:45-53); Documents that need to be published in multiple languages; Next is when the original text contains a lot of jargon; The last is the original text has a corresponding translation memory bank. If the original text is edited, it can better match the content of the translation memory bank. Pre-editing is a process of identifying problems. It requires to pre-edit the source texts before putting it into machine translation according to the requirements, listing the expressions or sentences that may have trouble in machine translation and then pre-edit it by human. The purpose is to enable the computer to translate better, improve the translatability of machine translation. (Slype G V & Guinet J F & Seitz F,1984:115)<br />
<br />
===1.3 Machine Translation Mode===<br />
According to the different knowledge acquisition methods, machine translation modes can be classified as follows: one is rule-based machine translation, which is based on bilingual dictionaries and a library of language rules for each language. The quality of translation depends on whether the source language conforms to the existing rules, but the inexhaustible rules are the hinders of this model. The second mode is machine translation based on statistics. This translation model relies on the principles of mathematics and statistics to find various existing translations corresponding to the translation tasks through the employment of corpus, analyzing the frequency of their occurrence and selecting the translation with the highest frequency for output. The disadvantage of this translation model is that it ignores the flexibility of language and the importance of context. The last one is neural network language model. This model is different from previous translation models in that it uses end-to-end neural network to realize automatic translation between natural languages. At present, the quality of its translation is much higher than that of the previous translation models.<br />
===1.4 Source of Study Abstracts===<br />
The core medical journals from domestic are selected in order to make the paper more representative and authoritative, such as National Medical Journal of China, Journal of Peking University (Health Science), and Journal of Third Military Medical University. All of them include basic medical science, biomedical technology, laboratory medical science and other fields. In this way, the pre-editing approaches included are applicable enough to machine translation of medical abstracts.<br />
===1.5 Selection of Translation Software===<br />
Generally, machine translation software includes Google Translation, Youdao Translator and Niu Translation, which has their own special use in translation. Take Google as an example, it is more like neurons in human brain, enabling to learn and collect information to establish connections with its neural machine translator’s neurons. However, it also causes many errors because of the lack of enough information. In this paper, contrastive analysis will be carried on by using Google translation. On the one hand, being a pioneer in the translation of NET, it is inevitably to sue Google as the translation software to translate medical texts in this paper. Comparing the program called “Google brain”, other NMT of translation software are relatively disadvantages. On the other hand, the Google translation enjoys the largest users in the world, with its downloads of more than one billion. The output quality of Google translation is more correct and complete than other machine translation software. With years of development and improvement, Google Translation has been greatly promoted. In this paper, Chinese medical abstract will be automatically translated by Google translation, and then the translation output will be compared with the translation by human and the publication of English abstracts. The main purpose is to prove that the improvement and promotion of quality and accuracy in the medical abstracts will be obtained through the pre-editing approaches.<br />
<br />
===2.Language Characteristics and Error Division===<br />
Since Chinese and English are two different languages, it is quite neccessary to identify their own characteristics so as to better analyze and understand the two languages. Tytler (1978) argued in his Essay on the Principles of Translation that there are three principles of translation that in all the translation should give readers the same feelings as the source text, except for complete transcript of ideas. There actually also exit some mistakes of these two languages. So the following will make some clarrifications of these two languages to ensure more accurate translation.<br />
<br />
===2.1 Language Characteristics of Medical Abstracts===<br />
Chinese and English belong to different language systems, so there are differences in their language structure and the way users think of the languages. When using machine translation from Chinese to English, due to the unequal language levels, there will be many mistakes in the translation process, especially for ESP texts, such as medical papers. <br />
Here, these differences mainly refer to the linguistic characteristics of medical abstracts. In medical abstracts, it usually includes structured and unstructured abstracts. Although in different forms, they both describe the purpose, methods and conclusions of the research.<br />
In the method section of medial abstract, several Chinese sentences can be connected with commas, but each sentence may convey different information. In contrast, English sentences contain a great deal of information, but in order to ensure clarity, some modifiers need to be isolated and then reconstructed. However, in Chinese-English machine translation, a lot of information is put into the sentence because the machine segments the sentence on the basis of the full comma.<br />
In addition, subjectless sentence is used in the objective and method parts of Chinese medical abstracts. The subjectless sentence means the sentence without or free from subject and is usually employed in two contexts. The first is "needless to say". In Chinese sentences, it is common to omit the subject of the sentence. Chinese sentences can convey meanings by using incomplete sentence structures, so Chinese speakers can understand the meanings of the sentences even though the sentence subject is omitted. The second is "emphasis of action". In this context, subjectless sentences are used to describe behaviors, especially in the study of traditional Chinese medicine abstracts. Comparatively speaking, it should be avoided in English medical abstracts. Abstract sentences are more subjective when describing the learning process, while the essential requirement of English medical abstract is objectivity. From this point, sentences without subject should be avoided in English medical abstracts. Another feature is voice. Few words with passive meanings appear in Chinese abstracts. English sentences are more favoured in using passive voice. When translating from Chinese to English, passive voice should be used to make the contents objective, which is also the basic requirement of medical papers.<br />
These are the linguistic features of Chinese medical abstracts. There are great differences in sentence structure and expression between Chinese and English medical abstracts. These differences may reduce the accuracy of machine translation, so it is necessary to introduce pre-editing to edit the source text to ensure that the source text can be accurately recognized by the machine and fully translated into English.<br />
===2.2 Error Division of Machine Translation in Translating Abstracts of Medical Papers from Chinese to English===<br />
In this paper, errors in machine translation are listed out after analyzing. Some are due to the principles, such as statistical-based MT and NMT, employed by machine translation. Based on the original errors, they can be studied from two levels. One is from the macro-level, referring to mistakes caused by objective factors. These are not human factors, but the disadvantage of machine translation and the limitations of language. The limitations of machine translation are derived from the principles and models adopted and manifests itself as a reliance on reference sources. In this case, semantic ambiguity and textual incoherence may occur in the absence of reference sources.<br />
However, for ESP texts such as medical papers, the requirements for terms and sentence patterns are far beyond the existing corpora. For unfamiliar texts (that is, the corresponding texts cannot be found in the corpora). The quality of output translation will be relatively low, which is determined by the principles of machine translation. As mentioned above, machine translation has evolved over the past few decades from rules-based to corp-based statistics to NMT. However, there still exist some limitations in machine translation.<br />
Mistakes on micro-level are mainly caused by the variations and differences of linguistic structure between Chinese and English. Chinese is an implicit language, while English is an explicit one (Lian, 2010) To put it in another way, Chinese expression does not depend on language structures, while English does the opposite. This may result in mismatches between the original and machine translated sentences. This kind of error is mainly divided into two types: component fragment and component missing. For a medical paper, such errors are very serious. As a kind of ESP discourse, medical paper has the characteristics of fixed, objective and accurate language structure. In order to reproduce the characteristics of medical abstracts in translation, it is necessary to avoid errors at the level of words and sentences and pay attention to logic and consistency.<br />
Lexical errors here refer to the inconsistency of fixed expressions in the translation of terms. Although these terms are machine translation based on the corpus, the corpus may not be perfect for ESP texts such as medical papers. Therefore, fixed expressions are not used in term translation. This is also the most common mistake in machine translation (the term here includes but is not limited to nouns). For ESP texts, medical text in particular, the fixed expressions and the accuracy of terms are of great importance.<br />
<br />
===3.Approaches Proposed for Pre-editing ===<br />
Generally speaking, there is a close relationship between pre-editing and post-editing, both of which aim to convey information and ensure high or publishable translation quality. Proper pre-editing can improve the quality of machine translation in terms of adequacy and consistency. <br />
Complexity of natural language and people use language arbitrarily, bringing many difficulties to Chinese-English machine translation, Hu Qingping (2005:24) has proposed "the research of translation software and the development of the controlled language are the two directions to improve the quality of machine translation : the former aims at the difficulty in the natural language processing, the latter overcomes the arbitrariness of natural language". Feng Quangong and Gao Lin (2017: 63-68) put forward: "The writing principles of controlled language can be applied to pre-editing of machine translation. Pre-editing based on controlled language can effectively reduce the complexity and ambiguity of source text, improve the identifiability of machine translation (the translatability of source text itself), and thus reduce (fully) the workload of post-editing".<br />
The central task of pre-editing is to transform human-friendly content into machine-friendly content, so words and sentences need to be repositioned or even changed. Based on the analysis of the language characteristics of Chinese and English, the Chinese ideographic group should be split before the Chinese source text is input into the machine software to translate so that the sentence structure is complete which can be easily recognized by the machine translation software.<br />
===3.1 Extraction and Replacement of Terms in the Source Text===<br />
As a branch of applied English, medical English is the product of the combination of English language knowledge and medical knowledge. The terms in medical papers have the characteristics of fixed and complex language structure. Although Google Translation is based on a corpus, the language structure is not fixed due to the limited size of the corpus. Therefore, the following two steps should be followed before machine translation. The first is to extract terms and create a glossary, and then replace the Chinese terms in the source text with the corresponding English expressions in the glossary. Of course, the terms of extraction should be searched and verified according to the actual situation. All of these words are verified before they can be replaced. This method can not only ensure the accuracy of term translation, but also maintain the consistency of expression, especially when dealing with long texts.<br />
Example1<br />
Source abstract: 口腔白斑病癌变相关缺氧应答基因和微小RNA的芯片及表达验证。<br />
Google translation: Microarray detection/Chip detection and expression verification of hypoxia response genes and microRNAs related to oral leukoplakia canceration.<br />
Published translation:Transcriptome array screening and verification of oral leukoplakia carcinogenesis-related hypoxia-responsive gene and microRNA. <br />
Analysis: The expression “ Affymetrix GeneChip” empolyed in this thesis is a human transcription array for transcriptome array. In the source abstract, “芯片检测“ can be meant “screening” but not detection, so the proper and appropriate translation of these expression should be “transcription array screening”, but the Google translates it into “microarry detection of chip detection”. Therefore, term extraction is essential and inevitable before putting the source abstracts into the machine translation software.<br />
After pre-editing source abstracts: 对 oral leukoplakia carcinogenesis 相关 hypoxia-responsive gene 和微小RNA进行 transcriptome array screening 及表达验证。<br />
After pre-editing translation: Transcriptome array screening and expression- verification of hypoxia-responsive genes and microRNAs related to oral leukoplakia carcinogenesis.<br />
===3.2 Explication of Subordination===<br />
As mentioned above, the subordination of Chinese sentences is judged by certain specific words in machine translation . Chinese is a paratactic language, and sentences are often connected by internal logical relationships; while English depends on sentence structure, so sentences are often closely linked by various language forms. In order to improve the accuracy of machine translation, we can adjust the sentence structure and adjust the position of adjectives and their modifiers. <br />
Example 2<br />
Source abstracts:回顾性分析南京医科大学附属儿童医院中重度HIE患儿49例及同期就诊的无神经系统症状体征的足月新生儿为对照组31例的头颅磁共振成像（MRI）资料。<br />
Google translation: A retrospective analysis that the brain magnetic resonance imaging (MRI) data of 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University and full-term neonates with no neurological symptoms and signs during the same period were included in the control group.<br />
Published translation: A total of 49 children with moderate to severe HIE admitted to the children’s Hospital Affiliated to Nanjing Medical University were retrospectively analyzed. Cranial magnetic resonance imaging (MRI) date of 31 full-term neonates without neurological symptoms and signs who visited the hospital during the same period were recruited as the control group. <br />
Analysis: As the above example shows that “中重度HIE患儿” and “足月新生儿” in the source abstracts are from the Children’s Hospital Affiliated Nanjing Medical University. The Google translation shows that 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University. There is an error due to the misuse of subordination. There are some advice in order to solve the problem. That is, first to segment the sentence and then reconstruct the sentence. For instance, the Chinese expression “南京医科大学附属儿童医院” carries many modifiers, which requires to cut the modifiers and reconstruct the sentence. Therefore, the Chinese expression “南京医科大学附属儿童医院’can be divided into abstractions, leaving two sentences instead of one long sentence with modifiers..<br />
After pre-editing source abstracts: 对49例中重度HIE患儿以及31例无神经系统症状体征的足月新生儿的MRI资料进行回顾性分析。这些患者收治于南京医科大学儿童附属医院。<br />
After pre-editing translation: The MRI data of 49 children with moderate to severe HIE and 31 full-term newborns without neurological symptoms and signs were retrospectively analyzed. These patients were admitted to the Children’s Hospital of Nanjing Medical University.<br />
===3.3 Explication of Subject through Voice Changing===<br />
The extensive use of subjectless sentences are quite common in the Chinese abstracts, while English prefers to employ passive voice in the sentences so as to make them object and accurate. In order to take the characteristics of English sentences into great and careful consideration, the sentences written in passive voice in particular, it will be helpful for machine translation to find out the subject and reconstruct a sentence in passive voice (Wang Yan: 2008). The first is to discover the “doee” in a sentence and then is to reconstruct the sentence structure and put the “doee” before the verb. The last is to explicate the passive relation between the noun and the verb.<br />
Example 3<br />
Source abstract: 回顾性分析2018年1月至2020年12月解放军总医院第七医学中心收治的500例老年髋部骨折患者的资料。<br />
Google translation: A retrospective analysis of the data of 500 elderly patients with hip fractures admitted to the Seventh Medical Center of the PLA General Hospital from January 2018 to December 2020.<br />
Published translation: From January 2018 to December 2020, the data of 500 elderly patients with hip fracture treated in the Seventh Medical Center of PLA General Hospital were analyzed retrospectively.<br />
Analysis: The above example indicates that the “回顾性分析”in the Google Translate is a noun phrase, but the source abstracts actually describes an action or a behavior. The reason for such a mistake is that the machine can’t recognize the Chinese subjetless sentences. To find the subject of the sentence, the source abstracts can be revised and adjusted. Finding the “doee” is the first step, which refers to “500例老年髋部骨折患者的资料”in the source abstracts. And next is to put it in front of the verb, that is to place it in the beginning of the sentence. Last but not least, the passive voice should be explicated in the sentence. <br />
After pre-editing source abstract: 500例老年髋部骨折患者的资料被回顾性分析，他们在2018年1月之2020年12月期间收治于解放军总医院第七医学中心。<br />
After pre-editing translation: The data of 500 elderly hip fracture patients were retrospectively analyzed. They were admitted to the Seventh Medical Center of the PLA General Hospital between January 2018 and December 2020.<br />
===3.4 Relocation of Modifiers===<br />
Modifiers, including noun, adverbial and attributive are constantly employed in the Chinese medical papers in order to add information to subject and object in the sentence. By doing this, complicated sentences can be structured, thus causing obstacles to machine translation for recognizing this complex sentence structures when translating Chinese-English sentences. To make the machine translation successfully recognize the sentence structure, simplifying the sentence structure is quite necessary. The key is to moving the location of the modifiers and thus making the modifiers an independent sentence. In other words, the modifiers ought to be put before or behind the main part of the sentence to satisfy the common use of English. <br />
Usually, the modifiers in Chinese sentence can only be placed in front of the core word, while in English, modifiers are very flexible. It is all right to place the modifiers in front of or behind the major part of the sentences with adjective or a connective noun.<br />
Example 4<br />
Source abstracts: 利用DNA重组技术以pET-28a表达系统在E.coli BL21(DE3) 中重组表达Hepl。<br />
Google Translation: Hepl is recombined in E.coli BL21 (DE3) using DNA recombination technology with pET-28a expression system.<br />
Published translation: The recombinant Hcpl protein was expressed by using DNA recombination technology through pET-28a expression system in E. coli BL21 (De3).<br />
Analysis: The Chinese medical text includes two modifiers, “利用DNA”and “以pET-28a)表达系统”. These two modifiers will be translated by machine, which catches more attention to the two constituents. From the Google translation above, one failure is obvious that it misplaces the location of the two modifiers and presents it in not accurate form. To make it translate correctly by machine translation, dividing the sentences is very important so that the source abstracts can be correctly recognized by machine translation.<br />
After pre-editing source abstract: 利用DNA重组技术，Hcp1重组表达在E.coli BL21 (DE3)中， 通过pET-28a表达系统在E. coli BL21 (DE3)中。<br />
Google translation: Using DNA recombination technology, Hcp1 recombination is expressed in E.coli BL21 (DE3), via pET-28a expression system in E. coli BL21 (DE3).<br />
The second is the independence of modifiers, that is, modifiers can also be reconstructed into clauses to modify core words. Compared with English, There is no subordinate clause in Chinese, so redundant Chinese modifiers need to be reconstructed into subordinate clauses in Chinese-English translation to meet the characteristics of English. English, especially sentences with multiple modifiers. Otherwise, sentence structure may be confused, such as scattered modifiers of core words. To avoid this mistake, it is necessary to separate the modifier from the main part of the sentence. These modifiers should be reconstituted into clauses. Also, keep your sentences simple and easy to understand.<br />
===3.5 Proper Omission and Deletion of Category Words===<br />
Category words are commonly used in Chinese. Category words complement the meaning of words, including problems, positions, situations and jobs. Adding this supplementary word is more in line with Chinese custom. In many cases, it has no real meaning, so it can be omitted in translation. In Chinese, category words are frequently used. However, it is rarely used in English, which is one of the differences between Chinese and English. There are many kinds of category words. Considering objectivity and the fixed structure of language, redundant category words should be deleted in Chinese-English translation. In the general, the most commonly used category of words in the Chinese medical abstracts are "process", "behavior", and "situation". These categories of words hinder the language conversion of Chinese and English, resulting in redundancy. <br />
Example 5<br />
Source abstract: 探讨口腔白斑病癌变进程中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia response genes and associated microRNAs in the process of oral leukoplakia cancer was discussed.<br />
Published translation: To study the hypoxia response gene and microRNA (miRNA)expression profiles in the pathogenesis and progression of oral leukoplakia (OLK).<br />
Analysis: As the above example shows, the Chinese words “进程” belongs to a category word. However, the Chinese expression “癌变”contains the process, so the “进程” expression can be deleted before placing it into the machine translation, because the meaning of it has been overlapping between the expression “癌变”. Therefore, its place can be replaced with preposition “during”.<br />
After pre-editing source abstract: 探讨口腔白斑病癌变中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia responsive genes and miRNA in oral leukoplakia cancer was investigated.<br />
<br />
===Conclusion===<br />
<br />
===References===<br />
<br />
=12 蔡珠凤=(The Mistranslation of C-J Machine Translation of Political Statements)=<br />
[[Machine_Trans_EN_12]]<br />
===Abstract===<br />
Language is the main way of communication between people. With the continuous development of globalization, the scale of cross-border exchanges is also expanding. However, due to cultural differences and diversity, the languages of different countries and regions are very different, which seriously hinders people's communication. The demand for efficient and convenient translation tools is increasing. At the same time, with the development of network technology and artificial intelligence, recognition technology based on deep learning is more and more widely used in English, Japanese and other fields.<br />
<br />
===Key words===<br />
machine translation; political statements; mistranslation of C-J machine translation<br />
===题目===<br />
The Mistranslation of C-J Machine Translation of Political Statements<br />
===摘要===<br />
语言是人与人之间交流的主要方式。随着全球化的不断发展，跨境交流的规模也在不断扩大。然而，由于文化的差异和多样性，不同国家和地区的语言差异很大，这严重阻碍了人们的交流。对高效便捷的翻译工具的需求正在增加。同时，随着网络技术和人工智能的发展，基于深度学习的识别技术在英语、日语等领域的应用越来越广泛。<br />
<br />
===关键词===<br />
机器翻译；政治发言；政治发言中译日的误译<br />
===1. Introduction===<br />
===Introduction to machine translation===<br />
Machine translation, also known as automatic translation, is a process of using computers to convert one natural language (source language) into another natural language (target language). It is a branch of computational linguistics, one of the ultimate goals of artificial intelligence, and has important scientific research value.At the same time, machine translation has important practical value. With the rapid development of economic globalization and the Internet, machine translation technology plays a more and more important role in promoting political, economic and cultural exchanges.The development of machine translation technology has been closely accompanied by the development of computer technology, information theory, linguistics and other disciplines. From the early dictionary matching, to the rule translation of dictionaries combined with linguistic expert knowledge, and then to the statistical machine translation based on corpus, with the improvement of computer computing power and the explosive growth of multilingual information, machine translation technology gradually stepped out of the ivory tower and began to provide real-time and convenient translation services for general users.<br />
<br />
=== C-J machine translation software===<br />
Today's online machine translation software includes Baidu translation, Tencent translation, Google translation, Youdao translation, Bing translation and so on. Google was the first company to launch the machine translation system, and Baidu was the first company to import the machine translation system in China. In addition, Tencent and Youdao have attracted much attention.Machine translation is the process of using computers to convert one natural language into another. It usually refers to sentence and full-text translation between natural languages. In order to continuously improve the translation quality, R & D personnel have added artificial intelligence technologies such as speech recognition, image processing and deep neural network to machine translation on the basis of traditional machine translation based on rules, statistics and examples.With the increase of using machine translation, the joint cooperation between manual translation and machine translation will also increase significantly in the future. What criteria should be used to evaluate the quality of machine translation? In the evolving field of machine translation, there is an urgent need to clarify the unsolvable questions and solved problems.<br />
<br />
===The history of machine translation===<br />
The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. alchuni put forward the idea of using machines for translation. In 1933, Soviet inventor П.П. Trojansky designed a machine to translate one language into another, and registered his invention on September 5 of the same year; However, due to the low technical level in the 1930s, his translation machine was not made. In 1946, the first modern electronic computer ENIAC was born. Shortly after that, W. weaver, an American scientist and A. D. booth, a British engineer, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947 when discussing the application scope of electronic computer. In 1949, W. Weaver published the translation memorandum, which formally put forward the idea of machine translation. After 60 years of ups and downs, machine translation has experienced a tortuous and long development path. The academic community generally divides it into the following four stages:<br />
Pioneering period（1947-1964）<br />
In 1954, with the cooperation of IBM, Georgetown University completed the English Russian machine translation experiment with ibm-701 computer for the first time, showing the feasibility of machine translation to the public and the scientific community, thus opening the prelude to the study of machine translation.<br />
It is not too late for China to start this research. As early as 1956, the state included this research in the national scientific work development plan. The topic name is "machine translation, the construction of natural language translation rules and the mathematical theory of natural language". In 1957, the Institute of language and the Institute of computing technology of the Chinese Academy of Sciences cooperated in the Russian Chinese machine translation experiment, translating 9 different types of more complex sentences.<br />
From the 1950s to the first half of the 1960s, machine translation research has been on the rise. The United States and the former Soviet Union, two superpowers, have provided a lot of financial support for machine translation projects for military, political and economic purposes, while European countries have also paid considerable attention to machine translation research due to geopolitical and economic needs, and machine translation has become an upsurge for a time. In this period, although machine translation is just in the pioneering stage, it has entered an optimistic period of prosperity.<br />
Frustrated period（1964-1975）<br />
In 1964, in order to evaluate the research progress of machine translation, the American Academy of Sciences established the automatic language processing Advisory Committee (Alpac Committee) and began a two-year comprehensive investigation, analysis and test.<br />
In November 1966, the committee published a report entitled "language and machine" (Alpac report for short), which comprehensively denied the feasibility of machine translation and suggested stopping the financial support for machine translation projects. The publication of this report has dealt a blow to the booming machine translation, and the research of machine translation has fallen into a standstill. Coincidentally, during this period, China broke out the "ten-year Cultural Revolution", and basically these studies also stagnated. Machine translation has entered a depression.<br />
convalescence（1975-1989）<br />
Since the 1970s, with the development of science and technology and the increasingly frequent exchange of scientific and technological information among countries, the language barriers between countries have become more serious. The traditional manual operation mode has been far from meeting the needs, and there is an urgent need for computers to engage in translation. At the same time, the development of computer science and linguistics, especially the substantial improvement of computer hardware technology and the application of artificial intelligence in natural language processing, have promoted the recovery of machine translation research from the technical level. Machine translation projects have begun to develop again, and various practical and experimental systems have been launched successively, such as weinder system Eurpotra multilingual translation system, taum-meteo system, etc.<br />
However, after the end of the "ten-year holocaust", China has perked up again, and machine translation research has been put on the agenda again. The "784" project has paid enough attention to machine translation research. After the mid-1980s, the development of machine translation research in China has further accelerated. Firstly, two English Chinese machine translation systems, ky-1 and MT / ec863, have been successfully developed, indicating that China has made great progress in machine translation technology.<br />
New period(1990 present)<br />
With the universal application of the Internet, the acceleration of the process of world economic integration and the increasingly frequent exchanges in the international community, the traditional way of manual operation is far from meeting the rapidly growing needs of translation. People's demand for machine translation has increased unprecedentedly, and machine translation has ushered in a new development opportunity. International conferences on machine translation research have been held frequently, and China has made unprecedented achievements. A series of machine translation software have been launched, such as "Yixing", "Yaxin", "Tongyi", "Huajian", etc. Driven by the market demand, the commercial machine translation system has entered the practical stage, entered the market and came to the users.<br />
Since the new century, with the emergence and popularization of the Internet, the amount of data has increased sharply, and statistical methods have been fully applied. Internet companies have set up machine translation research groups and developed machine translation systems based on Internet big data, so as to make machine translation really practical, such as "Baidu translation", "Google translation", etc. In recent years, with the progress of in-depth learning, machine translation technology has further developed, which has promoted the rapid improvement of translation quality, and the translation in oral and other fields is more authentic and fluent.<br />
<br />
===The problem of machine translation at present===<br />
Error is inevitable<br />
Many people have misunderstandings about machine translation. They think that machine translation has great deviation and can't help people solve any problems. In fact, the error is inevitable. The reason is that machine translation uses linguistic principles. The machine automatically recognizes grammar, calls the stored thesaurus and automatically performs corresponding translation. However, errors are inevitable due to changes or irregularities in grammar, morphology and syntax, such as sentences with adverbials after "give me a reason to kill you first" in Dahua journey to the West. After all, a machine is a machine. No one has special feelings for language. How can it feel the lasting charm of "the tenderness of lowering its head, like the shame of a water lotus? After all, the meaning of Chinese is very different due to the changes of morphology, grammar and syntax and the change of context. Even many Chinese people are zhanger monks - they can't touch their heads, let alone machines.<br />
Bottleneck<br />
In fact, no matter which method, the biggest factor affecting the development of machine translation lies in the quality of translation. Judging from the achievements, the quality of machine translation is still far from the ultimate goal.<br />
Chinese mathematician and linguist Zhou Haizhong once pointed out in his paper "fifty years of machine translation": to improve the quality of machine translation, the first thing to solve is the problem of language itself rather than programming; It is certainly impossible to improve the quality of machine translation by relying on several programs alone. At the same time, he also pointed out that it is impossible for machine translation to achieve the degree of "faithfulness, expressiveness and elegance" when human beings have not yet understood how the brain performs fuzzy recognition and logical judgment of language. This view may reveal the bottleneck restricting the quality of translation. <br />
It is worth mentioning that American inventor and futurist ray cozwell predicted in an interview with Huffington Post that the quality of machine translation will reach the level of human translation by 2029. There are still many disputes about this thesis in the academic circles.<br />
<br />
===2.Mistranslation of Chinese Japanese machine translation===<br />
===2.1Vocabulary mistranslation in Chinese Japanese machine translation===<br />
===2.1.1Mistranslation of proper nouns===<br />
In political materials, there are often political dignitaries' names, place names or a large number of proper nouns in the political field. The morphemes of such words are definite and inseparable. Mistranslation will make the source language lose its specific meaning. <br />
<br />
Chinese translation into Japanese Japanese translation into Chinese<br />
<br />
original text translation by Youdao reference translation original text translation by Youdao reference translation<br />
栗战书 栗戰史書 栗戰書 労安 劳安 劳安<br />
李克强 李克強 李克強 朱鎔基 朱基 朱镕基<br />
习近平 習近平 習近平 筑紫哲也 筑紫哲也 筑紫哲也<br />
韩正 韓中 韓正 山口百惠 山口百惠 山口百惠<br />
王沪宁 王上海氏 王滬寧 田中角栄 田中角荣 田中角荣<br />
汪洋 汪洋 汪洋 東条英機 东条英社 东条英机<br />
赵乐际 趙樂南 趙樂際 毛沢东 毛泽东 毛泽东<br />
江泽民 江沢民 江沢民 トウ・ショウヘイ 大酱 邓小平<br />
周恩来 周恩来 周恩来<br />
クリントン 克林顿 克林顿<br />
<br />
The above table counts 18 special names in the two texts, and 7 machine translation errors. In terms of mistranslation, there are not only "战书" but also "戰史" and "史書" in Chinese. "沪" is the abbreviation of Shanghai. In other words, since "战书" and "沪" are originally common nouns, this disrupts the choice of target language in machine translation. Except Katakana interference (except for トウシゃウヘイ, most of the mistranslations appear in the new Standing Committee. It can be seen that the machine translation system does not update the thesaurus in time. Different from other words, people's names have particularity. Especially as members of the Standing Committee of the Political Bureau of the CPC Central Committee, the translation of their names is officially unified. In this regard, public figures such as political dignitaries, stars, famous hosts and important. In addition, the machine also translates Japanese surnames such as "タ二モト" (谷本), "アンドウ" (安藤) into "塔尼莫特" and "龙胆". It can be found that the language feature that Japanese will be written together with Chinese characters and Hiragana also directly affects the translation quality of Japanese Chinese machine translation. Japanese people often use Katakana pronunciation. For example, when Japanese people talk with Premier Zhu, they often use "二ーハウ" (Hello). However, the machine only recognizes the two pseudonyms "二" and "ハ" and transliterates them into "尼哈" , ignoring the long sound after "二" and "ハ".<br />
<br />
original text Translation by Youdao reference translation<br />
日美安全体制 日米の安全体制 日米安保体制<br />
中国共产党第十九次全国代表大会 中国共産党第19回全国代表大会 中国共産党第19回全国代表大会（第19回党大会）<br />
十八大 十八大 第18回党大会<br />
中国特色社会主义 中国特色社会主義 中国の特色ある社会主義<br />
中国共产党中央委员会 中国共産党中央委員会 中国共産党中央委員会<br />
十八届中共中央政治局常委 第18代中国共產党中央政治局常務委員 第18期中共中央政治局常務委員<br />
十八届中共中央政治局委员 18期の中国共產党中央政治局委員 第18期中共中央政治局委員<br />
十九届中共中央政治局常委 十九回中国共產党中央政治局常務委員 第19期中央政治局常務委員<br />
中共十九届一中全会 中国共產党第十九回一中央委員会 第19期中央委員会第1回全体会議<br />
The above table is a comparison of the original and translated versions of some proper nouns. As shown in the table, the mistranslation problems are mainly reflected in the mismatch of numerals + quantifiers, the wrong addition of case auxiliary word "の", the lack of connectors, the Mistranslation of abbreviations, dead translation, and the writing errors of Chinese characters. The following is a specific analysis one by one.<br />
"十八届中共中央政治局常委", "十八届中共中央政治局委员", "十九届中共中央政治局常委" and "中共十九届一中全会" all have quantifiers "届", which are translated into "代", "期" and "回" respectively. The meaning is vague and should be uniformly translated into "期"; Among them, the translation of the last three proper nouns lacks the Chinese conjunction "第". The case auxiliary word "の" was added by mistake in the translation of "十八届中共中央政治局委员" and "日美安全体制". The "中国共产党中央委员会" did not write in the form of Japanese Ming Dynasty characters, but directly used simplified Chinese characters.<br />
The full name of "中共十九届一中全会" is "中国共产党第十九届中央委员会第一次全体会议", in which "一" stands for "第一次", which is an ordinal word rather than a cardinal word. Machine translation does not produce a correct translation. The fundamental reason is that there are no "规则" for translating such words in machine translation. If we can formulate corresponding rules for such words (for example, 中共m'1届m2 中全会→第m1期中央委员会第m2回全体会议), the translation system must be able to translate well no matter how many plenary sessions of the CPC Central Committee.<br />
<br />
===2.1.2Mistranslation of Polysemy===<br />
<br />
original text Translation by Youdao reference translation<br />
スタジオ 摄影棚/工作室 直播现场/演播厅<br />
日中関係の話 中日关系的故事 就中日关系(话题)<br />
溝 水沟 鸿沟<br />
それでは日中の問題について質問のある方。 那么对白天的问题有提问的人。 关于中日问题的话题，举手提问。<br />
私たちのクラスは20人ちょっとですが、 我们班有20人左右， 我们班二十多人的意见统一很难，<br />
いろいろな意見が出て、まとめるのは大変です。 但是又各种各样的意见，总结起来很困难。 中国是怎样把13亿人凝聚在一起的？<br />
一体どうやって、13億人もの人をまとめているんですか。 到底是怎么处理13亿的人的呢？ <br />
In the original text, the word "スタジオ" appeared four times and was translated into "摄影棚" or "工作室" respectively, but the context is on-site interview, not the production site of photography or film. "話" has many semantics, such as "说话", "事情", "道理", etc. In accordance with the practice of the interview program, Premier Zhu Rongji was invited to answer five questions on the topic of China Japan relations. Obviously, the meaning of the word "故事" is very abrupt. The inherent Japanese word "日中" means "晌午、白天". At the same time, it is also the abbreviation of the names of the two countries. The machine failed to deal with it correctly according to the context. "溝" refers to the gap between China and Japan, not a "水沟". The former "まとめる" appears in the original text with the word "意见", which is intended to describe that it is difficult to unify everyone's opinions. The latter "まとめている" also refers to unifying the thoughts of 1.3 billion people, not "处理". Therefore, it is more appropriate to use "统一" and "处理" in human translation, rather than "总结意见" or "处理意见".<br />
Mistranslation of polysemy has always been a difficult problem in machine translation research. The translation of each word is correct, but it is often very different from the original expression in the context. Zhang Zhengzeng said that ambiguity is a common phenomenon in natural language. Its essence is that the same language form may have different meanings, which is also one of the differences between natural language and artificial language. Therefore, one of the difficulties faced by machine translation is language disambiguation (Zhang Zheng, 2005:60). In this regard, we can mark all the meanings of polysemous words and judge which meaning to choose by common collocation with other words. At the same time, strengthen the text recognition ability of the machine to avoid the translation inconsistent with the current context. In this way, we can avoid the mistake of "大酱" in the place where famous figures such as Deng Xiaoping should have appeared in the previous political articles.<br />
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===2.1.3Mistranslation of compound words===<br />
Multi class words refer to a word with two or more parts of speech, also known as the same word and different classes.<br />
original text Translation by Youdao reference translation<br />
1998年江泽民主席曾经访问日本, 1998年の江沢民国家主席の日本訪問し、 1998年、江沢民総書記が日本を訪問し、かつ、<br />
同已故小渊首相签署了联合宣言。 かつて同じ故小渕首相が署名した共同宣言 亡くなられた小渕総理と宣言に調印されました<br />
<br />
Chinese "同" has two parts of speech: prepositions and conjunctions: "故" has many parts of speech, such as nouns, verbs, adjectives and conjunctions. This directly affects the judgment of the machine at the source language level. The word "同" in the above table is used as a conjunction to indicate the other party of a common act. "故" is used as a verb with the semantic meaning of "死亡", which is a modifier of "小渊首相". The machine regards "同" as an adjective and "故" as a noun "原因", which leads to confusion in the structure and unclear semantics of the translation. Different from the polysemy problem, multi category words have at least two parts of speech, and there is often not only one meaning under each part of speech. In this regard, software R & D personnel should fully consider the existence of multi category words, so that the translation machine can distinguish the meaning of words on the basis of marking the part of speech, so as to select the translation through the context and the components of the word in the sentence. Of course, the realization of this function is difficult, and we need to give full play to the wisdom of R & D personnel.<br />
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===2.2 Syntactic mistranslation in Chinese Japanese machine translation===<br />
===2.2.1Mistranslation of tenses===<br />
original text：History is written by the people, and all achievements are attributed to the people. <br />
Translation by Youdao：歴史は人民が書いたものであり、すべての成果は人民のためである。<br />
reference translation：歴史は人民が綴っていくものであり、すべての成果は人民に帰することとなります。<br />
The original sentence meaning is "历史是人民书写的历史" or "历史是人民书写的东西". When translated into Japanese, due to the influence of Japanese language habits, the formal noun "もの" should be supplemented accordingly. In this sentence, both the machine and the interpreter have translated correctly. However, the machine's recognition of "的" is biased, resulting in tense translation errors. The past, present and future will become "history", and this is a continuous action. The form translated as "~ ていく" not only reflects that the action is a continuous action, but also conforms to the tense and semantic information of the original text. In addition, the machine's treatment of the preposition "于" is also inappropriate. In the original text, "人民" is the recipient of action, not the target language. As the most obvious feature of isolated language, function words in Chinese play an important role in semantic expression, and their translation should be regarded as a focus of machine translation research.<br />
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original text：李克强同志是十六届中共中央政治局常委,其他五位同志都是十六届中共中央政治局委员。<br />
Translation by Youdao：李克強総理は第16代中国共産党中央政治局常務委員であり、他の５人の同志はいずれも16期の中国共産党中央政治局委員である。<br />
reference translation：李克強同志は第16期中国共産党中央政治局常務委員を務め、他の５人は第16期中共中央政治局委員を務めました。<br />
Judging the verb "是" in the original text plays its most basic positive role, but due to the complexity of Chinese language, "是" often can not be completely transformed into the form of "だ / である" in the process of translation. This sentence is a judgment of what has happened. Compared with the 19th session, the 18th session has become history. This is an implicit temporal information. It is very difficult for machines without human brain to recognize this implicit information. "中共中央政治局常委" is the abbreviation of "中共中央政治局常务委员会委员" and it is a kind of position. In Japanese, often use it with verbs such as "務める","担当する",etc. The translator adopts the past tense of "務める", which conforms to the expression habit of Japanese and deals with the problem of tense at the same time. However, machine translation only mechanically translates this sentence into judgment sentence, and fails to correctly deal with the past information implied by the word "十八届".<br />
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===2.2.2Mistranslation of honorifics===<br />
Honorific language is a language means to show respect to the listener. Different from Japanese, there is no grammatical category of honorifics in Chinese. There is no specific fixed grammatical form to express honorifics, self modesty and politeness. Instead, specific words such as "您", "请" and "劳驾" are used to express all kinds of respect or self modesty. <br />
Original text translation by Youdao reference translation<br />
女士们，先生们，同志们，朋友们 さんたち、先生たち、同志たち、お友达さん ご列席の皆さん<br />
谢谢大家！ ありがとうございます！ ご清聴ありがとうございました。<br />
これはどうされますか。 这是怎么回事呢? 您将如何解决这一问题？<br />
こうした問題をどうお考えでしょうか。 我们会如何考虑这些问题呢？ 您如何看待这一问题？ <br />
For example, for the processing of "女士们，先生们，同志们，朋友们", the machine makes the translation correspond to the original one by one based on the principle of unchanged format, but there are errors in semantic communication and pragmatic habits. When speaking on formal occasions, Chinese expression tends to be comprehensive and detailed, as well as the address of the audience. In contrast, Japanese usually uses general terms such as "皆様", "ご臨席の皆さん" or "代表団の方々" as the opening remarks. In terms of Japanese Chinese translation, the machine also failed to recognize the usage of Japanese honorifics such as "さ れ る" and "お考え". It can be seen that Chinese Japanese machine translation has a low ability to deal with honorific expressions. The occasion is more formal. A good translation should not only be fluent in meaning, but also conform to the expression habits of the target language and match the current translation environment.<br />
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===Conclusion===<br />
In the process of discussing the Mistranslation of machine translation, this paper mainly cites the Mistranslation of Youdao translator. When I studied the problem of machine translation mistranslation, I also used a large number of translation software such as Google and Baidu for parallel comparison, and found that these translation software also had similar problems with Youdao translator. For example, the "新世界新未来" in Chinese ABAC structural phrases is translated by Baidu as "新し世界の新しい未来", which, like Youdao, is not translated in the form of parallel phrases; Google online translation translates the word "“全心全意" into a completely wrong "全面的に"; The original sentence "我吃了很多亏" in the Mistranslation of the unique expression in the language is translated by Google online as "私はたくさんの損失を食べました". Because the "吃" and "亏" in the original text are not closely adjacent, the machine can not recognize this echo and mistakenly treats "亏" as a kind of food, so the machine translates the predicate as "食べました"; Japanese "こうした問題をどう考えでしょうか", Google's online translation is "你如何看待这些问题?", "你" tone can not reflect the tone of Japanese honorific, while Baidu translates as "你怎么想这样的问题呢?" Although the meaning can be understood, it is an irregular spoken language. Google and Baidu also made the same mistakes as Youdao in the translation of proper nouns. For example, they translated "十七届(中共中央政治局常委)”" into "第17回..." .In short, similar mistranslations of Youdao are also common in Baidu and Google. Due to space constraints, they will not be listed one by one here. <br />
Mr. Liu Yongquan, Institute of language, Chinese Academy of Social Sciences (1997) It has been pointed out that machine translation is a linguistic problem in the final analysis. Although corpus based machine translation does not require a lot of linguistic knowledge, language expression is ever-changing. Machine translation based solely on statistical ideas can not avoid the translation quality problems caused by the lack of language rules. The following is based on the analysis of lexical, syntactic and other mistranslations From the perspective of the characteristics of the source language, this paper summarizes some difficulties of Chinese and Japanese in machine translation. <br />
(1) The difficulties of Chinese in machine translation <br />
Chinese is a typical isolated language. The relationship between words needs to be reflected by word order and function words. We should pay attention to the transformation of function words such as "的", "在", "向" and "了". Some special verbs, such as "是", "做" and "作", are widely used. How to translate on the basis of conforming to Japanese pragmatic habits and expressions is a difficulty in machine translation research. In terms of word order, Chinese is basically "subject→predicate→object". At the same time, Chinese pays attention to parataxis, and there is no need to be clear in expression with meaningful cohesion, which increases the difficulty of Chinese Japanese machine translation. When there are multiple verbs or modifier components in complex long sentences, machine translation usually can not accurately divide the components of the sentence, resulting in the result that the translation is completely unreadable. <br />
(2) Difficulties of Japanese in machine translation <br />
Both Chinese and Japanese languages use Chinese characters, and most machines produce the target language through the corresponding translation of Chinese characters. However, pseudonyms in Japanese play an important role in judging the part of speech and meaning, and can not only recognize Chinese characters and judge the structure and semantics of sentences. Japanese vocabulary is composed of Chinese vocabulary, inherent vocabulary and foreign vocabulary. Among them, the first two have a great impact on Chinese Japanese machine translation. For example "提出" is translated as "提出する" or "打ち出す"; and "重视" is translated as "重視する" or "大切にする". Japanese is an adhesive language, which contains a lot of "けど", "が" and "けれども" Some of them are transitional and progressive structures, but there are also some sequential expressions that do not need translation, and these translation software often can not accurately grasp them.<br />
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===References===<br />
[1] Navroz Kaur Kahlon;Williamjeet Singh.Machine translation from text to sign language: a systematic review[J].Universal Access in the Information Society,2021(prepublish):1-35.<br />
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[2] Cao Qianyu;Hao Hanmei;Ahmed Syed Hassan.A Chaotic Neural Network Model for English Machine Translation Based on Big Data Analysis[J].Computational Intelligence and Neuroscience,2021,2021:3274326-3274326.<br />
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[3]Hwang Yongkeun;Kim Yanghoon;Jung Kyomin.Context-Aware Neural Machine Translation for Korean Honorific Expressions[J].Electronics,2021,10(13):1589-1589.<br />
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[4]Zakaryia Almahasees.Analysing English-Arabic Machine Translation:Google Translate, Microsoft Translator and Sakhr,2021.<br />
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[5]Machine learning in translation[J].Nature Biomedical Engineering,2021,5(6):485-486.<br />
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[6]Shaimaa Marzouk.An in-depth analysis of the individual impact of controlled language rules on machine translation output: a mixed-methods approach[J].Machine Translation,2021(prepublish):1-37.<br />
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[7]Welnitzová Katarína;Munková Daša.Sentence-structure errors of machine translation into Slovak[J].Topics in Linguistics,2021,22(1):78-92.<br />
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[8]Xu Xueyuan.Machine learning-based prediction of urban soil environment and corpus translation teaching[J].Arabian Journal of Geosciences,2021,14(11). <br />
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[9]陈丙昌.機械翻訳の誤訳分析【D】.贵州大学.2016(05) <br />
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[10]呂寅秋.機械翻訳の言語規則と伝統文法との相違点.日本学研究.1996(00):21-22 <br />
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[11]刘君.基于语料库的中日同形词词义用法对比及其日中机器翻译研究【D】.广西大学.2014(03) <br />
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[12]崔倩倩.机器翻译错误与译后编辑策略研究【D】.北京外国语大学.2019(09) <br />
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[13]张义.机器翻译的译文分析【D】.西安外国语大学.2019(10) <br />
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[14]张琳婧.在线机器翻译中日翻译错误原因及对策【D】.山西大学.2019(02)<br />
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[15]王丹.基于机器翻译的专利文本译后编辑对策研究【D】.大连理工大学.2020(06)<br />
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[16]杨晓琨.日中机器翻译中的前编辑规则与效果验证【D】.大连理工大学.2020(06)<br />
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[17]左嘉. 机器翻译日译汉误译研究[D]. 北京第二外国语学院, 2021.<br />
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[18]关碧莹.关于政治类发言的汉日机器翻译误译分析[D].哈尔滨理工大学, 2018.<br />
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[19]车彤.汉译日机器翻译质量评估及译后编辑策略研究【D】.北京外国语大学.2021(09)<br />
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Networking Linking<br />
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http://www.elecfans.com/rengongzhineng/692245.html<br />
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https://baike.baidu.com/item/%E6%9C%BA%E5%99%A8%E7%BF%BB%E8%AF%91/411793<br />
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=13 陈湘琼Chen Xiangqiong（Study on Post-editing from the Perspective of Functional Equivalence Theory ）=<br />
[[Machine_Trans_EN_13]]<br />
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=14 Bi bi Nadia（Machine Translation a Challenge for Human Translators)=<br />
[[Machine_Trans_EN_14]]<br />
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===Abstract===<br />
Machine translation is a big obstacle in the way of Human translators or interpretors although it is quick and less time consuming.people are trying to get translation of their target language using through source language.For this purpose they are using digital apps like Google translation Google translation does not give accurate and exact interpretation.Google translator is translates word to word translate that doesn't clarifies it's true and actual meaning.On the other hand human translators can give exact and accurate translation ,they take care of grammatical errors , diction and sentence structure.They clarify the purpose of target language through using source language.<br />
===Keywords===<br />
Descriptive translation, academic, interdiscipline, comparative literature, localization,Translatology,school of thought, translation , studies, linguistics, corresponding<br />
===Body of article===<br />
Translation is the process of reworking text from one language into another to maintain the original message and communication. But, like everything else, there are different methods of translation, and they vary in form and function. What is translation? The term has become widely used among knowledge transfer researchers and practitioners, especially in the fields of health and health care. In a landmark review, Jonathan Lomas began to argue that 'The tasks… may be defined as to establish and maintain links between researchers and their audience, via the appropriate translation of research findings' (Lomas 1997, p 4). In 2004, the World Health Organization's World Report on Knowledge for Better Health suggested that 'One of the key contributions of research to health systems is the translation of knowledge into actions' (WHO 2004, p 33 and p 100). By 2006, special issues of WHO's Bulletin as well as the journals Evaluation and the Health Professions and the Journal of Continuing Education in the Health Professions were dedicated to translation.But what does 'translation' mean? It may be a new word for an old problem, meaning nothing more than 'transfer'. The rapidly emerging field of 'translational medicine' seems to take translation to mean generally what transfer might have meant, that is the transmission of knowledge and evidence 'from bench to bedside'. As one commentator has observed, "Translational medicine" as a fashionable term is being increasingly used to describe the wish of biomedical researchers to ultimately help patients' (Wehling 2008, abstract). <br />
Semantic uncertainty persists, not least because of the different interests of scientists, clinicians, patients and commercial firms (Littman et al 2007): 'Translational research means different things to different people, but it seems important to almost everyone' (Woolf 2008, p 211).<br />
In related fields, such as public health (Armstrong et al 2006), 'translation' seems to signify dissatisfaction with 'transfer'. It wants to move away from thinking of knowledge transfer as a form of technology transfer or dissemination, rejecting if only by implication its mechanistic assumptions and its model of linear messaging from A to B. But still, what does it signify?<br />
<br />
===Why translation?===<br />
'Translation' indicates a closer attention to the problem of shared meaning and how it might be developed. It seems to represent some new epistemological lubricant, facilitating the dissemination of texts and the application and use of the knowledge and information they in. Simply, translation might be the key to transfer. And yet, when we stop to think, we are more ambivalent. What is translated often seems somehow inferior, not real or original. Note how readily commentators reach for the idea that things might be 'lost in translation'. Knowing at a distance – made in and mediated by translation - makes for incomplete renditions, blurred images, partial truths. So what might 'translation' really mean? The purpose of this paper is to set out, for policy makers and practitioners, the theoretical and conceptual resources translation holds and seems to represent. In doing so, it explores understandings of translation in the fields of literature and linguistics and in the sociology of science and technology. It begins by setting out just why this idea of translation should make immediate, intuitive sense in relation to research, policy and practice.<br />
1translation in research, policy and practice research as translation<br />
Research often entails translation from one language to another: where data is collected from more than one ethnic group, for example, or where the language of the researcher is other than that of the research subject. It may draw on a secondary literature or source documents written in different languages, and may be published and disseminated in languages other than the one in which it is first written up.<br />
2In a different way, to conduct an interview is to ask for an account of experience and its meanings, but it is also to construct and translate that experience in terms defined at least in part by the researcher. In representing what is said, transcripts then select data, usually excluding significant gesture and eye-contact, for example. Often, certain characteristics of speech-acts (such as hesitations) will be edited out. In turn, the format of the transcript shapes the analytic use the researcher may make of it. The basis of research 'findings', then, is an artefact, a transcript or translation, not an original interaction (Ochs 1979, Barnes, Bloor and Henry 1996, Ross 2009).<br />
3In this way, the researcher recasts aspects of his or her problem or topic in new, scientific form: 'All researchers "translate" the experiences of others' (Temple 1997, p 609). Research is invariably conducted in a sort of 'metalanguage' (Hantrais and Ager 1985): the research process can be conceived as one of successive translations (from theoretical formulation to operationalization, transcription, interpretation and dissemination). Theorization is a process of reciprocal back and forth between theory and fact, in which conceptions of each are revised in order that one fit the other (Baldamus 1974).<br />
4 It is a kind of translation: a rereading, re-use, re-application or re-representation of what we know in new terms (Turner 1980). Referencing, too, is an act of translation, a form of appropriation and incorporation of one text by another (Gilbert 1977).policy as translation Each of the fields covered by the paper is diverse and ill-defined, and there is no intention here to provide a comprehensive account of any them. Sources have been chosen for their relevance: main references are cited in the text, and additional sources listed in footnotes.<br />
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2For a brief introduction to the technical issues involved in social research in more than one language, see Birbili (2000). For translation issues in survey research and question design in general, see Ervin and Bower (1952) and Deutscher (1968); on translating survey research instruments (in this instance health-related quality of life measures), Bowden and Fox-Rushby (2003). On the use of translators and interpreters, see Temple (1997) and Jentsch (1998).<br />
3For an interesting discussion of this problem, see Bourdieu (1999), esp pp 621-626, 'The risks of writing'. <br />
===Difference between machine translation and human translators===<br />
Few people disagree on the differences between the two, but many argue over the quality of the translations. How accurate are machine translations? How reliable are human translations? Some say machine translation produces near-perfect translations while others are adamant that translations are incomprehensible and cause more problems than they solve. Results will, of course, vary depending on the source and target languages, the machine translation service used (e.g. Google Translate), and the complexity of the original text.<br />
Machine translation, love it or hate it, is here to stay. In fact, the machine translation market is growing at such a fast pace that it is predicted to reach $980 million by 2022.<br />
===Machine translation: the pros and cons===<br />
The advantages of machine translation generally come down to two factors: it’s faster and cheaper. The downside to this is the standard of translation can be anywhere from inaccurate, to incomprehensible, and potentially dangerous (more on that shortly).<br />
==The advantages of machine translation==<br />
Many free tools are readily available (Google Translate, Skype Translator, etc.)<br />
Quick turnaround time <br />
You can translate between multiple languages using one tool<br />
Translation technology is constantly improving<br />
The disadvantages of machine translation<br />
Level of accuracy can be very low <br />
Accuracy is also very inconsistent across different languages<br />
==Machines can't translate context ==<br />
Mistakes are sometimes costly <br />
Sometimes translation simply doesn’t work<br />
The most important thing to consider with any kind of translation is the cost of potential mistakes. Translating instructions for medical equipment, aviation manuals, legal documents and many other kinds of content require 100% accuracy. In such cases, mistakes can cost lives, huge amounts of money and irreparable damage to your company’s image. So choose carefully!<br />
Human translation: the pros and cons<br />
Human translation essentially switches the table in terms of pros and cons. A higher standard of accuracy comes at the price of longer turnaround times and higher costs. What you have to decide is whether that initial investment outweighs the potential cost of mistakes. Alternatively, whether mistakes simply aren’t an option, like the cases we looked at in the previous section.<br />
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==The advantages of human translation ==<br />
It’s a translator’s job to ensure the highest accuracy<br />
Humans can interpret context and capture the same meaning, rather than simply translating words<br />
Human translators can review their work and provide a quality process<br />
Humans can interpret the creative use of language, e.g. puns, metaphors, slogans, etc.<br />
Professional translators understand the idiomatic differences between their languages<br />
Humans can spot pieces of content where literal translation isn’t possible and find the most suitable alternative<br />
The disadvantages of human translation<br />
Turnaround time is longer <br />
Translators rarely work for free <br />
Unless you use a translation agency, with access to thousands of translators, you’re limited to the languages any one translator can work with<br />
Simply put, human translation is your best option when accuracy is even remotely important. Other considerations to make are the complexity of your source material and the two languages you’re translating between – both of which can render machines pretty useless.<br />
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When to use machine and human translation<br />
The truth is, the debate over machine vs human translation is an unnecessary distraction. What we should really be talking about is when to use these two different types of translation services, because they both serve a very valid purpose.<br />
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Examples of when to use machine translation<br />
When you have a large bulk of content to translate and the general meaning is enough<br />
When your translation never reaches the final audience, e.g. you’re translating a resource as research for another piece of content<br />
Translating documents for internal use within a company, provided 100% accuracy isn’t needed<br />
To partially translate large chunks of content for a human translator to improve upon<br />
Examples of when to use human translation<br />
When accuracy is important<br />
Most cases where your translated content is received by a consumer audience<br />
When you have a duty of care to provide accurate translations (e.g. legal documents, product instructions, medical guidelines or health and safety content)<br />
When translating marketing material or other texts for creative language uses.<br />
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===Conclusion ===<br />
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In the light of above mentioned facts and figures here by we would say that machine translation is challenge for human translators because it can reduces the wokload of translation but can't give accurate and exact translation of the traget language.It can be less reliable than human translation..</div>Chen Huinihttps://bou.de/u/index.php?title=Machine_translation&diff=129758Machine translation2021-12-08T01:31:13Z<p>Chen Huini: /* 3.Approaches Proposed for Pre-editing */</p>
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=1 卫怡雯(A Comparison Between the Quality of Machine Translation and Human Translation——A Case Study of the Application of artificial intelligence in Sports Events)=<br />
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=2 吴映红（The Introduction of Machine Translation)= <br />
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=3 肖毅瑶(On the Realm Advantages And Symbiotic Development of Machine Translation And Huamn Translation)=<br />
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=4 王李菲 （Comparison Between Neural Machine Translation of Netease and Traditional Human Translation—A Case Study of The Economist Articles)= <br />
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=6 徐敏赟(Machine Translation Based on Neural Network --Challenge or Chance?)=<br />
[[Machine_Trans_EN_6]]<br />
<br />
=7 颜莉莉(一带一路背景下人工智能与翻译人才的培养)=<br />
[[Machine_Trans_EN_7]]<br />
<br />
===Abstract===<br />
In the era of artificial intelligence, artificial intelligence has been applied to various fields. In the field of translation, traditional translation models can no longer meet the rapid development and updating of the information age. The development of machine translation has brought structural changes to the language service industry, which poses challenges to the cultivation of translation talents. Under the background of "The Belt and Road initiative", translation talents have higher and higher requirements on translation literacy. Artificial intelligence and translation technology are used to reform the training mode of translation talents, so as to better serve the development of The Times. This paper mainly explores the cultivation of artificial intelligence and translation talents under the background of the Belt and Road Initiative. The cultivation of translation talents is moving towards comprehensive cultivation of talents. On the contrary, artificial intelligence and machine translation can also be used to improve the teaching mode and teaching content, so as to win together in cooperation.<br />
===Key words===<br />
Artificial intelligence,Machine translation,cultivation of translation talents,"The Belt and Road initiative"<br />
===题目===<br />
一带一路背景下人工智能与翻译人才的培养<br />
<br />
===摘要===<br />
进入人工智能时代，人工智能被应用于各个领域。在翻译领域，传统的翻译模式已无法满足信息化时代的飞速发展和更新，机器翻译的发展给语言服务行业带来了结构性改变，这对翻译人才的培养提出了挑战。“一带一路”背景下，对翻译人才的翻译素养要求越来越高，利用人工智能和翻译技术对翻译人才培养模式进行革新，更好为时代发展服务。本文主要探究在一带一路背景下人工智能和翻译人才培养，翻译人才的培养过程中正向对人才的综合性培养，反之也可以利用人工智能和机器翻译完善教学模式和教学内容，在合作中共赢。<br />
===关键词===<br />
人工智能；机器翻译；翻译人才培养；一带一路<br />
<br />
===1. Introduction===<br />
With the development of science and technology in China, artificial intelligence has also been greatly improved, and related technologies have been applied to various fields, such as the use of intelligent robots to deliver food to quarantined people during the epidemic, which has made people's lives more convenient. The most controversial and widely discussed issue is machine translation. Before the emergence of machine translation, translation was generally dominated by human translation, including translation and interpretation, which was divided into simultaneous interpretation and hand transmission, etc. It takes a lot of time and energy to cultivate a translation talent. However, nowadays, the era is developing rapidly and information is updated rapidly. As a translation talent, it is necessary to constantly update its knowledge reserve to keep up with the pace of The Times. The emergence of machine translation has also posed challenges to translation talents and the training of translation talents. Although machine translation had some problems in the early stage, it is now constantly improving its functions. In the context of the belt and Road Initiative, both machine translation and human translation are facing difficulties. Regardless of whether human translation is still needed, what is more important at present is how to train translators to adapt to difficulties and promote the cooperation between human translation and machine translation.<br />
<br />
===2.Development status of machine translation in the era of artificial intelligence ===<br />
With the development of AI technology, machine translation has made great progress and has been applied to people's lives. For example, more and more tourists choose to download translation software when traveling abroad, which makes machine translation take an absolute advantage in daily email reply and other translation activities that do not require high accuracy. The translation software commonly used by netizens include Google Translation, Baidu Translation, Youdao Translation, IFly.com Translation, etc. Even wechat and other chat software can also carry out instant Translation into English. Some companies have also launched translation pens, translation machines and other equipment, which enables even native speakers to rely on machine translation to carry out basic communication with other Chinese people.<br />
But so far, machine translation still faces huge problems. Although machine translation has made great progress, it is highly dependent on corpus and other big data matching. It does not reach the thinking level of human brain, and cannot deal with the problem of translation differences caused by culture and religion. In addition, many minor languages cannot be translated by machine due to lack of corpus.<br />
<br />
What's more, most of the corpus is about developed countries such as Britain and France, and most of the corpus is about diplomacy, politics, science and technology, etc., while there are very few about nationality, culture, religion, etc.<br />
<br />
In addition, machine translation can only be used for daily communication at present. If it involves important occasions such as large conferences and international affairs, it is impossible to risk using machine translation for translation work. Professional translators are required to carry out translation work. So machine translation still has a long way to go.<br />
<br />
===3.Challenges in the training of translation talents in universities===<br />
The cultivation of translators is targeted at the market. Professors Zhu Yifan and Guan Xinchao from the School of Foreign Languages at Shanghai Jiao Tong University believe that the cultivation of translators can be divided into four types: high-end translators and interpreters, senior translators and researchers, compound translators and applied translators.<br />
<br />
From their names, it can be seen that high-end translators and interpreters and senior translators and researchers talents have high requirements on the knowledge and quality of interpreters, because they have to face the changing international situation, and have to deal with all kinds of sensitive relations and political related content, they should have flexible cross-cultural communication skills. In addition, for literature, sociology and humanities academic works, it is not only necessary to translate their content, but also to understand their essence. Therefore, translators should not only have humanistic feelings, but also need to have a deep understanding of Chinese and western culture.<br />
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However, there is not much demand for this kind of translation in the society. Such high-level translation requirements are not needed in daily life and work. The greatest demand is for compound translators, which means that they should master knowledge in a specific field while mastering a foreign language. For example, compound translators in the financial field should not only be good at foreign languages, but also master financial knowledge, including professional terms, special expressions and sentence patterns.<br />
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Now we say that machine translation can replace human translation should refer to the field of compound translation talents. Although AI technology has enabled machine translation to participate in creation, it does not mean that compound translation talents will be replaced by machines. The complexity of language and the flexible cross-cultural awareness required in communication make it impossible for machine translation to completely replace human translation.<br />
<br />
The last type of applied translation talents are mostly involved in the general text without too much technical content and few professional terms, so it is easy to be replaced by machine translation.<br />
<br />
Therefore, the author thinks that what universities are facing at present is not only how to train translation talents to cope with the development of machine translation, but to consider the application of machine translation in the process of training translation talents to achieve human-machine integration, so as to better complete the translation work.<br />
<br />
===4.The Language environment and opportunities and challenges of the Belt and Road initiative===<br />
During visits to Central and Southeast Asian countries in September and October 2013, Chinese President Xi Jinping put forward the major initiative of jointly building the Silk Road Economic Belt and the 21st Century Maritime Silk Road. And began to be abbreviated as the Belt and Road Initiative.<br />
<br />
According to the Vision and Actions for Jointly Building silk Road Economic Belt and 21st Century Maritime Silk Road, the Silk Road Economic Belt focuses on connecting China, Central Asia, Russia and Europe (the Baltic Sea). From China to the Persian Gulf and the Mediterranean Sea via Central and West Asia; China to Southeast Asia, South Asia, Indian Ocean. The focus of the 21st Century Maritime Silk Road is to stretch from China's coastal ports to Europe, through the South China Sea and the Indian Ocean. From China's coastal ports across the South China Sea to the South Pacific.<br />
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The Belt and Road "construction is comply with the world multi-polarization and economic globalization, cultural diversity, the initiative of social informatization tide, drive along the countries achieve economic policy coordination, to carry out a wider range, higher level, the deeper regional cooperation and jointly create open, inclusive and balanced, pratt &whitney regional economic cooperation framework.<br />
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====4.1The language environment of the Belt and Road====<br />
The "Belt and Road" involves a wide range of countries and regions, and their languages and cultures are very complex. How to make good use of language, do a good job in translation services, actively spread Chinese culture to the world, strengthen the ability of discourse, and tell Chinese stories well, the first thing to do is to understand the language situation of the countries along the "Belt and Road".<br />
<br />
=====4.1.1The most common language in countries along the "Belt and Road" =====<br />
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There are a wide variety of languages spoken in 65 countries along the Belt and Road, involving nine language families. However, The status of English as the first language in the world is undeniable. Most of the countries participating in the Belt and Road are developing countries, and many of them speak English as their first foreign language. Especially in southeast Asian and South Asian countries, English plays an important role in foreign communication, whether as the official language or the first foreign language. Besides English, more than 100 million people speak Russian, Hindi, Bengali, Arabic and other major languages in the "Belt and Road" countries. It can also be seen that a common feature of languages in countries along the "Belt and Road" is the popularization of English education. English is widely used in international politics, economy, culture, education, science and technology, playing the role of the most important language in the world.<br />
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=====4.1.2The complex language conditions of countries along the "Belt and Road" =====<br />
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The languages spoken in countries along the Belt and Road involve nine major language families and almost all the world's religious types. Differences in religious beliefs also result in differences in culture, customs and social values behind languages. The languages of some countries along the belt and Road have also been influenced by historical and realistic factors, such as colonization, internal division and immigration. <br />
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India, for example, has no national language, but more than 20 official languages. India is a multi-ethnic country, a total of more than 100 people, one of the most obvious difference between nation and nation is the language problem. Therefore, according to the difference of language, India divides different ethnic groups into different states, big and small. Ethnic groups that use the same language are divided into one state. If there are two languages in a state, the state is divided into two parts. And Indian languages differ not only in word order but also in the way they are written. In India, for example, Hindi is spoken by the largest number of people in the north, with about 700 million speakers and 530 million as their first language. It is written in The Hindu language and belongs to the Indo-European language family. Telugu in the east is spoken by about 95 million people and 81.13 million as their first language. It is written in Telugu, which belongs to the Dravidian language family and is quite different from Hindi. As a result, a parliamentary session in India requires dozens of interpreters. <br />
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These factors cannot be ignored in the process of translation, from language communication to cultural understanding, from text to thought exchange, through the bridge of language to truly connect the people, so as to avoid misreading and misunderstanding caused by differences in language and national conditions.<br />
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====4.2 Opportunities and challenges of the "Belt and Road" ====<br />
With the promotion of the Belt and Road Initiative, there has been an unprecedented boom in translation. In the previous translation boom in China, most of the foreign languages were translated into Chinese, and most of the foreign cultures were imported into China. However, this time, in the context of the "Belt and Road" initiative, translating Chinese into foreign languages has become an important task for translators. As is known to all, there are many different kinds of "One Belt And One Road" along the national language and culture is complex, the service "area" construction has become a factor in Chinese translation talents training mode reform, one of the foreign language universities have action, many colleges and universities to establish the "area" all the way along the country's small language major, as a result, "One Belt And One Road" initiative to promote, It has brought unprecedented opportunities for human translation. The cultivation of diversified translation talents and the cultivation of translation talents in small languages is an urgent problem to be solved in China. The cultivation of translation talents cannot be completed overnight, and the state needs to reform the training mode of translation talents from the perspective of language strategic development. Only in this way can we meet the new demand for human translation under the new situation of the belt and Road Initiative.<br />
<br />
For a long time, the traditional orientation of translation curriculum and training goal in colleges and universities is to train translation teachers and translators in need of society through translation theory and practice and literary translation practice, which cannot meet the needs of society. Since 2007, in order to meet the needs of the socialist market economy for application-oriented high-level professionals, the Academic Degrees Committee of The State Council approved the establishment of Master of Translation and Interpreting (MTI for short). After joining the pilot program of MTI, more and more universities are reforming the curriculum and training mode of master of Translation in order to cultivate translators who meet the needs of the society.<br />
<br />
Language is an important carrier of culture, and translation is an important link for exporting culture. The quality of translation output also reflects the cultural soft power of a country. With the rise of China, more and more people are interested in Chinese culture, and the number of Chinese learners keeps increasing. Under the background of "One Belt and One Road", excellent translators are urgently needed to spread Chinese culture. With the promotion of "One Belt and One Road" Initiative, the number of other countries learning mutual learning and cultural exchanges with China has increased unprecedeningly, bringing vigorous opportunities for the spread of Chinese culture. Translation talents who understand small languages and multi-lingual translators are needed. They should not only use language to convey information, but also use language as a lubricant for communication.<br />
<br />
===5.Training translation talents from the perspective of machine translation===<br />
Under the prevailing environment of machine translation, it poses a great challenge to the cultivation of translation talents. According to the current situation, translation needs and the shortage of translation talents, colleges and universities should reform and innovate the existing training programs for translation talents in terms of the quality of translation talents, the reform of training mode and the use of artificial intelligence. Based on the obtained data and literature, the author discusses how to train translation talents in the perspective of machine translation from the following aspects.<br />
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====5.1 Quality requirements for translation talents ====<br />
Zhong Weihe and Murray made a more detailed and profound discussion on translator's literacy, believing that "translators should not only be proficient in two languages, but also have extensive cultural and encyclopedic knowledge and relevant professional knowledge; Master a variety of translation skills, a lot of translation practice; Have a clear translator role awareness, good professional ethics, practical and enterprising style of work, conscious team spirit and calm psychological quality ". According to the collected data, the author will elaborate the requirements for translation talents from four aspects: language literacy, humanistic literacy, translation ability and innovation ability.<br />
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The first is language literacy, which is the most basic and important requirement. MAO Dun pointed out that "mastery of mother tongue and target language are the foundation of translation". A solid foundation of bilingual skills is the basic skills of translators. Poor language proficiency seems to be a common problem among students majoring in translation and interpreting. Many translation diseases are caused by poor Chinese foundation. As part of going global, the belt and Road initiative is to tell Chinese culture and Chinese stories, which requires translators to be able to use both languages flexibly. Therefore, the first problem that colleges and universities face to solve is to improve the language level of foreign language learners.<br />
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The second is humanistic literacy. Humanistic literacy is mainly manifested by a good command of politics, economy, history, literature and other knowledge, which is particularly important for interpreters. In addition, cross-cultural communication cannot be ignored. In the process of communicating with foreigners or translating, translators often encounter the first cross-cultural contradiction. Cross-culture refers to having a full and correct understanding of cultural phenomena, customs and habits that differ or conflict with the national culture, and accepting and adapting to them in an inclusive manner on this basis. So the interpreter can first fully understand and master the national conditions and culture of the target country, which is particularly important in the "Belt and Road". There are more than 60 countries along the "Belt and Road", and it takes a lot of energy to master their national conditions and culture.<br />
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The third is translation ability. We should distinguish between translation ability and language ability. Translation ability is actually a system of knowledge and skills necessary for translation, the core of which is conversion ability. First of all, it reflects the ability to use tools to assist translation, such as computer application, translation technology and so on. In addition, interpreters should have enough healthy psychological quality and good professional quality. In terms of translation ability, the current training model of translation talents is inadequate.<br />
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The last one is innovation. The cultivation of learners' thinking ability is the key to translation teaching and the cultivation of thoughtful translators should be the connotation of translation teaching. Therefore, the interpreter is not only a translation tool, which is no different from machine translation. More importantly, it is necessary to explore translation with thoughts, have a sense of lifelong learning and innovation consciousness. Translators must constantly innovate themselves, learn new knowledge, and strive to seek reform and innovation. Many colleges and universities should also consciously cultivate students' innovation ability and broaden their thinking and vision.<br />
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====5.2 The reform of college curriculum setting====<br />
First, we will further reform the curriculum of colleges and universities. Add economics, law and engineering to the curriculum, these contents in the "belt and Road".<br />
"One Road" is very important in the construction. According to the author's personal experience, the most typical problem of foreign language majors in colleges and universities is the single learning of foreign languages. More professional foreign language colleges and universities will add some literature courses and national conditions courses of the language target countries. Obviously, whether foreign language graduates are engaged in translation work or not, these knowledge is not enough. Of course, great reforms have been carried out in foreign language teaching, such as combining foreign language with finance, law, diplomacy and so on, and taking the way of minor training foreign language majors.<br />
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Domestic enterprises with a high degree of internationalization attach great importance to translation. Their translation research includes cutting-edge theoretical and applied research, involving machine translation, natural language processing and AI theory, algorithm and model. With such a foundation, enterprises can solve problems by themselves, such as embedding automatic translation functions in mobile phones. International enterprises not only do technical translation, but also deal with all forms of translation and localization in society. At present, translation teaching in most colleges and universities is still in the early mode, and it is an objective fact that it is divorced from the workplace and has a gap with the needs of enterprises.<br />
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Second, we should adjust and strengthen the construction of second foreign language teaching for foreign language majors. In the 1980s, our country was in urgent need of Russian translation. At that time, students majoring in English could translate microelectronic product manuals and related business documents in English and Russian at the same time after learning Russian for half a year. The mutual conversion between English and Russian played a great role in practice. According to the author, in the Graduate Institute of Interpretation and Translation of Beijing Foreign Studies University a very few students majored in multiple languages at the graduate level, that is, they majored in minor languages at the undergraduate level and were admitted to the Graduate Institute of Interpretation and Translation in English. Their training mode is to study English in the Graduate Institute of Interpretation and Translation for two years and the third year in the corresponding department of the undergraduate major. Such training mode in my opinion is a bigger model, cost It's more difficult for students. <br />
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In addition, there is a great disparity in the development of second foreign language teaching in colleges and universities, and the overall level is not high enough. Part of the second foreign language university foreign language professional may still be too much focus in languages such as German, French and Japanese, should as far as possible, considering the need of the construction of the "region", like Croatia, Serbia, Turkish, Hungarian, Italian, Indonesian, Albanian, these are the countries along the "area" the language of the two countries, Colleges and universities should encourage the teaching of a second foreign language.<br />
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Third, the teaching of translation technology should be strengthened. Traditional translation teaching teaches translation skills, such as the translation of words, sentences, texts and figures of speech. Translation technology refers to a series of practical workplace technologies with computer-aided translation software and translation project management as the core, which can greatly improve translation efficiency. However, due to the relative lack of translation technology teachers and equipment in colleges and universities, there is a disconnect between talent training and the requirements of translation technology in the translation field.<br />
<br />
====5.3 Application of artificial intelligence to translation teaching practice====<br />
In order to improve the teaching quality and train students' English translation ability, it is necessary to realize the effective integration of ARTIFICIAL intelligence and translation activity courses, which should not only reflect the effectiveness of artificial intelligence translation technology, but also help students establish a healthy concept of English communication. Through the application of artificial intelligence technology, students can strengthen their flexible translation skills through close communication with "AI program" during the learning stage of English translation activity class. For example, teachers can ask students to translate directly against the translation content provided on the translation screen of the ARTIFICIAL intelligence system. After that, the system can collect the translation answers with the help of speech recognition function, and then judge the accuracy of the translation content, thus providing important feedback to students.<br />
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China has used such artificial intelligence technology in the Putonghua test to ensure that every student can find a suitable translation method in practical communication. The so-called artificial intelligence technology is a new kind of technology modeled after the characteristics of human neural network thinking, can combine the human mind to respond. If it can be integrated into English translation activity teaching, it can not only improve the teaching efficiency, but also enhance students' enthusiasm in learning the course.<br />
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At the same time, during the training of translation talents, teachers also need to take into account the importance of influencing education factors, so that students can form a higher disciplinary quality in translation, so as to fit the concept of quality education in the new era. Only when artificial intelligence translation content is fully integrated into college English translation activity courses can the overall translation ability of college students be maximized.<br />
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====5.4The improvement of translator's technical ability====<br />
In the previous part, the author roughly mentioned that translation teaching should be improved, which will be elaborated here. At present, only a few universities can make full use of the advantages of translation technology in translation teaching and focus on cultivating professional translation talents. Most universities still cannot get rid of the traditional teaching mode of "language + relevant professional knowledge" in translation teaching, and generally lack a correct understanding of COMPUTER-aided translation teaching.<br />
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According to Wang Huashu et al., the courses that can be offered around the composition of translators' technical literacy include computer-assisted translation, translation and corpus, machine translation and post-translation editing, localization and internationalization, film and television translation (subtitle), technical communication and technical writing, and computer programming. The course modules involved are: Fundamentals of COMPUTER-aided Translation, CAT tool application, corpus alignment and processing, term management, QA technology for translation quality assurance, OFFICE fundamentals, translation management technology, basic computer knowledge, desktop typesetting, localization and internationalization, project management system and content management system, technical writing, basic knowledge of computer programming, basic knowledge of web code, etc.<br />
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<br />
===6针对一带一路的机器翻译与翻译人才的合作===<br />
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===Conclusion===<br />
<br />
===References===<br />
<br />
=8 颜静(On Machine Translation Under Lanuguage Intelligence——An Option and Opportunity for Human Translators)=<br />
[[Machine_Trans_EN_8]]<br />
<br />
=9 谢佳芬（人工智能时代下的机器翻译与人工翻译）=<br />
[[Machine_Trans_EN_9]]<br />
===Abstract===<br />
With the continuous development of information technology, many industries are facing the competitive pressure of artificial intelligence, and so is the field of translation. Artificial intelligence technology has developed rapidly and combined with the field of translation，which has brought great impact and changes to traditional translation, but artificial intelligence translation and artificial translation have their own advantages and disadvantages. Artificial translation is in the leading position in adapting to human language logical habits and understanding characteristics, but in terms of translation threshold and economic value, the efficiency of artificial intelligence translation is even better. In a word, we need to know that machine translation and human translation are complementary rather than antagonistic.<br />
<br />
===Key Words===<br />
Machine Translation; Artificial Translation; Artificial Intelligence<br />
<br />
===题目===<br />
人工智能时代下的机器翻译与人工翻译<br />
<br />
===摘要===<br />
伴随着信息技术的不断发展，多个行业面临着人工智能的竞争压力，翻译领域也是如此。人工智能技术快速发展并与翻译领域结合，人工智能翻译给传统翻译带来了巨大的冲击和变革，但人工智能翻译与人工翻译存在着各自的优劣特点和发展空间，在适应人类语言逻辑习惯和理解特点的翻译效果上，人工翻译处于领先地位，但在翻译门槛和经济价值上，人工智能翻译的效率则更胜一筹。总的来说，我们要知道机器翻译与人工翻译是互补而非对立的关系。<br />
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===关键词===<br />
机器翻译;人工翻译;人工智能<br />
<br />
===1. Introduction===<br />
====1.1 The History of Machine Translation Aborad====<br />
The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. Alchuni put forward the idea of using machines for translation. In 1933, the Soviet inventor Troyansky designed a machine to translate one language into another. [1]In 1946, the world's first modern electronic computer ENIAC was born. Soon after, American scientist Warren Weaver, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947. In 1949, Warren Weaver published a memorandum entitled Translation, which formally raised the issue of machine translation. In 1954, Georgetown University, with the cooperation of IBM, completed the English-Russian machine translation experiment with IBM-701 computer for the first time, which opened the prelude of machine translation research. [2] In 2006, Google translation was officially released as a free service software, bringing a big upsurge of statistical machine translation research. It was Franz Och who joined Google in 2004 and led Google translation. What’s more, it is precisely because of the unremitting efforts of generations of scientists that science fiction has been brought into reality step by step.<br />
====1.2 The History of Machine Translation in China====<br />
In 1956, the research and development of machine translation has been named in the scientific and technological work and made little achievements in China. On the eve of the tenth anniversary of the National Day in 1959, our country successfully carried out experiments, which translated nine different types of complicated sentences on large general-purpose electronic computers. The dictionary includes 2030 entries, and the grammar rule system consists of 29 circuit diagrams. [3]. After a period of stagnation, China's machine translation ushered in a high-speed development stage after the 1980s in the wave of the third scientific and technological revolution. With the rapid development of economy and science and technology, China has made a qualitative leap in the field of machine translation research with the pace of reform and opening up. In 1978, Institute of Scientific and Technological Information of China, Institute of Computing Technology and Institute of Linguistics carried out an English-Chinese translation experiment with 20 Metallurgical Title examples as the objects and achieved satisfactory results. Subsequently, they developed a JYE-I machine translation system, which based on 200 sentences from metallurgical documents. Its principles and methods were also widely used in the machine translation system developed in the future. In addition, the research achievements of machine translation in China during the 1980s and 1990s also include that Institute of Post and Telecommunication Sciences developed a machine translation system, C Retrieval and automatic typesetting system with good performance and strong practicability in October 1986; In 1988, ISTC launched the ISTIC-I English-Chinese Title System for the translation of applied literature of metallurgy, Information Research Institute of Railway developed an English-Chinese Title Recording machine translation system for railway documents; the Language Institute of the Academy of Social Sciences developed "Tianyu" English-Chinese machine translation system and Matr English-Chinese machine translation system developed by the computer department of National University of Defense Technology. After many explorations and studies, machine translation in China has gradually moved towards application, popularization and commercialization. China Software Technology Corporation launched "Yixing I" in 1988, marking China's machine translation system officially going to the market. After "Yixing", a series of machine translation systems such as Gaoli system in Beijing, Tongyi system in Tianjin and Langwei system in Shaanxi have also entered the public. In the 21st century, the development of a series of apps such as Kingsoft Powerword, Youdao translation and Baidu translation has greatly met the needs of ordinary users for translation. According to the working principle, machine translation has roughly experienced three stages: rule-based machine translation, statistics-based machine translation and deep learning based neural machine translation. [4] These three stages witnessed a leap in the quality of machine translation. Machine translation is more and more used in daily life and even the translation of some texts is almost comparable to artificial translation. In addition to text translation, voice translation, photo translation and other functions have also been listed, which provides great convenience for people's life. It is undeniable that machine translation has become the development trend of translation in the future.<br />
====1.3 The Status Quo of Machine Translation====<br />
In this big data era of information explosion, the prospect of machine translation is also bright. At present, the circular neural network system launched by Google has supported universal translation in more than 60 languages. Many Internet companies such as Microsoft Bing, Sogou, Tencent, Baidu and NetEase Youdao have also launched their own Internet free machine translation systems. [5] Users can obtain translation results free of charge by logging in to the corresponding websites. At present, the circular neural network translation system launched by Google can support real-time translation of more than 60 languages, and the domestic Baidu online machine translation system can also support real-time translation of 28 languages. These Internet online machine translation systems are suitable for a variety of terminal platforms such as mobile phone, PC, tablet and web and its functions are also quite diverse, supporting many translation forms, such as screen word selection, text scanning translation, photo translation, offline translation, web page translation and so on. Although its translation quality needs to be improved, it has been outstanding in the fields of daily dialogue, news translation and so on.<br />
===2. Advantages and Disadvantages of Machine Translation===<br />
Generally speaking, machine translation has the characteristics of high efficiency, low cost, accurate term translation and great development potential and etc. Machine translation is fast and efficient, this is something that artificial translation can’t catch up with. In addition, with the continuous emergence of all kinds of translation software in the market, compared with artificial translation, machine translation is cheap and sometimes even free, which greatly saves the economic cost and time for users with low translation quality requirements. What's more, compared with artificial translation, machine translation has a huge corpus, which makes the translation of some terms, especially the latest scientific and technological terms, more rapid and accurate. The accurate translation of these terms requires the translator to constantly learn, but learning needs a process, which has a certain test on the translator's learning ability and learning speed. In this regard, artificial translation has uncertainty and hysteretic nature. At the same time, with the progress of science and technology and the development of society, the function of machine translation will be more perfect and the quality of translation will be better.Today's machine translation tools and software are easy to carry, all you need to do is just to use the software and electronic dictionary in the mobile phone. There is no need to carry paper dictionaries and books for translation, which saves time and space. At the same time, machine translation covers many fields and is suitable for translation practice in different situations, such as academic, education, commercial trade, social networking, tourism, production technology, etc, it is also easy to deal with various professional terms. However, due to the limitation of translators' own knowledge, artificial translation is often limited to one or a few fields or industries. For example, it is difficult for an interpreter specializing in medical English to translate legal English.<br />
At the same time, machine translation also has its limitations. At first, machine can only operate word to word translation, which only plays the function and role of dictionary. Then, the application of syntax enables the process of sentence translation and it can be solved by using the direct translation method. When the original text and the target language are highly similar, it can be translated directly. For example, the original text "他是个老师." The target language is "he is a teacher ". With the increase of the structural complexity of the original text, the effect of machine translation is greatly reduced. Therefore, at the syntactic level, machine translation still stays in sentences with relatively simple structure. Meanwhile, the original text and the results of machine translation cannot be interchanged equally, indicating that English-Chinese translation has strong randomness, and is not rigorous and scientific enough. <br />
Nowadays, machine translation is highly dependent on parallel corpora, but the construction of parallel corpora is not perfect. At present, the resources of some mainstream languages such as Chinese and English are relatively rich, while the data collection of many small languages is not satisfactory. Moreover, the current corpus is mainly concentrated in the fields of government literature, science and technology, current affairs and news, while there is a serious lack of data in other fields, which can’t reflect the advantages of machine translation. At the same time, corpus construction lags behind. Some informative texts introducing the latest cutting-edge research results often spread the latest academic knowledge and use a large number of new professional terms, such as academic papers and teaching materials while the corpus often lacks the corresponding words of the target language, which makes machine translation powerless<br />
Besides, machine translation is not culturally sensitive. Human may never be able to program machines to understand and experience a particular culture. Different cultures have unique and different language systems, and machines do not have complexity to understand or recognize slang, jargon, puns and idioms. Therefore, their translation may not conform to cultural values and specific norms. This is also one of the challenges that the machine needs to overcome.[6] Artificial intelligence may have human abstract thinking ability in the future, but it is difficult to have image thinking ability including imagination and emotion. [7] Therefore, machine translation is often used in news, science and technology, patents, specifications and other text fields with the purpose of fact description, knowledge and information transmission. These words rarely involve emotional and cultural background. When translating expressive texts, the limitations of machine translation are exposed. The so-called expressive text refers to the text that pays attention to emotional expression and is full of imagination. Its main characteristics are subjectivity, emotion and imagination, such as novels, poetry, prose, art and so on. This kind of text attaches importance to the emotional expression of the author or character image, and uses a lot of metaphors, symbols and other expressions. Machine translation is difficult to catch up with artificial translation in this kind of text, it can only translate the main idea, lack of connotation and literary grace and it cannot have subjective feelings and rational analysis like human beings. In fact, it is not difficult to simulate the human brain, the difficulty is that it is impossible to learn from the rich social experience and life experience of excellent translators. In other words, machine translation lacks the personalization and creativity of human translation. It is this personalization and creativity that promote the development and evolution of language, and what machine translation can only output is mechanical "machine language".<br />
<br />
===3.The Irreplaceability of Artificial Translation ===<br />
====3.1 Translation is Constrained by Context====<br />
At present, machine translation can help people deal with language communication in people's daily life and work, such as clothing, food, housing and transportation, but there is a big gap from the "faithfulness, expressiveness and elegance" emphasized by high-level translation. Language itself is art，which pays more attention to artistry than functionality, and the discipline of art is difficult to quantify and unify. Sometimes it is regular, rigorous, logical and clear, and sometimes it is random, free and logical. If it is translated by machine, it is difficult to grasp this degree. Sometimes, machine translation cannot connect words with contextual meaning. In many languages, the same word may have multiple completely unrelated meanings. In this case, context will have a great impact on word meaning, and the understanding of word meaning depends largely on the meaning read from context. Only human beings can combine words with context, determine their true meaning, and creatively adjust and modify the language to obtain a complete and accurate translation. This is undoubtedly very difficult for machine translation. Artificial translation can get rid of the constraints of the source language and translate the translation in line with the grammar, sentence patterns and word habits of the target language. In the process of translation, translators can use their own knowledge reserves to analyze the differences between the source language and the target language in thinking mode, cultural characteristics, social background, customs and habits, so as to translate a more accurate translation. Artificial translation can also add, delete, domesticate, modify and polish the translation according to the style, make up for the lack of culture, try to maintain the thought, artistic conception and charm of the original text and the style of the source language. In addition, translators can also judge and consider the words with multiple meanings or easy to produce ambiguity according to the context, so as to make the translation more clear and more accurate and improve the quality of the translation.<br />
<br />
<br />
===4. Discussion on the Relationship Between Machine Translation and Artificial Translation ===<br />
<br />
===5. Suggestions on the Combined Development of Machine Translation and Artificial Translation===<br />
<br />
===6. ===<br />
<br />
===7. ===<br />
<br />
===Conclusion===<br />
<br />
===References===<br />
<br />
=10 熊敏(Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts)=<br />
[[Machine_Trans_EN_10]]<br />
===Abstract===<br />
The history of machine translation can be traced to 1940s, undergoing germination, recession, revival and flourish. Nowadays, with the rapid development of information technology, machine translation technology emerged and is gradually becoming mature. Whether manual translation can be replaced by machine translation becomes a widely-disputed topic. So in order to explore the ability of machine translation, I adopts two versions of translation, which are manual translation and machine translation(this paper uses Youdao translation) for different types of texts(according to Peter Newmark's types of text：informative text, expressive text and vocative text). The results are quite different in terms of quality and accuracy. The results show that machine translation is more suitable for informative text for its brevity, while the expressive texts especially literary works and vocative texts are difficult to be translated, because there are too many loaded words and cultural images in expressive text and dependence on the readers. To draw a conclusion, the relationship between machine translation and manual translation should be complementary rather than competitive.<br />
<br />
===Key words===<br />
machine translation; manual translation; Newmark's type of texts<br />
<br />
===题目===<br />
Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts<br />
<br />
===摘要===<br />
机器翻译的历史可以追溯到上个世纪40年代，经历了萌芽期、萧条期、复兴期和繁荣期四个阶段。如今，随着信息技术的高速发展，机器翻译技术日益成熟，于是机器翻译能不能替代人工翻译这个话题引起了广泛热议。为了探究机器翻译的能力水平是否足以替代人工翻译，本人根据皮特纽马克的文本类型分类理论（信息类文本，表达文本，呼吁文本），选择了相应的译文类型，并且将其机器翻译的版本以及人工翻译的版本进行对比。结果发现，就质量和准确度而言，译文的水平大相径庭。因为其简洁的特性，机器比较适合翻译信息文本。而就表达文本，尤其是文学作品，因其存在文化负载词及相应的文化现象，所以机器翻译这类文本存在难度。而呼唤文本因其目的性以及对读者的依赖性较强，翻译难度较大。由此得知，机器翻译和人工翻译的关系应该是互补的，而不是竞争的。<br />
<br />
===关键词===<br />
机器翻译；人工翻译；纽马克文本类型<br />
<br />
===1. Introduction===<br />
====1.1.Introduction to machine translation====<br />
Machine translation, also known as computer translation is a technique to translating through machine translator, such as Google translation and Youdao translation. Machine translation is one of the branches of computational linguistics, ranging from computer science, statistics, information science and so on. Machine translation plays an important role in all aspects.<br />
Machine translation can be traced back to 1940s, when British engineer Booth and American engineer Weaver proposed using computer to translate and started to study machines used for translation. And the first machine translating system launched in 1954, used in English and Russian translation. And this means machine translation becomes a reality. However, the arrival of new things always accompanies barriers and setbacks. In the 1960s, reports from ALPAC (Automated Language Processing Advisory Committee) showed studies on machine translation had stagnated for a decade. At that time, due to the high cost of machine, choosing human translators to finish translating works was more economical for most companies. What’s worse, machine had difficulty in understanding semantic and pragmatic meanings. Fortunately, in the 1970s, with the advance and popularity of computer, machine translation was gradually back on track. With the development of science and technology, machine translation has better technological guarantee, such as artificial intelligence and computer technology. In the last decades, from the late 90s till now, machine translation is becoming more and more mature. The initial rule-oriented machine translation has been updated and Corpus-aided machine translation appears. And nowadays, technology in voice recognition has been further developed. This technique is frequently applied in speech translation products. Users only need to speak source language to the online translator and instantly it will speak out the target version. But machine can’t fully provide precise and accurate translation without any grammatical or semantic problems. Machine can understand the literal meaning of texts, but not the meaning in context. For example, there is polysemy in English, which refers to words having multiple meanings. So when translating these words, translators should take contexts into consideration. But machine has troubles in this way. In addition, machine has no feeling or intention. Hence, it is also difficult to convey the feelings from the source language.<br />
<br />
====1.2.Process of machine translation====<br />
The process of manual translation is different from that of machine translation. Here is the process of the former. (1) Understand source language. (2) Use target language to organize language. (3) Generate translation. Unlike manual translation, machine translation tends to analyze and code source language first, then look for related codes in corpus, and work out the code that represents target language, generating translation. But they share a common feature, which is that Lexicon, grammatical rules and syntactic structure are taken into consideration. This is one of the biggest challenges for machine translation.<br />
<br />
===2.Theorectical framework===<br />
====2.1Newmark’s text typology====<br />
Text typology is a theory from linguistics. It undergoes several phrases. Karl Buhler, a German psychologist and linguist defines communicative functions into expressive, information and vocative. Then Roman Jakobson proposes categorization of texts, which are intralingual translation, interlingual translation and intersemiotic translation. Accordingly, he defines six main functions of language, including the referential function, conative function, emotive function, poetic function, metalingual function and the phatic function. Based on the two theories, Peter Newmark, a translation theorist, summarizes the language function into six parts: the informative function, the vocative function, the expressive function, the phatic function, the aesthetic function, and the metalingual function. Later, he further divided texts into informative type, expressive text and vocative type according to the linguistic functions of various texts. <br />
The core of the informative texts is the truth. It is to convey facts, information, knowledge of certain topics, reality and theories. The language style of the text is objective, logical and standardized. Reports, papers, scientific and technological textbooks are all attributed to informative texts. Translators are required to take format into account for different styles.<br />
The core of the expressive text is the sender’s emotions and attitudes. It is to express sender’s preferences, feelings, views and so on. The language style of it is subjective. Imaginative literature, including fictions, poems and dramas, autobiography and authoritative statements belong to expressive text. It requires translators to be faithful to the source language and maintain the style.<br />
The core of the vocative text is readership. It is to call upon readers to act, think, feel and react in the way intended by the text. So it is reader-oriented. Such texts as advertisement, propaganda and notices are of vocative text. Newmark noted that translators need to consider cultural background of the source language and the semantic and pragmatic effect of target language. If readers can comprehend the meaning at once and do as the text says, then the goal of information communication is achieved.<br />
To draw a conclusion, informative texts focus on the information and facts. Expressive texts center on the mind of addressers, including feelings, prejudices and so on. And vocative texts are oriented on readers and call on them to practice as the texts say.<br />
<br />
====2.2Study method====<br />
Manual translations of the three texts are selected from authoritative versions and universally acknowledged. And machine translations of those come from Youdao Translator. And in this thesis I will compare and evaluate the two methods in word diction, sentence structure, word order and redundancy.<br />
<br />
===3. ===<br />
<br />
===4. ===<br />
<br />
===5. ===<br />
<br />
===6. ===<br />
<br />
===7. ===<br />
<br />
===Conclusion===<br />
<br />
===References===<br />
<br />
=11 陈惠妮=(Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts)=<br />
[[Machine_Trans_EN_11]]<br />
===Abstract===<br />
At present, globalization is accelerating and the market demand for language services is rapidly increasing . Machine translation, as an important translation method, can greatly improve translation efficiency due to its low cost and high speed. However, because of the limitations of machine translation and the differences between Chinese and English language, machine translation is not accurate enough. In order to balance translation efficiency and translation quality, a great number of manual revisions in translation are required for the machine translating texts. Medical papers are specialized, special and purposeful, so it requires accurate,qualified and professional translation. However, the quality of translations by machine is inefficient to meet the high-quality requirements of medical papers translation. Therefore, the introduction of pre-editing can greatly improve the efficiency and quality of machine translation.<br />
<br />
===Key words===<br />
Pre-editing, Machine translation, Medical texts<br />
<br />
===题目===<br />
Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts<br />
<br />
===摘要===<br />
在全球化加速发展的今天，市场对语言服务的需求迅速增加。机器翻译作为一种重要的翻译途径，由于其成本低、速度快，可以大大提高翻译效率。然而，由于机器翻译的局限性以及中英文语言的差异，机器翻译的准确性不高。为了平衡翻译效率和翻译质量，机器翻译文本需要大量的手工修改。<br />
医学论文具有专业性、特殊性和目的性，要求其译文准确、合格、专业。然而，机器翻译的质量较低，无法满足医学论文对翻译的高质量要求。因此，译前编辑的引入可以大大提高机器翻译的效率和质量。<br />
<br />
===关键词===<br />
译前编辑；机器翻译；医学文本<br />
<br />
===1. Introduction===<br />
===1.1 Definition of Machine Translation===<br />
As Cronin(2013) revealed: "Translation is undergoing a revolutionary upheaval. The influence of digital technology and the Internet on translation is continuous, extensive and profound. From the popularity of automatic online translation applications, translation revolution is everywhere (Cronin,2013) However,the concept of machine translation was firstly proposed in the 1930s. Since 1940s, the machine translation technology has been evolving from rule-based machine translation (RBMT) to statistical machine translation (SMT), and to neural machine translation (NMT). Machine translation refers to the automatic translation of source language into target language by using a computer system. That is, machine translation refers to the automatic translation of text from one language into another natural language by computer software or other online translation webs. Machine translation is also defined as the process of “using a computer system to automatically translate text or speech from one natural language to another” according to the definition by ISO (Cui, 2014).O 'Brien (2002) defines it as "the behavior of modifying errors in machine translation to ensure that the target translation meets certain quality requirements". On the basic level, machine translation performs mechanical substitution of words in one language for words in another language, but that rarely produces a good translation, therefore, recognition of the whole phrases and their closest counterparts in the target language is needed. Not all words in one language have equivalents in another language, and many words have more than one meaning. A huge demand for translation is greatly needed in today’s global world, which creates new opportunities for the development of machine translation, attracts more and more attention and becomes one of the current research focuses.<br />
<br />
===1.2 Definition of Pre-editing===<br />
Pre-editing means to adjust and modify the source language to make it fit more with the characteristics of the machine translation software before putting the source language before the into machine translation, so as to improve the quality of the translation machine translation (Wei Changhong, 2008:93-94). Pre-editing is to modify the original text before putting it into machine translation software, in order to improve the recognition rate of machine translation, optimize the output quality of translated text and reduce the workload of post-editing. Because pre-translation editing only needs to be modified in one language, the operation is simpler than post-translation editing, which can realize the double improvement of quality and efficiency. A good pre-editing translation can help machine translation more smoothly, thus improving the machine readability and quality of the output translation. Pre-editing is mostly applied in the following situations: one is when the original text is of poor quality and the machine is difficult to recognize the meaning of the sentence, such as user generated content with poor readability and translatability (Gerlach et al, 2003:45-53); Documents that need to be published in multiple languages; Next is when the original text contains a lot of jargon; The last is the original text has a corresponding translation memory bank. If the original text is edited, it can better match the content of the translation memory bank. Pre-editing is a process of identifying problems. It requires to pre-edit the source texts before putting it into machine translation according to the requirements, listing the expressions or sentences that may have trouble in machine translation and then pre-edit it by human. The purpose is to enable the computer to translate better, improve the translatability of machine translation. (Slype G V & Guinet J F & Seitz F,1984:115)<br />
<br />
===1.3 Machine Translation Mode===<br />
According to the different knowledge acquisition methods, machine translation modes can be classified as follows: one is rule-based machine translation, which is based on bilingual dictionaries and a library of language rules for each language. The quality of translation depends on whether the source language conforms to the existing rules, but the inexhaustible rules are the hinders of this model. The second mode is machine translation based on statistics. This translation model relies on the principles of mathematics and statistics to find various existing translations corresponding to the translation tasks through the employment of corpus, analyzing the frequency of their occurrence and selecting the translation with the highest frequency for output. The disadvantage of this translation model is that it ignores the flexibility of language and the importance of context. The last one is neural network language model. This model is different from previous translation models in that it uses end-to-end neural network to realize automatic translation between natural languages. At present, the quality of its translation is much higher than that of the previous translation models.<br />
===1.4 Source of Study Abstracts===<br />
The core medical journals from domestic are selected in order to make the paper more representative and authoritative, such as National Medical Journal of China, Journal of Peking University (Health Science), and Journal of Third Military Medical University. All of them include basic medical science, biomedical technology, laboratory medical science and other fields. In this way, the pre-editing approaches included are applicable enough to machine translation of medical abstracts.<br />
===1.5 Selection of Translation Software===<br />
Generally, machine translation software includes Google Translation, Youdao Translator and Niu Translation, which has their own special use in translation. Take Google as an example, it is more like neurons in human brain, enabling to learn and collect information to establish connections with its neural machine translator’s neurons. However, it also causes many errors because of the lack of enough information. In this paper, contrastive analysis will be carried on by using Google translation. On the one hand, being a pioneer in the translation of NET, it is inevitably to sue Google as the translation software to translate medical texts in this paper. Comparing the program called “Google brain”, other NMT of translation software are relatively disadvantages. On the other hand, the Google translation enjoys the largest users in the world, with its downloads of more than one billion. The output quality of Google translation is more correct and complete than other machine translation software. With years of development and improvement, Google Translation has been greatly promoted. In this paper, Chinese medical abstract will be automatically translated by Google translation, and then the translation output will be compared with the translation by human and the publication of English abstracts. The main purpose is to prove that the improvement and promotion of quality and accuracy in the medical abstracts will be obtained through the pre-editing approaches.<br />
<br />
===2.Language Characteristics and Error Division===<br />
Since Chinese and English are two different languages, it is quite neccessary to identify their own characteristics so as to better analyze and understand the two languages. Tytler (1978) argued in his Essay on the Principles of Translation that there are three principles of translation that in all the translation should give readers the same feelings as the source text, except for complete transcript of ideas. There actually also exit some mistakes of these two languages. So the following will make some clarrifications of these two languages to ensure more accurate translation.<br />
<br />
===2.1 Language Characteristics of Medical Abstracts===<br />
Chinese and English belong to different language systems, so there are differences in their language structure and the way users think of the languages. When using machine translation from Chinese to English, due to the unequal language levels, there will be many mistakes in the translation process, especially for ESP texts, such as medical papers. <br />
Here, these differences mainly refer to the linguistic characteristics of medical abstracts. In medical abstracts, it usually includes structured and unstructured abstracts. Although in different forms, they both describe the purpose, methods and conclusions of the research.<br />
In the method section of medial abstract, several Chinese sentences can be connected with commas, but each sentence may convey different information. In contrast, English sentences contain a great deal of information, but in order to ensure clarity, some modifiers need to be isolated and then reconstructed. However, in Chinese-English machine translation, a lot of information is put into the sentence because the machine segments the sentence on the basis of the full comma.<br />
In addition, subjectless sentence is used in the objective and method parts of Chinese medical abstracts. The subjectless sentence means the sentence without or free from subject and is usually employed in two contexts. The first is "needless to say". In Chinese sentences, it is common to omit the subject of the sentence. Chinese sentences can convey meanings by using incomplete sentence structures, so Chinese speakers can understand the meanings of the sentences even though the sentence subject is omitted. The second is "emphasis of action". In this context, subjectless sentences are used to describe behaviors, especially in the study of traditional Chinese medicine abstracts. Comparatively speaking, it should be avoided in English medical abstracts. Abstract sentences are more subjective when describing the learning process, while the essential requirement of English medical abstract is objectivity. From this point, sentences without subject should be avoided in English medical abstracts. Another feature is voice. Few words with passive meanings appear in Chinese abstracts. English sentences are more favoured in using passive voice. When translating from Chinese to English, passive voice should be used to make the contents objective, which is also the basic requirement of medical papers.<br />
These are the linguistic features of Chinese medical abstracts. There are great differences in sentence structure and expression between Chinese and English medical abstracts. These differences may reduce the accuracy of machine translation, so it is necessary to introduce pre-editing to edit the source text to ensure that the source text can be accurately recognized by the machine and fully translated into English.<br />
===2.2 Error Division of Machine Translation in Translating Abstracts of Medical Papers from Chinese to English===<br />
In this paper, errors in machine translation are listed out after analyzing. Some are due to the principles, such as statistical-based MT and NMT, employed by machine translation. Based on the original errors, they can be studied from two levels. One is from the macro-level, referring to mistakes caused by objective factors. These are not human factors, but the disadvantage of machine translation and the limitations of language. The limitations of machine translation are derived from the principles and models adopted and manifests itself as a reliance on reference sources. In this case, semantic ambiguity and textual incoherence may occur in the absence of reference sources.<br />
However, for ESP texts such as medical papers, the requirements for terms and sentence patterns are far beyond the existing corpora. For unfamiliar texts (that is, the corresponding texts cannot be found in the corpora). The quality of output translation will be relatively low, which is determined by the principles of machine translation. As mentioned above, machine translation has evolved over the past few decades from rules-based to corp-based statistics to NMT. However, there still exist some limitations in machine translation.<br />
Mistakes on micro-level are mainly caused by the variations and differences of linguistic structure between Chinese and English. Chinese is an implicit language, while English is an explicit one (Lian, 2010) To put it in another way, Chinese expression does not depend on language structures, while English does the opposite. This may result in mismatches between the original and machine translated sentences. This kind of error is mainly divided into two types: component fragment and component missing. For a medical paper, such errors are very serious. As a kind of ESP discourse, medical paper has the characteristics of fixed, objective and accurate language structure. In order to reproduce the characteristics of medical abstracts in translation, it is necessary to avoid errors at the level of words and sentences and pay attention to logic and consistency.<br />
Lexical errors here refer to the inconsistency of fixed expressions in the translation of terms. Although these terms are machine translation based on the corpus, the corpus may not be perfect for ESP texts such as medical papers. Therefore, fixed expressions are not used in term translation. This is also the most common mistake in machine translation (the term here includes but is not limited to nouns). For ESP texts, medical text in particular, the fixed expressions and the accuracy of terms are of great importance.<br />
<br />
===3.Approaches Proposed for Pre-editing ===<br />
Generally speaking, there is a close relationship between pre-editing and post-editing, both of which aim to convey information and ensure high or publishable translation quality. Proper pre-editing can improve the quality of machine translation in terms of adequacy and consistency. <br />
Complexity of natural language and people use language arbitrarily, bringing many difficulties to Chinese-English machine translation, Hu Qingping (2005:24) has proposed "the research of translation software and the development of the controlled language are the two directions to improve the quality of machine translation : the former aims at the difficulty in the natural language processing, the latter overcomes the arbitrariness of natural language". Feng Quangong and Gao Lin (2017: 63-68) put forward: "The writing principles of controlled language can be applied to pre-editing of machine translation. Pre-editing based on controlled language can effectively reduce the complexity and ambiguity of source text, improve the identifiability of machine translation (the translatability of source text itself), and thus reduce (fully) the workload of post-editing".<br />
The central task of pre-editing is to transform human-friendly content into machine-friendly content, so words and sentences need to be repositioned or even changed. Based on the analysis of the language characteristics of Chinese and English, the Chinese ideographic group should be split before the Chinese source text is input into the machine software to translate so that the sentence structure is complete which can be easily recognized by the machine translation software.<br />
===3.1 Extraction and Replacement of Terms in the Source Text===<br />
As a branch of applied English, medical English is the product of the combination of English language knowledge and medical knowledge. The terms in medical papers have the characteristics of fixed and complex language structure. Although Google Translation is based on a corpus, the language structure is not fixed due to the limited size of the corpus. Therefore, the following two steps should be followed before machine translation. The first is to extract terms and create a glossary, and then replace the Chinese terms in the source text with the corresponding English expressions in the glossary. Of course, the terms of extraction should be searched and verified according to the actual situation. All of these words are verified before they can be replaced. This method can not only ensure the accuracy of term translation, but also maintain the consistency of expression, especially when dealing with long texts.<br />
Example1<br />
Source abstract: 口腔白斑病癌变相关缺氧应答基因和微小RNA的芯片及表达验证。<br />
Google translation: Microarray detection/Chip detection and expression verification of hypoxia response genes and microRNAs related to oral leukoplakia canceration.<br />
Published translation:Transcriptome array screening and verification of oral leukoplakia carcinogenesis-related hypoxia-responsive gene and microRNA. <br />
Analysis: The expression “ Affymetrix GeneChip” empolyed in this thesis is a human transcription array for transcriptome array. In the source abstract, “芯片检测“ can be meant “screening” but not detection, so the proper and appropriate translation of these expression should be “transcription array screening”, but the Google translates it into “microarry detection of chip detection”. Therefore, term extraction is essential and inevitable before putting the source abstracts into the machine translation software.<br />
After pre-editing source abstracts: 对 oral leukoplakia carcinogenesis 相关 hypoxia-responsive gene 和微小RNA进行 transcriptome array screening 及表达验证。<br />
After pre-editing translation: Transcriptome array screening and expression- verification of hypoxia-responsive genes and microRNAs related to oral leukoplakia carcinogenesis.<br />
===3.2 Explication of Subordination===<br />
As mentioned above, the subordination of Chinese sentences is judged by certain specific words in machine translation . Chinese is a paratactic language, and sentences are often connected by internal logical relationships; while English depends on sentence structure, so sentences are often closely linked by various language forms. In order to improve the accuracy of machine translation, we can adjust the sentence structure and adjust the position of adjectives and their modifiers. <br />
Example 2<br />
Source abstracts:回顾性分析南京医科大学附属儿童医院中重度HIE患儿49例及同期就诊的无神经系统症状体征的足月新生儿为对照组31例的头颅磁共振成像（MRI）资料。<br />
Google translation: A retrospective analysis that the brain magnetic resonance imaging (MRI) data of 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University and full-term neonates with no neurological symptoms and signs during the same period were included in the control group.<br />
Published translation: A total of 49 children with moderate to severe HIE admitted to the children’s Hospital Affiliated to Nanjing Medical University were retrospectively analyzed. Cranial magnetic resonance imaging (MRI) date of 31 full-term neonates without neurological symptoms and signs who visited the hospital during the same period were recruited as the control group. <br />
Analysis: As the above example shows that “中重度HIE患儿” and “足月新生儿” in the source abstracts are from the Children’s Hospital Affiliated Nanjing Medical University. The Google translation shows that 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University. There is an error due to the misuse of subordination. There are some advice in order to solve the problem. That is, first to segment the sentence and then reconstruct the sentence. For instance, the Chinese expression “南京医科大学附属儿童医院” carries many modifiers, which requires to cut the modifiers and reconstruct the sentence. Therefore, the Chinese expression “南京医科大学附属儿童医院’can be divided into abstractions, leaving two sentences instead of one long sentence with modifiers..<br />
After pre-editing source abstracts: 对49例中重度HIE患儿以及31例无神经系统症状体征的足月新生儿的MRI资料进行回顾性分析。这些患者收治于南京医科大学儿童附属医院。<br />
After pre-editing translation: The MRI data of 49 children with moderate to severe HIE and 31 full-term newborns without neurological symptoms and signs were retrospectively analyzed. These patients were admitted to the Children’s Hospital of Nanjing Medical University.<br />
===3.3 Explication of Subject through Voice Changing===<br />
The extensive use of subjectless sentences are quite common in the Chinese abstracts, while English prefers to employ passive voice in the sentences so as to make them object and accurate. In order to take the characteristics of English sentences into great and careful consideration, the sentences written in passive voice in particular, it will be helpful for machine translation to find out the subject and reconstruct a sentence in passive voice (Wang Yan: 2008). The first is to discover the “doee” in a sentence and then is to reconstruct the sentence structure and put the “doee” before the verb. The last is to explicate the passive relation between the noun and the verb.<br />
Example 3<br />
Source abstract: 回顾性分析2018年1月至2020年12月解放军总医院第七医学中心收治的500例老年髋部骨折患者的资料。<br />
Google translation: A retrospective analysis of the data of 500 elderly patients with hip fractures admitted to the Seventh Medical Center of the PLA General Hospital from January 2018 to December 2020.<br />
Published translation: From January 2018 to December 2020, the data of 500 elderly patients with hip fracture treated in the Seventh Medical Center of PLA General Hospital were analyzed retrospectively.<br />
Analysis: The above example indicates that the “回顾性分析”in the Google Translate is a noun phrase, but the source abstracts actually describes an action or a behavior. The reason for such a mistake is that the machine can’t recognize the Chinese subjetless sentences. To find the subject of the sentence, the source abstracts can be revised and adjusted. Finding the “doee” is the first step, which refers to “500例老年髋部骨折患者的资料”in the source abstracts. And next is to put it in front of the verb, that is to place it in the beginning of the sentence. Last but not least, the passive voice should be explicated in the sentence. <br />
After pre-editing source abstract: 500例老年髋部骨折患者的资料被回顾性分析，他们在2018年1月之2020年12月期间收治于解放军总医院第七医学中心。<br />
After pre-editing translation: The data of 500 elderly hip fracture patients were retrospectively analyzed. They were admitted to the Seventh Medical Center of the PLA General Hospital between January 2018 and December 2020.<br />
===3.4 Relocation of Modifiers===<br />
Modifiers, including noun, adverbial and attributive are constantly employed in the Chinese medical papers in order to add information to subject and object in the sentence. By doing this, complicated sentences can be structured, thus causing obstacles to machine translation for recognizing this complex sentence structures when translating Chinese-English sentences. To make the machine translation successfully recognize the sentence structure, simplifying the sentence structure is quite necessary. The key is to moving the location of the modifiers and thus making the modifiers an independent sentence. In other words, the modifiers ought to be put before or behind the main part of the sentence to satisfy the common use of English. <br />
Usually, the modifiers in Chinese sentence can only be placed in front of the core word, while in English, modifiers are very flexible. It is all right to place the modifiers in front of or behind the major part of the sentences with adjective or a connective noun.<br />
Example 4<br />
Source abstracts: 利用DNA重组技术以pET-28a表达系统在E.coli BL21(DE3) 中重组表达Hepl。<br />
Google Translation: Hepl is recombined in E.coli BL21 (DE3) using DNA recombination technology with pET-28a expression system.<br />
Published translation: The recombinant Hcpl protein was expressed by using DNA recombination technology through pET-28a expression system in E. coli BL21 (De3).<br />
Analysis: The Chinese medical text includes two modifiers, “利用DNA”and “以pET-28a)表达系统”. These two modifiers will be translated by machine, which catches more attention to the two constituents. From the Google translation above, one failure is obvious that it misplaces the location of the two modifiers and presents it in not accurate form. To make it translate correctly by machine translation, dividing the sentences is very important so that the source abstracts can be correctly recognized by machine translation.<br />
After pre-editing source abstract: 利用DNA重组技术，Hcp1重组表达在E.coli BL21 (DE3)中， 通过pET-28a表达系统在E. coli BL21 (DE3)中。<br />
Google translation: Using DNA recombination technology, Hcp1 recombination is expressed in E.coli BL21 (DE3), via pET-28a expression system in E. coli BL21 (DE3).<br />
The second is the independence of modifiers, that is, modifiers can also be reconstructed into clauses to modify core words. Compared with English, There is no subordinate clause in Chinese, so redundant Chinese modifiers need to be reconstructed into subordinate clauses in Chinese-English translation to meet the characteristics of English. English, especially sentences with multiple modifiers. Otherwise, sentence structure may be confused, such as scattered modifiers of core words. To avoid this mistake, it is necessary to separate the modifier from the main part of the sentence. These modifiers should be reconstituted into clauses. Also, keep your sentences simple and easy to understand.<br />
===3.5 Proper Omission and Deletion of Category Words===<br />
Category words are commonly used in Chinese. Category words complement the meaning of words, including problems, positions, situations and jobs. Adding this supplementary word is more in line with Chinese custom. In many cases, it has no real meaning, so it can be omitted in translation. In Chinese, category words are frequently used. However, it is rarely used in English, which is one of the differences between Chinese and English. There are many kinds of category words. Considering objectivity and the fixed structure of language, redundant category words should be deleted in Chinese-English translation. In the general, the most commonly used category of words in the Chinese medical abstracts are "process", "behavior", and "situation". These categories of words hinder the language conversion of Chinese and English, resulting in redundancy. <br />
Example 5<br />
Source abstract: 探讨口腔白斑病癌变进程中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia response genes and associated microRNAs in the process of oral leukoplakia cancer was discussed.<br />
Published translation: To study the hypoxia response gene and microRNA (miRNA)expression profiles in the pathogenesis and progression of oral leukoplakia (OLK).<br />
Analysis: As the above example shows, the Chinese words “进程” belongs to a category word. However, the Chinese expression “癌变”contains the process, so the “进程” expression can be deleted before placing it into the machine translation, because the meaning of it has been overlapping between the expression “癌变”. Therefore, its place can be replaced with preposition “during”.<br />
After pre-editing source abstract: 探讨口腔白斑病癌变中的缺氧应答基因及相关微小RNA (miRNA) 的表达。<br />
Google translation: The expression of hypoxia responsive genes and miRNA in oral leukoplakia cancer was investigated.<br />
<br />
===4.===<br />
<br />
===Conclusion===<br />
<br />
===References===<br />
<br />
=12 蔡珠凤=(The Mistranslation of C-J Machine Translation of Political Statements)=<br />
[[Machine_Trans_EN_12]]<br />
===Abstract===<br />
Language is the main way of communication between people. With the continuous development of globalization, the scale of cross-border exchanges is also expanding. However, due to cultural differences and diversity, the languages of different countries and regions are very different, which seriously hinders people's communication. The demand for efficient and convenient translation tools is increasing. At the same time, with the development of network technology and artificial intelligence, recognition technology based on deep learning is more and more widely used in English, Japanese and other fields.<br />
<br />
===Key words===<br />
machine translation; political statements; mistranslation of C-J machine translation<br />
===题目===<br />
The Mistranslation of C-J Machine Translation of Political Statements<br />
===摘要===<br />
语言是人与人之间交流的主要方式。随着全球化的不断发展，跨境交流的规模也在不断扩大。然而，由于文化的差异和多样性，不同国家和地区的语言差异很大，这严重阻碍了人们的交流。对高效便捷的翻译工具的需求正在增加。同时，随着网络技术和人工智能的发展，基于深度学习的识别技术在英语、日语等领域的应用越来越广泛。<br />
<br />
===关键词===<br />
机器翻译；政治发言；政治发言中译日的误译<br />
===1. Introduction===<br />
===Introduction to machine translation===<br />
Machine translation, also known as automatic translation, is a process of using computers to convert one natural language (source language) into another natural language (target language). It is a branch of computational linguistics, one of the ultimate goals of artificial intelligence, and has important scientific research value.At the same time, machine translation has important practical value. With the rapid development of economic globalization and the Internet, machine translation technology plays a more and more important role in promoting political, economic and cultural exchanges.The development of machine translation technology has been closely accompanied by the development of computer technology, information theory, linguistics and other disciplines. From the early dictionary matching, to the rule translation of dictionaries combined with linguistic expert knowledge, and then to the statistical machine translation based on corpus, with the improvement of computer computing power and the explosive growth of multilingual information, machine translation technology gradually stepped out of the ivory tower and began to provide real-time and convenient translation services for general users.<br />
<br />
=== C-J machine translation software===<br />
Today's online machine translation software includes Baidu translation, Tencent translation, Google translation, Youdao translation, Bing translation and so on. Google was the first company to launch the machine translation system, and Baidu was the first company to import the machine translation system in China. In addition, Tencent and Youdao have attracted much attention.Machine translation is the process of using computers to convert one natural language into another. It usually refers to sentence and full-text translation between natural languages. In order to continuously improve the translation quality, R & D personnel have added artificial intelligence technologies such as speech recognition, image processing and deep neural network to machine translation on the basis of traditional machine translation based on rules, statistics and examples.With the increase of using machine translation, the joint cooperation between manual translation and machine translation will also increase significantly in the future. What criteria should be used to evaluate the quality of machine translation? In the evolving field of machine translation, there is an urgent need to clarify the unsolvable questions and solved problems.<br />
<br />
===The history of machine translation===<br />
The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. alchuni put forward the idea of using machines for translation. In 1933, Soviet inventor П.П. Trojansky designed a machine to translate one language into another, and registered his invention on September 5 of the same year; However, due to the low technical level in the 1930s, his translation machine was not made. In 1946, the first modern electronic computer ENIAC was born. Shortly after that, W. weaver, an American scientist and A. D. booth, a British engineer, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947 when discussing the application scope of electronic computer. In 1949, W. Weaver published the translation memorandum, which formally put forward the idea of machine translation. After 60 years of ups and downs, machine translation has experienced a tortuous and long development path. The academic community generally divides it into the following four stages:<br />
Pioneering period（1947-1964）<br />
In 1954, with the cooperation of IBM, Georgetown University completed the English Russian machine translation experiment with ibm-701 computer for the first time, showing the feasibility of machine translation to the public and the scientific community, thus opening the prelude to the study of machine translation.<br />
It is not too late for China to start this research. As early as 1956, the state included this research in the national scientific work development plan. The topic name is "machine translation, the construction of natural language translation rules and the mathematical theory of natural language". In 1957, the Institute of language and the Institute of computing technology of the Chinese Academy of Sciences cooperated in the Russian Chinese machine translation experiment, translating 9 different types of more complex sentences.<br />
From the 1950s to the first half of the 1960s, machine translation research has been on the rise. The United States and the former Soviet Union, two superpowers, have provided a lot of financial support for machine translation projects for military, political and economic purposes, while European countries have also paid considerable attention to machine translation research due to geopolitical and economic needs, and machine translation has become an upsurge for a time. In this period, although machine translation is just in the pioneering stage, it has entered an optimistic period of prosperity.<br />
Frustrated period（1964-1975）<br />
In 1964, in order to evaluate the research progress of machine translation, the American Academy of Sciences established the automatic language processing Advisory Committee (Alpac Committee) and began a two-year comprehensive investigation, analysis and test.<br />
In November 1966, the committee published a report entitled "language and machine" (Alpac report for short), which comprehensively denied the feasibility of machine translation and suggested stopping the financial support for machine translation projects. The publication of this report has dealt a blow to the booming machine translation, and the research of machine translation has fallen into a standstill. Coincidentally, during this period, China broke out the "ten-year Cultural Revolution", and basically these studies also stagnated. Machine translation has entered a depression.<br />
convalescence（1975-1989）<br />
Since the 1970s, with the development of science and technology and the increasingly frequent exchange of scientific and technological information among countries, the language barriers between countries have become more serious. The traditional manual operation mode has been far from meeting the needs, and there is an urgent need for computers to engage in translation. At the same time, the development of computer science and linguistics, especially the substantial improvement of computer hardware technology and the application of artificial intelligence in natural language processing, have promoted the recovery of machine translation research from the technical level. Machine translation projects have begun to develop again, and various practical and experimental systems have been launched successively, such as weinder system Eurpotra multilingual translation system, taum-meteo system, etc.<br />
However, after the end of the "ten-year holocaust", China has perked up again, and machine translation research has been put on the agenda again. The "784" project has paid enough attention to machine translation research. After the mid-1980s, the development of machine translation research in China has further accelerated. Firstly, two English Chinese machine translation systems, ky-1 and MT / ec863, have been successfully developed, indicating that China has made great progress in machine translation technology.<br />
New period(1990 present)<br />
With the universal application of the Internet, the acceleration of the process of world economic integration and the increasingly frequent exchanges in the international community, the traditional way of manual operation is far from meeting the rapidly growing needs of translation. People's demand for machine translation has increased unprecedentedly, and machine translation has ushered in a new development opportunity. International conferences on machine translation research have been held frequently, and China has made unprecedented achievements. A series of machine translation software have been launched, such as "Yixing", "Yaxin", "Tongyi", "Huajian", etc. Driven by the market demand, the commercial machine translation system has entered the practical stage, entered the market and came to the users.<br />
Since the new century, with the emergence and popularization of the Internet, the amount of data has increased sharply, and statistical methods have been fully applied. Internet companies have set up machine translation research groups and developed machine translation systems based on Internet big data, so as to make machine translation really practical, such as "Baidu translation", "Google translation", etc. In recent years, with the progress of in-depth learning, machine translation technology has further developed, which has promoted the rapid improvement of translation quality, and the translation in oral and other fields is more authentic and fluent.<br />
<br />
===The problem of machine translation at present===<br />
Error is inevitable<br />
Many people have misunderstandings about machine translation. They think that machine translation has great deviation and can't help people solve any problems. In fact, the error is inevitable. The reason is that machine translation uses linguistic principles. The machine automatically recognizes grammar, calls the stored thesaurus and automatically performs corresponding translation. However, errors are inevitable due to changes or irregularities in grammar, morphology and syntax, such as sentences with adverbials after "give me a reason to kill you first" in Dahua journey to the West. After all, a machine is a machine. No one has special feelings for language. How can it feel the lasting charm of "the tenderness of lowering its head, like the shame of a water lotus? After all, the meaning of Chinese is very different due to the changes of morphology, grammar and syntax and the change of context. Even many Chinese people are zhanger monks - they can't touch their heads, let alone machines.<br />
Bottleneck<br />
In fact, no matter which method, the biggest factor affecting the development of machine translation lies in the quality of translation. Judging from the achievements, the quality of machine translation is still far from the ultimate goal.<br />
Chinese mathematician and linguist Zhou Haizhong once pointed out in his paper "fifty years of machine translation": to improve the quality of machine translation, the first thing to solve is the problem of language itself rather than programming; It is certainly impossible to improve the quality of machine translation by relying on several programs alone. At the same time, he also pointed out that it is impossible for machine translation to achieve the degree of "faithfulness, expressiveness and elegance" when human beings have not yet understood how the brain performs fuzzy recognition and logical judgment of language. This view may reveal the bottleneck restricting the quality of translation. <br />
It is worth mentioning that American inventor and futurist ray cozwell predicted in an interview with Huffington Post that the quality of machine translation will reach the level of human translation by 2029. There are still many disputes about this thesis in the academic circles.<br />
<br />
===2.Mistranslation of Chinese Japanese machine translation===<br />
===2.1Vocabulary mistranslation in Chinese Japanese machine translation===<br />
===2.1.1Mistranslation of proper nouns===<br />
In political materials, there are often political dignitaries' names, place names or a large number of proper nouns in the political field. The morphemes of such words are definite and inseparable. Mistranslation will make the source language lose its specific meaning. <br />
<br />
Chinese translation into Japanese Japanese translation into Chinese<br />
<br />
original text translation by Youdao reference translation original text translation by Youdao reference translation<br />
栗战书 栗戰史書 栗戰書 労安 劳安 劳安<br />
李克强 李克強 李克強 朱鎔基 朱基 朱镕基<br />
习近平 習近平 習近平 筑紫哲也 筑紫哲也 筑紫哲也<br />
韩正 韓中 韓正 山口百惠 山口百惠 山口百惠<br />
王沪宁 王上海氏 王滬寧 田中角栄 田中角荣 田中角荣<br />
汪洋 汪洋 汪洋 東条英機 东条英社 东条英机<br />
赵乐际 趙樂南 趙樂際 毛沢东 毛泽东 毛泽东<br />
江泽民 江沢民 江沢民 トウ・ショウヘイ 大酱 邓小平<br />
周恩来 周恩来 周恩来<br />
クリントン 克林顿 克林顿<br />
<br />
The above table counts 18 special names in the two texts, and 7 machine translation errors. In terms of mistranslation, there are not only "战书" but also "戰史" and "史書" in Chinese. "沪" is the abbreviation of Shanghai. In other words, since "战书" and "沪" are originally common nouns, this disrupts the choice of target language in machine translation. Except Katakana interference (except for トウシゃウヘイ, most of the mistranslations appear in the new Standing Committee. It can be seen that the machine translation system does not update the thesaurus in time. Different from other words, people's names have particularity. Especially as members of the Standing Committee of the Political Bureau of the CPC Central Committee, the translation of their names is officially unified. In this regard, public figures such as political dignitaries, stars, famous hosts and important. In addition, the machine also translates Japanese surnames such as "タ二モト" (谷本), "アンドウ" (安藤) into "塔尼莫特" and "龙胆". It can be found that the language feature that Japanese will be written together with Chinese characters and Hiragana also directly affects the translation quality of Japanese Chinese machine translation. Japanese people often use Katakana pronunciation. For example, when Japanese people talk with Premier Zhu, they often use "二ーハウ" (Hello). However, the machine only recognizes the two pseudonyms "二" and "ハ" and transliterates them into "尼哈" , ignoring the long sound after "二" and "ハ".<br />
<br />
original text Translation by Youdao reference translation<br />
日美安全体制 日米の安全体制 日米安保体制<br />
中国共产党第十九次全国代表大会 中国共産党第19回全国代表大会 中国共産党第19回全国代表大会（第19回党大会）<br />
十八大 十八大 第18回党大会<br />
中国特色社会主义 中国特色社会主義 中国の特色ある社会主義<br />
中国共产党中央委员会 中国共産党中央委員会 中国共産党中央委員会<br />
十八届中共中央政治局常委 第18代中国共產党中央政治局常務委員 第18期中共中央政治局常務委員<br />
十八届中共中央政治局委员 18期の中国共產党中央政治局委員 第18期中共中央政治局委員<br />
十九届中共中央政治局常委 十九回中国共產党中央政治局常務委員 第19期中央政治局常務委員<br />
中共十九届一中全会 中国共產党第十九回一中央委員会 第19期中央委員会第1回全体会議<br />
The above table is a comparison of the original and translated versions of some proper nouns. As shown in the table, the mistranslation problems are mainly reflected in the mismatch of numerals + quantifiers, the wrong addition of case auxiliary word "の", the lack of connectors, the Mistranslation of abbreviations, dead translation, and the writing errors of Chinese characters. The following is a specific analysis one by one.<br />
"十八届中共中央政治局常委", "十八届中共中央政治局委员", "十九届中共中央政治局常委" and "中共十九届一中全会" all have quantifiers "届", which are translated into "代", "期" and "回" respectively. The meaning is vague and should be uniformly translated into "期"; Among them, the translation of the last three proper nouns lacks the Chinese conjunction "第". The case auxiliary word "の" was added by mistake in the translation of "十八届中共中央政治局委员" and "日美安全体制". The "中国共产党中央委员会" did not write in the form of Japanese Ming Dynasty characters, but directly used simplified Chinese characters.<br />
The full name of "中共十九届一中全会" is "中国共产党第十九届中央委员会第一次全体会议", in which "一" stands for "第一次", which is an ordinal word rather than a cardinal word. Machine translation does not produce a correct translation. The fundamental reason is that there are no "规则" for translating such words in machine translation. If we can formulate corresponding rules for such words (for example, 中共m'1届m2 中全会→第m1期中央委员会第m2回全体会议), the translation system must be able to translate well no matter how many plenary sessions of the CPC Central Committee.<br />
<br />
===2.1.2Mistranslation of Polysemy===<br />
<br />
original text Translation by Youdao reference translation<br />
スタジオ 摄影棚/工作室 直播现场/演播厅<br />
日中関係の話 中日关系的故事 就中日关系(话题)<br />
溝 水沟 鸿沟<br />
それでは日中の問題について質問のある方。 那么对白天的问题有提问的人。 关于中日问题的话题，举手提问。<br />
私たちのクラスは20人ちょっとですが、 我们班有20人左右， 我们班二十多人的意见统一很难，<br />
いろいろな意見が出て、まとめるのは大変です。 但是又各种各样的意见，总结起来很困难。 中国是怎样把13亿人凝聚在一起的？<br />
一体どうやって、13億人もの人をまとめているんですか。 到底是怎么处理13亿的人的呢？ <br />
In the original text, the word "スタジオ" appeared four times and was translated into "摄影棚" or "工作室" respectively, but the context is on-site interview, not the production site of photography or film. "話" has many semantics, such as "说话", "事情", "道理", etc. In accordance with the practice of the interview program, Premier Zhu Rongji was invited to answer five questions on the topic of China Japan relations. Obviously, the meaning of the word "故事" is very abrupt. The inherent Japanese word "日中" means "晌午、白天". At the same time, it is also the abbreviation of the names of the two countries. The machine failed to deal with it correctly according to the context. "溝" refers to the gap between China and Japan, not a "水沟". The former "まとめる" appears in the original text with the word "意见", which is intended to describe that it is difficult to unify everyone's opinions. The latter "まとめている" also refers to unifying the thoughts of 1.3 billion people, not "处理". Therefore, it is more appropriate to use "统一" and "处理" in human translation, rather than "总结意见" or "处理意见".<br />
Mistranslation of polysemy has always been a difficult problem in machine translation research. The translation of each word is correct, but it is often very different from the original expression in the context. Zhang Zhengzeng said that ambiguity is a common phenomenon in natural language. Its essence is that the same language form may have different meanings, which is also one of the differences between natural language and artificial language. Therefore, one of the difficulties faced by machine translation is language disambiguation (Zhang Zheng, 2005:60). In this regard, we can mark all the meanings of polysemous words and judge which meaning to choose by common collocation with other words. At the same time, strengthen the text recognition ability of the machine to avoid the translation inconsistent with the current context. In this way, we can avoid the mistake of "大酱" in the place where famous figures such as Deng Xiaoping should have appeared in the previous political articles.<br />
<br />
===2.1.3Mistranslation of compound words===<br />
Multi class words refer to a word with two or more parts of speech, also known as the same word and different classes.<br />
original text Translation by Youdao reference translation<br />
1998年江泽民主席曾经访问日本, 1998年の江沢民国家主席の日本訪問し、 1998年、江沢民総書記が日本を訪問し、かつ、<br />
同已故小渊首相签署了联合宣言。 かつて同じ故小渕首相が署名した共同宣言 亡くなられた小渕総理と宣言に調印されました<br />
<br />
Chinese "同" has two parts of speech: prepositions and conjunctions: "故" has many parts of speech, such as nouns, verbs, adjectives and conjunctions. This directly affects the judgment of the machine at the source language level. The word "同" in the above table is used as a conjunction to indicate the other party of a common act. "故" is used as a verb with the semantic meaning of "死亡", which is a modifier of "小渊首相". The machine regards "同" as an adjective and "故" as a noun "原因", which leads to confusion in the structure and unclear semantics of the translation. Different from the polysemy problem, multi category words have at least two parts of speech, and there is often not only one meaning under each part of speech. In this regard, software R & D personnel should fully consider the existence of multi category words, so that the translation machine can distinguish the meaning of words on the basis of marking the part of speech, so as to select the translation through the context and the components of the word in the sentence. Of course, the realization of this function is difficult, and we need to give full play to the wisdom of R & D personnel.<br />
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===2.2 Syntactic mistranslation in Chinese Japanese machine translation===<br />
===2.2.1Mistranslation of tenses===<br />
original text：History is written by the people, and all achievements are attributed to the people. <br />
Translation by Youdao：歴史は人民が書いたものであり、すべての成果は人民のためである。<br />
reference translation：歴史は人民が綴っていくものであり、すべての成果は人民に帰することとなります。<br />
The original sentence meaning is "历史是人民书写的历史" or "历史是人民书写的东西". When translated into Japanese, due to the influence of Japanese language habits, the formal noun "もの" should be supplemented accordingly. In this sentence, both the machine and the interpreter have translated correctly. However, the machine's recognition of "的" is biased, resulting in tense translation errors. The past, present and future will become "history", and this is a continuous action. The form translated as "~ ていく" not only reflects that the action is a continuous action, but also conforms to the tense and semantic information of the original text. In addition, the machine's treatment of the preposition "于" is also inappropriate. In the original text, "人民" is the recipient of action, not the target language. As the most obvious feature of isolated language, function words in Chinese play an important role in semantic expression, and their translation should be regarded as a focus of machine translation research.<br />
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original text：李克强同志是十六届中共中央政治局常委,其他五位同志都是十六届中共中央政治局委员。<br />
Translation by Youdao：李克強総理は第16代中国共産党中央政治局常務委員であり、他の５人の同志はいずれも16期の中国共産党中央政治局委員である。<br />
reference translation：李克強同志は第16期中国共産党中央政治局常務委員を務め、他の５人は第16期中共中央政治局委員を務めました。<br />
Judging the verb "是" in the original text plays its most basic positive role, but due to the complexity of Chinese language, "是" often can not be completely transformed into the form of "だ / である" in the process of translation. This sentence is a judgment of what has happened. Compared with the 19th session, the 18th session has become history. This is an implicit temporal information. It is very difficult for machines without human brain to recognize this implicit information. "中共中央政治局常委" is the abbreviation of "中共中央政治局常务委员会委员" and it is a kind of position. In Japanese, often use it with verbs such as "務める","担当する",etc. The translator adopts the past tense of "務める", which conforms to the expression habit of Japanese and deals with the problem of tense at the same time. However, machine translation only mechanically translates this sentence into judgment sentence, and fails to correctly deal with the past information implied by the word "十八届".<br />
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===2.2.2Mistranslation of honorifics===<br />
Honorific language is a language means to show respect to the listener. Different from Japanese, there is no grammatical category of honorifics in Chinese. There is no specific fixed grammatical form to express honorifics, self modesty and politeness. Instead, specific words such as "您", "请" and "劳驾" are used to express all kinds of respect or self modesty. <br />
Original text translation by Youdao reference translation<br />
女士们，先生们，同志们，朋友们 さんたち、先生たち、同志たち、お友达さん ご列席の皆さん<br />
谢谢大家！ ありがとうございます！ ご清聴ありがとうございました。<br />
これはどうされますか。 这是怎么回事呢? 您将如何解决这一问题？<br />
こうした問題をどうお考えでしょうか。 我们会如何考虑这些问题呢？ 您如何看待这一问题？ <br />
For example, for the processing of "女士们，先生们，同志们，朋友们", the machine makes the translation correspond to the original one by one based on the principle of unchanged format, but there are errors in semantic communication and pragmatic habits. When speaking on formal occasions, Chinese expression tends to be comprehensive and detailed, as well as the address of the audience. In contrast, Japanese usually uses general terms such as "皆様", "ご臨席の皆さん" or "代表団の方々" as the opening remarks. In terms of Japanese Chinese translation, the machine also failed to recognize the usage of Japanese honorifics such as "さ れ る" and "お考え". It can be seen that Chinese Japanese machine translation has a low ability to deal with honorific expressions. The occasion is more formal. A good translation should not only be fluent in meaning, but also conform to the expression habits of the target language and match the current translation environment.<br />
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===Conclusion===<br />
In the process of discussing the Mistranslation of machine translation, this paper mainly cites the Mistranslation of Youdao translator. When I studied the problem of machine translation mistranslation, I also used a large number of translation software such as Google and Baidu for parallel comparison, and found that these translation software also had similar problems with Youdao translator. For example, the "新世界新未来" in Chinese ABAC structural phrases is translated by Baidu as "新し世界の新しい未来", which, like Youdao, is not translated in the form of parallel phrases; Google online translation translates the word "“全心全意" into a completely wrong "全面的に"; The original sentence "我吃了很多亏" in the Mistranslation of the unique expression in the language is translated by Google online as "私はたくさんの損失を食べました". Because the "吃" and "亏" in the original text are not closely adjacent, the machine can not recognize this echo and mistakenly treats "亏" as a kind of food, so the machine translates the predicate as "食べました"; Japanese "こうした問題をどう考えでしょうか", Google's online translation is "你如何看待这些问题?", "你" tone can not reflect the tone of Japanese honorific, while Baidu translates as "你怎么想这样的问题呢?" Although the meaning can be understood, it is an irregular spoken language. Google and Baidu also made the same mistakes as Youdao in the translation of proper nouns. For example, they translated "十七届(中共中央政治局常委)”" into "第17回..." .In short, similar mistranslations of Youdao are also common in Baidu and Google. Due to space constraints, they will not be listed one by one here. <br />
Mr. Liu Yongquan, Institute of language, Chinese Academy of Social Sciences (1997) It has been pointed out that machine translation is a linguistic problem in the final analysis. Although corpus based machine translation does not require a lot of linguistic knowledge, language expression is ever-changing. Machine translation based solely on statistical ideas can not avoid the translation quality problems caused by the lack of language rules. The following is based on the analysis of lexical, syntactic and other mistranslations From the perspective of the characteristics of the source language, this paper summarizes some difficulties of Chinese and Japanese in machine translation. <br />
(1) The difficulties of Chinese in machine translation <br />
Chinese is a typical isolated language. The relationship between words needs to be reflected by word order and function words. We should pay attention to the transformation of function words such as "的", "在", "向" and "了". Some special verbs, such as "是", "做" and "作", are widely used. How to translate on the basis of conforming to Japanese pragmatic habits and expressions is a difficulty in machine translation research. In terms of word order, Chinese is basically "subject→predicate→object". At the same time, Chinese pays attention to parataxis, and there is no need to be clear in expression with meaningful cohesion, which increases the difficulty of Chinese Japanese machine translation. When there are multiple verbs or modifier components in complex long sentences, machine translation usually can not accurately divide the components of the sentence, resulting in the result that the translation is completely unreadable. <br />
(2) Difficulties of Japanese in machine translation <br />
Both Chinese and Japanese languages use Chinese characters, and most machines produce the target language through the corresponding translation of Chinese characters. However, pseudonyms in Japanese play an important role in judging the part of speech and meaning, and can not only recognize Chinese characters and judge the structure and semantics of sentences. Japanese vocabulary is composed of Chinese vocabulary, inherent vocabulary and foreign vocabulary. Among them, the first two have a great impact on Chinese Japanese machine translation. For example "提出" is translated as "提出する" or "打ち出す"; and "重视" is translated as "重視する" or "大切にする". Japanese is an adhesive language, which contains a lot of "けど", "が" and "けれども" Some of them are transitional and progressive structures, but there are also some sequential expressions that do not need translation, and these translation software often can not accurately grasp them.<br />
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===References===<br />
[1] Navroz Kaur Kahlon;Williamjeet Singh.Machine translation from text to sign language: a systematic review[J].Universal Access in the Information Society,2021(prepublish):1-35.<br />
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[2] Cao Qianyu;Hao Hanmei;Ahmed Syed Hassan.A Chaotic Neural Network Model for English Machine Translation Based on Big Data Analysis[J].Computational Intelligence and Neuroscience,2021,2021:3274326-3274326.<br />
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[3]Hwang Yongkeun;Kim Yanghoon;Jung Kyomin.Context-Aware Neural Machine Translation for Korean Honorific Expressions[J].Electronics,2021,10(13):1589-1589.<br />
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[4]Zakaryia Almahasees.Analysing English-Arabic Machine Translation:Google Translate, Microsoft Translator and Sakhr,2021.<br />
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[5]Machine learning in translation[J].Nature Biomedical Engineering,2021,5(6):485-486.<br />
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[6]Shaimaa Marzouk.An in-depth analysis of the individual impact of controlled language rules on machine translation output: a mixed-methods approach[J].Machine Translation,2021(prepublish):1-37.<br />
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[7]Welnitzová Katarína;Munková Daša.Sentence-structure errors of machine translation into Slovak[J].Topics in Linguistics,2021,22(1):78-92.<br />
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[8]Xu Xueyuan.Machine learning-based prediction of urban soil environment and corpus translation teaching[J].Arabian Journal of Geosciences,2021,14(11). <br />
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[9]陈丙昌.機械翻訳の誤訳分析【D】.贵州大学.2016(05) <br />
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[10]呂寅秋.機械翻訳の言語規則と伝統文法との相違点.日本学研究.1996(00):21-22 <br />
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[11]刘君.基于语料库的中日同形词词义用法对比及其日中机器翻译研究【D】.广西大学.2014(03) <br />
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[12]崔倩倩.机器翻译错误与译后编辑策略研究【D】.北京外国语大学.2019(09) <br />
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[13]张义.机器翻译的译文分析【D】.西安外国语大学.2019(10) <br />
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[14]张琳婧.在线机器翻译中日翻译错误原因及对策【D】.山西大学.2019(02)<br />
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[15]王丹.基于机器翻译的专利文本译后编辑对策研究【D】.大连理工大学.2020(06)<br />
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[16]杨晓琨.日中机器翻译中的前编辑规则与效果验证【D】.大连理工大学.2020(06)<br />
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[17]左嘉. 机器翻译日译汉误译研究[D]. 北京第二外国语学院, 2021.<br />
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[18]关碧莹.关于政治类发言的汉日机器翻译误译分析[D].哈尔滨理工大学, 2018.<br />
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[19]车彤.汉译日机器翻译质量评估及译后编辑策略研究【D】.北京外国语大学.2021(09)<br />
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Networking Linking<br />
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http://www.elecfans.com/rengongzhineng/692245.html<br />
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https://baike.baidu.com/item/%E6%9C%BA%E5%99%A8%E7%BF%BB%E8%AF%91/411793<br />
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=13 陈湘琼Chen Xiangqiong（Study on Post-editing from the Perspective of Functional Equivalence Theory ）=<br />
[[Machine_Trans_EN_13]]<br />
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=14 Bi bi Nadia（Machine Translation a Challenge for Human Translators)=<br />
[[Machine_Trans_EN_14]]<br />
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===Abstract===<br />
Machine translation is a big obstacle in the way of Human translators or interpretors although it is quick and less time consuming.people are trying to get translation of their target language using through source language.For this purpose they are using digital apps like Google translation Google translation does not give accurate and exact interpretation.Google translator is translates word to word translate that doesn't clarifies it's true and actual meaning.On the other hand human translators can give exact and accurate translation ,they take care of grammatical errors , diction and sentence structure.They clarify the purpose of target language through using source language.<br />
===Keywords===<br />
Descriptive translation, academic, interdiscipline, comparative literature, localization,Translatology,school of thought, translation , studies, linguistics, corresponding<br />
===Body of article===<br />
Translation is the process of reworking text from one language into another to maintain the original message and communication. But, like everything else, there are different methods of translation, and they vary in form and function. What is translation? The term has become widely used among knowledge transfer researchers and practitioners, especially in the fields of health and health care. In a landmark review, Jonathan Lomas began to argue that 'The tasks… may be defined as to establish and maintain links between researchers and their audience, via the appropriate translation of research findings' (Lomas 1997, p 4). In 2004, the World Health Organization's World Report on Knowledge for Better Health suggested that 'One of the key contributions of research to health systems is the translation of knowledge into actions' (WHO 2004, p 33 and p 100). By 2006, special issues of WHO's Bulletin as well as the journals Evaluation and the Health Professions and the Journal of Continuing Education in the Health Professions were dedicated to translation.But what does 'translation' mean? It may be a new word for an old problem, meaning nothing more than 'transfer'. The rapidly emerging field of 'translational medicine' seems to take translation to mean generally what transfer might have meant, that is the transmission of knowledge and evidence 'from bench to bedside'. As one commentator has observed, "Translational medicine" as a fashionable term is being increasingly used to describe the wish of biomedical researchers to ultimately help patients' (Wehling 2008, abstract). <br />
Semantic uncertainty persists, not least because of the different interests of scientists, clinicians, patients and commercial firms (Littman et al 2007): 'Translational research means different things to different people, but it seems important to almost everyone' (Woolf 2008, p 211).<br />
In related fields, such as public health (Armstrong et al 2006), 'translation' seems to signify dissatisfaction with 'transfer'. It wants to move away from thinking of knowledge transfer as a form of technology transfer or dissemination, rejecting if only by implication its mechanistic assumptions and its model of linear messaging from A to B. But still, what does it signify?<br />
<br />
===Why translation?===<br />
'Translation' indicates a closer attention to the problem of shared meaning and how it might be developed. It seems to represent some new epistemological lubricant, facilitating the dissemination of texts and the application and use of the knowledge and information they in. Simply, translation might be the key to transfer. And yet, when we stop to think, we are more ambivalent. What is translated often seems somehow inferior, not real or original. Note how readily commentators reach for the idea that things might be 'lost in translation'. Knowing at a distance – made in and mediated by translation - makes for incomplete renditions, blurred images, partial truths. So what might 'translation' really mean? The purpose of this paper is to set out, for policy makers and practitioners, the theoretical and conceptual resources translation holds and seems to represent. In doing so, it explores understandings of translation in the fields of literature and linguistics and in the sociology of science and technology. It begins by setting out just why this idea of translation should make immediate, intuitive sense in relation to research, policy and practice.<br />
1translation in research, policy and practice research as translation<br />
Research often entails translation from one language to another: where data is collected from more than one ethnic group, for example, or where the language of the researcher is other than that of the research subject. It may draw on a secondary literature or source documents written in different languages, and may be published and disseminated in languages other than the one in which it is first written up.<br />
2In a different way, to conduct an interview is to ask for an account of experience and its meanings, but it is also to construct and translate that experience in terms defined at least in part by the researcher. In representing what is said, transcripts then select data, usually excluding significant gesture and eye-contact, for example. Often, certain characteristics of speech-acts (such as hesitations) will be edited out. In turn, the format of the transcript shapes the analytic use the researcher may make of it. The basis of research 'findings', then, is an artefact, a transcript or translation, not an original interaction (Ochs 1979, Barnes, Bloor and Henry 1996, Ross 2009).<br />
3In this way, the researcher recasts aspects of his or her problem or topic in new, scientific form: 'All researchers "translate" the experiences of others' (Temple 1997, p 609). Research is invariably conducted in a sort of 'metalanguage' (Hantrais and Ager 1985): the research process can be conceived as one of successive translations (from theoretical formulation to operationalization, transcription, interpretation and dissemination). Theorization is a process of reciprocal back and forth between theory and fact, in which conceptions of each are revised in order that one fit the other (Baldamus 1974).<br />
4 It is a kind of translation: a rereading, re-use, re-application or re-representation of what we know in new terms (Turner 1980). Referencing, too, is an act of translation, a form of appropriation and incorporation of one text by another (Gilbert 1977).policy as translation Each of the fields covered by the paper is diverse and ill-defined, and there is no intention here to provide a comprehensive account of any them. Sources have been chosen for their relevance: main references are cited in the text, and additional sources listed in footnotes.<br />
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2For a brief introduction to the technical issues involved in social research in more than one language, see Birbili (2000). For translation issues in survey research and question design in general, see Ervin and Bower (1952) and Deutscher (1968); on translating survey research instruments (in this instance health-related quality of life measures), Bowden and Fox-Rushby (2003). On the use of translators and interpreters, see Temple (1997) and Jentsch (1998).<br />
3For an interesting discussion of this problem, see Bourdieu (1999), esp pp 621-626, 'The risks of writing'. <br />
===Difference between machine translation and human translators===<br />
Few people disagree on the differences between the two, but many argue over the quality of the translations. How accurate are machine translations? How reliable are human translations? Some say machine translation produces near-perfect translations while others are adamant that translations are incomprehensible and cause more problems than they solve. Results will, of course, vary depending on the source and target languages, the machine translation service used (e.g. Google Translate), and the complexity of the original text.<br />
Machine translation, love it or hate it, is here to stay. In fact, the machine translation market is growing at such a fast pace that it is predicted to reach $980 million by 2022.<br />
===Machine translation: the pros and cons===<br />
The advantages of machine translation generally come down to two factors: it’s faster and cheaper. The downside to this is the standard of translation can be anywhere from inaccurate, to incomprehensible, and potentially dangerous (more on that shortly).<br />
==The advantages of machine translation==<br />
Many free tools are readily available (Google Translate, Skype Translator, etc.)<br />
Quick turnaround time <br />
You can translate between multiple languages using one tool<br />
Translation technology is constantly improving<br />
The disadvantages of machine translation<br />
Level of accuracy can be very low <br />
Accuracy is also very inconsistent across different languages<br />
==Machines can't translate context ==<br />
Mistakes are sometimes costly <br />
Sometimes translation simply doesn’t work<br />
The most important thing to consider with any kind of translation is the cost of potential mistakes. Translating instructions for medical equipment, aviation manuals, legal documents and many other kinds of content require 100% accuracy. In such cases, mistakes can cost lives, huge amounts of money and irreparable damage to your company’s image. So choose carefully!<br />
Human translation: the pros and cons<br />
Human translation essentially switches the table in terms of pros and cons. A higher standard of accuracy comes at the price of longer turnaround times and higher costs. What you have to decide is whether that initial investment outweighs the potential cost of mistakes. Alternatively, whether mistakes simply aren’t an option, like the cases we looked at in the previous section.<br />
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==The advantages of human translation ==<br />
It’s a translator’s job to ensure the highest accuracy<br />
Humans can interpret context and capture the same meaning, rather than simply translating words<br />
Human translators can review their work and provide a quality process<br />
Humans can interpret the creative use of language, e.g. puns, metaphors, slogans, etc.<br />
Professional translators understand the idiomatic differences between their languages<br />
Humans can spot pieces of content where literal translation isn’t possible and find the most suitable alternative<br />
The disadvantages of human translation<br />
Turnaround time is longer <br />
Translators rarely work for free <br />
Unless you use a translation agency, with access to thousands of translators, you’re limited to the languages any one translator can work with<br />
Simply put, human translation is your best option when accuracy is even remotely important. Other considerations to make are the complexity of your source material and the two languages you’re translating between – both of which can render machines pretty useless.<br />
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When to use machine and human translation<br />
The truth is, the debate over machine vs human translation is an unnecessary distraction. What we should really be talking about is when to use these two different types of translation services, because they both serve a very valid purpose.<br />
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Examples of when to use machine translation<br />
When you have a large bulk of content to translate and the general meaning is enough<br />
When your translation never reaches the final audience, e.g. you’re translating a resource as research for another piece of content<br />
Translating documents for internal use within a company, provided 100% accuracy isn’t needed<br />
To partially translate large chunks of content for a human translator to improve upon<br />
Examples of when to use human translation<br />
When accuracy is important<br />
Most cases where your translated content is received by a consumer audience<br />
When you have a duty of care to provide accurate translations (e.g. legal documents, product instructions, medical guidelines or health and safety content)<br />
When translating marketing material or other texts for creative language uses.<br />
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===Conclusion ===<br />
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In the light of above mentioned facts and figures here by we would say that machine translation is challenge for human translators because it can reduces the wokload of translation but can't give accurate and exact translation of the traget language.It can be less reliable than human translation..</div>Chen Huinihttps://bou.de/u/index.php?title=Machine_translation&diff=129755Machine translation2021-12-08T01:27:05Z<p>Chen Huini: /* 3. */</p>
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<div>'''Machine Translation - A challenge or a chance for human translators?'''<br />
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=1 卫怡雯(A Comparison Between the Quality of Machine Translation and Human Translation——A Case Study of the Application of artificial intelligence in Sports Events)=<br />
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=2 吴映红（The Introduction of Machine Translation)= <br />
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=3 肖毅瑶(On the Realm Advantages And Symbiotic Development of Machine Translation And Huamn Translation)=<br />
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=4 王李菲 （Comparison Between Neural Machine Translation of Netease and Traditional Human Translation—A Case Study of The Economist Articles)= <br />
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=6 徐敏赟(Machine Translation Based on Neural Network --Challenge or Chance?)=<br />
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=7 颜莉莉(一带一路背景下人工智能与翻译人才的培养)=<br />
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===Abstract===<br />
In the era of artificial intelligence, artificial intelligence has been applied to various fields. In the field of translation, traditional translation models can no longer meet the rapid development and updating of the information age. The development of machine translation has brought structural changes to the language service industry, which poses challenges to the cultivation of translation talents. Under the background of "The Belt and Road initiative", translation talents have higher and higher requirements on translation literacy. Artificial intelligence and translation technology are used to reform the training mode of translation talents, so as to better serve the development of The Times. This paper mainly explores the cultivation of artificial intelligence and translation talents under the background of the Belt and Road Initiative. The cultivation of translation talents is moving towards comprehensive cultivation of talents. On the contrary, artificial intelligence and machine translation can also be used to improve the teaching mode and teaching content, so as to win together in cooperation.<br />
===Key words===<br />
Artificial intelligence,Machine translation,cultivation of translation talents,"The Belt and Road initiative"<br />
===题目===<br />
一带一路背景下人工智能与翻译人才的培养<br />
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===摘要===<br />
进入人工智能时代，人工智能被应用于各个领域。在翻译领域，传统的翻译模式已无法满足信息化时代的飞速发展和更新，机器翻译的发展给语言服务行业带来了结构性改变，这对翻译人才的培养提出了挑战。“一带一路”背景下，对翻译人才的翻译素养要求越来越高，利用人工智能和翻译技术对翻译人才培养模式进行革新，更好为时代发展服务。本文主要探究在一带一路背景下人工智能和翻译人才培养，翻译人才的培养过程中正向对人才的综合性培养，反之也可以利用人工智能和机器翻译完善教学模式和教学内容，在合作中共赢。<br />
===关键词===<br />
人工智能；机器翻译；翻译人才培养；一带一路<br />
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===1. Introduction===<br />
With the development of science and technology in China, artificial intelligence has also been greatly improved, and related technologies have been applied to various fields, such as the use of intelligent robots to deliver food to quarantined people during the epidemic, which has made people's lives more convenient. The most controversial and widely discussed issue is machine translation. Before the emergence of machine translation, translation was generally dominated by human translation, including translation and interpretation, which was divided into simultaneous interpretation and hand transmission, etc. It takes a lot of time and energy to cultivate a translation talent. However, nowadays, the era is developing rapidly and information is updated rapidly. As a translation talent, it is necessary to constantly update its knowledge reserve to keep up with the pace of The Times. The emergence of machine translation has also posed challenges to translation talents and the training of translation talents. Although machine translation had some problems in the early stage, it is now constantly improving its functions. In the context of the belt and Road Initiative, both machine translation and human translation are facing difficulties. Regardless of whether human translation is still needed, what is more important at present is how to train translators to adapt to difficulties and promote the cooperation between human translation and machine translation.<br />
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===2.Development status of machine translation in the era of artificial intelligence ===<br />
With the development of AI technology, machine translation has made great progress and has been applied to people's lives. For example, more and more tourists choose to download translation software when traveling abroad, which makes machine translation take an absolute advantage in daily email reply and other translation activities that do not require high accuracy. The translation software commonly used by netizens include Google Translation, Baidu Translation, Youdao Translation, IFly.com Translation, etc. Even wechat and other chat software can also carry out instant Translation into English. Some companies have also launched translation pens, translation machines and other equipment, which enables even native speakers to rely on machine translation to carry out basic communication with other Chinese people.<br />
But so far, machine translation still faces huge problems. Although machine translation has made great progress, it is highly dependent on corpus and other big data matching. It does not reach the thinking level of human brain, and cannot deal with the problem of translation differences caused by culture and religion. In addition, many minor languages cannot be translated by machine due to lack of corpus.<br />
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What's more, most of the corpus is about developed countries such as Britain and France, and most of the corpus is about diplomacy, politics, science and technology, etc., while there are very few about nationality, culture, religion, etc.<br />
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In addition, machine translation can only be used for daily communication at present. If it involves important occasions such as large conferences and international affairs, it is impossible to risk using machine translation for translation work. Professional translators are required to carry out translation work. So machine translation still has a long way to go.<br />
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===3.Challenges in the training of translation talents in universities===<br />
The cultivation of translators is targeted at the market. Professors Zhu Yifan and Guan Xinchao from the School of Foreign Languages at Shanghai Jiao Tong University believe that the cultivation of translators can be divided into four types: high-end translators and interpreters, senior translators and researchers, compound translators and applied translators.<br />
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From their names, it can be seen that high-end translators and interpreters and senior translators and researchers talents have high requirements on the knowledge and quality of interpreters, because they have to face the changing international situation, and have to deal with all kinds of sensitive relations and political related content, they should have flexible cross-cultural communication skills. In addition, for literature, sociology and humanities academic works, it is not only necessary to translate their content, but also to understand their essence. Therefore, translators should not only have humanistic feelings, but also need to have a deep understanding of Chinese and western culture.<br />
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However, there is not much demand for this kind of translation in the society. Such high-level translation requirements are not needed in daily life and work. The greatest demand is for compound translators, which means that they should master knowledge in a specific field while mastering a foreign language. For example, compound translators in the financial field should not only be good at foreign languages, but also master financial knowledge, including professional terms, special expressions and sentence patterns.<br />
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Now we say that machine translation can replace human translation should refer to the field of compound translation talents. Although AI technology has enabled machine translation to participate in creation, it does not mean that compound translation talents will be replaced by machines. The complexity of language and the flexible cross-cultural awareness required in communication make it impossible for machine translation to completely replace human translation.<br />
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The last type of applied translation talents are mostly involved in the general text without too much technical content and few professional terms, so it is easy to be replaced by machine translation.<br />
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Therefore, the author thinks that what universities are facing at present is not only how to train translation talents to cope with the development of machine translation, but to consider the application of machine translation in the process of training translation talents to achieve human-machine integration, so as to better complete the translation work.<br />
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===4.The Language environment and opportunities and challenges of the Belt and Road initiative===<br />
During visits to Central and Southeast Asian countries in September and October 2013, Chinese President Xi Jinping put forward the major initiative of jointly building the Silk Road Economic Belt and the 21st Century Maritime Silk Road. And began to be abbreviated as the Belt and Road Initiative.<br />
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According to the Vision and Actions for Jointly Building silk Road Economic Belt and 21st Century Maritime Silk Road, the Silk Road Economic Belt focuses on connecting China, Central Asia, Russia and Europe (the Baltic Sea). From China to the Persian Gulf and the Mediterranean Sea via Central and West Asia; China to Southeast Asia, South Asia, Indian Ocean. The focus of the 21st Century Maritime Silk Road is to stretch from China's coastal ports to Europe, through the South China Sea and the Indian Ocean. From China's coastal ports across the South China Sea to the South Pacific.<br />
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The Belt and Road "construction is comply with the world multi-polarization and economic globalization, cultural diversity, the initiative of social informatization tide, drive along the countries achieve economic policy coordination, to carry out a wider range, higher level, the deeper regional cooperation and jointly create open, inclusive and balanced, pratt &whitney regional economic cooperation framework.<br />
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====4.1The language environment of the Belt and Road====<br />
The "Belt and Road" involves a wide range of countries and regions, and their languages and cultures are very complex. How to make good use of language, do a good job in translation services, actively spread Chinese culture to the world, strengthen the ability of discourse, and tell Chinese stories well, the first thing to do is to understand the language situation of the countries along the "Belt and Road".<br />
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=====4.1.1The most common language in countries along the "Belt and Road" =====<br />
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There are a wide variety of languages spoken in 65 countries along the Belt and Road, involving nine language families. However, The status of English as the first language in the world is undeniable. Most of the countries participating in the Belt and Road are developing countries, and many of them speak English as their first foreign language. Especially in southeast Asian and South Asian countries, English plays an important role in foreign communication, whether as the official language or the first foreign language. Besides English, more than 100 million people speak Russian, Hindi, Bengali, Arabic and other major languages in the "Belt and Road" countries. It can also be seen that a common feature of languages in countries along the "Belt and Road" is the popularization of English education. English is widely used in international politics, economy, culture, education, science and technology, playing the role of the most important language in the world.<br />
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=====4.1.2The complex language conditions of countries along the "Belt and Road" =====<br />
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The languages spoken in countries along the Belt and Road involve nine major language families and almost all the world's religious types. Differences in religious beliefs also result in differences in culture, customs and social values behind languages. The languages of some countries along the belt and Road have also been influenced by historical and realistic factors, such as colonization, internal division and immigration. <br />
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India, for example, has no national language, but more than 20 official languages. India is a multi-ethnic country, a total of more than 100 people, one of the most obvious difference between nation and nation is the language problem. Therefore, according to the difference of language, India divides different ethnic groups into different states, big and small. Ethnic groups that use the same language are divided into one state. If there are two languages in a state, the state is divided into two parts. And Indian languages differ not only in word order but also in the way they are written. In India, for example, Hindi is spoken by the largest number of people in the north, with about 700 million speakers and 530 million as their first language. It is written in The Hindu language and belongs to the Indo-European language family. Telugu in the east is spoken by about 95 million people and 81.13 million as their first language. It is written in Telugu, which belongs to the Dravidian language family and is quite different from Hindi. As a result, a parliamentary session in India requires dozens of interpreters. <br />
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These factors cannot be ignored in the process of translation, from language communication to cultural understanding, from text to thought exchange, through the bridge of language to truly connect the people, so as to avoid misreading and misunderstanding caused by differences in language and national conditions.<br />
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====4.2 Opportunities and challenges of the "Belt and Road" ====<br />
With the promotion of the Belt and Road Initiative, there has been an unprecedented boom in translation. In the previous translation boom in China, most of the foreign languages were translated into Chinese, and most of the foreign cultures were imported into China. However, this time, in the context of the "Belt and Road" initiative, translating Chinese into foreign languages has become an important task for translators. As is known to all, there are many different kinds of "One Belt And One Road" along the national language and culture is complex, the service "area" construction has become a factor in Chinese translation talents training mode reform, one of the foreign language universities have action, many colleges and universities to establish the "area" all the way along the country's small language major, as a result, "One Belt And One Road" initiative to promote, It has brought unprecedented opportunities for human translation. The cultivation of diversified translation talents and the cultivation of translation talents in small languages is an urgent problem to be solved in China. The cultivation of translation talents cannot be completed overnight, and the state needs to reform the training mode of translation talents from the perspective of language strategic development. Only in this way can we meet the new demand for human translation under the new situation of the belt and Road Initiative.<br />
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For a long time, the traditional orientation of translation curriculum and training goal in colleges and universities is to train translation teachers and translators in need of society through translation theory and practice and literary translation practice, which cannot meet the needs of society. Since 2007, in order to meet the needs of the socialist market economy for application-oriented high-level professionals, the Academic Degrees Committee of The State Council approved the establishment of Master of Translation and Interpreting (MTI for short). After joining the pilot program of MTI, more and more universities are reforming the curriculum and training mode of master of Translation in order to cultivate translators who meet the needs of the society.<br />
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Language is an important carrier of culture, and translation is an important link for exporting culture. The quality of translation output also reflects the cultural soft power of a country. With the rise of China, more and more people are interested in Chinese culture, and the number of Chinese learners keeps increasing. Under the background of "One Belt and One Road", excellent translators are urgently needed to spread Chinese culture. With the promotion of "One Belt and One Road" Initiative, the number of other countries learning mutual learning and cultural exchanges with China has increased unprecedeningly, bringing vigorous opportunities for the spread of Chinese culture. Translation talents who understand small languages and multi-lingual translators are needed. They should not only use language to convey information, but also use language as a lubricant for communication.<br />
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===5.Training translation talents from the perspective of machine translation===<br />
Under the prevailing environment of machine translation, it poses a great challenge to the cultivation of translation talents. According to the current situation, translation needs and the shortage of translation talents, colleges and universities should reform and innovate the existing training programs for translation talents in terms of the quality of translation talents, the reform of training mode and the use of artificial intelligence. Based on the obtained data and literature, the author discusses how to train translation talents in the perspective of machine translation from the following aspects.<br />
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====5.1 Quality requirements for translation talents ====<br />
Zhong Weihe and Murray made a more detailed and profound discussion on translator's literacy, believing that "translators should not only be proficient in two languages, but also have extensive cultural and encyclopedic knowledge and relevant professional knowledge; Master a variety of translation skills, a lot of translation practice; Have a clear translator role awareness, good professional ethics, practical and enterprising style of work, conscious team spirit and calm psychological quality ". According to the collected data, the author will elaborate the requirements for translation talents from four aspects: language literacy, humanistic literacy, translation ability and innovation ability.<br />
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The first is language literacy, which is the most basic and important requirement. MAO Dun pointed out that "mastery of mother tongue and target language are the foundation of translation". A solid foundation of bilingual skills is the basic skills of translators. Poor language proficiency seems to be a common problem among students majoring in translation and interpreting. Many translation diseases are caused by poor Chinese foundation. As part of going global, the belt and Road initiative is to tell Chinese culture and Chinese stories, which requires translators to be able to use both languages flexibly. Therefore, the first problem that colleges and universities face to solve is to improve the language level of foreign language learners.<br />
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The second is humanistic literacy. Humanistic literacy is mainly manifested by a good command of politics, economy, history, literature and other knowledge, which is particularly important for interpreters. In addition, cross-cultural communication cannot be ignored. In the process of communicating with foreigners or translating, translators often encounter the first cross-cultural contradiction. Cross-culture refers to having a full and correct understanding of cultural phenomena, customs and habits that differ or conflict with the national culture, and accepting and adapting to them in an inclusive manner on this basis. So the interpreter can first fully understand and master the national conditions and culture of the target country, which is particularly important in the "Belt and Road". There are more than 60 countries along the "Belt and Road", and it takes a lot of energy to master their national conditions and culture.<br />
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The third is translation ability. We should distinguish between translation ability and language ability. Translation ability is actually a system of knowledge and skills necessary for translation, the core of which is conversion ability. First of all, it reflects the ability to use tools to assist translation, such as computer application, translation technology and so on. In addition, interpreters should have enough healthy psychological quality and good professional quality. In terms of translation ability, the current training model of translation talents is inadequate.<br />
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The last one is innovation. The cultivation of learners' thinking ability is the key to translation teaching and the cultivation of thoughtful translators should be the connotation of translation teaching. Therefore, the interpreter is not only a translation tool, which is no different from machine translation. More importantly, it is necessary to explore translation with thoughts, have a sense of lifelong learning and innovation consciousness. Translators must constantly innovate themselves, learn new knowledge, and strive to seek reform and innovation. Many colleges and universities should also consciously cultivate students' innovation ability and broaden their thinking and vision.<br />
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====5.2 The reform of college curriculum setting====<br />
First, we will further reform the curriculum of colleges and universities. Add economics, law and engineering to the curriculum, these contents in the "belt and Road".<br />
"One Road" is very important in the construction. According to the author's personal experience, the most typical problem of foreign language majors in colleges and universities is the single learning of foreign languages. More professional foreign language colleges and universities will add some literature courses and national conditions courses of the language target countries. Obviously, whether foreign language graduates are engaged in translation work or not, these knowledge is not enough. Of course, great reforms have been carried out in foreign language teaching, such as combining foreign language with finance, law, diplomacy and so on, and taking the way of minor training foreign language majors.<br />
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Domestic enterprises with a high degree of internationalization attach great importance to translation. Their translation research includes cutting-edge theoretical and applied research, involving machine translation, natural language processing and AI theory, algorithm and model. With such a foundation, enterprises can solve problems by themselves, such as embedding automatic translation functions in mobile phones. International enterprises not only do technical translation, but also deal with all forms of translation and localization in society. At present, translation teaching in most colleges and universities is still in the early mode, and it is an objective fact that it is divorced from the workplace and has a gap with the needs of enterprises.<br />
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Second, we should adjust and strengthen the construction of second foreign language teaching for foreign language majors. In the 1980s, our country was in urgent need of Russian translation. At that time, students majoring in English could translate microelectronic product manuals and related business documents in English and Russian at the same time after learning Russian for half a year. The mutual conversion between English and Russian played a great role in practice. According to the author, in the Graduate Institute of Interpretation and Translation of Beijing Foreign Studies University a very few students majored in multiple languages at the graduate level, that is, they majored in minor languages at the undergraduate level and were admitted to the Graduate Institute of Interpretation and Translation in English. Their training mode is to study English in the Graduate Institute of Interpretation and Translation for two years and the third year in the corresponding department of the undergraduate major. Such training mode in my opinion is a bigger model, cost It's more difficult for students. <br />
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In addition, there is a great disparity in the development of second foreign language teaching in colleges and universities, and the overall level is not high enough. Part of the second foreign language university foreign language professional may still be too much focus in languages such as German, French and Japanese, should as far as possible, considering the need of the construction of the "region", like Croatia, Serbia, Turkish, Hungarian, Italian, Indonesian, Albanian, these are the countries along the "area" the language of the two countries, Colleges and universities should encourage the teaching of a second foreign language.<br />
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Third, the teaching of translation technology should be strengthened. Traditional translation teaching teaches translation skills, such as the translation of words, sentences, texts and figures of speech. Translation technology refers to a series of practical workplace technologies with computer-aided translation software and translation project management as the core, which can greatly improve translation efficiency. However, due to the relative lack of translation technology teachers and equipment in colleges and universities, there is a disconnect between talent training and the requirements of translation technology in the translation field.<br />
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====5.3 Application of artificial intelligence to translation teaching practice====<br />
In order to improve the teaching quality and train students' English translation ability, it is necessary to realize the effective integration of ARTIFICIAL intelligence and translation activity courses, which should not only reflect the effectiveness of artificial intelligence translation technology, but also help students establish a healthy concept of English communication. Through the application of artificial intelligence technology, students can strengthen their flexible translation skills through close communication with "AI program" during the learning stage of English translation activity class. For example, teachers can ask students to translate directly against the translation content provided on the translation screen of the ARTIFICIAL intelligence system. After that, the system can collect the translation answers with the help of speech recognition function, and then judge the accuracy of the translation content, thus providing important feedback to students.<br />
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China has used such artificial intelligence technology in the Putonghua test to ensure that every student can find a suitable translation method in practical communication. The so-called artificial intelligence technology is a new kind of technology modeled after the characteristics of human neural network thinking, can combine the human mind to respond. If it can be integrated into English translation activity teaching, it can not only improve the teaching efficiency, but also enhance students' enthusiasm in learning the course.<br />
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At the same time, during the training of translation talents, teachers also need to take into account the importance of influencing education factors, so that students can form a higher disciplinary quality in translation, so as to fit the concept of quality education in the new era. Only when artificial intelligence translation content is fully integrated into college English translation activity courses can the overall translation ability of college students be maximized.<br />
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====5.4The improvement of translator's technical ability====<br />
In the previous part, the author roughly mentioned that translation teaching should be improved, which will be elaborated here. At present, only a few universities can make full use of the advantages of translation technology in translation teaching and focus on cultivating professional translation talents. Most universities still cannot get rid of the traditional teaching mode of "language + relevant professional knowledge" in translation teaching, and generally lack a correct understanding of COMPUTER-aided translation teaching.<br />
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According to Wang Huashu et al., the courses that can be offered around the composition of translators' technical literacy include computer-assisted translation, translation and corpus, machine translation and post-translation editing, localization and internationalization, film and television translation (subtitle), technical communication and technical writing, and computer programming. The course modules involved are: Fundamentals of COMPUTER-aided Translation, CAT tool application, corpus alignment and processing, term management, QA technology for translation quality assurance, OFFICE fundamentals, translation management technology, basic computer knowledge, desktop typesetting, localization and internationalization, project management system and content management system, technical writing, basic knowledge of computer programming, basic knowledge of web code, etc.<br />
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===6针对一带一路的机器翻译与翻译人才的合作===<br />
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===Conclusion===<br />
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===References===<br />
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=8 颜静(On Machine Translation Under Lanuguage Intelligence——An Option and Opportunity for Human Translators)=<br />
[[Machine_Trans_EN_8]]<br />
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=9 谢佳芬（人工智能时代下的机器翻译与人工翻译）=<br />
[[Machine_Trans_EN_9]]<br />
===Abstract===<br />
With the continuous development of information technology, many industries are facing the competitive pressure of artificial intelligence, and so is the field of translation. Artificial intelligence technology has developed rapidly and combined with the field of translation，which has brought great impact and changes to traditional translation, but artificial intelligence translation and artificial translation have their own advantages and disadvantages. Artificial translation is in the leading position in adapting to human language logical habits and understanding characteristics, but in terms of translation threshold and economic value, the efficiency of artificial intelligence translation is even better. In a word, we need to know that machine translation and human translation are complementary rather than antagonistic.<br />
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===Key Words===<br />
Machine Translation; Artificial Translation; Artificial Intelligence<br />
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===题目===<br />
人工智能时代下的机器翻译与人工翻译<br />
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===摘要===<br />
伴随着信息技术的不断发展，多个行业面临着人工智能的竞争压力，翻译领域也是如此。人工智能技术快速发展并与翻译领域结合，人工智能翻译给传统翻译带来了巨大的冲击和变革，但人工智能翻译与人工翻译存在着各自的优劣特点和发展空间，在适应人类语言逻辑习惯和理解特点的翻译效果上，人工翻译处于领先地位，但在翻译门槛和经济价值上，人工智能翻译的效率则更胜一筹。总的来说，我们要知道机器翻译与人工翻译是互补而非对立的关系。<br />
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===关键词===<br />
机器翻译;人工翻译;人工智能<br />
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===1. Introduction===<br />
====1.1 The History of Machine Translation Aborad====<br />
The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. Alchuni put forward the idea of using machines for translation. In 1933, the Soviet inventor Troyansky designed a machine to translate one language into another. [1]In 1946, the world's first modern electronic computer ENIAC was born. Soon after, American scientist Warren Weaver, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947. In 1949, Warren Weaver published a memorandum entitled Translation, which formally raised the issue of machine translation. In 1954, Georgetown University, with the cooperation of IBM, completed the English-Russian machine translation experiment with IBM-701 computer for the first time, which opened the prelude of machine translation research. [2] In 2006, Google translation was officially released as a free service software, bringing a big upsurge of statistical machine translation research. It was Franz Och who joined Google in 2004 and led Google translation. What’s more, it is precisely because of the unremitting efforts of generations of scientists that science fiction has been brought into reality step by step.<br />
====1.2 The History of Machine Translation in China====<br />
In 1956, the research and development of machine translation has been named in the scientific and technological work and made little achievements in China. On the eve of the tenth anniversary of the National Day in 1959, our country successfully carried out experiments, which translated nine different types of complicated sentences on large general-purpose electronic computers. The dictionary includes 2030 entries, and the grammar rule system consists of 29 circuit diagrams. [3]. After a period of stagnation, China's machine translation ushered in a high-speed development stage after the 1980s in the wave of the third scientific and technological revolution. With the rapid development of economy and science and technology, China has made a qualitative leap in the field of machine translation research with the pace of reform and opening up. In 1978, Institute of Scientific and Technological Information of China, Institute of Computing Technology and Institute of Linguistics carried out an English-Chinese translation experiment with 20 Metallurgical Title examples as the objects and achieved satisfactory results. Subsequently, they developed a JYE-I machine translation system, which based on 200 sentences from metallurgical documents. Its principles and methods were also widely used in the machine translation system developed in the future. In addition, the research achievements of machine translation in China during the 1980s and 1990s also include that Institute of Post and Telecommunication Sciences developed a machine translation system, C Retrieval and automatic typesetting system with good performance and strong practicability in October 1986; In 1988, ISTC launched the ISTIC-I English-Chinese Title System for the translation of applied literature of metallurgy, Information Research Institute of Railway developed an English-Chinese Title Recording machine translation system for railway documents; the Language Institute of the Academy of Social Sciences developed "Tianyu" English-Chinese machine translation system and Matr English-Chinese machine translation system developed by the computer department of National University of Defense Technology. After many explorations and studies, machine translation in China has gradually moved towards application, popularization and commercialization. China Software Technology Corporation launched "Yixing I" in 1988, marking China's machine translation system officially going to the market. After "Yixing", a series of machine translation systems such as Gaoli system in Beijing, Tongyi system in Tianjin and Langwei system in Shaanxi have also entered the public. In the 21st century, the development of a series of apps such as Kingsoft Powerword, Youdao translation and Baidu translation has greatly met the needs of ordinary users for translation. According to the working principle, machine translation has roughly experienced three stages: rule-based machine translation, statistics-based machine translation and deep learning based neural machine translation. [4] These three stages witnessed a leap in the quality of machine translation. Machine translation is more and more used in daily life and even the translation of some texts is almost comparable to artificial translation. In addition to text translation, voice translation, photo translation and other functions have also been listed, which provides great convenience for people's life. It is undeniable that machine translation has become the development trend of translation in the future.<br />
====1.3 The Status Quo of Machine Translation====<br />
In this big data era of information explosion, the prospect of machine translation is also bright. At present, the circular neural network system launched by Google has supported universal translation in more than 60 languages. Many Internet companies such as Microsoft Bing, Sogou, Tencent, Baidu and NetEase Youdao have also launched their own Internet free machine translation systems. [5] Users can obtain translation results free of charge by logging in to the corresponding websites. At present, the circular neural network translation system launched by Google can support real-time translation of more than 60 languages, and the domestic Baidu online machine translation system can also support real-time translation of 28 languages. These Internet online machine translation systems are suitable for a variety of terminal platforms such as mobile phone, PC, tablet and web and its functions are also quite diverse, supporting many translation forms, such as screen word selection, text scanning translation, photo translation, offline translation, web page translation and so on. Although its translation quality needs to be improved, it has been outstanding in the fields of daily dialogue, news translation and so on.<br />
===2. Advantages and Disadvantages of Machine Translation===<br />
Generally speaking, machine translation has the characteristics of high efficiency, low cost, accurate term translation and great development potential and etc. Machine translation is fast and efficient, this is something that artificial translation can’t catch up with. In addition, with the continuous emergence of all kinds of translation software in the market, compared with artificial translation, machine translation is cheap and sometimes even free, which greatly saves the economic cost and time for users with low translation quality requirements. What's more, compared with artificial translation, machine translation has a huge corpus, which makes the translation of some terms, especially the latest scientific and technological terms, more rapid and accurate. The accurate translation of these terms requires the translator to constantly learn, but learning needs a process, which has a certain test on the translator's learning ability and learning speed. In this regard, artificial translation has uncertainty and hysteretic nature. At the same time, with the progress of science and technology and the development of society, the function of machine translation will be more perfect and the quality of translation will be better.Today's machine translation tools and software are easy to carry, all you need to do is just to use the software and electronic dictionary in the mobile phone. There is no need to carry paper dictionaries and books for translation, which saves time and space. At the same time, machine translation covers many fields and is suitable for translation practice in different situations, such as academic, education, commercial trade, social networking, tourism, production technology, etc, it is also easy to deal with various professional terms. However, due to the limitation of translators' own knowledge, artificial translation is often limited to one or a few fields or industries. For example, it is difficult for an interpreter specializing in medical English to translate legal English.<br />
At the same time, machine translation also has its limitations. At first, machine can only operate word to word translation, which only plays the function and role of dictionary. Then, the application of syntax enables the process of sentence translation and it can be solved by using the direct translation method. When the original text and the target language are highly similar, it can be translated directly. For example, the original text "他是个老师." The target language is "he is a teacher ". With the increase of the structural complexity of the original text, the effect of machine translation is greatly reduced. Therefore, at the syntactic level, machine translation still stays in sentences with relatively simple structure. Meanwhile, the original text and the results of machine translation cannot be interchanged equally, indicating that English-Chinese translation has strong randomness, and is not rigorous and scientific enough. <br />
Nowadays, machine translation is highly dependent on parallel corpora, but the construction of parallel corpora is not perfect. At present, the resources of some mainstream languages such as Chinese and English are relatively rich, while the data collection of many small languages is not satisfactory. Moreover, the current corpus is mainly concentrated in the fields of government literature, science and technology, current affairs and news, while there is a serious lack of data in other fields, which can’t reflect the advantages of machine translation. At the same time, corpus construction lags behind. Some informative texts introducing the latest cutting-edge research results often spread the latest academic knowledge and use a large number of new professional terms, such as academic papers and teaching materials while the corpus often lacks the corresponding words of the target language, which makes machine translation powerless<br />
Besides, machine translation is not culturally sensitive. Human may never be able to program machines to understand and experience a particular culture. Different cultures have unique and different language systems, and machines do not have complexity to understand or recognize slang, jargon, puns and idioms. Therefore, their translation may not conform to cultural values and specific norms. This is also one of the challenges that the machine needs to overcome.[6] Artificial intelligence may have human abstract thinking ability in the future, but it is difficult to have image thinking ability including imagination and emotion. [7] Therefore, machine translation is often used in news, science and technology, patents, specifications and other text fields with the purpose of fact description, knowledge and information transmission. These words rarely involve emotional and cultural background. When translating expressive texts, the limitations of machine translation are exposed. The so-called expressive text refers to the text that pays attention to emotional expression and is full of imagination. Its main characteristics are subjectivity, emotion and imagination, such as novels, poetry, prose, art and so on. This kind of text attaches importance to the emotional expression of the author or character image, and uses a lot of metaphors, symbols and other expressions. Machine translation is difficult to catch up with artificial translation in this kind of text, it can only translate the main idea, lack of connotation and literary grace and it cannot have subjective feelings and rational analysis like human beings. In fact, it is not difficult to simulate the human brain, the difficulty is that it is impossible to learn from the rich social experience and life experience of excellent translators. In other words, machine translation lacks the personalization and creativity of human translation. It is this personalization and creativity that promote the development and evolution of language, and what machine translation can only output is mechanical "machine language".<br />
<br />
===3.The Irreplaceability of Artificial Translation ===<br />
====3.1 Translation is Constrained by Context====<br />
At present, machine translation can help people deal with language communication in people's daily life and work, such as clothing, food, housing and transportation, but there is a big gap from the "faithfulness, expressiveness and elegance" emphasized by high-level translation. Language itself is art，which pays more attention to artistry than functionality, and the discipline of art is difficult to quantify and unify. Sometimes it is regular, rigorous, logical and clear, and sometimes it is random, free and logical. If it is translated by machine, it is difficult to grasp this degree. Sometimes, machine translation cannot connect words with contextual meaning. In many languages, the same word may have multiple completely unrelated meanings. In this case, context will have a great impact on word meaning, and the understanding of word meaning depends largely on the meaning read from context. Only human beings can combine words with context, determine their true meaning, and creatively adjust and modify the language to obtain a complete and accurate translation. This is undoubtedly very difficult for machine translation. Artificial translation can get rid of the constraints of the source language and translate the translation in line with the grammar, sentence patterns and word habits of the target language. In the process of translation, translators can use their own knowledge reserves to analyze the differences between the source language and the target language in thinking mode, cultural characteristics, social background, customs and habits, so as to translate a more accurate translation. Artificial translation can also add, delete, domesticate, modify and polish the translation according to the style, make up for the lack of culture, try to maintain the thought, artistic conception and charm of the original text and the style of the source language. In addition, translators can also judge and consider the words with multiple meanings or easy to produce ambiguity according to the context, so as to make the translation more clear and more accurate and improve the quality of the translation.<br />
<br />
<br />
===4. Discussion on the Relationship Between Machine Translation and Artificial Translation ===<br />
<br />
===5. Suggestions on the Combined Development of Machine Translation and Artificial Translation===<br />
<br />
===6. ===<br />
<br />
===7. ===<br />
<br />
===Conclusion===<br />
<br />
===References===<br />
<br />
=10 熊敏(Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts)=<br />
[[Machine_Trans_EN_10]]<br />
===Abstract===<br />
The history of machine translation can be traced to 1940s, undergoing germination, recession, revival and flourish. Nowadays, with the rapid development of information technology, machine translation technology emerged and is gradually becoming mature. Whether manual translation can be replaced by machine translation becomes a widely-disputed topic. So in order to explore the ability of machine translation, I adopts two versions of translation, which are manual translation and machine translation(this paper uses Youdao translation) for different types of texts(according to Peter Newmark's types of text：informative text, expressive text and vocative text). The results are quite different in terms of quality and accuracy. The results show that machine translation is more suitable for informative text for its brevity, while the expressive texts especially literary works and vocative texts are difficult to be translated, because there are too many loaded words and cultural images in expressive text and dependence on the readers. To draw a conclusion, the relationship between machine translation and manual translation should be complementary rather than competitive.<br />
<br />
===Key words===<br />
machine translation; manual translation; Newmark's type of texts<br />
<br />
===题目===<br />
Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts<br />
<br />
===摘要===<br />
机器翻译的历史可以追溯到上个世纪40年代，经历了萌芽期、萧条期、复兴期和繁荣期四个阶段。如今，随着信息技术的高速发展，机器翻译技术日益成熟，于是机器翻译能不能替代人工翻译这个话题引起了广泛热议。为了探究机器翻译的能力水平是否足以替代人工翻译，本人根据皮特纽马克的文本类型分类理论（信息类文本，表达文本，呼吁文本），选择了相应的译文类型，并且将其机器翻译的版本以及人工翻译的版本进行对比。结果发现，就质量和准确度而言，译文的水平大相径庭。因为其简洁的特性，机器比较适合翻译信息文本。而就表达文本，尤其是文学作品，因其存在文化负载词及相应的文化现象，所以机器翻译这类文本存在难度。而呼唤文本因其目的性以及对读者的依赖性较强，翻译难度较大。由此得知，机器翻译和人工翻译的关系应该是互补的，而不是竞争的。<br />
<br />
===关键词===<br />
机器翻译；人工翻译；纽马克文本类型<br />
<br />
===1. Introduction===<br />
====1.1.Introduction to machine translation====<br />
Machine translation, also known as computer translation is a technique to translating through machine translator, such as Google translation and Youdao translation. Machine translation is one of the branches of computational linguistics, ranging from computer science, statistics, information science and so on. Machine translation plays an important role in all aspects.<br />
Machine translation can be traced back to 1940s, when British engineer Booth and American engineer Weaver proposed using computer to translate and started to study machines used for translation. And the first machine translating system launched in 1954, used in English and Russian translation. And this means machine translation becomes a reality. However, the arrival of new things always accompanies barriers and setbacks. In the 1960s, reports from ALPAC (Automated Language Processing Advisory Committee) showed studies on machine translation had stagnated for a decade. At that time, due to the high cost of machine, choosing human translators to finish translating works was more economical for most companies. What’s worse, machine had difficulty in understanding semantic and pragmatic meanings. Fortunately, in the 1970s, with the advance and popularity of computer, machine translation was gradually back on track. With the development of science and technology, machine translation has better technological guarantee, such as artificial intelligence and computer technology. In the last decades, from the late 90s till now, machine translation is becoming more and more mature. The initial rule-oriented machine translation has been updated and Corpus-aided machine translation appears. And nowadays, technology in voice recognition has been further developed. This technique is frequently applied in speech translation products. Users only need to speak source language to the online translator and instantly it will speak out the target version. But machine can’t fully provide precise and accurate translation without any grammatical or semantic problems. Machine can understand the literal meaning of texts, but not the meaning in context. For example, there is polysemy in English, which refers to words having multiple meanings. So when translating these words, translators should take contexts into consideration. But machine has troubles in this way. In addition, machine has no feeling or intention. Hence, it is also difficult to convey the feelings from the source language.<br />
<br />
====1.2.Process of machine translation====<br />
The process of manual translation is different from that of machine translation. Here is the process of the former. (1) Understand source language. (2) Use target language to organize language. (3) Generate translation. Unlike manual translation, machine translation tends to analyze and code source language first, then look for related codes in corpus, and work out the code that represents target language, generating translation. But they share a common feature, which is that Lexicon, grammatical rules and syntactic structure are taken into consideration. This is one of the biggest challenges for machine translation.<br />
<br />
===2.Theorectical framework===<br />
====2.1Newmark’s text typology====<br />
Text typology is a theory from linguistics. It undergoes several phrases. Karl Buhler, a German psychologist and linguist defines communicative functions into expressive, information and vocative. Then Roman Jakobson proposes categorization of texts, which are intralingual translation, interlingual translation and intersemiotic translation. Accordingly, he defines six main functions of language, including the referential function, conative function, emotive function, poetic function, metalingual function and the phatic function. Based on the two theories, Peter Newmark, a translation theorist, summarizes the language function into six parts: the informative function, the vocative function, the expressive function, the phatic function, the aesthetic function, and the metalingual function. Later, he further divided texts into informative type, expressive text and vocative type according to the linguistic functions of various texts. <br />
The core of the informative texts is the truth. It is to convey facts, information, knowledge of certain topics, reality and theories. The language style of the text is objective, logical and standardized. Reports, papers, scientific and technological textbooks are all attributed to informative texts. Translators are required to take format into account for different styles.<br />
The core of the expressive text is the sender’s emotions and attitudes. It is to express sender’s preferences, feelings, views and so on. The language style of it is subjective. Imaginative literature, including fictions, poems and dramas, autobiography and authoritative statements belong to expressive text. It requires translators to be faithful to the source language and maintain the style.<br />
The core of the vocative text is readership. It is to call upon readers to act, think, feel and react in the way intended by the text. So it is reader-oriented. Such texts as advertisement, propaganda and notices are of vocative text. Newmark noted that translators need to consider cultural background of the source language and the semantic and pragmatic effect of target language. If readers can comprehend the meaning at once and do as the text says, then the goal of information communication is achieved.<br />
To draw a conclusion, informative texts focus on the information and facts. Expressive texts center on the mind of addressers, including feelings, prejudices and so on. And vocative texts are oriented on readers and call on them to practice as the texts say.<br />
<br />
====2.2Study method====<br />
Manual translations of the three texts are selected from authoritative versions and universally acknowledged. And machine translations of those come from Youdao Translator. And in this thesis I will compare and evaluate the two methods in word diction, sentence structure, word order and redundancy.<br />
<br />
===3. ===<br />
<br />
===4. ===<br />
<br />
===5. ===<br />
<br />
===6. ===<br />
<br />
===7. ===<br />
<br />
===Conclusion===<br />
<br />
===References===<br />
<br />
=11 陈惠妮=(Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts)=<br />
[[Machine_Trans_EN_11]]<br />
===Abstract===<br />
At present, globalization is accelerating and the market demand for language services is rapidly increasing . Machine translation, as an important translation method, can greatly improve translation efficiency due to its low cost and high speed. However, because of the limitations of machine translation and the differences between Chinese and English language, machine translation is not accurate enough. In order to balance translation efficiency and translation quality, a great number of manual revisions in translation are required for the machine translating texts. Medical papers are specialized, special and purposeful, so it requires accurate,qualified and professional translation. However, the quality of translations by machine is inefficient to meet the high-quality requirements of medical papers translation. Therefore, the introduction of pre-editing can greatly improve the efficiency and quality of machine translation.<br />
<br />
===Key words===<br />
Pre-editing, Machine translation, Medical texts<br />
<br />
===题目===<br />
Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts<br />
<br />
===摘要===<br />
在全球化加速发展的今天，市场对语言服务的需求迅速增加。机器翻译作为一种重要的翻译途径，由于其成本低、速度快，可以大大提高翻译效率。然而，由于机器翻译的局限性以及中英文语言的差异，机器翻译的准确性不高。为了平衡翻译效率和翻译质量，机器翻译文本需要大量的手工修改。<br />
医学论文具有专业性、特殊性和目的性，要求其译文准确、合格、专业。然而，机器翻译的质量较低，无法满足医学论文对翻译的高质量要求。因此，译前编辑的引入可以大大提高机器翻译的效率和质量。<br />
<br />
===关键词===<br />
译前编辑；机器翻译；医学文本<br />
<br />
===1. Introduction===<br />
===1.1 Definition of Machine Translation===<br />
As Cronin(2013) revealed: "Translation is undergoing a revolutionary upheaval. The influence of digital technology and the Internet on translation is continuous, extensive and profound. From the popularity of automatic online translation applications, translation revolution is everywhere (Cronin,2013) However,the concept of machine translation was firstly proposed in the 1930s. Since 1940s, the machine translation technology has been evolving from rule-based machine translation (RBMT) to statistical machine translation (SMT), and to neural machine translation (NMT). Machine translation refers to the automatic translation of source language into target language by using a computer system. That is, machine translation refers to the automatic translation of text from one language into another natural language by computer software or other online translation webs. Machine translation is also defined as the process of “using a computer system to automatically translate text or speech from one natural language to another” according to the definition by ISO (Cui, 2014).O 'Brien (2002) defines it as "the behavior of modifying errors in machine translation to ensure that the target translation meets certain quality requirements". On the basic level, machine translation performs mechanical substitution of words in one language for words in another language, but that rarely produces a good translation, therefore, recognition of the whole phrases and their closest counterparts in the target language is needed. Not all words in one language have equivalents in another language, and many words have more than one meaning. A huge demand for translation is greatly needed in today’s global world, which creates new opportunities for the development of machine translation, attracts more and more attention and becomes one of the current research focuses.<br />
<br />
===1.2 Definition of Pre-editing===<br />
Pre-editing means to adjust and modify the source language to make it fit more with the characteristics of the machine translation software before putting the source language before the into machine translation, so as to improve the quality of the translation machine translation (Wei Changhong, 2008:93-94). Pre-editing is to modify the original text before putting it into machine translation software, in order to improve the recognition rate of machine translation, optimize the output quality of translated text and reduce the workload of post-editing. Because pre-translation editing only needs to be modified in one language, the operation is simpler than post-translation editing, which can realize the double improvement of quality and efficiency. A good pre-editing translation can help machine translation more smoothly, thus improving the machine readability and quality of the output translation. Pre-editing is mostly applied in the following situations: one is when the original text is of poor quality and the machine is difficult to recognize the meaning of the sentence, such as user generated content with poor readability and translatability (Gerlach et al, 2003:45-53); Documents that need to be published in multiple languages; Next is when the original text contains a lot of jargon; The last is the original text has a corresponding translation memory bank. If the original text is edited, it can better match the content of the translation memory bank. Pre-editing is a process of identifying problems. It requires to pre-edit the source texts before putting it into machine translation according to the requirements, listing the expressions or sentences that may have trouble in machine translation and then pre-edit it by human. The purpose is to enable the computer to translate better, improve the translatability of machine translation. (Slype G V & Guinet J F & Seitz F,1984:115)<br />
<br />
===1.3 Machine Translation Mode===<br />
According to the different knowledge acquisition methods, machine translation modes can be classified as follows: one is rule-based machine translation, which is based on bilingual dictionaries and a library of language rules for each language. The quality of translation depends on whether the source language conforms to the existing rules, but the inexhaustible rules are the hinders of this model. The second mode is machine translation based on statistics. This translation model relies on the principles of mathematics and statistics to find various existing translations corresponding to the translation tasks through the employment of corpus, analyzing the frequency of their occurrence and selecting the translation with the highest frequency for output. The disadvantage of this translation model is that it ignores the flexibility of language and the importance of context. The last one is neural network language model. This model is different from previous translation models in that it uses end-to-end neural network to realize automatic translation between natural languages. At present, the quality of its translation is much higher than that of the previous translation models.<br />
===1.4 Source of Study Abstracts===<br />
The core medical journals from domestic are selected in order to make the paper more representative and authoritative, such as National Medical Journal of China, Journal of Peking University (Health Science), and Journal of Third Military Medical University. All of them include basic medical science, biomedical technology, laboratory medical science and other fields. In this way, the pre-editing approaches included are applicable enough to machine translation of medical abstracts.<br />
===1.5 Selection of Translation Software===<br />
Generally, machine translation software includes Google Translation, Youdao Translator and Niu Translation, which has their own special use in translation. Take Google as an example, it is more like neurons in human brain, enabling to learn and collect information to establish connections with its neural machine translator’s neurons. However, it also causes many errors because of the lack of enough information. In this paper, contrastive analysis will be carried on by using Google translation. On the one hand, being a pioneer in the translation of NET, it is inevitably to sue Google as the translation software to translate medical texts in this paper. Comparing the program called “Google brain”, other NMT of translation software are relatively disadvantages. On the other hand, the Google translation enjoys the largest users in the world, with its downloads of more than one billion. The output quality of Google translation is more correct and complete than other machine translation software. With years of development and improvement, Google Translation has been greatly promoted. In this paper, Chinese medical abstract will be automatically translated by Google translation, and then the translation output will be compared with the translation by human and the publication of English abstracts. The main purpose is to prove that the improvement and promotion of quality and accuracy in the medical abstracts will be obtained through the pre-editing approaches.<br />
<br />
===2.Language Characteristics and Error Division===<br />
Since Chinese and English are two different languages, it is quite neccessary to identify their own characteristics so as to better analyze and understand the two languages. Tytler (1978) argued in his Essay on the Principles of Translation that there are three principles of translation that in all the translation should give readers the same feelings as the source text, except for complete transcript of ideas. There actually also exit some mistakes of these two languages. So the following will make some clarrifications of these two languages to ensure more accurate translation.<br />
<br />
===2.1 Language Characteristics of Medical Abstracts===<br />
Chinese and English belong to different language systems, so there are differences in their language structure and the way users think of the languages. When using machine translation from Chinese to English, due to the unequal language levels, there will be many mistakes in the translation process, especially for ESP texts, such as medical papers. <br />
Here, these differences mainly refer to the linguistic characteristics of medical abstracts. In medical abstracts, it usually includes structured and unstructured abstracts. Although in different forms, they both describe the purpose, methods and conclusions of the research.<br />
In the method section of medial abstract, several Chinese sentences can be connected with commas, but each sentence may convey different information. In contrast, English sentences contain a great deal of information, but in order to ensure clarity, some modifiers need to be isolated and then reconstructed. However, in Chinese-English machine translation, a lot of information is put into the sentence because the machine segments the sentence on the basis of the full comma.<br />
In addition, subjectless sentence is used in the objective and method parts of Chinese medical abstracts. The subjectless sentence means the sentence without or free from subject and is usually employed in two contexts. The first is "needless to say". In Chinese sentences, it is common to omit the subject of the sentence. Chinese sentences can convey meanings by using incomplete sentence structures, so Chinese speakers can understand the meanings of the sentences even though the sentence subject is omitted. The second is "emphasis of action". In this context, subjectless sentences are used to describe behaviors, especially in the study of traditional Chinese medicine abstracts. Comparatively speaking, it should be avoided in English medical abstracts. Abstract sentences are more subjective when describing the learning process, while the essential requirement of English medical abstract is objectivity. From this point, sentences without subject should be avoided in English medical abstracts. Another feature is voice. Few words with passive meanings appear in Chinese abstracts. English sentences are more favoured in using passive voice. When translating from Chinese to English, passive voice should be used to make the contents objective, which is also the basic requirement of medical papers.<br />
These are the linguistic features of Chinese medical abstracts. There are great differences in sentence structure and expression between Chinese and English medical abstracts. These differences may reduce the accuracy of machine translation, so it is necessary to introduce pre-editing to edit the source text to ensure that the source text can be accurately recognized by the machine and fully translated into English.<br />
===2.2 Error Division of Machine Translation in Translating Abstracts of Medical Papers from Chinese to English===<br />
In this paper, errors in machine translation are listed out after analyzing. Some are due to the principles, such as statistical-based MT and NMT, employed by machine translation. Based on the original errors, they can be studied from two levels. One is from the macro-level, referring to mistakes caused by objective factors. These are not human factors, but the disadvantage of machine translation and the limitations of language. The limitations of machine translation are derived from the principles and models adopted and manifests itself as a reliance on reference sources. In this case, semantic ambiguity and textual incoherence may occur in the absence of reference sources.<br />
However, for ESP texts such as medical papers, the requirements for terms and sentence patterns are far beyond the existing corpora. For unfamiliar texts (that is, the corresponding texts cannot be found in the corpora). The quality of output translation will be relatively low, which is determined by the principles of machine translation. As mentioned above, machine translation has evolved over the past few decades from rules-based to corp-based statistics to NMT. However, there still exist some limitations in machine translation.<br />
Mistakes on micro-level are mainly caused by the variations and differences of linguistic structure between Chinese and English. Chinese is an implicit language, while English is an explicit one (Lian, 2010) To put it in another way, Chinese expression does not depend on language structures, while English does the opposite. This may result in mismatches between the original and machine translated sentences. This kind of error is mainly divided into two types: component fragment and component missing. For a medical paper, such errors are very serious. As a kind of ESP discourse, medical paper has the characteristics of fixed, objective and accurate language structure. In order to reproduce the characteristics of medical abstracts in translation, it is necessary to avoid errors at the level of words and sentences and pay attention to logic and consistency.<br />
Lexical errors here refer to the inconsistency of fixed expressions in the translation of terms. Although these terms are machine translation based on the corpus, the corpus may not be perfect for ESP texts such as medical papers. Therefore, fixed expressions are not used in term translation. This is also the most common mistake in machine translation (the term here includes but is not limited to nouns). For ESP texts, medical text in particular, the fixed expressions and the accuracy of terms are of great importance.<br />
<br />
===3.Approaches Proposed for Pre-editing ===<br />
Generally speaking, there is a close relationship between pre-editing and post-editing, both of which aim to convey information and ensure high or publishable translation quality. Proper pre-editing can improve the quality of machine translation in terms of adequacy and consistency. <br />
Complexity of natural language and people use language arbitrarily, bringing many difficulties to Chinese-English machine translation, Hu Qingping (2005:24) has proposed "the research of translation software and the development of the controlled language are the two directions to improve the quality of machine translation : the former aims at the difficulty in the natural language processing, the latter overcomes the arbitrariness of natural language". Feng Quangong and Gao Lin (2017: 63-68) put forward: "The writing principles of controlled language can be applied to pre-editing of machine translation. Pre-editing based on controlled language can effectively reduce the complexity and ambiguity of source text, improve the identifiability of machine translation (the translatability of source text itself), and thus reduce (fully) the workload of post-editing".<br />
The central task of pre-editing is to transform human-friendly content into machine-friendly content, so words and sentences need to be repositioned or even changed. Based on the analysis of the language characteristics of Chinese and English, the Chinese ideographic group should be split before the Chinese source text is input into the machine software to translate so that the sentence structure is complete which can be easily recognized by the machine translation software.<br />
<br />
===4.===<br />
<br />
===Conclusion===<br />
<br />
===References===<br />
<br />
=12 蔡珠凤=(The Mistranslation of C-J Machine Translation of Political Statements)=<br />
[[Machine_Trans_EN_12]]<br />
===Abstract===<br />
Language is the main way of communication between people. With the continuous development of globalization, the scale of cross-border exchanges is also expanding. However, due to cultural differences and diversity, the languages of different countries and regions are very different, which seriously hinders people's communication. The demand for efficient and convenient translation tools is increasing. At the same time, with the development of network technology and artificial intelligence, recognition technology based on deep learning is more and more widely used in English, Japanese and other fields.<br />
<br />
===Key words===<br />
machine translation; political statements; mistranslation of C-J machine translation<br />
===题目===<br />
The Mistranslation of C-J Machine Translation of Political Statements<br />
===摘要===<br />
语言是人与人之间交流的主要方式。随着全球化的不断发展，跨境交流的规模也在不断扩大。然而，由于文化的差异和多样性，不同国家和地区的语言差异很大，这严重阻碍了人们的交流。对高效便捷的翻译工具的需求正在增加。同时，随着网络技术和人工智能的发展，基于深度学习的识别技术在英语、日语等领域的应用越来越广泛。<br />
<br />
===关键词===<br />
机器翻译；政治发言；政治发言中译日的误译<br />
===1. Introduction===<br />
===Introduction to machine translation===<br />
Machine translation, also known as automatic translation, is a process of using computers to convert one natural language (source language) into another natural language (target language). It is a branch of computational linguistics, one of the ultimate goals of artificial intelligence, and has important scientific research value.At the same time, machine translation has important practical value. With the rapid development of economic globalization and the Internet, machine translation technology plays a more and more important role in promoting political, economic and cultural exchanges.The development of machine translation technology has been closely accompanied by the development of computer technology, information theory, linguistics and other disciplines. From the early dictionary matching, to the rule translation of dictionaries combined with linguistic expert knowledge, and then to the statistical machine translation based on corpus, with the improvement of computer computing power and the explosive growth of multilingual information, machine translation technology gradually stepped out of the ivory tower and began to provide real-time and convenient translation services for general users.<br />
<br />
=== C-J machine translation software===<br />
Today's online machine translation software includes Baidu translation, Tencent translation, Google translation, Youdao translation, Bing translation and so on. Google was the first company to launch the machine translation system, and Baidu was the first company to import the machine translation system in China. In addition, Tencent and Youdao have attracted much attention.Machine translation is the process of using computers to convert one natural language into another. It usually refers to sentence and full-text translation between natural languages. In order to continuously improve the translation quality, R & D personnel have added artificial intelligence technologies such as speech recognition, image processing and deep neural network to machine translation on the basis of traditional machine translation based on rules, statistics and examples.With the increase of using machine translation, the joint cooperation between manual translation and machine translation will also increase significantly in the future. What criteria should be used to evaluate the quality of machine translation? In the evolving field of machine translation, there is an urgent need to clarify the unsolvable questions and solved problems.<br />
<br />
===The history of machine translation===<br />
The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. alchuni put forward the idea of using machines for translation. In 1933, Soviet inventor П.П. Trojansky designed a machine to translate one language into another, and registered his invention on September 5 of the same year; However, due to the low technical level in the 1930s, his translation machine was not made. In 1946, the first modern electronic computer ENIAC was born. Shortly after that, W. weaver, an American scientist and A. D. booth, a British engineer, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947 when discussing the application scope of electronic computer. In 1949, W. Weaver published the translation memorandum, which formally put forward the idea of machine translation. After 60 years of ups and downs, machine translation has experienced a tortuous and long development path. The academic community generally divides it into the following four stages:<br />
Pioneering period（1947-1964）<br />
In 1954, with the cooperation of IBM, Georgetown University completed the English Russian machine translation experiment with ibm-701 computer for the first time, showing the feasibility of machine translation to the public and the scientific community, thus opening the prelude to the study of machine translation.<br />
It is not too late for China to start this research. As early as 1956, the state included this research in the national scientific work development plan. The topic name is "machine translation, the construction of natural language translation rules and the mathematical theory of natural language". In 1957, the Institute of language and the Institute of computing technology of the Chinese Academy of Sciences cooperated in the Russian Chinese machine translation experiment, translating 9 different types of more complex sentences.<br />
From the 1950s to the first half of the 1960s, machine translation research has been on the rise. The United States and the former Soviet Union, two superpowers, have provided a lot of financial support for machine translation projects for military, political and economic purposes, while European countries have also paid considerable attention to machine translation research due to geopolitical and economic needs, and machine translation has become an upsurge for a time. In this period, although machine translation is just in the pioneering stage, it has entered an optimistic period of prosperity.<br />
Frustrated period（1964-1975）<br />
In 1964, in order to evaluate the research progress of machine translation, the American Academy of Sciences established the automatic language processing Advisory Committee (Alpac Committee) and began a two-year comprehensive investigation, analysis and test.<br />
In November 1966, the committee published a report entitled "language and machine" (Alpac report for short), which comprehensively denied the feasibility of machine translation and suggested stopping the financial support for machine translation projects. The publication of this report has dealt a blow to the booming machine translation, and the research of machine translation has fallen into a standstill. Coincidentally, during this period, China broke out the "ten-year Cultural Revolution", and basically these studies also stagnated. Machine translation has entered a depression.<br />
convalescence（1975-1989）<br />
Since the 1970s, with the development of science and technology and the increasingly frequent exchange of scientific and technological information among countries, the language barriers between countries have become more serious. The traditional manual operation mode has been far from meeting the needs, and there is an urgent need for computers to engage in translation. At the same time, the development of computer science and linguistics, especially the substantial improvement of computer hardware technology and the application of artificial intelligence in natural language processing, have promoted the recovery of machine translation research from the technical level. Machine translation projects have begun to develop again, and various practical and experimental systems have been launched successively, such as weinder system Eurpotra multilingual translation system, taum-meteo system, etc.<br />
However, after the end of the "ten-year holocaust", China has perked up again, and machine translation research has been put on the agenda again. The "784" project has paid enough attention to machine translation research. After the mid-1980s, the development of machine translation research in China has further accelerated. Firstly, two English Chinese machine translation systems, ky-1 and MT / ec863, have been successfully developed, indicating that China has made great progress in machine translation technology.<br />
New period(1990 present)<br />
With the universal application of the Internet, the acceleration of the process of world economic integration and the increasingly frequent exchanges in the international community, the traditional way of manual operation is far from meeting the rapidly growing needs of translation. People's demand for machine translation has increased unprecedentedly, and machine translation has ushered in a new development opportunity. International conferences on machine translation research have been held frequently, and China has made unprecedented achievements. A series of machine translation software have been launched, such as "Yixing", "Yaxin", "Tongyi", "Huajian", etc. Driven by the market demand, the commercial machine translation system has entered the practical stage, entered the market and came to the users.<br />
Since the new century, with the emergence and popularization of the Internet, the amount of data has increased sharply, and statistical methods have been fully applied. Internet companies have set up machine translation research groups and developed machine translation systems based on Internet big data, so as to make machine translation really practical, such as "Baidu translation", "Google translation", etc. In recent years, with the progress of in-depth learning, machine translation technology has further developed, which has promoted the rapid improvement of translation quality, and the translation in oral and other fields is more authentic and fluent.<br />
<br />
===The problem of machine translation at present===<br />
Error is inevitable<br />
Many people have misunderstandings about machine translation. They think that machine translation has great deviation and can't help people solve any problems. In fact, the error is inevitable. The reason is that machine translation uses linguistic principles. The machine automatically recognizes grammar, calls the stored thesaurus and automatically performs corresponding translation. However, errors are inevitable due to changes or irregularities in grammar, morphology and syntax, such as sentences with adverbials after "give me a reason to kill you first" in Dahua journey to the West. After all, a machine is a machine. No one has special feelings for language. How can it feel the lasting charm of "the tenderness of lowering its head, like the shame of a water lotus? After all, the meaning of Chinese is very different due to the changes of morphology, grammar and syntax and the change of context. Even many Chinese people are zhanger monks - they can't touch their heads, let alone machines.<br />
Bottleneck<br />
In fact, no matter which method, the biggest factor affecting the development of machine translation lies in the quality of translation. Judging from the achievements, the quality of machine translation is still far from the ultimate goal.<br />
Chinese mathematician and linguist Zhou Haizhong once pointed out in his paper "fifty years of machine translation": to improve the quality of machine translation, the first thing to solve is the problem of language itself rather than programming; It is certainly impossible to improve the quality of machine translation by relying on several programs alone. At the same time, he also pointed out that it is impossible for machine translation to achieve the degree of "faithfulness, expressiveness and elegance" when human beings have not yet understood how the brain performs fuzzy recognition and logical judgment of language. This view may reveal the bottleneck restricting the quality of translation. <br />
It is worth mentioning that American inventor and futurist ray cozwell predicted in an interview with Huffington Post that the quality of machine translation will reach the level of human translation by 2029. There are still many disputes about this thesis in the academic circles.<br />
<br />
===2.Mistranslation of Chinese Japanese machine translation===<br />
===2.1Vocabulary mistranslation in Chinese Japanese machine translation===<br />
===2.1.1Mistranslation of proper nouns===<br />
In political materials, there are often political dignitaries' names, place names or a large number of proper nouns in the political field. The morphemes of such words are definite and inseparable. Mistranslation will make the source language lose its specific meaning. <br />
<br />
Chinese translation into Japanese Japanese translation into Chinese<br />
<br />
original text translation by Youdao reference translation original text translation by Youdao reference translation<br />
栗战书 栗戰史書 栗戰書 労安 劳安 劳安<br />
李克强 李克強 李克強 朱鎔基 朱基 朱镕基<br />
习近平 習近平 習近平 筑紫哲也 筑紫哲也 筑紫哲也<br />
韩正 韓中 韓正 山口百惠 山口百惠 山口百惠<br />
王沪宁 王上海氏 王滬寧 田中角栄 田中角荣 田中角荣<br />
汪洋 汪洋 汪洋 東条英機 东条英社 东条英机<br />
赵乐际 趙樂南 趙樂際 毛沢东 毛泽东 毛泽东<br />
江泽民 江沢民 江沢民 トウ・ショウヘイ 大酱 邓小平<br />
周恩来 周恩来 周恩来<br />
クリントン 克林顿 克林顿<br />
<br />
The above table counts 18 special names in the two texts, and 7 machine translation errors. In terms of mistranslation, there are not only "战书" but also "戰史" and "史書" in Chinese. "沪" is the abbreviation of Shanghai. In other words, since "战书" and "沪" are originally common nouns, this disrupts the choice of target language in machine translation. Except Katakana interference (except for トウシゃウヘイ, most of the mistranslations appear in the new Standing Committee. It can be seen that the machine translation system does not update the thesaurus in time. Different from other words, people's names have particularity. Especially as members of the Standing Committee of the Political Bureau of the CPC Central Committee, the translation of their names is officially unified. In this regard, public figures such as political dignitaries, stars, famous hosts and important. In addition, the machine also translates Japanese surnames such as "タ二モト" (谷本), "アンドウ" (安藤) into "塔尼莫特" and "龙胆". It can be found that the language feature that Japanese will be written together with Chinese characters and Hiragana also directly affects the translation quality of Japanese Chinese machine translation. Japanese people often use Katakana pronunciation. For example, when Japanese people talk with Premier Zhu, they often use "二ーハウ" (Hello). However, the machine only recognizes the two pseudonyms "二" and "ハ" and transliterates them into "尼哈" , ignoring the long sound after "二" and "ハ".<br />
<br />
original text Translation by Youdao reference translation<br />
日美安全体制 日米の安全体制 日米安保体制<br />
中国共产党第十九次全国代表大会 中国共産党第19回全国代表大会 中国共産党第19回全国代表大会（第19回党大会）<br />
十八大 十八大 第18回党大会<br />
中国特色社会主义 中国特色社会主義 中国の特色ある社会主義<br />
中国共产党中央委员会 中国共産党中央委員会 中国共産党中央委員会<br />
十八届中共中央政治局常委 第18代中国共產党中央政治局常務委員 第18期中共中央政治局常務委員<br />
十八届中共中央政治局委员 18期の中国共產党中央政治局委員 第18期中共中央政治局委員<br />
十九届中共中央政治局常委 十九回中国共產党中央政治局常務委員 第19期中央政治局常務委員<br />
中共十九届一中全会 中国共產党第十九回一中央委員会 第19期中央委員会第1回全体会議<br />
The above table is a comparison of the original and translated versions of some proper nouns. As shown in the table, the mistranslation problems are mainly reflected in the mismatch of numerals + quantifiers, the wrong addition of case auxiliary word "の", the lack of connectors, the Mistranslation of abbreviations, dead translation, and the writing errors of Chinese characters. The following is a specific analysis one by one.<br />
"十八届中共中央政治局常委", "十八届中共中央政治局委员", "十九届中共中央政治局常委" and "中共十九届一中全会" all have quantifiers "届", which are translated into "代", "期" and "回" respectively. The meaning is vague and should be uniformly translated into "期"; Among them, the translation of the last three proper nouns lacks the Chinese conjunction "第". The case auxiliary word "の" was added by mistake in the translation of "十八届中共中央政治局委员" and "日美安全体制". The "中国共产党中央委员会" did not write in the form of Japanese Ming Dynasty characters, but directly used simplified Chinese characters.<br />
The full name of "中共十九届一中全会" is "中国共产党第十九届中央委员会第一次全体会议", in which "一" stands for "第一次", which is an ordinal word rather than a cardinal word. Machine translation does not produce a correct translation. The fundamental reason is that there are no "规则" for translating such words in machine translation. If we can formulate corresponding rules for such words (for example, 中共m'1届m2 中全会→第m1期中央委员会第m2回全体会议), the translation system must be able to translate well no matter how many plenary sessions of the CPC Central Committee.<br />
<br />
===2.1.2Mistranslation of Polysemy===<br />
<br />
original text Translation by Youdao reference translation<br />
スタジオ 摄影棚/工作室 直播现场/演播厅<br />
日中関係の話 中日关系的故事 就中日关系(话题)<br />
溝 水沟 鸿沟<br />
それでは日中の問題について質問のある方。 那么对白天的问题有提问的人。 关于中日问题的话题，举手提问。<br />
私たちのクラスは20人ちょっとですが、 我们班有20人左右， 我们班二十多人的意见统一很难，<br />
いろいろな意見が出て、まとめるのは大変です。 但是又各种各样的意见，总结起来很困难。 中国是怎样把13亿人凝聚在一起的？<br />
一体どうやって、13億人もの人をまとめているんですか。 到底是怎么处理13亿的人的呢？ <br />
In the original text, the word "スタジオ" appeared four times and was translated into "摄影棚" or "工作室" respectively, but the context is on-site interview, not the production site of photography or film. "話" has many semantics, such as "说话", "事情", "道理", etc. In accordance with the practice of the interview program, Premier Zhu Rongji was invited to answer five questions on the topic of China Japan relations. Obviously, the meaning of the word "故事" is very abrupt. The inherent Japanese word "日中" means "晌午、白天". At the same time, it is also the abbreviation of the names of the two countries. The machine failed to deal with it correctly according to the context. "溝" refers to the gap between China and Japan, not a "水沟". The former "まとめる" appears in the original text with the word "意见", which is intended to describe that it is difficult to unify everyone's opinions. The latter "まとめている" also refers to unifying the thoughts of 1.3 billion people, not "处理". Therefore, it is more appropriate to use "统一" and "处理" in human translation, rather than "总结意见" or "处理意见".<br />
Mistranslation of polysemy has always been a difficult problem in machine translation research. The translation of each word is correct, but it is often very different from the original expression in the context. Zhang Zhengzeng said that ambiguity is a common phenomenon in natural language. Its essence is that the same language form may have different meanings, which is also one of the differences between natural language and artificial language. Therefore, one of the difficulties faced by machine translation is language disambiguation (Zhang Zheng, 2005:60). In this regard, we can mark all the meanings of polysemous words and judge which meaning to choose by common collocation with other words. At the same time, strengthen the text recognition ability of the machine to avoid the translation inconsistent with the current context. In this way, we can avoid the mistake of "大酱" in the place where famous figures such as Deng Xiaoping should have appeared in the previous political articles.<br />
<br />
===2.1.3Mistranslation of compound words===<br />
Multi class words refer to a word with two or more parts of speech, also known as the same word and different classes.<br />
original text Translation by Youdao reference translation<br />
1998年江泽民主席曾经访问日本, 1998年の江沢民国家主席の日本訪問し、 1998年、江沢民総書記が日本を訪問し、かつ、<br />
同已故小渊首相签署了联合宣言。 かつて同じ故小渕首相が署名した共同宣言 亡くなられた小渕総理と宣言に調印されました<br />
<br />
Chinese "同" has two parts of speech: prepositions and conjunctions: "故" has many parts of speech, such as nouns, verbs, adjectives and conjunctions. This directly affects the judgment of the machine at the source language level. The word "同" in the above table is used as a conjunction to indicate the other party of a common act. "故" is used as a verb with the semantic meaning of "死亡", which is a modifier of "小渊首相". The machine regards "同" as an adjective and "故" as a noun "原因", which leads to confusion in the structure and unclear semantics of the translation. Different from the polysemy problem, multi category words have at least two parts of speech, and there is often not only one meaning under each part of speech. In this regard, software R & D personnel should fully consider the existence of multi category words, so that the translation machine can distinguish the meaning of words on the basis of marking the part of speech, so as to select the translation through the context and the components of the word in the sentence. Of course, the realization of this function is difficult, and we need to give full play to the wisdom of R & D personnel.<br />
<br />
===2.2 Syntactic mistranslation in Chinese Japanese machine translation===<br />
===2.2.1Mistranslation of tenses===<br />
original text：History is written by the people, and all achievements are attributed to the people. <br />
Translation by Youdao：歴史は人民が書いたものであり、すべての成果は人民のためである。<br />
reference translation：歴史は人民が綴っていくものであり、すべての成果は人民に帰することとなります。<br />
The original sentence meaning is "历史是人民书写的历史" or "历史是人民书写的东西". When translated into Japanese, due to the influence of Japanese language habits, the formal noun "もの" should be supplemented accordingly. In this sentence, both the machine and the interpreter have translated correctly. However, the machine's recognition of "的" is biased, resulting in tense translation errors. The past, present and future will become "history", and this is a continuous action. The form translated as "~ ていく" not only reflects that the action is a continuous action, but also conforms to the tense and semantic information of the original text. In addition, the machine's treatment of the preposition "于" is also inappropriate. In the original text, "人民" is the recipient of action, not the target language. As the most obvious feature of isolated language, function words in Chinese play an important role in semantic expression, and their translation should be regarded as a focus of machine translation research.<br />
<br />
original text：李克强同志是十六届中共中央政治局常委,其他五位同志都是十六届中共中央政治局委员。<br />
Translation by Youdao：李克強総理は第16代中国共産党中央政治局常務委員であり、他の５人の同志はいずれも16期の中国共産党中央政治局委員である。<br />
reference translation：李克強同志は第16期中国共産党中央政治局常務委員を務め、他の５人は第16期中共中央政治局委員を務めました。<br />
Judging the verb "是" in the original text plays its most basic positive role, but due to the complexity of Chinese language, "是" often can not be completely transformed into the form of "だ / である" in the process of translation. This sentence is a judgment of what has happened. Compared with the 19th session, the 18th session has become history. This is an implicit temporal information. It is very difficult for machines without human brain to recognize this implicit information. "中共中央政治局常委" is the abbreviation of "中共中央政治局常务委员会委员" and it is a kind of position. In Japanese, often use it with verbs such as "務める","担当する",etc. The translator adopts the past tense of "務める", which conforms to the expression habit of Japanese and deals with the problem of tense at the same time. However, machine translation only mechanically translates this sentence into judgment sentence, and fails to correctly deal with the past information implied by the word "十八届".<br />
<br />
===2.2.2Mistranslation of honorifics===<br />
Honorific language is a language means to show respect to the listener. Different from Japanese, there is no grammatical category of honorifics in Chinese. There is no specific fixed grammatical form to express honorifics, self modesty and politeness. Instead, specific words such as "您", "请" and "劳驾" are used to express all kinds of respect or self modesty. <br />
Original text translation by Youdao reference translation<br />
女士们，先生们，同志们，朋友们 さんたち、先生たち、同志たち、お友达さん ご列席の皆さん<br />
谢谢大家！ ありがとうございます！ ご清聴ありがとうございました。<br />
これはどうされますか。 这是怎么回事呢? 您将如何解决这一问题？<br />
こうした問題をどうお考えでしょうか。 我们会如何考虑这些问题呢？ 您如何看待这一问题？ <br />
For example, for the processing of "女士们，先生们，同志们，朋友们", the machine makes the translation correspond to the original one by one based on the principle of unchanged format, but there are errors in semantic communication and pragmatic habits. When speaking on formal occasions, Chinese expression tends to be comprehensive and detailed, as well as the address of the audience. In contrast, Japanese usually uses general terms such as "皆様", "ご臨席の皆さん" or "代表団の方々" as the opening remarks. In terms of Japanese Chinese translation, the machine also failed to recognize the usage of Japanese honorifics such as "さ れ る" and "お考え". It can be seen that Chinese Japanese machine translation has a low ability to deal with honorific expressions. The occasion is more formal. A good translation should not only be fluent in meaning, but also conform to the expression habits of the target language and match the current translation environment.<br />
<br />
===Conclusion===<br />
In the process of discussing the Mistranslation of machine translation, this paper mainly cites the Mistranslation of Youdao translator. When I studied the problem of machine translation mistranslation, I also used a large number of translation software such as Google and Baidu for parallel comparison, and found that these translation software also had similar problems with Youdao translator. For example, the "新世界新未来" in Chinese ABAC structural phrases is translated by Baidu as "新し世界の新しい未来", which, like Youdao, is not translated in the form of parallel phrases; Google online translation translates the word "“全心全意" into a completely wrong "全面的に"; The original sentence "我吃了很多亏" in the Mistranslation of the unique expression in the language is translated by Google online as "私はたくさんの損失を食べました". Because the "吃" and "亏" in the original text are not closely adjacent, the machine can not recognize this echo and mistakenly treats "亏" as a kind of food, so the machine translates the predicate as "食べました"; Japanese "こうした問題をどう考えでしょうか", Google's online translation is "你如何看待这些问题?", "你" tone can not reflect the tone of Japanese honorific, while Baidu translates as "你怎么想这样的问题呢?" Although the meaning can be understood, it is an irregular spoken language. Google and Baidu also made the same mistakes as Youdao in the translation of proper nouns. For example, they translated "十七届(中共中央政治局常委)”" into "第17回..." .In short, similar mistranslations of Youdao are also common in Baidu and Google. Due to space constraints, they will not be listed one by one here. <br />
Mr. Liu Yongquan, Institute of language, Chinese Academy of Social Sciences (1997) It has been pointed out that machine translation is a linguistic problem in the final analysis. Although corpus based machine translation does not require a lot of linguistic knowledge, language expression is ever-changing. Machine translation based solely on statistical ideas can not avoid the translation quality problems caused by the lack of language rules. The following is based on the analysis of lexical, syntactic and other mistranslations From the perspective of the characteristics of the source language, this paper summarizes some difficulties of Chinese and Japanese in machine translation. <br />
(1) The difficulties of Chinese in machine translation <br />
Chinese is a typical isolated language. The relationship between words needs to be reflected by word order and function words. We should pay attention to the transformation of function words such as "的", "在", "向" and "了". Some special verbs, such as "是", "做" and "作", are widely used. How to translate on the basis of conforming to Japanese pragmatic habits and expressions is a difficulty in machine translation research. In terms of word order, Chinese is basically "subject→predicate→object". At the same time, Chinese pays attention to parataxis, and there is no need to be clear in expression with meaningful cohesion, which increases the difficulty of Chinese Japanese machine translation. When there are multiple verbs or modifier components in complex long sentences, machine translation usually can not accurately divide the components of the sentence, resulting in the result that the translation is completely unreadable. <br />
(2) Difficulties of Japanese in machine translation <br />
Both Chinese and Japanese languages use Chinese characters, and most machines produce the target language through the corresponding translation of Chinese characters. However, pseudonyms in Japanese play an important role in judging the part of speech and meaning, and can not only recognize Chinese characters and judge the structure and semantics of sentences. Japanese vocabulary is composed of Chinese vocabulary, inherent vocabulary and foreign vocabulary. Among them, the first two have a great impact on Chinese Japanese machine translation. For example "提出" is translated as "提出する" or "打ち出す"; and "重视" is translated as "重視する" or "大切にする". Japanese is an adhesive language, which contains a lot of "けど", "が" and "けれども" Some of them are transitional and progressive structures, but there are also some sequential expressions that do not need translation, and these translation software often can not accurately grasp them.<br />
<br />
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[13]张义.机器翻译的译文分析【D】.西安外国语大学.2019(10) <br />
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[14]张琳婧.在线机器翻译中日翻译错误原因及对策【D】.山西大学.2019(02)<br />
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[15]王丹.基于机器翻译的专利文本译后编辑对策研究【D】.大连理工大学.2020(06)<br />
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[16]杨晓琨.日中机器翻译中的前编辑规则与效果验证【D】.大连理工大学.2020(06)<br />
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[17]左嘉. 机器翻译日译汉误译研究[D]. 北京第二外国语学院, 2021.<br />
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[18]关碧莹.关于政治类发言的汉日机器翻译误译分析[D].哈尔滨理工大学, 2018.<br />
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[19]车彤.汉译日机器翻译质量评估及译后编辑策略研究【D】.北京外国语大学.2021(09)<br />
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Networking Linking<br />
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http://www.elecfans.com/rengongzhineng/692245.html<br />
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https://baike.baidu.com/item/%E6%9C%BA%E5%99%A8%E7%BF%BB%E8%AF%91/411793<br />
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=13 陈湘琼Chen Xiangqiong（Study on Post-editing from the Perspective of Functional Equivalence Theory ）=<br />
[[Machine_Trans_EN_13]]<br />
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=14 Bi bi Nadia（Machine Translation a Challenge for Human Translators)=<br />
[[Machine_Trans_EN_14]]<br />
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===Abstract===<br />
Machine translation is a big obstacle in the way of Human translators or interpretors although it is quick and less time consuming.people are trying to get translation of their target language using through source language.For this purpose they are using digital apps like Google translation Google translation does not give accurate and exact interpretation.Google translator is translates word to word translate that doesn't clarifies it's true and actual meaning.On the other hand human translators can give exact and accurate translation ,they take care of grammatical errors , diction and sentence structure.They clarify the purpose of target language through using source language.<br />
===Keywords===<br />
Descriptive translation, academic, interdiscipline, comparative literature, localization,Translatology,school of thought, translation , studies, linguistics, corresponding<br />
===Body of article===<br />
Translation is the process of reworking text from one language into another to maintain the original message and communication. But, like everything else, there are different methods of translation, and they vary in form and function. What is translation? The term has become widely used among knowledge transfer researchers and practitioners, especially in the fields of health and health care. In a landmark review, Jonathan Lomas began to argue that 'The tasks… may be defined as to establish and maintain links between researchers and their audience, via the appropriate translation of research findings' (Lomas 1997, p 4). In 2004, the World Health Organization's World Report on Knowledge for Better Health suggested that 'One of the key contributions of research to health systems is the translation of knowledge into actions' (WHO 2004, p 33 and p 100). By 2006, special issues of WHO's Bulletin as well as the journals Evaluation and the Health Professions and the Journal of Continuing Education in the Health Professions were dedicated to translation.But what does 'translation' mean? It may be a new word for an old problem, meaning nothing more than 'transfer'. The rapidly emerging field of 'translational medicine' seems to take translation to mean generally what transfer might have meant, that is the transmission of knowledge and evidence 'from bench to bedside'. As one commentator has observed, "Translational medicine" as a fashionable term is being increasingly used to describe the wish of biomedical researchers to ultimately help patients' (Wehling 2008, abstract). <br />
Semantic uncertainty persists, not least because of the different interests of scientists, clinicians, patients and commercial firms (Littman et al 2007): 'Translational research means different things to different people, but it seems important to almost everyone' (Woolf 2008, p 211).<br />
In related fields, such as public health (Armstrong et al 2006), 'translation' seems to signify dissatisfaction with 'transfer'. It wants to move away from thinking of knowledge transfer as a form of technology transfer or dissemination, rejecting if only by implication its mechanistic assumptions and its model of linear messaging from A to B. But still, what does it signify?<br />
<br />
===Why translation?===<br />
'Translation' indicates a closer attention to the problem of shared meaning and how it might be developed. It seems to represent some new epistemological lubricant, facilitating the dissemination of texts and the application and use of the knowledge and information they in. Simply, translation might be the key to transfer. And yet, when we stop to think, we are more ambivalent. What is translated often seems somehow inferior, not real or original. Note how readily commentators reach for the idea that things might be 'lost in translation'. Knowing at a distance – made in and mediated by translation - makes for incomplete renditions, blurred images, partial truths. So what might 'translation' really mean? The purpose of this paper is to set out, for policy makers and practitioners, the theoretical and conceptual resources translation holds and seems to represent. In doing so, it explores understandings of translation in the fields of literature and linguistics and in the sociology of science and technology. It begins by setting out just why this idea of translation should make immediate, intuitive sense in relation to research, policy and practice.<br />
1translation in research, policy and practice research as translation<br />
Research often entails translation from one language to another: where data is collected from more than one ethnic group, for example, or where the language of the researcher is other than that of the research subject. It may draw on a secondary literature or source documents written in different languages, and may be published and disseminated in languages other than the one in which it is first written up.<br />
2In a different way, to conduct an interview is to ask for an account of experience and its meanings, but it is also to construct and translate that experience in terms defined at least in part by the researcher. In representing what is said, transcripts then select data, usually excluding significant gesture and eye-contact, for example. Often, certain characteristics of speech-acts (such as hesitations) will be edited out. In turn, the format of the transcript shapes the analytic use the researcher may make of it. The basis of research 'findings', then, is an artefact, a transcript or translation, not an original interaction (Ochs 1979, Barnes, Bloor and Henry 1996, Ross 2009).<br />
3In this way, the researcher recasts aspects of his or her problem or topic in new, scientific form: 'All researchers "translate" the experiences of others' (Temple 1997, p 609). Research is invariably conducted in a sort of 'metalanguage' (Hantrais and Ager 1985): the research process can be conceived as one of successive translations (from theoretical formulation to operationalization, transcription, interpretation and dissemination). Theorization is a process of reciprocal back and forth between theory and fact, in which conceptions of each are revised in order that one fit the other (Baldamus 1974).<br />
4 It is a kind of translation: a rereading, re-use, re-application or re-representation of what we know in new terms (Turner 1980). Referencing, too, is an act of translation, a form of appropriation and incorporation of one text by another (Gilbert 1977).policy as translation Each of the fields covered by the paper is diverse and ill-defined, and there is no intention here to provide a comprehensive account of any them. Sources have been chosen for their relevance: main references are cited in the text, and additional sources listed in footnotes.<br />
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2For a brief introduction to the technical issues involved in social research in more than one language, see Birbili (2000). For translation issues in survey research and question design in general, see Ervin and Bower (1952) and Deutscher (1968); on translating survey research instruments (in this instance health-related quality of life measures), Bowden and Fox-Rushby (2003). On the use of translators and interpreters, see Temple (1997) and Jentsch (1998).<br />
3For an interesting discussion of this problem, see Bourdieu (1999), esp pp 621-626, 'The risks of writing'. <br />
===Difference between machine translation and human translators===<br />
Few people disagree on the differences between the two, but many argue over the quality of the translations. How accurate are machine translations? How reliable are human translations? Some say machine translation produces near-perfect translations while others are adamant that translations are incomprehensible and cause more problems than they solve. Results will, of course, vary depending on the source and target languages, the machine translation service used (e.g. Google Translate), and the complexity of the original text.<br />
Machine translation, love it or hate it, is here to stay. In fact, the machine translation market is growing at such a fast pace that it is predicted to reach $980 million by 2022.<br />
===Machine translation: the pros and cons===<br />
The advantages of machine translation generally come down to two factors: it’s faster and cheaper. The downside to this is the standard of translation can be anywhere from inaccurate, to incomprehensible, and potentially dangerous (more on that shortly).<br />
==The advantages of machine translation==<br />
Many free tools are readily available (Google Translate, Skype Translator, etc.)<br />
Quick turnaround time <br />
You can translate between multiple languages using one tool<br />
Translation technology is constantly improving<br />
The disadvantages of machine translation<br />
Level of accuracy can be very low <br />
Accuracy is also very inconsistent across different languages<br />
==Machines can't translate context ==<br />
Mistakes are sometimes costly <br />
Sometimes translation simply doesn’t work<br />
The most important thing to consider with any kind of translation is the cost of potential mistakes. Translating instructions for medical equipment, aviation manuals, legal documents and many other kinds of content require 100% accuracy. In such cases, mistakes can cost lives, huge amounts of money and irreparable damage to your company’s image. So choose carefully!<br />
Human translation: the pros and cons<br />
Human translation essentially switches the table in terms of pros and cons. A higher standard of accuracy comes at the price of longer turnaround times and higher costs. What you have to decide is whether that initial investment outweighs the potential cost of mistakes. Alternatively, whether mistakes simply aren’t an option, like the cases we looked at in the previous section.<br />
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==The advantages of human translation ==<br />
It’s a translator’s job to ensure the highest accuracy<br />
Humans can interpret context and capture the same meaning, rather than simply translating words<br />
Human translators can review their work and provide a quality process<br />
Humans can interpret the creative use of language, e.g. puns, metaphors, slogans, etc.<br />
Professional translators understand the idiomatic differences between their languages<br />
Humans can spot pieces of content where literal translation isn’t possible and find the most suitable alternative<br />
The disadvantages of human translation<br />
Turnaround time is longer <br />
Translators rarely work for free <br />
Unless you use a translation agency, with access to thousands of translators, you’re limited to the languages any one translator can work with<br />
Simply put, human translation is your best option when accuracy is even remotely important. Other considerations to make are the complexity of your source material and the two languages you’re translating between – both of which can render machines pretty useless.<br />
<br />
When to use machine and human translation<br />
The truth is, the debate over machine vs human translation is an unnecessary distraction. What we should really be talking about is when to use these two different types of translation services, because they both serve a very valid purpose.<br />
<br />
Examples of when to use machine translation<br />
When you have a large bulk of content to translate and the general meaning is enough<br />
When your translation never reaches the final audience, e.g. you’re translating a resource as research for another piece of content<br />
Translating documents for internal use within a company, provided 100% accuracy isn’t needed<br />
To partially translate large chunks of content for a human translator to improve upon<br />
Examples of when to use human translation<br />
When accuracy is important<br />
Most cases where your translated content is received by a consumer audience<br />
When you have a duty of care to provide accurate translations (e.g. legal documents, product instructions, medical guidelines or health and safety content)<br />
When translating marketing material or other texts for creative language uses.<br />
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===Conclusion ===<br />
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In the light of above mentioned facts and figures here by we would say that machine translation is challenge for human translators because it can reduces the wokload of translation but can't give accurate and exact translation of the traget language.It can be less reliable than human translation..</div>Chen Huinihttps://bou.de/u/index.php?title=Machine_translation&diff=129248Machine translation2021-12-06T02:31:49Z<p>Chen Huini: /* 1.2 Definition of Pre-editing */</p>
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<div>'''Machine Translation - A challenge or a chance for human translators?'''<br />
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[[Machine_translation|Overview Page of Machine Translation]]<br />
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=1 卫怡雯(A Comparison Between the Quality of Machine Translation and Human Translation——A Case Study of the Application of artificial intelligence in Sports Events)=<br />
[[Machine_Trans_EN_1]]<br />
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=2 吴映红（The Introduction of Machine Translation)= <br />
[[Machine_Trans_EN_2]]<br />
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=3 肖毅瑶(On the Realm Advantages And Symbiotic Development of Machine Translation And Huamn Translation)=<br />
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=4 王李菲 （Comparison Between Neural Machine Translation of Netease and Traditional Human Translation—A Case Study of The Economist Articles)= <br />
[[Machine_Trans_EN_4]]<br />
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===Abstract===<br />
Machine translation is a subfield of artificial intelligence and natural language processing that investigates transforming the source language into the target language. On this basis, the emergence of neural machine translation, a new method based on sequence-to-sequence model, improves the quality and accuracy of translation to a new level. As one of the earliest companies to invest in machine translation in China, Netease launched neural machine translation in 2017, which adopts the unique structure of neural network to encode sentences, imitating the working mechanism of human brain, and generates a translation that is more professional and more in line with the target language context. This paper takes the articles in The Economist as the corpus for analysis, and aims to explore the types and causes of common errors, as well as the advantages and challenges of each, through the comparative analysis of Netease neural machine translation and human translation, and finally to forecast the future development trend and make a summary of this paper.<br />
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===Key words===<br />
Neural Machine Translation; Human Translation; Contrastive Analysis<br />
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===题目===<br />
有道神经网络机器翻译与传统人工翻译的译文对比——以经济学人语料为例<br />
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===摘要===<br />
机器翻译研究将源语言所表达的语义自动转换为目标语言的相同语义，是人工智能和自然语言处理的重要研究分区。在此基础上，一种基于序列到序列模型的全新机器翻译方法——神经机器翻译的出现让译文的质量和准确度提升到了新的层次。网易作为国内最早投身机器翻译的公司之一，在2017年上线的神经网络翻译采用了独到的神经网络结构，模仿人脑的工作机制对句子进行编码，生成的译文更具专业性，也更符合目的语语境。本文以经济学人内的文章为分析语料，旨在通过对网易神经机器翻译和人工翻译的英汉译文进行对比分析，探究常见错误类型及生成原因，以及各自存在的优势与挑战，最后展望未来发展趋势，并对本文做出总结。 <br />
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===关键词===<br />
神经网络翻译；人工翻译；对比分析<br />
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===1. Introduction===<br />
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Nowadays, the process of economic globalization has accelerated overwhelmingly, and considerable resources are poured into the business field. As a branch of global language English, business English is proposed under the theoretical framework of English for Specific Purpose (ESP), serves the international business activities which is a professional subject requiring specialized English. As the medium that helps people with different cultural backgrounds to understand each other, business translation is required to be “formal, accurate, standardized and smooth”, which challenged both the machine translation and human translator.<br />
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With the urgent requirement for more precise and higher quality translation, recent years have witnessed the rapid development of neural machine translation (NMT), which has replaced traditional statistical machine translation (SMT) to become a new mainstream technique, playing a crucial part in many fields, like business, academic and industry. Compared with SMT, NMT model is more like an organism. There are many parameters in the model that can be adjusted and optimized for the same goal, making the combination and interaction more organic and the overall translation effect better, which greatly matched the demands of business translation. <br />
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The Economist is an international news and Business Weekly offering clear coverage, commentary and analysis of global politics, business, finance, science and technology. A huge number of terminologies plus the polysemy contained in the texts, put forward a tricky problem to both machine translation and human translator. In view of this, this paper makes a comparative analysis of Netease neural machine translation and human translation, aiming to explore the types and causes of common errors, as well as the advantages and challenges of each. In the end, this paper will forecast the future development, hoping to promote the development of translation studies in China.<br />
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===2.2. The Development Process of Machine Translation ===<br />
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Since the IBM model was put forward by the researcher Peter Brown in the early 1990s, statistical methods have gradually become the mainstream of machine translation research. This method has greatly promoted the development of machine translation technology. In recent years, a variety of statistical machine translation models have emerged, such as phrase-based translation model, hierarchical phrase translation model and syntactic translation model, then the translation quality has been greatly improved.<br />
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Since 2002, BLEU, an automatic translation quality evaluation method, has greatly promoted the development of statistical machine translation technology and effectively reduced the cost of manual evaluation. In recent years, with the technical maturity and stability of statistical machine translation, especially phrase-based machine translation, statistical machine translation technology has been making strong strides towards practical and commercial application. Therefore, with the rapid development of technology, people have gradually built-up confidence in machine translation, and the social demand for machine translation has been increasing day by day, with higher and higher expectations.<br />
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However, from the perspective of academic research, both phrase-based translation models and syntactic translation models have experienced a rapid development stage, and the existing theoretical methods and technical models have begun to show "bottlenecks" in the improvement of translation performance. In addition, from the perspective of industrialization and utilitarianism, there is an urgent need for a more practical machine translation system, but the gap between the results of machine translation and the requirements of human beings is still very large. Therefore, for the researchers of machine translation, while excited to see the BLEU score of machine translation system evaluation is getting higher and higher, and the performance of online machine translation system developed by Google, Baidu, Netease and other enterprises is developing with each passing day, they are facing more and more challenges.<br />
<br />
Aiming to solve these problems, many technological giants are striving to find a new way to improve both the quality and efficiency of machine translation. There was a breakthrough which bought machine translation to a new level. Since 2014, the end-to-end neural machine translation has developed rapidly, compared with the statistical machine translation, the translation quality received a significant boost.<br />
<br />
The previous statistical machine translation was more like a mechanical system. Each module has its own function and goal, and then outputs the translation results through mechanical splicing. Its main disadvantage is that the model contains low syntactic and semantic components, so it will encounter problems when dealing with languages with large syntactic differences, such as Chinese-English. Sometimes the result is unreadable even though it is “word-for-word”.<br />
On the contrary, neural machine translation are consisted of several components, including phrase conditions, partial conditions, sequential conditions, primitive models, and so on. Its core is deep learning of artificial intelligence which can imitate the working mechanism of human brain and adopt unique neural network structure to model the whole process of translation. The whole model is composed of a large number of “neurons”, and each “neuron” has to complete some simple tasks, and then through the combination of all of them to coordinate the work, a much better translation text appears. <br />
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Since neural machine translation puts more emphasis on context and the whole text, it produces more coherent and comprehensible content to readers than traditional statistical machine translation, and be widely accepted and used in various field in a very short time. In 2017, at the GMIC (Global Mobile Internet Congress), Duan Yitao, the chief scientist of Netease, delivered a keynote speech titled “Machine Translation has Its Own Way” and announced an exciting news: the neural machine translation technology independently developed by Netease has been officially launched. This technology launched by Youdao this time has been jointly developed by Netease Youdao and Netease Hangzhou Research Institute for over two years. It will serve Youdao Dictionary, Youdao Translator, Youdao Web version, Youdao E-reader and other products, expecting to bring super-convenient product experience to users. In addition, Youdao Translation officer also launched photo translation, users only need to take pictures of the text, can show the results of neural network translation in real time. <br />
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As a pioneer of machine translation in China, the development process of Netease YouDao is exactly the paradigm of the history of machine translation in China. Therefore, in this paper, the neural machine translation technology developed by Netease will be compared with human translators. The same excerpts selected from The Economist are translated by both of them, then the different versions will be analyzed by the translation criterion so as to figure out their respective strengths and weaknesses, bringing consideration to current translation situation and references to future development.<br />
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===3.Comparative Analysis of Errors in English-Chinese Translation ===<br />
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===4.===<br />
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===5. ===<br />
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===Conclusion===<br />
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===References===<br />
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=6 徐敏赟(Machine Translation Based on Neural Network --Challenge or Chance?)=<br />
[[Machine_Trans_EN_6]]<br />
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=7 颜莉莉(一带一路背景下人工智能与翻译人才的培养)=<br />
[[Machine_Trans_EN_7]]<br />
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===Abstract===<br />
In the era of artificial intelligence, artificial intelligence has been applied to various fields. In the field of translation, traditional translation models can no longer meet the rapid development and updating of the information age. The development of machine translation has brought structural changes to the language service industry, which poses challenges to the cultivation of translation talents. Under the background of "The Belt and Road initiative", translation talents have higher and higher requirements on translation literacy. Artificial intelligence and translation technology are used to reform the training mode of translation talents, so as to better serve the development of The Times. This paper mainly explores the cultivation of artificial intelligence and translation talents under the background of the Belt and Road Initiative. The cultivation of translation talents is moving towards comprehensive cultivation of talents. On the contrary, artificial intelligence and machine translation can also be used to improve the teaching mode and teaching content, so as to win together in cooperation.<br />
===Key words===<br />
Artificial intelligence,Machine translation,cultivation of translation talents,"The Belt and Road initiative"<br />
===题目===<br />
一带一路背景下人工智能与翻译人才的培养<br />
<br />
===摘要===<br />
进入人工智能时代，人工智能被应用于各个领域。在翻译领域，传统的翻译模式已无法满足信息化时代的飞速发展和更新，机器翻译的发展给语言服务行业带来了结构性改变，这对翻译人才的培养提出了挑战。“一带一路”背景下，对翻译人才的翻译素养要求越来越高，利用人工智能和翻译技术对翻译人才培养模式进行革新，更好为时代发展服务。本文主要探究在一带一路背景下人工智能和翻译人才培养，翻译人才的培养过程中正向对人才的综合性培养，反之也可以利用人工智能和机器翻译完善教学模式和教学内容，在合作中共赢。<br />
===关键词===<br />
人工智能；机器翻译；翻译人才培养；一带一路<br />
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===1. Introduction===<br />
With the development of science and technology in China, artificial intelligence has also been greatly improved, and related technologies have been applied to various fields, such as the use of intelligent robots to deliver food to quarantined people during the epidemic, which has made people's lives more convenient. The most controversial and widely discussed issue is machine translation. Before the emergence of machine translation, translation was generally dominated by human translation, including translation and interpretation, which was divided into simultaneous interpretation and hand transmission, etc. It takes a lot of time and energy to cultivate a translation talent. However, nowadays, the era is developing rapidly and information is updated rapidly. As a translation talent, it is necessary to constantly update its knowledge reserve to keep up with the pace of The Times. The emergence of machine translation has also posed challenges to translation talents and the training of translation talents. Although machine translation had some problems in the early stage, it is now constantly improving its functions. In the context of the belt and Road Initiative, both machine translation and human translation are facing difficulties. Regardless of whether human translation is still needed, what is more important at present is how to train translators to adapt to difficulties and promote the cooperation between human translation and machine translation.<br />
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===2.Development status of machine translation in the era of artificial intelligence ===<br />
With the development of AI technology, machine translation has made great progress and has been applied to people's lives. For example, more and more tourists choose to download translation software when traveling abroad, which makes machine translation take an absolute advantage in daily email reply and other translation activities that do not require high accuracy. The translation software commonly used by netizens include Google Translation, Baidu Translation, Youdao Translation, IFly.com Translation, etc. Even wechat and other chat software can also carry out instant Translation into English. Some companies have also launched translation pens, translation machines and other equipment, which enables even native speakers to rely on machine translation to carry out basic communication with other Chinese people.<br />
But so far, machine translation still faces huge problems. Although machine translation has made great progress, it is highly dependent on corpus and other big data matching. It does not reach the thinking level of human brain, and cannot deal with the problem of translation differences caused by culture and religion. In addition, many minor languages cannot be translated by machine due to lack of corpus.<br />
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What's more, most of the corpus is about developed countries such as Britain and France, and most of the corpus is about diplomacy, politics, science and technology, etc., while there are very few about nationality, culture, religion, etc.<br />
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In addition, machine translation can only be used for daily communication at present. If it involves important occasions such as large conferences and international affairs, it is impossible to risk using machine translation for translation work. Professional translators are required to carry out translation work. So machine translation still has a long way to go.<br />
<br />
===3.Challenges in the training of translation talents in universities===<br />
The cultivation of translators is targeted at the market. Professors Zhu Yifan and Guan Xinchao from the School of Foreign Languages at Shanghai Jiao Tong University believe that the cultivation of translators can be divided into four types: high-end translators and interpreters, senior translators and researchers, compound translators and applied translators.<br />
<br />
From their names, it can be seen that high-end translators and interpreters and senior translators and researchers talents have high requirements on the knowledge and quality of interpreters, because they have to face the changing international situation, and have to deal with all kinds of sensitive relations and political related content, they should have flexible cross-cultural communication skills. In addition, for literature, sociology and humanities academic works, it is not only necessary to translate their content, but also to understand their essence. Therefore, translators should not only have humanistic feelings, but also need to have a deep understanding of Chinese and western culture.<br />
<br />
However, there is not much demand for this kind of translation in the society. Such high-level translation requirements are not needed in daily life and work. The greatest demand is for compound translators, which means that they should master knowledge in a specific field while mastering a foreign language. For example, compound translators in the financial field should not only be good at foreign languages, but also master financial knowledge, including professional terms, special expressions and sentence patterns.<br />
<br />
Now we say that machine translation can replace human translation should refer to the field of compound translation talents. Although AI technology has enabled machine translation to participate in creation, it does not mean that compound translation talents will be replaced by machines. The complexity of language and the flexible cross-cultural awareness required in communication make it impossible for machine translation to completely replace human translation.<br />
<br />
The last type of applied translation talents are mostly involved in the general text without too much technical content and few professional terms, so it is easy to be replaced by machine translation.<br />
<br />
Therefore, the author thinks that what universities are facing at present is not only how to train translation talents to cope with the development of machine translation, but to consider the application of machine translation in the process of training translation talents to achieve human-machine integration, so as to better complete the translation work.<br />
<br />
===4.The Language environment and opportunities and challenges of the Belt and Road initiative===<br />
During visits to Central and Southeast Asian countries in September and October 2013, Chinese President Xi Jinping put forward the major initiative of jointly building the Silk Road Economic Belt and the 21st Century Maritime Silk Road. And began to be abbreviated as the Belt and Road Initiative.<br />
<br />
According to the Vision and Actions for Jointly Building silk Road Economic Belt and 21st Century Maritime Silk Road, the Silk Road Economic Belt focuses on connecting China, Central Asia, Russia and Europe (the Baltic Sea). From China to the Persian Gulf and the Mediterranean Sea via Central and West Asia; China to Southeast Asia, South Asia, Indian Ocean. The focus of the 21st Century Maritime Silk Road is to stretch from China's coastal ports to Europe, through the South China Sea and the Indian Ocean. From China's coastal ports across the South China Sea to the South Pacific.<br />
<br />
The Belt and Road "construction is comply with the world multi-polarization and economic globalization, cultural diversity, the initiative of social informatization tide, drive along the countries achieve economic policy coordination, to carry out a wider range, higher level, the deeper regional cooperation and jointly create open, inclusive and balanced, pratt &whitney regional economic cooperation framework.<br />
<br />
====4.1The language environment of the Belt and Road====<br />
The "Belt and Road" involves a wide range of countries and regions, and their languages and cultures are very complex. How to make good use of language, do a good job in translation services, actively spread Chinese culture to the world, strengthen the ability of discourse, and tell Chinese stories well, the first thing to do is to understand the language situation of the countries along the "Belt and Road".<br />
<br />
=====4.1.1The most common language in countries along the "Belt and Road" =====<br />
<br />
There are a wide variety of languages spoken in 65 countries along the Belt and Road, involving nine language families. However, The status of English as the first language in the world is undeniable. Most of the countries participating in the Belt and Road are developing countries, and many of them speak English as their first foreign language. Especially in southeast Asian and South Asian countries, English plays an important role in foreign communication, whether as the official language or the first foreign language. Besides English, more than 100 million people speak Russian, Hindi, Bengali, Arabic and other major languages in the "Belt and Road" countries. It can also be seen that a common feature of languages in countries along the "Belt and Road" is the popularization of English education. English is widely used in international politics, economy, culture, education, science and technology, playing the role of the most important language in the world.<br />
<br />
=====4.1.2The complex language conditions of countries along the "Belt and Road" =====<br />
<br />
The languages spoken in countries along the Belt and Road involve nine major language families and almost all the world's religious types. Differences in religious beliefs also result in differences in culture, customs and social values behind languages. The languages of some countries along the belt and Road have also been influenced by historical and realistic factors, such as colonization, internal division and immigration. <br />
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India, for example, has no national language, but more than 20 official languages. India is a multi-ethnic country, a total of more than 100 people, one of the most obvious difference between nation and nation is the language problem. Therefore, according to the difference of language, India divides different ethnic groups into different states, big and small. Ethnic groups that use the same language are divided into one state. If there are two languages in a state, the state is divided into two parts. And Indian languages differ not only in word order but also in the way they are written. In India, for example, Hindi is spoken by the largest number of people in the north, with about 700 million speakers and 530 million as their first language. It is written in The Hindu language and belongs to the Indo-European language family. Telugu in the east is spoken by about 95 million people and 81.13 million as their first language. It is written in Telugu, which belongs to the Dravidian language family and is quite different from Hindi. As a result, a parliamentary session in India requires dozens of interpreters. <br />
<br />
These factors cannot be ignored in the process of translation, from language communication to cultural understanding, from text to thought exchange, through the bridge of language to truly connect the people, so as to avoid misreading and misunderstanding caused by differences in language and national conditions.<br />
<br />
====4.2 Opportunities and challenges of the "Belt and Road" ====<br />
With the promotion of the Belt and Road Initiative, there has been an unprecedented boom in translation. In the previous translation boom in China, most of the foreign languages were translated into Chinese, and most of the foreign cultures were imported into China. However, this time, in the context of the "Belt and Road" initiative, translating Chinese into foreign languages has become an important task for translators. As is known to all, there are many different kinds of "One Belt And One Road" along the national language and culture is complex, the service "area" construction has become a factor in Chinese translation talents training mode reform, one of the foreign language universities have action, many colleges and universities to establish the "area" all the way along the country's small language major, as a result, "One Belt And One Road" initiative to promote, It has brought unprecedented opportunities for human translation. The cultivation of diversified translation talents and the cultivation of translation talents in small languages is an urgent problem to be solved in China. The cultivation of translation talents cannot be completed overnight, and the state needs to reform the training mode of translation talents from the perspective of language strategic development. Only in this way can we meet the new demand for human translation under the new situation of the belt and Road Initiative.<br />
<br />
For a long time, the traditional orientation of translation curriculum and training goal in colleges and universities is to train translation teachers and translators in need of society through translation theory and practice and literary translation practice, which cannot meet the needs of society. Since 2007, in order to meet the needs of the socialist market economy for application-oriented high-level professionals, the Academic Degrees Committee of The State Council approved the establishment of Master of Translation and Interpreting (MTI for short). After joining the pilot program of MTI, more and more universities are reforming the curriculum and training mode of master of Translation in order to cultivate translators who meet the needs of the society.<br />
<br />
Language is an important carrier of culture, and translation is an important link for exporting culture. The quality of translation output also reflects the cultural soft power of a country. With the rise of China, more and more people are interested in Chinese culture, and the number of Chinese learners keeps increasing. Under the background of "One Belt and One Road", excellent translators are urgently needed to spread Chinese culture. With the promotion of "One Belt and One Road" Initiative, the number of other countries learning mutual learning and cultural exchanges with China has increased unprecedeningly, bringing vigorous opportunities for the spread of Chinese culture. Translation talents who understand small languages and multi-lingual translators are needed. They should not only use language to convey information, but also use language as a lubricant for communication.<br />
<br />
===5.Training translation talents from the perspective of machine translation===<br />
Under the prevailing environment of machine translation, it poses a great challenge to the cultivation of translation talents. According to the current situation, translation needs and the shortage of translation talents, colleges and universities should reform and innovate the existing training programs for translation talents in terms of the quality of translation talents, the reform of training mode and the use of artificial intelligence. Based on the obtained data and literature, the author discusses how to train translation talents in the perspective of machine translation from the following aspects.<br />
<br />
====5.1 Quality requirements for translation talents ====<br />
Zhong Weihe and Murray made a more detailed and profound discussion on translator's literacy, believing that "translators should not only be proficient in two languages, but also have extensive cultural and encyclopedic knowledge and relevant professional knowledge; Master a variety of translation skills, a lot of translation practice; Have a clear translator role awareness, good professional ethics, practical and enterprising style of work, conscious team spirit and calm psychological quality ". According to the collected data, the author will elaborate the requirements for translation talents from four aspects: language literacy, humanistic literacy, translation ability and innovation ability.<br />
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The first is language literacy, which is the most basic and important requirement. MAO Dun pointed out that "mastery of mother tongue and target language are the foundation of translation". A solid foundation of bilingual skills is the basic skills of translators. Poor language proficiency seems to be a common problem among students majoring in translation and interpreting. Many translation diseases are caused by poor Chinese foundation. As part of going global, the belt and Road initiative is to tell Chinese culture and Chinese stories, which requires translators to be able to use both languages flexibly. Therefore, the first problem that colleges and universities face to solve is to improve the language level of foreign language learners.<br />
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The second is humanistic literacy. Humanistic literacy is mainly manifested by a good command of politics, economy, history, literature and other knowledge, which is particularly important for interpreters. In addition, cross-cultural communication cannot be ignored. In the process of communicating with foreigners or translating, translators often encounter the first cross-cultural contradiction. Cross-culture refers to having a full and correct understanding of cultural phenomena, customs and habits that differ or conflict with the national culture, and accepting and adapting to them in an inclusive manner on this basis. So the interpreter can first fully understand and master the national conditions and culture of the target country, which is particularly important in the "Belt and Road". There are more than 60 countries along the "Belt and Road", and it takes a lot of energy to master their national conditions and culture.<br />
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The third is translation ability. We should distinguish between translation ability and language ability. Translation ability is actually a system of knowledge and skills necessary for translation, the core of which is conversion ability. First of all, it reflects the ability to use tools to assist translation, such as computer application, translation technology and so on. In addition, interpreters should have enough healthy psychological quality and good professional quality. In terms of translation ability, the current training model of translation talents is inadequate.<br />
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The last one is innovation. The cultivation of learners' thinking ability is the key to translation teaching and the cultivation of thoughtful translators should be the connotation of translation teaching. Therefore, the interpreter is not only a translation tool, which is no different from machine translation. More importantly, it is necessary to explore translation with thoughts, have a sense of lifelong learning and innovation consciousness. Translators must constantly innovate themselves, learn new knowledge, and strive to seek reform and innovation. Many colleges and universities should also consciously cultivate students' innovation ability and broaden their thinking and vision.<br />
<br />
====5.2 The reform of college curriculum setting====<br />
First, we will further reform the curriculum of colleges and universities. Add economics, law and engineering to the curriculum, these contents in the "belt and Road".<br />
"One Road" is very important in the construction. According to the author's personal experience, the most typical problem of foreign language majors in colleges and universities is the single learning of foreign languages. More professional foreign language colleges and universities will add some literature courses and national conditions courses of the language target countries. Obviously, whether foreign language graduates are engaged in translation work or not, these knowledge is not enough. Of course, great reforms have been carried out in foreign language teaching, such as combining foreign language with finance, law, diplomacy and so on, and taking the way of minor training foreign language majors.<br />
<br />
Domestic enterprises with a high degree of internationalization attach great importance to translation. Their translation research includes cutting-edge theoretical and applied research, involving machine translation, natural language processing and AI theory, algorithm and model. With such a foundation, enterprises can solve problems by themselves, such as embedding automatic translation functions in mobile phones. International enterprises not only do technical translation, but also deal with all forms of translation and localization in society. At present, translation teaching in most colleges and universities is still in the early mode, and it is an objective fact that it is divorced from the workplace and has a gap with the needs of enterprises.<br />
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Second, we should adjust and strengthen the construction of second foreign language teaching for foreign language majors. In the 1980s, our country was in urgent need of Russian translation. At that time, students majoring in English could translate microelectronic product manuals and related business documents in English and Russian at the same time after learning Russian for half a year. The mutual conversion between English and Russian played a great role in practice. According to the author, in the Graduate Institute of Interpretation and Translation of Beijing Foreign Studies University a very few students majored in multiple languages at the graduate level, that is, they majored in minor languages at the undergraduate level and were admitted to the Graduate Institute of Interpretation and Translation in English. Their training mode is to study English in the Graduate Institute of Interpretation and Translation for two years and the third year in the corresponding department of the undergraduate major. Such training mode in my opinion is a bigger model, cost It's more difficult for students. <br />
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In addition, there is a great disparity in the development of second foreign language teaching in colleges and universities, and the overall level is not high enough. Part of the second foreign language university foreign language professional may still be too much focus in languages such as German, French and Japanese, should as far as possible, considering the need of the construction of the "region", like Croatia, Serbia, Turkish, Hungarian, Italian, Indonesian, Albanian, these are the countries along the "area" the language of the two countries, Colleges and universities should encourage the teaching of a second foreign language.<br />
<br />
Third, the teaching of translation technology should be strengthened. Traditional translation teaching teaches translation skills, such as the translation of words, sentences, texts and figures of speech. Translation technology refers to a series of practical workplace technologies with computer-aided translation software and translation project management as the core, which can greatly improve translation efficiency. However, due to the relative lack of translation technology teachers and equipment in colleges and universities, there is a disconnect between talent training and the requirements of translation technology in the translation field.<br />
<br />
====5.3 Application of artificial intelligence to translation teaching practice====<br />
In order to improve the teaching quality and train students' English translation ability, it is necessary to realize the effective integration of ARTIFICIAL intelligence and translation activity courses, which should not only reflect the effectiveness of artificial intelligence translation technology, but also help students establish a healthy concept of English communication. Through the application of artificial intelligence technology, students can strengthen their flexible translation skills through close communication with "AI program" during the learning stage of English translation activity class. For example, teachers can ask students to translate directly against the translation content provided on the translation screen of the ARTIFICIAL intelligence system. After that, the system can collect the translation answers with the help of speech recognition function, and then judge the accuracy of the translation content, thus providing important feedback to students.<br />
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China has used such artificial intelligence technology in the Putonghua test to ensure that every student can find a suitable translation method in practical communication. The so-called artificial intelligence technology is a new kind of technology modeled after the characteristics of human neural network thinking, can combine the human mind to respond. If it can be integrated into English translation activity teaching, it can not only improve the teaching efficiency, but also enhance students' enthusiasm in learning the course.<br />
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At the same time, during the training of translation talents, teachers also need to take into account the importance of influencing education factors, so that students can form a higher disciplinary quality in translation, so as to fit the concept of quality education in the new era. Only when artificial intelligence translation content is fully integrated into college English translation activity courses can the overall translation ability of college students be maximized.<br />
<br />
====5.4The improvement of translator's technical ability====<br />
In the previous part, the author roughly mentioned that translation teaching should be improved, which will be elaborated here. At present, only a few universities can make full use of the advantages of translation technology in translation teaching and focus on cultivating professional translation talents. Most universities still cannot get rid of the traditional teaching mode of "language + relevant professional knowledge" in translation teaching, and generally lack a correct understanding of COMPUTER-aided translation teaching.<br />
<br />
According to Wang Huashu et al., the courses that can be offered around the composition of translators' technical literacy include computer-assisted translation, translation and corpus, machine translation and post-translation editing, localization and internationalization, film and television translation (subtitle), technical communication and technical writing, and computer programming. The course modules involved are: Fundamentals of COMPUTER-aided Translation, CAT tool application, corpus alignment and processing, term management, QA technology for translation quality assurance, OFFICE fundamentals, translation management technology, basic computer knowledge, desktop typesetting, localization and internationalization, project management system and content management system, technical writing, basic knowledge of computer programming, basic knowledge of web code, etc.<br />
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<br />
===6针对一带一路的机器翻译与翻译人才的合作===<br />
<br />
===Conclusion===<br />
<br />
===References===<br />
<br />
=8 颜静(On Machine Translation Under Lanuguage Intelligence——An Option and Opportunity for Human Translators)=<br />
[[Machine_Trans_EN_8]]<br />
<br />
=9 谢佳芬（人工智能时代下的机器翻译与人工翻译）=<br />
[[Machine_Trans_EN_9]]<br />
===Abstract===<br />
With the continuous development of information technology, many industries are facing the competitive pressure of artificial intelligence, and so is the field of translation. Artificial intelligence technology has developed rapidly and combined with the field of translation，which has brought great impact and changes to traditional translation, but artificial intelligence translation and artificial translation have their own advantages and disadvantages. Artificial translation is in the leading position in adapting to human language logical habits and understanding characteristics, but in terms of translation threshold and economic value, the efficiency of artificial intelligence translation is even better. In a word, we need to know that machine translation and human translation are complementary rather than antagonistic.<br />
<br />
===Key Words===<br />
Machine Translation; Artificial Translation; Artificial Intelligence<br />
<br />
===题目===<br />
人工智能时代下的机器翻译与人工翻译<br />
<br />
===摘要===<br />
伴随着信息技术的不断发展，多个行业面临着人工智能的竞争压力，翻译领域也是如此。人工智能技术快速发展并与翻译领域结合，人工智能翻译给传统翻译带来了巨大的冲击和变革，但人工智能翻译与人工翻译存在着各自的优劣特点和发展空间，在适应人类语言逻辑习惯和理解特点的翻译效果上，人工翻译处于领先地位，但在翻译门槛和经济价值上，人工智能翻译的效率则更胜一筹。总的来说，我们要知道机器翻译与人工翻译是互补而非对立的关系。<br />
<br />
===关键词===<br />
机器翻译;人工翻译;人工智能<br />
<br />
===1. Introduction===<br />
====1.1 The History of Machine Translation Aborad====<br />
The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. Alchuni put forward the idea of using machines for translation. In 1933, the Soviet inventor Troyansky designed a machine to translate one language into another. [1]In 1946, the world's first modern electronic computer ENIAC was born. Soon after, American scientist Warren Weaver, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947. In 1949, Warren Weaver published a memorandum entitled Translation, which formally raised the issue of machine translation. In 1954, Georgetown University, with the cooperation of IBM, completed the English-Russian machine translation experiment with IBM-701 computer for the first time, which opened the prelude of machine translation research. [2] In 2006, Google translation was officially released as a free service software, bringing a big upsurge of statistical machine translation research. It was Franz Och who joined Google in 2004 and led Google translation. What’s more, it is precisely because of the unremitting efforts of generations of scientists that science fiction has been brought into reality step by step.<br />
====1.2 The History of Machine Translation in China====<br />
In 1956, the research and development of machine translation has been named in the scientific and technological work and made little achievements in China. On the eve of the tenth anniversary of the National Day in 1959, our country successfully carried out experiments, which translated nine different types of complicated sentences on large general-purpose electronic computers. The dictionary includes 2030 entries, and the grammar rule system consists of 29 circuit diagrams. [3]. After a period of stagnation, China's machine translation ushered in a high-speed development stage after the 1980s in the wave of the third scientific and technological revolution. With the rapid development of economy and science and technology, China has made a qualitative leap in the field of machine translation research with the pace of reform and opening up. In 1978, Institute of Scientific and Technological Information of China, Institute of Computing Technology and Institute of Linguistics carried out an English-Chinese translation experiment with 20 Metallurgical Title examples as the objects and achieved satisfactory results. Subsequently, they developed a JYE-I machine translation system, which based on 200 sentences from metallurgical documents. Its principles and methods were also widely used in the machine translation system developed in the future. In addition, the research achievements of machine translation in China during the 1980s and 1990s also include that Institute of Post and Telecommunication Sciences developed a machine translation system, C Retrieval and automatic typesetting system with good performance and strong practicability in October 1986; In 1988, ISTC launched the ISTIC-I English-Chinese Title System for the translation of applied literature of metallurgy, Information Research Institute of Railway developed an English-Chinese Title Recording machine translation system for railway documents; the Language Institute of the Academy of Social Sciences developed "Tianyu" English-Chinese machine translation system and Matr English-Chinese machine translation system developed by the computer department of National University of Defense Technology. After many explorations and studies, machine translation in China has gradually moved towards application, popularization and commercialization. China Software Technology Corporation launched "Yixing I" in 1988, marking China's machine translation system officially going to the market. After "Yixing", a series of machine translation systems such as Gaoli system in Beijing, Tongyi system in Tianjin and Langwei system in Shaanxi have also entered the public. In the 21st century, the development of a series of apps such as Kingsoft Powerword, Youdao translation and Baidu translation has greatly met the needs of ordinary users for translation. According to the working principle, machine translation has roughly experienced three stages: rule-based machine translation, statistics-based machine translation and deep learning based neural machine translation. [4] These three stages witnessed a leap in the quality of machine translation. Machine translation is more and more used in daily life and even the translation of some texts is almost comparable to artificial translation. In addition to text translation, voice translation, photo translation and other functions have also been listed, which provides great convenience for people's life. It is undeniable that machine translation has become the development trend of translation in the future.<br />
====1.3 The Status Quo of Machine Translation====<br />
In this big data era of information explosion, the prospect of machine translation is also bright. At present, the circular neural network system launched by Google has supported universal translation in more than 60 languages. Many Internet companies such as Microsoft Bing, Sogou, Tencent, Baidu and NetEase Youdao have also launched their own Internet free machine translation systems. [5] Users can obtain translation results free of charge by logging in to the corresponding websites. At present, the circular neural network translation system launched by Google can support real-time translation of more than 60 languages, and the domestic Baidu online machine translation system can also support real-time translation of 28 languages. These Internet online machine translation systems are suitable for a variety of terminal platforms such as mobile phone, PC, tablet and web and its functions are also quite diverse, supporting many translation forms, such as screen word selection, text scanning translation, photo translation, offline translation, web page translation and so on. Although its translation quality needs to be improved, it has been outstanding in the fields of daily dialogue, news translation and so on.<br />
===2. Advantages and Disadvantages of Machine Translation===<br />
Generally speaking, machine translation has the characteristics of high efficiency, low cost, accurate term translation and great development potential and etc. Machine translation is fast and efficient, this is something that artificial translation can’t catch up with. In addition, with the continuous emergence of all kinds of translation software in the market, compared with artificial translation, machine translation is cheap and sometimes even free, which greatly saves the economic cost and time for users with low translation quality requirements. What's more, compared with artificial translation, machine translation has a huge corpus, which makes the translation of some terms, especially the latest scientific and technological terms, more rapid and accurate. The accurate translation of these terms requires the translator to constantly learn, but learning needs a process, which has a certain test on the translator's learning ability and learning speed. In this regard, artificial translation has uncertainty and hysteretic nature. At the same time, with the progress of science and technology and the development of society, the function of machine translation will be more perfect and the quality of translation will be better.Today's machine translation tools and software are easy to carry, all you need to do is just to use the software and electronic dictionary in the mobile phone. There is no need to carry paper dictionaries and books for translation, which saves time and space. At the same time, machine translation covers many fields and is suitable for translation practice in different situations, such as academic, education, commercial trade, social networking, tourism, production technology, etc, it is also easy to deal with various professional terms. However, due to the limitation of translators' own knowledge, artificial translation is often limited to one or a few fields or industries. For example, it is difficult for an interpreter specializing in medical English to translate legal English.<br />
At the same time, machine translation also has its limitations. At first, machine can only operate word to word translation, which only plays the function and role of dictionary. Then, the application of syntax enables the process of sentence translation and it can be solved by using the direct translation method. When the original text and the target language are highly similar, it can be translated directly. For example, the original text "他是个老师." The target language is "he is a teacher ". With the increase of the structural complexity of the original text, the effect of machine translation is greatly reduced. Therefore, at the syntactic level, machine translation still stays in sentences with relatively simple structure. Meanwhile, the original text and the results of machine translation cannot be interchanged equally, indicating that English-Chinese translation has strong randomness, and is not rigorous and scientific enough. <br />
Nowadays, machine translation is highly dependent on parallel corpora, but the construction of parallel corpora is not perfect. At present, the resources of some mainstream languages such as Chinese and English are relatively rich, while the data collection of many small languages is not satisfactory. Moreover, the current corpus is mainly concentrated in the fields of government literature, science and technology, current affairs and news, while there is a serious lack of data in other fields, which can’t reflect the advantages of machine translation. At the same time, corpus construction lags behind. Some informative texts introducing the latest cutting-edge research results often spread the latest academic knowledge and use a large number of new professional terms, such as academic papers and teaching materials while the corpus often lacks the corresponding words of the target language, which makes machine translation powerless<br />
Besides, machine translation is not culturally sensitive. Human may never be able to program machines to understand and experience a particular culture. Different cultures have unique and different language systems, and machines do not have complexity to understand or recognize slang, jargon, puns and idioms. Therefore, their translation may not conform to cultural values and specific norms. This is also one of the challenges that the machine needs to overcome.[6] Artificial intelligence may have human abstract thinking ability in the future, but it is difficult to have image thinking ability including imagination and emotion. [7] Therefore, machine translation is often used in news, science and technology, patents, specifications and other text fields with the purpose of fact description, knowledge and information transmission. These words rarely involve emotional and cultural background. When translating expressive texts, the limitations of machine translation are exposed. The so-called expressive text refers to the text that pays attention to emotional expression and is full of imagination. Its main characteristics are subjectivity, emotion and imagination, such as novels, poetry, prose, art and so on. This kind of text attaches importance to the emotional expression of the author or character image, and uses a lot of metaphors, symbols and other expressions. Machine translation is difficult to catch up with artificial translation in this kind of text, it can only translate the main idea, lack of connotation and literary grace and it cannot have subjective feelings and rational analysis like human beings. In fact, it is not difficult to simulate the human brain, the difficulty is that it is impossible to learn from the rich social experience and life experience of excellent translators. In other words, machine translation lacks the personalization and creativity of human translation. It is this personalization and creativity that promote the development and evolution of language, and what machine translation can only output is mechanical "machine language".<br />
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===3.The Irreplaceability of Artificial Translation ===<br />
====3.1 Translation is Constrained by Context====<br />
At present, machine translation can help people deal with language communication in people's daily life and work, such as clothing, food, housing and transportation, but there is a big gap from the "faithfulness, expressiveness and elegance" emphasized by high-level translation. Language itself is art，which pays more attention to artistry than functionality, and the discipline of art is difficult to quantify and unify. Sometimes it is regular, rigorous, logical and clear, and sometimes it is random, free and logical. If it is translated by machine, it is difficult to grasp this degree. Sometimes, machine translation cannot connect words with contextual meaning. In many languages, the same word may have multiple completely unrelated meanings. In this case, context will have a great impact on word meaning, and the understanding of word meaning depends largely on the meaning read from context. Only human beings can combine words with context, determine their true meaning, and creatively adjust and modify the language to obtain a complete and accurate translation. This is undoubtedly very difficult for machine translation. Artificial translation can get rid of the constraints of the source language and translate the translation in line with the grammar, sentence patterns and word habits of the target language. In the process of translation, translators can use their own knowledge reserves to analyze the differences between the source language and the target language in thinking mode, cultural characteristics, social background, customs and habits, so as to translate a more accurate translation. Artificial translation can also add, delete, domesticate, modify and polish the translation according to the style, make up for the lack of culture, try to maintain the thought, artistic conception and charm of the original text and the style of the source language. In addition, translators can also judge and consider the words with multiple meanings or easy to produce ambiguity according to the context, so as to make the translation more clear and more accurate and improve the quality of the translation.<br />
<br />
<br />
===4. Discussion on the Relationship Between Machine Translation and Artificial Translation ===<br />
<br />
===5. Suggestions on the Combined Development of Machine Translation and Artificial Translation===<br />
<br />
===6. ===<br />
<br />
===7. ===<br />
<br />
===Conclusion===<br />
<br />
===References===<br />
<br />
=10 熊敏(Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts)=<br />
[[Machine_Trans_EN_10]]<br />
===Abstract===<br />
With the rapid development of information technology,machine translation technology emerged and is gradually becoming mature.In order to explore the ability of machine translation, I adopts two versions of translation, which are manual translation and machine translation(this paper uses Youdao translation) for different types of texts(according to Peter Newmark's types of text). The results are quite different in terms of quality and accuracy.<br />
<br />
===Key words===<br />
machine translation; manual translation; Newmark's type of texts<br />
<br />
===题目===<br />
Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts<br />
<br />
===摘要===<br />
随着信息技术的高速发展，机器翻译技术出现了，并且逐渐成熟。为了探究机器翻译的能力水平，本人根据纽马克的文本类型分类，选择了相应的译文类型，并且将其机器翻译的版本以及人工翻译的版本进行对比。就质量和准确度而言，译文的水平大相径庭。<br />
<br />
===关键词===<br />
机器翻译；人工翻译；纽马克文本类型<br />
<br />
===1. Introduction===<br />
====1.1.Introduction to machine translation====<br />
Machine translation, also known as computer translation is a technique to translating through machine translator, such as Google translation and Youdao translation. Machine translation is one of the branches of computational linguistics, ranging from computer science, statistics, information science and so on. Machine translation plays an important role in all aspects.<br />
Machine translation can be traced back to 1940s, when British engineer Booth and American engineer Weaver proposed using computer to translate and started to study machines used for translation. However in the 1960s, reports from ALPAC (Automated Language Processing Advisory Committee) showed studies on machine translation had stagnated for a decade. In the 1970s, with the advancement of computer, machine translation was back to track. In the last decades, machine translation has mainly developed into four stages: rule-based machine translation, statistic machine translation, example-based machine translation and neural machine translation.<br />
<br />
====1.2.Process of machine translation====<br />
The process of manual translation is different from that of machine translation. Here is the process of the former. (1) Understand source language. (2) Use target language to organize language. (3) Generate translation. Unlike manual translation, machine translation tends to analyze and code source language first, then look for related codes in corpus, and work out the code that represents target language, generating translation. But they share a common feature, which is that Lexicon, grammatical rules and syntactic structure are taken into consideration. This is one of the biggest challenges for machine translation.<br />
<br />
===2.Newmark’s type of texts===<br />
Peter Newmark divided texts into informative type, expressive text and vocative type according to the linguistic functions of various texts. <br />
====2.11Informative text====<br />
The core of the informative texts is the truth. It is to convey facts, information, knowledge and the like. The language style of the text is objective and logical. Reports, papers, scientific and technological textbooks are all attributed to informative texts.<br />
====2.2Expressive text====<br />
The core of the expressive text is the emotion. It is to express preferences, feelings, views and so on. The language style of it is subjective. Literary works, including fictions, poems and drama, autobiography and authoritative statements belong to expressive text.<br />
====2.3Vocative text====<br />
The core of the vocative text is readership. It is to call upon readers to act in the way intended by the text. So it is reader-oriented. Such texts advertisement, propaganda and notices are of vocative text.<br />
====2.4Study Method====<br />
Manual translations of the three texts are selected from authoritative versions and universally acknowledged. And machine translations of those come from Youdao Translator. And in this thesis I will compare and evaluate the two methods in word diction, sentence structure, word order and redundancy.<br />
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===7. ===<br />
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===Conclusion===<br />
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===References===<br />
<br />
=11 陈惠妮=(Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts)=<br />
[[Machine_Trans_EN_11]]<br />
===Abstract===<br />
At present, globalization is accelerating and the market demand for language services is rapidly increasing . Machine translation, as an important translation method, can greatly improve translation efficiency due to its low cost and high speed. However, because of the limitations of machine translation and the differences between Chinese and English language, machine translation is not accurate enough. In order to balance translation efficiency and translation quality, a great number of manual revisions in translation are required for the machine translating texts. Medical papers are specialized, special and purposeful, so it requires accurate,qualified and professional translation. However, the quality of translations by machine is inefficient to meet the high-quality requirements of medical papers translation. Therefore, the introduction of pre-editing can greatly improve the efficiency and quality of machine translation.<br />
<br />
===Key words===<br />
Pre-editing, Machine translation, Medical texts<br />
<br />
===题目===<br />
Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts<br />
<br />
===摘要===<br />
在全球化加速发展的今天，市场对语言服务的需求迅速增加。机器翻译作为一种重要的翻译途径，由于其成本低、速度快，可以大大提高翻译效率。然而，由于机器翻译的局限性以及中英文语言的差异，机器翻译的准确性不高。为了平衡翻译效率和翻译质量，机器翻译文本需要大量的手工修改。<br />
医学论文具有专业性、特殊性和目的性，要求其译文准确、合格、专业。然而，机器翻译的质量较低，无法满足医学论文对翻译的高质量要求。因此，译前编辑的引入可以大大提高机器翻译的效率和质量。<br />
<br />
===关键词===<br />
译前编辑；机器翻译；医学文本<br />
<br />
===1. Introduction===<br />
===1.1 Definition of Machine Translation===<br />
As Cronin(2013) revealed: "Translation is undergoing a revolutionary upheaval. The influence of digital technology and the Internet on translation is continuous, extensive and profound. From the popularity of automatic online translation applications, translation revolution is everywhere (Cronin,2013) However,the concept of machine translation was firstly proposed in the 1930s. Since 1940s, the machine translation technology has been evolving from rule-based machine translation (RBMT) to statistical machine translation (SMT), and to neural machine translation (NMT). Machine translation refers to the automatic translation of source language into target language by using a computer system. That is, machine translation refers to the automatic translation of text from one language into another natural language by computer software or other online translation webs. Machine translation is also defined as the process of “using a computer system to automatically translate text or speech from one natural language to another” according to the definition by ISO (Cui, 2014).O 'Brien (2002) defines it as "the behavior of modifying errors in machine translation to ensure that the target translation meets certain quality requirements". On the basic level, machine translation performs mechanical substitution of words in one language for words in another language, but that rarely produces a good translation, therefore, recognition of the whole phrases and their closest counterparts in the target language is needed. Not all words in one language have equivalents in another language, and many words have more than one meaning. A huge demand for translation is greatly needed in today’s global world, which creates new opportunities for the development of machine translation, attracts more and more attention and becomes one of the current research focuses.<br />
<br />
===1.2 Definition of Pre-editing===<br />
Pre-editing means to adjust and modify the source language to make it fit more with the characteristics of the machine translation software before putting the source language before the into machine translation, so as to improve the quality of the translation machine translation (Wei Changhong, 2008:93-94). Pre-editing is to modify the original text before putting it into machine translation software, in order to improve the recognition rate of machine translation, optimize the output quality of translated text and reduce the workload of post-editing. Because pre-translation editing only needs to be modified in one language, the operation is simpler than post-translation editing, which can realize the double improvement of quality and efficiency. A good pre-editing translation can help machine translation more smoothly, thus improving the machine readability and quality of the output translation. Pre-editing is mostly applied in the following situations: one is when the original text is of poor quality and the machine is difficult to recognize the meaning of the sentence, such as user generated content with poor readability and translatability (Gerlach et al, 2003:45-53); Documents that need to be published in multiple languages; Next is when the original text contains a lot of jargon; The last is the original text has a corresponding translation memory bank. If the original text is edited, it can better match the content of the translation memory bank. Pre-editing is a process of identifying problems. It requires to pre-edit the source texts before putting it into machine translation according to the requirements, listing the expressions or sentences that may have trouble in machine translation and then pre-edit it by human. The purpose is to enable the computer to translate better, improve the translatability of machine translation. (Slype G V & Guinet J F & Seitz F,1984:115)<br />
<br />
===1.3 Machine Translation Mode===<br />
According to the different knowledge acquisition methods, machine translation modes can be classified as follows: one is rule-based machine translation, which is based on bilingual dictionaries and a library of language rules for each language. The quality of translation depends on whether the source language conforms to the existing rules, but the inexhaustible rules are the hinders of this model. The second mode is machine translation based on statistics. This translation model relies on the principles of mathematics and statistics to find various existing translations corresponding to the translation tasks through the employment of corpus, analyzing the frequency of their occurrence and selecting the translation with the highest frequency for output. The disadvantage of this translation model is that it ignores the flexibility of language and the importance of context. The last one is neural network language model. This model is different from previous translation models in that it uses end-to-end neural network to realize automatic translation between natural languages. At present, the quality of its translation is much higher than that of the previous translation models.<br />
===1.4 Source of Study Abstracts===<br />
The core medical journals from domestic are selected in order to make the paper more representative and authoritative, such as National Medical Journal of China, Journal of Peking University (Health Science), and Journal of Third Military Medical University. All of them include basic medical science, biomedical technology, laboratory medical science and other fields. In this way, the pre-editing approaches included are applicable enough to machine translation of medical abstracts.<br />
===1.5 Selection of Translation Software===<br />
Generally, machine translation software includes Google Translation, Youdao Translator and Niu Translation, which has their own special use in translation. Take Google as an example, it is more like neurons in human brain, enabling to learn and collect information to establish connections with its neural machine translator’s neurons. However, it also causes many errors because of the lack of enough information. In this paper, contrastive analysis will be carried on by using Google translation. On the one hand, being a pioneer in the translation of NET, it is inevitably to sue Google as the translation software to translate medical texts in this paper. Comparing the program called “Google brain”, other NMT of translation software are relatively disadvantages. On the other hand, the Google translation enjoys the largest users in the world, with its downloads of more than one billion. The output quality of Google translation is more correct and complete than other machine translation software. With years of development and improvement, Google Translation has been greatly promoted. In this paper, Chinese medical abstract will be automatically translated by Google translation, and then the translation output will be compared with the translation by human and the publication of English abstracts. The main purpose is to prove that the improvement and promotion of quality and accuracy in the medical abstracts will be obtained through the pre-editing approaches.<br />
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===2.Language Characteristics and Error Division===<br />
Since Chinese and English are two different languages, it is quite neccessary to identify their own characteristics so as to better analyze and understand the two languages. Tytler (1978) argued in his Essay on the Principles of Translation that there are three principles of translation that in all the translation should give readers the same feelings as the source text, except for complete transcript of ideas. There actually also exit some mistakes of these two languages. So the following will make some clarrifications of these two languages to ensure more accurate translation.<br />
<br />
===2.1 Language Characteristics of Medical Abstracts===<br />
Chinese and English belong to different language systems, so there are differences in their language structure and the way users think of the languages. When using machine translation from Chinese to English, due to the unequal language levels, there will be many mistakes in the translation process, especially for ESP texts, such as medical papers. <br />
Here, these differences mainly refer to the linguistic characteristics of medical abstracts. In medical abstracts, it usually includes structured and unstructured abstracts. Although in different forms, they both describe the purpose, methods and conclusions of the research.<br />
In the method section of medial abstract, several Chinese sentences can be connected with commas, but each sentence may convey different information. In contrast, English sentences contain a great deal of information, but in order to ensure clarity, some modifiers need to be isolated and then reconstructed. However, in Chinese-English machine translation, a lot of information is put into the sentence because the machine segments the sentence on the basis of the full comma.<br />
In addition, subjectless sentence is used in the objective and method parts of Chinese medical abstracts. The subjectless sentence means the sentence without or free from subject and is usually employed in two contexts. The first is "needless to say". In Chinese sentences, it is common to omit the subject of the sentence. Chinese sentences can convey meanings by using incomplete sentence structures, so Chinese speakers can understand the meanings of the sentences even though the sentence subject is omitted. The second is "emphasis of action". In this context, subjectless sentences are used to describe behaviors, especially in the study of traditional Chinese medicine abstracts. Comparatively speaking, it should be avoided in English medical abstracts. Abstract sentences are more subjective when describing the learning process, while the essential requirement of English medical abstract is objectivity. From this point, sentences without subject should be avoided in English medical abstracts. Another feature is voice. Few words with passive meanings appear in Chinese abstracts. English sentences are more favoured in using passive voice. When translating from Chinese to English, passive voice should be used to make the contents objective, which is also the basic requirement of medical papers.<br />
These are the linguistic features of Chinese medical abstracts. There are great differences in sentence structure and expression between Chinese and English medical abstracts. These differences may reduce the accuracy of machine translation, so it is necessary to introduce pre-editing to edit the source text to ensure that the source text can be accurately recognized by the machine and fully translated into English.<br />
===2.2 Error Division of Machine Translation in Translating Abstracts of Medical Papers from Chinese to English===<br />
In this paper, errors in machine translation are listed out after analyzing. Some are due to the principles, such as statistical-based MT and NMT, employed by machine translation. Based on the original errors, they can be studied from two levels. One is from the macro-level, referring to mistakes caused by objective factors. These are not human factors, but the disadvantage of machine translation and the limitations of language. The limitations of machine translation are derived from the principles and models adopted and manifests itself as a reliance on reference sources. In this case, semantic ambiguity and textual incoherence may occur in the absence of reference sources.<br />
However, for ESP texts such as medical papers, the requirements for terms and sentence patterns are far beyond the existing corpora. For unfamiliar texts (that is, the corresponding texts cannot be found in the corpora). The quality of output translation will be relatively low, which is determined by the principles of machine translation. As mentioned above, machine translation has evolved over the past few decades from rules-based to corp-based statistics to NMT. However, there still exist some limitations in machine translation.<br />
Mistakes on micro-level are mainly caused by the variations and differences of linguistic structure between Chinese and English. Chinese is an implicit language, while English is an explicit one (Lian, 2010) To put it in another way, Chinese expression does not depend on language structures, while English does the opposite. This may result in mismatches between the original and machine translated sentences. This kind of error is mainly divided into two types: component fragment and component missing. For a medical paper, such errors are very serious. As a kind of ESP discourse, medical paper has the characteristics of fixed, objective and accurate language structure. In order to reproduce the characteristics of medical abstracts in translation, it is necessary to avoid errors at the level of words and sentences and pay attention to logic and consistency.<br />
Lexical errors here refer to the inconsistency of fixed expressions in the translation of terms. Although these terms are machine translation based on the corpus, the corpus may not be perfect for ESP texts such as medical papers. Therefore, fixed expressions are not used in term translation. This is also the most common mistake in machine translation (the term here includes but is not limited to nouns). For ESP texts, medical text in particular, the fixed expressions and the accuracy of terms are of great importance.<br />
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===3.===<br />
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===4.===<br />
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===Conclusion===<br />
<br />
===References===<br />
<br />
=12 蔡珠凤=(The Mistranslation of C-J Machine Translation of Political Statements)=<br />
[[Machine_Trans_EN_12]]<br />
===Abstract===<br />
Language is the main way of communication between people. With the continuous development of globalization, the scale of cross-border exchanges is also expanding. However, due to cultural differences and diversity, the languages of different countries and regions are very different, which seriously hinders people's communication. The demand for efficient and convenient translation tools is increasing. At the same time, with the development of network technology and artificial intelligence, recognition technology based on deep learning is more and more widely used in English, Japanese and other fields.<br />
<br />
===Key words===<br />
machine translation; political statements; mistranslation of C-J machine translation<br />
===题目===<br />
The Mistranslation of C-J Machine Translation of Political Statements<br />
===摘要===<br />
语言是人与人之间交流的主要方式。随着全球化的不断发展，跨境交流的规模也在不断扩大。然而，由于文化的差异和多样性，不同国家和地区的语言差异很大，这严重阻碍了人们的交流。对高效便捷的翻译工具的需求正在增加。同时，随着网络技术和人工智能的发展，基于深度学习的识别技术在英语、日语等领域的应用越来越广泛。<br />
<br />
===关键词===<br />
机器翻译；政治发言；政治发言中译日的误译<br />
===1. Introduction===<br />
===Introduction to machine translation===<br />
Machine translation, also known as automatic translation, is a process of using computers to convert one natural language (source language) into another natural language (target language). It is a branch of computational linguistics, one of the ultimate goals of artificial intelligence, and has important scientific research value.At the same time, machine translation has important practical value. With the rapid development of economic globalization and the Internet, machine translation technology plays a more and more important role in promoting political, economic and cultural exchanges.The development of machine translation technology has been closely accompanied by the development of computer technology, information theory, linguistics and other disciplines. From the early dictionary matching, to the rule translation of dictionaries combined with linguistic expert knowledge, and then to the statistical machine translation based on corpus, with the improvement of computer computing power and the explosive growth of multilingual information, machine translation technology gradually stepped out of the ivory tower and began to provide real-time and convenient translation services for general users.<br />
<br />
=== C-J machine translation software===<br />
Today's online machine translation software includes Baidu translation, Tencent translation, Google translation, Youdao translation, Bing translation and so on. Google was the first company to launch the machine translation system, and Baidu was the first company to import the machine translation system in China. In addition, Tencent and Youdao have attracted much attention.Machine translation is the process of using computers to convert one natural language into another. It usually refers to sentence and full-text translation between natural languages. In order to continuously improve the translation quality, R & D personnel have added artificial intelligence technologies such as speech recognition, image processing and deep neural network to machine translation on the basis of traditional machine translation based on rules, statistics and examples.With the increase of using machine translation, the joint cooperation between manual translation and machine translation will also increase significantly in the future. What criteria should be used to evaluate the quality of machine translation? In the evolving field of machine translation, there is an urgent need to clarify the unsolvable questions and solved problems.<br />
<br />
===The history of machine translation===<br />
The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. alchuni put forward the idea of using machines for translation. In 1933, Soviet inventor П.П. Trojansky designed a machine to translate one language into another, and registered his invention on September 5 of the same year; However, due to the low technical level in the 1930s, his translation machine was not made. In 1946, the first modern electronic computer ENIAC was born. Shortly after that, W. weaver, an American scientist and A. D. booth, a British engineer, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947 when discussing the application scope of electronic computer. In 1949, W. Weaver published the translation memorandum, which formally put forward the idea of machine translation. After 60 years of ups and downs, machine translation has experienced a tortuous and long development path. The academic community generally divides it into the following four stages:<br />
Pioneering period（1947-1964）<br />
In 1954, with the cooperation of IBM, Georgetown University completed the English Russian machine translation experiment with ibm-701 computer for the first time, showing the feasibility of machine translation to the public and the scientific community, thus opening the prelude to the study of machine translation.<br />
It is not too late for China to start this research. As early as 1956, the state included this research in the national scientific work development plan. The topic name is "machine translation, the construction of natural language translation rules and the mathematical theory of natural language". In 1957, the Institute of language and the Institute of computing technology of the Chinese Academy of Sciences cooperated in the Russian Chinese machine translation experiment, translating 9 different types of more complex sentences.<br />
From the 1950s to the first half of the 1960s, machine translation research has been on the rise. The United States and the former Soviet Union, two superpowers, have provided a lot of financial support for machine translation projects for military, political and economic purposes, while European countries have also paid considerable attention to machine translation research due to geopolitical and economic needs, and machine translation has become an upsurge for a time. In this period, although machine translation is just in the pioneering stage, it has entered an optimistic period of prosperity.<br />
Frustrated period（1964-1975）<br />
In 1964, in order to evaluate the research progress of machine translation, the American Academy of Sciences established the automatic language processing Advisory Committee (Alpac Committee) and began a two-year comprehensive investigation, analysis and test.<br />
In November 1966, the committee published a report entitled "language and machine" (Alpac report for short), which comprehensively denied the feasibility of machine translation and suggested stopping the financial support for machine translation projects. The publication of this report has dealt a blow to the booming machine translation, and the research of machine translation has fallen into a standstill. Coincidentally, during this period, China broke out the "ten-year Cultural Revolution", and basically these studies also stagnated. Machine translation has entered a depression.<br />
convalescence（1975-1989）<br />
Since the 1970s, with the development of science and technology and the increasingly frequent exchange of scientific and technological information among countries, the language barriers between countries have become more serious. The traditional manual operation mode has been far from meeting the needs, and there is an urgent need for computers to engage in translation. At the same time, the development of computer science and linguistics, especially the substantial improvement of computer hardware technology and the application of artificial intelligence in natural language processing, have promoted the recovery of machine translation research from the technical level. Machine translation projects have begun to develop again, and various practical and experimental systems have been launched successively, such as weinder system Eurpotra multilingual translation system, taum-meteo system, etc.<br />
However, after the end of the "ten-year holocaust", China has perked up again, and machine translation research has been put on the agenda again. The "784" project has paid enough attention to machine translation research. After the mid-1980s, the development of machine translation research in China has further accelerated. Firstly, two English Chinese machine translation systems, ky-1 and MT / ec863, have been successfully developed, indicating that China has made great progress in machine translation technology.<br />
New period(1990 present)<br />
With the universal application of the Internet, the acceleration of the process of world economic integration and the increasingly frequent exchanges in the international community, the traditional way of manual operation is far from meeting the rapidly growing needs of translation. People's demand for machine translation has increased unprecedentedly, and machine translation has ushered in a new development opportunity. International conferences on machine translation research have been held frequently, and China has made unprecedented achievements. A series of machine translation software have been launched, such as "Yixing", "Yaxin", "Tongyi", "Huajian", etc. Driven by the market demand, the commercial machine translation system has entered the practical stage, entered the market and came to the users.<br />
Since the new century, with the emergence and popularization of the Internet, the amount of data has increased sharply, and statistical methods have been fully applied. Internet companies have set up machine translation research groups and developed machine translation systems based on Internet big data, so as to make machine translation really practical, such as "Baidu translation", "Google translation", etc. In recent years, with the progress of in-depth learning, machine translation technology has further developed, which has promoted the rapid improvement of translation quality, and the translation in oral and other fields is more authentic and fluent.<br />
<br />
===The problem of machine translation at present===<br />
Error is inevitable<br />
Many people have misunderstandings about machine translation. They think that machine translation has great deviation and can't help people solve any problems. In fact, the error is inevitable. The reason is that machine translation uses linguistic principles. The machine automatically recognizes grammar, calls the stored thesaurus and automatically performs corresponding translation. However, errors are inevitable due to changes or irregularities in grammar, morphology and syntax, such as sentences with adverbials after "give me a reason to kill you first" in Dahua journey to the West. After all, a machine is a machine. No one has special feelings for language. How can it feel the lasting charm of "the tenderness of lowering its head, like the shame of a water lotus? After all, the meaning of Chinese is very different due to the changes of morphology, grammar and syntax and the change of context. Even many Chinese people are zhanger monks - they can't touch their heads, let alone machines.<br />
Bottleneck<br />
In fact, no matter which method, the biggest factor affecting the development of machine translation lies in the quality of translation. Judging from the achievements, the quality of machine translation is still far from the ultimate goal.<br />
Chinese mathematician and linguist Zhou Haizhong once pointed out in his paper "fifty years of machine translation": to improve the quality of machine translation, the first thing to solve is the problem of language itself rather than programming; It is certainly impossible to improve the quality of machine translation by relying on several programs alone. At the same time, he also pointed out that it is impossible for machine translation to achieve the degree of "faithfulness, expressiveness and elegance" when human beings have not yet understood how the brain performs fuzzy recognition and logical judgment of language. This view may reveal the bottleneck restricting the quality of translation. <br />
It is worth mentioning that American inventor and futurist ray cozwell predicted in an interview with Huffington Post that the quality of machine translation will reach the level of human translation by 2029. There are still many disputes about this thesis in the academic circles.<br />
<br />
===2.Mistranslation of Chinese Japanese machine translation===<br />
===2.1Vocabulary mistranslation in Chinese Japanese machine translation===<br />
===2.1.1Mistranslation of proper nouns===<br />
===2.1.2Mistranslation of Polysemy===<br />
===2.1.3Mistranslation of compound words===<br />
===2.2 Syntactic mistranslation in Chinese Japanese machine translation===<br />
===2.2.1Main dynamic Mistranslation===<br />
===2.2.2Dynamic Mistranslation===<br />
===2.2.3Mistranslation of tenses===<br />
===2.2.4Mistranslation of honorifics===<br />
===3.===<br />
===4.===<br />
===Conclusion===<br />
===References===<br />
<br />
=13 陈湘琼Chen Xiangqiong（Study on Post-editing from the Perspective of Functional Equivalence Theory ）=<br />
[[Machine_Trans_EN_13]]<br />
<br />
=14 Bi bi Nadia（Machine Translation a Challenge for Human Translators)=<br />
[[Machine_Trans_EN_14]]<br />
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===Abstract===<br />
Machine translation is a big obstacle in the way of Human translators or interpretors although it is quick and less time consuming.people are trying to get translation of their target language using through source language.For this purpose they are using digital apps like Google translation Google translation does not give accurate and exact interpretation.Google translator is translates word to word translate that doesn't clarifies it's true and actual meaning.On the other hand human translators can give exact and accurate translation ,they take care of grammatical errors , diction and sentence structure.They clarify the purpose of target language through using source language.<br />
===Keywords===<br />
Descriptive translation, academic, interdiscipline, comparative literature, localization,Translatology,school of thought, translation , studies, linguistics, corresponding<br />
===Body of article===<br />
Translation is the process of reworking text from one language into another to maintain the original message and communication. But, like everything else, there are different methods of translation, and they vary in form and function. What is translation? The term has become widely used among knowledge transfer researchers and practitioners, especially in the fields of health and health care. In a landmark review, Jonathan Lomas began to argue that 'The tasks… may be defined as to establish and maintain links between researchers and their audience, via the appropriate translation of research findings' (Lomas 1997, p 4). In 2004, the World Health Organization's World Report on Knowledge for Better Health suggested that 'One of the key contributions of research to health systems is the translation of knowledge into actions' (WHO 2004, p 33 and p 100). By 2006, special issues of WHO's Bulletin as well as the journals Evaluation and the Health Professions and the Journal of Continuing Education in the Health Professions were dedicated to translation.But what does 'translation' mean? It may be a new word for an old problem, meaning nothing more than 'transfer'. The rapidly emerging field of 'translational medicine' seems to take translation to mean generally what transfer might have meant, that is the transmission of knowledge and evidence 'from bench to bedside'. As one commentator has observed, "Translational medicine" as a fashionable term is being increasingly used to describe the wish of biomedical researchers to ultimately help patients' (Wehling 2008, abstract). <br />
Semantic uncertainty persists, not least because of the different interests of scientists, clinicians, patients and commercial firms (Littman et al 2007): 'Translational research means different things to different people, but it seems important to almost everyone' (Woolf 2008, p 211).<br />
In related fields, such as public health (Armstrong et al 2006), 'translation' seems to signify dissatisfaction with 'transfer'. It wants to move away from thinking of knowledge transfer as a form of technology transfer or dissemination, rejecting if only by implication its mechanistic assumptions and its model of linear messaging from A to B. But still, what does it signify?<br />
<br />
===Why translation?===<br />
'Translation' indicates a closer attention to the problem of shared meaning and how it might be developed. It seems to represent some new epistemological lubricant, facilitating the dissemination of texts and the application and use of the knowledge and information they in. Simply, translation might be the key to transfer. And yet, when we stop to think, we are more ambivalent. What is translated often seems somehow inferior, not real or original. Note how readily commentators reach for the idea that things might be 'lost in translation'. Knowing at a distance – made in and mediated by translation - makes for incomplete renditions, blurred images, partial truths. So what might 'translation' really mean? The purpose of this paper is to set out, for policy makers and practitioners, the theoretical and conceptual resources translation holds and seems to represent. In doing so, it explores understandings of translation in the fields of literature and linguistics and in the sociology of science and technology. It begins by setting out just why this idea of translation should make immediate, intuitive sense in relation to research, policy and practice.<br />
1translation in research, policy and practice research as translation<br />
Research often entails translation from one language to another: where data is collected from more than one ethnic group, for example, or where the language of the researcher is other than that of the research subject. It may draw on a secondary literature or source documents written in different languages, and may be published and disseminated in languages other than the one in which it is first written up.<br />
2In a different way, to conduct an interview is to ask for an account of experience and its meanings, but it is also to construct and translate that experience in terms defined at least in part by the researcher. In representing what is said, transcripts then select data, usually excluding significant gesture and eye-contact, for example. Often, certain characteristics of speech-acts (such as hesitations) will be edited out. In turn, the format of the transcript shapes the analytic use the researcher may make of it. The basis of research 'findings', then, is an artefact, a transcript or translation, not an original interaction (Ochs 1979, Barnes, Bloor and Henry 1996, Ross 2009).<br />
3In this way, the researcher recasts aspects of his or her problem or topic in new, scientific form: 'All researchers "translate" the experiences of others' (Temple 1997, p 609). Research is invariably conducted in a sort of 'metalanguage' (Hantrais and Ager 1985): the research process can be conceived as one of successive translations (from theoretical formulation to operationalization, transcription, interpretation and dissemination). Theorization is a process of reciprocal back and forth between theory and fact, in which conceptions of each are revised in order that one fit the other (Baldamus 1974).<br />
4 It is a kind of translation: a rereading, re-use, re-application or re-representation of what we know in new terms (Turner 1980). Referencing, too, is an act of translation, a form of appropriation and incorporation of one text by another (Gilbert 1977).policy as translation Each of the fields covered by the paper is diverse and ill-defined, and there is no intention here to provide a comprehensive account of any them. Sources have been chosen for their relevance: main references are cited in the text, and additional sources listed in footnotes.<br />
<br />
2For a brief introduction to the technical issues involved in social research in more than one language, see Birbili (2000). For translation issues in survey research and question design in general, see Ervin and Bower (1952) and Deutscher (1968); on translating survey research instruments (in this instance health-related quality of life measures), Bowden and Fox-Rushby (2003). On the use of translators and interpreters, see Temple (1997) and Jentsch (1998).<br />
3For an interesting discussion of this problem, see Bourdieu (1999), esp pp 621-626, 'The risks of writing'. <br />
===Difference between machine translation and human translators===<br />
Few people disagree on the differences between the two, but many argue over the quality of the translations. How accurate are machine translations? How reliable are human translations? Some say machine translation produces near-perfect translations while others are adamant that translations are incomprehensible and cause more problems than they solve. Results will, of course, vary depending on the source and target languages, the machine translation service used (e.g. Google Translate), and the complexity of the original text.<br />
Machine translation, love it or hate it, is here to stay. In fact, the machine translation market is growing at such a fast pace that it is predicted to reach $980 million by 2022.<br />
===Machine translation: the pros and cons===<br />
The advantages of machine translation generally come down to two factors: it’s faster and cheaper. The downside to this is the standard of translation can be anywhere from inaccurate, to incomprehensible, and potentially dangerous (more on that shortly).<br />
==The advantages of machine translation==<br />
Many free tools are readily available (Google Translate, Skype Translator, etc.)<br />
Quick turnaround time <br />
You can translate between multiple languages using one tool<br />
Translation technology is constantly improving<br />
The disadvantages of machine translation<br />
Level of accuracy can be very low <br />
Accuracy is also very inconsistent across different languages<br />
==Machines can't translate context ==<br />
Mistakes are sometimes costly <br />
Sometimes translation simply doesn’t work<br />
The most important thing to consider with any kind of translation is the cost of potential mistakes. Translating instructions for medical equipment, aviation manuals, legal documents and many other kinds of content require 100% accuracy. In such cases, mistakes can cost lives, huge amounts of money and irreparable damage to your company’s image. So choose carefully!<br />
Human translation: the pros and cons<br />
Human translation essentially switches the table in terms of pros and cons. A higher standard of accuracy comes at the price of longer turnaround times and higher costs. What you have to decide is whether that initial investment outweighs the potential cost of mistakes. Alternatively, whether mistakes simply aren’t an option, like the cases we looked at in the previous section.<br />
<br />
==The advantages of human translation ==<br />
It’s a translator’s job to ensure the highest accuracy<br />
Humans can interpret context and capture the same meaning, rather than simply translating words<br />
Human translators can review their work and provide a quality process<br />
Humans can interpret the creative use of language, e.g. puns, metaphors, slogans, etc.<br />
Professional translators understand the idiomatic differences between their languages<br />
Humans can spot pieces of content where literal translation isn’t possible and find the most suitable alternative<br />
The disadvantages of human translation<br />
Turnaround time is longer <br />
Translators rarely work for free <br />
Unless you use a translation agency, with access to thousands of translators, you’re limited to the languages any one translator can work with<br />
Simply put, human translation is your best option when accuracy is even remotely important. Other considerations to make are the complexity of your source material and the two languages you’re translating between – both of which can render machines pretty useless.<br />
<br />
When to use machine and human translation<br />
The truth is, the debate over machine vs human translation is an unnecessary distraction. What we should really be talking about is when to use these two different types of translation services, because they both serve a very valid purpose.<br />
<br />
Examples of when to use machine translation<br />
When you have a large bulk of content to translate and the general meaning is enough<br />
When your translation never reaches the final audience, e.g. you’re translating a resource as research for another piece of content<br />
Translating documents for internal use within a company, provided 100% accuracy isn’t needed<br />
To partially translate large chunks of content for a human translator to improve upon<br />
Examples of when to use human translation<br />
When accuracy is important<br />
Most cases where your translated content is received by a consumer audience<br />
When you have a duty of care to provide accurate translations (e.g. legal documents, product instructions, medical guidelines or health and safety content)<br />
When translating marketing material or other texts for creative language uses.<br />
<br />
===Conclusion ===<br />
<br />
In the light of above mentioned facts and figures here by we would say that machine translation is challenge for human translators because it can reduces the wokload of translation but can't give accurate and exact translation of the traget language.It can be less reliable than human translation..</div>Chen Huinihttps://bou.de/u/index.php?title=Machine_translation&diff=129218Machine translation2021-12-06T02:12:40Z<p>Chen Huini: /* 1.1 Definition of Machine Translation */</p>
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<div>'''Machine Translation - A challenge or a chance for human translators?'''<br />
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=1 卫怡雯(A Comparison Between the Quality of Machine Translation and Human Translation——A Case Study of the Application of artificial intelligence in Sports Events)=<br />
[[Machine_Trans_EN_1]]<br />
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=2 吴映红（The Introduction of Machine Translation)= <br />
[[Machine_Trans_EN_2]]<br />
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=3 肖毅瑶(On the Realm Advantages And Symbiotic Development of Machine Translation And Huamn Translation)=<br />
[[Machine_Trans_EN_3]]<br />
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=4 王李菲 （Comparison Between Neural Machine Translation of Netease and Traditional Human Translation—A Case Study of The Economist Articles)= <br />
[[Machine_Trans_EN_4]]<br />
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===Abstract===<br />
Machine translation is a subfield of artificial intelligence and natural language processing that investigates transforming the source language into the target language. On this basis, the emergence of neural machine translation, a new method based on sequence-to-sequence model, improves the quality and accuracy of translation to a new level. As one of the earliest companies to invest in machine translation in China, Netease launched neural machine translation in 2017, which adopts the unique structure of neural network to encode sentences, imitating the working mechanism of human brain, and generates a translation that is more professional and more in line with the target language context. This paper takes the articles in The Economist as the corpus for analysis, and aims to explore the types and causes of common errors, as well as the advantages and challenges of each, through the comparative analysis of Netease neural machine translation and human translation, and finally to forecast the future development trend and make a summary of this paper.<br />
<br />
===Key words===<br />
Neural Machine Translation; Human Translation; Contrastive Analysis<br />
<br />
===题目===<br />
有道神经网络机器翻译与传统人工翻译的译文对比——以经济学人语料为例<br />
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===摘要===<br />
机器翻译研究将源语言所表达的语义自动转换为目标语言的相同语义，是人工智能和自然语言处理的重要研究分区。在此基础上，一种基于序列到序列模型的全新机器翻译方法——神经机器翻译的出现让译文的质量和准确度提升到了新的层次。网易作为国内最早投身机器翻译的公司之一，在2017年上线的神经网络翻译采用了独到的神经网络结构，模仿人脑的工作机制对句子进行编码，生成的译文更具专业性，也更符合目的语语境。本文以经济学人内的文章为分析语料，旨在通过对网易神经机器翻译和人工翻译的英汉译文进行对比分析，探究常见错误类型及生成原因，以及各自存在的优势与挑战，最后展望未来发展趋势，并对本文做出总结。 <br />
<br />
===关键词===<br />
神经网络翻译；人工翻译；对比分析<br />
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===1. Introduction===<br />
<br />
Nowadays, the process of economic globalization has accelerated overwhelmingly, and considerable resources are poured into the business field. As a branch of global language English, business English is proposed under the theoretical framework of English for Specific Purpose (ESP), serves the international business activities which is a professional subject requiring specialized English. As the medium that helps people with different cultural backgrounds to understand each other, business translation is required to be “formal, accurate, standardized and smooth”, which challenged both the machine translation and human translator.<br />
<br />
With the urgent requirement for more precise and higher quality translation, recent years have witnessed the rapid development of neural machine translation (NMT), which has replaced traditional statistical machine translation (SMT) to become a new mainstream technique, playing a crucial part in many fields, like business, academic and industry. Compared with SMT, NMT model is more like an organism. There are many parameters in the model that can be adjusted and optimized for the same goal, making the combination and interaction more organic and the overall translation effect better, which greatly matched the demands of business translation. <br />
<br />
The Economist is an international news and Business Weekly offering clear coverage, commentary and analysis of global politics, business, finance, science and technology. A huge number of terminologies plus the polysemy contained in the texts, put forward a tricky problem to both machine translation and human translator. In view of this, this paper makes a comparative analysis of Netease neural machine translation and human translation, aiming to explore the types and causes of common errors, as well as the advantages and challenges of each. In the end, this paper will forecast the future development, hoping to promote the development of translation studies in China.<br />
<br />
===2.2. The Development Process of Machine Translation ===<br />
<br />
Since the IBM model was put forward by the researcher Peter Brown in the early 1990s, statistical methods have gradually become the mainstream of machine translation research. This method has greatly promoted the development of machine translation technology. In recent years, a variety of statistical machine translation models have emerged, such as phrase-based translation model, hierarchical phrase translation model and syntactic translation model, then the translation quality has been greatly improved.<br />
<br />
Since 2002, BLEU, an automatic translation quality evaluation method, has greatly promoted the development of statistical machine translation technology and effectively reduced the cost of manual evaluation. In recent years, with the technical maturity and stability of statistical machine translation, especially phrase-based machine translation, statistical machine translation technology has been making strong strides towards practical and commercial application. Therefore, with the rapid development of technology, people have gradually built-up confidence in machine translation, and the social demand for machine translation has been increasing day by day, with higher and higher expectations.<br />
<br />
However, from the perspective of academic research, both phrase-based translation models and syntactic translation models have experienced a rapid development stage, and the existing theoretical methods and technical models have begun to show "bottlenecks" in the improvement of translation performance. In addition, from the perspective of industrialization and utilitarianism, there is an urgent need for a more practical machine translation system, but the gap between the results of machine translation and the requirements of human beings is still very large. Therefore, for the researchers of machine translation, while excited to see the BLEU score of machine translation system evaluation is getting higher and higher, and the performance of online machine translation system developed by Google, Baidu, Netease and other enterprises is developing with each passing day, they are facing more and more challenges.<br />
<br />
Aiming to solve these problems, many technological giants are striving to find a new way to improve both the quality and efficiency of machine translation. There was a breakthrough which bought machine translation to a new level. Since 2014, the end-to-end neural machine translation has developed rapidly, compared with the statistical machine translation, the translation quality received a significant boost.<br />
<br />
The previous statistical machine translation was more like a mechanical system. Each module has its own function and goal, and then outputs the translation results through mechanical splicing. Its main disadvantage is that the model contains low syntactic and semantic components, so it will encounter problems when dealing with languages with large syntactic differences, such as Chinese-English. Sometimes the result is unreadable even though it is “word-for-word”.<br />
On the contrary, neural machine translation are consisted of several components, including phrase conditions, partial conditions, sequential conditions, primitive models, and so on. Its core is deep learning of artificial intelligence which can imitate the working mechanism of human brain and adopt unique neural network structure to model the whole process of translation. The whole model is composed of a large number of “neurons”, and each “neuron” has to complete some simple tasks, and then through the combination of all of them to coordinate the work, a much better translation text appears. <br />
<br />
Since neural machine translation puts more emphasis on context and the whole text, it produces more coherent and comprehensible content to readers than traditional statistical machine translation, and be widely accepted and used in various field in a very short time. In 2017, at the GMIC (Global Mobile Internet Congress), Duan Yitao, the chief scientist of Netease, delivered a keynote speech titled “Machine Translation has Its Own Way” and announced an exciting news: the neural machine translation technology independently developed by Netease has been officially launched. This technology launched by Youdao this time has been jointly developed by Netease Youdao and Netease Hangzhou Research Institute for over two years. It will serve Youdao Dictionary, Youdao Translator, Youdao Web version, Youdao E-reader and other products, expecting to bring super-convenient product experience to users. In addition, Youdao Translation officer also launched photo translation, users only need to take pictures of the text, can show the results of neural network translation in real time. <br />
<br />
As a pioneer of machine translation in China, the development process of Netease YouDao is exactly the paradigm of the history of machine translation in China. Therefore, in this paper, the neural machine translation technology developed by Netease will be compared with human translators. The same excerpts selected from The Economist are translated by both of them, then the different versions will be analyzed by the translation criterion so as to figure out their respective strengths and weaknesses, bringing consideration to current translation situation and references to future development.<br />
<br />
===3.Comparative Analysis of Errors in English-Chinese Translation ===<br />
<br />
===4.===<br />
<br />
===5. ===<br />
<br />
===Conclusion===<br />
<br />
===References===<br />
<br />
=6 徐敏赟(Machine Translation Based on Neural Network --Challenge or Chance?)=<br />
[[Machine_Trans_EN_6]]<br />
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=7 颜莉莉(一带一路背景下人工智能与翻译人才的培养)=<br />
[[Machine_Trans_EN_7]]<br />
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===Abstract===<br />
In the era of artificial intelligence, artificial intelligence has been applied to various fields. In the field of translation, traditional translation models can no longer meet the rapid development and updating of the information age. The development of machine translation has brought structural changes to the language service industry, which poses challenges to the cultivation of translation talents. Under the background of "The Belt and Road initiative", translation talents have higher and higher requirements on translation literacy. Artificial intelligence and translation technology are used to reform the training mode of translation talents, so as to better serve the development of The Times. This paper mainly explores the cultivation of artificial intelligence and translation talents under the background of the Belt and Road Initiative. The cultivation of translation talents is moving towards comprehensive cultivation of talents. On the contrary, artificial intelligence and machine translation can also be used to improve the teaching mode and teaching content, so as to win together in cooperation.<br />
===Key words===<br />
Artificial intelligence,Machine translation,cultivation of translation talents,"The Belt and Road initiative"<br />
===题目===<br />
一带一路背景下人工智能与翻译人才的培养<br />
<br />
===摘要===<br />
进入人工智能时代，人工智能被应用于各个领域。在翻译领域，传统的翻译模式已无法满足信息化时代的飞速发展和更新，机器翻译的发展给语言服务行业带来了结构性改变，这对翻译人才的培养提出了挑战。“一带一路”背景下，对翻译人才的翻译素养要求越来越高，利用人工智能和翻译技术对翻译人才培养模式进行革新，更好为时代发展服务。本文主要探究在一带一路背景下人工智能和翻译人才培养，翻译人才的培养过程中正向对人才的综合性培养，反之也可以利用人工智能和机器翻译完善教学模式和教学内容，在合作中共赢。<br />
===关键词===<br />
人工智能；机器翻译；翻译人才培养；一带一路<br />
<br />
===1. Introduction===<br />
With the development of science and technology in China, artificial intelligence has also been greatly improved, and related technologies have been applied to various fields, such as the use of intelligent robots to deliver food to quarantined people during the epidemic, which has made people's lives more convenient. The most controversial and widely discussed issue is machine translation. Before the emergence of machine translation, translation was generally dominated by human translation, including translation and interpretation, which was divided into simultaneous interpretation and hand transmission, etc. It takes a lot of time and energy to cultivate a translation talent. However, nowadays, the era is developing rapidly and information is updated rapidly. As a translation talent, it is necessary to constantly update its knowledge reserve to keep up with the pace of The Times. The emergence of machine translation has also posed challenges to translation talents and the training of translation talents. Although machine translation had some problems in the early stage, it is now constantly improving its functions. In the context of the belt and Road Initiative, both machine translation and human translation are facing difficulties. Regardless of whether human translation is still needed, what is more important at present is how to train translators to adapt to difficulties and promote the cooperation between human translation and machine translation.<br />
<br />
===2.Development status of machine translation in the era of artificial intelligence ===<br />
With the development of AI technology, machine translation has made great progress and has been applied to people's lives. For example, more and more tourists choose to download translation software when traveling abroad, which makes machine translation take an absolute advantage in daily email reply and other translation activities that do not require high accuracy. The translation software commonly used by netizens include Google Translation, Baidu Translation, Youdao Translation, IFly.com Translation, etc. Even wechat and other chat software can also carry out instant Translation into English. Some companies have also launched translation pens, translation machines and other equipment, which enables even native speakers to rely on machine translation to carry out basic communication with other Chinese people.<br />
But so far, machine translation still faces huge problems. Although machine translation has made great progress, it is highly dependent on corpus and other big data matching. It does not reach the thinking level of human brain, and cannot deal with the problem of translation differences caused by culture and religion. In addition, many minor languages cannot be translated by machine due to lack of corpus.<br />
<br />
What's more, most of the corpus is about developed countries such as Britain and France, and most of the corpus is about diplomacy, politics, science and technology, etc., while there are very few about nationality, culture, religion, etc.<br />
<br />
In addition, machine translation can only be used for daily communication at present. If it involves important occasions such as large conferences and international affairs, it is impossible to risk using machine translation for translation work. Professional translators are required to carry out translation work. So machine translation still has a long way to go.<br />
<br />
===3.Challenges in the training of translation talents in universities===<br />
The cultivation of translators is targeted at the market. Professors Zhu Yifan and Guan Xinchao from the School of Foreign Languages at Shanghai Jiao Tong University believe that the cultivation of translators can be divided into four types: high-end translators and interpreters, senior translators and researchers, compound translators and applied translators.<br />
<br />
From their names, it can be seen that high-end translators and interpreters and senior translators and researchers talents have high requirements on the knowledge and quality of interpreters, because they have to face the changing international situation, and have to deal with all kinds of sensitive relations and political related content, they should have flexible cross-cultural communication skills. In addition, for literature, sociology and humanities academic works, it is not only necessary to translate their content, but also to understand their essence. Therefore, translators should not only have humanistic feelings, but also need to have a deep understanding of Chinese and western culture.<br />
<br />
However, there is not much demand for this kind of translation in the society. Such high-level translation requirements are not needed in daily life and work. The greatest demand is for compound translators, which means that they should master knowledge in a specific field while mastering a foreign language. For example, compound translators in the financial field should not only be good at foreign languages, but also master financial knowledge, including professional terms, special expressions and sentence patterns.<br />
<br />
Now we say that machine translation can replace human translation should refer to the field of compound translation talents. Although AI technology has enabled machine translation to participate in creation, it does not mean that compound translation talents will be replaced by machines. The complexity of language and the flexible cross-cultural awareness required in communication make it impossible for machine translation to completely replace human translation.<br />
<br />
The last type of applied translation talents are mostly involved in the general text without too much technical content and few professional terms, so it is easy to be replaced by machine translation.<br />
<br />
Therefore, the author thinks that what universities are facing at present is not only how to train translation talents to cope with the development of machine translation, but to consider the application of machine translation in the process of training translation talents to achieve human-machine integration, so as to better complete the translation work.<br />
<br />
===4.The Language environment and opportunities and challenges of the Belt and Road initiative===<br />
During visits to Central and Southeast Asian countries in September and October 2013, Chinese President Xi Jinping put forward the major initiative of jointly building the Silk Road Economic Belt and the 21st Century Maritime Silk Road. And began to be abbreviated as the Belt and Road Initiative.<br />
<br />
According to the Vision and Actions for Jointly Building silk Road Economic Belt and 21st Century Maritime Silk Road, the Silk Road Economic Belt focuses on connecting China, Central Asia, Russia and Europe (the Baltic Sea). From China to the Persian Gulf and the Mediterranean Sea via Central and West Asia; China to Southeast Asia, South Asia, Indian Ocean. The focus of the 21st Century Maritime Silk Road is to stretch from China's coastal ports to Europe, through the South China Sea and the Indian Ocean. From China's coastal ports across the South China Sea to the South Pacific.<br />
<br />
The Belt and Road "construction is comply with the world multi-polarization and economic globalization, cultural diversity, the initiative of social informatization tide, drive along the countries achieve economic policy coordination, to carry out a wider range, higher level, the deeper regional cooperation and jointly create open, inclusive and balanced, pratt &whitney regional economic cooperation framework.<br />
<br />
====4.1The language environment of the Belt and Road====<br />
The "Belt and Road" involves a wide range of countries and regions, and their languages and cultures are very complex. How to make good use of language, do a good job in translation services, actively spread Chinese culture to the world, strengthen the ability of discourse, and tell Chinese stories well, the first thing to do is to understand the language situation of the countries along the "Belt and Road".<br />
<br />
=====4.1.1The most common language in countries along the "Belt and Road" =====<br />
<br />
There are a wide variety of languages spoken in 65 countries along the Belt and Road, involving nine language families. However, The status of English as the first language in the world is undeniable. Most of the countries participating in the Belt and Road are developing countries, and many of them speak English as their first foreign language. Especially in southeast Asian and South Asian countries, English plays an important role in foreign communication, whether as the official language or the first foreign language. Besides English, more than 100 million people speak Russian, Hindi, Bengali, Arabic and other major languages in the "Belt and Road" countries. It can also be seen that a common feature of languages in countries along the "Belt and Road" is the popularization of English education. English is widely used in international politics, economy, culture, education, science and technology, playing the role of the most important language in the world.<br />
<br />
=====4.1.2The complex language conditions of countries along the "Belt and Road" =====<br />
<br />
The languages spoken in countries along the Belt and Road involve nine major language families and almost all the world's religious types. Differences in religious beliefs also result in differences in culture, customs and social values behind languages. The languages of some countries along the belt and Road have also been influenced by historical and realistic factors, such as colonization, internal division and immigration. <br />
<br />
India, for example, has no national language, but more than 20 official languages. India is a multi-ethnic country, a total of more than 100 people, one of the most obvious difference between nation and nation is the language problem. Therefore, according to the difference of language, India divides different ethnic groups into different states, big and small. Ethnic groups that use the same language are divided into one state. If there are two languages in a state, the state is divided into two parts. And Indian languages differ not only in word order but also in the way they are written. In India, for example, Hindi is spoken by the largest number of people in the north, with about 700 million speakers and 530 million as their first language. It is written in The Hindu language and belongs to the Indo-European language family. Telugu in the east is spoken by about 95 million people and 81.13 million as their first language. It is written in Telugu, which belongs to the Dravidian language family and is quite different from Hindi. As a result, a parliamentary session in India requires dozens of interpreters. <br />
<br />
These factors cannot be ignored in the process of translation, from language communication to cultural understanding, from text to thought exchange, through the bridge of language to truly connect the people, so as to avoid misreading and misunderstanding caused by differences in language and national conditions.<br />
<br />
====4.2 Opportunities and challenges of the "Belt and Road" ====<br />
With the promotion of the Belt and Road Initiative, there has been an unprecedented boom in translation. In the previous translation boom in China, most of the foreign languages were translated into Chinese, and most of the foreign cultures were imported into China. However, this time, in the context of the "Belt and Road" initiative, translating Chinese into foreign languages has become an important task for translators. As is known to all, there are many different kinds of "One Belt And One Road" along the national language and culture is complex, the service "area" construction has become a factor in Chinese translation talents training mode reform, one of the foreign language universities have action, many colleges and universities to establish the "area" all the way along the country's small language major, as a result, "One Belt And One Road" initiative to promote, It has brought unprecedented opportunities for human translation. The cultivation of diversified translation talents and the cultivation of translation talents in small languages is an urgent problem to be solved in China. The cultivation of translation talents cannot be completed overnight, and the state needs to reform the training mode of translation talents from the perspective of language strategic development. Only in this way can we meet the new demand for human translation under the new situation of the belt and Road Initiative.<br />
<br />
For a long time, the traditional orientation of translation curriculum and training goal in colleges and universities is to train translation teachers and translators in need of society through translation theory and practice and literary translation practice, which cannot meet the needs of society. Since 2007, in order to meet the needs of the socialist market economy for application-oriented high-level professionals, the Academic Degrees Committee of The State Council approved the establishment of Master of Translation and Interpreting (MTI for short). After joining the pilot program of MTI, more and more universities are reforming the curriculum and training mode of master of Translation in order to cultivate translators who meet the needs of the society.<br />
<br />
Language is an important carrier of culture, and translation is an important link for exporting culture. The quality of translation output also reflects the cultural soft power of a country. With the rise of China, more and more people are interested in Chinese culture, and the number of Chinese learners keeps increasing. Under the background of "One Belt and One Road", excellent translators are urgently needed to spread Chinese culture. With the promotion of "One Belt and One Road" Initiative, the number of other countries learning mutual learning and cultural exchanges with China has increased unprecedeningly, bringing vigorous opportunities for the spread of Chinese culture. Translation talents who understand small languages and multi-lingual translators are needed. They should not only use language to convey information, but also use language as a lubricant for communication.<br />
<br />
===5.Training translation talents from the perspective of machine translation===<br />
Under the prevailing environment of machine translation, it poses a great challenge to the cultivation of translation talents. According to the current situation, translation needs and the shortage of translation talents, colleges and universities should reform and innovate the existing training programs for translation talents in terms of the quality of translation talents, the reform of training mode and the use of artificial intelligence. Based on the obtained data and literature, the author discusses how to train translation talents in the perspective of machine translation from the following aspects.<br />
<br />
====5.1 Quality requirements for translation talents ====<br />
Zhong Weihe and Murray made a more detailed and profound discussion on translator's literacy, believing that "translators should not only be proficient in two languages, but also have extensive cultural and encyclopedic knowledge and relevant professional knowledge; Master a variety of translation skills, a lot of translation practice; Have a clear translator role awareness, good professional ethics, practical and enterprising style of work, conscious team spirit and calm psychological quality ". According to the collected data, the author will elaborate the requirements for translation talents from four aspects: language literacy, humanistic literacy, translation ability and innovation ability.<br />
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The first is language literacy, which is the most basic and important requirement. MAO Dun pointed out that "mastery of mother tongue and target language are the foundation of translation". A solid foundation of bilingual skills is the basic skills of translators. Poor language proficiency seems to be a common problem among students majoring in translation and interpreting. Many translation diseases are caused by poor Chinese foundation. As part of going global, the belt and Road initiative is to tell Chinese culture and Chinese stories, which requires translators to be able to use both languages flexibly. Therefore, the first problem that colleges and universities face to solve is to improve the language level of foreign language learners.<br />
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The second is humanistic literacy. Humanistic literacy is mainly manifested by a good command of politics, economy, history, literature and other knowledge, which is particularly important for interpreters. In addition, cross-cultural communication cannot be ignored. In the process of communicating with foreigners or translating, translators often encounter the first cross-cultural contradiction. Cross-culture refers to having a full and correct understanding of cultural phenomena, customs and habits that differ or conflict with the national culture, and accepting and adapting to them in an inclusive manner on this basis. So the interpreter can first fully understand and master the national conditions and culture of the target country, which is particularly important in the "Belt and Road". There are more than 60 countries along the "Belt and Road", and it takes a lot of energy to master their national conditions and culture.<br />
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The third is translation ability. We should distinguish between translation ability and language ability. Translation ability is actually a system of knowledge and skills necessary for translation, the core of which is conversion ability. First of all, it reflects the ability to use tools to assist translation, such as computer application, translation technology and so on. In addition, interpreters should have enough healthy psychological quality and good professional quality. In terms of translation ability, the current training model of translation talents is inadequate.<br />
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The last one is innovation. The cultivation of learners' thinking ability is the key to translation teaching and the cultivation of thoughtful translators should be the connotation of translation teaching. Therefore, the interpreter is not only a translation tool, which is no different from machine translation. More importantly, it is necessary to explore translation with thoughts, have a sense of lifelong learning and innovation consciousness. Translators must constantly innovate themselves, learn new knowledge, and strive to seek reform and innovation. Many colleges and universities should also consciously cultivate students' innovation ability and broaden their thinking and vision.<br />
<br />
====5.2 The reform of college curriculum setting====<br />
First, we will further reform the curriculum of colleges and universities. Add economics, law and engineering to the curriculum, these contents in the "belt and Road".<br />
"One Road" is very important in the construction. According to the author's personal experience, the most typical problem of foreign language majors in colleges and universities is the single learning of foreign languages. More professional foreign language colleges and universities will add some literature courses and national conditions courses of the language target countries. Obviously, whether foreign language graduates are engaged in translation work or not, these knowledge is not enough. Of course, great reforms have been carried out in foreign language teaching, such as combining foreign language with finance, law, diplomacy and so on, and taking the way of minor training foreign language majors.<br />
<br />
Domestic enterprises with a high degree of internationalization attach great importance to translation. Their translation research includes cutting-edge theoretical and applied research, involving machine translation, natural language processing and AI theory, algorithm and model. With such a foundation, enterprises can solve problems by themselves, such as embedding automatic translation functions in mobile phones. International enterprises not only do technical translation, but also deal with all forms of translation and localization in society. At present, translation teaching in most colleges and universities is still in the early mode, and it is an objective fact that it is divorced from the workplace and has a gap with the needs of enterprises.<br />
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Second, we should adjust and strengthen the construction of second foreign language teaching for foreign language majors. In the 1980s, our country was in urgent need of Russian translation. At that time, students majoring in English could translate microelectronic product manuals and related business documents in English and Russian at the same time after learning Russian for half a year. The mutual conversion between English and Russian played a great role in practice. According to the author, in the Graduate Institute of Interpretation and Translation of Beijing Foreign Studies University a very few students majored in multiple languages at the graduate level, that is, they majored in minor languages at the undergraduate level and were admitted to the Graduate Institute of Interpretation and Translation in English. Their training mode is to study English in the Graduate Institute of Interpretation and Translation for two years and the third year in the corresponding department of the undergraduate major. Such training mode in my opinion is a bigger model, cost It's more difficult for students. <br />
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In addition, there is a great disparity in the development of second foreign language teaching in colleges and universities, and the overall level is not high enough. Part of the second foreign language university foreign language professional may still be too much focus in languages such as German, French and Japanese, should as far as possible, considering the need of the construction of the "region", like Croatia, Serbia, Turkish, Hungarian, Italian, Indonesian, Albanian, these are the countries along the "area" the language of the two countries, Colleges and universities should encourage the teaching of a second foreign language.<br />
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Third, the teaching of translation technology should be strengthened. Traditional translation teaching teaches translation skills, such as the translation of words, sentences, texts and figures of speech. Translation technology refers to a series of practical workplace technologies with computer-aided translation software and translation project management as the core, which can greatly improve translation efficiency. However, due to the relative lack of translation technology teachers and equipment in colleges and universities, there is a disconnect between talent training and the requirements of translation technology in the translation field.<br />
<br />
====5.3 Application of artificial intelligence to translation teaching practice====<br />
In order to improve the teaching quality and train students' English translation ability, it is necessary to realize the effective integration of ARTIFICIAL intelligence and translation activity courses, which should not only reflect the effectiveness of artificial intelligence translation technology, but also help students establish a healthy concept of English communication. Through the application of artificial intelligence technology, students can strengthen their flexible translation skills through close communication with "AI program" during the learning stage of English translation activity class. For example, teachers can ask students to translate directly against the translation content provided on the translation screen of the ARTIFICIAL intelligence system. After that, the system can collect the translation answers with the help of speech recognition function, and then judge the accuracy of the translation content, thus providing important feedback to students.<br />
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China has used such artificial intelligence technology in the Putonghua test to ensure that every student can find a suitable translation method in practical communication. The so-called artificial intelligence technology is a new kind of technology modeled after the characteristics of human neural network thinking, can combine the human mind to respond. If it can be integrated into English translation activity teaching, it can not only improve the teaching efficiency, but also enhance students' enthusiasm in learning the course.<br />
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At the same time, during the training of translation talents, teachers also need to take into account the importance of influencing education factors, so that students can form a higher disciplinary quality in translation, so as to fit the concept of quality education in the new era. Only when artificial intelligence translation content is fully integrated into college English translation activity courses can the overall translation ability of college students be maximized.<br />
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====5.4The improvement of translator's technical ability====<br />
In the previous part, the author roughly mentioned that translation teaching should be improved, which will be elaborated here. At present, only a few universities can make full use of the advantages of translation technology in translation teaching and focus on cultivating professional translation talents. Most universities still cannot get rid of the traditional teaching mode of "language + relevant professional knowledge" in translation teaching, and generally lack a correct understanding of COMPUTER-aided translation teaching.<br />
<br />
According to Wang Huashu et al., the courses that can be offered around the composition of translators' technical literacy include computer-assisted translation, translation and corpus, machine translation and post-translation editing, localization and internationalization, film and television translation (subtitle), technical communication and technical writing, and computer programming. The course modules involved are: Fundamentals of COMPUTER-aided Translation, CAT tool application, corpus alignment and processing, term management, QA technology for translation quality assurance, OFFICE fundamentals, translation management technology, basic computer knowledge, desktop typesetting, localization and internationalization, project management system and content management system, technical writing, basic knowledge of computer programming, basic knowledge of web code, etc.<br />
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<br />
===6针对一带一路的机器翻译与翻译人才的合作===<br />
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===Conclusion===<br />
<br />
===References===<br />
<br />
=8 颜静(On Machine Translation Under Lanuguage Intelligence——An Option and Opportunity for Human Translators)=<br />
[[Machine_Trans_EN_8]]<br />
<br />
=9 谢佳芬（人工智能时代下的机器翻译与人工翻译）=<br />
[[Machine_Trans_EN_9]]<br />
===Abstract===<br />
With the continuous development of information technology, many industries are facing the competitive pressure of artificial intelligence, and so is the field of translation. Artificial intelligence technology has developed rapidly and combined with the field of translation，which has brought great impact and changes to traditional translation, but artificial intelligence translation and artificial translation have their own advantages and disadvantages. Artificial translation is in the leading position in adapting to human language logical habits and understanding characteristics, but in terms of translation threshold and economic value, the efficiency of artificial intelligence translation is even better. In a word, we need to know that machine translation and human translation are complementary rather than antagonistic.<br />
<br />
===Key Words===<br />
Machine Translation; Artificial Translation; Artificial Intelligence<br />
<br />
===题目===<br />
人工智能时代下的机器翻译与人工翻译<br />
<br />
===摘要===<br />
伴随着信息技术的不断发展，多个行业面临着人工智能的竞争压力，翻译领域也是如此。人工智能技术快速发展并与翻译领域结合，人工智能翻译给传统翻译带来了巨大的冲击和变革，但人工智能翻译与人工翻译存在着各自的优劣特点和发展空间，在适应人类语言逻辑习惯和理解特点的翻译效果上，人工翻译处于领先地位，但在翻译门槛和经济价值上，人工智能翻译的效率则更胜一筹。总的来说，我们要知道机器翻译与人工翻译是互补而非对立的关系。<br />
<br />
===关键词===<br />
机器翻译;人工翻译;人工智能<br />
<br />
===1. Introduction===<br />
====1.1 The History of Machine Translation Aborad====<br />
The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. Alchuni put forward the idea of using machines for translation. In 1933, the Soviet inventor Troyansky designed a machine to translate one language into another. [1]In 1946, the world's first modern electronic computer ENIAC was born. Soon after, American scientist Warren Weaver, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947. In 1949, Warren Weaver published a memorandum entitled Translation, which formally raised the issue of machine translation. In 1954, Georgetown University, with the cooperation of IBM, completed the English-Russian machine translation experiment with IBM-701 computer for the first time, which opened the prelude of machine translation research. [2] In 2006, Google translation was officially released as a free service software, bringing a big upsurge of statistical machine translation research. It was Franz Och who joined Google in 2004 and led Google translation. What’s more, it is precisely because of the unremitting efforts of generations of scientists that science fiction has been brought into reality step by step.<br />
====1.2 The History of Machine Translation in China====<br />
In 1956, the research and development of machine translation has been named in the scientific and technological work and made little achievements in China. On the eve of the tenth anniversary of the National Day in 1959, our country successfully carried out experiments, which translated nine different types of complicated sentences on large general-purpose electronic computers. The dictionary includes 2030 entries, and the grammar rule system consists of 29 circuit diagrams. [3]. After a period of stagnation, China's machine translation ushered in a high-speed development stage after the 1980s in the wave of the third scientific and technological revolution. With the rapid development of economy and science and technology, China has made a qualitative leap in the field of machine translation research with the pace of reform and opening up. In 1978, Institute of Scientific and Technological Information of China, Institute of Computing Technology and Institute of Linguistics carried out an English-Chinese translation experiment with 20 Metallurgical Title examples as the objects and achieved satisfactory results. Subsequently, they developed a JYE-I machine translation system, which based on 200 sentences from metallurgical documents. Its principles and methods were also widely used in the machine translation system developed in the future. In addition, the research achievements of machine translation in China during the 1980s and 1990s also include that Institute of Post and Telecommunication Sciences developed a machine translation system, C Retrieval and automatic typesetting system with good performance and strong practicability in October 1986; In 1988, ISTC launched the ISTIC-I English-Chinese Title System for the translation of applied literature of metallurgy, Information Research Institute of Railway developed an English-Chinese Title Recording machine translation system for railway documents; the Language Institute of the Academy of Social Sciences developed "Tianyu" English-Chinese machine translation system and Matr English-Chinese machine translation system developed by the computer department of National University of Defense Technology. After many explorations and studies, machine translation in China has gradually moved towards application, popularization and commercialization. China Software Technology Corporation launched "Yixing I" in 1988, marking China's machine translation system officially going to the market. After "Yixing", a series of machine translation systems such as Gaoli system in Beijing, Tongyi system in Tianjin and Langwei system in Shaanxi have also entered the public. In the 21st century, the development of a series of apps such as Kingsoft Powerword, Youdao translation and Baidu translation has greatly met the needs of ordinary users for translation. According to the working principle, machine translation has roughly experienced three stages: rule-based machine translation, statistics-based machine translation and deep learning based neural machine translation. [4] These three stages witnessed a leap in the quality of machine translation. Machine translation is more and more used in daily life and even the translation of some texts is almost comparable to artificial translation. In addition to text translation, voice translation, photo translation and other functions have also been listed, which provides great convenience for people's life. It is undeniable that machine translation has become the development trend of translation in the future.<br />
====1.3 The Status Quo of Machine Translation====<br />
In this big data era of information explosion, the prospect of machine translation is also bright. At present, the circular neural network system launched by Google has supported universal translation in more than 60 languages. Many Internet companies such as Microsoft Bing, Sogou, Tencent, Baidu and NetEase Youdao have also launched their own Internet free machine translation systems. [5] Users can obtain translation results free of charge by logging in to the corresponding websites. At present, the circular neural network translation system launched by Google can support real-time translation of more than 60 languages, and the domestic Baidu online machine translation system can also support real-time translation of 28 languages. These Internet online machine translation systems are suitable for a variety of terminal platforms such as mobile phone, PC, tablet and web and its functions are also quite diverse, supporting many translation forms, such as screen word selection, text scanning translation, photo translation, offline translation, web page translation and so on. Although its translation quality needs to be improved, it has been outstanding in the fields of daily dialogue, news translation and so on.<br />
===2. Advantages and Disadvantages of Machine Translation===<br />
Generally speaking, machine translation has the characteristics of high efficiency, low cost, accurate term translation and great development potential and etc. Machine translation is fast and efficient, this is something that artificial translation can’t catch up with. In addition, with the continuous emergence of all kinds of translation software in the market, compared with artificial translation, machine translation is cheap and sometimes even free, which greatly saves the economic cost and time for users with low translation quality requirements. What's more, compared with artificial translation, machine translation has a huge corpus, which makes the translation of some terms, especially the latest scientific and technological terms, more rapid and accurate. The accurate translation of these terms requires the translator to constantly learn, but learning needs a process, which has a certain test on the translator's learning ability and learning speed. In this regard, artificial translation has uncertainty and hysteretic nature. At the same time, with the progress of science and technology and the development of society, the function of machine translation will be more perfect and the quality of translation will be better.Today's machine translation tools and software are easy to carry, all you need to do is just to use the software and electronic dictionary in the mobile phone. There is no need to carry paper dictionaries and books for translation, which saves time and space. At the same time, machine translation covers many fields and is suitable for translation practice in different situations, such as academic, education, commercial trade, social networking, tourism, production technology, etc, it is also easy to deal with various professional terms. However, due to the limitation of translators' own knowledge, artificial translation is often limited to one or a few fields or industries. For example, it is difficult for an interpreter specializing in medical English to translate legal English.<br />
At the same time, machine translation also has its limitations. At first, machine can only operate word to word translation, which only plays the function and role of dictionary. Then, the application of syntax enables the process of sentence translation and it can be solved by using the direct translation method. When the original text and the target language are highly similar, it can be translated directly. For example, the original text "他是个老师." The target language is "he is a teacher ". With the increase of the structural complexity of the original text, the effect of machine translation is greatly reduced. Therefore, at the syntactic level, machine translation still stays in sentences with relatively simple structure. Meanwhile, the original text and the results of machine translation cannot be interchanged equally, indicating that English-Chinese translation has strong randomness, and is not rigorous and scientific enough. <br />
Nowadays, machine translation is highly dependent on parallel corpora, but the construction of parallel corpora is not perfect. At present, the resources of some mainstream languages such as Chinese and English are relatively rich, while the data collection of many small languages is not satisfactory. Moreover, the current corpus is mainly concentrated in the fields of government literature, science and technology, current affairs and news, while there is a serious lack of data in other fields, which can’t reflect the advantages of machine translation. At the same time, corpus construction lags behind. Some informative texts introducing the latest cutting-edge research results often spread the latest academic knowledge and use a large number of new professional terms, such as academic papers and teaching materials while the corpus often lacks the corresponding words of the target language, which makes machine translation powerless<br />
Besides, machine translation is not culturally sensitive. Human may never be able to program machines to understand and experience a particular culture. Different cultures have unique and different language systems, and machines do not have complexity to understand or recognize slang, jargon, puns and idioms. Therefore, their translation may not conform to cultural values and specific norms. This is also one of the challenges that the machine needs to overcome.[6] Artificial intelligence may have human abstract thinking ability in the future, but it is difficult to have image thinking ability including imagination and emotion. [7] Therefore, machine translation is often used in news, science and technology, patents, specifications and other text fields with the purpose of fact description, knowledge and information transmission. These words rarely involve emotional and cultural background. When translating expressive texts, the limitations of machine translation are exposed. The so-called expressive text refers to the text that pays attention to emotional expression and is full of imagination. Its main characteristics are subjectivity, emotion and imagination, such as novels, poetry, prose, art and so on. This kind of text attaches importance to the emotional expression of the author or character image, and uses a lot of metaphors, symbols and other expressions. Machine translation is difficult to catch up with artificial translation in this kind of text, it can only translate the main idea, lack of connotation and literary grace and it cannot have subjective feelings and rational analysis like human beings. In fact, it is not difficult to simulate the human brain, the difficulty is that it is impossible to learn from the rich social experience and life experience of excellent translators. In other words, machine translation lacks the personalization and creativity of human translation. It is this personalization and creativity that promote the development and evolution of language, and what machine translation can only output is mechanical "machine language".<br />
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===3.The Irreplaceability of Artificial Translation ===<br />
====3.1 Translation is Constrained by Context====<br />
At present, machine translation can help people deal with language communication in people's daily life and work, such as clothing, food, housing and transportation, but there is a big gap from the "faithfulness, expressiveness and elegance" emphasized by high-level translation. Language itself is art，which pays more attention to artistry than functionality, and the discipline of art is difficult to quantify and unify. Sometimes it is regular, rigorous, logical and clear, and sometimes it is random, free and logical. If it is translated by machine, it is difficult to grasp this degree. Sometimes, machine translation cannot connect words with contextual meaning. In many languages, the same word may have multiple completely unrelated meanings. In this case, context will have a great impact on word meaning, and the understanding of word meaning depends largely on the meaning read from context. Only human beings can combine words with context, determine their true meaning, and creatively adjust and modify the language to obtain a complete and accurate translation. This is undoubtedly very difficult for machine translation. Artificial translation can get rid of the constraints of the source language and translate the translation in line with the grammar, sentence patterns and word habits of the target language. In the process of translation, translators can use their own knowledge reserves to analyze the differences between the source language and the target language in thinking mode, cultural characteristics, social background, customs and habits, so as to translate a more accurate translation. Artificial translation can also add, delete, domesticate, modify and polish the translation according to the style, make up for the lack of culture, try to maintain the thought, artistic conception and charm of the original text and the style of the source language. In addition, translators can also judge and consider the words with multiple meanings or easy to produce ambiguity according to the context, so as to make the translation more clear and more accurate and improve the quality of the translation.<br />
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===4. Discussion on the Relationship Between Machine Translation and Artificial Translation ===<br />
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===5. Suggestions on the Combined Development of Machine Translation and Artificial Translation===<br />
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===6. ===<br />
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===7. ===<br />
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===Conclusion===<br />
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===References===<br />
<br />
=10 熊敏(Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts)=<br />
[[Machine_Trans_EN_10]]<br />
===Abstract===<br />
With the rapid development of information technology,machine translation technology emerged and is gradually becoming mature.In order to explore the ability of machine translation, I adopts two versions of translation, which are manual translation and machine translation(this paper uses Youdao translation) for different types of texts(according to Peter Newmark's types of text). The results are quite different in terms of quality and accuracy.<br />
<br />
===Key words===<br />
machine translation; manual translation; Newmark's type of texts<br />
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===题目===<br />
Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts<br />
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===摘要===<br />
随着信息技术的高速发展，机器翻译技术出现了，并且逐渐成熟。为了探究机器翻译的能力水平，本人根据纽马克的文本类型分类，选择了相应的译文类型，并且将其机器翻译的版本以及人工翻译的版本进行对比。就质量和准确度而言，译文的水平大相径庭。<br />
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===关键词===<br />
机器翻译；人工翻译；纽马克文本类型<br />
<br />
===1. Introduction===<br />
====1.1.Introduction to machine translation====<br />
Machine translation, also known as computer translation is a technique to translating through machine translator, such as Google translation and Youdao translation. Machine translation is one of the branches of computational linguistics, ranging from computer science, statistics, information science and so on. Machine translation plays an important role in all aspects.<br />
Machine translation can be traced back to 1940s, when British engineer Booth and American engineer Weaver proposed using computer to translate and started to study machines used for translation. However in the 1960s, reports from ALPAC (Automated Language Processing Advisory Committee) showed studies on machine translation had stagnated for a decade. In the 1970s, with the advancement of computer, machine translation was back to track. In the last decades, machine translation has mainly developed into four stages: rule-based machine translation, statistic machine translation, example-based machine translation and neural machine translation.<br />
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====1.2.Process of machine translation====<br />
The process of manual translation is different from that of machine translation. Here is the process of the former. (1) Understand source language. (2) Use target language to organize language. (3) Generate translation. Unlike manual translation, machine translation tends to analyze and code source language first, then look for related codes in corpus, and work out the code that represents target language, generating translation. But they share a common feature, which is that Lexicon, grammatical rules and syntactic structure are taken into consideration. This is one of the biggest challenges for machine translation.<br />
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===2.Newmark’s type of texts===<br />
Peter Newmark divided texts into informative type, expressive text and vocative type according to the linguistic functions of various texts. <br />
====2.11Informative text====<br />
The core of the informative texts is the truth. It is to convey facts, information, knowledge and the like. The language style of the text is objective and logical. Reports, papers, scientific and technological textbooks are all attributed to informative texts.<br />
====2.2Expressive text====<br />
The core of the expressive text is the emotion. It is to express preferences, feelings, views and so on. The language style of it is subjective. Literary works, including fictions, poems and drama, autobiography and authoritative statements belong to expressive text.<br />
====2.3Vocative text====<br />
The core of the vocative text is readership. It is to call upon readers to act in the way intended by the text. So it is reader-oriented. Such texts advertisement, propaganda and notices are of vocative text.<br />
====2.4Study Method====<br />
Manual translations of the three texts are selected from authoritative versions and universally acknowledged. And machine translations of those come from Youdao Translator. And in this thesis I will compare and evaluate the two methods in word diction, sentence structure, word order and redundancy.<br />
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===3. ===<br />
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===4. ===<br />
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===5. ===<br />
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===6. ===<br />
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===7. ===<br />
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===Conclusion===<br />
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===References===<br />
<br />
=11 陈惠妮=(Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts)=<br />
[[Machine_Trans_EN_11]]<br />
===Abstract===<br />
At present, globalization is accelerating and the market demand for language services is rapidly increasing . Machine translation, as an important translation method, can greatly improve translation efficiency due to its low cost and high speed. However, because of the limitations of machine translation and the differences between Chinese and English language, machine translation is not accurate enough. In order to balance translation efficiency and translation quality, a great number of manual revisions in translation are required for the machine translating texts. Medical papers are specialized, special and purposeful, so it requires accurate,qualified and professional translation. However, the quality of translations by machine is inefficient to meet the high-quality requirements of medical papers translation. Therefore, the introduction of pre-editing can greatly improve the efficiency and quality of machine translation.<br />
<br />
===Key words===<br />
Pre-editing, Machine translation, Medical texts<br />
<br />
===题目===<br />
Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts<br />
<br />
===摘要===<br />
在全球化加速发展的今天，市场对语言服务的需求迅速增加。机器翻译作为一种重要的翻译途径，由于其成本低、速度快，可以大大提高翻译效率。然而，由于机器翻译的局限性以及中英文语言的差异，机器翻译的准确性不高。为了平衡翻译效率和翻译质量，机器翻译文本需要大量的手工修改。<br />
医学论文具有专业性、特殊性和目的性，要求其译文准确、合格、专业。然而，机器翻译的质量较低，无法满足医学论文对翻译的高质量要求。因此，译前编辑的引入可以大大提高机器翻译的效率和质量。<br />
<br />
===关键词===<br />
译前编辑；机器翻译；医学文本<br />
<br />
===1. Introduction===<br />
===1.1 Definition of Machine Translation===<br />
As Cronin(2013) revealed: "Translation is undergoing a revolutionary upheaval. The influence of digital technology and the Internet on translation is continuous, extensive and profound. From the popularity of automatic online translation applications, translation revolution is everywhere (Cronin,2013) However,the concept of machine translation was firstly proposed in the 1930s. Since 1940s, the machine translation technology has been evolving from rule-based machine translation (RBMT) to statistical machine translation (SMT), and to neural machine translation (NMT). Machine translation refers to the automatic translation of source language into target language by using a computer system. That is, machine translation refers to the automatic translation of text from one language into another natural language by computer software or other online translation webs. Machine translation is also defined as the process of “using a computer system to automatically translate text or speech from one natural language to another” according to the definition by ISO (Cui, 2014).O 'Brien (2002) defines it as "the behavior of modifying errors in machine translation to ensure that the target translation meets certain quality requirements". On the basic level, machine translation performs mechanical substitution of words in one language for words in another language, but that rarely produces a good translation, therefore, recognition of the whole phrases and their closest counterparts in the target language is needed. Not all words in one language have equivalents in another language, and many words have more than one meaning. A huge demand for translation is greatly needed in today’s global world, which creates new opportunities for the development of machine translation, attracts more and more attention and becomes one of the current research focuses.<br />
<br />
===1.2 Definition of Pre-editing===<br />
Pre-editing means to adjust and modify the source language to make it fit more with the characteristics of the machine translation software before putting the source language before the into machine translation, so as to improve the quality of the translation machine translation (Wei Changhong, 2008:93-94). Pre-editing is to modify the original text before putting it into machine translation software, in order to improve the recognition rate of machine translation, optimize the output quality of translated text and reduce the workload of post-editing. Because pre-translation editing only needs to be modified in one language, the operation is simpler than post-translation editing, which can realize the double improvement of quality and efficiency. A good pre-editing translation can help machine translation more smoothly, thus improving the machine readability and quality of the output translation. Pre-editing is mostly applied in the following situations: one is when the original text is of poor quality and the machine is difficult to recognize the meaning of the sentence, such as user generated content with poor readability and translatability (Gerlach et al, 2003:45-53); Documents that need to be published in multiple languages; Next is when the original text contains a lot of jargon; The last is the original text has a corresponding translation memory bank. If the original text is edited, it can better match the content of the translation memory bank.<br />
<br />
===1.3 Machine Translation Mode===<br />
According to the different knowledge acquisition methods, machine translation modes can be classified as follows: one is rule-based machine translation, which is based on bilingual dictionaries and a library of language rules for each language. The quality of translation depends on whether the source language conforms to the existing rules, but the inexhaustible rules are the hinders of this model. The second mode is machine translation based on statistics. This translation model relies on the principles of mathematics and statistics to find various existing translations corresponding to the translation tasks through the employment of corpus, analyzing the frequency of their occurrence and selecting the translation with the highest frequency for output. The disadvantage of this translation model is that it ignores the flexibility of language and the importance of context. The last one is neural network language model. This model is different from previous translation models in that it uses end-to-end neural network to realize automatic translation between natural languages. At present, the quality of its translation is much higher than that of the previous translation models.<br />
===1.4 Source of Study Abstracts===<br />
The core medical journals from domestic are selected in order to make the paper more representative and authoritative, such as National Medical Journal of China, Journal of Peking University (Health Science), and Journal of Third Military Medical University. All of them include basic medical science, biomedical technology, laboratory medical science and other fields. In this way, the pre-editing approaches included are applicable enough to machine translation of medical abstracts.<br />
===1.5 Selection of Translation Software===<br />
Generally, machine translation software includes Google Translation, Youdao Translator and Niu Translation, which has their own special use in translation. Take Google as an example, it is more like neurons in human brain, enabling to learn and collect information to establish connections with its neural machine translator’s neurons. However, it also causes many errors because of the lack of enough information. In this paper, contrastive analysis will be carried on by using Google translation. On the one hand, being a pioneer in the translation of NET, it is inevitably to sue Google as the translation software to translate medical texts in this paper. Comparing the program called “Google brain”, other NMT of translation software are relatively disadvantages. On the other hand, the Google translation enjoys the largest users in the world, with its downloads of more than one billion. The output quality of Google translation is more correct and complete than other machine translation software. With years of development and improvement, Google Translation has been greatly promoted. In this paper, Chinese medical abstract will be automatically translated by Google translation, and then the translation output will be compared with the translation by human and the publication of English abstracts. The main purpose is to prove that the improvement and promotion of quality and accuracy in the medical abstracts will be obtained through the pre-editing approaches.<br />
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===2.Language Characteristics and Error Division===<br />
Since Chinese and English are two different languages, it is quite neccessary to identify their own characteristics so as to better analyze and understand the two languages. Tytler (1978) argued in his Essay on the Principles of Translation that there are three principles of translation that in all the translation should give readers the same feelings as the source text, except for complete transcript of ideas. There actually also exit some mistakes of these two languages. So the following will make some clarrifications of these two languages to ensure more accurate translation.<br />
<br />
===2.1 Language Characteristics of Medical Abstracts===<br />
Chinese and English belong to different language systems, so there are differences in their language structure and the way users think of the languages. When using machine translation from Chinese to English, due to the unequal language levels, there will be many mistakes in the translation process, especially for ESP texts, such as medical papers. <br />
Here, these differences mainly refer to the linguistic characteristics of medical abstracts. In medical abstracts, it usually includes structured and unstructured abstracts. Although in different forms, they both describe the purpose, methods and conclusions of the research.<br />
In the method section of medial abstract, several Chinese sentences can be connected with commas, but each sentence may convey different information. In contrast, English sentences contain a great deal of information, but in order to ensure clarity, some modifiers need to be isolated and then reconstructed. However, in Chinese-English machine translation, a lot of information is put into the sentence because the machine segments the sentence on the basis of the full comma.<br />
In addition, subjectless sentence is used in the objective and method parts of Chinese medical abstracts. The subjectless sentence means the sentence without or free from subject and is usually employed in two contexts. The first is "needless to say". In Chinese sentences, it is common to omit the subject of the sentence. Chinese sentences can convey meanings by using incomplete sentence structures, so Chinese speakers can understand the meanings of the sentences even though the sentence subject is omitted. The second is "emphasis of action". In this context, subjectless sentences are used to describe behaviors, especially in the study of traditional Chinese medicine abstracts. Comparatively speaking, it should be avoided in English medical abstracts. Abstract sentences are more subjective when describing the learning process, while the essential requirement of English medical abstract is objectivity. From this point, sentences without subject should be avoided in English medical abstracts. Another feature is voice. Few words with passive meanings appear in Chinese abstracts. English sentences are more favoured in using passive voice. When translating from Chinese to English, passive voice should be used to make the contents objective, which is also the basic requirement of medical papers.<br />
These are the linguistic features of Chinese medical abstracts. There are great differences in sentence structure and expression between Chinese and English medical abstracts. These differences may reduce the accuracy of machine translation, so it is necessary to introduce pre-editing to edit the source text to ensure that the source text can be accurately recognized by the machine and fully translated into English.<br />
===2.2 Error Division of Machine Translation in Translating Abstracts of Medical Papers from Chinese to English===<br />
In this paper, errors in machine translation are listed out after analyzing. Some are due to the principles, such as statistical-based MT and NMT, employed by machine translation. Based on the original errors, they can be studied from two levels. One is from the macro-level, referring to mistakes caused by objective factors. These are not human factors, but the disadvantage of machine translation and the limitations of language. The limitations of machine translation are derived from the principles and models adopted and manifests itself as a reliance on reference sources. In this case, semantic ambiguity and textual incoherence may occur in the absence of reference sources.<br />
However, for ESP texts such as medical papers, the requirements for terms and sentence patterns are far beyond the existing corpora. For unfamiliar texts (that is, the corresponding texts cannot be found in the corpora). The quality of output translation will be relatively low, which is determined by the principles of machine translation. As mentioned above, machine translation has evolved over the past few decades from rules-based to corp-based statistics to NMT. However, there still exist some limitations in machine translation.<br />
Mistakes on micro-level are mainly caused by the variations and differences of linguistic structure between Chinese and English. Chinese is an implicit language, while English is an explicit one (Lian, 2010) To put it in another way, Chinese expression does not depend on language structures, while English does the opposite. This may result in mismatches between the original and machine translated sentences. This kind of error is mainly divided into two types: component fragment and component missing. For a medical paper, such errors are very serious. As a kind of ESP discourse, medical paper has the characteristics of fixed, objective and accurate language structure. In order to reproduce the characteristics of medical abstracts in translation, it is necessary to avoid errors at the level of words and sentences and pay attention to logic and consistency.<br />
Lexical errors here refer to the inconsistency of fixed expressions in the translation of terms. Although these terms are machine translation based on the corpus, the corpus may not be perfect for ESP texts such as medical papers. Therefore, fixed expressions are not used in term translation. This is also the most common mistake in machine translation (the term here includes but is not limited to nouns). For ESP texts, medical text in particular, the fixed expressions and the accuracy of terms are of great importance.<br />
<br />
===3.===<br />
<br />
===4.===<br />
<br />
===Conclusion===<br />
<br />
===References===<br />
<br />
=12 蔡珠凤=(The Mistranslation of C-J Machine Translation of Political Statements)=<br />
[[Machine_Trans_EN_12]]<br />
===Abstract===<br />
Language is the main way of communication between people. With the continuous development of globalization, the scale of cross-border exchanges is also expanding. However, due to cultural differences and diversity, the languages of different countries and regions are very different, which seriously hinders people's communication. The demand for efficient and convenient translation tools is increasing. At the same time, with the development of network technology and artificial intelligence, recognition technology based on deep learning is more and more widely used in English, Japanese and other fields.<br />
<br />
===Key words===<br />
machine translation; political statements; mistranslation of C-J machine translation<br />
===题目===<br />
The Mistranslation of C-J Machine Translation of Political Statements<br />
===摘要===<br />
语言是人与人之间交流的主要方式。随着全球化的不断发展，跨境交流的规模也在不断扩大。然而，由于文化的差异和多样性，不同国家和地区的语言差异很大，这严重阻碍了人们的交流。对高效便捷的翻译工具的需求正在增加。同时，随着网络技术和人工智能的发展，基于深度学习的识别技术在英语、日语等领域的应用越来越广泛。<br />
<br />
===关键词===<br />
机器翻译；政治发言；政治发言中译日的误译<br />
===1. Introduction===<br />
===Introduction to machine translation===<br />
Machine translation, also known as automatic translation, is a process of using computers to convert one natural language (source language) into another natural language (target language). It is a branch of computational linguistics, one of the ultimate goals of artificial intelligence, and has important scientific research value.At the same time, machine translation has important practical value. With the rapid development of economic globalization and the Internet, machine translation technology plays a more and more important role in promoting political, economic and cultural exchanges.The development of machine translation technology has been closely accompanied by the development of computer technology, information theory, linguistics and other disciplines. From the early dictionary matching, to the rule translation of dictionaries combined with linguistic expert knowledge, and then to the statistical machine translation based on corpus, with the improvement of computer computing power and the explosive growth of multilingual information, machine translation technology gradually stepped out of the ivory tower and began to provide real-time and convenient translation services for general users.<br />
<br />
=== C-J machine translation software===<br />
Today's online machine translation software includes Baidu translation, Tencent translation, Google translation, Youdao translation, Bing translation and so on. Google was the first company to launch the machine translation system, and Baidu was the first company to import the machine translation system in China. In addition, Tencent and Youdao have attracted much attention.Machine translation is the process of using computers to convert one natural language into another. It usually refers to sentence and full-text translation between natural languages. In order to continuously improve the translation quality, R & D personnel have added artificial intelligence technologies such as speech recognition, image processing and deep neural network to machine translation on the basis of traditional machine translation based on rules, statistics and examples.With the increase of using machine translation, the joint cooperation between manual translation and machine translation will also increase significantly in the future. What criteria should be used to evaluate the quality of machine translation? In the evolving field of machine translation, there is an urgent need to clarify the unsolvable questions and solved problems.<br />
<br />
===The history of machine translation===<br />
The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. alchuni put forward the idea of using machines for translation. In 1933, Soviet inventor П.П. Trojansky designed a machine to translate one language into another, and registered his invention on September 5 of the same year; However, due to the low technical level in the 1930s, his translation machine was not made. In 1946, the first modern electronic computer ENIAC was born. Shortly after that, W. weaver, an American scientist and A. D. booth, a British engineer, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947 when discussing the application scope of electronic computer. In 1949, W. Weaver published the translation memorandum, which formally put forward the idea of machine translation. After 60 years of ups and downs, machine translation has experienced a tortuous and long development path. The academic community generally divides it into the following four stages:<br />
Pioneering period（1947-1964）<br />
In 1954, with the cooperation of IBM, Georgetown University completed the English Russian machine translation experiment with ibm-701 computer for the first time, showing the feasibility of machine translation to the public and the scientific community, thus opening the prelude to the study of machine translation.<br />
It is not too late for China to start this research. As early as 1956, the state included this research in the national scientific work development plan. The topic name is "machine translation, the construction of natural language translation rules and the mathematical theory of natural language". In 1957, the Institute of language and the Institute of computing technology of the Chinese Academy of Sciences cooperated in the Russian Chinese machine translation experiment, translating 9 different types of more complex sentences.<br />
From the 1950s to the first half of the 1960s, machine translation research has been on the rise. The United States and the former Soviet Union, two superpowers, have provided a lot of financial support for machine translation projects for military, political and economic purposes, while European countries have also paid considerable attention to machine translation research due to geopolitical and economic needs, and machine translation has become an upsurge for a time. In this period, although machine translation is just in the pioneering stage, it has entered an optimistic period of prosperity.<br />
Frustrated period（1964-1975）<br />
In 1964, in order to evaluate the research progress of machine translation, the American Academy of Sciences established the automatic language processing Advisory Committee (Alpac Committee) and began a two-year comprehensive investigation, analysis and test.<br />
In November 1966, the committee published a report entitled "language and machine" (Alpac report for short), which comprehensively denied the feasibility of machine translation and suggested stopping the financial support for machine translation projects. The publication of this report has dealt a blow to the booming machine translation, and the research of machine translation has fallen into a standstill. Coincidentally, during this period, China broke out the "ten-year Cultural Revolution", and basically these studies also stagnated. Machine translation has entered a depression.<br />
convalescence（1975-1989）<br />
Since the 1970s, with the development of science and technology and the increasingly frequent exchange of scientific and technological information among countries, the language barriers between countries have become more serious. The traditional manual operation mode has been far from meeting the needs, and there is an urgent need for computers to engage in translation. At the same time, the development of computer science and linguistics, especially the substantial improvement of computer hardware technology and the application of artificial intelligence in natural language processing, have promoted the recovery of machine translation research from the technical level. Machine translation projects have begun to develop again, and various practical and experimental systems have been launched successively, such as weinder system Eurpotra multilingual translation system, taum-meteo system, etc.<br />
However, after the end of the "ten-year holocaust", China has perked up again, and machine translation research has been put on the agenda again. The "784" project has paid enough attention to machine translation research. After the mid-1980s, the development of machine translation research in China has further accelerated. Firstly, two English Chinese machine translation systems, ky-1 and MT / ec863, have been successfully developed, indicating that China has made great progress in machine translation technology.<br />
New period(1990 present)<br />
With the universal application of the Internet, the acceleration of the process of world economic integration and the increasingly frequent exchanges in the international community, the traditional way of manual operation is far from meeting the rapidly growing needs of translation. People's demand for machine translation has increased unprecedentedly, and machine translation has ushered in a new development opportunity. International conferences on machine translation research have been held frequently, and China has made unprecedented achievements. A series of machine translation software have been launched, such as "Yixing", "Yaxin", "Tongyi", "Huajian", etc. Driven by the market demand, the commercial machine translation system has entered the practical stage, entered the market and came to the users.<br />
Since the new century, with the emergence and popularization of the Internet, the amount of data has increased sharply, and statistical methods have been fully applied. Internet companies have set up machine translation research groups and developed machine translation systems based on Internet big data, so as to make machine translation really practical, such as "Baidu translation", "Google translation", etc. In recent years, with the progress of in-depth learning, machine translation technology has further developed, which has promoted the rapid improvement of translation quality, and the translation in oral and other fields is more authentic and fluent.<br />
<br />
===The problem of machine translation at present===<br />
Error is inevitable<br />
Many people have misunderstandings about machine translation. They think that machine translation has great deviation and can't help people solve any problems. In fact, the error is inevitable. The reason is that machine translation uses linguistic principles. The machine automatically recognizes grammar, calls the stored thesaurus and automatically performs corresponding translation. However, errors are inevitable due to changes or irregularities in grammar, morphology and syntax, such as sentences with adverbials after "give me a reason to kill you first" in Dahua journey to the West. After all, a machine is a machine. No one has special feelings for language. How can it feel the lasting charm of "the tenderness of lowering its head, like the shame of a water lotus? After all, the meaning of Chinese is very different due to the changes of morphology, grammar and syntax and the change of context. Even many Chinese people are zhanger monks - they can't touch their heads, let alone machines.<br />
Bottleneck<br />
In fact, no matter which method, the biggest factor affecting the development of machine translation lies in the quality of translation. Judging from the achievements, the quality of machine translation is still far from the ultimate goal.<br />
Chinese mathematician and linguist Zhou Haizhong once pointed out in his paper "fifty years of machine translation": to improve the quality of machine translation, the first thing to solve is the problem of language itself rather than programming; It is certainly impossible to improve the quality of machine translation by relying on several programs alone. At the same time, he also pointed out that it is impossible for machine translation to achieve the degree of "faithfulness, expressiveness and elegance" when human beings have not yet understood how the brain performs fuzzy recognition and logical judgment of language. This view may reveal the bottleneck restricting the quality of translation. <br />
It is worth mentioning that American inventor and futurist ray cozwell predicted in an interview with Huffington Post that the quality of machine translation will reach the level of human translation by 2029. There are still many disputes about this thesis in the academic circles.<br />
<br />
===2.Mistranslation of Chinese Japanese machine translation===<br />
===2.1Vocabulary mistranslation in Chinese Japanese machine translation===<br />
===2.1.1Mistranslation of proper nouns===<br />
===2.1.2Mistranslation of Polysemy===<br />
===2.1.3Mistranslation of compound words===<br />
===2.2 Syntactic mistranslation in Chinese Japanese machine translation===<br />
===2.2.1Main dynamic Mistranslation===<br />
===2.2.2Dynamic Mistranslation===<br />
===2.2.3Mistranslation of tenses===<br />
===2.2.4Mistranslation of honorifics===<br />
===3.===<br />
===4.===<br />
===Conclusion===<br />
===References===<br />
<br />
=13 陈湘琼Chen Xiangqiong（Study on Post-editing from the Perspective of Functional Equivalence Theory ）=<br />
[[Machine_Trans_EN_13]]<br />
<br />
=14 Bi bi Nadia（Machine Translation a Challenge for Human Translators)=<br />
[[Machine_Trans_EN_14]]<br />
<br />
===Abstract===<br />
Machine translation is a big obstacle in the way of Human translators or interpretors although it is quick and less time consuming.people are trying to get translation of their target language using through source language.For this purpose they are using digital apps like Google translation Google translation does not give accurate and exact interpretation.Google translator is translates word to word translate that doesn't clarifies it's true and actual meaning.On the other hand human translators can give exact and accurate translation ,they take care of grammatical errors , diction and sentence structure.They clarify the purpose of target language through using source language.<br />
===Keywords===<br />
Descriptive translation, academic, interdiscipline, comparative literature, localization,Translatology,school of thought, translation , studies, linguistics, corresponding<br />
===Body of article===<br />
Translation is the process of reworking text from one language into another to maintain the original message and communication. But, like everything else, there are different methods of translation, and they vary in form and function. What is translation? The term has become widely used among knowledge transfer researchers and practitioners, especially in the fields of health and health care. In a landmark review, Jonathan Lomas began to argue that 'The tasks… may be defined as to establish and maintain links between researchers and their audience, via the appropriate translation of research findings' (Lomas 1997, p 4). In 2004, the World Health Organization's World Report on Knowledge for Better Health suggested that 'One of the key contributions of research to health systems is the translation of knowledge into actions' (WHO 2004, p 33 and p 100). By 2006, special issues of WHO's Bulletin as well as the journals Evaluation and the Health Professions and the Journal of Continuing Education in the Health Professions were dedicated to translation.But what does 'translation' mean? It may be a new word for an old problem, meaning nothing more than 'transfer'. The rapidly emerging field of 'translational medicine' seems to take translation to mean generally what transfer might have meant, that is the transmission of knowledge and evidence 'from bench to bedside'. As one commentator has observed, "Translational medicine" as a fashionable term is being increasingly used to describe the wish of biomedical researchers to ultimately help patients' (Wehling 2008, abstract). <br />
Semantic uncertainty persists, not least because of the different interests of scientists, clinicians, patients and commercial firms (Littman et al 2007): 'Translational research means different things to different people, but it seems important to almost everyone' (Woolf 2008, p 211).<br />
In related fields, such as public health (Armstrong et al 2006), 'translation' seems to signify dissatisfaction with 'transfer'. It wants to move away from thinking of knowledge transfer as a form of technology transfer or dissemination, rejecting if only by implication its mechanistic assumptions and its model of linear messaging from A to B. But still, what does it signify?<br />
<br />
===Why translation?===<br />
'Translation' indicates a closer attention to the problem of shared meaning and how it might be developed. It seems to represent some new epistemological lubricant, facilitating the dissemination of texts and the application and use of the knowledge and information they in. Simply, translation might be the key to transfer. And yet, when we stop to think, we are more ambivalent. What is translated often seems somehow inferior, not real or original. Note how readily commentators reach for the idea that things might be 'lost in translation'. Knowing at a distance – made in and mediated by translation - makes for incomplete renditions, blurred images, partial truths. So what might 'translation' really mean? The purpose of this paper is to set out, for policy makers and practitioners, the theoretical and conceptual resources translation holds and seems to represent. In doing so, it explores understandings of translation in the fields of literature and linguistics and in the sociology of science and technology. It begins by setting out just why this idea of translation should make immediate, intuitive sense in relation to research, policy and practice.<br />
1translation in research, policy and practice research as translation<br />
Research often entails translation from one language to another: where data is collected from more than one ethnic group, for example, or where the language of the researcher is other than that of the research subject. It may draw on a secondary literature or source documents written in different languages, and may be published and disseminated in languages other than the one in which it is first written up.<br />
2In a different way, to conduct an interview is to ask for an account of experience and its meanings, but it is also to construct and translate that experience in terms defined at least in part by the researcher. In representing what is said, transcripts then select data, usually excluding significant gesture and eye-contact, for example. Often, certain characteristics of speech-acts (such as hesitations) will be edited out. In turn, the format of the transcript shapes the analytic use the researcher may make of it. The basis of research 'findings', then, is an artefact, a transcript or translation, not an original interaction (Ochs 1979, Barnes, Bloor and Henry 1996, Ross 2009).<br />
3In this way, the researcher recasts aspects of his or her problem or topic in new, scientific form: 'All researchers "translate" the experiences of others' (Temple 1997, p 609). Research is invariably conducted in a sort of 'metalanguage' (Hantrais and Ager 1985): the research process can be conceived as one of successive translations (from theoretical formulation to operationalization, transcription, interpretation and dissemination). Theorization is a process of reciprocal back and forth between theory and fact, in which conceptions of each are revised in order that one fit the other (Baldamus 1974).<br />
4 It is a kind of translation: a rereading, re-use, re-application or re-representation of what we know in new terms (Turner 1980). Referencing, too, is an act of translation, a form of appropriation and incorporation of one text by another (Gilbert 1977).policy as translation Each of the fields covered by the paper is diverse and ill-defined, and there is no intention here to provide a comprehensive account of any them. Sources have been chosen for their relevance: main references are cited in the text, and additional sources listed in footnotes.<br />
<br />
2For a brief introduction to the technical issues involved in social research in more than one language, see Birbili (2000). For translation issues in survey research and question design in general, see Ervin and Bower (1952) and Deutscher (1968); on translating survey research instruments (in this instance health-related quality of life measures), Bowden and Fox-Rushby (2003). On the use of translators and interpreters, see Temple (1997) and Jentsch (1998).<br />
3For an interesting discussion of this problem, see Bourdieu (1999), esp pp 621-626, 'The risks of writing'. <br />
===Difference between machine translation and human translators===<br />
Few people disagree on the differences between the two, but many argue over the quality of the translations. How accurate are machine translations? How reliable are human translations? Some say machine translation produces near-perfect translations while others are adamant that translations are incomprehensible and cause more problems than they solve. Results will, of course, vary depending on the source and target languages, the machine translation service used (e.g. Google Translate), and the complexity of the original text.<br />
Machine translation, love it or hate it, is here to stay. In fact, the machine translation market is growing at such a fast pace that it is predicted to reach $980 million by 2022.<br />
===Machine translation: the pros and cons===<br />
The advantages of machine translation generally come down to two factors: it’s faster and cheaper. The downside to this is the standard of translation can be anywhere from inaccurate, to incomprehensible, and potentially dangerous (more on that shortly).<br />
==The advantages of machine translation==<br />
Many free tools are readily available (Google Translate, Skype Translator, etc.)<br />
Quick turnaround time <br />
You can translate between multiple languages using one tool<br />
Translation technology is constantly improving<br />
The disadvantages of machine translation<br />
Level of accuracy can be very low <br />
Accuracy is also very inconsistent across different languages<br />
==Machines can't translate context ==<br />
Mistakes are sometimes costly <br />
Sometimes translation simply doesn’t work<br />
The most important thing to consider with any kind of translation is the cost of potential mistakes. Translating instructions for medical equipment, aviation manuals, legal documents and many other kinds of content require 100% accuracy. In such cases, mistakes can cost lives, huge amounts of money and irreparable damage to your company’s image. So choose carefully!<br />
Human translation: the pros and cons<br />
Human translation essentially switches the table in terms of pros and cons. A higher standard of accuracy comes at the price of longer turnaround times and higher costs. What you have to decide is whether that initial investment outweighs the potential cost of mistakes. Alternatively, whether mistakes simply aren’t an option, like the cases we looked at in the previous section.<br />
<br />
==The advantages of human translation ==<br />
It’s a translator’s job to ensure the highest accuracy<br />
Humans can interpret context and capture the same meaning, rather than simply translating words<br />
Human translators can review their work and provide a quality process<br />
Humans can interpret the creative use of language, e.g. puns, metaphors, slogans, etc.<br />
Professional translators understand the idiomatic differences between their languages<br />
Humans can spot pieces of content where literal translation isn’t possible and find the most suitable alternative<br />
The disadvantages of human translation<br />
Turnaround time is longer <br />
Translators rarely work for free <br />
Unless you use a translation agency, with access to thousands of translators, you’re limited to the languages any one translator can work with<br />
Simply put, human translation is your best option when accuracy is even remotely important. Other considerations to make are the complexity of your source material and the two languages you’re translating between – both of which can render machines pretty useless.<br />
<br />
When to use machine and human translation<br />
The truth is, the debate over machine vs human translation is an unnecessary distraction. What we should really be talking about is when to use these two different types of translation services, because they both serve a very valid purpose.<br />
<br />
Examples of when to use machine translation<br />
When you have a large bulk of content to translate and the general meaning is enough<br />
When your translation never reaches the final audience, e.g. you’re translating a resource as research for another piece of content<br />
Translating documents for internal use within a company, provided 100% accuracy isn’t needed<br />
To partially translate large chunks of content for a human translator to improve upon<br />
Examples of when to use human translation<br />
When accuracy is important<br />
Most cases where your translated content is received by a consumer audience<br />
When you have a duty of care to provide accurate translations (e.g. legal documents, product instructions, medical guidelines or health and safety content)<br />
When translating marketing material or other texts for creative language uses.<br />
<br />
===Conclusion ===<br />
<br />
In the light of above mentioned facts and figures here by we would say that machine translation is challenge for human translators because it can reduces the wokload of translation but can't give accurate and exact translation of the traget language.It can be less reliable than human translation..</div>Chen Huinihttps://bou.de/u/index.php?title=Machine_translation&diff=129204Machine translation2021-12-06T02:00:36Z<p>Chen Huini: /* 1.1 Definition of Machine Translation */</p>
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<div>'''Machine Translation - A challenge or a chance for human translators?'''<br />
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[[Machine_translation|Overview Page of Machine Translation]]<br />
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=1 卫怡雯(A Comparison Between the Quality of Machine Translation and Human Translation——A Case Study of the Application of artificial intelligence in Sports Events)=<br />
[[Machine_Trans_EN_1]]<br />
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=2 吴映红（The Introduction of Machine Translation)= <br />
[[Machine_Trans_EN_2]]<br />
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=3 肖毅瑶(On the Realm Advantages And Symbiotic Development of Machine Translation And Huamn Translation)=<br />
[[Machine_Trans_EN_3]]<br />
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=4 王李菲 （Comparison Between Neural Machine Translation of Netease and Traditional Human Translation—A Case Study of The Economist Articles)= <br />
[[Machine_Trans_EN_4]]<br />
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===Abstract===<br />
Machine translation is a subfield of artificial intelligence and natural language processing that investigates transforming the source language into the target language. On this basis, the emergence of neural machine translation, a new method based on sequence-to-sequence model, improves the quality and accuracy of translation to a new level. As one of the earliest companies to invest in machine translation in China, Netease launched neural machine translation in 2017, which adopts the unique structure of neural network to encode sentences, imitating the working mechanism of human brain, and generates a translation that is more professional and more in line with the target language context. This paper takes the articles in The Economist as the corpus for analysis, and aims to explore the types and causes of common errors, as well as the advantages and challenges of each, through the comparative analysis of Netease neural machine translation and human translation, and finally to forecast the future development trend and make a summary of this paper.<br />
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===Key words===<br />
Neural Machine Translation; Human Translation; Contrastive Analysis<br />
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===题目===<br />
有道神经网络机器翻译与传统人工翻译的译文对比——以经济学人语料为例<br />
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===摘要===<br />
机器翻译研究将源语言所表达的语义自动转换为目标语言的相同语义，是人工智能和自然语言处理的重要研究分区。在此基础上，一种基于序列到序列模型的全新机器翻译方法——神经机器翻译的出现让译文的质量和准确度提升到了新的层次。网易作为国内最早投身机器翻译的公司之一，在2017年上线的神经网络翻译采用了独到的神经网络结构，模仿人脑的工作机制对句子进行编码，生成的译文更具专业性，也更符合目的语语境。本文以经济学人内的文章为分析语料，旨在通过对网易神经机器翻译和人工翻译的英汉译文进行对比分析，探究常见错误类型及生成原因，以及各自存在的优势与挑战，最后展望未来发展趋势，并对本文做出总结。 <br />
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===关键词===<br />
神经网络翻译；人工翻译；对比分析<br />
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===1. Introduction===<br />
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Nowadays, the process of economic globalization has accelerated overwhelmingly, and considerable resources are poured into the business field. As a branch of global language English, business English is proposed under the theoretical framework of English for Specific Purpose (ESP), serves the international business activities which is a professional subject requiring specialized English. As the medium that helps people with different cultural backgrounds to understand each other, business translation is required to be “formal, accurate, standardized and smooth”, which challenged both the machine translation and human translator.<br />
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With the urgent requirement for more precise and higher quality translation, recent years have witnessed the rapid development of neural machine translation (NMT), which has replaced traditional statistical machine translation (SMT) to become a new mainstream technique, playing a crucial part in many fields, like business, academic and industry. Compared with SMT, NMT model is more like an organism. There are many parameters in the model that can be adjusted and optimized for the same goal, making the combination and interaction more organic and the overall translation effect better, which greatly matched the demands of business translation. <br />
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The Economist is an international news and Business Weekly offering clear coverage, commentary and analysis of global politics, business, finance, science and technology. A huge number of terminologies plus the polysemy contained in the texts, put forward a tricky problem to both machine translation and human translator. In view of this, this paper makes a comparative analysis of Netease neural machine translation and human translation, aiming to explore the types and causes of common errors, as well as the advantages and challenges of each. In the end, this paper will forecast the future development, hoping to promote the development of translation studies in China.<br />
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===2.2. The Development Process of Machine Translation ===<br />
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Since the IBM model was put forward by the researcher Peter Brown in the early 1990s, statistical methods have gradually become the mainstream of machine translation research. This method has greatly promoted the development of machine translation technology. In recent years, a variety of statistical machine translation models have emerged, such as phrase-based translation model, hierarchical phrase translation model and syntactic translation model, then the translation quality has been greatly improved.<br />
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Since 2002, BLEU, an automatic translation quality evaluation method, has greatly promoted the development of statistical machine translation technology and effectively reduced the cost of manual evaluation. In recent years, with the technical maturity and stability of statistical machine translation, especially phrase-based machine translation, statistical machine translation technology has been making strong strides towards practical and commercial application. Therefore, with the rapid development of technology, people have gradually built-up confidence in machine translation, and the social demand for machine translation has been increasing day by day, with higher and higher expectations.<br />
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However, from the perspective of academic research, both phrase-based translation models and syntactic translation models have experienced a rapid development stage, and the existing theoretical methods and technical models have begun to show "bottlenecks" in the improvement of translation performance. In addition, from the perspective of industrialization and utilitarianism, there is an urgent need for a more practical machine translation system, but the gap between the results of machine translation and the requirements of human beings is still very large. Therefore, for the researchers of machine translation, while excited to see the BLEU score of machine translation system evaluation is getting higher and higher, and the performance of online machine translation system developed by Google, Baidu, Netease and other enterprises is developing with each passing day, they are facing more and more challenges.<br />
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Aiming to solve these problems, many technological giants are striving to find a new way to improve both the quality and efficiency of machine translation. There was a breakthrough which bought machine translation to a new level. Since 2014, the end-to-end neural machine translation has developed rapidly, compared with the statistical machine translation, the translation quality received a significant boost.<br />
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The previous statistical machine translation was more like a mechanical system. Each module has its own function and goal, and then outputs the translation results through mechanical splicing. Its main disadvantage is that the model contains low syntactic and semantic components, so it will encounter problems when dealing with languages with large syntactic differences, such as Chinese-English. Sometimes the result is unreadable even though it is “word-for-word”.<br />
On the contrary, neural machine translation are consisted of several components, including phrase conditions, partial conditions, sequential conditions, primitive models, and so on. Its core is deep learning of artificial intelligence which can imitate the working mechanism of human brain and adopt unique neural network structure to model the whole process of translation. The whole model is composed of a large number of “neurons”, and each “neuron” has to complete some simple tasks, and then through the combination of all of them to coordinate the work, a much better translation text appears. <br />
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Since neural machine translation puts more emphasis on context and the whole text, it produces more coherent and comprehensible content to readers than traditional statistical machine translation, and be widely accepted and used in various field in a very short time. In 2017, at the GMIC (Global Mobile Internet Congress), Duan Yitao, the chief scientist of Netease, delivered a keynote speech titled “Machine Translation has Its Own Way” and announced an exciting news: the neural machine translation technology independently developed by Netease has been officially launched. This technology launched by Youdao this time has been jointly developed by Netease Youdao and Netease Hangzhou Research Institute for over two years. It will serve Youdao Dictionary, Youdao Translator, Youdao Web version, Youdao E-reader and other products, expecting to bring super-convenient product experience to users. In addition, Youdao Translation officer also launched photo translation, users only need to take pictures of the text, can show the results of neural network translation in real time. <br />
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As a pioneer of machine translation in China, the development process of Netease YouDao is exactly the paradigm of the history of machine translation in China. Therefore, in this paper, the neural machine translation technology developed by Netease will be compared with human translators. The same excerpts selected from The Economist are translated by both of them, then the different versions will be analyzed by the translation criterion so as to figure out their respective strengths and weaknesses, bringing consideration to current translation situation and references to future development.<br />
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===3.Comparative Analysis of Errors in English-Chinese Translation ===<br />
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===4.===<br />
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===5. ===<br />
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===Conclusion===<br />
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===References===<br />
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=6 徐敏赟(Machine Translation Based on Neural Network --Challenge or Chance?)=<br />
[[Machine_Trans_EN_6]]<br />
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=7 颜莉莉(一带一路背景下人工智能与翻译人才的培养)=<br />
[[Machine_Trans_EN_7]]<br />
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===Abstract===<br />
In the era of artificial intelligence, artificial intelligence has been applied to various fields. In the field of translation, traditional translation models can no longer meet the rapid development and updating of the information age. The development of machine translation has brought structural changes to the language service industry, which poses challenges to the cultivation of translation talents. Under the background of "The Belt and Road initiative", translation talents have higher and higher requirements on translation literacy. Artificial intelligence and translation technology are used to reform the training mode of translation talents, so as to better serve the development of The Times. This paper mainly explores the cultivation of artificial intelligence and translation talents under the background of the Belt and Road Initiative. The cultivation of translation talents is moving towards comprehensive cultivation of talents. On the contrary, artificial intelligence and machine translation can also be used to improve the teaching mode and teaching content, so as to win together in cooperation.<br />
===Key words===<br />
Artificial intelligence,Machine translation,cultivation of translation talents,"The Belt and Road initiative"<br />
===题目===<br />
一带一路背景下人工智能与翻译人才的培养<br />
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===摘要===<br />
进入人工智能时代，人工智能被应用于各个领域。在翻译领域，传统的翻译模式已无法满足信息化时代的飞速发展和更新，机器翻译的发展给语言服务行业带来了结构性改变，这对翻译人才的培养提出了挑战。“一带一路”背景下，对翻译人才的翻译素养要求越来越高，利用人工智能和翻译技术对翻译人才培养模式进行革新，更好为时代发展服务。本文主要探究在一带一路背景下人工智能和翻译人才培养，翻译人才的培养过程中正向对人才的综合性培养，反之也可以利用人工智能和机器翻译完善教学模式和教学内容，在合作中共赢。<br />
===关键词===<br />
人工智能；机器翻译；翻译人才培养；一带一路<br />
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===1. Introduction===<br />
With the development of science and technology in China, artificial intelligence has also been greatly improved, and related technologies have been applied to various fields, such as the use of intelligent robots to deliver food to quarantined people during the epidemic, which has made people's lives more convenient. The most controversial and widely discussed issue is machine translation. Before the emergence of machine translation, translation was generally dominated by human translation, including translation and interpretation, which was divided into simultaneous interpretation and hand transmission, etc. It takes a lot of time and energy to cultivate a translation talent. However, nowadays, the era is developing rapidly and information is updated rapidly. As a translation talent, it is necessary to constantly update its knowledge reserve to keep up with the pace of The Times. The emergence of machine translation has also posed challenges to translation talents and the training of translation talents. Although machine translation had some problems in the early stage, it is now constantly improving its functions. In the context of the belt and Road Initiative, both machine translation and human translation are facing difficulties. Regardless of whether human translation is still needed, what is more important at present is how to train translators to adapt to difficulties and promote the cooperation between human translation and machine translation.<br />
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===2.Development status of machine translation in the era of artificial intelligence ===<br />
With the development of AI technology, machine translation has made great progress and has been applied to people's lives. For example, more and more tourists choose to download translation software when traveling abroad, which makes machine translation take an absolute advantage in daily email reply and other translation activities that do not require high accuracy. The translation software commonly used by netizens include Google Translation, Baidu Translation, Youdao Translation, IFly.com Translation, etc. Even wechat and other chat software can also carry out instant Translation into English. Some companies have also launched translation pens, translation machines and other equipment, which enables even native speakers to rely on machine translation to carry out basic communication with other Chinese people.<br />
But so far, machine translation still faces huge problems. Although machine translation has made great progress, it is highly dependent on corpus and other big data matching. It does not reach the thinking level of human brain, and cannot deal with the problem of translation differences caused by culture and religion. In addition, many minor languages cannot be translated by machine due to lack of corpus.<br />
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What's more, most of the corpus is about developed countries such as Britain and France, and most of the corpus is about diplomacy, politics, science and technology, etc., while there are very few about nationality, culture, religion, etc.<br />
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In addition, machine translation can only be used for daily communication at present. If it involves important occasions such as large conferences and international affairs, it is impossible to risk using machine translation for translation work. Professional translators are required to carry out translation work. So machine translation still has a long way to go.<br />
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===3.Challenges in the training of translation talents in universities===<br />
The cultivation of translators is targeted at the market. Professors Zhu Yifan and Guan Xinchao from the School of Foreign Languages at Shanghai Jiao Tong University believe that the cultivation of translators can be divided into four types: high-end translators and interpreters, senior translators and researchers, compound translators and applied translators.<br />
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From their names, it can be seen that high-end translators and interpreters and senior translators and researchers talents have high requirements on the knowledge and quality of interpreters, because they have to face the changing international situation, and have to deal with all kinds of sensitive relations and political related content, they should have flexible cross-cultural communication skills. In addition, for literature, sociology and humanities academic works, it is not only necessary to translate their content, but also to understand their essence. Therefore, translators should not only have humanistic feelings, but also need to have a deep understanding of Chinese and western culture.<br />
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However, there is not much demand for this kind of translation in the society. Such high-level translation requirements are not needed in daily life and work. The greatest demand is for compound translators, which means that they should master knowledge in a specific field while mastering a foreign language. For example, compound translators in the financial field should not only be good at foreign languages, but also master financial knowledge, including professional terms, special expressions and sentence patterns.<br />
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Now we say that machine translation can replace human translation should refer to the field of compound translation talents. Although AI technology has enabled machine translation to participate in creation, it does not mean that compound translation talents will be replaced by machines. The complexity of language and the flexible cross-cultural awareness required in communication make it impossible for machine translation to completely replace human translation.<br />
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The last type of applied translation talents are mostly involved in the general text without too much technical content and few professional terms, so it is easy to be replaced by machine translation.<br />
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Therefore, the author thinks that what universities are facing at present is not only how to train translation talents to cope with the development of machine translation, but to consider the application of machine translation in the process of training translation talents to achieve human-machine integration, so as to better complete the translation work.<br />
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===4.The Language environment and opportunities and challenges of the Belt and Road initiative===<br />
During visits to Central and Southeast Asian countries in September and October 2013, Chinese President Xi Jinping put forward the major initiative of jointly building the Silk Road Economic Belt and the 21st Century Maritime Silk Road. And began to be abbreviated as the Belt and Road Initiative.<br />
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According to the Vision and Actions for Jointly Building silk Road Economic Belt and 21st Century Maritime Silk Road, the Silk Road Economic Belt focuses on connecting China, Central Asia, Russia and Europe (the Baltic Sea). From China to the Persian Gulf and the Mediterranean Sea via Central and West Asia; China to Southeast Asia, South Asia, Indian Ocean. The focus of the 21st Century Maritime Silk Road is to stretch from China's coastal ports to Europe, through the South China Sea and the Indian Ocean. From China's coastal ports across the South China Sea to the South Pacific.<br />
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The Belt and Road "construction is comply with the world multi-polarization and economic globalization, cultural diversity, the initiative of social informatization tide, drive along the countries achieve economic policy coordination, to carry out a wider range, higher level, the deeper regional cooperation and jointly create open, inclusive and balanced, pratt &whitney regional economic cooperation framework.<br />
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====4.1The language environment of the Belt and Road====<br />
The "Belt and Road" involves a wide range of countries and regions, and their languages and cultures are very complex. How to make good use of language, do a good job in translation services, actively spread Chinese culture to the world, strengthen the ability of discourse, and tell Chinese stories well, the first thing to do is to understand the language situation of the countries along the "Belt and Road".<br />
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=====4.1.1The most common language in countries along the "Belt and Road" =====<br />
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There are a wide variety of languages spoken in 65 countries along the Belt and Road, involving nine language families. However, The status of English as the first language in the world is undeniable. Most of the countries participating in the Belt and Road are developing countries, and many of them speak English as their first foreign language. Especially in southeast Asian and South Asian countries, English plays an important role in foreign communication, whether as the official language or the first foreign language. Besides English, more than 100 million people speak Russian, Hindi, Bengali, Arabic and other major languages in the "Belt and Road" countries. It can also be seen that a common feature of languages in countries along the "Belt and Road" is the popularization of English education. English is widely used in international politics, economy, culture, education, science and technology, playing the role of the most important language in the world.<br />
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=====4.1.2The complex language conditions of countries along the "Belt and Road" =====<br />
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The languages spoken in countries along the Belt and Road involve nine major language families and almost all the world's religious types. Differences in religious beliefs also result in differences in culture, customs and social values behind languages. The languages of some countries along the belt and Road have also been influenced by historical and realistic factors, such as colonization, internal division and immigration. <br />
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India, for example, has no national language, but more than 20 official languages. India is a multi-ethnic country, a total of more than 100 people, one of the most obvious difference between nation and nation is the language problem. Therefore, according to the difference of language, India divides different ethnic groups into different states, big and small. Ethnic groups that use the same language are divided into one state. If there are two languages in a state, the state is divided into two parts. And Indian languages differ not only in word order but also in the way they are written. In India, for example, Hindi is spoken by the largest number of people in the north, with about 700 million speakers and 530 million as their first language. It is written in The Hindu language and belongs to the Indo-European language family. Telugu in the east is spoken by about 95 million people and 81.13 million as their first language. It is written in Telugu, which belongs to the Dravidian language family and is quite different from Hindi. As a result, a parliamentary session in India requires dozens of interpreters. <br />
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These factors cannot be ignored in the process of translation, from language communication to cultural understanding, from text to thought exchange, through the bridge of language to truly connect the people, so as to avoid misreading and misunderstanding caused by differences in language and national conditions.<br />
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====4.2 Opportunities and challenges of the "Belt and Road" ====<br />
With the promotion of the Belt and Road Initiative, there has been an unprecedented boom in translation. In the previous translation boom in China, most of the foreign languages were translated into Chinese, and most of the foreign cultures were imported into China. However, this time, in the context of the "Belt and Road" initiative, translating Chinese into foreign languages has become an important task for translators. As is known to all, there are many different kinds of "One Belt And One Road" along the national language and culture is complex, the service "area" construction has become a factor in Chinese translation talents training mode reform, one of the foreign language universities have action, many colleges and universities to establish the "area" all the way along the country's small language major, as a result, "One Belt And One Road" initiative to promote, It has brought unprecedented opportunities for human translation. The cultivation of diversified translation talents and the cultivation of translation talents in small languages is an urgent problem to be solved in China. The cultivation of translation talents cannot be completed overnight, and the state needs to reform the training mode of translation talents from the perspective of language strategic development. Only in this way can we meet the new demand for human translation under the new situation of the belt and Road Initiative.<br />
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For a long time, the traditional orientation of translation curriculum and training goal in colleges and universities is to train translation teachers and translators in need of society through translation theory and practice and literary translation practice, which cannot meet the needs of society. Since 2007, in order to meet the needs of the socialist market economy for application-oriented high-level professionals, the Academic Degrees Committee of The State Council approved the establishment of Master of Translation and Interpreting (MTI for short). After joining the pilot program of MTI, more and more universities are reforming the curriculum and training mode of master of Translation in order to cultivate translators who meet the needs of the society.<br />
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Language is an important carrier of culture, and translation is an important link for exporting culture. The quality of translation output also reflects the cultural soft power of a country. With the rise of China, more and more people are interested in Chinese culture, and the number of Chinese learners keeps increasing. Under the background of "One Belt and One Road", excellent translators are urgently needed to spread Chinese culture. With the promotion of "One Belt and One Road" Initiative, the number of other countries learning mutual learning and cultural exchanges with China has increased unprecedeningly, bringing vigorous opportunities for the spread of Chinese culture. Translation talents who understand small languages and multi-lingual translators are needed. They should not only use language to convey information, but also use language as a lubricant for communication.<br />
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===5.Training translation talents from the perspective of machine translation===<br />
Under the prevailing environment of machine translation, it poses a great challenge to the cultivation of translation talents. According to the current situation, translation needs and the shortage of translation talents, colleges and universities should reform and innovate the existing training programs for translation talents in terms of the quality of translation talents, the reform of training mode and the use of artificial intelligence. Based on the obtained data and literature, the author discusses how to train translation talents in the perspective of machine translation from the following aspects.<br />
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====5.1 Quality requirements for translation talents ====<br />
Zhong Weihe and Murray made a more detailed and profound discussion on translator's literacy, believing that "translators should not only be proficient in two languages, but also have extensive cultural and encyclopedic knowledge and relevant professional knowledge; Master a variety of translation skills, a lot of translation practice; Have a clear translator role awareness, good professional ethics, practical and enterprising style of work, conscious team spirit and calm psychological quality ". According to the collected data, the author will elaborate the requirements for translation talents from four aspects: language literacy, humanistic literacy, translation ability and innovation ability.<br />
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The first is language literacy, which is the most basic and important requirement. MAO Dun pointed out that "mastery of mother tongue and target language are the foundation of translation". A solid foundation of bilingual skills is the basic skills of translators. Poor language proficiency seems to be a common problem among students majoring in translation and interpreting. Many translation diseases are caused by poor Chinese foundation. As part of going global, the belt and Road initiative is to tell Chinese culture and Chinese stories, which requires translators to be able to use both languages flexibly. Therefore, the first problem that colleges and universities face to solve is to improve the language level of foreign language learners.<br />
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The second is humanistic literacy. Humanistic literacy is mainly manifested by a good command of politics, economy, history, literature and other knowledge, which is particularly important for interpreters. In addition, cross-cultural communication cannot be ignored. In the process of communicating with foreigners or translating, translators often encounter the first cross-cultural contradiction. Cross-culture refers to having a full and correct understanding of cultural phenomena, customs and habits that differ or conflict with the national culture, and accepting and adapting to them in an inclusive manner on this basis. So the interpreter can first fully understand and master the national conditions and culture of the target country, which is particularly important in the "Belt and Road". There are more than 60 countries along the "Belt and Road", and it takes a lot of energy to master their national conditions and culture.<br />
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The third is translation ability. We should distinguish between translation ability and language ability. Translation ability is actually a system of knowledge and skills necessary for translation, the core of which is conversion ability. First of all, it reflects the ability to use tools to assist translation, such as computer application, translation technology and so on. In addition, interpreters should have enough healthy psychological quality and good professional quality. In terms of translation ability, the current training model of translation talents is inadequate.<br />
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The last one is innovation. The cultivation of learners' thinking ability is the key to translation teaching and the cultivation of thoughtful translators should be the connotation of translation teaching. Therefore, the interpreter is not only a translation tool, which is no different from machine translation. More importantly, it is necessary to explore translation with thoughts, have a sense of lifelong learning and innovation consciousness. Translators must constantly innovate themselves, learn new knowledge, and strive to seek reform and innovation. Many colleges and universities should also consciously cultivate students' innovation ability and broaden their thinking and vision.<br />
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====5.2 The reform of college curriculum setting====<br />
First, we will further reform the curriculum of colleges and universities. Add economics, law and engineering to the curriculum, these contents in the "belt and Road".<br />
"One Road" is very important in the construction. According to the author's personal experience, the most typical problem of foreign language majors in colleges and universities is the single learning of foreign languages. More professional foreign language colleges and universities will add some literature courses and national conditions courses of the language target countries. Obviously, whether foreign language graduates are engaged in translation work or not, these knowledge is not enough. Of course, great reforms have been carried out in foreign language teaching, such as combining foreign language with finance, law, diplomacy and so on, and taking the way of minor training foreign language majors.<br />
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Domestic enterprises with a high degree of internationalization attach great importance to translation. Their translation research includes cutting-edge theoretical and applied research, involving machine translation, natural language processing and AI theory, algorithm and model. With such a foundation, enterprises can solve problems by themselves, such as embedding automatic translation functions in mobile phones. International enterprises not only do technical translation, but also deal with all forms of translation and localization in society. At present, translation teaching in most colleges and universities is still in the early mode, and it is an objective fact that it is divorced from the workplace and has a gap with the needs of enterprises.<br />
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Second, we should adjust and strengthen the construction of second foreign language teaching for foreign language majors. In the 1980s, our country was in urgent need of Russian translation. At that time, students majoring in English could translate microelectronic product manuals and related business documents in English and Russian at the same time after learning Russian for half a year. The mutual conversion between English and Russian played a great role in practice. According to the author, in the Graduate Institute of Interpretation and Translation of Beijing Foreign Studies University a very few students majored in multiple languages at the graduate level, that is, they majored in minor languages at the undergraduate level and were admitted to the Graduate Institute of Interpretation and Translation in English. Their training mode is to study English in the Graduate Institute of Interpretation and Translation for two years and the third year in the corresponding department of the undergraduate major. Such training mode in my opinion is a bigger model, cost It's more difficult for students. <br />
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In addition, there is a great disparity in the development of second foreign language teaching in colleges and universities, and the overall level is not high enough. Part of the second foreign language university foreign language professional may still be too much focus in languages such as German, French and Japanese, should as far as possible, considering the need of the construction of the "region", like Croatia, Serbia, Turkish, Hungarian, Italian, Indonesian, Albanian, these are the countries along the "area" the language of the two countries, Colleges and universities should encourage the teaching of a second foreign language.<br />
<br />
Third, the teaching of translation technology should be strengthened. Traditional translation teaching teaches translation skills, such as the translation of words, sentences, texts and figures of speech. Translation technology refers to a series of practical workplace technologies with computer-aided translation software and translation project management as the core, which can greatly improve translation efficiency. However, due to the relative lack of translation technology teachers and equipment in colleges and universities, there is a disconnect between talent training and the requirements of translation technology in the translation field.<br />
<br />
====5.3 Application of artificial intelligence to translation teaching practice====<br />
In order to improve the teaching quality and train students' English translation ability, it is necessary to realize the effective integration of ARTIFICIAL intelligence and translation activity courses, which should not only reflect the effectiveness of artificial intelligence translation technology, but also help students establish a healthy concept of English communication. Through the application of artificial intelligence technology, students can strengthen their flexible translation skills through close communication with "AI program" during the learning stage of English translation activity class. For example, teachers can ask students to translate directly against the translation content provided on the translation screen of the ARTIFICIAL intelligence system. After that, the system can collect the translation answers with the help of speech recognition function, and then judge the accuracy of the translation content, thus providing important feedback to students.<br />
<br />
China has used such artificial intelligence technology in the Putonghua test to ensure that every student can find a suitable translation method in practical communication. The so-called artificial intelligence technology is a new kind of technology modeled after the characteristics of human neural network thinking, can combine the human mind to respond. If it can be integrated into English translation activity teaching, it can not only improve the teaching efficiency, but also enhance students' enthusiasm in learning the course.<br />
<br />
At the same time, during the training of translation talents, teachers also need to take into account the importance of influencing education factors, so that students can form a higher disciplinary quality in translation, so as to fit the concept of quality education in the new era. Only when artificial intelligence translation content is fully integrated into college English translation activity courses can the overall translation ability of college students be maximized.<br />
<br />
====5.4The improvement of translator's technical ability====<br />
In the previous part, the author roughly mentioned that translation teaching should be improved, which will be elaborated here. At present, only a few universities can make full use of the advantages of translation technology in translation teaching and focus on cultivating professional translation talents. Most universities still cannot get rid of the traditional teaching mode of "language + relevant professional knowledge" in translation teaching, and generally lack a correct understanding of COMPUTER-aided translation teaching.<br />
<br />
According to Wang Huashu et al., the courses that can be offered around the composition of translators' technical literacy include computer-assisted translation, translation and corpus, machine translation and post-translation editing, localization and internationalization, film and television translation (subtitle), technical communication and technical writing, and computer programming. The course modules involved are: Fundamentals of COMPUTER-aided Translation, CAT tool application, corpus alignment and processing, term management, QA technology for translation quality assurance, OFFICE fundamentals, translation management technology, basic computer knowledge, desktop typesetting, localization and internationalization, project management system and content management system, technical writing, basic knowledge of computer programming, basic knowledge of web code, etc.<br />
<br />
<br />
===6针对一带一路的机器翻译与翻译人才的合作===<br />
<br />
===Conclusion===<br />
<br />
===References===<br />
<br />
=8 颜静(On Machine Translation Under Lanuguage Intelligence——An Option and Opportunity for Human Translators)=<br />
[[Machine_Trans_EN_8]]<br />
<br />
=9 谢佳芬（人工智能时代下的机器翻译与人工翻译）=<br />
[[Machine_Trans_EN_9]]<br />
===Abstract===<br />
With the continuous development of information technology, many industries are facing the competitive pressure of artificial intelligence, and so is the field of translation. Artificial intelligence technology has developed rapidly and combined with the field of translation，which has brought great impact and changes to traditional translation, but artificial intelligence translation and artificial translation have their own advantages and disadvantages. Artificial translation is in the leading position in adapting to human language logical habits and understanding characteristics, but in terms of translation threshold and economic value, the efficiency of artificial intelligence translation is even better. In a word, we need to know that machine translation and human translation are complementary rather than antagonistic.<br />
<br />
===Key Words===<br />
Machine Translation; Artificial Translation; Artificial Intelligence<br />
<br />
===题目===<br />
人工智能时代下的机器翻译与人工翻译<br />
<br />
===摘要===<br />
伴随着信息技术的不断发展，多个行业面临着人工智能的竞争压力，翻译领域也是如此。人工智能技术快速发展并与翻译领域结合，人工智能翻译给传统翻译带来了巨大的冲击和变革，但人工智能翻译与人工翻译存在着各自的优劣特点和发展空间，在适应人类语言逻辑习惯和理解特点的翻译效果上，人工翻译处于领先地位，但在翻译门槛和经济价值上，人工智能翻译的效率则更胜一筹。总的来说，我们要知道机器翻译与人工翻译是互补而非对立的关系。<br />
<br />
===关键词===<br />
机器翻译;人工翻译;人工智能<br />
<br />
===1. Introduction===<br />
====1.1 The History of Machine Translation Aborad====<br />
The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. Alchuni put forward the idea of using machines for translation. In 1933, the Soviet inventor Troyansky designed a machine to translate one language into another. [1]In 1946, the world's first modern electronic computer ENIAC was born. Soon after, American scientist Warren Weaver, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947. In 1949, Warren Weaver published a memorandum entitled Translation, which formally raised the issue of machine translation. In 1954, Georgetown University, with the cooperation of IBM, completed the English-Russian machine translation experiment with IBM-701 computer for the first time, which opened the prelude of machine translation research. [2] In 2006, Google translation was officially released as a free service software, bringing a big upsurge of statistical machine translation research. It was Franz Och who joined Google in 2004 and led Google translation. What’s more, it is precisely because of the unremitting efforts of generations of scientists that science fiction has been brought into reality step by step.<br />
====1.2 The History of Machine Translation in China====<br />
In 1956, the research and development of machine translation has been named in the scientific and technological work and made little achievements in China. On the eve of the tenth anniversary of the National Day in 1959, our country successfully carried out experiments, which translated nine different types of complicated sentences on large general-purpose electronic computers. The dictionary includes 2030 entries, and the grammar rule system consists of 29 circuit diagrams. [3]. After a period of stagnation, China's machine translation ushered in a high-speed development stage after the 1980s in the wave of the third scientific and technological revolution. With the rapid development of economy and science and technology, China has made a qualitative leap in the field of machine translation research with the pace of reform and opening up. In 1978, Institute of Scientific and Technological Information of China, Institute of Computing Technology and Institute of Linguistics carried out an English-Chinese translation experiment with 20 Metallurgical Title examples as the objects and achieved satisfactory results. Subsequently, they developed a JYE-I machine translation system, which based on 200 sentences from metallurgical documents. Its principles and methods were also widely used in the machine translation system developed in the future. In addition, the research achievements of machine translation in China during the 1980s and 1990s also include that Institute of Post and Telecommunication Sciences developed a machine translation system, C Retrieval and automatic typesetting system with good performance and strong practicability in October 1986; In 1988, ISTC launched the ISTIC-I English-Chinese Title System for the translation of applied literature of metallurgy, Information Research Institute of Railway developed an English-Chinese Title Recording machine translation system for railway documents; the Language Institute of the Academy of Social Sciences developed "Tianyu" English-Chinese machine translation system and Matr English-Chinese machine translation system developed by the computer department of National University of Defense Technology. After many explorations and studies, machine translation in China has gradually moved towards application, popularization and commercialization. China Software Technology Corporation launched "Yixing I" in 1988, marking China's machine translation system officially going to the market. After "Yixing", a series of machine translation systems such as Gaoli system in Beijing, Tongyi system in Tianjin and Langwei system in Shaanxi have also entered the public. In the 21st century, the development of a series of apps such as Kingsoft Powerword, Youdao translation and Baidu translation has greatly met the needs of ordinary users for translation. According to the working principle, machine translation has roughly experienced three stages: rule-based machine translation, statistics-based machine translation and deep learning based neural machine translation. [4] These three stages witnessed a leap in the quality of machine translation. Machine translation is more and more used in daily life and even the translation of some texts is almost comparable to artificial translation. In addition to text translation, voice translation, photo translation and other functions have also been listed, which provides great convenience for people's life. It is undeniable that machine translation has become the development trend of translation in the future.<br />
====1.3 The Status Quo of Machine Translation====<br />
In this big data era of information explosion, the prospect of machine translation is also bright. At present, the circular neural network system launched by Google has supported universal translation in more than 60 languages. Many Internet companies such as Microsoft Bing, Sogou, Tencent, Baidu and NetEase Youdao have also launched their own Internet free machine translation systems. [5] Users can obtain translation results free of charge by logging in to the corresponding websites. At present, the circular neural network translation system launched by Google can support real-time translation of more than 60 languages, and the domestic Baidu online machine translation system can also support real-time translation of 28 languages. These Internet online machine translation systems are suitable for a variety of terminal platforms such as mobile phone, PC, tablet and web and its functions are also quite diverse, supporting many translation forms, such as screen word selection, text scanning translation, photo translation, offline translation, web page translation and so on. Although its translation quality needs to be improved, it has been outstanding in the fields of daily dialogue, news translation and so on.<br />
===2. Advantages and Disadvantages of Machine Translation===<br />
Generally speaking, machine translation has the characteristics of high efficiency, low cost, accurate term translation and great development potential and etc. Machine translation is fast and efficient, this is something that artificial translation can’t catch up with. In addition, with the continuous emergence of all kinds of translation software in the market, compared with artificial translation, machine translation is cheap and sometimes even free, which greatly saves the economic cost and time for users with low translation quality requirements. What's more, compared with artificial translation, machine translation has a huge corpus, which makes the translation of some terms, especially the latest scientific and technological terms, more rapid and accurate. The accurate translation of these terms requires the translator to constantly learn, but learning needs a process, which has a certain test on the translator's learning ability and learning speed. In this regard, artificial translation has uncertainty and hysteretic nature. At the same time, with the progress of science and technology and the development of society, the function of machine translation will be more perfect and the quality of translation will be better.Today's machine translation tools and software are easy to carry, all you need to do is just to use the software and electronic dictionary in the mobile phone. There is no need to carry paper dictionaries and books for translation, which saves time and space. At the same time, machine translation covers many fields and is suitable for translation practice in different situations, such as academic, education, commercial trade, social networking, tourism, production technology, etc, it is also easy to deal with various professional terms. However, due to the limitation of translators' own knowledge, artificial translation is often limited to one or a few fields or industries. For example, it is difficult for an interpreter specializing in medical English to translate legal English.<br />
At the same time, machine translation also has its limitations. At first, machine can only operate word to word translation, which only plays the function and role of dictionary. Then, the application of syntax enables the process of sentence translation and it can be solved by using the direct translation method. When the original text and the target language are highly similar, it can be translated directly. For example, the original text "他是个老师." The target language is "he is a teacher ". With the increase of the structural complexity of the original text, the effect of machine translation is greatly reduced. Therefore, at the syntactic level, machine translation still stays in sentences with relatively simple structure. Meanwhile, the original text and the results of machine translation cannot be interchanged equally, indicating that English-Chinese translation has strong randomness, and is not rigorous and scientific enough. <br />
Nowadays, machine translation is highly dependent on parallel corpora, but the construction of parallel corpora is not perfect. At present, the resources of some mainstream languages such as Chinese and English are relatively rich, while the data collection of many small languages is not satisfactory. Moreover, the current corpus is mainly concentrated in the fields of government literature, science and technology, current affairs and news, while there is a serious lack of data in other fields, which can’t reflect the advantages of machine translation. At the same time, corpus construction lags behind. Some informative texts introducing the latest cutting-edge research results often spread the latest academic knowledge and use a large number of new professional terms, such as academic papers and teaching materials while the corpus often lacks the corresponding words of the target language, which makes machine translation powerless<br />
Besides, machine translation is not culturally sensitive. Human may never be able to program machines to understand and experience a particular culture. Different cultures have unique and different language systems, and machines do not have complexity to understand or recognize slang, jargon, puns and idioms. Therefore, their translation may not conform to cultural values and specific norms. This is also one of the challenges that the machine needs to overcome.[6] Artificial intelligence may have human abstract thinking ability in the future, but it is difficult to have image thinking ability including imagination and emotion. [7] Therefore, machine translation is often used in news, science and technology, patents, specifications and other text fields with the purpose of fact description, knowledge and information transmission. These words rarely involve emotional and cultural background. When translating expressive texts, the limitations of machine translation are exposed. The so-called expressive text refers to the text that pays attention to emotional expression and is full of imagination. Its main characteristics are subjectivity, emotion and imagination, such as novels, poetry, prose, art and so on. This kind of text attaches importance to the emotional expression of the author or character image, and uses a lot of metaphors, symbols and other expressions. Machine translation is difficult to catch up with artificial translation in this kind of text, it can only translate the main idea, lack of connotation and literary grace and it cannot have subjective feelings and rational analysis like human beings. In fact, it is not difficult to simulate the human brain, the difficulty is that it is impossible to learn from the rich social experience and life experience of excellent translators. In other words, machine translation lacks the personalization and creativity of human translation. It is this personalization and creativity that promote the development and evolution of language, and what machine translation can only output is mechanical "machine language".<br />
<br />
===3.The Irreplaceability of Artificial Translation ===<br />
====3.1 Translation is Constrained by Context====<br />
At present, machine translation can help people deal with language communication in people's daily life and work, such as clothing, food, housing and transportation, but there is a big gap from the "faithfulness, expressiveness and elegance" emphasized by high-level translation. Language itself is art，which pays more attention to artistry than functionality, and the discipline of art is difficult to quantify and unify. Sometimes it is regular, rigorous, logical and clear, and sometimes it is random, free and logical. If it is translated by machine, it is difficult to grasp this degree. Sometimes, machine translation cannot connect words with contextual meaning. In many languages, the same word may have multiple completely unrelated meanings. In this case, context will have a great impact on word meaning, and the understanding of word meaning depends largely on the meaning read from context. Only human beings can combine words with context, determine their true meaning, and creatively adjust and modify the language to obtain a complete and accurate translation. This is undoubtedly very difficult for machine translation. Artificial translation can get rid of the constraints of the source language and translate the translation in line with the grammar, sentence patterns and word habits of the target language. In the process of translation, translators can use their own knowledge reserves to analyze the differences between the source language and the target language in thinking mode, cultural characteristics, social background, customs and habits, so as to translate a more accurate translation. Artificial translation can also add, delete, domesticate, modify and polish the translation according to the style, make up for the lack of culture, try to maintain the thought, artistic conception and charm of the original text and the style of the source language. In addition, translators can also judge and consider the words with multiple meanings or easy to produce ambiguity according to the context, so as to make the translation more clear and more accurate and improve the quality of the translation.<br />
<br />
<br />
===4. Discussion on the Relationship Between Machine Translation and Artificial Translation ===<br />
<br />
===5. Suggestions on the Combined Development of Machine Translation and Artificial Translation===<br />
<br />
===6. ===<br />
<br />
===7. ===<br />
<br />
===Conclusion===<br />
<br />
===References===<br />
<br />
=10 熊敏(Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts)=<br />
[[Machine_Trans_EN_10]]<br />
===Abstract===<br />
With the rapid development of information technology,machine translation technology emerged and is gradually becoming mature.In order to explore the ability of machine translation, I adopts two versions of translation, which are manual translation and machine translation(this paper uses Youdao translation) for different types of texts(according to Peter Newmark's types of text). The results are quite different in terms of quality and accuracy.<br />
<br />
===Key words===<br />
machine translation; manual translation; Newmark's type of texts<br />
<br />
===题目===<br />
Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts<br />
<br />
===摘要===<br />
随着信息技术的高速发展，机器翻译技术出现了，并且逐渐成熟。为了探究机器翻译的能力水平，本人根据纽马克的文本类型分类，选择了相应的译文类型，并且将其机器翻译的版本以及人工翻译的版本进行对比。就质量和准确度而言，译文的水平大相径庭。<br />
<br />
===关键词===<br />
机器翻译；人工翻译；纽马克文本类型<br />
<br />
===1. Introduction===<br />
====1.1.Introduction to machine translation====<br />
Machine translation, also known as computer translation is a technique to translating through machine translator, such as Google translation and Youdao translation. Machine translation is one of the branches of computational linguistics, ranging from computer science, statistics, information science and so on. Machine translation plays an important role in all aspects.<br />
Machine translation can be traced back to 1940s, when British engineer Booth and American engineer Weaver proposed using computer to translate and started to study machines used for translation. However in the 1960s, reports from ALPAC (Automated Language Processing Advisory Committee) showed studies on machine translation had stagnated for a decade. In the 1970s, with the advancement of computer, machine translation was back to track. In the last decades, machine translation has mainly developed into four stages: rule-based machine translation, statistic machine translation, example-based machine translation and neural machine translation.<br />
<br />
====1.2.Process of machine translation====<br />
The process of manual translation is different from that of machine translation. Here is the process of the former. (1) Understand source language. (2) Use target language to organize language. (3) Generate translation. Unlike manual translation, machine translation tends to analyze and code source language first, then look for related codes in corpus, and work out the code that represents target language, generating translation. But they share a common feature, which is that Lexicon, grammatical rules and syntactic structure are taken into consideration. This is one of the biggest challenges for machine translation.<br />
<br />
===2.Newmark’s type of texts===<br />
Peter Newmark divided texts into informative type, expressive text and vocative type according to the linguistic functions of various texts. <br />
====2.11Informative text====<br />
The core of the informative texts is the truth. It is to convey facts, information, knowledge and the like. The language style of the text is objective and logical. Reports, papers, scientific and technological textbooks are all attributed to informative texts.<br />
====2.2Expressive text====<br />
The core of the expressive text is the emotion. It is to express preferences, feelings, views and so on. The language style of it is subjective. Literary works, including fictions, poems and drama, autobiography and authoritative statements belong to expressive text.<br />
====2.3Vocative text====<br />
The core of the vocative text is readership. It is to call upon readers to act in the way intended by the text. So it is reader-oriented. Such texts advertisement, propaganda and notices are of vocative text.<br />
====2.4Study Method====<br />
Manual translations of the three texts are selected from authoritative versions and universally acknowledged. And machine translations of those come from Youdao Translator. And in this thesis I will compare and evaluate the two methods in word diction, sentence structure, word order and redundancy.<br />
<br />
===3. ===<br />
<br />
===4. ===<br />
<br />
===5. ===<br />
<br />
===6. ===<br />
<br />
===7. ===<br />
<br />
===Conclusion===<br />
<br />
===References===<br />
<br />
=11 陈惠妮=(Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts)=<br />
[[Machine_Trans_EN_11]]<br />
===Abstract===<br />
At present, globalization is accelerating and the market demand for language services is rapidly increasing . Machine translation, as an important translation method, can greatly improve translation efficiency due to its low cost and high speed. However, because of the limitations of machine translation and the differences between Chinese and English language, machine translation is not accurate enough. In order to balance translation efficiency and translation quality, a great number of manual revisions in translation are required for the machine translating texts. Medical papers are specialized, special and purposeful, so it requires accurate,qualified and professional translation. However, the quality of translations by machine is inefficient to meet the high-quality requirements of medical papers translation. Therefore, the introduction of pre-editing can greatly improve the efficiency and quality of machine translation.<br />
<br />
===Key words===<br />
Pre-editing, Machine translation, Medical texts<br />
<br />
===题目===<br />
Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts<br />
<br />
===摘要===<br />
在全球化加速发展的今天，市场对语言服务的需求迅速增加。机器翻译作为一种重要的翻译途径，由于其成本低、速度快，可以大大提高翻译效率。然而，由于机器翻译的局限性以及中英文语言的差异，机器翻译的准确性不高。为了平衡翻译效率和翻译质量，机器翻译文本需要大量的手工修改。<br />
医学论文具有专业性、特殊性和目的性，要求其译文准确、合格、专业。然而，机器翻译的质量较低，无法满足医学论文对翻译的高质量要求。因此，译前编辑的引入可以大大提高机器翻译的效率和质量。<br />
<br />
===关键词===<br />
译前编辑；机器翻译；医学文本<br />
<br />
===1. Introduction===<br />
===1.1 Definition of Machine Translation===<br />
As Cronin(2013) revealed: "Translation is undergoing a revolutionary upheaval. The influence of digital technology and the Internet on translation is continuous, extensive and profound. From the popularity of automatic online translation applications, translation revolution is everywhere (Cronin,2013) However,the concept of machine translation was firstly proposed in the 1930s. Since 1940s, the machine translation technology has been evolving from rule-based machine translation (RBMT) to statistical machine translation (SMT), and to neural machine translation (NMT). Machine translation refers to the automatic translation of source language into target language by using a computer system. That is, machine translation refers to the automatic translation of text from one language into another natural language by computer software or other online translation webs. Machine translation is also defined as the process of “using a computer system to automatically translate text or speech from one natural language to another” according to the definition by ISO (Cui, 2014). On the basic level, machine translation performs mechanical substitution of words in one language for words in another language, but that rarely produces a good translation, therefore, recognition of the whole phrases and their closest counterparts in the target language is needed. Not all words in one language have equivalents in another language, and many words have more than one meaning. A huge demand for translation is greatly needed in today’s global world, which creates new opportunities for the development of machine translation, attracts more and more attention and becomes one of the current research focuses.<br />
<br />
===1.2 Definition of Pre-editing===<br />
Pre-editing means to adjust and modify the source language to make it fit more with the characteristics of the machine translation software before putting the source language before the into machine translation, so as to improve the quality of the translation machine translation (Wei Changhong, 2008:93-94). Pre-editing is to modify the original text before putting it into machine translation software, in order to improve the recognition rate of machine translation, optimize the output quality of translated text and reduce the workload of post-editing. Because pre-translation editing only needs to be modified in one language, the operation is simpler than post-translation editing, which can realize the double improvement of quality and efficiency. A good pre-editing translation can help machine translation more smoothly, thus improving the machine readability and quality of the output translation. Pre-editing is mostly applied in the following situations: one is when the original text is of poor quality and the machine is difficult to recognize the meaning of the sentence, such as user generated content with poor readability and translatability (Gerlach et al, 2003:45-53); Documents that need to be published in multiple languages; Next is when the original text contains a lot of jargon; The last is the original text has a corresponding translation memory bank. If the original text is edited, it can better match the content of the translation memory bank.<br />
<br />
===1.3 Machine Translation Mode===<br />
According to the different knowledge acquisition methods, machine translation modes can be classified as follows: one is rule-based machine translation, which is based on bilingual dictionaries and a library of language rules for each language. The quality of translation depends on whether the source language conforms to the existing rules, but the inexhaustible rules are the hinders of this model. The second mode is machine translation based on statistics. This translation model relies on the principles of mathematics and statistics to find various existing translations corresponding to the translation tasks through the employment of corpus, analyzing the frequency of their occurrence and selecting the translation with the highest frequency for output. The disadvantage of this translation model is that it ignores the flexibility of language and the importance of context. The last one is neural network language model. This model is different from previous translation models in that it uses end-to-end neural network to realize automatic translation between natural languages. At present, the quality of its translation is much higher than that of the previous translation models.<br />
===1.4 Source of Study Abstracts===<br />
The core medical journals from domestic are selected in order to make the paper more representative and authoritative, such as National Medical Journal of China, Journal of Peking University (Health Science), and Journal of Third Military Medical University. All of them include basic medical science, biomedical technology, laboratory medical science and other fields. In this way, the pre-editing approaches included are applicable enough to machine translation of medical abstracts.<br />
===1.5 Selection of Translation Software===<br />
Generally, machine translation software includes Google Translation, Youdao Translator and Niu Translation, which has their own special use in translation. Take Google as an example, it is more like neurons in human brain, enabling to learn and collect information to establish connections with its neural machine translator’s neurons. However, it also causes many errors because of the lack of enough information. In this paper, contrastive analysis will be carried on by using Google translation. On the one hand, being a pioneer in the translation of NET, it is inevitably to sue Google as the translation software to translate medical texts in this paper. Comparing the program called “Google brain”, other NMT of translation software are relatively disadvantages. On the other hand, the Google translation enjoys the largest users in the world, with its downloads of more than one billion. The output quality of Google translation is more correct and complete than other machine translation software. With years of development and improvement, Google Translation has been greatly promoted. In this paper, Chinese medical abstract will be automatically translated by Google translation, and then the translation output will be compared with the translation by human and the publication of English abstracts. The main purpose is to prove that the improvement and promotion of quality and accuracy in the medical abstracts will be obtained through the pre-editing approaches.<br />
<br />
===2.Language Characteristics and Error Division===<br />
Since Chinese and English are two different languages, it is quite neccessary to identify their own characteristics so as to better analyze and understand the two languages. Tytler (1978) argued in his Essay on the Principles of Translation that there are three principles of translation that in all the translation should give readers the same feelings as the source text, except for complete transcript of ideas. There actually also exit some mistakes of these two languages. So the following will make some clarrifications of these two languages to ensure more accurate translation.<br />
<br />
===2.1 Language Characteristics of Medical Abstracts===<br />
Chinese and English belong to different language systems, so there are differences in their language structure and the way users think of the languages. When using machine translation from Chinese to English, due to the unequal language levels, there will be many mistakes in the translation process, especially for ESP texts, such as medical papers. <br />
Here, these differences mainly refer to the linguistic characteristics of medical abstracts. In medical abstracts, it usually includes structured and unstructured abstracts. Although in different forms, they both describe the purpose, methods and conclusions of the research.<br />
In the method section of medial abstract, several Chinese sentences can be connected with commas, but each sentence may convey different information. In contrast, English sentences contain a great deal of information, but in order to ensure clarity, some modifiers need to be isolated and then reconstructed. However, in Chinese-English machine translation, a lot of information is put into the sentence because the machine segments the sentence on the basis of the full comma.<br />
In addition, subjectless sentence is used in the objective and method parts of Chinese medical abstracts. The subjectless sentence means the sentence without or free from subject and is usually employed in two contexts. The first is "needless to say". In Chinese sentences, it is common to omit the subject of the sentence. Chinese sentences can convey meanings by using incomplete sentence structures, so Chinese speakers can understand the meanings of the sentences even though the sentence subject is omitted. The second is "emphasis of action". In this context, subjectless sentences are used to describe behaviors, especially in the study of traditional Chinese medicine abstracts. Comparatively speaking, it should be avoided in English medical abstracts. Abstract sentences are more subjective when describing the learning process, while the essential requirement of English medical abstract is objectivity. From this point, sentences without subject should be avoided in English medical abstracts. Another feature is voice. Few words with passive meanings appear in Chinese abstracts. English sentences are more favoured in using passive voice. When translating from Chinese to English, passive voice should be used to make the contents objective, which is also the basic requirement of medical papers.<br />
These are the linguistic features of Chinese medical abstracts. There are great differences in sentence structure and expression between Chinese and English medical abstracts. These differences may reduce the accuracy of machine translation, so it is necessary to introduce pre-editing to edit the source text to ensure that the source text can be accurately recognized by the machine and fully translated into English.<br />
===2.2 Error Division of Machine Translation in Translating Abstracts of Medical Papers from Chinese to English===<br />
In this paper, errors in machine translation are listed out after analyzing. Some are due to the principles, such as statistical-based MT and NMT, employed by machine translation. Based on the original errors, they can be studied from two levels. One is from the macro-level, referring to mistakes caused by objective factors. These are not human factors, but the disadvantage of machine translation and the limitations of language. The limitations of machine translation are derived from the principles and models adopted and manifests itself as a reliance on reference sources. In this case, semantic ambiguity and textual incoherence may occur in the absence of reference sources.<br />
However, for ESP texts such as medical papers, the requirements for terms and sentence patterns are far beyond the existing corpora. For unfamiliar texts (that is, the corresponding texts cannot be found in the corpora). The quality of output translation will be relatively low, which is determined by the principles of machine translation. As mentioned above, machine translation has evolved over the past few decades from rules-based to corp-based statistics to NMT. However, there still exist some limitations in machine translation.<br />
Mistakes on micro-level are mainly caused by the variations and differences of linguistic structure between Chinese and English. Chinese is an implicit language, while English is an explicit one (Lian, 2010) To put it in another way, Chinese expression does not depend on language structures, while English does the opposite. This may result in mismatches between the original and machine translated sentences. This kind of error is mainly divided into two types: component fragment and component missing. For a medical paper, such errors are very serious. As a kind of ESP discourse, medical paper has the characteristics of fixed, objective and accurate language structure. In order to reproduce the characteristics of medical abstracts in translation, it is necessary to avoid errors at the level of words and sentences and pay attention to logic and consistency.<br />
Lexical errors here refer to the inconsistency of fixed expressions in the translation of terms. Although these terms are machine translation based on the corpus, the corpus may not be perfect for ESP texts such as medical papers. Therefore, fixed expressions are not used in term translation. This is also the most common mistake in machine translation (the term here includes but is not limited to nouns). For ESP texts, medical text in particular, the fixed expressions and the accuracy of terms are of great importance.<br />
<br />
===3.===<br />
<br />
===4.===<br />
<br />
===Conclusion===<br />
<br />
===References===<br />
<br />
=12 蔡珠凤=(The Mistranslation of C-J Machine Translation of Political Statements)=<br />
[[Machine_Trans_EN_12]]<br />
===Abstract===<br />
Language is the main way of communication between people. With the continuous development of globalization, the scale of cross-border exchanges is also expanding. However, due to cultural differences and diversity, the languages of different countries and regions are very different, which seriously hinders people's communication. The demand for efficient and convenient translation tools is increasing. At the same time, with the development of network technology and artificial intelligence, recognition technology based on deep learning is more and more widely used in English, Japanese and other fields.<br />
<br />
===Key words===<br />
machine translation; political statements; mistranslation of C-J machine translation<br />
===题目===<br />
The Mistranslation of C-J Machine Translation of Political Statements<br />
===摘要===<br />
语言是人与人之间交流的主要方式。随着全球化的不断发展，跨境交流的规模也在不断扩大。然而，由于文化的差异和多样性，不同国家和地区的语言差异很大，这严重阻碍了人们的交流。对高效便捷的翻译工具的需求正在增加。同时，随着网络技术和人工智能的发展，基于深度学习的识别技术在英语、日语等领域的应用越来越广泛。<br />
<br />
===关键词===<br />
机器翻译；政治发言；政治发言中译日的误译<br />
===1. Introduction===<br />
===Introduction to machine translation===<br />
Machine translation, also known as automatic translation, is a process of using computers to convert one natural language (source language) into another natural language (target language). It is a branch of computational linguistics, one of the ultimate goals of artificial intelligence, and has important scientific research value.At the same time, machine translation has important practical value. With the rapid development of economic globalization and the Internet, machine translation technology plays a more and more important role in promoting political, economic and cultural exchanges.The development of machine translation technology has been closely accompanied by the development of computer technology, information theory, linguistics and other disciplines. From the early dictionary matching, to the rule translation of dictionaries combined with linguistic expert knowledge, and then to the statistical machine translation based on corpus, with the improvement of computer computing power and the explosive growth of multilingual information, machine translation technology gradually stepped out of the ivory tower and began to provide real-time and convenient translation services for general users.<br />
<br />
=== C-J machine translation software===<br />
Today's online machine translation software includes Baidu translation, Tencent translation, Google translation, Youdao translation, Bing translation and so on. Google was the first company to launch the machine translation system, and Baidu was the first company to import the machine translation system in China. In addition, Tencent and Youdao have attracted much attention.Machine translation is the process of using computers to convert one natural language into another. It usually refers to sentence and full-text translation between natural languages. In order to continuously improve the translation quality, R & D personnel have added artificial intelligence technologies such as speech recognition, image processing and deep neural network to machine translation on the basis of traditional machine translation based on rules, statistics and examples.With the increase of using machine translation, the joint cooperation between manual translation and machine translation will also increase significantly in the future. What criteria should be used to evaluate the quality of machine translation? In the evolving field of machine translation, there is an urgent need to clarify the unsolvable questions and solved problems.<br />
<br />
===The history of machine translation===<br />
The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. alchuni put forward the idea of using machines for translation. In 1933, Soviet inventor П.П. Trojansky designed a machine to translate one language into another, and registered his invention on September 5 of the same year; However, due to the low technical level in the 1930s, his translation machine was not made. In 1946, the first modern electronic computer ENIAC was born. Shortly after that, W. weaver, an American scientist and A. D. booth, a British engineer, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947 when discussing the application scope of electronic computer. In 1949, W. Weaver published the translation memorandum, which formally put forward the idea of machine translation. After 60 years of ups and downs, machine translation has experienced a tortuous and long development path. The academic community generally divides it into the following four stages:<br />
Pioneering period（1947-1964）<br />
In 1954, with the cooperation of IBM, Georgetown University completed the English Russian machine translation experiment with ibm-701 computer for the first time, showing the feasibility of machine translation to the public and the scientific community, thus opening the prelude to the study of machine translation.<br />
It is not too late for China to start this research. As early as 1956, the state included this research in the national scientific work development plan. The topic name is "machine translation, the construction of natural language translation rules and the mathematical theory of natural language". In 1957, the Institute of language and the Institute of computing technology of the Chinese Academy of Sciences cooperated in the Russian Chinese machine translation experiment, translating 9 different types of more complex sentences.<br />
From the 1950s to the first half of the 1960s, machine translation research has been on the rise. The United States and the former Soviet Union, two superpowers, have provided a lot of financial support for machine translation projects for military, political and economic purposes, while European countries have also paid considerable attention to machine translation research due to geopolitical and economic needs, and machine translation has become an upsurge for a time. In this period, although machine translation is just in the pioneering stage, it has entered an optimistic period of prosperity.<br />
Frustrated period（1964-1975）<br />
In 1964, in order to evaluate the research progress of machine translation, the American Academy of Sciences established the automatic language processing Advisory Committee (Alpac Committee) and began a two-year comprehensive investigation, analysis and test.<br />
In November 1966, the committee published a report entitled "language and machine" (Alpac report for short), which comprehensively denied the feasibility of machine translation and suggested stopping the financial support for machine translation projects. The publication of this report has dealt a blow to the booming machine translation, and the research of machine translation has fallen into a standstill. Coincidentally, during this period, China broke out the "ten-year Cultural Revolution", and basically these studies also stagnated. Machine translation has entered a depression.<br />
convalescence（1975-1989）<br />
Since the 1970s, with the development of science and technology and the increasingly frequent exchange of scientific and technological information among countries, the language barriers between countries have become more serious. The traditional manual operation mode has been far from meeting the needs, and there is an urgent need for computers to engage in translation. At the same time, the development of computer science and linguistics, especially the substantial improvement of computer hardware technology and the application of artificial intelligence in natural language processing, have promoted the recovery of machine translation research from the technical level. Machine translation projects have begun to develop again, and various practical and experimental systems have been launched successively, such as weinder system Eurpotra multilingual translation system, taum-meteo system, etc.<br />
However, after the end of the "ten-year holocaust", China has perked up again, and machine translation research has been put on the agenda again. The "784" project has paid enough attention to machine translation research. After the mid-1980s, the development of machine translation research in China has further accelerated. Firstly, two English Chinese machine translation systems, ky-1 and MT / ec863, have been successfully developed, indicating that China has made great progress in machine translation technology.<br />
New period(1990 present)<br />
With the universal application of the Internet, the acceleration of the process of world economic integration and the increasingly frequent exchanges in the international community, the traditional way of manual operation is far from meeting the rapidly growing needs of translation. People's demand for machine translation has increased unprecedentedly, and machine translation has ushered in a new development opportunity. International conferences on machine translation research have been held frequently, and China has made unprecedented achievements. A series of machine translation software have been launched, such as "Yixing", "Yaxin", "Tongyi", "Huajian", etc. Driven by the market demand, the commercial machine translation system has entered the practical stage, entered the market and came to the users.<br />
Since the new century, with the emergence and popularization of the Internet, the amount of data has increased sharply, and statistical methods have been fully applied. Internet companies have set up machine translation research groups and developed machine translation systems based on Internet big data, so as to make machine translation really practical, such as "Baidu translation", "Google translation", etc. In recent years, with the progress of in-depth learning, machine translation technology has further developed, which has promoted the rapid improvement of translation quality, and the translation in oral and other fields is more authentic and fluent.<br />
<br />
===The problem of machine translation at present===<br />
Error is inevitable<br />
Many people have misunderstandings about machine translation. They think that machine translation has great deviation and can't help people solve any problems. In fact, the error is inevitable. The reason is that machine translation uses linguistic principles. The machine automatically recognizes grammar, calls the stored thesaurus and automatically performs corresponding translation. However, errors are inevitable due to changes or irregularities in grammar, morphology and syntax, such as sentences with adverbials after "give me a reason to kill you first" in Dahua journey to the West. After all, a machine is a machine. No one has special feelings for language. How can it feel the lasting charm of "the tenderness of lowering its head, like the shame of a water lotus? After all, the meaning of Chinese is very different due to the changes of morphology, grammar and syntax and the change of context. Even many Chinese people are zhanger monks - they can't touch their heads, let alone machines.<br />
Bottleneck<br />
In fact, no matter which method, the biggest factor affecting the development of machine translation lies in the quality of translation. Judging from the achievements, the quality of machine translation is still far from the ultimate goal.<br />
Chinese mathematician and linguist Zhou Haizhong once pointed out in his paper "fifty years of machine translation": to improve the quality of machine translation, the first thing to solve is the problem of language itself rather than programming; It is certainly impossible to improve the quality of machine translation by relying on several programs alone. At the same time, he also pointed out that it is impossible for machine translation to achieve the degree of "faithfulness, expressiveness and elegance" when human beings have not yet understood how the brain performs fuzzy recognition and logical judgment of language. This view may reveal the bottleneck restricting the quality of translation. <br />
It is worth mentioning that American inventor and futurist ray cozwell predicted in an interview with Huffington Post that the quality of machine translation will reach the level of human translation by 2029. There are still many disputes about this thesis in the academic circles.<br />
<br />
===2.Mistranslation of Chinese Japanese machine translation===<br />
===2.1Vocabulary mistranslation in Chinese Japanese machine translation===<br />
===2.1.1Mistranslation of proper nouns===<br />
===2.1.2Mistranslation of Polysemy===<br />
===2.1.3Mistranslation of compound words===<br />
===2.2 Syntactic mistranslation in Chinese Japanese machine translation===<br />
===2.2.1Main dynamic Mistranslation===<br />
===2.2.2Dynamic Mistranslation===<br />
===2.2.3Mistranslation of tenses===<br />
===2.2.4Mistranslation of honorifics===<br />
===3.===<br />
===4.===<br />
===Conclusion===<br />
===References===<br />
<br />
=13 陈湘琼Chen Xiangqiong（Study on Post-editing from the Perspective of Functional Equivalence Theory ）=<br />
[[Machine_Trans_EN_13]]<br />
<br />
=14 Bi bi Nadia（Machine Translation a Challenge for Human Translators)=<br />
[[Machine_Trans_EN_14]]<br />
<br />
===Abstract===<br />
Machine translation is a big obstacle in the way of Human translators or interpretors although it is quick and less time consuming.people are trying to get translation of their target language using through source language.For this purpose they are using digital apps like Google translation Google translation does not give accurate and exact interpretation.Google translator is translates word to word translate that doesn't clarifies it's true and actual meaning.On the other hand human translators can give exact and accurate translation ,they take care of grammatical errors , diction and sentence structure.They clarify the purpose of target language through using source language.<br />
===Keywords===<br />
Descriptive translation, academic, interdiscipline, comparative literature, localization,Translatology,school of thought, translation , studies, linguistics, corresponding<br />
===Body of article===<br />
Translation is the process of reworking text from one language into another to maintain the original message and communication. But, like everything else, there are different methods of translation, and they vary in form and function. What is translation? The term has become widely used among knowledge transfer researchers and practitioners, especially in the fields of health and health care. In a landmark review, Jonathan Lomas began to argue that 'The tasks… may be defined as to establish and maintain links between researchers and their audience, via the appropriate translation of research findings' (Lomas 1997, p 4). In 2004, the World Health Organization's World Report on Knowledge for Better Health suggested that 'One of the key contributions of research to health systems is the translation of knowledge into actions' (WHO 2004, p 33 and p 100). By 2006, special issues of WHO's Bulletin as well as the journals Evaluation and the Health Professions and the Journal of Continuing Education in the Health Professions were dedicated to translation.But what does 'translation' mean? It may be a new word for an old problem, meaning nothing more than 'transfer'. The rapidly emerging field of 'translational medicine' seems to take translation to mean generally what transfer might have meant, that is the transmission of knowledge and evidence 'from bench to bedside'. As one commentator has observed, "Translational medicine" as a fashionable term is being increasingly used to describe the wish of biomedical researchers to ultimately help patients' (Wehling 2008, abstract). <br />
Semantic uncertainty persists, not least because of the different interests of scientists, clinicians, patients and commercial firms (Littman et al 2007): 'Translational research means different things to different people, but it seems important to almost everyone' (Woolf 2008, p 211).<br />
In related fields, such as public health (Armstrong et al 2006), 'translation' seems to signify dissatisfaction with 'transfer'. It wants to move away from thinking of knowledge transfer as a form of technology transfer or dissemination, rejecting if only by implication its mechanistic assumptions and its model of linear messaging from A to B. But still, what does it signify?<br />
<br />
===Why translation?===<br />
'Translation' indicates a closer attention to the problem of shared meaning and how it might be developed. It seems to represent some new epistemological lubricant, facilitating the dissemination of texts and the application and use of the knowledge and information they in. Simply, translation might be the key to transfer. And yet, when we stop to think, we are more ambivalent. What is translated often seems somehow inferior, not real or original. Note how readily commentators reach for the idea that things might be 'lost in translation'. Knowing at a distance – made in and mediated by translation - makes for incomplete renditions, blurred images, partial truths. So what might 'translation' really mean? The purpose of this paper is to set out, for policy makers and practitioners, the theoretical and conceptual resources translation holds and seems to represent. In doing so, it explores understandings of translation in the fields of literature and linguistics and in the sociology of science and technology. It begins by setting out just why this idea of translation should make immediate, intuitive sense in relation to research, policy and practice.<br />
1translation in research, policy and practice research as translation<br />
Research often entails translation from one language to another: where data is collected from more than one ethnic group, for example, or where the language of the researcher is other than that of the research subject. It may draw on a secondary literature or source documents written in different languages, and may be published and disseminated in languages other than the one in which it is first written up.<br />
2In a different way, to conduct an interview is to ask for an account of experience and its meanings, but it is also to construct and translate that experience in terms defined at least in part by the researcher. In representing what is said, transcripts then select data, usually excluding significant gesture and eye-contact, for example. Often, certain characteristics of speech-acts (such as hesitations) will be edited out. In turn, the format of the transcript shapes the analytic use the researcher may make of it. The basis of research 'findings', then, is an artefact, a transcript or translation, not an original interaction (Ochs 1979, Barnes, Bloor and Henry 1996, Ross 2009).<br />
3In this way, the researcher recasts aspects of his or her problem or topic in new, scientific form: 'All researchers "translate" the experiences of others' (Temple 1997, p 609). Research is invariably conducted in a sort of 'metalanguage' (Hantrais and Ager 1985): the research process can be conceived as one of successive translations (from theoretical formulation to operationalization, transcription, interpretation and dissemination). Theorization is a process of reciprocal back and forth between theory and fact, in which conceptions of each are revised in order that one fit the other (Baldamus 1974).<br />
4 It is a kind of translation: a rereading, re-use, re-application or re-representation of what we know in new terms (Turner 1980). Referencing, too, is an act of translation, a form of appropriation and incorporation of one text by another (Gilbert 1977).policy as translation Each of the fields covered by the paper is diverse and ill-defined, and there is no intention here to provide a comprehensive account of any them. Sources have been chosen for their relevance: main references are cited in the text, and additional sources listed in footnotes.<br />
<br />
2For a brief introduction to the technical issues involved in social research in more than one language, see Birbili (2000). For translation issues in survey research and question design in general, see Ervin and Bower (1952) and Deutscher (1968); on translating survey research instruments (in this instance health-related quality of life measures), Bowden and Fox-Rushby (2003). On the use of translators and interpreters, see Temple (1997) and Jentsch (1998).<br />
3For an interesting discussion of this problem, see Bourdieu (1999), esp pp 621-626, 'The risks of writing'. <br />
===Difference between machine translation and human translators===<br />
Few people disagree on the differences between the two, but many argue over the quality of the translations. How accurate are machine translations? How reliable are human translations? Some say machine translation produces near-perfect translations while others are adamant that translations are incomprehensible and cause more problems than they solve. Results will, of course, vary depending on the source and target languages, the machine translation service used (e.g. Google Translate), and the complexity of the original text.<br />
Machine translation, love it or hate it, is here to stay. In fact, the machine translation market is growing at such a fast pace that it is predicted to reach $980 million by 2022.<br />
===Machine translation: the pros and cons===<br />
The advantages of machine translation generally come down to two factors: it’s faster and cheaper. The downside to this is the standard of translation can be anywhere from inaccurate, to incomprehensible, and potentially dangerous (more on that shortly).<br />
==The advantages of machine translation==<br />
Many free tools are readily available (Google Translate, Skype Translator, etc.)<br />
Quick turnaround time <br />
You can translate between multiple languages using one tool<br />
Translation technology is constantly improving<br />
The disadvantages of machine translation<br />
Level of accuracy can be very low <br />
Accuracy is also very inconsistent across different languages<br />
==Machines can't translate context ==<br />
Mistakes are sometimes costly <br />
Sometimes translation simply doesn’t work<br />
The most important thing to consider with any kind of translation is the cost of potential mistakes. Translating instructions for medical equipment, aviation manuals, legal documents and many other kinds of content require 100% accuracy. In such cases, mistakes can cost lives, huge amounts of money and irreparable damage to your company’s image. So choose carefully!<br />
Human translation: the pros and cons<br />
Human translation essentially switches the table in terms of pros and cons. A higher standard of accuracy comes at the price of longer turnaround times and higher costs. What you have to decide is whether that initial investment outweighs the potential cost of mistakes. Alternatively, whether mistakes simply aren’t an option, like the cases we looked at in the previous section.<br />
<br />
==The advantages of human translation ==<br />
It’s a translator’s job to ensure the highest accuracy<br />
Humans can interpret context and capture the same meaning, rather than simply translating words<br />
Human translators can review their work and provide a quality process<br />
Humans can interpret the creative use of language, e.g. puns, metaphors, slogans, etc.<br />
Professional translators understand the idiomatic differences between their languages<br />
Humans can spot pieces of content where literal translation isn’t possible and find the most suitable alternative<br />
The disadvantages of human translation<br />
Turnaround time is longer <br />
Translators rarely work for free <br />
Unless you use a translation agency, with access to thousands of translators, you’re limited to the languages any one translator can work with<br />
Simply put, human translation is your best option when accuracy is even remotely important. Other considerations to make are the complexity of your source material and the two languages you’re translating between – both of which can render machines pretty useless.<br />
<br />
When to use machine and human translation<br />
The truth is, the debate over machine vs human translation is an unnecessary distraction. What we should really be talking about is when to use these two different types of translation services, because they both serve a very valid purpose.<br />
<br />
Examples of when to use machine translation<br />
When you have a large bulk of content to translate and the general meaning is enough<br />
When your translation never reaches the final audience, e.g. you’re translating a resource as research for another piece of content<br />
Translating documents for internal use within a company, provided 100% accuracy isn’t needed<br />
To partially translate large chunks of content for a human translator to improve upon<br />
Examples of when to use human translation<br />
When accuracy is important<br />
Most cases where your translated content is received by a consumer audience<br />
When you have a duty of care to provide accurate translations (e.g. legal documents, product instructions, medical guidelines or health and safety content)<br />
When translating marketing material or other texts for creative language uses.<br />
<br />
===Conclusion ===<br />
<br />
In the light of above mentioned facts and figures here by we would say that machine translation is challenge for human translators because it can reduces the wokload of translation but can't give accurate and exact translation of the traget language.It can be less reliable than human translation..</div>Chen Huinihttps://bou.de/u/index.php?title=Machine_translation&diff=129200Machine translation2021-12-06T01:54:11Z<p>Chen Huini: /* 2.Language Characteristics and Error Division */</p>
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<div>'''Machine Translation - A challenge or a chance for human translators?'''<br />
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[[Machine_translation|Overview Page of Machine Translation]]<br />
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=1 卫怡雯(A Comparison Between the Quality of Machine Translation and Human Translation——A Case Study of the Application of artificial intelligence in Sports Events)=<br />
[[Machine_Trans_EN_1]]<br />
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=2 吴映红（The Introduction of Machine Translation)= <br />
[[Machine_Trans_EN_2]]<br />
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=3 肖毅瑶(On the Realm Advantages And Symbiotic Development of Machine Translation And Huamn Translation)=<br />
[[Machine_Trans_EN_3]]<br />
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=4 王李菲 （Comparison Between Neural Machine Translation of Netease and Traditional Human Translation—A Case Study of The Economist Articles)= <br />
[[Machine_Trans_EN_4]]<br />
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===Abstract===<br />
Machine translation is a subfield of artificial intelligence and natural language processing that investigates transforming the source language into the target language. On this basis, the emergence of neural machine translation, a new method based on sequence-to-sequence model, improves the quality and accuracy of translation to a new level. As one of the earliest companies to invest in machine translation in China, Netease launched neural machine translation in 2017, which adopts the unique structure of neural network to encode sentences, imitating the working mechanism of human brain, and generates a translation that is more professional and more in line with the target language context. This paper takes the articles in The Economist as the corpus for analysis, and aims to explore the types and causes of common errors, as well as the advantages and challenges of each, through the comparative analysis of Netease neural machine translation and human translation, and finally to forecast the future development trend and make a summary of this paper.<br />
<br />
===Key words===<br />
Neural Machine Translation; Human Translation; Contrastive Analysis<br />
<br />
===题目===<br />
有道神经网络机器翻译与传统人工翻译的译文对比——以经济学人语料为例<br />
<br />
===摘要===<br />
机器翻译研究将源语言所表达的语义自动转换为目标语言的相同语义，是人工智能和自然语言处理的重要研究分区。在此基础上，一种基于序列到序列模型的全新机器翻译方法——神经机器翻译的出现让译文的质量和准确度提升到了新的层次。网易作为国内最早投身机器翻译的公司之一，在2017年上线的神经网络翻译采用了独到的神经网络结构，模仿人脑的工作机制对句子进行编码，生成的译文更具专业性，也更符合目的语语境。本文以经济学人内的文章为分析语料，旨在通过对网易神经机器翻译和人工翻译的英汉译文进行对比分析，探究常见错误类型及生成原因，以及各自存在的优势与挑战，最后展望未来发展趋势，并对本文做出总结。 <br />
<br />
===关键词===<br />
神经网络翻译；人工翻译；对比分析<br />
<br />
===1. Introduction===<br />
<br />
Nowadays, the process of economic globalization has accelerated overwhelmingly, and considerable resources are poured into the business field. As a branch of global language English, business English is proposed under the theoretical framework of English for Specific Purpose (ESP), serves the international business activities which is a professional subject requiring specialized English. As the medium that helps people with different cultural backgrounds to understand each other, business translation is required to be “formal, accurate, standardized and smooth”, which challenged both the machine translation and human translator.<br />
<br />
With the urgent requirement for more precise and higher quality translation, recent years have witnessed the rapid development of neural machine translation (NMT), which has replaced traditional statistical machine translation (SMT) to become a new mainstream technique, playing a crucial part in many fields, like business, academic and industry. Compared with SMT, NMT model is more like an organism. There are many parameters in the model that can be adjusted and optimized for the same goal, making the combination and interaction more organic and the overall translation effect better, which greatly matched the demands of business translation. <br />
<br />
The Economist is an international news and Business Weekly offering clear coverage, commentary and analysis of global politics, business, finance, science and technology. A huge number of terminologies plus the polysemy contained in the texts, put forward a tricky problem to both machine translation and human translator. In view of this, this paper makes a comparative analysis of Netease neural machine translation and human translation, aiming to explore the types and causes of common errors, as well as the advantages and challenges of each. In the end, this paper will forecast the future development, hoping to promote the development of translation studies in China.<br />
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===2.2. The Development Process of Machine Translation ===<br />
<br />
Since the IBM model was put forward by the researcher Peter Brown in the early 1990s, statistical methods have gradually become the mainstream of machine translation research. This method has greatly promoted the development of machine translation technology. In recent years, a variety of statistical machine translation models have emerged, such as phrase-based translation model, hierarchical phrase translation model and syntactic translation model, then the translation quality has been greatly improved.<br />
<br />
Since 2002, BLEU, an automatic translation quality evaluation method, has greatly promoted the development of statistical machine translation technology and effectively reduced the cost of manual evaluation. In recent years, with the technical maturity and stability of statistical machine translation, especially phrase-based machine translation, statistical machine translation technology has been making strong strides towards practical and commercial application. Therefore, with the rapid development of technology, people have gradually built-up confidence in machine translation, and the social demand for machine translation has been increasing day by day, with higher and higher expectations.<br />
<br />
However, from the perspective of academic research, both phrase-based translation models and syntactic translation models have experienced a rapid development stage, and the existing theoretical methods and technical models have begun to show "bottlenecks" in the improvement of translation performance. In addition, from the perspective of industrialization and utilitarianism, there is an urgent need for a more practical machine translation system, but the gap between the results of machine translation and the requirements of human beings is still very large. Therefore, for the researchers of machine translation, while excited to see the BLEU score of machine translation system evaluation is getting higher and higher, and the performance of online machine translation system developed by Google, Baidu, Netease and other enterprises is developing with each passing day, they are facing more and more challenges.<br />
<br />
Aiming to solve these problems, many technological giants are striving to find a new way to improve both the quality and efficiency of machine translation. There was a breakthrough which bought machine translation to a new level. Since 2014, the end-to-end neural machine translation has developed rapidly, compared with the statistical machine translation, the translation quality received a significant boost.<br />
<br />
The previous statistical machine translation was more like a mechanical system. Each module has its own function and goal, and then outputs the translation results through mechanical splicing. Its main disadvantage is that the model contains low syntactic and semantic components, so it will encounter problems when dealing with languages with large syntactic differences, such as Chinese-English. Sometimes the result is unreadable even though it is “word-for-word”.<br />
On the contrary, neural machine translation are consisted of several components, including phrase conditions, partial conditions, sequential conditions, primitive models, and so on. Its core is deep learning of artificial intelligence which can imitate the working mechanism of human brain and adopt unique neural network structure to model the whole process of translation. The whole model is composed of a large number of “neurons”, and each “neuron” has to complete some simple tasks, and then through the combination of all of them to coordinate the work, a much better translation text appears. <br />
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Since neural machine translation puts more emphasis on context and the whole text, it produces more coherent and comprehensible content to readers than traditional statistical machine translation, and be widely accepted and used in various field in a very short time. In 2017, at the GMIC (Global Mobile Internet Congress), Duan Yitao, the chief scientist of Netease, delivered a keynote speech titled “Machine Translation has Its Own Way” and announced an exciting news: the neural machine translation technology independently developed by Netease has been officially launched. This technology launched by Youdao this time has been jointly developed by Netease Youdao and Netease Hangzhou Research Institute for over two years. It will serve Youdao Dictionary, Youdao Translator, Youdao Web version, Youdao E-reader and other products, expecting to bring super-convenient product experience to users. In addition, Youdao Translation officer also launched photo translation, users only need to take pictures of the text, can show the results of neural network translation in real time. <br />
<br />
As a pioneer of machine translation in China, the development process of Netease YouDao is exactly the paradigm of the history of machine translation in China. Therefore, in this paper, the neural machine translation technology developed by Netease will be compared with human translators. The same excerpts selected from The Economist are translated by both of them, then the different versions will be analyzed by the translation criterion so as to figure out their respective strengths and weaknesses, bringing consideration to current translation situation and references to future development.<br />
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===3.Comparative Analysis of Errors in English-Chinese Translation ===<br />
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===4.===<br />
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===5. ===<br />
<br />
===Conclusion===<br />
<br />
===References===<br />
<br />
=6 徐敏赟(Machine Translation Based on Neural Network --Challenge or Chance?)=<br />
[[Machine_Trans_EN_6]]<br />
<br />
=7 颜莉莉(一带一路背景下人工智能与翻译人才的培养)=<br />
[[Machine_Trans_EN_7]]<br />
<br />
===Abstract===<br />
In the era of artificial intelligence, artificial intelligence has been applied to various fields. In the field of translation, traditional translation models can no longer meet the rapid development and updating of the information age. The development of machine translation has brought structural changes to the language service industry, which poses challenges to the cultivation of translation talents. Under the background of "The Belt and Road initiative", translation talents have higher and higher requirements on translation literacy. Artificial intelligence and translation technology are used to reform the training mode of translation talents, so as to better serve the development of The Times. This paper mainly explores the cultivation of artificial intelligence and translation talents under the background of the Belt and Road Initiative. The cultivation of translation talents is moving towards comprehensive cultivation of talents. On the contrary, artificial intelligence and machine translation can also be used to improve the teaching mode and teaching content, so as to win together in cooperation.<br />
===Key words===<br />
Artificial intelligence,Machine translation,cultivation of translation talents,"The Belt and Road initiative"<br />
===题目===<br />
一带一路背景下人工智能与翻译人才的培养<br />
<br />
===摘要===<br />
进入人工智能时代，人工智能被应用于各个领域。在翻译领域，传统的翻译模式已无法满足信息化时代的飞速发展和更新，机器翻译的发展给语言服务行业带来了结构性改变，这对翻译人才的培养提出了挑战。“一带一路”背景下，对翻译人才的翻译素养要求越来越高，利用人工智能和翻译技术对翻译人才培养模式进行革新，更好为时代发展服务。本文主要探究在一带一路背景下人工智能和翻译人才培养，翻译人才的培养过程中正向对人才的综合性培养，反之也可以利用人工智能和机器翻译完善教学模式和教学内容，在合作中共赢。<br />
===关键词===<br />
人工智能；机器翻译；翻译人才培养；一带一路<br />
<br />
===1. Introduction===<br />
With the development of science and technology in China, artificial intelligence has also been greatly improved, and related technologies have been applied to various fields, such as the use of intelligent robots to deliver food to quarantined people during the epidemic, which has made people's lives more convenient. The most controversial and widely discussed issue is machine translation. Before the emergence of machine translation, translation was generally dominated by human translation, including translation and interpretation, which was divided into simultaneous interpretation and hand transmission, etc. It takes a lot of time and energy to cultivate a translation talent. However, nowadays, the era is developing rapidly and information is updated rapidly. As a translation talent, it is necessary to constantly update its knowledge reserve to keep up with the pace of The Times. The emergence of machine translation has also posed challenges to translation talents and the training of translation talents. Although machine translation had some problems in the early stage, it is now constantly improving its functions. In the context of the belt and Road Initiative, both machine translation and human translation are facing difficulties. Regardless of whether human translation is still needed, what is more important at present is how to train translators to adapt to difficulties and promote the cooperation between human translation and machine translation.<br />
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===2.Development status of machine translation in the era of artificial intelligence ===<br />
With the development of AI technology, machine translation has made great progress and has been applied to people's lives. For example, more and more tourists choose to download translation software when traveling abroad, which makes machine translation take an absolute advantage in daily email reply and other translation activities that do not require high accuracy. The translation software commonly used by netizens include Google Translation, Baidu Translation, Youdao Translation, IFly.com Translation, etc. Even wechat and other chat software can also carry out instant Translation into English. Some companies have also launched translation pens, translation machines and other equipment, which enables even native speakers to rely on machine translation to carry out basic communication with other Chinese people.<br />
But so far, machine translation still faces huge problems. Although machine translation has made great progress, it is highly dependent on corpus and other big data matching. It does not reach the thinking level of human brain, and cannot deal with the problem of translation differences caused by culture and religion. In addition, many minor languages cannot be translated by machine due to lack of corpus.<br />
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What's more, most of the corpus is about developed countries such as Britain and France, and most of the corpus is about diplomacy, politics, science and technology, etc., while there are very few about nationality, culture, religion, etc.<br />
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In addition, machine translation can only be used for daily communication at present. If it involves important occasions such as large conferences and international affairs, it is impossible to risk using machine translation for translation work. Professional translators are required to carry out translation work. So machine translation still has a long way to go.<br />
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===3.Challenges in the training of translation talents in universities===<br />
The cultivation of translators is targeted at the market. Professors Zhu Yifan and Guan Xinchao from the School of Foreign Languages at Shanghai Jiao Tong University believe that the cultivation of translators can be divided into four types: high-end translators and interpreters, senior translators and researchers, compound translators and applied translators.<br />
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From their names, it can be seen that high-end translators and interpreters and senior translators and researchers talents have high requirements on the knowledge and quality of interpreters, because they have to face the changing international situation, and have to deal with all kinds of sensitive relations and political related content, they should have flexible cross-cultural communication skills. In addition, for literature, sociology and humanities academic works, it is not only necessary to translate their content, but also to understand their essence. Therefore, translators should not only have humanistic feelings, but also need to have a deep understanding of Chinese and western culture.<br />
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However, there is not much demand for this kind of translation in the society. Such high-level translation requirements are not needed in daily life and work. The greatest demand is for compound translators, which means that they should master knowledge in a specific field while mastering a foreign language. For example, compound translators in the financial field should not only be good at foreign languages, but also master financial knowledge, including professional terms, special expressions and sentence patterns.<br />
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Now we say that machine translation can replace human translation should refer to the field of compound translation talents. Although AI technology has enabled machine translation to participate in creation, it does not mean that compound translation talents will be replaced by machines. The complexity of language and the flexible cross-cultural awareness required in communication make it impossible for machine translation to completely replace human translation.<br />
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The last type of applied translation talents are mostly involved in the general text without too much technical content and few professional terms, so it is easy to be replaced by machine translation.<br />
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Therefore, the author thinks that what universities are facing at present is not only how to train translation talents to cope with the development of machine translation, but to consider the application of machine translation in the process of training translation talents to achieve human-machine integration, so as to better complete the translation work.<br />
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===4.The Language environment and opportunities and challenges of the Belt and Road initiative===<br />
During visits to Central and Southeast Asian countries in September and October 2013, Chinese President Xi Jinping put forward the major initiative of jointly building the Silk Road Economic Belt and the 21st Century Maritime Silk Road. And began to be abbreviated as the Belt and Road Initiative.<br />
<br />
According to the Vision and Actions for Jointly Building silk Road Economic Belt and 21st Century Maritime Silk Road, the Silk Road Economic Belt focuses on connecting China, Central Asia, Russia and Europe (the Baltic Sea). From China to the Persian Gulf and the Mediterranean Sea via Central and West Asia; China to Southeast Asia, South Asia, Indian Ocean. The focus of the 21st Century Maritime Silk Road is to stretch from China's coastal ports to Europe, through the South China Sea and the Indian Ocean. From China's coastal ports across the South China Sea to the South Pacific.<br />
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The Belt and Road "construction is comply with the world multi-polarization and economic globalization, cultural diversity, the initiative of social informatization tide, drive along the countries achieve economic policy coordination, to carry out a wider range, higher level, the deeper regional cooperation and jointly create open, inclusive and balanced, pratt &whitney regional economic cooperation framework.<br />
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====4.1The language environment of the Belt and Road====<br />
The "Belt and Road" involves a wide range of countries and regions, and their languages and cultures are very complex. How to make good use of language, do a good job in translation services, actively spread Chinese culture to the world, strengthen the ability of discourse, and tell Chinese stories well, the first thing to do is to understand the language situation of the countries along the "Belt and Road".<br />
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=====4.1.1The most common language in countries along the "Belt and Road" =====<br />
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There are a wide variety of languages spoken in 65 countries along the Belt and Road, involving nine language families. However, The status of English as the first language in the world is undeniable. Most of the countries participating in the Belt and Road are developing countries, and many of them speak English as their first foreign language. Especially in southeast Asian and South Asian countries, English plays an important role in foreign communication, whether as the official language or the first foreign language. Besides English, more than 100 million people speak Russian, Hindi, Bengali, Arabic and other major languages in the "Belt and Road" countries. It can also be seen that a common feature of languages in countries along the "Belt and Road" is the popularization of English education. English is widely used in international politics, economy, culture, education, science and technology, playing the role of the most important language in the world.<br />
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=====4.1.2The complex language conditions of countries along the "Belt and Road" =====<br />
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The languages spoken in countries along the Belt and Road involve nine major language families and almost all the world's religious types. Differences in religious beliefs also result in differences in culture, customs and social values behind languages. The languages of some countries along the belt and Road have also been influenced by historical and realistic factors, such as colonization, internal division and immigration. <br />
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India, for example, has no national language, but more than 20 official languages. India is a multi-ethnic country, a total of more than 100 people, one of the most obvious difference between nation and nation is the language problem. Therefore, according to the difference of language, India divides different ethnic groups into different states, big and small. Ethnic groups that use the same language are divided into one state. If there are two languages in a state, the state is divided into two parts. And Indian languages differ not only in word order but also in the way they are written. In India, for example, Hindi is spoken by the largest number of people in the north, with about 700 million speakers and 530 million as their first language. It is written in The Hindu language and belongs to the Indo-European language family. Telugu in the east is spoken by about 95 million people and 81.13 million as their first language. It is written in Telugu, which belongs to the Dravidian language family and is quite different from Hindi. As a result, a parliamentary session in India requires dozens of interpreters. <br />
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These factors cannot be ignored in the process of translation, from language communication to cultural understanding, from text to thought exchange, through the bridge of language to truly connect the people, so as to avoid misreading and misunderstanding caused by differences in language and national conditions.<br />
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====4.2 Opportunities and challenges of the "Belt and Road" ====<br />
With the promotion of the Belt and Road Initiative, there has been an unprecedented boom in translation. In the previous translation boom in China, most of the foreign languages were translated into Chinese, and most of the foreign cultures were imported into China. However, this time, in the context of the "Belt and Road" initiative, translating Chinese into foreign languages has become an important task for translators. As is known to all, there are many different kinds of "One Belt And One Road" along the national language and culture is complex, the service "area" construction has become a factor in Chinese translation talents training mode reform, one of the foreign language universities have action, many colleges and universities to establish the "area" all the way along the country's small language major, as a result, "One Belt And One Road" initiative to promote, It has brought unprecedented opportunities for human translation. The cultivation of diversified translation talents and the cultivation of translation talents in small languages is an urgent problem to be solved in China. The cultivation of translation talents cannot be completed overnight, and the state needs to reform the training mode of translation talents from the perspective of language strategic development. Only in this way can we meet the new demand for human translation under the new situation of the belt and Road Initiative.<br />
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For a long time, the traditional orientation of translation curriculum and training goal in colleges and universities is to train translation teachers and translators in need of society through translation theory and practice and literary translation practice, which cannot meet the needs of society. Since 2007, in order to meet the needs of the socialist market economy for application-oriented high-level professionals, the Academic Degrees Committee of The State Council approved the establishment of Master of Translation and Interpreting (MTI for short). After joining the pilot program of MTI, more and more universities are reforming the curriculum and training mode of master of Translation in order to cultivate translators who meet the needs of the society.<br />
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Language is an important carrier of culture, and translation is an important link for exporting culture. The quality of translation output also reflects the cultural soft power of a country. With the rise of China, more and more people are interested in Chinese culture, and the number of Chinese learners keeps increasing. Under the background of "One Belt and One Road", excellent translators are urgently needed to spread Chinese culture. With the promotion of "One Belt and One Road" Initiative, the number of other countries learning mutual learning and cultural exchanges with China has increased unprecedeningly, bringing vigorous opportunities for the spread of Chinese culture. Translation talents who understand small languages and multi-lingual translators are needed. They should not only use language to convey information, but also use language as a lubricant for communication.<br />
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===5.Training translation talents from the perspective of machine translation===<br />
Under the prevailing environment of machine translation, it poses a great challenge to the cultivation of translation talents. According to the current situation, translation needs and the shortage of translation talents, colleges and universities should reform and innovate the existing training programs for translation talents in terms of the quality of translation talents, the reform of training mode and the use of artificial intelligence. Based on the obtained data and literature, the author discusses how to train translation talents in the perspective of machine translation from the following aspects.<br />
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====5.1 Quality requirements for translation talents ====<br />
Zhong Weihe and Murray made a more detailed and profound discussion on translator's literacy, believing that "translators should not only be proficient in two languages, but also have extensive cultural and encyclopedic knowledge and relevant professional knowledge; Master a variety of translation skills, a lot of translation practice; Have a clear translator role awareness, good professional ethics, practical and enterprising style of work, conscious team spirit and calm psychological quality ". According to the collected data, the author will elaborate the requirements for translation talents from four aspects: language literacy, humanistic literacy, translation ability and innovation ability.<br />
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The first is language literacy, which is the most basic and important requirement. MAO Dun pointed out that "mastery of mother tongue and target language are the foundation of translation". A solid foundation of bilingual skills is the basic skills of translators. Poor language proficiency seems to be a common problem among students majoring in translation and interpreting. Many translation diseases are caused by poor Chinese foundation. As part of going global, the belt and Road initiative is to tell Chinese culture and Chinese stories, which requires translators to be able to use both languages flexibly. Therefore, the first problem that colleges and universities face to solve is to improve the language level of foreign language learners.<br />
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The second is humanistic literacy. Humanistic literacy is mainly manifested by a good command of politics, economy, history, literature and other knowledge, which is particularly important for interpreters. In addition, cross-cultural communication cannot be ignored. In the process of communicating with foreigners or translating, translators often encounter the first cross-cultural contradiction. Cross-culture refers to having a full and correct understanding of cultural phenomena, customs and habits that differ or conflict with the national culture, and accepting and adapting to them in an inclusive manner on this basis. So the interpreter can first fully understand and master the national conditions and culture of the target country, which is particularly important in the "Belt and Road". There are more than 60 countries along the "Belt and Road", and it takes a lot of energy to master their national conditions and culture.<br />
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The third is translation ability. We should distinguish between translation ability and language ability. Translation ability is actually a system of knowledge and skills necessary for translation, the core of which is conversion ability. First of all, it reflects the ability to use tools to assist translation, such as computer application, translation technology and so on. In addition, interpreters should have enough healthy psychological quality and good professional quality. In terms of translation ability, the current training model of translation talents is inadequate.<br />
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The last one is innovation. The cultivation of learners' thinking ability is the key to translation teaching and the cultivation of thoughtful translators should be the connotation of translation teaching. Therefore, the interpreter is not only a translation tool, which is no different from machine translation. More importantly, it is necessary to explore translation with thoughts, have a sense of lifelong learning and innovation consciousness. Translators must constantly innovate themselves, learn new knowledge, and strive to seek reform and innovation. Many colleges and universities should also consciously cultivate students' innovation ability and broaden their thinking and vision.<br />
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====5.2 The reform of college curriculum setting====<br />
First, we will further reform the curriculum of colleges and universities. Add economics, law and engineering to the curriculum, these contents in the "belt and Road".<br />
"One Road" is very important in the construction. According to the author's personal experience, the most typical problem of foreign language majors in colleges and universities is the single learning of foreign languages. More professional foreign language colleges and universities will add some literature courses and national conditions courses of the language target countries. Obviously, whether foreign language graduates are engaged in translation work or not, these knowledge is not enough. Of course, great reforms have been carried out in foreign language teaching, such as combining foreign language with finance, law, diplomacy and so on, and taking the way of minor training foreign language majors.<br />
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Domestic enterprises with a high degree of internationalization attach great importance to translation. Their translation research includes cutting-edge theoretical and applied research, involving machine translation, natural language processing and AI theory, algorithm and model. With such a foundation, enterprises can solve problems by themselves, such as embedding automatic translation functions in mobile phones. International enterprises not only do technical translation, but also deal with all forms of translation and localization in society. At present, translation teaching in most colleges and universities is still in the early mode, and it is an objective fact that it is divorced from the workplace and has a gap with the needs of enterprises.<br />
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Second, we should adjust and strengthen the construction of second foreign language teaching for foreign language majors. In the 1980s, our country was in urgent need of Russian translation. At that time, students majoring in English could translate microelectronic product manuals and related business documents in English and Russian at the same time after learning Russian for half a year. The mutual conversion between English and Russian played a great role in practice. According to the author, in the Graduate Institute of Interpretation and Translation of Beijing Foreign Studies University a very few students majored in multiple languages at the graduate level, that is, they majored in minor languages at the undergraduate level and were admitted to the Graduate Institute of Interpretation and Translation in English. Their training mode is to study English in the Graduate Institute of Interpretation and Translation for two years and the third year in the corresponding department of the undergraduate major. Such training mode in my opinion is a bigger model, cost It's more difficult for students. <br />
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In addition, there is a great disparity in the development of second foreign language teaching in colleges and universities, and the overall level is not high enough. Part of the second foreign language university foreign language professional may still be too much focus in languages such as German, French and Japanese, should as far as possible, considering the need of the construction of the "region", like Croatia, Serbia, Turkish, Hungarian, Italian, Indonesian, Albanian, these are the countries along the "area" the language of the two countries, Colleges and universities should encourage the teaching of a second foreign language.<br />
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Third, the teaching of translation technology should be strengthened. Traditional translation teaching teaches translation skills, such as the translation of words, sentences, texts and figures of speech. Translation technology refers to a series of practical workplace technologies with computer-aided translation software and translation project management as the core, which can greatly improve translation efficiency. However, due to the relative lack of translation technology teachers and equipment in colleges and universities, there is a disconnect between talent training and the requirements of translation technology in the translation field.<br />
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====5.3 Application of artificial intelligence to translation teaching practice====<br />
In order to improve the teaching quality and train students' English translation ability, it is necessary to realize the effective integration of ARTIFICIAL intelligence and translation activity courses, which should not only reflect the effectiveness of artificial intelligence translation technology, but also help students establish a healthy concept of English communication. Through the application of artificial intelligence technology, students can strengthen their flexible translation skills through close communication with "AI program" during the learning stage of English translation activity class. For example, teachers can ask students to translate directly against the translation content provided on the translation screen of the ARTIFICIAL intelligence system. After that, the system can collect the translation answers with the help of speech recognition function, and then judge the accuracy of the translation content, thus providing important feedback to students.<br />
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China has used such artificial intelligence technology in the Putonghua test to ensure that every student can find a suitable translation method in practical communication. The so-called artificial intelligence technology is a new kind of technology modeled after the characteristics of human neural network thinking, can combine the human mind to respond. If it can be integrated into English translation activity teaching, it can not only improve the teaching efficiency, but also enhance students' enthusiasm in learning the course.<br />
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At the same time, during the training of translation talents, teachers also need to take into account the importance of influencing education factors, so that students can form a higher disciplinary quality in translation, so as to fit the concept of quality education in the new era. Only when artificial intelligence translation content is fully integrated into college English translation activity courses can the overall translation ability of college students be maximized.<br />
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====5.4The improvement of translator's technical ability====<br />
In the previous part, the author roughly mentioned that translation teaching should be improved, which will be elaborated here. At present, only a few universities can make full use of the advantages of translation technology in translation teaching and focus on cultivating professional translation talents. Most universities still cannot get rid of the traditional teaching mode of "language + relevant professional knowledge" in translation teaching, and generally lack a correct understanding of COMPUTER-aided translation teaching.<br />
<br />
According to Wang Huashu et al., the courses that can be offered around the composition of translators' technical literacy include computer-assisted translation, translation and corpus, machine translation and post-translation editing, localization and internationalization, film and television translation (subtitle), technical communication and technical writing, and computer programming. The course modules involved are: Fundamentals of COMPUTER-aided Translation, CAT tool application, corpus alignment and processing, term management, QA technology for translation quality assurance, OFFICE fundamentals, translation management technology, basic computer knowledge, desktop typesetting, localization and internationalization, project management system and content management system, technical writing, basic knowledge of computer programming, basic knowledge of web code, etc.<br />
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<br />
===6针对一带一路的机器翻译与翻译人才的合作===<br />
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===Conclusion===<br />
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===References===<br />
<br />
=8 颜静(On Machine Translation Under Lanuguage Intelligence——An Option and Opportunity for Human Translators)=<br />
[[Machine_Trans_EN_8]]<br />
<br />
=9 谢佳芬（人工智能时代下的机器翻译与人工翻译）=<br />
[[Machine_Trans_EN_9]]<br />
===Abstract===<br />
With the continuous development of information technology, many industries are facing the competitive pressure of artificial intelligence, and so is the field of translation. Artificial intelligence technology has developed rapidly and combined with the field of translation，which has brought great impact and changes to traditional translation, but artificial intelligence translation and artificial translation have their own advantages and disadvantages. Artificial translation is in the leading position in adapting to human language logical habits and understanding characteristics, but in terms of translation threshold and economic value, the efficiency of artificial intelligence translation is even better. In a word, we need to know that machine translation and human translation are complementary rather than antagonistic.<br />
<br />
===Key Words===<br />
Machine Translation; Artificial Translation; Artificial Intelligence<br />
<br />
===题目===<br />
人工智能时代下的机器翻译与人工翻译<br />
<br />
===摘要===<br />
伴随着信息技术的不断发展，多个行业面临着人工智能的竞争压力，翻译领域也是如此。人工智能技术快速发展并与翻译领域结合，人工智能翻译给传统翻译带来了巨大的冲击和变革，但人工智能翻译与人工翻译存在着各自的优劣特点和发展空间，在适应人类语言逻辑习惯和理解特点的翻译效果上，人工翻译处于领先地位，但在翻译门槛和经济价值上，人工智能翻译的效率则更胜一筹。总的来说，我们要知道机器翻译与人工翻译是互补而非对立的关系。<br />
<br />
===关键词===<br />
机器翻译;人工翻译;人工智能<br />
<br />
===1. Introduction===<br />
====1.1 The History of Machine Translation Aborad====<br />
The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. Alchuni put forward the idea of using machines for translation. In 1933, the Soviet inventor Troyansky designed a machine to translate one language into another. [1]In 1946, the world's first modern electronic computer ENIAC was born. Soon after, American scientist Warren Weaver, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947. In 1949, Warren Weaver published a memorandum entitled Translation, which formally raised the issue of machine translation. In 1954, Georgetown University, with the cooperation of IBM, completed the English-Russian machine translation experiment with IBM-701 computer for the first time, which opened the prelude of machine translation research. [2] In 2006, Google translation was officially released as a free service software, bringing a big upsurge of statistical machine translation research. It was Franz Och who joined Google in 2004 and led Google translation. What’s more, it is precisely because of the unremitting efforts of generations of scientists that science fiction has been brought into reality step by step.<br />
====1.2 The History of Machine Translation in China====<br />
In 1956, the research and development of machine translation has been named in the scientific and technological work and made little achievements in China. On the eve of the tenth anniversary of the National Day in 1959, our country successfully carried out experiments, which translated nine different types of complicated sentences on large general-purpose electronic computers. The dictionary includes 2030 entries, and the grammar rule system consists of 29 circuit diagrams. [3]. After a period of stagnation, China's machine translation ushered in a high-speed development stage after the 1980s in the wave of the third scientific and technological revolution. With the rapid development of economy and science and technology, China has made a qualitative leap in the field of machine translation research with the pace of reform and opening up. In 1978, Institute of Scientific and Technological Information of China, Institute of Computing Technology and Institute of Linguistics carried out an English-Chinese translation experiment with 20 Metallurgical Title examples as the objects and achieved satisfactory results. Subsequently, they developed a JYE-I machine translation system, which based on 200 sentences from metallurgical documents. Its principles and methods were also widely used in the machine translation system developed in the future. In addition, the research achievements of machine translation in China during the 1980s and 1990s also include that Institute of Post and Telecommunication Sciences developed a machine translation system, C Retrieval and automatic typesetting system with good performance and strong practicability in October 1986; In 1988, ISTC launched the ISTIC-I English-Chinese Title System for the translation of applied literature of metallurgy, Information Research Institute of Railway developed an English-Chinese Title Recording machine translation system for railway documents; the Language Institute of the Academy of Social Sciences developed "Tianyu" English-Chinese machine translation system and Matr English-Chinese machine translation system developed by the computer department of National University of Defense Technology. After many explorations and studies, machine translation in China has gradually moved towards application, popularization and commercialization. China Software Technology Corporation launched "Yixing I" in 1988, marking China's machine translation system officially going to the market. After "Yixing", a series of machine translation systems such as Gaoli system in Beijing, Tongyi system in Tianjin and Langwei system in Shaanxi have also entered the public. In the 21st century, the development of a series of apps such as Kingsoft Powerword, Youdao translation and Baidu translation has greatly met the needs of ordinary users for translation. According to the working principle, machine translation has roughly experienced three stages: rule-based machine translation, statistics-based machine translation and deep learning based neural machine translation. [4] These three stages witnessed a leap in the quality of machine translation. Machine translation is more and more used in daily life and even the translation of some texts is almost comparable to artificial translation. In addition to text translation, voice translation, photo translation and other functions have also been listed, which provides great convenience for people's life. It is undeniable that machine translation has become the development trend of translation in the future.<br />
====1.3 The Status Quo of Machine Translation====<br />
In this big data era of information explosion, the prospect of machine translation is also bright. At present, the circular neural network system launched by Google has supported universal translation in more than 60 languages. Many Internet companies such as Microsoft Bing, Sogou, Tencent, Baidu and NetEase Youdao have also launched their own Internet free machine translation systems. [5] Users can obtain translation results free of charge by logging in to the corresponding websites. At present, the circular neural network translation system launched by Google can support real-time translation of more than 60 languages, and the domestic Baidu online machine translation system can also support real-time translation of 28 languages. These Internet online machine translation systems are suitable for a variety of terminal platforms such as mobile phone, PC, tablet and web and its functions are also quite diverse, supporting many translation forms, such as screen word selection, text scanning translation, photo translation, offline translation, web page translation and so on. Although its translation quality needs to be improved, it has been outstanding in the fields of daily dialogue, news translation and so on.<br />
===2. Advantages and Disadvantages of Machine Translation===<br />
Generally speaking, machine translation has the characteristics of high efficiency, low cost, accurate term translation and great development potential and etc. Machine translation is fast and efficient, this is something that artificial translation can’t catch up with. In addition, with the continuous emergence of all kinds of translation software in the market, compared with artificial translation, machine translation is cheap and sometimes even free, which greatly saves the economic cost and time for users with low translation quality requirements. What's more, compared with artificial translation, machine translation has a huge corpus, which makes the translation of some terms, especially the latest scientific and technological terms, more rapid and accurate. The accurate translation of these terms requires the translator to constantly learn, but learning needs a process, which has a certain test on the translator's learning ability and learning speed. In this regard, artificial translation has uncertainty and hysteretic nature. At the same time, with the progress of science and technology and the development of society, the function of machine translation will be more perfect and the quality of translation will be better.Today's machine translation tools and software are easy to carry, all you need to do is just to use the software and electronic dictionary in the mobile phone. There is no need to carry paper dictionaries and books for translation, which saves time and space. At the same time, machine translation covers many fields and is suitable for translation practice in different situations, such as academic, education, commercial trade, social networking, tourism, production technology, etc, it is also easy to deal with various professional terms. However, due to the limitation of translators' own knowledge, artificial translation is often limited to one or a few fields or industries. For example, it is difficult for an interpreter specializing in medical English to translate legal English.<br />
At the same time, machine translation also has its limitations. At first, machine can only operate word to word translation, which only plays the function and role of dictionary. Then, the application of syntax enables the process of sentence translation and it can be solved by using the direct translation method. When the original text and the target language are highly similar, it can be translated directly. For example, the original text "他是个老师." The target language is "he is a teacher ". With the increase of the structural complexity of the original text, the effect of machine translation is greatly reduced. Therefore, at the syntactic level, machine translation still stays in sentences with relatively simple structure. Meanwhile, the original text and the results of machine translation cannot be interchanged equally, indicating that English-Chinese translation has strong randomness, and is not rigorous and scientific enough. <br />
Nowadays, machine translation is highly dependent on parallel corpora, but the construction of parallel corpora is not perfect. At present, the resources of some mainstream languages such as Chinese and English are relatively rich, while the data collection of many small languages is not satisfactory. Moreover, the current corpus is mainly concentrated in the fields of government literature, science and technology, current affairs and news, while there is a serious lack of data in other fields, which can’t reflect the advantages of machine translation. At the same time, corpus construction lags behind. Some informative texts introducing the latest cutting-edge research results often spread the latest academic knowledge and use a large number of new professional terms, such as academic papers and teaching materials while the corpus often lacks the corresponding words of the target language, which makes machine translation powerless<br />
Besides, machine translation is not culturally sensitive. Human may never be able to program machines to understand and experience a particular culture. Different cultures have unique and different language systems, and machines do not have complexity to understand or recognize slang, jargon, puns and idioms. Therefore, their translation may not conform to cultural values and specific norms. This is also one of the challenges that the machine needs to overcome.[6] Artificial intelligence may have human abstract thinking ability in the future, but it is difficult to have image thinking ability including imagination and emotion. [7] Therefore, machine translation is often used in news, science and technology, patents, specifications and other text fields with the purpose of fact description, knowledge and information transmission. These words rarely involve emotional and cultural background. When translating expressive texts, the limitations of machine translation are exposed. The so-called expressive text refers to the text that pays attention to emotional expression and is full of imagination. Its main characteristics are subjectivity, emotion and imagination, such as novels, poetry, prose, art and so on. This kind of text attaches importance to the emotional expression of the author or character image, and uses a lot of metaphors, symbols and other expressions. Machine translation is difficult to catch up with artificial translation in this kind of text, it can only translate the main idea, lack of connotation and literary grace and it cannot have subjective feelings and rational analysis like human beings. In fact, it is not difficult to simulate the human brain, the difficulty is that it is impossible to learn from the rich social experience and life experience of excellent translators. In other words, machine translation lacks the personalization and creativity of human translation. It is this personalization and creativity that promote the development and evolution of language, and what machine translation can only output is mechanical "machine language".<br />
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===3.The Irreplaceability of Artificial Translation ===<br />
====3.1 Translation is Constrained by Context====<br />
At present, machine translation can help people deal with language communication in people's daily life and work, such as clothing, food, housing and transportation, but there is a big gap from the "faithfulness, expressiveness and elegance" emphasized by high-level translation. Language itself is art，which pays more attention to artistry than functionality, and the discipline of art is difficult to quantify and unify. Sometimes it is regular, rigorous, logical and clear, and sometimes it is random, free and logical. If it is translated by machine, it is difficult to grasp this degree. Sometimes, machine translation cannot connect words with contextual meaning. In many languages, the same word may have multiple completely unrelated meanings. In this case, context will have a great impact on word meaning, and the understanding of word meaning depends largely on the meaning read from context. Only human beings can combine words with context, determine their true meaning, and creatively adjust and modify the language to obtain a complete and accurate translation. This is undoubtedly very difficult for machine translation. Artificial translation can get rid of the constraints of the source language and translate the translation in line with the grammar, sentence patterns and word habits of the target language. In the process of translation, translators can use their own knowledge reserves to analyze the differences between the source language and the target language in thinking mode, cultural characteristics, social background, customs and habits, so as to translate a more accurate translation. Artificial translation can also add, delete, domesticate, modify and polish the translation according to the style, make up for the lack of culture, try to maintain the thought, artistic conception and charm of the original text and the style of the source language. In addition, translators can also judge and consider the words with multiple meanings or easy to produce ambiguity according to the context, so as to make the translation more clear and more accurate and improve the quality of the translation.<br />
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===4. Discussion on the Relationship Between Machine Translation and Artificial Translation ===<br />
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===5. Suggestions on the Combined Development of Machine Translation and Artificial Translation===<br />
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===7. ===<br />
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===Conclusion===<br />
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===References===<br />
<br />
=10 熊敏(Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts)=<br />
[[Machine_Trans_EN_10]]<br />
===Abstract===<br />
With the rapid development of information technology,machine translation technology emerged and is gradually becoming mature.In order to explore the ability of machine translation, I adopts two versions of translation, which are manual translation and machine translation(this paper uses Youdao translation) for different types of texts(according to Peter Newmark's types of text). The results are quite different in terms of quality and accuracy.<br />
<br />
===Key words===<br />
machine translation; manual translation; Newmark's type of texts<br />
<br />
===题目===<br />
Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts<br />
<br />
===摘要===<br />
随着信息技术的高速发展，机器翻译技术出现了，并且逐渐成熟。为了探究机器翻译的能力水平，本人根据纽马克的文本类型分类，选择了相应的译文类型，并且将其机器翻译的版本以及人工翻译的版本进行对比。就质量和准确度而言，译文的水平大相径庭。<br />
<br />
===关键词===<br />
机器翻译；人工翻译；纽马克文本类型<br />
<br />
===1. Introduction===<br />
====1.1.Introduction to machine translation====<br />
Machine translation, also known as computer translation is a technique to translating through machine translator, such as Google translation and Youdao translation. Machine translation is one of the branches of computational linguistics, ranging from computer science, statistics, information science and so on. Machine translation plays an important role in all aspects.<br />
Machine translation can be traced back to 1940s, when British engineer Booth and American engineer Weaver proposed using computer to translate and started to study machines used for translation. However in the 1960s, reports from ALPAC (Automated Language Processing Advisory Committee) showed studies on machine translation had stagnated for a decade. In the 1970s, with the advancement of computer, machine translation was back to track. In the last decades, machine translation has mainly developed into four stages: rule-based machine translation, statistic machine translation, example-based machine translation and neural machine translation.<br />
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====1.2.Process of machine translation====<br />
The process of manual translation is different from that of machine translation. Here is the process of the former. (1) Understand source language. (2) Use target language to organize language. (3) Generate translation. Unlike manual translation, machine translation tends to analyze and code source language first, then look for related codes in corpus, and work out the code that represents target language, generating translation. But they share a common feature, which is that Lexicon, grammatical rules and syntactic structure are taken into consideration. This is one of the biggest challenges for machine translation.<br />
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===2.Newmark’s type of texts===<br />
Peter Newmark divided texts into informative type, expressive text and vocative type according to the linguistic functions of various texts. <br />
====2.11Informative text====<br />
The core of the informative texts is the truth. It is to convey facts, information, knowledge and the like. The language style of the text is objective and logical. Reports, papers, scientific and technological textbooks are all attributed to informative texts.<br />
====2.2Expressive text====<br />
The core of the expressive text is the emotion. It is to express preferences, feelings, views and so on. The language style of it is subjective. Literary works, including fictions, poems and drama, autobiography and authoritative statements belong to expressive text.<br />
====2.3Vocative text====<br />
The core of the vocative text is readership. It is to call upon readers to act in the way intended by the text. So it is reader-oriented. Such texts advertisement, propaganda and notices are of vocative text.<br />
====2.4Study Method====<br />
Manual translations of the three texts are selected from authoritative versions and universally acknowledged. And machine translations of those come from Youdao Translator. And in this thesis I will compare and evaluate the two methods in word diction, sentence structure, word order and redundancy.<br />
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===3. ===<br />
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===5. ===<br />
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===6. ===<br />
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===7. ===<br />
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===Conclusion===<br />
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===References===<br />
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=11 陈惠妮=(Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts)=<br />
[[Machine_Trans_EN_11]]<br />
===Abstract===<br />
At present, globalization is accelerating and the market demand for language services is rapidly increasing . Machine translation, as an important translation method, can greatly improve translation efficiency due to its low cost and high speed. However, because of the limitations of machine translation and the differences between Chinese and English language, machine translation is not accurate enough. In order to balance translation efficiency and translation quality, a great number of manual revisions in translation are required for the machine translating texts. Medical papers are specialized, special and purposeful, so it requires accurate,qualified and professional translation. However, the quality of translations by machine is inefficient to meet the high-quality requirements of medical papers translation. Therefore, the introduction of pre-editing can greatly improve the efficiency and quality of machine translation.<br />
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===Key words===<br />
Pre-editing, Machine translation, Medical texts<br />
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===题目===<br />
Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts<br />
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===摘要===<br />
在全球化加速发展的今天，市场对语言服务的需求迅速增加。机器翻译作为一种重要的翻译途径，由于其成本低、速度快，可以大大提高翻译效率。然而，由于机器翻译的局限性以及中英文语言的差异，机器翻译的准确性不高。为了平衡翻译效率和翻译质量，机器翻译文本需要大量的手工修改。<br />
医学论文具有专业性、特殊性和目的性，要求其译文准确、合格、专业。然而，机器翻译的质量较低，无法满足医学论文对翻译的高质量要求。因此，译前编辑的引入可以大大提高机器翻译的效率和质量。<br />
<br />
===关键词===<br />
译前编辑；机器翻译；医学文本<br />
<br />
===1. Introduction===<br />
===1.1 Definition of Machine Translation===<br />
The concept of machine translation was firstly proposed in the 1930s. Since 1940s, the machine translation technology has been evolving from rule-based machine translation (RBMT) to statistical machine translation (SMT), and to neural machine translation (NMT). Machine translation refers to the automatic translation of source language into target language by using a computer system. That is, machine translation refers to the automatic translation of text from one language into another natural language by computer software or other online translation webs. Machine translation is also defined as the process of “using a computer system to automatically translate text or speech from one natural language to another” according to the definition by ISO (Cui, 2014). On the basic level, machine translation performs mechanical substitution of words in one language for words in another language, but that rarely produces a good translation, therefore, recognition of the whole phrases and their closest counterparts in the target language is needed. Not all words in one language have equivalents in another language, and many words have more than one meaning. A huge demand for translation is greatly needed in today’s global world, which creates new opportunities for the development of machine translation, attracts more and more attention and becomes one of the current research focuses.<br />
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===1.2 Definition of Pre-editing===<br />
Pre-editing means to adjust and modify the source language to make it fit more with the characteristics of the machine translation software before putting the source language before the into machine translation, so as to improve the quality of the translation machine translation (Wei Changhong, 2008:93-94). Pre-editing is to modify the original text before putting it into machine translation software, in order to improve the recognition rate of machine translation, optimize the output quality of translated text and reduce the workload of post-editing. Because pre-translation editing only needs to be modified in one language, the operation is simpler than post-translation editing, which can realize the double improvement of quality and efficiency. A good pre-editing translation can help machine translation more smoothly, thus improving the machine readability and quality of the output translation. Pre-editing is mostly applied in the following situations: one is when the original text is of poor quality and the machine is difficult to recognize the meaning of the sentence, such as user generated content with poor readability and translatability (Gerlach et al, 2003:45-53); Documents that need to be published in multiple languages; Next is when the original text contains a lot of jargon; The last is the original text has a corresponding translation memory bank. If the original text is edited, it can better match the content of the translation memory bank.<br />
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===1.3 Machine Translation Mode===<br />
According to the different knowledge acquisition methods, machine translation modes can be classified as follows: one is rule-based machine translation, which is based on bilingual dictionaries and a library of language rules for each language. The quality of translation depends on whether the source language conforms to the existing rules, but the inexhaustible rules are the hinders of this model. The second mode is machine translation based on statistics. This translation model relies on the principles of mathematics and statistics to find various existing translations corresponding to the translation tasks through the employment of corpus, analyzing the frequency of their occurrence and selecting the translation with the highest frequency for output. The disadvantage of this translation model is that it ignores the flexibility of language and the importance of context. The last one is neural network language model. This model is different from previous translation models in that it uses end-to-end neural network to realize automatic translation between natural languages. At present, the quality of its translation is much higher than that of the previous translation models.<br />
===1.4 Source of Study Abstracts===<br />
The core medical journals from domestic are selected in order to make the paper more representative and authoritative, such as National Medical Journal of China, Journal of Peking University (Health Science), and Journal of Third Military Medical University. All of them include basic medical science, biomedical technology, laboratory medical science and other fields. In this way, the pre-editing approaches included are applicable enough to machine translation of medical abstracts.<br />
===1.5 Selection of Translation Software===<br />
Generally, machine translation software includes Google Translation, Youdao Translator and Niu Translation, which has their own special use in translation. Take Google as an example, it is more like neurons in human brain, enabling to learn and collect information to establish connections with its neural machine translator’s neurons. However, it also causes many errors because of the lack of enough information. In this paper, contrastive analysis will be carried on by using Google translation. On the one hand, being a pioneer in the translation of NET, it is inevitably to sue Google as the translation software to translate medical texts in this paper. Comparing the program called “Google brain”, other NMT of translation software are relatively disadvantages. On the other hand, the Google translation enjoys the largest users in the world, with its downloads of more than one billion. The output quality of Google translation is more correct and complete than other machine translation software. With years of development and improvement, Google Translation has been greatly promoted. In this paper, Chinese medical abstract will be automatically translated by Google translation, and then the translation output will be compared with the translation by human and the publication of English abstracts. The main purpose is to prove that the improvement and promotion of quality and accuracy in the medical abstracts will be obtained through the pre-editing approaches.<br />
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===2.Language Characteristics and Error Division===<br />
Since Chinese and English are two different languages, it is quite neccessary to identify their own characteristics so as to better analyze and understand the two languages. Tytler (1978) argued in his Essay on the Principles of Translation that there are three principles of translation that in all the translation should give readers the same feelings as the source text, except for complete transcript of ideas. There actually also exit some mistakes of these two languages. So the following will make some clarrifications of these two languages to ensure more accurate translation.<br />
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===2.1 Language Characteristics of Medical Abstracts===<br />
Chinese and English belong to different language systems, so there are differences in their language structure and the way users think of the languages. When using machine translation from Chinese to English, due to the unequal language levels, there will be many mistakes in the translation process, especially for ESP texts, such as medical papers. <br />
Here, these differences mainly refer to the linguistic characteristics of medical abstracts. In medical abstracts, it usually includes structured and unstructured abstracts. Although in different forms, they both describe the purpose, methods and conclusions of the research.<br />
In the method section of medial abstract, several Chinese sentences can be connected with commas, but each sentence may convey different information. In contrast, English sentences contain a great deal of information, but in order to ensure clarity, some modifiers need to be isolated and then reconstructed. However, in Chinese-English machine translation, a lot of information is put into the sentence because the machine segments the sentence on the basis of the full comma.<br />
In addition, subjectless sentence is used in the objective and method parts of Chinese medical abstracts. The subjectless sentence means the sentence without or free from subject and is usually employed in two contexts. The first is "needless to say". In Chinese sentences, it is common to omit the subject of the sentence. Chinese sentences can convey meanings by using incomplete sentence structures, so Chinese speakers can understand the meanings of the sentences even though the sentence subject is omitted. The second is "emphasis of action". In this context, subjectless sentences are used to describe behaviors, especially in the study of traditional Chinese medicine abstracts. Comparatively speaking, it should be avoided in English medical abstracts. Abstract sentences are more subjective when describing the learning process, while the essential requirement of English medical abstract is objectivity. From this point, sentences without subject should be avoided in English medical abstracts. Another feature is voice. Few words with passive meanings appear in Chinese abstracts. English sentences are more favoured in using passive voice. When translating from Chinese to English, passive voice should be used to make the contents objective, which is also the basic requirement of medical papers.<br />
These are the linguistic features of Chinese medical abstracts. There are great differences in sentence structure and expression between Chinese and English medical abstracts. These differences may reduce the accuracy of machine translation, so it is necessary to introduce pre-editing to edit the source text to ensure that the source text can be accurately recognized by the machine and fully translated into English.<br />
===2.2 Error Division of Machine Translation in Translating Abstracts of Medical Papers from Chinese to English===<br />
In this paper, errors in machine translation are listed out after analyzing. Some are due to the principles, such as statistical-based MT and NMT, employed by machine translation. Based on the original errors, they can be studied from two levels. One is from the macro-level, referring to mistakes caused by objective factors. These are not human factors, but the disadvantage of machine translation and the limitations of language. The limitations of machine translation are derived from the principles and models adopted and manifests itself as a reliance on reference sources. In this case, semantic ambiguity and textual incoherence may occur in the absence of reference sources.<br />
However, for ESP texts such as medical papers, the requirements for terms and sentence patterns are far beyond the existing corpora. For unfamiliar texts (that is, the corresponding texts cannot be found in the corpora). The quality of output translation will be relatively low, which is determined by the principles of machine translation. As mentioned above, machine translation has evolved over the past few decades from rules-based to corp-based statistics to NMT. However, there still exist some limitations in machine translation.<br />
Mistakes on micro-level are mainly caused by the variations and differences of linguistic structure between Chinese and English. Chinese is an implicit language, while English is an explicit one (Lian, 2010) To put it in another way, Chinese expression does not depend on language structures, while English does the opposite. This may result in mismatches between the original and machine translated sentences. This kind of error is mainly divided into two types: component fragment and component missing. For a medical paper, such errors are very serious. As a kind of ESP discourse, medical paper has the characteristics of fixed, objective and accurate language structure. In order to reproduce the characteristics of medical abstracts in translation, it is necessary to avoid errors at the level of words and sentences and pay attention to logic and consistency.<br />
Lexical errors here refer to the inconsistency of fixed expressions in the translation of terms. Although these terms are machine translation based on the corpus, the corpus may not be perfect for ESP texts such as medical papers. Therefore, fixed expressions are not used in term translation. This is also the most common mistake in machine translation (the term here includes but is not limited to nouns). For ESP texts, medical text in particular, the fixed expressions and the accuracy of terms are of great importance.<br />
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===3.===<br />
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===4.===<br />
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===Conclusion===<br />
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===References===<br />
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=12 蔡珠凤=(The Mistranslation of C-J Machine Translation of Political Statements)=<br />
[[Machine_Trans_EN_12]]<br />
===Abstract===<br />
Language is the main way of communication between people. With the continuous development of globalization, the scale of cross-border exchanges is also expanding. However, due to cultural differences and diversity, the languages of different countries and regions are very different, which seriously hinders people's communication. The demand for efficient and convenient translation tools is increasing. At the same time, with the development of network technology and artificial intelligence, recognition technology based on deep learning is more and more widely used in English, Japanese and other fields.<br />
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===Key words===<br />
machine translation; political statements; mistranslation of C-J machine translation<br />
===题目===<br />
The Mistranslation of C-J Machine Translation of Political Statements<br />
===摘要===<br />
语言是人与人之间交流的主要方式。随着全球化的不断发展，跨境交流的规模也在不断扩大。然而，由于文化的差异和多样性，不同国家和地区的语言差异很大，这严重阻碍了人们的交流。对高效便捷的翻译工具的需求正在增加。同时，随着网络技术和人工智能的发展，基于深度学习的识别技术在英语、日语等领域的应用越来越广泛。<br />
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===关键词===<br />
机器翻译；政治发言；政治发言中译日的误译<br />
===1. Introduction===<br />
===Introduction to machine translation===<br />
Machine translation, also known as automatic translation, is a process of using computers to convert one natural language (source language) into another natural language (target language). It is a branch of computational linguistics, one of the ultimate goals of artificial intelligence, and has important scientific research value.At the same time, machine translation has important practical value. With the rapid development of economic globalization and the Internet, machine translation technology plays a more and more important role in promoting political, economic and cultural exchanges.The development of machine translation technology has been closely accompanied by the development of computer technology, information theory, linguistics and other disciplines. From the early dictionary matching, to the rule translation of dictionaries combined with linguistic expert knowledge, and then to the statistical machine translation based on corpus, with the improvement of computer computing power and the explosive growth of multilingual information, machine translation technology gradually stepped out of the ivory tower and began to provide real-time and convenient translation services for general users.<br />
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=== C-J machine translation software===<br />
Today's online machine translation software includes Baidu translation, Tencent translation, Google translation, Youdao translation, Bing translation and so on. Google was the first company to launch the machine translation system, and Baidu was the first company to import the machine translation system in China. In addition, Tencent and Youdao have attracted much attention.Machine translation is the process of using computers to convert one natural language into another. It usually refers to sentence and full-text translation between natural languages. In order to continuously improve the translation quality, R & D personnel have added artificial intelligence technologies such as speech recognition, image processing and deep neural network to machine translation on the basis of traditional machine translation based on rules, statistics and examples.With the increase of using machine translation, the joint cooperation between manual translation and machine translation will also increase significantly in the future. What criteria should be used to evaluate the quality of machine translation? In the evolving field of machine translation, there is an urgent need to clarify the unsolvable questions and solved problems.<br />
<br />
===The history of machine translation===<br />
The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. alchuni put forward the idea of using machines for translation. In 1933, Soviet inventor П.П. Trojansky designed a machine to translate one language into another, and registered his invention on September 5 of the same year; However, due to the low technical level in the 1930s, his translation machine was not made. In 1946, the first modern electronic computer ENIAC was born. Shortly after that, W. weaver, an American scientist and A. D. booth, a British engineer, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947 when discussing the application scope of electronic computer. In 1949, W. Weaver published the translation memorandum, which formally put forward the idea of machine translation. After 60 years of ups and downs, machine translation has experienced a tortuous and long development path. The academic community generally divides it into the following four stages:<br />
Pioneering period（1947-1964）<br />
In 1954, with the cooperation of IBM, Georgetown University completed the English Russian machine translation experiment with ibm-701 computer for the first time, showing the feasibility of machine translation to the public and the scientific community, thus opening the prelude to the study of machine translation.<br />
It is not too late for China to start this research. As early as 1956, the state included this research in the national scientific work development plan. The topic name is "machine translation, the construction of natural language translation rules and the mathematical theory of natural language". In 1957, the Institute of language and the Institute of computing technology of the Chinese Academy of Sciences cooperated in the Russian Chinese machine translation experiment, translating 9 different types of more complex sentences.<br />
From the 1950s to the first half of the 1960s, machine translation research has been on the rise. The United States and the former Soviet Union, two superpowers, have provided a lot of financial support for machine translation projects for military, political and economic purposes, while European countries have also paid considerable attention to machine translation research due to geopolitical and economic needs, and machine translation has become an upsurge for a time. In this period, although machine translation is just in the pioneering stage, it has entered an optimistic period of prosperity.<br />
Frustrated period（1964-1975）<br />
In 1964, in order to evaluate the research progress of machine translation, the American Academy of Sciences established the automatic language processing Advisory Committee (Alpac Committee) and began a two-year comprehensive investigation, analysis and test.<br />
In November 1966, the committee published a report entitled "language and machine" (Alpac report for short), which comprehensively denied the feasibility of machine translation and suggested stopping the financial support for machine translation projects. The publication of this report has dealt a blow to the booming machine translation, and the research of machine translation has fallen into a standstill. Coincidentally, during this period, China broke out the "ten-year Cultural Revolution", and basically these studies also stagnated. Machine translation has entered a depression.<br />
convalescence（1975-1989）<br />
Since the 1970s, with the development of science and technology and the increasingly frequent exchange of scientific and technological information among countries, the language barriers between countries have become more serious. The traditional manual operation mode has been far from meeting the needs, and there is an urgent need for computers to engage in translation. At the same time, the development of computer science and linguistics, especially the substantial improvement of computer hardware technology and the application of artificial intelligence in natural language processing, have promoted the recovery of machine translation research from the technical level. Machine translation projects have begun to develop again, and various practical and experimental systems have been launched successively, such as weinder system Eurpotra multilingual translation system, taum-meteo system, etc.<br />
However, after the end of the "ten-year holocaust", China has perked up again, and machine translation research has been put on the agenda again. The "784" project has paid enough attention to machine translation research. After the mid-1980s, the development of machine translation research in China has further accelerated. Firstly, two English Chinese machine translation systems, ky-1 and MT / ec863, have been successfully developed, indicating that China has made great progress in machine translation technology.<br />
New period(1990 present)<br />
With the universal application of the Internet, the acceleration of the process of world economic integration and the increasingly frequent exchanges in the international community, the traditional way of manual operation is far from meeting the rapidly growing needs of translation. People's demand for machine translation has increased unprecedentedly, and machine translation has ushered in a new development opportunity. International conferences on machine translation research have been held frequently, and China has made unprecedented achievements. A series of machine translation software have been launched, such as "Yixing", "Yaxin", "Tongyi", "Huajian", etc. Driven by the market demand, the commercial machine translation system has entered the practical stage, entered the market and came to the users.<br />
Since the new century, with the emergence and popularization of the Internet, the amount of data has increased sharply, and statistical methods have been fully applied. Internet companies have set up machine translation research groups and developed machine translation systems based on Internet big data, so as to make machine translation really practical, such as "Baidu translation", "Google translation", etc. In recent years, with the progress of in-depth learning, machine translation technology has further developed, which has promoted the rapid improvement of translation quality, and the translation in oral and other fields is more authentic and fluent.<br />
<br />
===The problem of machine translation at present===<br />
Error is inevitable<br />
Many people have misunderstandings about machine translation. They think that machine translation has great deviation and can't help people solve any problems. In fact, the error is inevitable. The reason is that machine translation uses linguistic principles. The machine automatically recognizes grammar, calls the stored thesaurus and automatically performs corresponding translation. However, errors are inevitable due to changes or irregularities in grammar, morphology and syntax, such as sentences with adverbials after "give me a reason to kill you first" in Dahua journey to the West. After all, a machine is a machine. No one has special feelings for language. How can it feel the lasting charm of "the tenderness of lowering its head, like the shame of a water lotus? After all, the meaning of Chinese is very different due to the changes of morphology, grammar and syntax and the change of context. Even many Chinese people are zhanger monks - they can't touch their heads, let alone machines.<br />
Bottleneck<br />
In fact, no matter which method, the biggest factor affecting the development of machine translation lies in the quality of translation. Judging from the achievements, the quality of machine translation is still far from the ultimate goal.<br />
Chinese mathematician and linguist Zhou Haizhong once pointed out in his paper "fifty years of machine translation": to improve the quality of machine translation, the first thing to solve is the problem of language itself rather than programming; It is certainly impossible to improve the quality of machine translation by relying on several programs alone. At the same time, he also pointed out that it is impossible for machine translation to achieve the degree of "faithfulness, expressiveness and elegance" when human beings have not yet understood how the brain performs fuzzy recognition and logical judgment of language. This view may reveal the bottleneck restricting the quality of translation. <br />
It is worth mentioning that American inventor and futurist ray cozwell predicted in an interview with Huffington Post that the quality of machine translation will reach the level of human translation by 2029. There are still many disputes about this thesis in the academic circles.<br />
<br />
===2.Mistranslation of Chinese Japanese machine translation===<br />
===2.1Vocabulary mistranslation in Chinese Japanese machine translation===<br />
===2.1.1Mistranslation of proper nouns===<br />
===2.1.2Mistranslation of Polysemy===<br />
===2.1.3Mistranslation of compound words===<br />
===2.2 Syntactic mistranslation in Chinese Japanese machine translation===<br />
===2.2.1Main dynamic Mistranslation===<br />
===2.2.2Dynamic Mistranslation===<br />
===2.2.3Mistranslation of tenses===<br />
===2.2.4Mistranslation of honorifics===<br />
===3.===<br />
===4.===<br />
===Conclusion===<br />
===References===<br />
<br />
=13 陈湘琼Chen Xiangqiong（Study on Post-editing from the Perspective of Functional Equivalence Theory ）=<br />
[[Machine_Trans_EN_13]]<br />
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=14 Bi bi Nadia（Machine Translation a Challenge for Human Translators)=<br />
[[Machine_Trans_EN_14]]<br />
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===Abstract===<br />
Machine translation is a big obstacle in the way of Human translators or interpretors although it is quick and less time consuming.people are trying to get translation of their target language using through source language.For this purpose they are using digital apps like Google translation Google translation does not give accurate and exact interpretation.Google translator is translates word to word translate that doesn't clarifies it's true and actual meaning.On the other hand human translators can give exact and accurate translation ,they take care of grammatical errors , diction and sentence structure.They clarify the purpose of target language through using source language.<br />
===Keywords===<br />
Descriptive translation, academic, interdiscipline, comparative literature, localization,Translatology,school of thought, translation , studies, linguistics, corresponding<br />
===Body of article===<br />
Translation is the process of reworking text from one language into another to maintain the original message and communication. But, like everything else, there are different methods of translation, and they vary in form and function. What is translation? The term has become widely used among knowledge transfer researchers and practitioners, especially in the fields of health and health care. In a landmark review, Jonathan Lomas began to argue that 'The tasks… may be defined as to establish and maintain links between researchers and their audience, via the appropriate translation of research findings' (Lomas 1997, p 4). In 2004, the World Health Organization's World Report on Knowledge for Better Health suggested that 'One of the key contributions of research to health systems is the translation of knowledge into actions' (WHO 2004, p 33 and p 100). By 2006, special issues of WHO's Bulletin as well as the journals Evaluation and the Health Professions and the Journal of Continuing Education in the Health Professions were dedicated to translation.But what does 'translation' mean? It may be a new word for an old problem, meaning nothing more than 'transfer'. The rapidly emerging field of 'translational medicine' seems to take translation to mean generally what transfer might have meant, that is the transmission of knowledge and evidence 'from bench to bedside'. As one commentator has observed, "Translational medicine" as a fashionable term is being increasingly used to describe the wish of biomedical researchers to ultimately help patients' (Wehling 2008, abstract). <br />
Semantic uncertainty persists, not least because of the different interests of scientists, clinicians, patients and commercial firms (Littman et al 2007): 'Translational research means different things to different people, but it seems important to almost everyone' (Woolf 2008, p 211).<br />
In related fields, such as public health (Armstrong et al 2006), 'translation' seems to signify dissatisfaction with 'transfer'. It wants to move away from thinking of knowledge transfer as a form of technology transfer or dissemination, rejecting if only by implication its mechanistic assumptions and its model of linear messaging from A to B. But still, what does it signify?<br />
<br />
===Why translation?===<br />
'Translation' indicates a closer attention to the problem of shared meaning and how it might be developed. It seems to represent some new epistemological lubricant, facilitating the dissemination of texts and the application and use of the knowledge and information they in. Simply, translation might be the key to transfer. And yet, when we stop to think, we are more ambivalent. What is translated often seems somehow inferior, not real or original. Note how readily commentators reach for the idea that things might be 'lost in translation'. Knowing at a distance – made in and mediated by translation - makes for incomplete renditions, blurred images, partial truths. So what might 'translation' really mean? The purpose of this paper is to set out, for policy makers and practitioners, the theoretical and conceptual resources translation holds and seems to represent. In doing so, it explores understandings of translation in the fields of literature and linguistics and in the sociology of science and technology. It begins by setting out just why this idea of translation should make immediate, intuitive sense in relation to research, policy and practice.<br />
1translation in research, policy and practice research as translation<br />
Research often entails translation from one language to another: where data is collected from more than one ethnic group, for example, or where the language of the researcher is other than that of the research subject. It may draw on a secondary literature or source documents written in different languages, and may be published and disseminated in languages other than the one in which it is first written up.<br />
2In a different way, to conduct an interview is to ask for an account of experience and its meanings, but it is also to construct and translate that experience in terms defined at least in part by the researcher. In representing what is said, transcripts then select data, usually excluding significant gesture and eye-contact, for example. Often, certain characteristics of speech-acts (such as hesitations) will be edited out. In turn, the format of the transcript shapes the analytic use the researcher may make of it. The basis of research 'findings', then, is an artefact, a transcript or translation, not an original interaction (Ochs 1979, Barnes, Bloor and Henry 1996, Ross 2009).<br />
3In this way, the researcher recasts aspects of his or her problem or topic in new, scientific form: 'All researchers "translate" the experiences of others' (Temple 1997, p 609). Research is invariably conducted in a sort of 'metalanguage' (Hantrais and Ager 1985): the research process can be conceived as one of successive translations (from theoretical formulation to operationalization, transcription, interpretation and dissemination). Theorization is a process of reciprocal back and forth between theory and fact, in which conceptions of each are revised in order that one fit the other (Baldamus 1974).<br />
4 It is a kind of translation: a rereading, re-use, re-application or re-representation of what we know in new terms (Turner 1980). Referencing, too, is an act of translation, a form of appropriation and incorporation of one text by another (Gilbert 1977).policy as translation Each of the fields covered by the paper is diverse and ill-defined, and there is no intention here to provide a comprehensive account of any them. Sources have been chosen for their relevance: main references are cited in the text, and additional sources listed in footnotes.<br />
<br />
2For a brief introduction to the technical issues involved in social research in more than one language, see Birbili (2000). For translation issues in survey research and question design in general, see Ervin and Bower (1952) and Deutscher (1968); on translating survey research instruments (in this instance health-related quality of life measures), Bowden and Fox-Rushby (2003). On the use of translators and interpreters, see Temple (1997) and Jentsch (1998).<br />
3For an interesting discussion of this problem, see Bourdieu (1999), esp pp 621-626, 'The risks of writing'. <br />
===Difference between machine translation and human translators===<br />
Few people disagree on the differences between the two, but many argue over the quality of the translations. How accurate are machine translations? How reliable are human translations? Some say machine translation produces near-perfect translations while others are adamant that translations are incomprehensible and cause more problems than they solve. Results will, of course, vary depending on the source and target languages, the machine translation service used (e.g. Google Translate), and the complexity of the original text.<br />
Machine translation, love it or hate it, is here to stay. In fact, the machine translation market is growing at such a fast pace that it is predicted to reach $980 million by 2022.<br />
===Machine translation: the pros and cons===<br />
The advantages of machine translation generally come down to two factors: it’s faster and cheaper. The downside to this is the standard of translation can be anywhere from inaccurate, to incomprehensible, and potentially dangerous (more on that shortly).<br />
==The advantages of machine translation==<br />
Many free tools are readily available (Google Translate, Skype Translator, etc.)<br />
Quick turnaround time <br />
You can translate between multiple languages using one tool<br />
Translation technology is constantly improving<br />
The disadvantages of machine translation<br />
Level of accuracy can be very low <br />
Accuracy is also very inconsistent across different languages<br />
==Machines can't translate context ==<br />
Mistakes are sometimes costly <br />
Sometimes translation simply doesn’t work<br />
The most important thing to consider with any kind of translation is the cost of potential mistakes. Translating instructions for medical equipment, aviation manuals, legal documents and many other kinds of content require 100% accuracy. In such cases, mistakes can cost lives, huge amounts of money and irreparable damage to your company’s image. So choose carefully!<br />
Human translation: the pros and cons<br />
Human translation essentially switches the table in terms of pros and cons. A higher standard of accuracy comes at the price of longer turnaround times and higher costs. What you have to decide is whether that initial investment outweighs the potential cost of mistakes. Alternatively, whether mistakes simply aren’t an option, like the cases we looked at in the previous section.<br />
<br />
==The advantages of human translation ==<br />
It’s a translator’s job to ensure the highest accuracy<br />
Humans can interpret context and capture the same meaning, rather than simply translating words<br />
Human translators can review their work and provide a quality process<br />
Humans can interpret the creative use of language, e.g. puns, metaphors, slogans, etc.<br />
Professional translators understand the idiomatic differences between their languages<br />
Humans can spot pieces of content where literal translation isn’t possible and find the most suitable alternative<br />
The disadvantages of human translation<br />
Turnaround time is longer <br />
Translators rarely work for free <br />
Unless you use a translation agency, with access to thousands of translators, you’re limited to the languages any one translator can work with<br />
Simply put, human translation is your best option when accuracy is even remotely important. Other considerations to make are the complexity of your source material and the two languages you’re translating between – both of which can render machines pretty useless.<br />
<br />
When to use machine and human translation<br />
The truth is, the debate over machine vs human translation is an unnecessary distraction. What we should really be talking about is when to use these two different types of translation services, because they both serve a very valid purpose.<br />
<br />
Examples of when to use machine translation<br />
When you have a large bulk of content to translate and the general meaning is enough<br />
When your translation never reaches the final audience, e.g. you’re translating a resource as research for another piece of content<br />
Translating documents for internal use within a company, provided 100% accuracy isn’t needed<br />
To partially translate large chunks of content for a human translator to improve upon<br />
Examples of when to use human translation<br />
When accuracy is important<br />
Most cases where your translated content is received by a consumer audience<br />
When you have a duty of care to provide accurate translations (e.g. legal documents, product instructions, medical guidelines or health and safety content)<br />
When translating marketing material or other texts for creative language uses.<br />
<br />
===Conclusion ===<br />
<br />
In the light of above mentioned facts and figures here by we would say that machine translation is challenge for human translators because it can reduces the wokload of translation but can't give accurate and exact translation of the traget language.It can be less reliable than human translation..</div>Chen Huinihttps://bou.de/u/index.php?title=Machine_translation&diff=129195Machine translation2021-12-06T01:45:17Z<p>Chen Huini: /* 2.Language Characteristics and Error Division */</p>
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=1 卫怡雯(A Comparison Between the Quality of Machine Translation and Human Translation——A Case Study of the Application of artificial intelligence in Sports Events)=<br />
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=2 吴映红（The Introduction of Machine Translation)= <br />
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=3 肖毅瑶(On the Realm Advantages And Symbiotic Development of Machine Translation And Huamn Translation)=<br />
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=4 王李菲 （Comparison Between Neural Machine Translation of Netease and Traditional Human Translation—A Case Study of The Economist Articles)= <br />
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===Abstract===<br />
Machine translation is a subfield of artificial intelligence and natural language processing that investigates transforming the source language into the target language. On this basis, the emergence of neural machine translation, a new method based on sequence-to-sequence model, improves the quality and accuracy of translation to a new level. As one of the earliest companies to invest in machine translation in China, Netease launched neural machine translation in 2017, which adopts the unique structure of neural network to encode sentences, imitating the working mechanism of human brain, and generates a translation that is more professional and more in line with the target language context. This paper takes the articles in The Economist as the corpus for analysis, and aims to explore the types and causes of common errors, as well as the advantages and challenges of each, through the comparative analysis of Netease neural machine translation and human translation, and finally to forecast the future development trend and make a summary of this paper.<br />
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===Key words===<br />
Neural Machine Translation; Human Translation; Contrastive Analysis<br />
<br />
===题目===<br />
有道神经网络机器翻译与传统人工翻译的译文对比——以经济学人语料为例<br />
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===摘要===<br />
机器翻译研究将源语言所表达的语义自动转换为目标语言的相同语义，是人工智能和自然语言处理的重要研究分区。在此基础上，一种基于序列到序列模型的全新机器翻译方法——神经机器翻译的出现让译文的质量和准确度提升到了新的层次。网易作为国内最早投身机器翻译的公司之一，在2017年上线的神经网络翻译采用了独到的神经网络结构，模仿人脑的工作机制对句子进行编码，生成的译文更具专业性，也更符合目的语语境。本文以经济学人内的文章为分析语料，旨在通过对网易神经机器翻译和人工翻译的英汉译文进行对比分析，探究常见错误类型及生成原因，以及各自存在的优势与挑战，最后展望未来发展趋势，并对本文做出总结。 <br />
<br />
===关键词===<br />
神经网络翻译；人工翻译；对比分析<br />
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===1. Introduction===<br />
<br />
Nowadays, the process of economic globalization has accelerated overwhelmingly, and considerable resources are poured into the business field. As a branch of global language English, business English is proposed under the theoretical framework of English for Specific Purpose (ESP), serves the international business activities which is a professional subject requiring specialized English. As the medium that helps people with different cultural backgrounds to understand each other, business translation is required to be “formal, accurate, standardized and smooth”, which challenged both the machine translation and human translator.<br />
<br />
With the urgent requirement for more precise and higher quality translation, recent years have witnessed the rapid development of neural machine translation (NMT), which has replaced traditional statistical machine translation (SMT) to become a new mainstream technique, playing a crucial part in many fields, like business, academic and industry. Compared with SMT, NMT model is more like an organism. There are many parameters in the model that can be adjusted and optimized for the same goal, making the combination and interaction more organic and the overall translation effect better, which greatly matched the demands of business translation. <br />
<br />
The Economist is an international news and Business Weekly offering clear coverage, commentary and analysis of global politics, business, finance, science and technology. A huge number of terminologies plus the polysemy contained in the texts, put forward a tricky problem to both machine translation and human translator. In view of this, this paper makes a comparative analysis of Netease neural machine translation and human translation, aiming to explore the types and causes of common errors, as well as the advantages and challenges of each. In the end, this paper will forecast the future development, hoping to promote the development of translation studies in China.<br />
<br />
===2.2. The Development Process of Machine Translation ===<br />
<br />
Since the IBM model was put forward by the researcher Peter Brown in the early 1990s, statistical methods have gradually become the mainstream of machine translation research. This method has greatly promoted the development of machine translation technology. In recent years, a variety of statistical machine translation models have emerged, such as phrase-based translation model, hierarchical phrase translation model and syntactic translation model, then the translation quality has been greatly improved.<br />
<br />
Since 2002, BLEU, an automatic translation quality evaluation method, has greatly promoted the development of statistical machine translation technology and effectively reduced the cost of manual evaluation. In recent years, with the technical maturity and stability of statistical machine translation, especially phrase-based machine translation, statistical machine translation technology has been making strong strides towards practical and commercial application. Therefore, with the rapid development of technology, people have gradually built-up confidence in machine translation, and the social demand for machine translation has been increasing day by day, with higher and higher expectations.<br />
<br />
However, from the perspective of academic research, both phrase-based translation models and syntactic translation models have experienced a rapid development stage, and the existing theoretical methods and technical models have begun to show "bottlenecks" in the improvement of translation performance. In addition, from the perspective of industrialization and utilitarianism, there is an urgent need for a more practical machine translation system, but the gap between the results of machine translation and the requirements of human beings is still very large. Therefore, for the researchers of machine translation, while excited to see the BLEU score of machine translation system evaluation is getting higher and higher, and the performance of online machine translation system developed by Google, Baidu, Netease and other enterprises is developing with each passing day, they are facing more and more challenges.<br />
<br />
Aiming to solve these problems, many technological giants are striving to find a new way to improve both the quality and efficiency of machine translation. There was a breakthrough which bought machine translation to a new level. Since 2014, the end-to-end neural machine translation has developed rapidly, compared with the statistical machine translation, the translation quality received a significant boost.<br />
<br />
The previous statistical machine translation was more like a mechanical system. Each module has its own function and goal, and then outputs the translation results through mechanical splicing. Its main disadvantage is that the model contains low syntactic and semantic components, so it will encounter problems when dealing with languages with large syntactic differences, such as Chinese-English. Sometimes the result is unreadable even though it is “word-for-word”.<br />
On the contrary, neural machine translation are consisted of several components, including phrase conditions, partial conditions, sequential conditions, primitive models, and so on. Its core is deep learning of artificial intelligence which can imitate the working mechanism of human brain and adopt unique neural network structure to model the whole process of translation. The whole model is composed of a large number of “neurons”, and each “neuron” has to complete some simple tasks, and then through the combination of all of them to coordinate the work, a much better translation text appears. <br />
<br />
Since neural machine translation puts more emphasis on context and the whole text, it produces more coherent and comprehensible content to readers than traditional statistical machine translation, and be widely accepted and used in various field in a very short time. In 2017, at the GMIC (Global Mobile Internet Congress), Duan Yitao, the chief scientist of Netease, delivered a keynote speech titled “Machine Translation has Its Own Way” and announced an exciting news: the neural machine translation technology independently developed by Netease has been officially launched. This technology launched by Youdao this time has been jointly developed by Netease Youdao and Netease Hangzhou Research Institute for over two years. It will serve Youdao Dictionary, Youdao Translator, Youdao Web version, Youdao E-reader and other products, expecting to bring super-convenient product experience to users. In addition, Youdao Translation officer also launched photo translation, users only need to take pictures of the text, can show the results of neural network translation in real time. <br />
<br />
As a pioneer of machine translation in China, the development process of Netease YouDao is exactly the paradigm of the history of machine translation in China. Therefore, in this paper, the neural machine translation technology developed by Netease will be compared with human translators. The same excerpts selected from The Economist are translated by both of them, then the different versions will be analyzed by the translation criterion so as to figure out their respective strengths and weaknesses, bringing consideration to current translation situation and references to future development.<br />
<br />
===3.Comparative Analysis of Errors in English-Chinese Translation ===<br />
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===4.===<br />
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===5. ===<br />
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===Conclusion===<br />
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===References===<br />
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=6 徐敏赟(Machine Translation Based on Neural Network --Challenge or Chance?)=<br />
[[Machine_Trans_EN_6]]<br />
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=7 颜莉莉(一带一路背景下人工智能与翻译人才的培养)=<br />
[[Machine_Trans_EN_7]]<br />
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===Abstract===<br />
In the era of artificial intelligence, artificial intelligence has been applied to various fields. In the field of translation, traditional translation models can no longer meet the rapid development and updating of the information age. The development of machine translation has brought structural changes to the language service industry, which poses challenges to the cultivation of translation talents. Under the background of "The Belt and Road initiative", translation talents have higher and higher requirements on translation literacy. Artificial intelligence and translation technology are used to reform the training mode of translation talents, so as to better serve the development of The Times. This paper mainly explores the cultivation of artificial intelligence and translation talents under the background of the Belt and Road Initiative. The cultivation of translation talents is moving towards comprehensive cultivation of talents. On the contrary, artificial intelligence and machine translation can also be used to improve the teaching mode and teaching content, so as to win together in cooperation.<br />
===Key words===<br />
Artificial intelligence,Machine translation,cultivation of translation talents,"The Belt and Road initiative"<br />
===题目===<br />
一带一路背景下人工智能与翻译人才的培养<br />
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===摘要===<br />
进入人工智能时代，人工智能被应用于各个领域。在翻译领域，传统的翻译模式已无法满足信息化时代的飞速发展和更新，机器翻译的发展给语言服务行业带来了结构性改变，这对翻译人才的培养提出了挑战。“一带一路”背景下，对翻译人才的翻译素养要求越来越高，利用人工智能和翻译技术对翻译人才培养模式进行革新，更好为时代发展服务。本文主要探究在一带一路背景下人工智能和翻译人才培养，翻译人才的培养过程中正向对人才的综合性培养，反之也可以利用人工智能和机器翻译完善教学模式和教学内容，在合作中共赢。<br />
===关键词===<br />
人工智能；机器翻译；翻译人才培养；一带一路<br />
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===1. Introduction===<br />
With the development of science and technology in China, artificial intelligence has also been greatly improved, and related technologies have been applied to various fields, such as the use of intelligent robots to deliver food to quarantined people during the epidemic, which has made people's lives more convenient. The most controversial and widely discussed issue is machine translation. Before the emergence of machine translation, translation was generally dominated by human translation, including translation and interpretation, which was divided into simultaneous interpretation and hand transmission, etc. It takes a lot of time and energy to cultivate a translation talent. However, nowadays, the era is developing rapidly and information is updated rapidly. As a translation talent, it is necessary to constantly update its knowledge reserve to keep up with the pace of The Times. The emergence of machine translation has also posed challenges to translation talents and the training of translation talents. Although machine translation had some problems in the early stage, it is now constantly improving its functions. In the context of the belt and Road Initiative, both machine translation and human translation are facing difficulties. Regardless of whether human translation is still needed, what is more important at present is how to train translators to adapt to difficulties and promote the cooperation between human translation and machine translation.<br />
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===2.Development status of machine translation in the era of artificial intelligence ===<br />
With the development of AI technology, machine translation has made great progress and has been applied to people's lives. For example, more and more tourists choose to download translation software when traveling abroad, which makes machine translation take an absolute advantage in daily email reply and other translation activities that do not require high accuracy. The translation software commonly used by netizens include Google Translation, Baidu Translation, Youdao Translation, IFly.com Translation, etc. Even wechat and other chat software can also carry out instant Translation into English. Some companies have also launched translation pens, translation machines and other equipment, which enables even native speakers to rely on machine translation to carry out basic communication with other Chinese people.<br />
But so far, machine translation still faces huge problems. Although machine translation has made great progress, it is highly dependent on corpus and other big data matching. It does not reach the thinking level of human brain, and cannot deal with the problem of translation differences caused by culture and religion. In addition, many minor languages cannot be translated by machine due to lack of corpus.<br />
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What's more, most of the corpus is about developed countries such as Britain and France, and most of the corpus is about diplomacy, politics, science and technology, etc., while there are very few about nationality, culture, religion, etc.<br />
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In addition, machine translation can only be used for daily communication at present. If it involves important occasions such as large conferences and international affairs, it is impossible to risk using machine translation for translation work. Professional translators are required to carry out translation work. So machine translation still has a long way to go.<br />
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===3.Challenges in the training of translation talents in universities===<br />
The cultivation of translators is targeted at the market. Professors Zhu Yifan and Guan Xinchao from the School of Foreign Languages at Shanghai Jiao Tong University believe that the cultivation of translators can be divided into four types: high-end translators and interpreters, senior translators and researchers, compound translators and applied translators.<br />
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From their names, it can be seen that high-end translators and interpreters and senior translators and researchers talents have high requirements on the knowledge and quality of interpreters, because they have to face the changing international situation, and have to deal with all kinds of sensitive relations and political related content, they should have flexible cross-cultural communication skills. In addition, for literature, sociology and humanities academic works, it is not only necessary to translate their content, but also to understand their essence. Therefore, translators should not only have humanistic feelings, but also need to have a deep understanding of Chinese and western culture.<br />
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However, there is not much demand for this kind of translation in the society. Such high-level translation requirements are not needed in daily life and work. The greatest demand is for compound translators, which means that they should master knowledge in a specific field while mastering a foreign language. For example, compound translators in the financial field should not only be good at foreign languages, but also master financial knowledge, including professional terms, special expressions and sentence patterns.<br />
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Now we say that machine translation can replace human translation should refer to the field of compound translation talents. Although AI technology has enabled machine translation to participate in creation, it does not mean that compound translation talents will be replaced by machines. The complexity of language and the flexible cross-cultural awareness required in communication make it impossible for machine translation to completely replace human translation.<br />
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The last type of applied translation talents are mostly involved in the general text without too much technical content and few professional terms, so it is easy to be replaced by machine translation.<br />
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Therefore, the author thinks that what universities are facing at present is not only how to train translation talents to cope with the development of machine translation, but to consider the application of machine translation in the process of training translation talents to achieve human-machine integration, so as to better complete the translation work.<br />
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===4.The Language environment and opportunities and challenges of the Belt and Road initiative===<br />
During visits to Central and Southeast Asian countries in September and October 2013, Chinese President Xi Jinping put forward the major initiative of jointly building the Silk Road Economic Belt and the 21st Century Maritime Silk Road. And began to be abbreviated as the Belt and Road Initiative.<br />
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According to the Vision and Actions for Jointly Building silk Road Economic Belt and 21st Century Maritime Silk Road, the Silk Road Economic Belt focuses on connecting China, Central Asia, Russia and Europe (the Baltic Sea). From China to the Persian Gulf and the Mediterranean Sea via Central and West Asia; China to Southeast Asia, South Asia, Indian Ocean. The focus of the 21st Century Maritime Silk Road is to stretch from China's coastal ports to Europe, through the South China Sea and the Indian Ocean. From China's coastal ports across the South China Sea to the South Pacific.<br />
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The Belt and Road "construction is comply with the world multi-polarization and economic globalization, cultural diversity, the initiative of social informatization tide, drive along the countries achieve economic policy coordination, to carry out a wider range, higher level, the deeper regional cooperation and jointly create open, inclusive and balanced, pratt &whitney regional economic cooperation framework.<br />
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====4.1The language environment of the Belt and Road====<br />
The "Belt and Road" involves a wide range of countries and regions, and their languages and cultures are very complex. How to make good use of language, do a good job in translation services, actively spread Chinese culture to the world, strengthen the ability of discourse, and tell Chinese stories well, the first thing to do is to understand the language situation of the countries along the "Belt and Road".<br />
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=====4.1.1The most common language in countries along the "Belt and Road" =====<br />
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There are a wide variety of languages spoken in 65 countries along the Belt and Road, involving nine language families. However, The status of English as the first language in the world is undeniable. Most of the countries participating in the Belt and Road are developing countries, and many of them speak English as their first foreign language. Especially in southeast Asian and South Asian countries, English plays an important role in foreign communication, whether as the official language or the first foreign language. Besides English, more than 100 million people speak Russian, Hindi, Bengali, Arabic and other major languages in the "Belt and Road" countries. It can also be seen that a common feature of languages in countries along the "Belt and Road" is the popularization of English education. English is widely used in international politics, economy, culture, education, science and technology, playing the role of the most important language in the world.<br />
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=====4.1.2The complex language conditions of countries along the "Belt and Road" =====<br />
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The languages spoken in countries along the Belt and Road involve nine major language families and almost all the world's religious types. Differences in religious beliefs also result in differences in culture, customs and social values behind languages. The languages of some countries along the belt and Road have also been influenced by historical and realistic factors, such as colonization, internal division and immigration. <br />
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India, for example, has no national language, but more than 20 official languages. India is a multi-ethnic country, a total of more than 100 people, one of the most obvious difference between nation and nation is the language problem. Therefore, according to the difference of language, India divides different ethnic groups into different states, big and small. Ethnic groups that use the same language are divided into one state. If there are two languages in a state, the state is divided into two parts. And Indian languages differ not only in word order but also in the way they are written. In India, for example, Hindi is spoken by the largest number of people in the north, with about 700 million speakers and 530 million as their first language. It is written in The Hindu language and belongs to the Indo-European language family. Telugu in the east is spoken by about 95 million people and 81.13 million as their first language. It is written in Telugu, which belongs to the Dravidian language family and is quite different from Hindi. As a result, a parliamentary session in India requires dozens of interpreters. <br />
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These factors cannot be ignored in the process of translation, from language communication to cultural understanding, from text to thought exchange, through the bridge of language to truly connect the people, so as to avoid misreading and misunderstanding caused by differences in language and national conditions.<br />
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====4.2 Opportunities and challenges of the "Belt and Road" ====<br />
With the promotion of the Belt and Road Initiative, there has been an unprecedented boom in translation. In the previous translation boom in China, most of the foreign languages were translated into Chinese, and most of the foreign cultures were imported into China. However, this time, in the context of the "Belt and Road" initiative, translating Chinese into foreign languages has become an important task for translators. As is known to all, there are many different kinds of "One Belt And One Road" along the national language and culture is complex, the service "area" construction has become a factor in Chinese translation talents training mode reform, one of the foreign language universities have action, many colleges and universities to establish the "area" all the way along the country's small language major, as a result, "One Belt And One Road" initiative to promote, It has brought unprecedented opportunities for human translation. The cultivation of diversified translation talents and the cultivation of translation talents in small languages is an urgent problem to be solved in China. The cultivation of translation talents cannot be completed overnight, and the state needs to reform the training mode of translation talents from the perspective of language strategic development. Only in this way can we meet the new demand for human translation under the new situation of the belt and Road Initiative.<br />
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For a long time, the traditional orientation of translation curriculum and training goal in colleges and universities is to train translation teachers and translators in need of society through translation theory and practice and literary translation practice, which cannot meet the needs of society. Since 2007, in order to meet the needs of the socialist market economy for application-oriented high-level professionals, the Academic Degrees Committee of The State Council approved the establishment of Master of Translation and Interpreting (MTI for short). After joining the pilot program of MTI, more and more universities are reforming the curriculum and training mode of master of Translation in order to cultivate translators who meet the needs of the society.<br />
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Language is an important carrier of culture, and translation is an important link for exporting culture. The quality of translation output also reflects the cultural soft power of a country. With the rise of China, more and more people are interested in Chinese culture, and the number of Chinese learners keeps increasing. Under the background of "One Belt and One Road", excellent translators are urgently needed to spread Chinese culture. With the promotion of "One Belt and One Road" Initiative, the number of other countries learning mutual learning and cultural exchanges with China has increased unprecedeningly, bringing vigorous opportunities for the spread of Chinese culture. Translation talents who understand small languages and multi-lingual translators are needed. They should not only use language to convey information, but also use language as a lubricant for communication.<br />
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===5.Training translation talents from the perspective of machine translation===<br />
Under the prevailing environment of machine translation, it poses a great challenge to the cultivation of translation talents. According to the current situation, translation needs and the shortage of translation talents, colleges and universities should reform and innovate the existing training programs for translation talents in terms of the quality of translation talents, the reform of training mode and the use of artificial intelligence. Based on the obtained data and literature, the author discusses how to train translation talents in the perspective of machine translation from the following aspects.<br />
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====5.1 Quality requirements for translation talents ====<br />
Zhong Weihe and Murray made a more detailed and profound discussion on translator's literacy, believing that "translators should not only be proficient in two languages, but also have extensive cultural and encyclopedic knowledge and relevant professional knowledge; Master a variety of translation skills, a lot of translation practice; Have a clear translator role awareness, good professional ethics, practical and enterprising style of work, conscious team spirit and calm psychological quality ". According to the collected data, the author will elaborate the requirements for translation talents from four aspects: language literacy, humanistic literacy, translation ability and innovation ability.<br />
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The first is language literacy, which is the most basic and important requirement. MAO Dun pointed out that "mastery of mother tongue and target language are the foundation of translation". A solid foundation of bilingual skills is the basic skills of translators. Poor language proficiency seems to be a common problem among students majoring in translation and interpreting. Many translation diseases are caused by poor Chinese foundation. As part of going global, the belt and Road initiative is to tell Chinese culture and Chinese stories, which requires translators to be able to use both languages flexibly. Therefore, the first problem that colleges and universities face to solve is to improve the language level of foreign language learners.<br />
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The second is humanistic literacy. Humanistic literacy is mainly manifested by a good command of politics, economy, history, literature and other knowledge, which is particularly important for interpreters. In addition, cross-cultural communication cannot be ignored. In the process of communicating with foreigners or translating, translators often encounter the first cross-cultural contradiction. Cross-culture refers to having a full and correct understanding of cultural phenomena, customs and habits that differ or conflict with the national culture, and accepting and adapting to them in an inclusive manner on this basis. So the interpreter can first fully understand and master the national conditions and culture of the target country, which is particularly important in the "Belt and Road". There are more than 60 countries along the "Belt and Road", and it takes a lot of energy to master their national conditions and culture.<br />
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The third is translation ability. We should distinguish between translation ability and language ability. Translation ability is actually a system of knowledge and skills necessary for translation, the core of which is conversion ability. First of all, it reflects the ability to use tools to assist translation, such as computer application, translation technology and so on. In addition, interpreters should have enough healthy psychological quality and good professional quality. In terms of translation ability, the current training model of translation talents is inadequate.<br />
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The last one is innovation. The cultivation of learners' thinking ability is the key to translation teaching and the cultivation of thoughtful translators should be the connotation of translation teaching. Therefore, the interpreter is not only a translation tool, which is no different from machine translation. More importantly, it is necessary to explore translation with thoughts, have a sense of lifelong learning and innovation consciousness. Translators must constantly innovate themselves, learn new knowledge, and strive to seek reform and innovation. Many colleges and universities should also consciously cultivate students' innovation ability and broaden their thinking and vision.<br />
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====5.2 The reform of college curriculum setting====<br />
First, we will further reform the curriculum of colleges and universities. Add economics, law and engineering to the curriculum, these contents in the "belt and Road".<br />
"One Road" is very important in the construction. According to the author's personal experience, the most typical problem of foreign language majors in colleges and universities is the single learning of foreign languages. More professional foreign language colleges and universities will add some literature courses and national conditions courses of the language target countries. Obviously, whether foreign language graduates are engaged in translation work or not, these knowledge is not enough. Of course, great reforms have been carried out in foreign language teaching, such as combining foreign language with finance, law, diplomacy and so on, and taking the way of minor training foreign language majors.<br />
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Domestic enterprises with a high degree of internationalization attach great importance to translation. Their translation research includes cutting-edge theoretical and applied research, involving machine translation, natural language processing and AI theory, algorithm and model. With such a foundation, enterprises can solve problems by themselves, such as embedding automatic translation functions in mobile phones. International enterprises not only do technical translation, but also deal with all forms of translation and localization in society. At present, translation teaching in most colleges and universities is still in the early mode, and it is an objective fact that it is divorced from the workplace and has a gap with the needs of enterprises.<br />
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Second, we should adjust and strengthen the construction of second foreign language teaching for foreign language majors. In the 1980s, our country was in urgent need of Russian translation. At that time, students majoring in English could translate microelectronic product manuals and related business documents in English and Russian at the same time after learning Russian for half a year. The mutual conversion between English and Russian played a great role in practice. According to the author, in the Graduate Institute of Interpretation and Translation of Beijing Foreign Studies University a very few students majored in multiple languages at the graduate level, that is, they majored in minor languages at the undergraduate level and were admitted to the Graduate Institute of Interpretation and Translation in English. Their training mode is to study English in the Graduate Institute of Interpretation and Translation for two years and the third year in the corresponding department of the undergraduate major. Such training mode in my opinion is a bigger model, cost It's more difficult for students. <br />
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In addition, there is a great disparity in the development of second foreign language teaching in colleges and universities, and the overall level is not high enough. Part of the second foreign language university foreign language professional may still be too much focus in languages such as German, French and Japanese, should as far as possible, considering the need of the construction of the "region", like Croatia, Serbia, Turkish, Hungarian, Italian, Indonesian, Albanian, these are the countries along the "area" the language of the two countries, Colleges and universities should encourage the teaching of a second foreign language.<br />
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Third, the teaching of translation technology should be strengthened. Traditional translation teaching teaches translation skills, such as the translation of words, sentences, texts and figures of speech. Translation technology refers to a series of practical workplace technologies with computer-aided translation software and translation project management as the core, which can greatly improve translation efficiency. However, due to the relative lack of translation technology teachers and equipment in colleges and universities, there is a disconnect between talent training and the requirements of translation technology in the translation field.<br />
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====5.3 Application of artificial intelligence to translation teaching practice====<br />
In order to improve the teaching quality and train students' English translation ability, it is necessary to realize the effective integration of ARTIFICIAL intelligence and translation activity courses, which should not only reflect the effectiveness of artificial intelligence translation technology, but also help students establish a healthy concept of English communication. Through the application of artificial intelligence technology, students can strengthen their flexible translation skills through close communication with "AI program" during the learning stage of English translation activity class. For example, teachers can ask students to translate directly against the translation content provided on the translation screen of the ARTIFICIAL intelligence system. After that, the system can collect the translation answers with the help of speech recognition function, and then judge the accuracy of the translation content, thus providing important feedback to students.<br />
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China has used such artificial intelligence technology in the Putonghua test to ensure that every student can find a suitable translation method in practical communication. The so-called artificial intelligence technology is a new kind of technology modeled after the characteristics of human neural network thinking, can combine the human mind to respond. If it can be integrated into English translation activity teaching, it can not only improve the teaching efficiency, but also enhance students' enthusiasm in learning the course.<br />
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At the same time, during the training of translation talents, teachers also need to take into account the importance of influencing education factors, so that students can form a higher disciplinary quality in translation, so as to fit the concept of quality education in the new era. Only when artificial intelligence translation content is fully integrated into college English translation activity courses can the overall translation ability of college students be maximized.<br />
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====5.4The improvement of translator's technical ability====<br />
In the previous part, the author roughly mentioned that translation teaching should be improved, which will be elaborated here. At present, only a few universities can make full use of the advantages of translation technology in translation teaching and focus on cultivating professional translation talents. Most universities still cannot get rid of the traditional teaching mode of "language + relevant professional knowledge" in translation teaching, and generally lack a correct understanding of COMPUTER-aided translation teaching.<br />
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According to Wang Huashu et al., the courses that can be offered around the composition of translators' technical literacy include computer-assisted translation, translation and corpus, machine translation and post-translation editing, localization and internationalization, film and television translation (subtitle), technical communication and technical writing, and computer programming. The course modules involved are: Fundamentals of COMPUTER-aided Translation, CAT tool application, corpus alignment and processing, term management, QA technology for translation quality assurance, OFFICE fundamentals, translation management technology, basic computer knowledge, desktop typesetting, localization and internationalization, project management system and content management system, technical writing, basic knowledge of computer programming, basic knowledge of web code, etc.<br />
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===6针对一带一路的机器翻译与翻译人才的合作===<br />
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===Conclusion===<br />
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===References===<br />
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=8 颜静(On Machine Translation Under Lanuguage Intelligence——An Option and Opportunity for Human Translators)=<br />
[[Machine_Trans_EN_8]]<br />
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=9 谢佳芬（人工智能时代下的机器翻译与人工翻译）=<br />
[[Machine_Trans_EN_9]]<br />
===Abstract===<br />
With the continuous development of information technology, many industries are facing the competitive pressure of artificial intelligence, and so is the field of translation. Artificial intelligence technology has developed rapidly and combined with the field of translation，which has brought great impact and changes to traditional translation, but artificial intelligence translation and artificial translation have their own advantages and disadvantages. Artificial translation is in the leading position in adapting to human language logical habits and understanding characteristics, but in terms of translation threshold and economic value, the efficiency of artificial intelligence translation is even better. In a word, we need to know that machine translation and human translation are complementary rather than antagonistic.<br />
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===Key Words===<br />
Machine Translation; Artificial Translation; Artificial Intelligence<br />
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===题目===<br />
人工智能时代下的机器翻译与人工翻译<br />
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===摘要===<br />
伴随着信息技术的不断发展，多个行业面临着人工智能的竞争压力，翻译领域也是如此。人工智能技术快速发展并与翻译领域结合，人工智能翻译给传统翻译带来了巨大的冲击和变革，但人工智能翻译与人工翻译存在着各自的优劣特点和发展空间，在适应人类语言逻辑习惯和理解特点的翻译效果上，人工翻译处于领先地位，但在翻译门槛和经济价值上，人工智能翻译的效率则更胜一筹。总的来说，我们要知道机器翻译与人工翻译是互补而非对立的关系。<br />
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===关键词===<br />
机器翻译;人工翻译;人工智能<br />
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===1. Introduction===<br />
====1.1 The History of Machine Translation Aborad====<br />
The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. Alchuni put forward the idea of using machines for translation. In 1933, the Soviet inventor Troyansky designed a machine to translate one language into another. [1]In 1946, the world's first modern electronic computer ENIAC was born. Soon after, American scientist Warren Weaver, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947. In 1949, Warren Weaver published a memorandum entitled Translation, which formally raised the issue of machine translation. In 1954, Georgetown University, with the cooperation of IBM, completed the English-Russian machine translation experiment with IBM-701 computer for the first time, which opened the prelude of machine translation research. [2] In 2006, Google translation was officially released as a free service software, bringing a big upsurge of statistical machine translation research. It was Franz Och who joined Google in 2004 and led Google translation. What’s more, it is precisely because of the unremitting efforts of generations of scientists that science fiction has been brought into reality step by step.<br />
====1.2 The History of Machine Translation in China====<br />
In 1956, the research and development of machine translation has been named in the scientific and technological work and made little achievements in China. On the eve of the tenth anniversary of the National Day in 1959, our country successfully carried out experiments, which translated nine different types of complicated sentences on large general-purpose electronic computers. The dictionary includes 2030 entries, and the grammar rule system consists of 29 circuit diagrams. [3]. After a period of stagnation, China's machine translation ushered in a high-speed development stage after the 1980s in the wave of the third scientific and technological revolution. With the rapid development of economy and science and technology, China has made a qualitative leap in the field of machine translation research with the pace of reform and opening up. In 1978, Institute of Scientific and Technological Information of China, Institute of Computing Technology and Institute of Linguistics carried out an English-Chinese translation experiment with 20 Metallurgical Title examples as the objects and achieved satisfactory results. Subsequently, they developed a JYE-I machine translation system, which based on 200 sentences from metallurgical documents. Its principles and methods were also widely used in the machine translation system developed in the future. In addition, the research achievements of machine translation in China during the 1980s and 1990s also include that Institute of Post and Telecommunication Sciences developed a machine translation system, C Retrieval and automatic typesetting system with good performance and strong practicability in October 1986; In 1988, ISTC launched the ISTIC-I English-Chinese Title System for the translation of applied literature of metallurgy, Information Research Institute of Railway developed an English-Chinese Title Recording machine translation system for railway documents; the Language Institute of the Academy of Social Sciences developed "Tianyu" English-Chinese machine translation system and Matr English-Chinese machine translation system developed by the computer department of National University of Defense Technology. After many explorations and studies, machine translation in China has gradually moved towards application, popularization and commercialization. China Software Technology Corporation launched "Yixing I" in 1988, marking China's machine translation system officially going to the market. After "Yixing", a series of machine translation systems such as Gaoli system in Beijing, Tongyi system in Tianjin and Langwei system in Shaanxi have also entered the public. In the 21st century, the development of a series of apps such as Kingsoft Powerword, Youdao translation and Baidu translation has greatly met the needs of ordinary users for translation. According to the working principle, machine translation has roughly experienced three stages: rule-based machine translation, statistics-based machine translation and deep learning based neural machine translation. [4] These three stages witnessed a leap in the quality of machine translation. Machine translation is more and more used in daily life and even the translation of some texts is almost comparable to artificial translation. In addition to text translation, voice translation, photo translation and other functions have also been listed, which provides great convenience for people's life. It is undeniable that machine translation has become the development trend of translation in the future.<br />
====1.3 The Status Quo of Machine Translation====<br />
In this big data era of information explosion, the prospect of machine translation is also bright. At present, the circular neural network system launched by Google has supported universal translation in more than 60 languages. Many Internet companies such as Microsoft Bing, Sogou, Tencent, Baidu and NetEase Youdao have also launched their own Internet free machine translation systems. [5] Users can obtain translation results free of charge by logging in to the corresponding websites. At present, the circular neural network translation system launched by Google can support real-time translation of more than 60 languages, and the domestic Baidu online machine translation system can also support real-time translation of 28 languages. These Internet online machine translation systems are suitable for a variety of terminal platforms such as mobile phone, PC, tablet and web and its functions are also quite diverse, supporting many translation forms, such as screen word selection, text scanning translation, photo translation, offline translation, web page translation and so on. Although its translation quality needs to be improved, it has been outstanding in the fields of daily dialogue, news translation and so on.<br />
===2. Advantages and Disadvantages of Machine Translation===<br />
Generally speaking, machine translation has the characteristics of high efficiency, low cost, accurate term translation and great development potential and etc. Machine translation is fast and efficient, this is something that artificial translation can’t catch up with. In addition, with the continuous emergence of all kinds of translation software in the market, compared with artificial translation, machine translation is cheap and sometimes even free, which greatly saves the economic cost and time for users with low translation quality requirements. What's more, compared with artificial translation, machine translation has a huge corpus, which makes the translation of some terms, especially the latest scientific and technological terms, more rapid and accurate. The accurate translation of these terms requires the translator to constantly learn, but learning needs a process, which has a certain test on the translator's learning ability and learning speed. In this regard, artificial translation has uncertainty and hysteretic nature. At the same time, with the progress of science and technology and the development of society, the function of machine translation will be more perfect and the quality of translation will be better.Today's machine translation tools and software are easy to carry, all you need to do is just to use the software and electronic dictionary in the mobile phone. There is no need to carry paper dictionaries and books for translation, which saves time and space. At the same time, machine translation covers many fields and is suitable for translation practice in different situations, such as academic, education, commercial trade, social networking, tourism, production technology, etc, it is also easy to deal with various professional terms. However, due to the limitation of translators' own knowledge, artificial translation is often limited to one or a few fields or industries. For example, it is difficult for an interpreter specializing in medical English to translate legal English.<br />
At the same time, machine translation also has its limitations. At first, machine can only operate word to word translation, which only plays the function and role of dictionary. Then, the application of syntax enables the process of sentence translation and it can be solved by using the direct translation method. When the original text and the target language are highly similar, it can be translated directly. For example, the original text "他是个老师." The target language is "he is a teacher ". With the increase of the structural complexity of the original text, the effect of machine translation is greatly reduced. Therefore, at the syntactic level, machine translation still stays in sentences with relatively simple structure. Meanwhile, the original text and the results of machine translation cannot be interchanged equally, indicating that English-Chinese translation has strong randomness, and is not rigorous and scientific enough. <br />
Nowadays, machine translation is highly dependent on parallel corpora, but the construction of parallel corpora is not perfect. At present, the resources of some mainstream languages such as Chinese and English are relatively rich, while the data collection of many small languages is not satisfactory. Moreover, the current corpus is mainly concentrated in the fields of government literature, science and technology, current affairs and news, while there is a serious lack of data in other fields, which can’t reflect the advantages of machine translation. At the same time, corpus construction lags behind. Some informative texts introducing the latest cutting-edge research results often spread the latest academic knowledge and use a large number of new professional terms, such as academic papers and teaching materials while the corpus often lacks the corresponding words of the target language, which makes machine translation powerless<br />
Besides, machine translation is not culturally sensitive. Human may never be able to program machines to understand and experience a particular culture. Different cultures have unique and different language systems, and machines do not have complexity to understand or recognize slang, jargon, puns and idioms. Therefore, their translation may not conform to cultural values and specific norms. This is also one of the challenges that the machine needs to overcome.[6] Artificial intelligence may have human abstract thinking ability in the future, but it is difficult to have image thinking ability including imagination and emotion. [7] Therefore, machine translation is often used in news, science and technology, patents, specifications and other text fields with the purpose of fact description, knowledge and information transmission. These words rarely involve emotional and cultural background. When translating expressive texts, the limitations of machine translation are exposed. The so-called expressive text refers to the text that pays attention to emotional expression and is full of imagination. Its main characteristics are subjectivity, emotion and imagination, such as novels, poetry, prose, art and so on. This kind of text attaches importance to the emotional expression of the author or character image, and uses a lot of metaphors, symbols and other expressions. Machine translation is difficult to catch up with artificial translation in this kind of text, it can only translate the main idea, lack of connotation and literary grace and it cannot have subjective feelings and rational analysis like human beings. In fact, it is not difficult to simulate the human brain, the difficulty is that it is impossible to learn from the rich social experience and life experience of excellent translators. In other words, machine translation lacks the personalization and creativity of human translation. It is this personalization and creativity that promote the development and evolution of language, and what machine translation can only output is mechanical "machine language".<br />
<br />
===3.The Irreplaceability of Artificial Translation ===<br />
====3.1 Translation is Constrained by Context====<br />
At present, machine translation can help people deal with language communication in people's daily life and work, such as clothing, food, housing and transportation, but there is a big gap from the "faithfulness, expressiveness and elegance" emphasized by high-level translation. Language itself is art，which pays more attention to artistry than functionality, and the discipline of art is difficult to quantify and unify. Sometimes it is regular, rigorous, logical and clear, and sometimes it is random, free and logical. If it is translated by machine, it is difficult to grasp this degree. Sometimes, machine translation cannot connect words with contextual meaning. In many languages, the same word may have multiple completely unrelated meanings. In this case, context will have a great impact on word meaning, and the understanding of word meaning depends largely on the meaning read from context. Only human beings can combine words with context, determine their true meaning, and creatively adjust and modify the language to obtain a complete and accurate translation. This is undoubtedly very difficult for machine translation. Artificial translation can get rid of the constraints of the source language and translate the translation in line with the grammar, sentence patterns and word habits of the target language. In the process of translation, translators can use their own knowledge reserves to analyze the differences between the source language and the target language in thinking mode, cultural characteristics, social background, customs and habits, so as to translate a more accurate translation. Artificial translation can also add, delete, domesticate, modify and polish the translation according to the style, make up for the lack of culture, try to maintain the thought, artistic conception and charm of the original text and the style of the source language. In addition, translators can also judge and consider the words with multiple meanings or easy to produce ambiguity according to the context, so as to make the translation more clear and more accurate and improve the quality of the translation.<br />
<br />
<br />
===4. Discussion on the Relationship Between Machine Translation and Artificial Translation ===<br />
<br />
===5. Suggestions on the Combined Development of Machine Translation and Artificial Translation===<br />
<br />
===6. ===<br />
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===7. ===<br />
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===Conclusion===<br />
<br />
===References===<br />
<br />
=10 熊敏(Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts)=<br />
[[Machine_Trans_EN_10]]<br />
===Abstract===<br />
With the rapid development of information technology,machine translation technology emerged and is gradually becoming mature.In order to explore the ability of machine translation, I adopts two versions of translation, which are manual translation and machine translation(this paper uses Youdao translation) for different types of texts(according to Peter Newmark's types of text). The results are quite different in terms of quality and accuracy.<br />
<br />
===Key words===<br />
machine translation; manual translation; Newmark's type of texts<br />
<br />
===题目===<br />
Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts<br />
<br />
===摘要===<br />
随着信息技术的高速发展，机器翻译技术出现了，并且逐渐成熟。为了探究机器翻译的能力水平，本人根据纽马克的文本类型分类，选择了相应的译文类型，并且将其机器翻译的版本以及人工翻译的版本进行对比。就质量和准确度而言，译文的水平大相径庭。<br />
<br />
===关键词===<br />
机器翻译；人工翻译；纽马克文本类型<br />
<br />
===1. Introduction===<br />
====1.1.Introduction to machine translation====<br />
Machine translation, also known as computer translation is a technique to translating through machine translator, such as Google translation and Youdao translation. Machine translation is one of the branches of computational linguistics, ranging from computer science, statistics, information science and so on. Machine translation plays an important role in all aspects.<br />
Machine translation can be traced back to 1940s, when British engineer Booth and American engineer Weaver proposed using computer to translate and started to study machines used for translation. However in the 1960s, reports from ALPAC (Automated Language Processing Advisory Committee) showed studies on machine translation had stagnated for a decade. In the 1970s, with the advancement of computer, machine translation was back to track. In the last decades, machine translation has mainly developed into four stages: rule-based machine translation, statistic machine translation, example-based machine translation and neural machine translation.<br />
<br />
====1.2.Process of machine translation====<br />
The process of manual translation is different from that of machine translation. Here is the process of the former. (1) Understand source language. (2) Use target language to organize language. (3) Generate translation. Unlike manual translation, machine translation tends to analyze and code source language first, then look for related codes in corpus, and work out the code that represents target language, generating translation. But they share a common feature, which is that Lexicon, grammatical rules and syntactic structure are taken into consideration. This is one of the biggest challenges for machine translation.<br />
<br />
===2.Newmark’s type of texts===<br />
Peter Newmark divided texts into informative type, expressive text and vocative type according to the linguistic functions of various texts. <br />
====2.11Informative text====<br />
The core of the informative texts is the truth. It is to convey facts, information, knowledge and the like. The language style of the text is objective and logical. Reports, papers, scientific and technological textbooks are all attributed to informative texts.<br />
====2.2Expressive text====<br />
The core of the expressive text is the emotion. It is to express preferences, feelings, views and so on. The language style of it is subjective. Literary works, including fictions, poems and drama, autobiography and authoritative statements belong to expressive text.<br />
====2.3Vocative text====<br />
The core of the vocative text is readership. It is to call upon readers to act in the way intended by the text. So it is reader-oriented. Such texts advertisement, propaganda and notices are of vocative text.<br />
====2.4Study Method====<br />
Manual translations of the three texts are selected from authoritative versions and universally acknowledged. And machine translations of those come from Youdao Translator. And in this thesis I will compare and evaluate the two methods in word diction, sentence structure, word order and redundancy.<br />
<br />
===3. ===<br />
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===4. ===<br />
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===5. ===<br />
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===6. ===<br />
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===7. ===<br />
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===Conclusion===<br />
<br />
===References===<br />
<br />
=11 陈惠妮=(Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts)=<br />
[[Machine_Trans_EN_11]]<br />
===Abstract===<br />
At present, globalization is accelerating and the market demand for language services is rapidly increasing . Machine translation, as an important translation method, can greatly improve translation efficiency due to its low cost and high speed. However, because of the limitations of machine translation and the differences between Chinese and English language, machine translation is not accurate enough. In order to balance translation efficiency and translation quality, a great number of manual revisions in translation are required for the machine translating texts. Medical papers are specialized, special and purposeful, so it requires accurate,qualified and professional translation. However, the quality of translations by machine is inefficient to meet the high-quality requirements of medical papers translation. Therefore, the introduction of pre-editing can greatly improve the efficiency and quality of machine translation.<br />
<br />
===Key words===<br />
Pre-editing, Machine translation, Medical texts<br />
<br />
===题目===<br />
Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts<br />
<br />
===摘要===<br />
在全球化加速发展的今天，市场对语言服务的需求迅速增加。机器翻译作为一种重要的翻译途径，由于其成本低、速度快，可以大大提高翻译效率。然而，由于机器翻译的局限性以及中英文语言的差异，机器翻译的准确性不高。为了平衡翻译效率和翻译质量，机器翻译文本需要大量的手工修改。<br />
医学论文具有专业性、特殊性和目的性，要求其译文准确、合格、专业。然而，机器翻译的质量较低，无法满足医学论文对翻译的高质量要求。因此，译前编辑的引入可以大大提高机器翻译的效率和质量。<br />
<br />
===关键词===<br />
译前编辑；机器翻译；医学文本<br />
<br />
===1. Introduction===<br />
===1.1 Definition of Machine Translation===<br />
The concept of machine translation was firstly proposed in the 1930s. Since 1940s, the machine translation technology has been evolving from rule-based machine translation (RBMT) to statistical machine translation (SMT), and to neural machine translation (NMT). Machine translation refers to the automatic translation of source language into target language by using a computer system. That is, machine translation refers to the automatic translation of text from one language into another natural language by computer software or other online translation webs. Machine translation is also defined as the process of “using a computer system to automatically translate text or speech from one natural language to another” according to the definition by ISO (Cui, 2014). On the basic level, machine translation performs mechanical substitution of words in one language for words in another language, but that rarely produces a good translation, therefore, recognition of the whole phrases and their closest counterparts in the target language is needed. Not all words in one language have equivalents in another language, and many words have more than one meaning. A huge demand for translation is greatly needed in today’s global world, which creates new opportunities for the development of machine translation, attracts more and more attention and becomes one of the current research focuses.<br />
<br />
===1.2 Definition of Pre-editing===<br />
Pre-editing means to adjust and modify the source language to make it fit more with the characteristics of the machine translation software before putting the source language before the into machine translation, so as to improve the quality of the translation machine translation (Wei Changhong, 2008:93-94). Pre-editing is to modify the original text before putting it into machine translation software, in order to improve the recognition rate of machine translation, optimize the output quality of translated text and reduce the workload of post-editing. Because pre-translation editing only needs to be modified in one language, the operation is simpler than post-translation editing, which can realize the double improvement of quality and efficiency. A good pre-editing translation can help machine translation more smoothly, thus improving the machine readability and quality of the output translation. Pre-editing is mostly applied in the following situations: one is when the original text is of poor quality and the machine is difficult to recognize the meaning of the sentence, such as user generated content with poor readability and translatability (Gerlach et al, 2003:45-53); Documents that need to be published in multiple languages; Next is when the original text contains a lot of jargon; The last is the original text has a corresponding translation memory bank. If the original text is edited, it can better match the content of the translation memory bank.<br />
<br />
===1.3 Machine Translation Mode===<br />
According to the different knowledge acquisition methods, machine translation modes can be classified as follows: one is rule-based machine translation, which is based on bilingual dictionaries and a library of language rules for each language. The quality of translation depends on whether the source language conforms to the existing rules, but the inexhaustible rules are the hinders of this model. The second mode is machine translation based on statistics. This translation model relies on the principles of mathematics and statistics to find various existing translations corresponding to the translation tasks through the employment of corpus, analyzing the frequency of their occurrence and selecting the translation with the highest frequency for output. The disadvantage of this translation model is that it ignores the flexibility of language and the importance of context. The last one is neural network language model. This model is different from previous translation models in that it uses end-to-end neural network to realize automatic translation between natural languages. At present, the quality of its translation is much higher than that of the previous translation models.<br />
===1.4 Source of Study Abstracts===<br />
The core medical journals from domestic are selected in order to make the paper more representative and authoritative, such as National Medical Journal of China, Journal of Peking University (Health Science), and Journal of Third Military Medical University. All of them include basic medical science, biomedical technology, laboratory medical science and other fields. In this way, the pre-editing approaches included are applicable enough to machine translation of medical abstracts.<br />
===1.5 Selection of Translation Software===<br />
Generally, machine translation software includes Google Translation, Youdao Translator and Niu Translation, which has their own special use in translation. Take Google as an example, it is more like neurons in human brain, enabling to learn and collect information to establish connections with its neural machine translator’s neurons. However, it also causes many errors because of the lack of enough information. In this paper, contrastive analysis will be carried on by using Google translation. On the one hand, being a pioneer in the translation of NET, it is inevitably to sue Google as the translation software to translate medical texts in this paper. Comparing the program called “Google brain”, other NMT of translation software are relatively disadvantages. On the other hand, the Google translation enjoys the largest users in the world, with its downloads of more than one billion. The output quality of Google translation is more correct and complete than other machine translation software. With years of development and improvement, Google Translation has been greatly promoted. In this paper, Chinese medical abstract will be automatically translated by Google translation, and then the translation output will be compared with the translation by human and the publication of English abstracts. The main purpose is to prove that the improvement and promotion of quality and accuracy in the medical abstracts will be obtained through the pre-editing approaches.<br />
<br />
===2.Language Characteristics and Error Division===<br />
Since Chinese and English are two different languages, it is quite neccessary to identify their own characteristics so as to better analyze and understand the two languages. Norman Shapiro (2012) said that "I see translation as the attempt to produce a text so transparent that it does not seem to be translated. A good translation is like a pane of glass". In addition, Tytler (1978) argued in his Essay on the Principles of Translation that there are three principles of translation that in all the translation should give readers the same feelings as the source text, except for complete transcript of ideas. There actually also exit some mistakes of these two languages. So the following will make some clarrifications of these two languages to ensure more accurate translation.<br />
<br />
===2.1 Language Characteristics of Medical Abstracts===<br />
Chinese and English belong to different language systems, so there are differences in their language structure and the way users think of the languages. When using machine translation from Chinese to English, due to the unequal language levels, there will be many mistakes in the translation process, especially for ESP texts, such as medical papers. <br />
Here, these differences mainly refer to the linguistic characteristics of medical abstracts. In medical abstracts, it usually includes structured and unstructured abstracts. Although in different forms, they both describe the purpose, methods and conclusions of the research.<br />
In the method section of medial abstract, several Chinese sentences can be connected with commas, but each sentence may convey different information. In contrast, English sentences contain a great deal of information, but in order to ensure clarity, some modifiers need to be isolated and then reconstructed. However, in Chinese-English machine translation, a lot of information is put into the sentence because the machine segments the sentence on the basis of the full comma.<br />
In addition, subjectless sentence is used in the objective and method parts of Chinese medical abstracts. The subjectless sentence means the sentence without or free from subject and is usually employed in two contexts. The first is "needless to say". In Chinese sentences, it is common to omit the subject of the sentence. Chinese sentences can convey meanings by using incomplete sentence structures, so Chinese speakers can understand the meanings of the sentences even though the sentence subject is omitted. The second is "emphasis of action". In this context, subjectless sentences are used to describe behaviors, especially in the study of traditional Chinese medicine abstracts. Comparatively speaking, it should be avoided in English medical abstracts. Abstract sentences are more subjective when describing the learning process, while the essential requirement of English medical abstract is objectivity. From this point, sentences without subject should be avoided in English medical abstracts. Another feature is voice. Few words with passive meanings appear in Chinese abstracts. English sentences are more favoured in using passive voice. When translating from Chinese to English, passive voice should be used to make the contents objective, which is also the basic requirement of medical papers.<br />
These are the linguistic features of Chinese medical abstracts. There are great differences in sentence structure and expression between Chinese and English medical abstracts. These differences may reduce the accuracy of machine translation, so it is necessary to introduce pre-editing to edit the source text to ensure that the source text can be accurately recognized by the machine and fully translated into English.<br />
===2.2 Error Division of Machine Translation in Translating Abstracts of Medical Papers from Chinese to English===<br />
In this paper, errors in machine translation are listed out after analyzing. Some are due to the principles, such as statistical-based MT and NMT, employed by machine translation. Based on the original errors, they can be studied from two levels. One is from the macro-level, referring to mistakes caused by objective factors. These are not human factors, but the disadvantage of machine translation and the limitations of language. The limitations of machine translation are derived from the principles and models adopted and manifests itself as a reliance on reference sources. In this case, semantic ambiguity and textual incoherence may occur in the absence of reference sources.<br />
However, for ESP texts such as medical papers, the requirements for terms and sentence patterns are far beyond the existing corpora. For unfamiliar texts (that is, the corresponding texts cannot be found in the corpora). The quality of output translation will be relatively low, which is determined by the principles of machine translation. As mentioned above, machine translation has evolved over the past few decades from rules-based to corp-based statistics to NMT. However, there still exist some limitations in machine translation.<br />
Mistakes on micro-level are mainly caused by the variations and differences of linguistic structure between Chinese and English. Chinese is an implicit language, while English is an explicit one (Lian, 2010) To put it in another way, Chinese expression does not depend on language structures, while English does the opposite. This may result in mismatches between the original and machine translated sentences. This kind of error is mainly divided into two types: component fragment and component missing. For a medical paper, such errors are very serious. As a kind of ESP discourse, medical paper has the characteristics of fixed, objective and accurate language structure. In order to reproduce the characteristics of medical abstracts in translation, it is necessary to avoid errors at the level of words and sentences and pay attention to logic and consistency.<br />
Lexical errors here refer to the inconsistency of fixed expressions in the translation of terms. Although these terms are machine translation based on the corpus, the corpus may not be perfect for ESP texts such as medical papers. Therefore, fixed expressions are not used in term translation. This is also the most common mistake in machine translation (the term here includes but is not limited to nouns). For ESP texts, medical text in particular, the fixed expressions and the accuracy of terms are of great importance.<br />
<br />
===3.===<br />
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===4.===<br />
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===Conclusion===<br />
<br />
===References===<br />
<br />
=12 蔡珠凤=(The Mistranslation of C-J Machine Translation of Political Statements)=<br />
[[Machine_Trans_EN_12]]<br />
===Abstract===<br />
Language is the main way of communication between people. With the continuous development of globalization, the scale of cross-border exchanges is also expanding. However, due to cultural differences and diversity, the languages of different countries and regions are very different, which seriously hinders people's communication. The demand for efficient and convenient translation tools is increasing. At the same time, with the development of network technology and artificial intelligence, recognition technology based on deep learning is more and more widely used in English, Japanese and other fields.<br />
<br />
===Key words===<br />
machine translation; political statements; mistranslation of C-J machine translation<br />
===题目===<br />
The Mistranslation of C-J Machine Translation of Political Statements<br />
===摘要===<br />
语言是人与人之间交流的主要方式。随着全球化的不断发展，跨境交流的规模也在不断扩大。然而，由于文化的差异和多样性，不同国家和地区的语言差异很大，这严重阻碍了人们的交流。对高效便捷的翻译工具的需求正在增加。同时，随着网络技术和人工智能的发展，基于深度学习的识别技术在英语、日语等领域的应用越来越广泛。<br />
<br />
===关键词===<br />
机器翻译；政治发言；政治发言中译日的误译<br />
===1. Introduction===<br />
===Introduction to machine translation===<br />
Machine translation, also known as automatic translation, is a process of using computers to convert one natural language (source language) into another natural language (target language). It is a branch of computational linguistics, one of the ultimate goals of artificial intelligence, and has important scientific research value.At the same time, machine translation has important practical value. With the rapid development of economic globalization and the Internet, machine translation technology plays a more and more important role in promoting political, economic and cultural exchanges.The development of machine translation technology has been closely accompanied by the development of computer technology, information theory, linguistics and other disciplines. From the early dictionary matching, to the rule translation of dictionaries combined with linguistic expert knowledge, and then to the statistical machine translation based on corpus, with the improvement of computer computing power and the explosive growth of multilingual information, machine translation technology gradually stepped out of the ivory tower and began to provide real-time and convenient translation services for general users.<br />
<br />
=== C-J machine translation software===<br />
Today's online machine translation software includes Baidu translation, Tencent translation, Google translation, Youdao translation, Bing translation and so on. Google was the first company to launch the machine translation system, and Baidu was the first company to import the machine translation system in China. In addition, Tencent and Youdao have attracted much attention.Machine translation is the process of using computers to convert one natural language into another. It usually refers to sentence and full-text translation between natural languages. In order to continuously improve the translation quality, R & D personnel have added artificial intelligence technologies such as speech recognition, image processing and deep neural network to machine translation on the basis of traditional machine translation based on rules, statistics and examples.With the increase of using machine translation, the joint cooperation between manual translation and machine translation will also increase significantly in the future. What criteria should be used to evaluate the quality of machine translation? In the evolving field of machine translation, there is an urgent need to clarify the unsolvable questions and solved problems.<br />
<br />
===The history of machine translation===<br />
The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. alchuni put forward the idea of using machines for translation. In 1933, Soviet inventor П.П. Trojansky designed a machine to translate one language into another, and registered his invention on September 5 of the same year; However, due to the low technical level in the 1930s, his translation machine was not made. In 1946, the first modern electronic computer ENIAC was born. Shortly after that, W. weaver, an American scientist and A. D. booth, a British engineer, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947 when discussing the application scope of electronic computer. In 1949, W. Weaver published the translation memorandum, which formally put forward the idea of machine translation. After 60 years of ups and downs, machine translation has experienced a tortuous and long development path. The academic community generally divides it into the following four stages:<br />
Pioneering period（1947-1964）<br />
In 1954, with the cooperation of IBM, Georgetown University completed the English Russian machine translation experiment with ibm-701 computer for the first time, showing the feasibility of machine translation to the public and the scientific community, thus opening the prelude to the study of machine translation.<br />
It is not too late for China to start this research. As early as 1956, the state included this research in the national scientific work development plan. The topic name is "machine translation, the construction of natural language translation rules and the mathematical theory of natural language". In 1957, the Institute of language and the Institute of computing technology of the Chinese Academy of Sciences cooperated in the Russian Chinese machine translation experiment, translating 9 different types of more complex sentences.<br />
From the 1950s to the first half of the 1960s, machine translation research has been on the rise. The United States and the former Soviet Union, two superpowers, have provided a lot of financial support for machine translation projects for military, political and economic purposes, while European countries have also paid considerable attention to machine translation research due to geopolitical and economic needs, and machine translation has become an upsurge for a time. In this period, although machine translation is just in the pioneering stage, it has entered an optimistic period of prosperity.<br />
Frustrated period（1964-1975）<br />
In 1964, in order to evaluate the research progress of machine translation, the American Academy of Sciences established the automatic language processing Advisory Committee (Alpac Committee) and began a two-year comprehensive investigation, analysis and test.<br />
In November 1966, the committee published a report entitled "language and machine" (Alpac report for short), which comprehensively denied the feasibility of machine translation and suggested stopping the financial support for machine translation projects. The publication of this report has dealt a blow to the booming machine translation, and the research of machine translation has fallen into a standstill. Coincidentally, during this period, China broke out the "ten-year Cultural Revolution", and basically these studies also stagnated. Machine translation has entered a depression.<br />
convalescence（1975-1989）<br />
Since the 1970s, with the development of science and technology and the increasingly frequent exchange of scientific and technological information among countries, the language barriers between countries have become more serious. The traditional manual operation mode has been far from meeting the needs, and there is an urgent need for computers to engage in translation. At the same time, the development of computer science and linguistics, especially the substantial improvement of computer hardware technology and the application of artificial intelligence in natural language processing, have promoted the recovery of machine translation research from the technical level. Machine translation projects have begun to develop again, and various practical and experimental systems have been launched successively, such as weinder system Eurpotra multilingual translation system, taum-meteo system, etc.<br />
However, after the end of the "ten-year holocaust", China has perked up again, and machine translation research has been put on the agenda again. The "784" project has paid enough attention to machine translation research. After the mid-1980s, the development of machine translation research in China has further accelerated. Firstly, two English Chinese machine translation systems, ky-1 and MT / ec863, have been successfully developed, indicating that China has made great progress in machine translation technology.<br />
New period(1990 present)<br />
With the universal application of the Internet, the acceleration of the process of world economic integration and the increasingly frequent exchanges in the international community, the traditional way of manual operation is far from meeting the rapidly growing needs of translation. People's demand for machine translation has increased unprecedentedly, and machine translation has ushered in a new development opportunity. International conferences on machine translation research have been held frequently, and China has made unprecedented achievements. A series of machine translation software have been launched, such as "Yixing", "Yaxin", "Tongyi", "Huajian", etc. Driven by the market demand, the commercial machine translation system has entered the practical stage, entered the market and came to the users.<br />
Since the new century, with the emergence and popularization of the Internet, the amount of data has increased sharply, and statistical methods have been fully applied. Internet companies have set up machine translation research groups and developed machine translation systems based on Internet big data, so as to make machine translation really practical, such as "Baidu translation", "Google translation", etc. In recent years, with the progress of in-depth learning, machine translation technology has further developed, which has promoted the rapid improvement of translation quality, and the translation in oral and other fields is more authentic and fluent.<br />
<br />
===The problem of machine translation at present===<br />
Error is inevitable<br />
Many people have misunderstandings about machine translation. They think that machine translation has great deviation and can't help people solve any problems. In fact, the error is inevitable. The reason is that machine translation uses linguistic principles. The machine automatically recognizes grammar, calls the stored thesaurus and automatically performs corresponding translation. However, errors are inevitable due to changes or irregularities in grammar, morphology and syntax, such as sentences with adverbials after "give me a reason to kill you first" in Dahua journey to the West. After all, a machine is a machine. No one has special feelings for language. How can it feel the lasting charm of "the tenderness of lowering its head, like the shame of a water lotus? After all, the meaning of Chinese is very different due to the changes of morphology, grammar and syntax and the change of context. Even many Chinese people are zhanger monks - they can't touch their heads, let alone machines.<br />
Bottleneck<br />
In fact, no matter which method, the biggest factor affecting the development of machine translation lies in the quality of translation. Judging from the achievements, the quality of machine translation is still far from the ultimate goal.<br />
Chinese mathematician and linguist Zhou Haizhong once pointed out in his paper "fifty years of machine translation": to improve the quality of machine translation, the first thing to solve is the problem of language itself rather than programming; It is certainly impossible to improve the quality of machine translation by relying on several programs alone. At the same time, he also pointed out that it is impossible for machine translation to achieve the degree of "faithfulness, expressiveness and elegance" when human beings have not yet understood how the brain performs fuzzy recognition and logical judgment of language. This view may reveal the bottleneck restricting the quality of translation. <br />
It is worth mentioning that American inventor and futurist ray cozwell predicted in an interview with Huffington Post that the quality of machine translation will reach the level of human translation by 2029. There are still many disputes about this thesis in the academic circles.<br />
<br />
===2.Mistranslation of Chinese Japanese machine translation===<br />
===2.1Vocabulary mistranslation in Chinese Japanese machine translation===<br />
===2.1.1Mistranslation of proper nouns===<br />
===2.1.2Mistranslation of Polysemy===<br />
===2.1.3Mistranslation of compound words===<br />
===2.2 Syntactic mistranslation in Chinese Japanese machine translation===<br />
===2.2.1Main dynamic Mistranslation===<br />
===2.2.2Dynamic Mistranslation===<br />
===2.2.3Mistranslation of tenses===<br />
===2.2.4Mistranslation of honorifics===<br />
===3.===<br />
===4.===<br />
===Conclusion===<br />
===References===<br />
<br />
=13 陈湘琼Chen Xiangqiong（Study on Post-editing from the Perspective of Functional Equivalence Theory ）=<br />
[[Machine_Trans_EN_13]]<br />
<br />
=14 Bi bi Nadia（Machine Translation a Challenge for Human Translators)=<br />
[[Machine_Trans_EN_14]]<br />
<br />
===Abstract===<br />
Machine translation is a big obstacle in the way of Human translators or interpretors although it is quick and less time consuming.people are trying to get translation of their target language using through source language.For this purpose they are using digital apps like Google translation Google translation does not give accurate and exact interpretation.Google translator is translates word to word translate that doesn't clarifies it's true and actual meaning.On the other hand human translators can give exact and accurate translation ,they take care of grammatical errors , diction and sentence structure.They clarify the purpose of target language through using source language.<br />
===Keywords===<br />
Descriptive translation, academic, interdiscipline, comparative literature, localization,Translatology,school of thought, translation , studies, linguistics, corresponding<br />
===Body of article===<br />
Translation is the process of reworking text from one language into another to maintain the original message and communication. But, like everything else, there are different methods of translation, and they vary in form and function. What is translation? The term has become widely used among knowledge transfer researchers and practitioners, especially in the fields of health and health care. In a landmark review, Jonathan Lomas began to argue that 'The tasks… may be defined as to establish and maintain links between researchers and their audience, via the appropriate translation of research findings' (Lomas 1997, p 4). In 2004, the World Health Organization's World Report on Knowledge for Better Health suggested that 'One of the key contributions of research to health systems is the translation of knowledge into actions' (WHO 2004, p 33 and p 100). By 2006, special issues of WHO's Bulletin as well as the journals Evaluation and the Health Professions and the Journal of Continuing Education in the Health Professions were dedicated to translation.But what does 'translation' mean? It may be a new word for an old problem, meaning nothing more than 'transfer'. The rapidly emerging field of 'translational medicine' seems to take translation to mean generally what transfer might have meant, that is the transmission of knowledge and evidence 'from bench to bedside'. As one commentator has observed, "Translational medicine" as a fashionable term is being increasingly used to describe the wish of biomedical researchers to ultimately help patients' (Wehling 2008, abstract). <br />
Semantic uncertainty persists, not least because of the different interests of scientists, clinicians, patients and commercial firms (Littman et al 2007): 'Translational research means different things to different people, but it seems important to almost everyone' (Woolf 2008, p 211).<br />
In related fields, such as public health (Armstrong et al 2006), 'translation' seems to signify dissatisfaction with 'transfer'. It wants to move away from thinking of knowledge transfer as a form of technology transfer or dissemination, rejecting if only by implication its mechanistic assumptions and its model of linear messaging from A to B. But still, what does it signify?<br />
<br />
===Why translation?===<br />
'Translation' indicates a closer attention to the problem of shared meaning and how it might be developed. It seems to represent some new epistemological lubricant, facilitating the dissemination of texts and the application and use of the knowledge and information they in. Simply, translation might be the key to transfer. And yet, when we stop to think, we are more ambivalent. What is translated often seems somehow inferior, not real or original. Note how readily commentators reach for the idea that things might be 'lost in translation'. Knowing at a distance – made in and mediated by translation - makes for incomplete renditions, blurred images, partial truths. So what might 'translation' really mean? The purpose of this paper is to set out, for policy makers and practitioners, the theoretical and conceptual resources translation holds and seems to represent. In doing so, it explores understandings of translation in the fields of literature and linguistics and in the sociology of science and technology. It begins by setting out just why this idea of translation should make immediate, intuitive sense in relation to research, policy and practice.<br />
1translation in research, policy and practice research as translation<br />
Research often entails translation from one language to another: where data is collected from more than one ethnic group, for example, or where the language of the researcher is other than that of the research subject. It may draw on a secondary literature or source documents written in different languages, and may be published and disseminated in languages other than the one in which it is first written up.<br />
2In a different way, to conduct an interview is to ask for an account of experience and its meanings, but it is also to construct and translate that experience in terms defined at least in part by the researcher. In representing what is said, transcripts then select data, usually excluding significant gesture and eye-contact, for example. Often, certain characteristics of speech-acts (such as hesitations) will be edited out. In turn, the format of the transcript shapes the analytic use the researcher may make of it. The basis of research 'findings', then, is an artefact, a transcript or translation, not an original interaction (Ochs 1979, Barnes, Bloor and Henry 1996, Ross 2009).<br />
3In this way, the researcher recasts aspects of his or her problem or topic in new, scientific form: 'All researchers "translate" the experiences of others' (Temple 1997, p 609). Research is invariably conducted in a sort of 'metalanguage' (Hantrais and Ager 1985): the research process can be conceived as one of successive translations (from theoretical formulation to operationalization, transcription, interpretation and dissemination). Theorization is a process of reciprocal back and forth between theory and fact, in which conceptions of each are revised in order that one fit the other (Baldamus 1974).<br />
4 It is a kind of translation: a rereading, re-use, re-application or re-representation of what we know in new terms (Turner 1980). Referencing, too, is an act of translation, a form of appropriation and incorporation of one text by another (Gilbert 1977).policy as translation Each of the fields covered by the paper is diverse and ill-defined, and there is no intention here to provide a comprehensive account of any them. Sources have been chosen for their relevance: main references are cited in the text, and additional sources listed in footnotes.<br />
<br />
2For a brief introduction to the technical issues involved in social research in more than one language, see Birbili (2000). For translation issues in survey research and question design in general, see Ervin and Bower (1952) and Deutscher (1968); on translating survey research instruments (in this instance health-related quality of life measures), Bowden and Fox-Rushby (2003). On the use of translators and interpreters, see Temple (1997) and Jentsch (1998).<br />
3For an interesting discussion of this problem, see Bourdieu (1999), esp pp 621-626, 'The risks of writing'. <br />
===Difference between machine translation and human translators===<br />
Few people disagree on the differences between the two, but many argue over the quality of the translations. How accurate are machine translations? How reliable are human translations? Some say machine translation produces near-perfect translations while others are adamant that translations are incomprehensible and cause more problems than they solve. Results will, of course, vary depending on the source and target languages, the machine translation service used (e.g. Google Translate), and the complexity of the original text.<br />
Machine translation, love it or hate it, is here to stay. In fact, the machine translation market is growing at such a fast pace that it is predicted to reach $980 million by 2022.<br />
===Machine translation: the pros and cons===<br />
The advantages of machine translation generally come down to two factors: it’s faster and cheaper. The downside to this is the standard of translation can be anywhere from inaccurate, to incomprehensible, and potentially dangerous (more on that shortly).<br />
==The advantages of machine translation==<br />
Many free tools are readily available (Google Translate, Skype Translator, etc.)<br />
Quick turnaround time <br />
You can translate between multiple languages using one tool<br />
Translation technology is constantly improving<br />
The disadvantages of machine translation<br />
Level of accuracy can be very low <br />
Accuracy is also very inconsistent across different languages<br />
==Machines can't translate context ==<br />
Mistakes are sometimes costly <br />
Sometimes translation simply doesn’t work<br />
The most important thing to consider with any kind of translation is the cost of potential mistakes. Translating instructions for medical equipment, aviation manuals, legal documents and many other kinds of content require 100% accuracy. In such cases, mistakes can cost lives, huge amounts of money and irreparable damage to your company’s image. So choose carefully!<br />
Human translation: the pros and cons<br />
Human translation essentially switches the table in terms of pros and cons. A higher standard of accuracy comes at the price of longer turnaround times and higher costs. What you have to decide is whether that initial investment outweighs the potential cost of mistakes. Alternatively, whether mistakes simply aren’t an option, like the cases we looked at in the previous section.<br />
<br />
==The advantages of human translation ==<br />
It’s a translator’s job to ensure the highest accuracy<br />
Humans can interpret context and capture the same meaning, rather than simply translating words<br />
Human translators can review their work and provide a quality process<br />
Humans can interpret the creative use of language, e.g. puns, metaphors, slogans, etc.<br />
Professional translators understand the idiomatic differences between their languages<br />
Humans can spot pieces of content where literal translation isn’t possible and find the most suitable alternative<br />
The disadvantages of human translation<br />
Turnaround time is longer <br />
Translators rarely work for free <br />
Unless you use a translation agency, with access to thousands of translators, you’re limited to the languages any one translator can work with<br />
Simply put, human translation is your best option when accuracy is even remotely important. Other considerations to make are the complexity of your source material and the two languages you’re translating between – both of which can render machines pretty useless.<br />
<br />
When to use machine and human translation<br />
The truth is, the debate over machine vs human translation is an unnecessary distraction. What we should really be talking about is when to use these two different types of translation services, because they both serve a very valid purpose.<br />
<br />
Examples of when to use machine translation<br />
When you have a large bulk of content to translate and the general meaning is enough<br />
When your translation never reaches the final audience, e.g. you’re translating a resource as research for another piece of content<br />
Translating documents for internal use within a company, provided 100% accuracy isn’t needed<br />
To partially translate large chunks of content for a human translator to improve upon<br />
Examples of when to use human translation<br />
When accuracy is important<br />
Most cases where your translated content is received by a consumer audience<br />
When you have a duty of care to provide accurate translations (e.g. legal documents, product instructions, medical guidelines or health and safety content)<br />
When translating marketing material or other texts for creative language uses.<br />
<br />
===Conclusion ===<br />
<br />
In the light of above mentioned facts and figures here by we would say that machine translation is challenge for human translators because it can reduces the wokload of translation but can't give accurate and exact translation of the traget language.It can be less reliable than human translation..</div>Chen Huinihttps://bou.de/u/index.php?title=20211208_homework&diff=12906420211208 homework2021-12-05T07:56:52Z<p>Chen Huini: /* 陈惠妮 Chén Huìnī 英语语言文学（英美文学） 女 202120081479 */</p>
<hr />
<div>Quicklinks: [[Introduction_to_Translation_Studies_2021|Back to course homepage]] [https://bou.de/u/wiki/uvu:Community_Portal#Frequently_asked_questions_FAQ FAQ] [https://bou.de/u/wiki/uvu:Community_Portal Manual] [[20210926_homework|Back to all homework webpages overview]] [[20220112_final_exam|final exam page]]<br />
<br />
==陈静 Chén Jìng 国别 女 202020080595==<br />
<br />
说了一会话，临走又送我二两银子。”甄家娘子听了，不觉感伤。一夜无话。<br />
<br />
==蔡珠凤 Cài Zhūfèng 法语语言文学 女 202120081477==<br />
<br />
次日，早有雨村遣人送了两封银子、四匹锦缎，答谢甄家娘子；又一封密书与封肃，托他向甄家娘子要那娇杏作二房。封肃喜得眉开眼笑，巴不得去奉承太爷，便在女儿前一力撺掇。<br />
<br />
The next day, Yucun sent two bundles of silver and four brocades to thank the Zhen lady; Another secret letter to Feng Su asked him to ask the Zhen lady for the delicate Jiaoxing as concubine. Feng Su was so happy that he was eager to flatter the Lord, so he tried his best to encourage his daughter.<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 14:01, 4 December 2021 (UTC)<br />
<br />
The next day, Yucun sent two bundles of silver and four brocades to thank Zhen lady; He also sent another secret letter to Feng Su, asking him to ask Jiaoxing, the daughter of Zhen Lady, to be his second wife. Feng Su was so happy that he was eager to flatter the Lord, so he tried his best to encourage his daughter.--[[User:Chen Huini|Chen Huini]] ([[User talk:Chen Huini|talk]]) 07:56, 5 December 2021 (UTC)Chen Huini<br />
<br />
==陈惠妮 Chén Huìnī 英语语言文学（英美文学） 女 202120081479==<br />
<br />
<br />
当夜用一乘小轿，便把娇杏送进衙内去了。雨村欢喜，自不必言；又封百金赠与封肃，又送甄家娘子许多礼物，令其且自过活，以待访寻女儿下落。<br />
<br />
One evening, Jiaoxing was sent to prison by a small sedan carriage. Undoutedbly, Yucun was very pleased and gave hundreds of golds to Fengsu and many gifts to Zhen's wife so that she can live by herself untill her daugther was found.--[[User:Chen Huini|Chen Huini]] ([[User talk:Chen Huini|talk]]) 11:17, 4 December 2021 (UTC)Chen Huini<br />
<br />
==陈湘琼 Chén Xiāngqióng 外国语言学及应用语言学 女 202120081480==<br />
<br />
却说娇杏那丫头，便是当年回顾雨村的，因偶然一看，便弄出这段奇缘，也是意想不到之事。谁知他命运两济：不承望自到雨村身边只一年，便生一子；又半载，雨村嫡配忽染疾下世，雨村便将他扶作正室夫人。<br />
==陈心怡 Chén Xīnyí 翻译学 女 202120081481==<br />
<br />
正是：偶因一回顾，便为人上人。原来雨村因那年士隐赠银之后，他于十六日便起身赴京。大比之期，十分得意，中了进士，选入外班，今已升了本县太爷。<br />
<br />
Jiaoxing looked back at Jia Yucun out of curiosity, not out of love. But because of such a chance, from a little girl who was serviced, she became a rich lady who serviced others. It turns out that Yucun because of silver given by Shiyin in that year, he left for Beijing on the 16th. He was lucky enough to won the scholar in the great competition and was selected into the outer class, now has been promoted to the county magistrate. --[[User:Chen Xinyi|Chen Xinyi]] ([[User talk:Chen Xinyi|talk]]) 09:33, 4 December 2021 (UTC)<br />
<br />
==程杨 Chéng Yáng 英语语言文学（英美文学） 女 202120081482==<br />
<br />
虽才干优长，未免贪酷，且恃才侮上，那同寅皆侧目而视。不上一年，便被上司参了一本，说他貌似有才，性实狡猾；又题了一两件徇庇蠹役、交结乡绅之事。<br />
<br />
==丁旋 Dīng Xuán 英语语言文学（英美文学） 女 202120081483==<br />
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龙颜大怒，即命革职。部文一到，本府各官无不喜悦。那雨村虽十分惭恨，面上却全无一点怨色，仍是嘻笑自若。<br />
<br />
==杜莉娜 Dù Lìnuó 英语语言文学（语言学） 女 202120081484==<br />
<br />
交代过了公事，将历年所积的宦囊，并家属人等，送至原籍，安顿妥当了，却自己担风袖月，游览天下胜迹。那日偶又游至维扬地方，闻得今年盐政点的是林如海。<br />
<br />
After giving official business,leaving the family his accumulated salary for several years and settled them in native home, he had given up high official positions and riches and travelled the famous historical sites everywhere.One day he arrived Weiyang（Yangzhou，Jiangsu Province，China）by accident and heared about the present salt administration officer was Lin Ruhai Salzinspektor.--[[User:Du Lina|Du Lina]] ([[User talk:Du Lina|talk]]) 04:23, 5 December 2021 (UTC)<br />
<br />
==付红岩 Fù Hóngyán 英语语言文学（英美文学） 女 202120081485==<br />
<br />
这林如海姓林名海，表字如海，乃是前科的探花，今已升兰台寺大夫，本贯姑苏人氏，今钦点为巡盐御史，到任未久。<br />
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==付诗雨 Fù Shīyǔ 日语语言文学 女 202120081486==<br />
<br />
原来这林如海之祖，也曾袭过列侯的，今到如海，业经五世。起初只袭三世，因当今隆恩盛德，额外加恩，至如海之父又袭了一代，到了如海便从科第出身。<br />
<br />
In fact, the ancestors of Lin Ju-hai had, from years back, successively inherited the title of Marquis, which rank, by its present descent to Ju-hai, had already been enjoyed by five generations. When first conferred, the hereditary right to the title had been limited to three generations; but of late years, by an act of magnanimous favour and generous beneficence, extraordinary bounty had been superadded; and on the arrival of the succession to the father of Ju-hai, the right had been extended to another degree. It had now descended to Ju-hai, who had, besides this title of nobility, begun his career as a successful graduate. --[[User:Fu Shiyu|Fu Shiyu]] ([[User talk:Fu Shiyu|talk]]) 00:55, 5 December 2021 (UTC)<br />
<br />
==高蜜 Gāo Mì 翻译学 女 202120081487==<br />
<br />
虽系世禄之家，却是书香之族。只可惜这林家支庶不盛，人丁有限，虽有几门，却与如海俱是堂族，没甚亲支嫡派的。今如海年已五十，只有一个三岁之子，又于去岁亡了；<br />
<br />
==宫博雅 Gōng Bóyǎ 俄语语言文学 女 202120081488==<br />
<br />
虽有几房姬妾，奈命中无子，亦无可如何之事。只嫡妻贾氏生得一女，乳名黛玉，年方五岁，夫妻爱之如掌上明珠。见他生得聪明俊秀，也欲使他识几个字，不过假充养子，聊解膝下荒凉之叹。<br />
<br />
Although he had several concubines, he was doomed to have no son (to inherit the family line). Only lady Jia, his legal wife, gave birth to a daughter, Daiyu, aged five. The couple doted on their daughter like a pearl on the palm of their eyes. Lin Ruhai wanted to teach him to read, because he was smart and handsome, and Lin Ruhai wanted to ease the loneliness of not having a son by pretending to adopt him.--[[User:Gong Boya|Gong Boya]] ([[User talk:Gong Boya|talk]]) 05:55, 5 December 2021 (UTC)<br />
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==何芩 Hé Qín 翻译学 女 202120081489==<br />
<br />
且说贾雨村在旅店偶感风寒，愈后又因盘费不继，正欲得一个居停之所，以为息肩之地。偶遇两个旧友，认得新盐政，知他正要请一西席教训女儿，遂将雨村荐进衙门去。<br />
<br />
==胡舒情 Hú Shūqíng 英语语言文学（语言学） 女 202120081490==<br />
<br />
这女学生年纪幼小，身体又弱，功课不限多寡，其馀不过两个伴读丫鬟，故雨村十分省力，正好养病。看看又是一载有馀，不料女学生之母贾氏夫人一病而亡。<br />
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==黄锦云 Huáng Jǐnyún 英语语言文学（语言学） 女 202120081491==<br />
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女学生奉侍汤药，守丧尽礼，过于哀痛，素本怯弱，因此旧病复发，有好些时不曾上学。雨村闲居无聊，每当风日晴和，饭后便出来闲步。这一日偶至郊外，意欲赏鉴那村野风光。<br />
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==黄逸妍 Huáng Yìyán 外国语言学及应用语言学 女 202120081492==<br />
<br />
信步至一山环水漩、茂林修竹之处，隐隐有座庙宇，门巷倾颓，墙垣剥落。有额题曰“智通寺”，门旁又有一副旧破的对联云：身后有馀忘缩手，眼前无路想回头。<br />
<br />
==曾俊霖 Zēng Jùnlín 国别 男 202120081493==<br />
<br />
雨村看了，因想道：“这两句文虽甚浅，其意则深。也曾游过些名山大刹，倒不曾见过这话头。其中想必有个翻过筋斗来的，也未可知。<br />
<br />
==黄柱梁 Huáng Zhùliáng 国别 男 202120081493==<br />
<br />
何不进去一访？”走入看时，只有一个龙锺老僧在那里煮粥。雨村见了，却不在意。及至问他两句话，那老僧既聋且昏，又齿落舌钝，所答非所问。雨村不耐烦，仍退出来。<br />
<br />
==金晓童 Jīn Xiǎotóng 202120081494==<br />
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意欲到那村肆中沽饮三杯，以助野趣，于是移步行来。刚入肆门，只见座上吃酒之客，有一人起身大笑，接了出来，口内说：“奇遇，奇遇！”<br />
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==邝艳丽 Kuàng Yànl 英语语言文学（语言学） 女 202120081495==<br />
<br />
雨村忙看时，此人是都中古董行中贸易，姓冷号子兴的，旧日在都相识。雨村最赞这冷子兴是个有作为大本领的人，这子兴又借雨村斯文之名，故二人最相投契。<br />
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==李爱璇 Lǐ Àixuán 英语语言文学（语言学） 女 202120081496==<br />
<br />
雨村忙亦笑问：“老兄何日到此？弟竟不知。今日偶遇，真奇缘也！”子兴道：“去年岁底到家，今因还要入都，从此顺路找个敝友，说一句话。<br />
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==李瑞洋 Lǐ Ruìyáng 英语语言文学（英美文学） 女 202120081497==<br />
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承他的情，留我多住两日。我也无甚紧事，且盘桓两日，待月半时也就起身了。今日敝友有事，我因闲走到此，不期这样巧遇。”一面说，一面让雨村同席坐了，另整上酒肴来。<br />
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==李姗 Lǐ Shān 英语语言文学（英美文学） 女 202120081498==<br />
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二人闲谈慢饮，叙些别后之事。雨村因问：“近日都中可有新闻没有？”子兴道：“倒没有什么新闻，倒是老先生的贵同宗家出了一件小小的异事。”<br />
<br />
Over drinking, they two talked about some plans of the near future after the farewell. Then Yucun asked: Is there anything new in the capital city? Zixing answered，“Nothing new. But in your dignified remote relative's house there is indeed a strange thing.”--[[User:Li Shan|Li Shan]] ([[User talk:Li Shan|talk]]) 14:49, 4 December 2021 (UTC)<br />
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==李双 Lǐ Shuāng 翻译学 女 202120081499==<br />
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雨村笑道：“弟族中无人在都，何谈及此？”子兴笑道：“你们同姓，岂非一族？”雨村问：“是谁家？”子兴笑道：“荣国贾府中，可也不玷辱老先生的门楣了。”<br />
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==李文璇 Lǐ Wénxuán 英语语言文学（英美文学） 女 202120081500==<br />
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雨村道：“原来是他家。若论起来，寒族人丁却自不少，东汉贾复以来，支派繁盛，各省皆有，谁能逐细考查？若论荣国一支，却是同谱。但他那等荣耀，我们不便去认他，故越发生疏了。”<br />
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Yucun said: "It's his house. If discussed explicitly, the people of Han's family were of great quantity since the Eastern Han Dynasty of Jiafu. Their branches were numerous in each province, who can examine one by one? If only discussed the branch of Rongguo, they were the same. But the Rongguo were glorious, it was inconvenient for us to make a connection with them, so we were getting more and more unfamiliar. --[[User:Li Wenxuan|Li Wenxuan]] ([[User talk:Li Wenxuan|talk]]) 08:22, 4 December 2021 (UTC)<br />
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==李雯 Lǐ Wén 英语语言文学（英美文学） 女 202120081501==<br />
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子兴叹道：“老先生休这样说。如今的这荣、宁两府，也都萧索了，不比先时的光景。”雨村道：“当日宁、荣两宅人口也极多，如何便萧索了呢？”子兴道：“正是，说来也话长。”<br />
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==李新星 Lǐ Xīnxīng 亚非语言文学 女 202120081503==<br />
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雨村道：“去岁我到金陵时，因欲游览六朝遗迹，那日进了石头城，从他宅门前经过：街东是宁国府，街西是荣国府，二宅相连，竟将大半条街占了。<br />
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==李怡 Lǐ Yí 法语语言文学 女 202120081504==<br />
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大门外虽冷落无人，隔着围墙一望，里面厅殿楼阁，也还都峥嵘轩峻；就是后边一带花园里，树木山石，也都还有葱蔚洇润之气：那里像个衰败之家？”<br />
Although deserted outside the gate, across the wall to see the hall hall pavilions, are also lofty xuan Jun; Even in the garden at the back, the trees and rocks were all luxuriant: it did not look at all like a run-down house--[[User:Li Yi|Li Yi]] ([[User talk:Li Yi|talk]]) 01:57, 5 December 2021 (UTC)<br />
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==刘沛婷 Liú Pèitíng 英语语言文学（英美文学） 女 202120081505==<br />
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子兴笑道：“亏你是进士出身，原来不通。古人有言：‘百足之虫，死而不僵。’如今虽说不似先年那样兴盛，较之平常仕宦人家，到底气象不同。<br />
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==刘胜楠 Liú Shèngnán 翻译学 女 202120081506==<br />
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如今生齿日繁，事务日盛，主仆上下都是安富尊荣，运筹谋画的竟无一个；那日用排场，又不能将就省俭。如今外面的架子虽没很倒，内囊却也尽上来了。这也是小事。<br />
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==刘薇 Liú Wēi 国别 女 202120081507==<br />
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更有一件大事：谁知这样钟鸣鼎食的人家儿，如今养的儿孙，竟一代不如一代了。”雨村听说，也道：“这样诗礼之家，岂有不善教育之理？别门不知，只说这宁、荣两宅，是最教子有方的，何至如此？”<br />
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==刘晓 Liú Xiǎo 英语语言文学（英美文学） 女 202120081508==<br />
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子兴叹道：“正说的是这两门呢！等我告诉你：当日宁国公是一母同胞弟兄两个。宁公居长，生了两个儿子。宁公死后，长子贾代化袭了官，也养了两个儿子：长子贾敷，八九岁上死了；<br />
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==刘越 Liú Yuè 亚非语言文学 女 202120081509==<br />
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只剩了一个次子贾敬，袭了官，如今一味好道，只爱烧丹炼汞，别事一概不管。幸而早年留下一个儿子，名唤贾珍，因他父亲一心想作神仙，把官倒让他袭了。<br />
Only his second son, Jia Jing, succeeded him as the official. Now he devoted himself only to Taoism and alchemy, and did nothing else. Fortunately, in his early years, he had left a son named Jia Zhen, for his father had set his heart on becoming a fairy, so he succeeded to the official. --[[User:Liu Yue|Liu Yue]] ([[User talk:Liu Yue|talk]]) 07:30, 4 December 2021 (UTC)<br />
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==刘运心 Liú Yùnxīn 英语语言文学（英美文学） 女 202120081510==<br />
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他父亲又不肯住在家里，只在都中城外，和那些道士们胡羼。这位珍爷也生了一个儿子，今年才十六岁，名叫贾蓉。如今敬老爷不管事了。<br />
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==罗安怡 Luó Ānyí 英语语言文学（英美文学） 女 202120081511==<br />
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这珍爷那里干正事，只一味高乐不了，把那宁国府竟翻过来了，也没有敢来管他的人。再说荣府你听，方才所说异事就出在这里。<br />
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==罗曦 Luó Xī 英语语言文学（英美文学） 女 202120081512==<br />
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自荣公死后，长子贾代善袭了官，娶的是金陵世家史侯的小姐为妻。生了两个儿子：长名贾赦，次名贾政。如今代善早已去世，太夫人尚在。<br />
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==马新 Mǎ Xīn 外国语言学及应用语言学 女 202120081513==<br />
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长子贾赦袭了官，为人却也中平，也不管理家事。惟有次子贾政，自幼酷喜读书，为人端方正直。祖父锺爱，原要他从科甲出身。<br />
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The eldest son, Jia She, inherited the official position from his ancestors but he was not top-notch and did not manage the family affairs as well. Only his second son, Jia Zheng, loved to read since childhood and was a man of upright. His grandfather (Jia Yuan) like him the most and originally planed to let him take the imperial examination before becoming an official.--[[User:Ma Xin|Ma Xin]] ([[User talk:Ma Xin|talk]]) 08:02, 4 December 2021 (UTC)<br />
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Their elder son Jia She inherited the official title; he was moderate and often remained neutral, and did not manage the family affairs. Only the younger son, Jia Zheng, was fond of studying as a child and was a man of upright so that he was his grandfather’s (Jia Yuan) favorite, and he hoped to make a career for himself through the imperial examinations. --[[User:Mao Yawen|Mao Yawen]] ([[User talk:Mao Yawen|talk]]) 09:05, 4 December 2021 (UTC)<br />
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==毛雅文 Máo Yǎwén 英语语言文学（英美文学） 女 202120081514==<br />
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不料代善临终遗本一上，皇上怜念先臣，即叫长子袭了官；又问还有几个儿子，立刻引见，又将这政老爷赐了个额外主事职衔，叫他入部习学，如今现已升了员外郎。<br />
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Unexpectedly, when Jia Daishan died, he left a valedictory memorial, and the Emperor, out of memory and regard for his former minister, not only conferred the official title on his elder son but also asked what other sons there were and ordered them to be introduced to the palace immediately. The Emperor also bestowed the rank of Assistant Secretary on Jia Zheng, and as an additional favor gave him instructions to familiarize himself with affairs in one of the ministries. He has now risen to the rank of Under-Secretary. --[[User:Mao Yawen|Mao Yawen]] ([[User talk:Mao Yawen|talk]]) 08:40, 4 December 2021 (UTC)<br />
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==毛优 Máo Yōu 俄语语言文学 女 202120081515==<br />
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这政老爷的夫人王氏，头胎生的公子名叫贾珠，十四岁进学，后来娶了妻，生了子，不到二十岁，一病就死了。第二胎生了一位小姐，生在大年初一，就奇了。<br />
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Mrs. Wang --- the wife of Lord Zheng. Their first child was a son named Jia Zhu, who entered school at the age of fourteen, then married and gave birth to a son, who died of an illness before the age of twenty. The second child was a young girl, born on the first day of the year. It was very surprising.--[[User:Mao You|Mao You]] ([[User talk:Mao You|talk]]) 08:45, 4 December 2021 (UTC)<br />
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==牟一心 Móu Yīxīn 英语语言文学（英美文学） 女 202120081516==<br />
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不想隔了十几年，又生了一位公子，说来更奇：一落胞胎，嘴里便衔下一块五彩晶莹的玉来，还有许多字迹。你道是新闻不是？”<br />
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==彭瑞雪 Péng Ruìxuě 法语语言文学 女 202120081517==<br />
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雨村笑道：“果然奇异。只怕这人的来历不小。”子兴冷笑道：“万人都这样说，因而他祖母爱如珍宝。那年周岁时，政老爷试他将来的志向，便将世上所有的东西摆了无数叫他抓。<br />
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==秦建安 Qín Jiànān 外国语言学及应用语言学 女 202120081518==<br />
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谁知他一概不取，伸手只把些脂粉钗环抓来玩弄。那政老爷便不喜欢，说将来不过酒色之徒，因此不甚爱惜。独那太君还是命根子一般。说<br />
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==邱婷婷 Qiū Tíngtíng 英语语言文学（语言学） 女 202120081519==<br />
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说来又奇：如今长了十来岁，虽然淘气异常，但聪明乖觉，百个不及他一个。说起孩子话来也奇，他说：‘女儿是水做的骨肉，男子是泥做的骨肉。我见了女儿便清爽，见了男子便觉浊臭逼人。’<br />
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Strange to say: now he is ten years old, abnormally naughty , but smart and clever, even better than one hundred other children of his age. What he says is also very odd. Once he said, ‘Girls are made of water, men of mud. He will feel debonaire when he see girls, but when he see men, what he can feel is only squalidness.’--[[User:Qiu Tingting|Qiu Tingting]] ([[User talk:Qiu Tingting|talk]]) 02:34, 5 December 2021 (UTC)<br />
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==饶金盈 Ráo Jīnyíng 英语语言文学（语言学） 女 202120081520==<br />
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你道好笑不好笑？将来色鬼无疑了。”雨村罕然厉色道：“非也。可惜你们不知道这人的来历，大约政老前辈也错以淫魔色鬼看待了。<br />
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“Have you realized the facetiosity of it? He or she will be beyond all doubt a lecher.” Yucun said with stern countenance: “ it is absolutely not the truth. It is a pity that you are insensible of the background of this person and the senior Zheng may also mistakenly regarded him or her as a lewd demon”.--[[User:Shi Liqing|Shi Liqing]] ([[User talk:Shi Liqing|talk]]) 00:59, 5 December 2021 (UTC)<br />
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==石丽青 Shí Lìqīng 英语语言文学（英美文学） 女 202120081521==<br />
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若非多读书识事，加以致知格物之功、悟道参玄之力者，不能知也。”子兴见他说得这样重大，忙请教其故。雨村道：“天地生人，除大仁大恶，馀者皆无大异。<br />
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If someone was not well-read, knowledge-inquiring and truth-enlightening, he or she would be ignorant of it. Zixing believed that Yucun took it so seriously that he was bursting with impatience to make clear the reasons within it. Yucun asserted: “the universe gives birth to mankind that boasts no differences except the benevolent and the evil.--[[User:Shi Liqing|Shi Liqing]] ([[User talk:Shi Liqing|talk]]) 00:36, 5 December 2021 (UTC)<br />
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==孙雅诗 Sūn Yǎshī 外国语言学及应用语言学 女 202120081522==<br />
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若大仁者则应运而生，大恶者则应劫而生；运生世治，劫生世危。尧、舜、禹、汤、文、武、周、召、孔、孟、董、韩、周、程、朱、张，皆应运而生者；<br />
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==王李菲 Wáng Lǐfēi 英语语言文学（英美文学） 女 202120081523==<br />
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蚩尤、共工、桀、纣、始皇、王莽、曹操、桓温、安禄山、秦桧等，皆应劫而生者。大仁者修治天下，大恶者扰乱天下。清明灵秀，天地之正气，仁者之所秉也；残忍乖僻，天地之邪气，恶者之所秉也。<br />
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==王逸凡 Wáng Yìfán 亚非语言文学 女 202120081524==<br />
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今当祚永运隆之日，太平无为之世，清明灵秀之气所秉者，上自朝廷，下至草野，比比皆是。所馀之秀气漫无所归，遂为甘露，为和风，洽然溉及四海。<br />
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In this day of eternal prosperity and peace and inaction, there are many people from the imperial court to the grasses who have been blessed with a clear, bright and spiritual spirit. The remainder of the spirit has no place to return to, so it has become a sweet dew and a harmonious breeze, which has irrigated the four seas.--[[User:Wang Yifan21|Wang Yifan21]] ([[User talk:Wang Yifan21|talk]]) 13:40, 4 December 2021 (UTC)<br />
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==王镇隆 Wáng Zhènlóng 英语语言文学（英美文学） 男 202120081525==<br />
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彼残忍乖邪之气，不能荡溢于光天化日之下，遂凝结充塞于深沟大壑之中。偶因风荡，或被云摧，略有摇动感发之意，一丝半缕误而逸出者，值灵秀之气适过，正不容邪，邪复妒正，两不相下；<br />
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==卫怡雯 Wèi Yíwén 英语语言文学（英美文学） 女 202120081526==<br />
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如风水雷电地中既遇，既不能消，又不能让，必致搏击掀发。既然发泄，那邪气亦必赋之于人。假使或男或女偶秉此气而生者，上则不能为仁人为君子，下亦不能为大凶大恶。<br />
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==魏楚璇 Wèi Chǔxuán 英语语言文学（英美文学） 女 202120081527==<br />
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置之千万人之中，其聪俊灵秀之气，则在千万人之上；其乖僻邪谬不近人情之态，又在千万人之下。若生于公侯富贵之家，则为情痴情种；若生于诗书清贫之族，则为逸士高人；纵然生于薄祚寒门，甚至为奇优，为名娼，亦断不至为走卒健仆，甘遭庸夫驱制。<br />
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==魏兆妍 Wèi Zhàoyán 英语语言文学（英美文学） 女 202120081528==<br />
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如前之许由、陶潜、阮籍、嵇康、刘伶、王谢二族、顾虎头、陈后主、唐明皇、宋徽宗、刘庭芝、温飞卿、米南宫、石曼卿、柳耆卿、秦少游，近日倪云林、唐伯虎、祝枝山，再如李龟年、黄幡绰、敬新磨、卓文君、红拂、薛涛、崔莺、朝云之流：此皆易地则同之人也。”<br />
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==吴婧悦 Wú Jìngyuè 俄语语言文学 女 202120081529==<br />
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子兴道：“依你说，成则公侯败则贼了？”雨村道：“正是这意。你还不知，我自革职以来，这两年遍游各省，也曾遇见两个异样孩子，所以方才你一说这宝玉，我就猜着了八九也是这一派人物。<br />
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==吴映红 Wú Yìnghóng 日语语言文学 女 202120081530==<br />
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不用远说，只这金陵城内钦差金陵省体仁院总裁甄家，你可知道？”子兴道：“谁人不知，这甄府就是贾府老亲，他们两家来往极亲热的。就是我也和他家往来非止一日了。”<br />
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==肖毅瑶 Xiāo Yìyáo 英语语言文学（英美文学） 女 202120081531==<br />
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雨村笑道：“去岁我在金陵，也曾有人荐我到甄府处馆。我进去看其光景，谁知他家那等荣贵，却是个富而好礼之家，倒是个难得之馆。但是这个学生虽是启蒙，却比一个举业的还劳神。<br />
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==谢佳芬 Xiè Jiāfēn 英语语言文学（英美文学） 女 202120081532==<br />
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说起来更可笑，他说：‘必得两个女儿陪着我读书，我方能认得字，心上也明白；不然，我心里自己糊涂。’又常对着跟他的小厮们说：‘这“女儿”两个字极尊贵极清净的，比那瑞兽珍禽、奇花异草更觉稀罕尊贵呢。<br />
Even it is more ridiculous when he said: "I must have two daughters to accompany me to study, so that I can recognize words and understand them in my heart; Otherwise, I will be confused. " He often said to his pageboys: "the word" daughter "is very noble and pure, which is more rare and noble than the auspicious animals, rare birds and exotic flowers and plants.<br />
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==谢庆琳 Xiè Qìnglín 俄语语言文学 女 202120081533==<br />
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你们这种浊口臭舌，万万不可唐突了这两个字，要紧，要紧！但凡要说的时节，必用净水香茶漱了口方可；设若失错，便要凿牙穿眼的。’其暴虐顽劣，种种异常。<br />
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==熊敏 Xióng Mǐn 英语语言文学（英美文学） 女 202120081534==<br />
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只放了学进去，见了那些女儿们，其温厚和平，聪敏文雅，竟变了一个样子。因此，他令尊也曾下死笞楚过几次，竟不能改。每打的吃疼不过时，他便姐姐妹妹的乱叫起来。<br />
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==徐敏赟 Xú Mǐnyūn 语言智能与跨文化传播研究 男 202120081535==<br />
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后来听得里面女儿们拿他取笑：‘因何打急了，只管叫姐妹作什么？莫不叫姐妹们去讨情讨饶？你岂不愧些？’他回答的最妙，他说：‘急痛之时，只叫姐姐妹妹字样，或可解疼，也未可知，因叫了一声，果觉疼得好些。<br />
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==颜静 Yán Jìng 语言智能与跨文化传播研究 女 202120081536==<br />
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遂得了秘法，每疼痛之极，便连叫姐妹起来了。’你说可笑不可笑？为他祖母溺爱不明，每因孙辱师责子，我所以辞了馆出来的。这等子弟，必不能守祖、父基业，从师友规劝的。只可惜他家几个好姊妹都是少有的。”<br />
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==颜莉莉 Yán Lìlì 国别 女 202120081537==<br />
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子兴道：“便是贾府中现在三个也不错。政老爷的长女名元春，因贤孝才德，选入宫作女史去了。二小姐乃是赦老爷姨娘所出，名迎春；三小姐政老爷庶出，名探春；四小姐乃宁府珍爷的胞妹，名惜春：<br />
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Zi Xing said, "The three girls in Jia's mansion are not bad either. Jia Zheng's eldest daughter was named Yuanchun. Because of her virtue and filial piety, she was chosen to be a female historian in the court. The second lady was born to Jia He'concubine, her name was Yingchun; The third lady was born to Jia Zheng's concubine and was named Tanchun. The fourth lady is the sister of Jia Zhen in Ning' mansion, named Xichun:<br />
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==颜子涵 Yán Zǐhán 国别 女 202120081538==<br />
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因史老夫人极爱孙女，都跟在祖母这边，一处读书，听得个个不错。”雨村道：“更妙在甄家风俗：女儿之名，亦皆从男子之名；不似别人家里，另外用这些‘春’、‘红’、‘香’、‘玉’等艳字。<br />
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==阳佳颖 Yáng Jiāyǐng 国别 女 202120081540==<br />
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何得贾府亦落此俗套？”子兴道：“不然。只因现今大小姐是正月初一所生，故名元春，馀者都从了‘春’字；上一排的却也是从弟兄而来的。现有对证：目今你贵东家林公的夫人，即荣府中赦、政二公的胞妹，在家时名字唤贾敏。<br />
<br />
==杨爱江 Yáng Àijiāng 英语语言文学（语言学） 女 202120081541==<br />
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不信时你回去细访可知。”雨村拍手笑道：“是极。我这女学生名叫黛玉，他读书凡‘敏’字，他皆念作‘密’字；写字遇着‘敏’字，亦减一二笔。我心中每每疑惑，今听你说，是为此无疑矣。<br />
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==杨堃 Yáng Kūn 法语语言文学 女 202120081542==<br />
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怪道我这女学生言语举止另是一样，不与凡女子相同，度其母不凡，故生此女。今知为荣府之外孙，又不足罕矣。可惜上月其母竟亡故了。”子兴叹道：“老姊妹三个，这是极小的，又没了；<br />
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==杨柳青 Yáng Liǔqīng 英语语言文学（英美文学） 女 202120081543==<br />
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长一辈的姊妹，一个也没了。只看这小一辈的将来的东床何如呢。”雨村道：“正是。方才说政公已有一个衔玉之子，又有长子所遗弱孙，这赦老竟无一个不成？”<br />
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==叶维杰 Yè Wéijié 国别 男 202120081544==<br />
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子兴道：“政公既有玉儿之后，其妾又生了一个，倒不知其好歹。只眼前现有二子一孙，却不知将来何如。若问那赦老爷，也有一子，名叫贾琏，今已二十多岁了，亲上做亲，娶的是政老爷夫人王氏内侄女，今已娶了四五年。<br />
<br />
Zi Xing says:“After Master Zheng had Yu er, his concubine gave birth to another child, don't know whether it is good or bad. Right now they already have two children and a grandson, but not knowing what should do in the future. Master Xie also has a son named Jia Lian, who is about 20 years old now. Jia Lian married Master Zheng's wife Wang's niece, it was an intermarry between their families, and it's been five years now.”<br />
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==易扬帆 Yì Yángfān 英语语言文学（英美文学） 女 202120081545==<br />
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这位琏爷身上现捐了个同知，也是不喜正务的；于世路上好机变，言谈去得，所以目今只在乃叔政老爷家住，帮着料理家务。谁知自娶了这位奶奶之后，倒上下无人不称颂他的夫人，琏爷倒退了一舍之地：模样又极标致，言谈又爽利，心机又极深细，竟是个男人万不及一的。”<br />
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==殷慧珍 Yīn Huìzhēn 英语语言文学（英美文学） 女 202120081546==<br />
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雨村听了，笑道：“可知我言不谬了。你我方才所说的这几个人，只怕都是那正邪两赋而来一路之人，未可知也。”子兴道：“正也罢，邪也罢，只顾算别人家的账，你也吃杯酒才好。”<br />
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==殷美达 Yīn Měidá 英语语言文学（语言学） 女 202120081547==<br />
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雨村道：“只顾说话，就多吃了几杯。”子兴笑道：“说着别人家的闲话，正好下酒，即多吃几杯何妨？”雨村向窗外看道：“天也晚了，仔细关了城，我们慢慢进城再谈，未为不可。”<br />
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==尹媛 Yǐn Yuán 英语语言文学（英美文学） 女 202120081548==<br />
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于是二人起身，算还酒钱。方欲走时，忽听得后面有人叫道：“雨村兄恭喜了！特来报个喜信的。”雨村忙回头看时……要知是谁，且听下回分解。<br />
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So they got up and paid for the wine. When they was leaving, he heard someone calling behind: "Congratulations! My friend Yucun. Someone brings a lucky message to you." Yucun looks back at once... Who is it? Please expect the next chapter--[[User:Yin Yuan|Yin Yuan]] ([[User talk:Yin Yuan|talk]]) 05:03, 5 December 2021 (UTC).<br />
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==詹若萱 Zhān Ruòxuān 英语语言文学（英美文学） 女 202120081549==<br />
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外班──清代会试考取进士后，留在朝中任官者称“京官”，分发外地任地方官者称“外班”。因新官分发到地方后要候补，按班次任官，故称“外班”。同寅皆侧目而视──同寅：即同僚。<br />
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==张秋怡 Zhāng Qiūyí 亚非语言文学 女 202120081550==<br />
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典出《尚书·虞书·皋陶谟》：“百僚师师，百工惟时……同寅协恭，和衷哉。”寅时是朝臣上朝之时，故称。 侧目而视：斜着眼看。语出《战国策·秦策一》：“(苏秦)将说楚王，路过洛阳。<br />
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==张扬 Zhāng Yáng 国别 男 202120081551==<br />
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父母闻之，清宫除道，张乐设饮，郊迎三十里；妻侧目而视，倾耳而听；嫂蛇行匍伏，四拜自跪而谢。”原表示敬畏。引申以表示愤怒或不齿。<br />
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==张怡然 Zhāng Yírán 俄语语言文学 女 202120081552==<br />
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维扬──扬州(在今江苏省)的别称。大禹所划分的“九州”之一。典出《尚书·夏书·禹贡》：“淮海惟扬州。”“惟”通“维”。<br />
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==钟义菲 Zhōng Yìfēi 英语语言文学（英美文学） 女 202120081553==<br />
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后人从“惟扬州”截取“惟扬”，又以“维”代“惟”，遂成“维扬”。如北朝周·庾信《哀江南赋》：“淮海维扬，三千馀里。”探花──科举考试中殿试(最高一级考试)一甲第三名(第一名为状元，第二名为榜眼)。<br />
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Later generations intercepted "Weiyang" from "weiyangzhou" and replaced "Weiyang" with "Wei", so it became "Weiyang". For example, Yuxin's Fu on mourning the south of the Yangtze River in the Northern Dynasty said, "the Huaihai sea is vast, more than 3000 miles." Tanhua—the third place in the first grade of the palace examination (the highest level examination) (the first place is called Zhuangyuan and the second place is called Bangyan）--[[User:Zhong Yifei|Zhong Yifei]] ([[User talk:Zhong Yifei|talk]]) 09:04, 4 December 2021 (UTC)<br />
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==钟雨露 Zhōng Yǔlù 英语语言文学（英美文学） 女 202120081554==<br />
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本于唐代的“探花使”，亦称“探花郎”。唐·李淖《秦中岁时记》：“进士杏园初宴，谓之探花宴。差少俊二人为探花使，遍游名园，若它人先折花，二使皆被罚。”<br />
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==周玖 Zhōu Jiǔ 英语语言文学（英美文学） 女 202120081555==<br />
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又宋·魏泰《东轩笔录》卷六：“进士及第后，例期集一月……又选最年少者二人为探花使，赋诗，世谓之探花郎。”<br />
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==周俊辉 Zhōu Jùnhuī 法语语言文学 女 202120081556==<br />
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兰台寺大夫──指专管弹劾的御史。兰台是汉朝宫内藏书之所，由御史大夫主管，故后世将御史台别称“兰台”，将御史府别称“兰台寺”，将御史别称“兰台寺大夫”。<br />
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==周巧 Zhōu Qiǎo 英语语言文学（语言学） 女 202120081557==<br />
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列侯──古代爵名。在秦称“彻侯”，为二十四级爵位中的最高一级。至汉代为避汉武帝刘彻之讳，改为“通侯”。“通”与“彻”同义，是改名不改义。“通侯”之意是表示受爵者功勋通于王室。<br />
Marquis - Ancient Baron name. In Qin Dynasty, it was called "chehou", which was the highest among twenty-four levels. In the Han Dynasty, in order to avoid the taboo of Liu Che, Emperor of the Han Dynasty, it was changed to "tonghou". "Tong" is synonymous with "Che" in Chinese, in this way changing the name without changing the meaning. "Tong Hou" means that the recipient has done meritorious services to the royal family.--[[User:Zhou Qiao1|Zhou Qiao1]] ([[User talk:Zhou Qiao1|talk]]) 09:17, 4 December 2021 (UTC)<br />
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==周清 Zhōu Qīng 法语语言文学 女 202120081558==<br />
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后又改为“列侯”，表示序列之意。见《汉书·高帝纪下》颜师古注。清代并无此爵，只是借指侯爵。清代爵位分公、侯、伯、子、男，侯爵为第二等。<br />
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==周小雪 Zhōu Xiǎoxuě 日语语言文学 女 202120081559==<br />
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膝下荒凉──意谓子女稀少，尤无儿子。 膝下：这里指子女。因幼儿多倚偎于父母膝旁，故称。《孝经·圣治》：“故亲生之膝下，以养父母日严。”唐玄宗注：“亲犹爱也，膝下谓孩童之时也。” <br />
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==朱素珍 Zhū Sùzhēn 英语语言文学（语言学） 女 202120081561==<br />
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荒凉：形容因子女稀少而家庭显得清冷凄凉。西席──古人座次以右(西)为尊，故右席为宾客和塾师之位，坐西面东，故称幕宾和塾师为“西席”或“西宾”。<br />
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==邹岳丽 Zōu Yuèlí 日语语言文学 女 202120081562==<br />
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清·梁章钜《称谓录》卷八：“汉明帝尊桓荣以师礼，上幸太常府，令荣坐东面(坐西面东)，设几。故师曰西席。”这里指家庭教师。“身后”一联──身后有馀：是说馀年还很长(“身后”不可解作死后)。<br />
Liang Zhangju, Qing Dynasty, wrote in Volume VIII of 《Appellation records》: "Emperor Mingdi After respected Huan Rong and treated him with teacher courtesy. He once visited Taichang mansion in person, asked Huan Rong to sit in the East, set a table and a walking stick。Therefore, master said it was a seat in the West." Here refers to a tutor.A couplet of "behind you" - there is surplus behind you: it means that the remaining years are still very long ("behind you" cannot be interpreted as after death).--[[User:Zou Yueli|Zou Yueli]] ([[User talk:Zou Yueli|talk]]) 14:23, 4 December 2021 (UTC)<br />
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==Nadia 202011080004==<br />
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忘缩手：是说不肯收手，还要争名夺利。 <br />
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==Mahzad Heydarian 玛莎 202021080004==<br />
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无路：走投无路。此联是说世人大多只顾眼前，不顾将来，等到走投无路，后悔无及。<br />
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==Mariam toure 2020GBJ002301==<br />
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刹──梵语音译省称，意译为佛塔的柱形尖顶，故又称“佛柱”。<br />
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==Rouabah Soumaya 202121080001==<br />
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引申为佛寺。贾复──东汉南阳冠军(今河南邓州市西北)人，累官至左将军，并封胶东侯。<br />
Extended to Buddhist temple. Jia Fu——A native of Nanyang Champion of the Eastern Han Dynasty (now northwest of Dengzhou City, Henan Province), he was tired from general to the left and sealed Donghou in Jiao.<br />
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==Muhammad Numan 202121080002==<br />
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《后汉书》有传。姓贾的成千上万，贾雨村却只拉千年前的贾复为一家，足见其拉大旗作虎皮之势利小人肺肝。<br />
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==Atta Ur Rahman 202121080003==<br />
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百足之虫，死而不僵——典出三国魏·曹冏《六代论》：<br />
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==Muhammad Saqib Mehran 202121080004==<br />
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“故语曰：‘百足之虫，死而不僵。’扶之者众也。”<br />
The old saying goes:'Hundred-legged worms die but are not stiff.' There are many who support them."<br />
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[[File:Example.jpg]]==Zohaib Chand 202121080005==<br />
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比喻世家大族虽然衰败，因家底雄厚，依傍众多，表面上仍能维持繁荣景象。<br />
It is a metaphor that despite the decline of the aristocratic family, because of the strong family background and numerous support, it can still maintain its prosperity on the surface.<br />
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==Jawad Ahmad 202121080006==<br />
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百足：虫名，即马陆。长约一寸，躯干由多节构成，每节有足一对或二对，切断后仍能蠕动。<br />
English: Centipede, Insect name, arthropods. Length, around an inch, Body is composed of multiple sections, each section has one or two pairs of feet, after cutting still can squirm.<br />
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==Nizam Uddin 202121080007==<br />
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僵：倒下。安富尊荣──语出《孟子·尽心上》：<br />
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==Öncü 202121080008==<br />
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“君子居是国也，其君用之，则安富尊荣。”<br />
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==Akira Jantarat 202121080009==<br />
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原意是君子因辅佐国君功勋卓著而享受荣华富贵。<br />
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==Benjamin Wellsand 202111080118==<br />
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这里反用其意，意谓不劳而获，安享荣华富贵。<br />
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==Asep Budiman 202111080020==<br />
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钟鸣鼎食——语出唐·王勃《滕王阁序》：“闾阎扑地，钟鸣鼎食之家。”<br />
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==Ei Mon Kyaw 202111080021==<br />
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古代贵族鸣钟列鼎而食。这里借以形容富贵豪华。</div>Chen Huinihttps://bou.de/u/index.php?title=20211208_homework&diff=12906320211208 homework2021-12-05T07:56:27Z<p>Chen Huini: /* 蔡珠凤 Cài Zhūfèng 法语语言文学 女 202120081477 */</p>
<hr />
<div>Quicklinks: [[Introduction_to_Translation_Studies_2021|Back to course homepage]] [https://bou.de/u/wiki/uvu:Community_Portal#Frequently_asked_questions_FAQ FAQ] [https://bou.de/u/wiki/uvu:Community_Portal Manual] [[20210926_homework|Back to all homework webpages overview]] [[20220112_final_exam|final exam page]]<br />
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==陈静 Chén Jìng 国别 女 202020080595==<br />
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说了一会话，临走又送我二两银子。”甄家娘子听了，不觉感伤。一夜无话。<br />
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==蔡珠凤 Cài Zhūfèng 法语语言文学 女 202120081477==<br />
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次日，早有雨村遣人送了两封银子、四匹锦缎，答谢甄家娘子；又一封密书与封肃，托他向甄家娘子要那娇杏作二房。封肃喜得眉开眼笑，巴不得去奉承太爷，便在女儿前一力撺掇。<br />
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The next day, Yucun sent two bundles of silver and four brocades to thank the Zhen lady; Another secret letter to Feng Su asked him to ask the Zhen lady for the delicate Jiaoxing as concubine. Feng Su was so happy that he was eager to flatter the Lord, so he tried his best to encourage his daughter.<br />
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 14:01, 4 December 2021 (UTC)<br />
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The next day, Yucun sent two bundles of silver and four brocades to thank Zhen lady; He also sent another secret letter to Feng Su, asking him to ask Jiaoxing, the daughter of Zhen Lady, to be his second wife. Feng Su was so happy that he was eager to flatter the Lord, so he tried his best to encourage his daughter.--[[User:Chen Huini|Chen Huini]] ([[User talk:Chen Huini|talk]]) 07:56, 5 December 2021 (UTC)Chen Huini<br />
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==陈惠妮 Chén Huìnī 英语语言文学（英美文学） 女 202120081479==<br />
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当夜用一乘小轿，便把娇杏送进衙内去了。雨村欢喜，自不必言；又封百金赠与封肃，又送甄家娘子许多礼物，令其且自过活，以待访寻女儿下落。<br />
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One evening, Jiaoxing was sent to prison by a small sedan carriage. Undoutedbly, Yuchun was very pleased and gave hundreds of golds to Fengsu and many gifts to Zhen's wife so that she can live by herself untill her daugther was found.--[[User:Chen Huini|Chen Huini]] ([[User talk:Chen Huini|talk]]) 11:17, 4 December 2021 (UTC)Chen Huini<br />
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==陈湘琼 Chén Xiāngqióng 外国语言学及应用语言学 女 202120081480==<br />
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却说娇杏那丫头，便是当年回顾雨村的，因偶然一看，便弄出这段奇缘，也是意想不到之事。谁知他命运两济：不承望自到雨村身边只一年，便生一子；又半载，雨村嫡配忽染疾下世，雨村便将他扶作正室夫人。<br />
==陈心怡 Chén Xīnyí 翻译学 女 202120081481==<br />
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正是：偶因一回顾，便为人上人。原来雨村因那年士隐赠银之后，他于十六日便起身赴京。大比之期，十分得意，中了进士，选入外班，今已升了本县太爷。<br />
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Jiaoxing looked back at Jia Yucun out of curiosity, not out of love. But because of such a chance, from a little girl who was serviced, she became a rich lady who serviced others. It turns out that Yucun because of silver given by Shiyin in that year, he left for Beijing on the 16th. He was lucky enough to won the scholar in the great competition and was selected into the outer class, now has been promoted to the county magistrate. --[[User:Chen Xinyi|Chen Xinyi]] ([[User talk:Chen Xinyi|talk]]) 09:33, 4 December 2021 (UTC)<br />
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==程杨 Chéng Yáng 英语语言文学（英美文学） 女 202120081482==<br />
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虽才干优长，未免贪酷，且恃才侮上，那同寅皆侧目而视。不上一年，便被上司参了一本，说他貌似有才，性实狡猾；又题了一两件徇庇蠹役、交结乡绅之事。<br />
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==丁旋 Dīng Xuán 英语语言文学（英美文学） 女 202120081483==<br />
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龙颜大怒，即命革职。部文一到，本府各官无不喜悦。那雨村虽十分惭恨，面上却全无一点怨色，仍是嘻笑自若。<br />
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==杜莉娜 Dù Lìnuó 英语语言文学（语言学） 女 202120081484==<br />
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交代过了公事，将历年所积的宦囊，并家属人等，送至原籍，安顿妥当了，却自己担风袖月，游览天下胜迹。那日偶又游至维扬地方，闻得今年盐政点的是林如海。<br />
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After giving official business,leaving the family his accumulated salary for several years and settled them in native home, he had given up high official positions and riches and travelled the famous historical sites everywhere.One day he arrived Weiyang（Yangzhou，Jiangsu Province，China）by accident and heared about the present salt administration officer was Lin Ruhai Salzinspektor.--[[User:Du Lina|Du Lina]] ([[User talk:Du Lina|talk]]) 04:23, 5 December 2021 (UTC)<br />
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==付红岩 Fù Hóngyán 英语语言文学（英美文学） 女 202120081485==<br />
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这林如海姓林名海，表字如海，乃是前科的探花，今已升兰台寺大夫，本贯姑苏人氏，今钦点为巡盐御史，到任未久。<br />
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==付诗雨 Fù Shīyǔ 日语语言文学 女 202120081486==<br />
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原来这林如海之祖，也曾袭过列侯的，今到如海，业经五世。起初只袭三世，因当今隆恩盛德，额外加恩，至如海之父又袭了一代，到了如海便从科第出身。<br />
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In fact, the ancestors of Lin Ju-hai had, from years back, successively inherited the title of Marquis, which rank, by its present descent to Ju-hai, had already been enjoyed by five generations. When first conferred, the hereditary right to the title had been limited to three generations; but of late years, by an act of magnanimous favour and generous beneficence, extraordinary bounty had been superadded; and on the arrival of the succession to the father of Ju-hai, the right had been extended to another degree. It had now descended to Ju-hai, who had, besides this title of nobility, begun his career as a successful graduate. --[[User:Fu Shiyu|Fu Shiyu]] ([[User talk:Fu Shiyu|talk]]) 00:55, 5 December 2021 (UTC)<br />
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==高蜜 Gāo Mì 翻译学 女 202120081487==<br />
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虽系世禄之家，却是书香之族。只可惜这林家支庶不盛，人丁有限，虽有几门，却与如海俱是堂族，没甚亲支嫡派的。今如海年已五十，只有一个三岁之子，又于去岁亡了；<br />
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==宫博雅 Gōng Bóyǎ 俄语语言文学 女 202120081488==<br />
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虽有几房姬妾，奈命中无子，亦无可如何之事。只嫡妻贾氏生得一女，乳名黛玉，年方五岁，夫妻爱之如掌上明珠。见他生得聪明俊秀，也欲使他识几个字，不过假充养子，聊解膝下荒凉之叹。<br />
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Although he had several concubines, he was doomed to have no son (to inherit the family line). Only lady Jia, his legal wife, gave birth to a daughter, Daiyu, aged five. The couple doted on their daughter like a pearl on the palm of their eyes. Lin Ruhai wanted to teach him to read, because he was smart and handsome, and Lin Ruhai wanted to ease the loneliness of not having a son by pretending to adopt him.--[[User:Gong Boya|Gong Boya]] ([[User talk:Gong Boya|talk]]) 05:55, 5 December 2021 (UTC)<br />
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==何芩 Hé Qín 翻译学 女 202120081489==<br />
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且说贾雨村在旅店偶感风寒，愈后又因盘费不继，正欲得一个居停之所，以为息肩之地。偶遇两个旧友，认得新盐政，知他正要请一西席教训女儿，遂将雨村荐进衙门去。<br />
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==胡舒情 Hú Shūqíng 英语语言文学（语言学） 女 202120081490==<br />
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这女学生年纪幼小，身体又弱，功课不限多寡，其馀不过两个伴读丫鬟，故雨村十分省力，正好养病。看看又是一载有馀，不料女学生之母贾氏夫人一病而亡。<br />
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==黄锦云 Huáng Jǐnyún 英语语言文学（语言学） 女 202120081491==<br />
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女学生奉侍汤药，守丧尽礼，过于哀痛，素本怯弱，因此旧病复发，有好些时不曾上学。雨村闲居无聊，每当风日晴和，饭后便出来闲步。这一日偶至郊外，意欲赏鉴那村野风光。<br />
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==黄逸妍 Huáng Yìyán 外国语言学及应用语言学 女 202120081492==<br />
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信步至一山环水漩、茂林修竹之处，隐隐有座庙宇，门巷倾颓，墙垣剥落。有额题曰“智通寺”，门旁又有一副旧破的对联云：身后有馀忘缩手，眼前无路想回头。<br />
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==曾俊霖 Zēng Jùnlín 国别 男 202120081493==<br />
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雨村看了，因想道：“这两句文虽甚浅，其意则深。也曾游过些名山大刹，倒不曾见过这话头。其中想必有个翻过筋斗来的，也未可知。<br />
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==黄柱梁 Huáng Zhùliáng 国别 男 202120081493==<br />
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何不进去一访？”走入看时，只有一个龙锺老僧在那里煮粥。雨村见了，却不在意。及至问他两句话，那老僧既聋且昏，又齿落舌钝，所答非所问。雨村不耐烦，仍退出来。<br />
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==金晓童 Jīn Xiǎotóng 202120081494==<br />
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意欲到那村肆中沽饮三杯，以助野趣，于是移步行来。刚入肆门，只见座上吃酒之客，有一人起身大笑，接了出来，口内说：“奇遇，奇遇！”<br />
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==邝艳丽 Kuàng Yànl 英语语言文学（语言学） 女 202120081495==<br />
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雨村忙看时，此人是都中古董行中贸易，姓冷号子兴的，旧日在都相识。雨村最赞这冷子兴是个有作为大本领的人，这子兴又借雨村斯文之名，故二人最相投契。<br />
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==李爱璇 Lǐ Àixuán 英语语言文学（语言学） 女 202120081496==<br />
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雨村忙亦笑问：“老兄何日到此？弟竟不知。今日偶遇，真奇缘也！”子兴道：“去年岁底到家，今因还要入都，从此顺路找个敝友，说一句话。<br />
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==李瑞洋 Lǐ Ruìyáng 英语语言文学（英美文学） 女 202120081497==<br />
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承他的情，留我多住两日。我也无甚紧事，且盘桓两日，待月半时也就起身了。今日敝友有事，我因闲走到此，不期这样巧遇。”一面说，一面让雨村同席坐了，另整上酒肴来。<br />
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==李姗 Lǐ Shān 英语语言文学（英美文学） 女 202120081498==<br />
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二人闲谈慢饮，叙些别后之事。雨村因问：“近日都中可有新闻没有？”子兴道：“倒没有什么新闻，倒是老先生的贵同宗家出了一件小小的异事。”<br />
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Over drinking, they two talked about some plans of the near future after the farewell. Then Yucun asked: Is there anything new in the capital city? Zixing answered，“Nothing new. But in your dignified remote relative's house there is indeed a strange thing.”--[[User:Li Shan|Li Shan]] ([[User talk:Li Shan|talk]]) 14:49, 4 December 2021 (UTC)<br />
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==李双 Lǐ Shuāng 翻译学 女 202120081499==<br />
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雨村笑道：“弟族中无人在都，何谈及此？”子兴笑道：“你们同姓，岂非一族？”雨村问：“是谁家？”子兴笑道：“荣国贾府中，可也不玷辱老先生的门楣了。”<br />
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==李文璇 Lǐ Wénxuán 英语语言文学（英美文学） 女 202120081500==<br />
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雨村道：“原来是他家。若论起来，寒族人丁却自不少，东汉贾复以来，支派繁盛，各省皆有，谁能逐细考查？若论荣国一支，却是同谱。但他那等荣耀，我们不便去认他，故越发生疏了。”<br />
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Yucun said: "It's his house. If discussed explicitly, the people of Han's family were of great quantity since the Eastern Han Dynasty of Jiafu. Their branches were numerous in each province, who can examine one by one? If only discussed the branch of Rongguo, they were the same. But the Rongguo were glorious, it was inconvenient for us to make a connection with them, so we were getting more and more unfamiliar. --[[User:Li Wenxuan|Li Wenxuan]] ([[User talk:Li Wenxuan|talk]]) 08:22, 4 December 2021 (UTC)<br />
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==李雯 Lǐ Wén 英语语言文学（英美文学） 女 202120081501==<br />
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子兴叹道：“老先生休这样说。如今的这荣、宁两府，也都萧索了，不比先时的光景。”雨村道：“当日宁、荣两宅人口也极多，如何便萧索了呢？”子兴道：“正是，说来也话长。”<br />
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==李新星 Lǐ Xīnxīng 亚非语言文学 女 202120081503==<br />
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雨村道：“去岁我到金陵时，因欲游览六朝遗迹，那日进了石头城，从他宅门前经过：街东是宁国府，街西是荣国府，二宅相连，竟将大半条街占了。<br />
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==李怡 Lǐ Yí 法语语言文学 女 202120081504==<br />
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大门外虽冷落无人，隔着围墙一望，里面厅殿楼阁，也还都峥嵘轩峻；就是后边一带花园里，树木山石，也都还有葱蔚洇润之气：那里像个衰败之家？”<br />
Although deserted outside the gate, across the wall to see the hall hall pavilions, are also lofty xuan Jun; Even in the garden at the back, the trees and rocks were all luxuriant: it did not look at all like a run-down house--[[User:Li Yi|Li Yi]] ([[User talk:Li Yi|talk]]) 01:57, 5 December 2021 (UTC)<br />
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==刘沛婷 Liú Pèitíng 英语语言文学（英美文学） 女 202120081505==<br />
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子兴笑道：“亏你是进士出身，原来不通。古人有言：‘百足之虫，死而不僵。’如今虽说不似先年那样兴盛，较之平常仕宦人家，到底气象不同。<br />
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==刘胜楠 Liú Shèngnán 翻译学 女 202120081506==<br />
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如今生齿日繁，事务日盛，主仆上下都是安富尊荣，运筹谋画的竟无一个；那日用排场，又不能将就省俭。如今外面的架子虽没很倒，内囊却也尽上来了。这也是小事。<br />
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==刘薇 Liú Wēi 国别 女 202120081507==<br />
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更有一件大事：谁知这样钟鸣鼎食的人家儿，如今养的儿孙，竟一代不如一代了。”雨村听说，也道：“这样诗礼之家，岂有不善教育之理？别门不知，只说这宁、荣两宅，是最教子有方的，何至如此？”<br />
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==刘晓 Liú Xiǎo 英语语言文学（英美文学） 女 202120081508==<br />
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子兴叹道：“正说的是这两门呢！等我告诉你：当日宁国公是一母同胞弟兄两个。宁公居长，生了两个儿子。宁公死后，长子贾代化袭了官，也养了两个儿子：长子贾敷，八九岁上死了；<br />
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==刘越 Liú Yuè 亚非语言文学 女 202120081509==<br />
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只剩了一个次子贾敬，袭了官，如今一味好道，只爱烧丹炼汞，别事一概不管。幸而早年留下一个儿子，名唤贾珍，因他父亲一心想作神仙，把官倒让他袭了。<br />
Only his second son, Jia Jing, succeeded him as the official. Now he devoted himself only to Taoism and alchemy, and did nothing else. Fortunately, in his early years, he had left a son named Jia Zhen, for his father had set his heart on becoming a fairy, so he succeeded to the official. --[[User:Liu Yue|Liu Yue]] ([[User talk:Liu Yue|talk]]) 07:30, 4 December 2021 (UTC)<br />
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==刘运心 Liú Yùnxīn 英语语言文学（英美文学） 女 202120081510==<br />
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他父亲又不肯住在家里，只在都中城外，和那些道士们胡羼。这位珍爷也生了一个儿子，今年才十六岁，名叫贾蓉。如今敬老爷不管事了。<br />
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==罗安怡 Luó Ānyí 英语语言文学（英美文学） 女 202120081511==<br />
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这珍爷那里干正事，只一味高乐不了，把那宁国府竟翻过来了，也没有敢来管他的人。再说荣府你听，方才所说异事就出在这里。<br />
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==罗曦 Luó Xī 英语语言文学（英美文学） 女 202120081512==<br />
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自荣公死后，长子贾代善袭了官，娶的是金陵世家史侯的小姐为妻。生了两个儿子：长名贾赦，次名贾政。如今代善早已去世，太夫人尚在。<br />
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==马新 Mǎ Xīn 外国语言学及应用语言学 女 202120081513==<br />
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长子贾赦袭了官，为人却也中平，也不管理家事。惟有次子贾政，自幼酷喜读书，为人端方正直。祖父锺爱，原要他从科甲出身。<br />
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The eldest son, Jia She, inherited the official position from his ancestors but he was not top-notch and did not manage the family affairs as well. Only his second son, Jia Zheng, loved to read since childhood and was a man of upright. His grandfather (Jia Yuan) like him the most and originally planed to let him take the imperial examination before becoming an official.--[[User:Ma Xin|Ma Xin]] ([[User talk:Ma Xin|talk]]) 08:02, 4 December 2021 (UTC)<br />
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Their elder son Jia She inherited the official title; he was moderate and often remained neutral, and did not manage the family affairs. Only the younger son, Jia Zheng, was fond of studying as a child and was a man of upright so that he was his grandfather’s (Jia Yuan) favorite, and he hoped to make a career for himself through the imperial examinations. --[[User:Mao Yawen|Mao Yawen]] ([[User talk:Mao Yawen|talk]]) 09:05, 4 December 2021 (UTC)<br />
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==毛雅文 Máo Yǎwén 英语语言文学（英美文学） 女 202120081514==<br />
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不料代善临终遗本一上，皇上怜念先臣，即叫长子袭了官；又问还有几个儿子，立刻引见，又将这政老爷赐了个额外主事职衔，叫他入部习学，如今现已升了员外郎。<br />
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Unexpectedly, when Jia Daishan died, he left a valedictory memorial, and the Emperor, out of memory and regard for his former minister, not only conferred the official title on his elder son but also asked what other sons there were and ordered them to be introduced to the palace immediately. The Emperor also bestowed the rank of Assistant Secretary on Jia Zheng, and as an additional favor gave him instructions to familiarize himself with affairs in one of the ministries. He has now risen to the rank of Under-Secretary. --[[User:Mao Yawen|Mao Yawen]] ([[User talk:Mao Yawen|talk]]) 08:40, 4 December 2021 (UTC)<br />
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==毛优 Máo Yōu 俄语语言文学 女 202120081515==<br />
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这政老爷的夫人王氏，头胎生的公子名叫贾珠，十四岁进学，后来娶了妻，生了子，不到二十岁，一病就死了。第二胎生了一位小姐，生在大年初一，就奇了。<br />
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Mrs. Wang --- the wife of Lord Zheng. Their first child was a son named Jia Zhu, who entered school at the age of fourteen, then married and gave birth to a son, who died of an illness before the age of twenty. The second child was a young girl, born on the first day of the year. It was very surprising.--[[User:Mao You|Mao You]] ([[User talk:Mao You|talk]]) 08:45, 4 December 2021 (UTC)<br />
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==牟一心 Móu Yīxīn 英语语言文学（英美文学） 女 202120081516==<br />
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不想隔了十几年，又生了一位公子，说来更奇：一落胞胎，嘴里便衔下一块五彩晶莹的玉来，还有许多字迹。你道是新闻不是？”<br />
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==彭瑞雪 Péng Ruìxuě 法语语言文学 女 202120081517==<br />
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雨村笑道：“果然奇异。只怕这人的来历不小。”子兴冷笑道：“万人都这样说，因而他祖母爱如珍宝。那年周岁时，政老爷试他将来的志向，便将世上所有的东西摆了无数叫他抓。<br />
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==秦建安 Qín Jiànān 外国语言学及应用语言学 女 202120081518==<br />
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谁知他一概不取，伸手只把些脂粉钗环抓来玩弄。那政老爷便不喜欢，说将来不过酒色之徒，因此不甚爱惜。独那太君还是命根子一般。说<br />
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==邱婷婷 Qiū Tíngtíng 英语语言文学（语言学） 女 202120081519==<br />
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说来又奇：如今长了十来岁，虽然淘气异常，但聪明乖觉，百个不及他一个。说起孩子话来也奇，他说：‘女儿是水做的骨肉，男子是泥做的骨肉。我见了女儿便清爽，见了男子便觉浊臭逼人。’<br />
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Strange to say: now he is ten years old, abnormally naughty , but smart and clever, even better than one hundred other children of his age. What he says is also very odd. Once he said, ‘Girls are made of water, men of mud. He will feel debonaire when he see girls, but when he see men, what he can feel is only squalidness.’--[[User:Qiu Tingting|Qiu Tingting]] ([[User talk:Qiu Tingting|talk]]) 02:34, 5 December 2021 (UTC)<br />
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==饶金盈 Ráo Jīnyíng 英语语言文学（语言学） 女 202120081520==<br />
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你道好笑不好笑？将来色鬼无疑了。”雨村罕然厉色道：“非也。可惜你们不知道这人的来历，大约政老前辈也错以淫魔色鬼看待了。<br />
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“Have you realized the facetiosity of it? He or she will be beyond all doubt a lecher.” Yucun said with stern countenance: “ it is absolutely not the truth. It is a pity that you are insensible of the background of this person and the senior Zheng may also mistakenly regarded him or her as a lewd demon”.--[[User:Shi Liqing|Shi Liqing]] ([[User talk:Shi Liqing|talk]]) 00:59, 5 December 2021 (UTC)<br />
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==石丽青 Shí Lìqīng 英语语言文学（英美文学） 女 202120081521==<br />
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若非多读书识事，加以致知格物之功、悟道参玄之力者，不能知也。”子兴见他说得这样重大，忙请教其故。雨村道：“天地生人，除大仁大恶，馀者皆无大异。<br />
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If someone was not well-read, knowledge-inquiring and truth-enlightening, he or she would be ignorant of it. Zixing believed that Yucun took it so seriously that he was bursting with impatience to make clear the reasons within it. Yucun asserted: “the universe gives birth to mankind that boasts no differences except the benevolent and the evil.--[[User:Shi Liqing|Shi Liqing]] ([[User talk:Shi Liqing|talk]]) 00:36, 5 December 2021 (UTC)<br />
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==孙雅诗 Sūn Yǎshī 外国语言学及应用语言学 女 202120081522==<br />
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若大仁者则应运而生，大恶者则应劫而生；运生世治，劫生世危。尧、舜、禹、汤、文、武、周、召、孔、孟、董、韩、周、程、朱、张，皆应运而生者；<br />
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==王李菲 Wáng Lǐfēi 英语语言文学（英美文学） 女 202120081523==<br />
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蚩尤、共工、桀、纣、始皇、王莽、曹操、桓温、安禄山、秦桧等，皆应劫而生者。大仁者修治天下，大恶者扰乱天下。清明灵秀，天地之正气，仁者之所秉也；残忍乖僻，天地之邪气，恶者之所秉也。<br />
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==王逸凡 Wáng Yìfán 亚非语言文学 女 202120081524==<br />
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今当祚永运隆之日，太平无为之世，清明灵秀之气所秉者，上自朝廷，下至草野，比比皆是。所馀之秀气漫无所归，遂为甘露，为和风，洽然溉及四海。<br />
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In this day of eternal prosperity and peace and inaction, there are many people from the imperial court to the grasses who have been blessed with a clear, bright and spiritual spirit. The remainder of the spirit has no place to return to, so it has become a sweet dew and a harmonious breeze, which has irrigated the four seas.--[[User:Wang Yifan21|Wang Yifan21]] ([[User talk:Wang Yifan21|talk]]) 13:40, 4 December 2021 (UTC)<br />
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==王镇隆 Wáng Zhènlóng 英语语言文学（英美文学） 男 202120081525==<br />
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彼残忍乖邪之气，不能荡溢于光天化日之下，遂凝结充塞于深沟大壑之中。偶因风荡，或被云摧，略有摇动感发之意，一丝半缕误而逸出者，值灵秀之气适过，正不容邪，邪复妒正，两不相下；<br />
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==卫怡雯 Wèi Yíwén 英语语言文学（英美文学） 女 202120081526==<br />
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如风水雷电地中既遇，既不能消，又不能让，必致搏击掀发。既然发泄，那邪气亦必赋之于人。假使或男或女偶秉此气而生者，上则不能为仁人为君子，下亦不能为大凶大恶。<br />
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==魏楚璇 Wèi Chǔxuán 英语语言文学（英美文学） 女 202120081527==<br />
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置之千万人之中，其聪俊灵秀之气，则在千万人之上；其乖僻邪谬不近人情之态，又在千万人之下。若生于公侯富贵之家，则为情痴情种；若生于诗书清贫之族，则为逸士高人；纵然生于薄祚寒门，甚至为奇优，为名娼，亦断不至为走卒健仆，甘遭庸夫驱制。<br />
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==魏兆妍 Wèi Zhàoyán 英语语言文学（英美文学） 女 202120081528==<br />
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如前之许由、陶潜、阮籍、嵇康、刘伶、王谢二族、顾虎头、陈后主、唐明皇、宋徽宗、刘庭芝、温飞卿、米南宫、石曼卿、柳耆卿、秦少游，近日倪云林、唐伯虎、祝枝山，再如李龟年、黄幡绰、敬新磨、卓文君、红拂、薛涛、崔莺、朝云之流：此皆易地则同之人也。”<br />
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==吴婧悦 Wú Jìngyuè 俄语语言文学 女 202120081529==<br />
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子兴道：“依你说，成则公侯败则贼了？”雨村道：“正是这意。你还不知，我自革职以来，这两年遍游各省，也曾遇见两个异样孩子，所以方才你一说这宝玉，我就猜着了八九也是这一派人物。<br />
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==吴映红 Wú Yìnghóng 日语语言文学 女 202120081530==<br />
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不用远说，只这金陵城内钦差金陵省体仁院总裁甄家，你可知道？”子兴道：“谁人不知，这甄府就是贾府老亲，他们两家来往极亲热的。就是我也和他家往来非止一日了。”<br />
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==肖毅瑶 Xiāo Yìyáo 英语语言文学（英美文学） 女 202120081531==<br />
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雨村笑道：“去岁我在金陵，也曾有人荐我到甄府处馆。我进去看其光景，谁知他家那等荣贵，却是个富而好礼之家，倒是个难得之馆。但是这个学生虽是启蒙，却比一个举业的还劳神。<br />
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==谢佳芬 Xiè Jiāfēn 英语语言文学（英美文学） 女 202120081532==<br />
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说起来更可笑，他说：‘必得两个女儿陪着我读书，我方能认得字，心上也明白；不然，我心里自己糊涂。’又常对着跟他的小厮们说：‘这“女儿”两个字极尊贵极清净的，比那瑞兽珍禽、奇花异草更觉稀罕尊贵呢。<br />
Even it is more ridiculous when he said: "I must have two daughters to accompany me to study, so that I can recognize words and understand them in my heart; Otherwise, I will be confused. " He often said to his pageboys: "the word" daughter "is very noble and pure, which is more rare and noble than the auspicious animals, rare birds and exotic flowers and plants.<br />
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==谢庆琳 Xiè Qìnglín 俄语语言文学 女 202120081533==<br />
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你们这种浊口臭舌，万万不可唐突了这两个字，要紧，要紧！但凡要说的时节，必用净水香茶漱了口方可；设若失错，便要凿牙穿眼的。’其暴虐顽劣，种种异常。<br />
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==熊敏 Xióng Mǐn 英语语言文学（英美文学） 女 202120081534==<br />
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只放了学进去，见了那些女儿们，其温厚和平，聪敏文雅，竟变了一个样子。因此，他令尊也曾下死笞楚过几次，竟不能改。每打的吃疼不过时，他便姐姐妹妹的乱叫起来。<br />
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==徐敏赟 Xú Mǐnyūn 语言智能与跨文化传播研究 男 202120081535==<br />
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后来听得里面女儿们拿他取笑：‘因何打急了，只管叫姐妹作什么？莫不叫姐妹们去讨情讨饶？你岂不愧些？’他回答的最妙，他说：‘急痛之时，只叫姐姐妹妹字样，或可解疼，也未可知，因叫了一声，果觉疼得好些。<br />
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==颜静 Yán Jìng 语言智能与跨文化传播研究 女 202120081536==<br />
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遂得了秘法，每疼痛之极，便连叫姐妹起来了。’你说可笑不可笑？为他祖母溺爱不明，每因孙辱师责子，我所以辞了馆出来的。这等子弟，必不能守祖、父基业，从师友规劝的。只可惜他家几个好姊妹都是少有的。”<br />
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==颜莉莉 Yán Lìlì 国别 女 202120081537==<br />
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子兴道：“便是贾府中现在三个也不错。政老爷的长女名元春，因贤孝才德，选入宫作女史去了。二小姐乃是赦老爷姨娘所出，名迎春；三小姐政老爷庶出，名探春；四小姐乃宁府珍爷的胞妹，名惜春：<br />
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Zi Xing said, "The three girls in Jia's mansion are not bad either. Jia Zheng's eldest daughter was named Yuanchun. Because of her virtue and filial piety, she was chosen to be a female historian in the court. The second lady was born to Jia He'concubine, her name was Yingchun; The third lady was born to Jia Zheng's concubine and was named Tanchun. The fourth lady is the sister of Jia Zhen in Ning' mansion, named Xichun:<br />
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==颜子涵 Yán Zǐhán 国别 女 202120081538==<br />
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因史老夫人极爱孙女，都跟在祖母这边，一处读书，听得个个不错。”雨村道：“更妙在甄家风俗：女儿之名，亦皆从男子之名；不似别人家里，另外用这些‘春’、‘红’、‘香’、‘玉’等艳字。<br />
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==阳佳颖 Yáng Jiāyǐng 国别 女 202120081540==<br />
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何得贾府亦落此俗套？”子兴道：“不然。只因现今大小姐是正月初一所生，故名元春，馀者都从了‘春’字；上一排的却也是从弟兄而来的。现有对证：目今你贵东家林公的夫人，即荣府中赦、政二公的胞妹，在家时名字唤贾敏。<br />
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==杨爱江 Yáng Àijiāng 英语语言文学（语言学） 女 202120081541==<br />
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不信时你回去细访可知。”雨村拍手笑道：“是极。我这女学生名叫黛玉，他读书凡‘敏’字，他皆念作‘密’字；写字遇着‘敏’字，亦减一二笔。我心中每每疑惑，今听你说，是为此无疑矣。<br />
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==杨堃 Yáng Kūn 法语语言文学 女 202120081542==<br />
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怪道我这女学生言语举止另是一样，不与凡女子相同，度其母不凡，故生此女。今知为荣府之外孙，又不足罕矣。可惜上月其母竟亡故了。”子兴叹道：“老姊妹三个，这是极小的，又没了；<br />
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==杨柳青 Yáng Liǔqīng 英语语言文学（英美文学） 女 202120081543==<br />
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长一辈的姊妹，一个也没了。只看这小一辈的将来的东床何如呢。”雨村道：“正是。方才说政公已有一个衔玉之子，又有长子所遗弱孙，这赦老竟无一个不成？”<br />
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==叶维杰 Yè Wéijié 国别 男 202120081544==<br />
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子兴道：“政公既有玉儿之后，其妾又生了一个，倒不知其好歹。只眼前现有二子一孙，却不知将来何如。若问那赦老爷，也有一子，名叫贾琏，今已二十多岁了，亲上做亲，娶的是政老爷夫人王氏内侄女，今已娶了四五年。<br />
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Zi Xing says:“After Master Zheng had Yu er, his concubine gave birth to another child, don't know whether it is good or bad. Right now they already have two children and a grandson, but not knowing what should do in the future. Master Xie also has a son named Jia Lian, who is about 20 years old now. Jia Lian married Master Zheng's wife Wang's niece, it was an intermarry between their families, and it's been five years now.”<br />
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==易扬帆 Yì Yángfān 英语语言文学（英美文学） 女 202120081545==<br />
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这位琏爷身上现捐了个同知，也是不喜正务的；于世路上好机变，言谈去得，所以目今只在乃叔政老爷家住，帮着料理家务。谁知自娶了这位奶奶之后，倒上下无人不称颂他的夫人，琏爷倒退了一舍之地：模样又极标致，言谈又爽利，心机又极深细，竟是个男人万不及一的。”<br />
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==殷慧珍 Yīn Huìzhēn 英语语言文学（英美文学） 女 202120081546==<br />
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雨村听了，笑道：“可知我言不谬了。你我方才所说的这几个人，只怕都是那正邪两赋而来一路之人，未可知也。”子兴道：“正也罢，邪也罢，只顾算别人家的账，你也吃杯酒才好。”<br />
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==殷美达 Yīn Měidá 英语语言文学（语言学） 女 202120081547==<br />
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雨村道：“只顾说话，就多吃了几杯。”子兴笑道：“说着别人家的闲话，正好下酒，即多吃几杯何妨？”雨村向窗外看道：“天也晚了，仔细关了城，我们慢慢进城再谈，未为不可。”<br />
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==尹媛 Yǐn Yuán 英语语言文学（英美文学） 女 202120081548==<br />
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于是二人起身，算还酒钱。方欲走时，忽听得后面有人叫道：“雨村兄恭喜了！特来报个喜信的。”雨村忙回头看时……要知是谁，且听下回分解。<br />
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So they got up and paid for the wine. When they was leaving, he heard someone calling behind: "Congratulations! My friend Yucun. Someone brings a lucky message to you." Yucun looks back at once... Who is it? Please expect the next chapter--[[User:Yin Yuan|Yin Yuan]] ([[User talk:Yin Yuan|talk]]) 05:03, 5 December 2021 (UTC).<br />
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==詹若萱 Zhān Ruòxuān 英语语言文学（英美文学） 女 202120081549==<br />
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外班──清代会试考取进士后，留在朝中任官者称“京官”，分发外地任地方官者称“外班”。因新官分发到地方后要候补，按班次任官，故称“外班”。同寅皆侧目而视──同寅：即同僚。<br />
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==张秋怡 Zhāng Qiūyí 亚非语言文学 女 202120081550==<br />
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典出《尚书·虞书·皋陶谟》：“百僚师师，百工惟时……同寅协恭，和衷哉。”寅时是朝臣上朝之时，故称。 侧目而视：斜着眼看。语出《战国策·秦策一》：“(苏秦)将说楚王，路过洛阳。<br />
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==张扬 Zhāng Yáng 国别 男 202120081551==<br />
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父母闻之，清宫除道，张乐设饮，郊迎三十里；妻侧目而视，倾耳而听；嫂蛇行匍伏，四拜自跪而谢。”原表示敬畏。引申以表示愤怒或不齿。<br />
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==张怡然 Zhāng Yírán 俄语语言文学 女 202120081552==<br />
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维扬──扬州(在今江苏省)的别称。大禹所划分的“九州”之一。典出《尚书·夏书·禹贡》：“淮海惟扬州。”“惟”通“维”。<br />
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==钟义菲 Zhōng Yìfēi 英语语言文学（英美文学） 女 202120081553==<br />
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后人从“惟扬州”截取“惟扬”，又以“维”代“惟”，遂成“维扬”。如北朝周·庾信《哀江南赋》：“淮海维扬，三千馀里。”探花──科举考试中殿试(最高一级考试)一甲第三名(第一名为状元，第二名为榜眼)。<br />
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Later generations intercepted "Weiyang" from "weiyangzhou" and replaced "Weiyang" with "Wei", so it became "Weiyang". For example, Yuxin's Fu on mourning the south of the Yangtze River in the Northern Dynasty said, "the Huaihai sea is vast, more than 3000 miles." Tanhua—the third place in the first grade of the palace examination (the highest level examination) (the first place is called Zhuangyuan and the second place is called Bangyan）--[[User:Zhong Yifei|Zhong Yifei]] ([[User talk:Zhong Yifei|talk]]) 09:04, 4 December 2021 (UTC)<br />
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==钟雨露 Zhōng Yǔlù 英语语言文学（英美文学） 女 202120081554==<br />
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本于唐代的“探花使”，亦称“探花郎”。唐·李淖《秦中岁时记》：“进士杏园初宴，谓之探花宴。差少俊二人为探花使，遍游名园，若它人先折花，二使皆被罚。”<br />
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==周玖 Zhōu Jiǔ 英语语言文学（英美文学） 女 202120081555==<br />
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又宋·魏泰《东轩笔录》卷六：“进士及第后，例期集一月……又选最年少者二人为探花使，赋诗，世谓之探花郎。”<br />
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==周俊辉 Zhōu Jùnhuī 法语语言文学 女 202120081556==<br />
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兰台寺大夫──指专管弹劾的御史。兰台是汉朝宫内藏书之所，由御史大夫主管，故后世将御史台别称“兰台”，将御史府别称“兰台寺”，将御史别称“兰台寺大夫”。<br />
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==周巧 Zhōu Qiǎo 英语语言文学（语言学） 女 202120081557==<br />
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列侯──古代爵名。在秦称“彻侯”，为二十四级爵位中的最高一级。至汉代为避汉武帝刘彻之讳，改为“通侯”。“通”与“彻”同义，是改名不改义。“通侯”之意是表示受爵者功勋通于王室。<br />
Marquis - Ancient Baron name. In Qin Dynasty, it was called "chehou", which was the highest among twenty-four levels. In the Han Dynasty, in order to avoid the taboo of Liu Che, Emperor of the Han Dynasty, it was changed to "tonghou". "Tong" is synonymous with "Che" in Chinese, in this way changing the name without changing the meaning. "Tong Hou" means that the recipient has done meritorious services to the royal family.--[[User:Zhou Qiao1|Zhou Qiao1]] ([[User talk:Zhou Qiao1|talk]]) 09:17, 4 December 2021 (UTC)<br />
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==周清 Zhōu Qīng 法语语言文学 女 202120081558==<br />
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后又改为“列侯”，表示序列之意。见《汉书·高帝纪下》颜师古注。清代并无此爵，只是借指侯爵。清代爵位分公、侯、伯、子、男，侯爵为第二等。<br />
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==周小雪 Zhōu Xiǎoxuě 日语语言文学 女 202120081559==<br />
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膝下荒凉──意谓子女稀少，尤无儿子。 膝下：这里指子女。因幼儿多倚偎于父母膝旁，故称。《孝经·圣治》：“故亲生之膝下，以养父母日严。”唐玄宗注：“亲犹爱也，膝下谓孩童之时也。” <br />
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==朱素珍 Zhū Sùzhēn 英语语言文学（语言学） 女 202120081561==<br />
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荒凉：形容因子女稀少而家庭显得清冷凄凉。西席──古人座次以右(西)为尊，故右席为宾客和塾师之位，坐西面东，故称幕宾和塾师为“西席”或“西宾”。<br />
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==邹岳丽 Zōu Yuèlí 日语语言文学 女 202120081562==<br />
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清·梁章钜《称谓录》卷八：“汉明帝尊桓荣以师礼，上幸太常府，令荣坐东面(坐西面东)，设几。故师曰西席。”这里指家庭教师。“身后”一联──身后有馀：是说馀年还很长(“身后”不可解作死后)。<br />
Liang Zhangju, Qing Dynasty, wrote in Volume VIII of 《Appellation records》: "Emperor Mingdi After respected Huan Rong and treated him with teacher courtesy. He once visited Taichang mansion in person, asked Huan Rong to sit in the East, set a table and a walking stick。Therefore, master said it was a seat in the West." Here refers to a tutor.A couplet of "behind you" - there is surplus behind you: it means that the remaining years are still very long ("behind you" cannot be interpreted as after death).--[[User:Zou Yueli|Zou Yueli]] ([[User talk:Zou Yueli|talk]]) 14:23, 4 December 2021 (UTC)<br />
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==Nadia 202011080004==<br />
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忘缩手：是说不肯收手，还要争名夺利。 <br />
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==Mahzad Heydarian 玛莎 202021080004==<br />
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无路：走投无路。此联是说世人大多只顾眼前，不顾将来，等到走投无路，后悔无及。<br />
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==Mariam toure 2020GBJ002301==<br />
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刹──梵语音译省称，意译为佛塔的柱形尖顶，故又称“佛柱”。<br />
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==Rouabah Soumaya 202121080001==<br />
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引申为佛寺。贾复──东汉南阳冠军(今河南邓州市西北)人，累官至左将军，并封胶东侯。<br />
Extended to Buddhist temple. Jia Fu——A native of Nanyang Champion of the Eastern Han Dynasty (now northwest of Dengzhou City, Henan Province), he was tired from general to the left and sealed Donghou in Jiao.<br />
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==Muhammad Numan 202121080002==<br />
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《后汉书》有传。姓贾的成千上万，贾雨村却只拉千年前的贾复为一家，足见其拉大旗作虎皮之势利小人肺肝。<br />
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==Atta Ur Rahman 202121080003==<br />
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百足之虫，死而不僵——典出三国魏·曹冏《六代论》：<br />
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==Muhammad Saqib Mehran 202121080004==<br />
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“故语曰：‘百足之虫，死而不僵。’扶之者众也。”<br />
The old saying goes:'Hundred-legged worms die but are not stiff.' There are many who support them."<br />
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[[File:Example.jpg]]==Zohaib Chand 202121080005==<br />
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比喻世家大族虽然衰败，因家底雄厚，依傍众多，表面上仍能维持繁荣景象。<br />
It is a metaphor that despite the decline of the aristocratic family, because of the strong family background and numerous support, it can still maintain its prosperity on the surface.<br />
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==Jawad Ahmad 202121080006==<br />
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百足：虫名，即马陆。长约一寸，躯干由多节构成，每节有足一对或二对，切断后仍能蠕动。<br />
English: Centipede, Insect name, arthropods. Length, around an inch, Body is composed of multiple sections, each section has one or two pairs of feet, after cutting still can squirm.<br />
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==Nizam Uddin 202121080007==<br />
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僵：倒下。安富尊荣──语出《孟子·尽心上》：<br />
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==Öncü 202121080008==<br />
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“君子居是国也，其君用之，则安富尊荣。”<br />
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==Akira Jantarat 202121080009==<br />
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原意是君子因辅佐国君功勋卓著而享受荣华富贵。<br />
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==Benjamin Wellsand 202111080118==<br />
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这里反用其意，意谓不劳而获，安享荣华富贵。<br />
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==Asep Budiman 202111080020==<br />
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钟鸣鼎食——语出唐·王勃《滕王阁序》：“闾阎扑地，钟鸣鼎食之家。”<br />
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==Ei Mon Kyaw 202111080021==<br />
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古代贵族鸣钟列鼎而食。这里借以形容富贵豪华。</div>Chen Huinihttps://bou.de/u/index.php?title=20211208_homework&diff=12896120211208 homework2021-12-04T11:17:26Z<p>Chen Huini: /* 陈惠妮 Chén Huìnī 英语语言文学（英美文学） 女 202120081479 */</p>
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<div>Quicklinks: [[Introduction_to_Translation_Studies_2021|Back to course homepage]] [https://bou.de/u/wiki/uvu:Community_Portal#Frequently_asked_questions_FAQ FAQ] [https://bou.de/u/wiki/uvu:Community_Portal Manual] [[20210926_homework|Back to all homework webpages overview]] [[20220112_final_exam|final exam page]]<br />
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==陈静 Chén Jìng 国别 女 202020080595==<br />
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说了一会话，临走又送我二两银子。”甄家娘子听了，不觉感伤。一夜无话。<br />
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==蔡珠凤 Cài Zhūfèng 法语语言文学 女 202120081477==<br />
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次日，早有雨村遣人送了两封银子、四匹锦缎，答谢甄家娘子；又一封密书与封肃，托他向甄家娘子要那娇杏作二房。封肃喜得眉开眼笑，巴不得去奉承太爷，便在女儿前一力撺掇。<br />
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==陈惠妮 Chén Huìnī 英语语言文学（英美文学） 女 202120081479==<br />
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当夜用一乘小轿，便把娇杏送进衙内去了。雨村欢喜，自不必言；又封百金赠与封肃，又送甄家娘子许多礼物，令其且自过活，以待访寻女儿下落。<br />
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One evening, Jiaoxing was sent to prison by a small sedan carriage. Undoutedbly, Yuchun was very pleased and gave hundreds of golds to Fengsu and many gifts to Zhen's wife so that she can live by herself untill her daugther was found.--[[User:Chen Huini|Chen Huini]] ([[User talk:Chen Huini|talk]]) 11:17, 4 December 2021 (UTC)Chen Huini<br />
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==陈湘琼 Chén Xiāngqióng 外国语言学及应用语言学 女 202120081480==<br />
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却说娇杏那丫头，便是当年回顾雨村的，因偶然一看，便弄出这段奇缘，也是意想不到之事。谁知他命运两济：不承望自到雨村身边只一年，便生一子；又半载，雨村嫡配忽染疾下世，雨村便将他扶作正室夫人。<br />
==陈心怡 Chén Xīnyí 翻译学 女 202120081481==<br />
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正是：偶因一回顾，便为人上人。原来雨村因那年士隐赠银之后，他于十六日便起身赴京。大比之期，十分得意，中了进士，选入外班，今已升了本县太爷。<br />
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Jiaoxing looked back at Jia Yucun out of curiosity, not out of love. But because of such a chance, from a little girl who was serviced, she became a rich lady who serviced others. It turns out that Yucun because of silver given by Shiyin in that year, he left for Beijing on the 16th. He was lucky enough to won the scholar in the great competition and was selected into the outer class, now has been promoted to the county magistrate. --[[User:Chen Xinyi|Chen Xinyi]] ([[User talk:Chen Xinyi|talk]]) 09:33, 4 December 2021 (UTC)<br />
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==程杨 Chéng Yáng 英语语言文学（英美文学） 女 202120081482==<br />
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虽才干优长，未免贪酷，且恃才侮上，那同寅皆侧目而视。不上一年，便被上司参了一本，说他貌似有才，性实狡猾；又题了一两件徇庇蠹役、交结乡绅之事。<br />
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==丁旋 Dīng Xuán 英语语言文学（英美文学） 女 202120081483==<br />
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龙颜大怒，即命革职。部文一到，本府各官无不喜悦。那雨村虽十分惭恨，面上却全无一点怨色，仍是嘻笑自若。<br />
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==杜莉娜 Dù Lìnuó 英语语言文学（语言学） 女 202120081484==<br />
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交代过了公事，将历年所积的宦囊，并家属人等，送至原籍，安顿妥当了，却自己担风袖月，游览天下胜迹。那日偶又游至维扬地方，闻得今年盐政点的是林如海。<br />
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==付红岩 Fù Hóngyán 英语语言文学（英美文学） 女 202120081485==<br />
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这林如海姓林名海，表字如海，乃是前科的探花，今已升兰台寺大夫，本贯姑苏人氏，今钦点为巡盐御史，到任未久。<br />
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==付诗雨 Fù Shīyǔ 日语语言文学 女 202120081486==<br />
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原来这林如海之祖，也曾袭过列侯的，今到如海，业经五世。起初只袭三世，因当今隆恩盛德，额外加恩，至如海之父又袭了一代，到了如海便从科第出身。<br />
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==高蜜 Gāo Mì 翻译学 女 202120081487==<br />
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虽系世禄之家，却是书香之族。只可惜这林家支庶不盛，人丁有限，虽有几门，却与如海俱是堂族，没甚亲支嫡派的。今如海年已五十，只有一个三岁之子，又于去岁亡了；<br />
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==宫博雅 Gōng Bóyǎ 俄语语言文学 女 202120081488==<br />
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虽有几房姬妾，奈命中无子，亦无可如何之事。只嫡妻贾氏生得一女，乳名黛玉，年方五岁，夫妻爱之如掌上明珠。见他生得聪明俊秀，也欲使他识几个字，不过假充养子，聊解膝下荒凉之叹。<br />
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==何芩 Hé Qín 翻译学 女 202120081489==<br />
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且说贾雨村在旅店偶感风寒，愈后又因盘费不继，正欲得一个居停之所，以为息肩之地。偶遇两个旧友，认得新盐政，知他正要请一西席教训女儿，遂将雨村荐进衙门去。<br />
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==胡舒情 Hú Shūqíng 英语语言文学（语言学） 女 202120081490==<br />
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这女学生年纪幼小，身体又弱，功课不限多寡，其馀不过两个伴读丫鬟，故雨村十分省力，正好养病。看看又是一载有馀，不料女学生之母贾氏夫人一病而亡。<br />
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==黄锦云 Huáng Jǐnyún 英语语言文学（语言学） 女 202120081491==<br />
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女学生奉侍汤药，守丧尽礼，过于哀痛，素本怯弱，因此旧病复发，有好些时不曾上学。雨村闲居无聊，每当风日晴和，饭后便出来闲步。这一日偶至郊外，意欲赏鉴那村野风光。<br />
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==黄逸妍 Huáng Yìyán 外国语言学及应用语言学 女 202120081492==<br />
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信步至一山环水漩、茂林修竹之处，隐隐有座庙宇，门巷倾颓，墙垣剥落。有额题曰“智通寺”，门旁又有一副旧破的对联云：身后有馀忘缩手，眼前无路想回头。<br />
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==曾俊霖 Zēng Jùnlín 国别 男 202120081493==<br />
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雨村看了，因想道：“这两句文虽甚浅，其意则深。也曾游过些名山大刹，倒不曾见过这话头。其中想必有个翻过筋斗来的，也未可知。<br />
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==黄柱梁 Huáng Zhùliáng 国别 男 202120081493==<br />
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何不进去一访？”走入看时，只有一个龙锺老僧在那里煮粥。雨村见了，却不在意。及至问他两句话，那老僧既聋且昏，又齿落舌钝，所答非所问。雨村不耐烦，仍退出来。<br />
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==金晓童 Jīn Xiǎotóng 202120081494==<br />
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意欲到那村肆中沽饮三杯，以助野趣，于是移步行来。刚入肆门，只见座上吃酒之客，有一人起身大笑，接了出来，口内说：“奇遇，奇遇！”<br />
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==邝艳丽 Kuàng Yànl 英语语言文学（语言学） 女 202120081495==<br />
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雨村忙看时，此人是都中古董行中贸易，姓冷号子兴的，旧日在都相识。雨村最赞这冷子兴是个有作为大本领的人，这子兴又借雨村斯文之名，故二人最相投契。<br />
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==李爱璇 Lǐ Àixuán 英语语言文学（语言学） 女 202120081496==<br />
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雨村忙亦笑问：“老兄何日到此？弟竟不知。今日偶遇，真奇缘也！”子兴道：“去年岁底到家，今因还要入都，从此顺路找个敝友，说一句话。<br />
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==李瑞洋 Lǐ Ruìyáng 英语语言文学（英美文学） 女 202120081497==<br />
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承他的情，留我多住两日。我也无甚紧事，且盘桓两日，待月半时也就起身了。今日敝友有事，我因闲走到此，不期这样巧遇。”一面说，一面让雨村同席坐了，另整上酒肴来。<br />
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==李姗 Lǐ Shān 英语语言文学（英美文学） 女 202120081498==<br />
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二人闲谈慢饮，叙些别后之事。雨村因问：“近日都中可有新闻没有？”子兴道：“倒没有什么新闻，倒是老先生的贵同宗家出了一件小小的异事。”<br />
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==李双 Lǐ Shuāng 翻译学 女 202120081499==<br />
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雨村笑道：“弟族中无人在都，何谈及此？”子兴笑道：“你们同姓，岂非一族？”雨村问：“是谁家？”子兴笑道：“荣国贾府中，可也不玷辱老先生的门楣了。”<br />
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==李文璇 Lǐ Wénxuán 英语语言文学（英美文学） 女 202120081500==<br />
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雨村道：“原来是他家。若论起来，寒族人丁却自不少，东汉贾复以来，支派繁盛，各省皆有，谁能逐细考查？若论荣国一支，却是同谱。但他那等荣耀，我们不便去认他，故越发生疏了。”<br />
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Yucun said: "It's his house. If discussed explicitly, the people of Han's family were of great quantity since the Eastern Han Dynasty of Jiafu. Their branches were numerous in each province, who can examine one by one? If only discussed the branch of Rongguo, they were the same. But the Rongguo were glorious, it was inconvenient for us to make a connection with them, so we were getting more and more unfamiliar. --[[User:Li Wenxuan|Li Wenxuan]] ([[User talk:Li Wenxuan|talk]]) 08:22, 4 December 2021 (UTC)<br />
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==李雯 Lǐ Wén 英语语言文学（英美文学） 女 202120081501==<br />
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子兴叹道：“老先生休这样说。如今的这荣、宁两府，也都萧索了，不比先时的光景。”雨村道：“当日宁、荣两宅人口也极多，如何便萧索了呢？”子兴道：“正是，说来也话长。”<br />
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==李新星 Lǐ Xīnxīng 亚非语言文学 女 202120081503==<br />
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雨村道：“去岁我到金陵时，因欲游览六朝遗迹，那日进了石头城，从他宅门前经过：街东是宁国府，街西是荣国府，二宅相连，竟将大半条街占了。<br />
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==李怡 Lǐ Yí 法语语言文学 女 202120081504==<br />
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大门外虽冷落无人，隔着围墙一望，里面厅殿楼阁，也还都峥嵘轩峻；就是后边一带花园里，树木山石，也都还有葱蔚洇润之气：那里像个衰败之家？”<br />
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==刘沛婷 Liú Pèitíng 英语语言文学（英美文学） 女 202120081505==<br />
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子兴笑道：“亏你是进士出身，原来不通。古人有言：‘百足之虫，死而不僵。’如今虽说不似先年那样兴盛，较之平常仕宦人家，到底气象不同。<br />
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==刘胜楠 Liú Shèngnán 翻译学 女 202120081506==<br />
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如今生齿日繁，事务日盛，主仆上下都是安富尊荣，运筹谋画的竟无一个；那日用排场，又不能将就省俭。如今外面的架子虽没很倒，内囊却也尽上来了。这也是小事。<br />
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==刘薇 Liú Wēi 国别 女 202120081507==<br />
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更有一件大事：谁知这样钟鸣鼎食的人家儿，如今养的儿孙，竟一代不如一代了。”雨村听说，也道：“这样诗礼之家，岂有不善教育之理？别门不知，只说这宁、荣两宅，是最教子有方的，何至如此？”<br />
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==刘晓 Liú Xiǎo 英语语言文学（英美文学） 女 202120081508==<br />
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子兴叹道：“正说的是这两门呢！等我告诉你：当日宁国公是一母同胞弟兄两个。宁公居长，生了两个儿子。宁公死后，长子贾代化袭了官，也养了两个儿子：长子贾敷，八九岁上死了；<br />
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==刘越 Liú Yuè 亚非语言文学 女 202120081509==<br />
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只剩了一个次子贾敬，袭了官，如今一味好道，只爱烧丹炼汞，别事一概不管。幸而早年留下一个儿子，名唤贾珍，因他父亲一心想作神仙，把官倒让他袭了。<br />
Only his second son, Jia Jing, succeeded him as the official. Now he devoted himself only to Taoism and alchemy, and did nothing else. Fortunately, in his early years, he had left a son named Jia Zhen, for his father had set his heart on becoming a fairy, so he succeeded to the official. --[[User:Liu Yue|Liu Yue]] ([[User talk:Liu Yue|talk]]) 07:30, 4 December 2021 (UTC)<br />
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==刘运心 Liú Yùnxīn 英语语言文学（英美文学） 女 202120081510==<br />
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他父亲又不肯住在家里，只在都中城外，和那些道士们胡羼。这位珍爷也生了一个儿子，今年才十六岁，名叫贾蓉。如今敬老爷不管事了。<br />
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==罗安怡 Luó Ānyí 英语语言文学（英美文学） 女 202120081511==<br />
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这珍爷那里干正事，只一味高乐不了，把那宁国府竟翻过来了，也没有敢来管他的人。再说荣府你听，方才所说异事就出在这里。<br />
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==罗曦 Luó Xī 英语语言文学（英美文学） 女 202120081512==<br />
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自荣公死后，长子贾代善袭了官，娶的是金陵世家史侯的小姐为妻。生了两个儿子：长名贾赦，次名贾政。如今代善早已去世，太夫人尚在。<br />
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==马新 Mǎ Xīn 外国语言学及应用语言学 女 202120081513==<br />
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长子贾赦袭了官，为人却也中平，也不管理家事。惟有次子贾政，自幼酷喜读书，为人端方正直。祖父锺爱，原要他从科甲出身。<br />
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The eldest son, Jia She, inherited the official position from his ancestors but he was not top-notch and did not manage the family affairs as well. Only his second son, Jia Zheng, loved to read since childhood and was a man of upright. His grandfather (Jia Yuan) like him the most and originally planed to let him take the imperial examination before becoming an official.--[[User:Ma Xin|Ma Xin]] ([[User talk:Ma Xin|talk]]) 08:02, 4 December 2021 (UTC)<br />
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Their elder son Jia She inherited the official title; he was moderate and often remained neutral, and did not manage the family affairs. Only the younger son, Jia Zheng, was fond of studying as a child and was a man of upright so that he was his grandfather’s (Jia Yuan) favorite, and he hoped to make a career for himself through the imperial examinations. --[[User:Mao Yawen|Mao Yawen]] ([[User talk:Mao Yawen|talk]]) 09:05, 4 December 2021 (UTC)<br />
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==毛雅文 Máo Yǎwén 英语语言文学（英美文学） 女 202120081514==<br />
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不料代善临终遗本一上，皇上怜念先臣，即叫长子袭了官；又问还有几个儿子，立刻引见，又将这政老爷赐了个额外主事职衔，叫他入部习学，如今现已升了员外郎。<br />
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Unexpectedly, when Jia Daishan died, he left a valedictory memorial, and the Emperor, out of memory and regard for his former minister, not only conferred the official title on his elder son but also asked what other sons there were and ordered them to be introduced to the palace immediately. The Emperor also bestowed the rank of Assistant Secretary on Jia Zheng, and as an additional favor gave him instructions to familiarize himself with affairs in one of the ministries. He has now risen to the rank of Under-Secretary. --[[User:Mao Yawen|Mao Yawen]] ([[User talk:Mao Yawen|talk]]) 08:40, 4 December 2021 (UTC)<br />
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==毛优 Máo Yōu 俄语语言文学 女 202120081515==<br />
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这政老爷的夫人王氏，头胎生的公子名叫贾珠，十四岁进学，后来娶了妻，生了子，不到二十岁，一病就死了。第二胎生了一位小姐，生在大年初一，就奇了。<br />
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Mrs. Wang --- the wife of Lord Zheng. Their first child was a son named Jia Zhu, who entered school at the age of fourteen, then married and gave birth to a son, who died of an illness before the age of twenty. The second child was a young girl, born on the first day of the year. It was very surprising.--[[User:Mao You|Mao You]] ([[User talk:Mao You|talk]]) 08:45, 4 December 2021 (UTC)<br />
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==牟一心 Móu Yīxīn 英语语言文学（英美文学） 女 202120081516==<br />
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不想隔了十几年，又生了一位公子，说来更奇：一落胞胎，嘴里便衔下一块五彩晶莹的玉来，还有许多字迹。你道是新闻不是？”<br />
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==彭瑞雪 Péng Ruìxuě 法语语言文学 女 202120081517==<br />
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雨村笑道：“果然奇异。只怕这人的来历不小。”子兴冷笑道：“万人都这样说，因而他祖母爱如珍宝。那年周岁时，政老爷试他将来的志向，便将世上所有的东西摆了无数叫他抓。<br />
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==秦建安 Qín Jiànān 外国语言学及应用语言学 女 202120081518==<br />
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谁知他一概不取，伸手只把些脂粉钗环抓来玩弄。那政老爷便不喜欢，说将来不过酒色之徒，因此不甚爱惜。独那太君还是命根子一般。说<br />
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==邱婷婷 Qiū Tíngtíng 英语语言文学（语言学） 女 202120081519==<br />
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来又奇：如今长了十来岁，虽然淘气异常，但聪明乖觉，百个不及他一个。说起孩子话来也奇，他说：‘女儿是水做的骨肉，男子是泥做的骨肉。我见了女儿便清爽，见了男子便觉浊臭逼人。’<br />
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==饶金盈 Ráo Jīnyíng 英语语言文学（语言学） 女 202120081520==<br />
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你道好笑不好笑？将来色鬼无疑了。”雨村罕然厉色道：“非也。可惜你们不知道这人的来历，大约政老前辈也错以淫魔色鬼看待了。<br />
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==石丽青 Shí Lìqīng 英语语言文学（英美文学） 女 202120081521==<br />
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若非多读书识事，加以致知格物之功、悟道参玄之力者，不能知也。”子兴见他说得这样重大，忙请教其故。雨村道：“天地生人，除大仁大恶，馀者皆无大异。<br />
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==孙雅诗 Sūn Yǎshī 外国语言学及应用语言学 女 202120081522==<br />
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若大仁者则应运而生，大恶者则应劫而生；运生世治，劫生世危。尧、舜、禹、汤、文、武、周、召、孔、孟、董、韩、周、程、朱、张，皆应运而生者；<br />
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==王李菲 Wáng Lǐfēi 英语语言文学（英美文学） 女 202120081523==<br />
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蚩尤、共工、桀、纣、始皇、王莽、曹操、桓温、安禄山、秦桧等，皆应劫而生者。大仁者修治天下，大恶者扰乱天下。清明灵秀，天地之正气，仁者之所秉也；残忍乖僻，天地之邪气，恶者之所秉也。<br />
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==王逸凡 Wáng Yìfán 亚非语言文学 女 202120081524==<br />
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今当祚永运隆之日，太平无为之世，清明灵秀之气所秉者，上自朝廷，下至草野，比比皆是。所馀之秀气漫无所归，遂为甘露，为和风，洽然溉及四海。<br />
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==王镇隆 Wáng Zhènlóng 英语语言文学（英美文学） 男 202120081525==<br />
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彼残忍乖邪之气，不能荡溢于光天化日之下，遂凝结充塞于深沟大壑之中。偶因风荡，或被云摧，略有摇动感发之意，一丝半缕误而逸出者，值灵秀之气适过，正不容邪，邪复妒正，两不相下；<br />
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==卫怡雯 Wèi Yíwén 英语语言文学（英美文学） 女 202120081526==<br />
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如风水雷电地中既遇，既不能消，又不能让，必致搏击掀发。既然发泄，那邪气亦必赋之于人。假使或男或女偶秉此气而生者，上则不能为仁人为君子，下亦不能为大凶大恶。<br />
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==魏楚璇 Wèi Chǔxuán 英语语言文学（英美文学） 女 202120081527==<br />
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置之千万人之中，其聪俊灵秀之气，则在千万人之上；其乖僻邪谬不近人情之态，又在千万人之下。若生于公侯富贵之家，则为情痴情种；若生于诗书清贫之族，则为逸士高人；纵然生于薄祚寒门，甚至为奇优，为名娼，亦断不至为走卒健仆，甘遭庸夫驱制。<br />
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==魏兆妍 Wèi Zhàoyán 英语语言文学（英美文学） 女 202120081528==<br />
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如前之许由、陶潜、阮籍、嵇康、刘伶、王谢二族、顾虎头、陈后主、唐明皇、宋徽宗、刘庭芝、温飞卿、米南宫、石曼卿、柳耆卿、秦少游，近日倪云林、唐伯虎、祝枝山，再如李龟年、黄幡绰、敬新磨、卓文君、红拂、薛涛、崔莺、朝云之流：此皆易地则同之人也。”<br />
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==吴婧悦 Wú Jìngyuè 俄语语言文学 女 202120081529==<br />
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子兴道：“依你说，成则公侯败则贼了？”雨村道：“正是这意。你还不知，我自革职以来，这两年遍游各省，也曾遇见两个异样孩子，所以方才你一说这宝玉，我就猜着了八九也是这一派人物。<br />
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==吴映红 Wú Yìnghóng 日语语言文学 女 202120081530==<br />
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不用远说，只这金陵城内钦差金陵省体仁院总裁甄家，你可知道？”子兴道：“谁人不知，这甄府就是贾府老亲，他们两家来往极亲热的。就是我也和他家往来非止一日了。”<br />
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==肖毅瑶 Xiāo Yìyáo 英语语言文学（英美文学） 女 202120081531==<br />
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雨村笑道：“去岁我在金陵，也曾有人荐我到甄府处馆。我进去看其光景，谁知他家那等荣贵，却是个富而好礼之家，倒是个难得之馆。但是这个学生虽是启蒙，却比一个举业的还劳神。<br />
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==谢佳芬 Xiè Jiāfēn 英语语言文学（英美文学） 女 202120081532==<br />
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说起来更可笑，他说：‘必得两个女儿陪着我读书，我方能认得字，心上也明白；不然，我心里自己糊涂。’又常对着跟他的小厮们说：‘这“女儿”两个字极尊贵极清净的，比那瑞兽珍禽、奇花异草更觉稀罕尊贵呢。<br />
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==谢庆琳 Xiè Qìnglín 俄语语言文学 女 202120081533==<br />
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你们这种浊口臭舌，万万不可唐突了这两个字，要紧，要紧！但凡要说的时节，必用净水香茶漱了口方可；设若失错，便要凿牙穿眼的。’其暴虐顽劣，种种异常。<br />
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==熊敏 Xióng Mǐn 英语语言文学（英美文学） 女 202120081534==<br />
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只放了学进去，见了那些女儿们，其温厚和平，聪敏文雅，竟变了一个样子。因此，他令尊也曾下死笞楚过几次，竟不能改。每打的吃疼不过时，他便姐姐妹妹的乱叫起来。<br />
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==徐敏赟 Xú Mǐnyūn 语言智能与跨文化传播研究 男 202120081535==<br />
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后来听得里面女儿们拿他取笑：‘因何打急了，只管叫姐妹作什么？莫不叫姐妹们去讨情讨饶？你岂不愧些？’他回答的最妙，他说：‘急痛之时，只叫姐姐妹妹字样，或可解疼，也未可知，因叫了一声，果觉疼得好些。<br />
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==颜静 Yán Jìng 语言智能与跨文化传播研究 女 202120081536==<br />
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遂得了秘法，每疼痛之极，便连叫姐妹起来了。’你说可笑不可笑？为他祖母溺爱不明，每因孙辱师责子，我所以辞了馆出来的。这等子弟，必不能守祖、父基业，从师友规劝的。只可惜他家几个好姊妹都是少有的。”<br />
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==颜莉莉 Yán Lìlì 国别 女 202120081537==<br />
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子兴道：“便是贾府中现在三个也不错。政老爷的长女名元春，因贤孝才德，选入宫作女史去了。二小姐乃是赦老爷姨娘所出，名迎春；三小姐政老爷庶出，名探春；四小姐乃宁府珍爷的胞妹，名惜春：<br />
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Zi Xing said, "The three girls in Jia's mansion are not bad either. Jia Zheng's eldest daughter was named Yuanchun. Because of her virtue and filial piety, she was chosen to be a female historian in the court. The second lady was born to Jia He'concubine, her name was Yingchun; The third lady was born to Jia Zheng's concubine and was named Tanchun. The fourth lady is the sister of Jia Zhen in Ning' mansion, named Xichun:<br />
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==颜子涵 Yán Zǐhán 国别 女 202120081538==<br />
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因史老夫人极爱孙女，都跟在祖母这边，一处读书，听得个个不错。”雨村道：“更妙在甄家风俗：女儿之名，亦皆从男子之名；不似别人家里，另外用这些‘春’、‘红’、‘香’、‘玉’等艳字。<br />
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==阳佳颖 Yáng Jiāyǐng 国别 女 202120081540==<br />
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何得贾府亦落此俗套？”子兴道：“不然。只因现今大小姐是正月初一所生，故名元春，馀者都从了‘春’字；上一排的却也是从弟兄而来的。现有对证：目今你贵东家林公的夫人，即荣府中赦、政二公的胞妹，在家时名字唤贾敏。<br />
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==杨爱江 Yáng Àijiāng 英语语言文学（语言学） 女 202120081541==<br />
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不信时你回去细访可知。”雨村拍手笑道：“是极。我这女学生名叫黛玉，他读书凡‘敏’字，他皆念作‘密’字；写字遇着‘敏’字，亦减一二笔。我心中每每疑惑，今听你说，是为此无疑矣。<br />
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==杨堃 Yáng Kūn 法语语言文学 女 202120081542==<br />
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怪道我这女学生言语举止另是一样，不与凡女子相同，度其母不凡，故生此女。今知为荣府之外孙，又不足罕矣。可惜上月其母竟亡故了。”子兴叹道：“老姊妹三个，这是极小的，又没了；<br />
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==杨柳青 Yáng Liǔqīng 英语语言文学（英美文学） 女 202120081543==<br />
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长一辈的姊妹，一个也没了。只看这小一辈的将来的东床何如呢。”雨村道：“正是。方才说政公已有一个衔玉之子，又有长子所遗弱孙，这赦老竟无一个不成？”<br />
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==叶维杰 Yè Wéijié 国别 男 202120081544==<br />
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子兴道：“政公既有玉儿之后，其妾又生了一个，倒不知其好歹。只眼前现有二子一孙，却不知将来何如。若问那赦老爷，也有一子，名叫贾琏，今已二十多岁了，亲上做亲，娶的是政老爷夫人王氏内侄女，今已娶了四五年。<br />
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==易扬帆 Yì Yángfān 英语语言文学（英美文学） 女 202120081545==<br />
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这位琏爷身上现捐了个同知，也是不喜正务的；于世路上好机变，言谈去得，所以目今只在乃叔政老爷家住，帮着料理家务。谁知自娶了这位奶奶之后，倒上下无人不称颂他的夫人，琏爷倒退了一舍之地：模样又极标致，言谈又爽利，心机又极深细，竟是个男人万不及一的。”<br />
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==殷慧珍 Yīn Huìzhēn 英语语言文学（英美文学） 女 202120081546==<br />
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雨村听了，笑道：“可知我言不谬了。你我方才所说的这几个人，只怕都是那正邪两赋而来一路之人，未可知也。”子兴道：“正也罢，邪也罢，只顾算别人家的账，你也吃杯酒才好。”<br />
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==殷美达 Yīn Měidá 英语语言文学（语言学） 女 202120081547==<br />
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雨村道：“只顾说话，就多吃了几杯。”子兴笑道：“说着别人家的闲话，正好下酒，即多吃几杯何妨？”雨村向窗外看道：“天也晚了，仔细关了城，我们慢慢进城再谈，未为不可。”<br />
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==尹媛 Yǐn Yuán 英语语言文学（英美文学） 女 202120081548==<br />
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于是二人起身，算还酒钱。方欲走时，忽听得后面有人叫道：“雨村兄恭喜了！特来报个喜信的。”雨村忙回头看时……要知是谁，且听下回分解。<br />
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==詹若萱 Zhān Ruòxuān 英语语言文学（英美文学） 女 202120081549==<br />
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外班──清代会试考取进士后，留在朝中任官者称“京官”，分发外地任地方官者称“外班”。因新官分发到地方后要候补，按班次任官，故称“外班”。同寅皆侧目而视──同寅：即同僚。<br />
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==张秋怡 Zhāng Qiūyí 亚非语言文学 女 202120081550==<br />
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典出《尚书·虞书·皋陶谟》：“百僚师师，百工惟时……同寅协恭，和衷哉。”寅时是朝臣上朝之时，故称。 侧目而视：斜着眼看。语出《战国策·秦策一》：“(苏秦)将说楚王，路过洛阳。<br />
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==张扬 Zhāng Yáng 国别 男 202120081551==<br />
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父母闻之，清宫除道，张乐设饮，郊迎三十里；妻侧目而视，倾耳而听；嫂蛇行匍伏，四拜自跪而谢。”原表示敬畏。引申以表示愤怒或不齿。<br />
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==张怡然 Zhāng Yírán 俄语语言文学 女 202120081552==<br />
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维扬──扬州(在今江苏省)的别称。大禹所划分的“九州”之一。典出《尚书·夏书·禹贡》：“淮海惟扬州。”“惟”通“维”。<br />
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==钟义菲 Zhōng Yìfēi 英语语言文学（英美文学） 女 202120081553==<br />
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后人从“惟扬州”截取“惟扬”，又以“维”代“惟”，遂成“维扬”。如北朝周·庾信《哀江南赋》：“淮海维扬，三千馀里。”探花──科举考试中殿试(最高一级考试)一甲第三名(第一名为状元，第二名为榜眼)。<br />
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Later generations intercepted "Weiyang" from "weiyangzhou" and replaced "Weiyang" with "Wei", so it became "Weiyang". For example, Yuxin's Fu on mourning the south of the Yangtze River in the Northern Dynasty said, "the Huaihai sea is vast, more than 3000 miles." Tanhua—the third place in the first grade of the palace examination (the highest level examination) (the first place is called Zhuangyuan and the second place is called Bangyan）--[[User:Zhong Yifei|Zhong Yifei]] ([[User talk:Zhong Yifei|talk]]) 09:04, 4 December 2021 (UTC)<br />
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==钟雨露 Zhōng Yǔlù 英语语言文学（英美文学） 女 202120081554==<br />
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本于唐代的“探花使”，亦称“探花郎”。唐·李淖《秦中岁时记》：“进士杏园初宴，谓之探花宴。差少俊二人为探花使，遍游名园，若它人先折花，二使皆被罚。”<br />
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==周玖 Zhōu Jiǔ 英语语言文学（英美文学） 女 202120081555==<br />
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又宋·魏泰《东轩笔录》卷六：“进士及第后，例期集一月……又选最年少者二人为探花使，赋诗，世谓之探花郎。”<br />
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==周俊辉 Zhōu Jùnhuī 法语语言文学 女 202120081556==<br />
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兰台寺大夫──指专管弹劾的御史。兰台是汉朝宫内藏书之所，由御史大夫主管，故后世将御史台别称“兰台”，将御史府别称“兰台寺”，将御史别称“兰台寺大夫”。<br />
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==周巧 Zhōu Qiǎo 英语语言文学（语言学） 女 202120081557==<br />
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列侯──古代爵名。在秦称“彻侯”，为二十四级爵位中的最高一级。至汉代为避汉武帝刘彻之讳，改为“通侯”。“通”与“彻”同义，是改名不改义。“通侯”之意是表示受爵者功勋通于王室。<br />
Marquis - Ancient Baron name. In Qin Dynasty, it was called "chehou", which was the highest among twenty-four levels. In the Han Dynasty, in order to avoid the taboo of Liu Che, Emperor of the Han Dynasty, it was changed to "tonghou". "Tong" is synonymous with "Che" in Chinese, in this way changing the name without changing the meaning. "Tong Hou" means that the recipient has done meritorious services to the royal family.--[[User:Zhou Qiao1|Zhou Qiao1]] ([[User talk:Zhou Qiao1|talk]]) 09:17, 4 December 2021 (UTC)<br />
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==周清 Zhōu Qīng 法语语言文学 女 202120081558==<br />
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后又改为“列侯”，表示序列之意。见《汉书·高帝纪下》颜师古注。清代并无此爵，只是借指侯爵。清代爵位分公、侯、伯、子、男，侯爵为第二等。<br />
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==周小雪 Zhōu Xiǎoxuě 日语语言文学 女 202120081559==<br />
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膝下荒凉──意谓子女稀少，尤无儿子。 膝下：这里指子女。因幼儿多倚偎于父母膝旁，故称。《孝经·圣治》：“故亲生之膝下，以养父母日严。”唐玄宗注：“亲犹爱也，膝下谓孩童之时也。” <br />
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==朱素珍 Zhū Sùzhēn 英语语言文学（语言学） 女 202120081561==<br />
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荒凉：形容因子女稀少而家庭显得清冷凄凉。西席──古人座次以右(西)为尊，故右席为宾客和塾师之位，坐西面东，故称幕宾和塾师为“西席”或“西宾”。<br />
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==邹岳丽 Zōu Yuèlí 日语语言文学 女 202120081562==<br />
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清·梁章钜《称谓录》卷八：“汉明帝尊桓荣以师礼，上幸太常府，令荣坐东面(坐西面东)，设几。故师曰西席。”这里指家庭教师。“身后”一联──身后有馀：是说馀年还很长(“身后”不可解作死后)。 <br />
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==Nadia 202011080004==<br />
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忘缩手：是说不肯收手，还要争名夺利。 <br />
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==Mahzad Heydarian 玛莎 202021080004==<br />
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无路：走投无路。此联是说世人大多只顾眼前，不顾将来，等到走投无路，后悔无及。<br />
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==Mariam toure 2020GBJ002301==<br />
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刹──梵语音译省称，意译为佛塔的柱形尖顶，故又称“佛柱”。<br />
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==Rouabah Soumaya 202121080001==<br />
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引申为佛寺。贾复──东汉南阳冠军(今河南邓州市西北)人，累官至左将军，并封胶东侯。<br />
Extended to Buddhist temple. Jia Fu——A native of Nanyang Champion of the Eastern Han Dynasty (now northwest of Dengzhou City, Henan Province), he was tired from general to the left and sealed Donghou in Jiao.<br />
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==Muhammad Numan 202121080002==<br />
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《后汉书》有传。姓贾的成千上万，贾雨村却只拉千年前的贾复为一家，足见其拉大旗作虎皮之势利小人肺肝。<br />
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==Atta Ur Rahman 202121080003==<br />
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百足之虫，死而不僵——典出三国魏·曹冏《六代论》：<br />
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==Muhammad Saqib Mehran 202121080004==<br />
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“故语曰：‘百足之虫，死而不僵。’扶之者众也。”<br />
The old saying goes:'Hundred-legged worms die but are not stiff.' There are many who support them."<br />
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==Zohaib Chand 202121080005==<br />
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比喻世家大族虽然衰败，因家底雄厚，依傍众多，表面上仍能维持繁荣景象。 <br />
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==Jawad Ahmad 202121080006==<br />
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百足：虫名，即马陆。长约一寸，躯干由多节构成，每节有足一对或二对，切断后仍能蠕动。<br />
English: Centipede, Insect name, arthropods. Length, around an inch, Body is composed of multiple sections, each section has one or two pairs of feet, after cutting still can squirm.<br />
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==Nizam Uddin 202121080007==<br />
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僵：倒下。安富尊荣──语出《孟子·尽心上》：<br />
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==Öncü 202121080008==<br />
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“君子居是国也，其君用之，则安富尊荣。”<br />
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==Akira Jantarat 202121080009==<br />
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原意是君子因辅佐国君功勋卓著而享受荣华富贵。<br />
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==Benjamin Wellsand 202111080118==<br />
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这里反用其意，意谓不劳而获，安享荣华富贵。<br />
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==Asep Budiman 202111080020==<br />
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钟鸣鼎食——语出唐·王勃《滕王阁序》：“闾阎扑地，钟鸣鼎食之家。”<br />
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==Ei Mon Kyaw 202111080021==<br />
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古代贵族鸣钟列鼎而食。这里借以形容富贵豪华。</div>Chen Huinihttps://bou.de/u/index.php?title=Machine_translation&diff=128794Machine translation2021-12-02T03:00:58Z<p>Chen Huini: /* 1.3 Machine Translation Mode */</p>
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<div>'''Machine Translation - A challenge or a chance for human translators?'''<br />
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[[Machine_translation|Overview Page of Machine Translation]]<br />
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[[Machine_Trans_EN_1]] [[Machine_Trans_EN_2]] [[Machine_Trans_EN_3]] [[Machine_Trans_EN_4]] [[Machine_Trans_EN_5]] [[Machine_Trans_EN_6]] [[Machine_Trans_EN_7]] [[Machine_Trans_EN_8]] [[Machine_Trans_EN_9]] [[Machine_Trans_EN_10]] [[Machine_Trans_EN_11]] [[Machine_Trans_EN_12]] [[Machine_Trans_EN_13]] [[Machine_Trans_EN_14]] [[Machine_Trans_EN_15]] [[Machine_Trans_EN_16]] [[Machine_Trans_EN_17]] [[Machine_Trans_EN_18]] [[Machine_Trans_EN_19]] [[Machine_Trans_EN_20]] [[Machine_Trans_EN_21]] [[Machine_Trans_EN_22]] [[Machine_Trans_EN_23]] [[Machine_Trans_EN_24]] [[Machine_Trans_EN_25]] [[Machine_Trans_EN_26]] [[Machine_Trans_EN_27]] [[Machine_Trans_EN_28]] [[Machine_Trans_EN_29]] [[Machine_Trans_EN_30]] ...<br />
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[[Book_projects|Back to translation project overview]]<br />
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[[DCG-To-Do|To the To Do list]]<br />
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=1 卫怡雯(A Comparison Between the Quality of Machine Translation and Human Translation——A Case Study of the Application of artificial intelligence in Sports Events)=<br />
[[Machine_Trans_EN_1]]<br />
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=2 吴映红（The Introduction of Machine Translation)= <br />
[[Machine_Trans_EN_2]]<br />
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=3 肖毅瑶(On the Realm Advantages And Symbiotic Development of Machine Translation And Huamn Translation)=<br />
[[Machine_Trans_EN_3]]<br />
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=4 王李菲 （Comparison Between Neural Machine Translation of Netease and Traditional Human Translation—A Case Study of The Economist Articles)= <br />
[[Machine_Trans_EN_4]]<br />
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===Abstract===<br />
Machine translation is a subfield of artificial intelligence and natural language processing that investigates transforming the source language into the target language. On this basis, the emergence of neural machine translation, a new method based on sequence-to-sequence model, improves the quality and accuracy of translation to a new level. As one of the earliest companies to invest in machine translation in China, Netease launched neural machine translation in 2017, which adopts the unique structure of neural network to encode sentences, imitating the working mechanism of human brain, and generates a translation that is more professional and more in line with the target language context. This paper takes the articles in The Economist as the corpus for analysis, and aims to explore the types and causes of common errors, as well as the advantages and challenges of each, through the comparative analysis of Netease neural machine translation and human translation, and finally to forecast the future development trend and make a summary of this paper.<br />
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===Key words===<br />
Neural Machine Translation; Human Translation; Contrastive Analysis<br />
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===题目===<br />
有道神经网络机器翻译与传统人工翻译的译文对比——以经济学人语料为例<br />
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===摘要===<br />
机器翻译研究将源语言所表达的语义自动转换为目标语言的相同语义，是人工智能和自然语言处理的重要研究分区。在此基础上，一种基于序列到序列模型的全新机器翻译方法——神经机器翻译的出现让译文的质量和准确度提升到了新的层次。网易作为国内最早投身机器翻译的公司之一，在2017年上线的神经网络翻译采用了独到的神经网络结构，模仿人脑的工作机制对句子进行编码，生成的译文更具专业性，也更符合目的语语境。本文以经济学人内的文章为分析语料，旨在通过对网易神经机器翻译和人工翻译的英汉译文进行对比分析，探究常见错误类型及生成原因，以及各自存在的优势与挑战，最后展望未来发展趋势，并对本文做出总结。 <br />
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===关键词===<br />
神经网络翻译；人工翻译；对比分析<br />
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===1. Introduction===<br />
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Nowadays, the process of economic globalization has accelerated overwhelmingly, and considerable resources are poured into the business field. As a branch of global language English, business English is proposed under the theoretical framework of English for Specific Purpose (ESP), serves the international business activities which is a professional subject requiring specialized English. As the medium that helps people with different cultural backgrounds to understand each other, business translation is required to be “formal, accurate, standardized and smooth”, which challenged both the machine translation and human translator.<br />
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With the urgent requirement for more precise and higher quality translation, recent years have witnessed the rapid development of neural machine translation (NMT), which has replaced traditional statistical machine translation (SMT) to become a new mainstream technique, playing a crucial part in many fields, like business, academic and industry. Compared with SMT, NMT model is more like an organism. There are many parameters in the model that can be adjusted and optimized for the same goal, making the combination and interaction more organic and the overall translation effect better, which greatly matched the demands of business translation. <br />
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The Economist is an international news and Business Weekly offering clear coverage, commentary and analysis of global politics, business, finance, science and technology. A huge number of terminologies plus the polysemy contained in the texts, put forward a tricky problem to both machine translation and human translator. In view of this, this paper makes a comparative analysis of Netease neural machine translation and human translation, aiming to explore the types and causes of common errors, as well as the advantages and challenges of each. In the end, this paper will forecast the future development, hoping to promote the development of translation studies in China.<br />
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===2.2. The Development Process of Machine Translation ===<br />
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Since the IBM model was put forward by the researcher Peter Brown in the early 1990s, statistical methods have gradually become the mainstream of machine translation research. This method has greatly promoted the development of machine translation technology. In recent years, a variety of statistical machine translation models have emerged, such as phrase-based translation model, hierarchical phrase translation model and syntactic translation model, then the translation quality has been greatly improved.<br />
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Since 2002, BLEU, an automatic translation quality evaluation method, has greatly promoted the development of statistical machine translation technology and effectively reduced the cost of manual evaluation. In recent years, with the technical maturity and stability of statistical machine translation, especially phrase-based machine translation, statistical machine translation technology has been making strong strides towards practical and commercial application. Therefore, with the rapid development of technology, people have gradually built-up confidence in machine translation, and the social demand for machine translation has been increasing day by day, with higher and higher expectations.<br />
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However, from the perspective of academic research, both phrase-based translation models and syntactic translation models have experienced a rapid development stage, and the existing theoretical methods and technical models have begun to show "bottlenecks" in the improvement of translation performance. In addition, from the perspective of industrialization and utilitarianism, there is an urgent need for a more practical machine translation system, but the gap between the results of machine translation and the requirements of human beings is still very large. Therefore, for the researchers of machine translation, while excited to see the BLEU score of machine translation system evaluation is getting higher and higher, and the performance of online machine translation system developed by Google, Baidu, Netease and other enterprises is developing with each passing day, they are facing more and more challenges.<br />
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Aiming to solve these problems, many technological giants are striving to find a new way to improve both the quality and efficiency of machine translation. There was a breakthrough which bought machine translation to a new level. Since 2014, the end-to-end neural machine translation has developed rapidly, compared with the statistical machine translation, the translation quality received a significant boost.<br />
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The previous statistical machine translation was more like a mechanical system. Each module has its own function and goal, and then outputs the translation results through mechanical splicing. Its main disadvantage is that the model contains low syntactic and semantic components, so it will encounter problems when dealing with languages with large syntactic differences, such as Chinese-English. Sometimes the result is unreadable even though it is “word-for-word”.<br />
On the contrary, neural machine translation are consisted of several components, including phrase conditions, partial conditions, sequential conditions, primitive models, and so on. Its core is deep learning of artificial intelligence which can imitate the working mechanism of human brain and adopt unique neural network structure to model the whole process of translation. The whole model is composed of a large number of “neurons”, and each “neuron” has to complete some simple tasks, and then through the combination of all of them to coordinate the work, a much better translation text appears. <br />
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Since neural machine translation puts more emphasis on context and the whole text, it produces more coherent and comprehensible content to readers than traditional statistical machine translation, and be widely accepted and used in various field in a very short time. In 2017, at the GMIC (Global Mobile Internet Congress), Duan Yitao, the chief scientist of Netease, delivered a keynote speech titled “Machine Translation has Its Own Way” and announced an exciting news: the neural machine translation technology independently developed by Netease has been officially launched. This technology launched by Youdao this time has been jointly developed by Netease Youdao and Netease Hangzhou Research Institute for over two years. It will serve Youdao Dictionary, Youdao Translator, Youdao Web version, Youdao E-reader and other products, expecting to bring super-convenient product experience to users. In addition, Youdao Translation officer also launched photo translation, users only need to take pictures of the text, can show the results of neural network translation in real time. <br />
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As a pioneer of machine translation in China, the development process of Netease YouDao is exactly the paradigm of the history of machine translation in China. Therefore, in this paper, the neural machine translation technology developed by Netease will be compared with human translators. The same excerpts selected from The Economist are translated by both of them, then the different versions will be analyzed by the translation criterion so as to figure out their respective strengths and weaknesses, bringing consideration to current translation situation and references to future development.<br />
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===3.Comparative Analysis of Errors in English-Chinese Translation ===<br />
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===4.===<br />
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===5. ===<br />
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===Conclusion===<br />
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===References===<br />
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=6 徐敏赟(Machine Translation Based on Neural Network --Challenge or Chance?)=<br />
[[Machine_Trans_EN_6]]<br />
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===Abstract===<br />
With the acceleration of economic globalization, there is a growing demand for translation services. In recent years, with the rapid development of neural networks and deep learning, the quality of machine translation has been significantly improved. Compared with human translation, machine translation has the advantages of low cost and high speed.<br />
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Neural machine translation brings both convenience and pressure to translators. Based on the principles of neural machine translation, this paper will objectively analyze the advantages and disadvantages of neural machine translation, and discuss whether neural machine translation is a chance or a challenge for human translators.<br />
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===Key words===<br />
Neural network; Deep learning; Machine translation; human translation<br />
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===题目===<br />
基于神经网络的机器翻译 --机遇还是挑战？<br />
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===摘要===<br />
随着经济全球化进程的加速，人们对翻译服务的需求也越来越大。近些年来，神经网络和深度学习等技术得到快速发展，机器翻译的质量得到了显著提高，较人工翻译来讲有着成本低，速度快等优势。<br />
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基于神经网络的机器翻译给翻译工作者带来便捷的同时，同时也给翻译工作者们带来了一定的压力。本文将会从神经机器翻译的原理出发，客观分析基于神经网络的机器翻译中存在的一些优势与劣势，并以此来探讨机器翻译对于翻译工作者来说到底是机遇还是挑战这一问题。<br />
===关键词===<br />
神经网络；深度学习；机器翻译；人工翻译；<br />
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===Introduction===<br />
Machine Translation, a branch of Natural Language Processing (NLP), refers to the process of using a machine to automatically translate a natural language (source language) sentence into another language (target language) sentence (Li Mu, Liu Shujie, Zhang Dongdong, Zhou Ming 2018, 2). The natural language here refers to human language used daily (such as English, Chinese, Japanese, etc.), which is different from languages created by humans for specific purposes (such as computer programming languages).<br />
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According to statistics, there are about 5600 human languages in existence. In China, as we are a big family composed of 56 ethnic groups, some ethnic minorities also have their own languages and scripts. In other countries, due to the history of colonization, these countries usually have multiple official languages, so some official documents usually need to be written in more than two languages. In the context of the “One Belt, One Road” initiative, communication among different languages has become an important part of building a community with a shared future for mankind. Therefore, the application of machine translation technology can help promote national unity, communication between different languages and cross-cultural communication.<br />
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Although the latest machine translation method, neural machine translation, has advantages such as speed and low cost, machine translation is still far from being as effective as human translation. Li Yao (Li Yao 2021, 39) selects ''Chronicle of a Blood Merchant'' translated by Andrew F. Jones, a classic work of Yu Hua, and the versions of Baidu Translation, Youdao Translation and Google Translation as corpus to conduct a comparative study on translation quality. Starting from the development of machine translation, Jin Wenlu (Jin Wenlu 2019, 82) analyzed the advantages and disadvantages of machine translation and manual translation, discussed the question of whether machine translation can replace human translation. In order to further explore the impact of machine translation on translators, this paper will take neural machine translation - the latest machine translation as an example to discuss whether machine translation is a chance or a challenge for translators.<br />
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===Comparison of different machine translation methods===<br />
Actually, the development of Machine Translation methods is going through four stages: rule-based methods, instance-based methods, statistical machine translation and neural machine translation. At present, thanks to the application of deep learning methods, neural machine translation has become the mainstream. Compared with statistical machine translation, neural machine translation has the following advantages:<br />
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1) End-to-end learning does not rely on too many prior assumptions. In the era of statistical machine translation, model design makes more or less assumptions about the process of translation. Phrase-based models, for example, assume that both source and target languages are sliced into sequences of phrases, with some alignment between them. This hypothesis has both advantages and disadvantages. On the one hand, it draws lessons from the relevant concepts of linguistics and helps to integrate the model into human prior knowledge. On the other hand, the more assumptions, the more constrained the model. If the assumptions are correct, the model can describe the problem well. But if the assumptions are wrong, the model can be biased. Deep learning does not rely on prior knowledge, nor does it require manual design of features. The model learns directly from the mapping of input and output (end-to-end learning), which also avoids possible deviations caused by assumptions to a certain extent.<br />
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2) The continuous space model of neural network has better representation ability. A basic problem in machine translation is how to represent a sentence. Statistical machine translation regards the process of sentence generation as the derivation of phrases or rules, which is essentially a symbol system in discrete space. Deep learning transforms traditional discrete-based representations into representations of continuous space. For example, a distributed representation of the space of real numbers replaces the discrete lexical representation, and the entire sentence can be described as a vector of real numbers. Therefore, the translation problem can be described in continuous space, which greatly alleviates the dimension disaster of traditional discrete space model. More importantly, continuous space model can be optimized by gradient descent and other methods, which has good mathematical properties and is easy to implement.<br />
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===Principles of Neural Machine Translation===<br />
=====Word Representation=====<br />
Actually, we know that machine translation is a branch of natural language processing. One of the things we need to decided is how to represent individual words in a sentence. The first thing we do is come up with a vocabulary which is also called a dictionary, and that means making a list of the words that we will use in our representations. What we can do is then use one-hot representation to represent each of words in a sentence, in which each word is represented as a long vector. The dimension of this vector is the size of vocabulary, most of the elements are 0, and only one dimension has a value of 1, and this dimension represents the current word.<br />
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For example, “tiger” is represented as [0, 0, 0, 0, 1, 0, 0, 0, 0 …] and “panda” is represented as [0, 1, 0, 0, 0, 0, 0, 0, 0 …]. Therefore, we can label “tiger” as 4 and label “panda” as “1”. However, there exists a critical problem in one-hot representation: any two words are isolated from each other. It's impossible to tell from these two vectors whether the words are related or not. There is a key idea which is a new way of representing words called words embeddings.<br />
In Deep Learning, what we commonly used isn’t one-hot representation just mentioned, but distributed representation which is often called word representation or word embedding. Such a vector would look something like this: [0.123, −0.258, −0.762, 0.556, −0.131 …]. And the dimension of this vector is far smaller than the vector dimension which is represented by one-hot representation.<br />
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It can often let our algorithms automatically understand analogies like that, man is to women, as king is to queen, and many other examples. And we will find a way to learn words embeddings later, what we should know that is these high dimensional feature vectors can give a better representation than one-hot vectors for representing different words.<br />
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If words are represented in one-hot representation, it may cause a dimension disaster when it comes to solving certain tasks, such as building language models (Bengio 2003, 1137–1155). But using lower-dimensional feature vectors doesn't have this problem. In practice, if high dimensional feature vectors are applied to deep learning, their complexity is almost unacceptable. Therefore, low dimensional feature vectors are also popular here. In my opinion, the biggest contribution of word embedding is to make related or similar words closer in distance.<br />
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=====Recurrent Neural Network Language Model=====<br />
Language modeling is one of the basic and important tasks in natural language processing. There’s also one that Recurrent Neural Networks (RNNs) do very well. The language model which is built as RNNs is called Recurrent Neural Network Language Model (RNNLM).<br />
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What is a language model? For example, if we are going to build a speech recognition system, and we hear a sentence, “The apple and pear(pair) salad were delicious.”, so what exactly did we hear? “The apple and pear salad were delicious.” or “The apple and pair salad were delicious.”? As human, we might think that we heard are more like the second. In fact, that's what a good speech recognition system helps output, even if the two sentences sound so similar. The way to get speech recognition to choose the second sentence is to use a language model that can calculate the probability of each sentence.<br />
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For example, a speech recognition model might calculate the probability of the first sentence being: 𝑃(The Apple and pear salad) = 2.6 × 10-13, whereas 𝑃(The Apple and pear salad) = 4.3 × 10-10. Compare the two values, because the second sentence is 1,000 times more likely than the first, which is why the speech recognition system is able to choose the correct answer between the two sentences.<br />
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what the language model does is that it tells you what the probability of a particular sentence is. Language model is a fundamental component for both speech recognition systems as we just mentioned, as well as for machine translation systems where translation systems want to output only sentences that are likely. The basic job of a language model is to input a sentence, then the language model will estimate the probability of that particular word in a sequence of sentences.<br />
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How to build a language model with an RNN? First of all, we need a training set comprising a large corpus of English text or text from whatever language we want to build a language model of. Here is the architecture of RNNML which is referenced by Mikolov (Mikolov 2012). As the picture, RNNML predicts the probability of the 5th position word based on the previous 4 words, so this model are more likely outputs the sentence: “The students opened their books”.<br />
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[[File:RNNLM.jpg]]<br />
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=====Encoders and Decoders=====<br />
Actually, in machine translation, the length of the source language sentence and the target language sentence are generally different. Therefore, the common language models cannot meet the needs of machine translation. In 2014, the researchers (Sutskever et al. 2014) (Cho et al. 2014) designed new models called sequence to sequence models, which is also often called encoder-decoder models.<br />
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Firstly, we should create a network, which we are going to call the encoder network be built as RNNs. Then, we should feed in the input a source language word at a time. And after ingesting the input sequence, the RNNs will output a vector that represents the input sequence. And after that we can build a decoder network, which takes as input the encoding output by the encoder network. The decoder network can be trained to output a translated word at a time until eventually it outputs the end of sequence or the sentence token.<br />
<br />
As shown in the figure below, the upper and lower dotted boxes respectively represent the encoder and decoder of neural machine translation, S and T sequences respectively represent the source language sentences and the target language sentences, </S> represents the end of sentence, and small circles represent feature vectors and neural network hidden layer.<br />
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[[File:Encoder_decoder.jpg|200px|thumb|bottom|Encoder and decoder model]]<br />
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However, the model also has some problems, and the main problem is that only constant length vector C can be used to represent the entire source language sentences. In other words, regardless of the length of source language sentences, it can only be encoded as a vector C of fixed length. If it is a long sentence, the information contained by the constant length vector C will decrease or even disappear. Therefore, the problem of gradient disappearance or explosion exists in model training.<br />
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=====Attention Model=====<br />
In order to solve the above problems, the researchers (Junczys-Dowmunt et al. 2016) introduced an attention model that can dynamically capture context. The attention model is an improvement of the traditional neural network model. The basic idea is that each target language word has nothing to do with most of the source language words, but only some words. By improving the representation of source language using bidirectional recurrent neural network, vector representation containing global information can be generated for each source word.<br />
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As the picture shows, in attention model, the encoder will use the forward recurrent neural network to encode the source language sentence from left to right in turn, generating a set of hidden states, and then use the backward recurrent neural network to generate another set of hidden states. Finally, the two sets of hidden states at the corresponding moments are spliced together to generate a new set of hidden states (Li Mu et al. 2018, 165), which is represented as the source feature vectors. Its advantage is that each source language feature vector representation contains the context information on the left and right sides, and the concatenation of the two means that it contains the entire source sentence information, so both can be used as the vector representation of the entire sentence.<br />
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[[File:Attention_model.jpg]]<br />
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Actually, there are some attention weights between encoder hidden states and decoder hidden states, and the wights is trained by comparing the hidden states of encoder and decoder. The attention model will use these attention wights to weight all the encoded hidden states by bit to obtain the source language sentence context vector C’ at that moment. For example, when the attention model is generating the target word “T2”, and “T2” is only most relevant to the source word “S1”, but is irrelevant or under-relevant to other words, so the weights between “T2” and “S1” will be large, and the other weights will be small. Repeatedly, we will get the target sentence finally.<br />
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===Drawbacks of Neural Machine Translation===<br />
As a new technology, neural machine translation still has many problems. Recently, relevant comprehensive studies mainly include: improvement of attention mechanism, integration of priors and constraints, model training and fusion, new model, architecture construction and evaluation of neural machine translation. The research on improving the quality of neural machine translation mainly includes:<br />
<br />
1)Due to insufficient computational space, we cannot save all the words in words embeddings, so there exits the problem of out-of-vocabulary words translation when we training models.<br />
<br />
2)Because of the scarcity of language source and insufficient training corpus, the minority languages translation becomes extremely difficult.<br />
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3)Because the model is sensitive to sentence length, it tends to produce short results, resulting in translation difficulties in long and difficult sentences.<br />
<br />
=====Translation of Out-of-Vocabulary Words=====<br />
In the decoding process, neural machine translation needs to normalize the probability distribution with the help of the whole translation word embeddings, which has a large time and space consumption and high computational complexity. In order to control the temporal and spatial overhead, only high-frequency words are generally used in model training, and the number is limited to 30,000 to 80,000. All other uncovered words, which is often called out-of-set words, are uniformly identified as <UNK> characters. <UNK> characters means that the semantic structure of parallel sentences in the training set is damaged, and the quality of model parameters will be affected. Also, it means that source language sentences are difficult to be expressed correctly, and the risk of ambiguity increases. Last but not least, unknown words will appear in the target language, and the quality and readability of the target language will be damaged. Moreover, language changes rapidly in this modern information society: old words add new ideas, new words continue to emerge, and named entities appear frequently. Therefore, translation of out-of-vocabulary words is one of the basic topics in the research of neural machine translation, which is of great significance to improve the quality of neural machine translation.<br />
<br />
Out-of-vocabulary words translation is a difficult issue in the current research of neural machine translation. There are two ways to alleviate it. One is word granularity processing, which is mainly achieved by replacing out-of-vocabulary words. Although this method can reduce sentence ambiguity and improve the quality of translation and model parameters, it is not accurate enough to replace low-frequency words and polysemous words, so it is difficult to effectively deal with the problem in certain cases. The other is sub-word and character granularity processing method, which is the most popular method in present, mainly solves the problems of parataxis language segmentation and hypotactic language deformation by reducing translation granularity and data sparsity. The sub-word and character granularity processing method does not need to use out-of-vocabulary words processing module alone, but decomposes out-of-vocabulary words into sub-words and characters, which is simple and effective and widely used. However, fine-grained lexical segmentation may change semantic information, increase the number of sentence words, the risk of ambiguity, and the difficulty of training.<br />
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=====Translation of Minority Languages=====<br />
According to statistics, there are about 5,600 languages in the world. The current neural network model for major languages will not be able to cope with the increasing translation needs in the era of big data. Therefore, it is of great practical value to improve the translation performance of neural network model under the condition of resource scarcity. Similar to statistical machine translation, neural machine translation is also a data-driven translation model, and its performance is highly dependent on the scale, quality and breadth of parallel corpus. The scale of artificial neural network parameters is huge, and only when the training corpus reaches a certain level, neural machine translation will significantly surpass traditional statistical machine translation (Zoph et al. 2016, 1568). However, the reality is that, except for some vertical fields in major languages with relatively rich resources, most minority languages or vertical fields still lack large-scale, high-quality and broad-covered parallel corpora. Therefore, how to use existing resources to alleviate the translation problems of minority languages is a focus of current research. At present, the latest methods to deal with this problem mainly include zero-resource, data augmentation, and diverse learning methods.<br />
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The zero-resource method is one of the effective ways to alleviate the problem of neural machine translation in minority languages. The specific method is as follows: If there are three languages of A, B and C, and you want to realize the translation between A and C, and there is no parallel corpus between them. But the parallel corpus of A and B is sufficient, and the parallel corpus of B and C is sufficient too. Then you can choose B as the pivot to realize the translation between A and C.<br />
<br />
The data augmentation method can also effectively alleviate the insufficient generalization ability of the model due to the scarcity of training data. According to the currently available paper, data augmentation techniques used for neural machine translation mainly include back translation and word exchange.<br />
<br />
The diverse learning methods, such as meta-learning, transfer learning, multi-task learning, unsupervised learning and so on are also an effective way to solve the shortage of minority language resources, although the latest results are mostly seen in the latter two.<br />
<br />
Although the methods above mentioned can alleviate the translation problems of minority languages to some extent, the breadth and depth of the experiment are still limited. Whether each method is applicable to all language pairs is a subject worthy of in-depth discussion. In addition, for major languages, the information processing technology of minority languages is often more backward. Some languages, such as Mongolian, even the basic problems of part-of-speech tagging and named entity recognition are still not well solved (Bao et al. 2018, 61). Therefore, while actively developing new training methods, we must also pay attention to improving the level of information processing technology in minor languages.<br />
<br />
=====Translation of Long and Difficult Sentence=====<br />
Due to the insufficient number of long sentences in the training corpus, and the long-term memory problem of the cyclic neural network (Li Yachao et al. 2018, 2745). Therefore, neural network model is not able to translate long and difficult sentences. It only has a comparative advantage over traditional statistical machine translation in the translation of sentences within about 60 words (Koehn, Knowles 2017, 28). If this limit is exceeded, the quality will drop sharply. Although the encoder-decoder model based on attention mechanism can dynamically capture context information, solve the problem of information transmission in long distance, improve the performance of neural machine translation, due to the complex structure of natural language, even the attention model cannot properly focus on all the information in the source language sentences. Therefore, in the translation of long and difficult sentences, there will be mistranslations such as over-translation and under-translation. Although the fluency of the target language has improved, the semantic fidelity is worrying.<br />
<br />
There are two main solutions to the problem of long and difficult sentences translation: one is to improve the capture ability of the model in long distance; the other is to adopt the long sentence divide and conquer strategy. These two methods can alleviate the sensitivity of sentence length to a certain extent, but their effects still need to be improved. In view of the complexity and diversity of languages, not all languages can be divided and conquered, so the first method can be considered to improve the quality of long and difficult sentences translation in the future.<br />
<br />
===Prospect of Neural Machine Translation===<br />
In the long term, neural machine translation, as a new technology, is in the ascendant and has a promising future. In recent years, especially since 2014, neural machine translation has made great progress and developed rapidly. It is not only outstanding in traditional text translation, but also excellent in image and speech translation. It is a machine translation model with great potential. At present, the following trends are promising, and we believe that neural machine translation will have a more brilliant future as time goes on.<br />
<br />
=====Unsupervised Translation=====<br />
Recurrent neural network is suitable for processing sequential data, especially variable length sequential data, and is the mainstream implementation of traditional neural machine translation. Recurrent neural network is a typical supervised learning model. During the training process, it is highly dependent on bilingual or multilingual parallel corpus, and the scale and quality of corpus will directly restrict the translation performance of the model. However, the reality is that most languages do not have ready-made parallel corpora, and they are not naturally tagged, so it is expensive to process the corpus such as alignment and labeling. At present, a number of scholars have tried to use different methods to achieve unsupervised translation, such as the unsupervised cross-language embedding method (Artetxe et al. 2018) and the latent semantic space sharing method (Lample et al. 2018). Unsupervised translation has shown great development potential, and will surely become one of the key exploration objects in the future.<br />
<br />
=====Multilingual Translation=====<br />
Barrier-free communication has always been a dream of mankind, and word vector technology provides the possibility for it to realize the dream of Babel Tower. By mapping the words to the latent semantic space and using low-dimensional continuous real number vectors to describe their features (Li Feng et al. 2017, 610), we can not only avoid dimension disaster, but also improve semantic representation accuracy. More importantly, different languages can not only share the same semantic space, but also share the same attention mechanism (Firat et al. 2016, 866), which lays a good foundation for multilingual neural machine translation. Although in practice, whether word embeddings can be used to represent all language vocabulary remains to be verified, in theory, it does play the role of universal language. How to use word embeddings technology to improve the existing neural network model and make it to achieve barrier-free communication truly is a topic to be explored.<br />
<br />
=====Cross-cultural Communication=====<br />
What is cross-cultural communication? Cross-cultural communication is not only the interpersonal communication and information dissemination activities between social members with different cultural backgrounds, but also involves the process of migration, diffusion and change of various cultural elements in the global society, and its impact on different groups, cultures, countries and even the human community. Language is the carrier of culture.<br />
<br />
Nowadays, cultural differences cannot be ignored. In my opinion, the quality of translation determines the quality of cross-cultural communication. If different cultures are compared to two lands that have never communicated with each other, then translation is a bridge of cross-cultural communication. The width and flatness of the bridge determine how many people can cross it on both sides, and the capacity and flow of the bridge are determined by the meaning that the translation can convey. Only more accurate and rigorous translation based on different culture can make participants more extensive and enthusiastic, so as to facilitate the smooth dissemination of culture.<br />
<br />
Therefore, the ultimate goal of neural machine translation to help translate high-quality cultural works such as classic literature and good domestic animation, to let Chinese culture go out and absorb excellent foreign cultures.<br />
<br />
===Impact on Human Translation Market===<br />
With the development of neural machine translation, what kind of impact has been or will have on the human translation market? Whether neural machine translation is a challenge for human translators?<br />
<br />
Firstly, human translation market will not shrink, just as the textile machine has expanded the textile market. Some applications that require only "near" translation, such as the localization of some e-commerce sites, which might be abandoned because of the high cost of human translation. But it might be survived with the help of machine translation. And in this process, the human translation market has created new demand which is called post-editing. when there was only human translation, this demand did not exist. But under machine translation, there were some human translation businesses. Not only the machine translation market, but also the human translation market has expanded.<br />
<br />
Secondly, high-end human translation will not die. Any excellent handmade work is still expensive today. After all, some translation tasks such as translation of poetry and literature is a creative labor. We should know that machine translation is not sensitive to culture. Different cultures have its unique language systems, so the translations they produce may not conform to the values and specific norms of the culture. Therefore, humans can play to their unique advantages to translate some certain translation tasks.<br />
<br />
Finally, technological improvements often lead to improvements in efficiency, allowing people to complete their work more efficiently. The steam engine improves the efficiency of moving bricks, but it still requires a driver. The development of technology will bring various positions around machine translation, such as post- editing, quality controller and so on. In fact, Machine Assisted Translation (CAT) has begun to become a compulsory course in many translation schools. If you can master these technologies proficiently, you will have the upper hand in the market.<br />
<br />
===Conclusion===<br />
Let’s go back to the question at the beginning of this article: whether neural machine translation is a chance or a challenge for human translators? Maybe we can say that the neural machine translation is not only a chance but also a challenge for human translators.<br />
<br />
From this paper, we can know that the neural machine translation has been developed a lot because of some critical methods such as deep learning, and the quality of machine translation has been greatly improved. Nowadays, machine translation also has a place in translation market. However, as far as I am concerned, we should not pay too much attention to the impact of neural machine translation on human translation, what we should really talk about is how to effectively combine two different types of translation services.<br />
<br />
As a new model, neural machine translation still has a long way to go. How to improve the existing neural network model and make itself more intelligent is a major challenge. Therefore, we should look at machine translation from a dialectical and developmental perspective. Humans do not need to be afraid of technology, but should learn to use technology to enhance the efficiency and value of their work.<br />
<br />
===References===<br />
Li Mu et al. 李沐 等. (2018). 机器翻译 [Machine Translation]. Beijing: Higher Education Press 高等教育出版社.<br />
<br />
Li Yao 李耀. (2021). 基于语料库的机器翻译文学作品质量研究--以《许三观卖血记》为例 [A Corpus-based Study on the Quality of Machine Translation Literary Works--Taking ''Chronicle of a Blood Merchant'' as an example]. 海外英语 Overseas English (18) 39-40+42.<br />
<br />
Jin Wenlu 靳文璐. (2019). 机器翻译可以取代人工翻译吗? [Can machine translation replace human translation?]. 智库时代 Think Tank Times (40) 282-284.<br />
<br />
Yoshua Bengio et al. (2003). A neural probabilistic language model. Journal of Machine Learning Research (JMLR) (3) 1137–1155.<br />
<br />
Mikolov Tomáš. (2012). Statistical Language Models based on Neural Networks. PhD thesis, Brno University of Technology. <br />
<br />
Sutskever et al. (2014). Sequence to sequence learning with neural networks.<br />
<br />
Cho et al. (2014). Learning phrase representation using RNN encoder-decoder for statistical machine translation.<br />
<br />
Junczys-Dowmunt et al. (2016). Is Neural Machine Translation Ready for Deployment? A Case Study on 30 Translation Directions.<br />
<br />
Zoph et al. (2016). Transfer Learning for Low-resource Neural Machine Translation.<br />
<br />
Bao et al. 包乌格德勒 等. (2018). 基于RNN和CNN的蒙汉神经机器翻译研究 [Mongolian-Chinese Neural Machine Translation Based on RNN and CNN]. 中文信息学报 Journal of Chinese Information Processing (8) 61.<br />
<br />
Li Yachao et al. 李亚超 等. (2018). 神经机器翻译综述 [A Survey of Neural Machine Translation]. 计算学报 Chinese Journal of Computers (12) 2745.<br />
<br />
Koehn, Knowles. (2017). Six Challenges for Neural Machine Translation. <br />
<br />
Artetxe et al. (2018). Unsupervised Neural Machine Translation.<br />
<br />
Lample et al. (2018). Unsupervised Machine Translation Using Monolingual Corpora Only.<br />
<br />
Firat et al. (2016). Multi-way, Multilingual Neural Machine Translation with a Shared AttentionMechanism.<br />
<br />
=7 颜莉莉(一带一路背景下人工智能与翻译人才的培养)=<br />
[[Machine_Trans_EN_7]]<br />
<br />
===Abstract===<br />
In the era of artificial intelligence, artificial intelligence has been applied to various fields. In the field of translation, traditional translation models can no longer meet the rapid development and updating of the information age. The development of machine translation has brought structural changes to the language service industry, which poses challenges to the cultivation of translation talents. Under the background of "The Belt and Road initiative", translation talents have higher and higher requirements on translation literacy. Artificial intelligence and translation technology are used to reform the training mode of translation talents, so as to better serve the development of The Times. This paper mainly explores the cultivation of artificial intelligence and translation talents under the background of the Belt and Road Initiative. The cultivation of translation talents is moving towards comprehensive cultivation of talents. On the contrary, artificial intelligence and machine translation can also be used to improve the teaching mode and teaching content, so as to win together in cooperation.<br />
===Key words===<br />
Artificial intelligence,Machine translation,cultivation of translation talents,"The Belt and Road initiative"<br />
===题目===<br />
一带一路背景下人工智能与翻译人才的培养<br />
<br />
===摘要===<br />
进入人工智能时代，人工智能被应用于各个领域。在翻译领域，传统的翻译模式已无法满足信息化时代的飞速发展和更新，机器翻译的发展给语言服务行业带来了结构性改变，这对翻译人才的培养提出了挑战。“一带一路”背景下，对翻译人才的翻译素养要求越来越高，利用人工智能和翻译技术对翻译人才培养模式进行革新，更好为时代发展服务。本文主要探究在一带一路背景下人工智能和翻译人才培养，翻译人才的培养过程中正向对人才的综合性培养，反之也可以利用人工智能和机器翻译完善教学模式和教学内容，在合作中共赢。<br />
===关键词===<br />
人工智能；机器翻译；翻译人才培养；一带一路<br />
<br />
===1. Introduction===<br />
With the development of science and technology in China, artificial intelligence has also been greatly improved, and related technologies have been applied to various fields, such as the use of intelligent robots to deliver food to quarantined people during the epidemic, which has made people's lives more convenient. The most controversial and widely discussed issue is machine translation. Before the emergence of machine translation, translation was generally dominated by human translation, including translation and interpretation, which was divided into simultaneous interpretation and hand transmission, etc. It takes a lot of time and energy to cultivate a translation talent. However, nowadays, the era is developing rapidly and information is updated rapidly. As a translation talent, it is necessary to constantly update its knowledge reserve to keep up with the pace of The Times. The emergence of machine translation has also posed challenges to translation talents and the training of translation talents. Although machine translation had some problems in the early stage, it is now constantly improving its functions. In the context of the belt and Road Initiative, both machine translation and human translation are facing difficulties. Regardless of whether human translation is still needed, what is more important at present is how to train translators to adapt to difficulties and promote the cooperation between human translation and machine translation.<br />
<br />
===2.Development status of machine translation in the era of artificial intelligence ===<br />
With the development of AI technology, machine translation has made great progress and has been applied to people's lives. For example, more and more tourists choose to download translation software when traveling abroad, which makes machine translation take an absolute advantage in daily email reply and other translation activities that do not require high accuracy. The translation software commonly used by netizens include Google Translation, Baidu Translation, Youdao Translation, IFly.com Translation, etc. Even wechat and other chat software can also carry out instant Translation into English. Some companies have also launched translation pens, translation machines and other equipment, which enables even native speakers to rely on machine translation to carry out basic communication with other Chinese people.<br />
But so far, machine translation still faces huge problems. Although machine translation has made great progress, it is highly dependent on corpus and other big data matching. It does not reach the thinking level of human brain, and cannot deal with the problem of translation differences caused by culture and religion. In addition, many minor languages cannot be translated by machine due to lack of corpus.<br />
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What's more, most of the corpus is about developed countries such as Britain and France, and most of the corpus is about diplomacy, politics, science and technology, etc., while there are very few about nationality, culture, religion, etc.<br />
<br />
In addition, machine translation can only be used for daily communication at present. If it involves important occasions such as large conferences and international affairs, it is impossible to risk using machine translation for translation work. Professional translators are required to carry out translation work. So machine translation still has a long way to go.<br />
<br />
===3.Challenges in the training of translation talents in universities===<br />
The cultivation of translators is targeted at the market. Professors Zhu Yifan and Guan Xinchao from the School of Foreign Languages at Shanghai Jiao Tong University believe that the cultivation of translators can be divided into four types: high-end translators and interpreters, senior translators and researchers, compound translators and applied translators.<br />
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From their names, it can be seen that high-end translators and interpreters and senior translators and researchers talents have high requirements on the knowledge and quality of interpreters, because they have to face the changing international situation, and have to deal with all kinds of sensitive relations and political related content, they should have flexible cross-cultural communication skills. In addition, for literature, sociology and humanities academic works, it is not only necessary to translate their content, but also to understand their essence. Therefore, translators should not only have humanistic feelings, but also need to have a deep understanding of Chinese and western culture.<br />
<br />
However, there is not much demand for this kind of translation in the society. Such high-level translation requirements are not needed in daily life and work. The greatest demand is for compound translators, which means that they should master knowledge in a specific field while mastering a foreign language. For example, compound translators in the financial field should not only be good at foreign languages, but also master financial knowledge, including professional terms, special expressions and sentence patterns.<br />
<br />
Now we say that machine translation can replace human translation should refer to the field of compound translation talents. Although AI technology has enabled machine translation to participate in creation, it does not mean that compound translation talents will be replaced by machines. The complexity of language and the flexible cross-cultural awareness required in communication make it impossible for machine translation to completely replace human translation.<br />
<br />
The last type of applied translation talents are mostly involved in the general text without too much technical content and few professional terms, so it is easy to be replaced by machine translation.<br />
<br />
Therefore, the author thinks that what universities are facing at present is not only how to train translation talents to cope with the development of machine translation, but to consider the application of machine translation in the process of training translation talents to achieve human-machine integration, so as to better complete the translation work.<br />
<br />
===4.The Language environment and opportunities and challenges of the Belt and Road initiative===<br />
During visits to Central and Southeast Asian countries in September and October 2013, Chinese President Xi Jinping put forward the major initiative of jointly building the Silk Road Economic Belt and the 21st Century Maritime Silk Road. And began to be abbreviated as the Belt and Road Initiative.<br />
<br />
According to the Vision and Actions for Jointly Building silk Road Economic Belt and 21st Century Maritime Silk Road, the Silk Road Economic Belt focuses on connecting China, Central Asia, Russia and Europe (the Baltic Sea). From China to the Persian Gulf and the Mediterranean Sea via Central and West Asia; China to Southeast Asia, South Asia, Indian Ocean. The focus of the 21st Century Maritime Silk Road is to stretch from China's coastal ports to Europe, through the South China Sea and the Indian Ocean. From China's coastal ports across the South China Sea to the South Pacific.<br />
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The Belt and Road "construction is comply with the world multi-polarization and economic globalization, cultural diversity, the initiative of social informatization tide, drive along the countries achieve economic policy coordination, to carry out a wider range, higher level, the deeper regional cooperation and jointly create open, inclusive and balanced, pratt &whitney regional economic cooperation framework.<br />
<br />
====4.1一带一路的语言环境====<br />
The "Belt and Road" involves a wide range of countries and regions, and their languages and cultures are very complex. How to make good use of language, do a good job in translation services, actively spread Chinese culture to the world, strengthen the ability of discourse, and tell Chinese stories well, the first thing to do is to understand the language situation of the countries along the "Belt and Road".<br />
<br />
=====4.1.1The most common language in countries along the "Belt and Road" =====<br />
<br />
There are a wide variety of languages spoken in 65 countries along the Belt and Road, involving nine language families. However, The status of English as the first language in the world is undeniable. Most of the countries participating in the Belt and Road are developing countries, and many of them speak English as their first foreign language. Especially in southeast Asian and South Asian countries, English plays an important role in foreign communication, whether as the official language or the first foreign language. Besides English, more than 100 million people speak Russian, Hindi, Bengali, Arabic and other major languages in the "Belt and Road" countries. It can also be seen that a common feature of languages in countries along the "Belt and Road" is the popularization of English education. English is widely used in international politics, economy, culture, education, science and technology, playing the role of the most important language in the world.<br />
<br />
=====4.1.2The complex language conditions of countries along the "Belt and Road" =====<br />
<br />
The languages spoken in countries along the Belt and Road involve nine major language families and almost all the world's religious types. Differences in religious beliefs also result in differences in culture, customs and social values behind languages. The languages of some countries along the belt and Road have also been influenced by historical and realistic factors, such as colonization, internal division and immigration. <br />
<br />
India, for example, has no national language, but more than 20 official languages. India is a multi-ethnic country, a total of more than 100 people, one of the most obvious difference between nation and nation is the language problem. Therefore, according to the difference of language, India divides different ethnic groups into different states, big and small. Ethnic groups that use the same language are divided into one state. If there are two languages in a state, the state is divided into two parts. And Indian languages differ not only in word order but also in the way they are written. In India, for example, Hindi is spoken by the largest number of people in the north, with about 700 million speakers and 530 million as their first language. It is written in The Hindu language and belongs to the Indo-European language family. Telugu in the east is spoken by about 95 million people and 81.13 million as their first language. It is written in Telugu, which belongs to the Dravidian language family and is quite different from Hindi. As a result, a parliamentary session in India requires dozens of interpreters. <br />
<br />
These factors cannot be ignored in the process of translation, from language communication to cultural understanding, from text to thought exchange, through the bridge of language to truly connect the people, so as to avoid misreading and misunderstanding caused by differences in language and national conditions.<br />
<br />
====4.2Opportunities and challenges of the "Belt and Road" ====<br />
With the promotion of the Belt and Road Initiative, there has been an unprecedented boom in translation. In the previous translation boom in China, most of the foreign languages were translated into Chinese, and most of the foreign cultures were imported into China. However, this time, in the context of the "Belt and Road" initiative, translating Chinese into foreign languages has become an important task for translators. As is known to all, there are many different kinds of "One Belt And One Road" along the national language and culture is complex, the service "area" construction has become a factor in Chinese translation talents training mode reform, one of the foreign language universities have action, many colleges and universities to establish the "area" all the way along the country's small language major, as a result, "One Belt And One Road" initiative to promote, It has brought unprecedented opportunities for human translation. The cultivation of diversified translation talents and the cultivation of translation talents in small languages is an urgent problem to be solved in China. The cultivation of translation talents cannot be completed overnight, and the state needs to reform the training mode of translation talents from the perspective of language strategic development. Only in this way can we meet the new demand for human translation under the new situation of the belt and Road Initiative.<br />
<br />
For a long time, the traditional orientation of translation curriculum and training goal in colleges and universities is to train translation teachers and translators in need of society through translation theory and practice and literary translation practice, which cannot meet the needs of society. Since 2007, in order to meet the needs of the socialist market economy for application-oriented high-level professionals, the Academic Degrees Committee of The State Council approved the establishment of Master of Translation and Interpreting (MTI for short). After joining the pilot program of MTI, more and more universities are reforming the curriculum and training mode of master of Translation in order to cultivate translators who meet the needs of the society.<br />
<br />
Language is an important carrier of culture, and translation is an important link for exporting culture. The quality of translation output also reflects the cultural soft power of a country. With the rise of China, more and more people are interested in Chinese culture, and the number of Chinese learners keeps increasing. Under the background of "One Belt and One Road", excellent translators are urgently needed to spread Chinese culture. With the promotion of "One Belt and One Road" Initiative, the number of other countries learning mutual learning and cultural exchanges with China has increased unprecedeningly, bringing vigorous opportunities for the spread of Chinese culture. Translation talents who understand small languages and multi-lingual translators are needed. They should not only use language to convey information, but also use language as a lubricant for communication.<br />
<br />
===5.在机器翻译视域下如何培养翻译人才 ===<br />
<br />
====5.1 对翻译人才的素养要求 ====<br />
<br />
====5.2 利用人工智能进行翻译实践活动====<br />
<br />
====5.3 大数据、术语库和语料库的应用====<br />
<br />
===6针对一带一路的机器翻译与翻译人才的合作===<br />
<br />
===Conclusion===<br />
<br />
===References===<br />
<br />
=8 颜静(On Machine Translation Under Lanuguage Intelligence——An Option and Opportunity for Human Translators)=<br />
[[Machine_Trans_EN_8]]<br />
<br />
=9 谢佳芬（人工智能时代下的机器翻译与人工翻译）=<br />
[[Machine_Trans_EN_9]]<br />
===Abstract===<br />
With the continuous development of information technology, many industries are facing the competitive pressure of artificial intelligence, and so is the field of translation. Artificial intelligence technology has developed rapidly and combined with the field of translation，which has brought great impact and changes to traditional translation, but artificial intelligence translation and artificial translation have their own advantages and disadvantages. Artificial translation is in the leading position in adapting to human language logical habits and understanding characteristics, but in terms of translation threshold and economic value, the efficiency of artificial intelligence translation is even better. In a word, we need to know that machine translation and human translation are complementary rather than antagonistic.<br />
<br />
===Key Words===<br />
Machine Translation; Artificial Translation; Artificial Intelligence<br />
<br />
===题目===<br />
人工智能时代下的机器翻译与人工翻译<br />
<br />
===摘要===<br />
伴随着信息技术的不断发展，多个行业面临着人工智能的竞争压力，翻译领域也是如此。人工智能技术快速发展并与翻译领域结合，人工智能翻译给传统翻译带来了巨大的冲击和变革，但人工智能翻译与人工翻译存在着各自的优劣特点和发展空间，在适应人类语言逻辑习惯和理解特点的翻译效果上，人工翻译处于领先地位，但在翻译门槛和经济价值上，人工智能翻译的效率则更胜一筹。总的来说，我们要知道机器翻译与人工翻译是互补而非对立的关系。<br />
<br />
===关键词===<br />
机器翻译;人工翻译;人工智能<br />
<br />
===1. Introduction===<br />
====1.1 The History of Machine Translation Aborad====<br />
The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. Alchuni put forward the idea of using machines for translation. In 1933, the Soviet inventor Troyansky designed a machine to translate one language into another. [1]In 1946, the world's first modern electronic computer ENIAC was born. Soon after, American scientist Warren Weaver, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947. In 1949, Warren Weaver published a memorandum entitled Translation, which formally raised the issue of machine translation. In 1954, Georgetown University, with the cooperation of IBM, completed the English-Russian machine translation experiment with IBM-701 computer for the first time, which opened the prelude of machine translation research. [2] In 2006, Google translation was officially released as a free service software, bringing a big upsurge of statistical machine translation research. It was Franz Och who joined Google in 2004 and led Google translation. What’s more, it is precisely because of the unremitting efforts of generations of scientists that science fiction has been brought into reality step by step.<br />
====1.2 The History of Machine Translation in China====<br />
In 1956, the research and development of machine translation has been named in the scientific and technological work and made little achievements in China. On the eve of the tenth anniversary of the National Day in 1959, our country successfully carried out experiments, which translated nine different types of complicated sentences on large general-purpose electronic computers. The dictionary includes 2030 entries, and the grammar rule system consists of 29 circuit diagrams. [3]. After a period of stagnation, China's machine translation ushered in a high-speed development stage after the 1980s in the wave of the third scientific and technological revolution. With the rapid development of economy and science and technology, China has made a qualitative leap in the field of machine translation research with the pace of reform and opening up. In 1978, Institute of Scientific and Technological Information of China, Institute of Computing Technology and Institute of Linguistics carried out an English-Chinese translation experiment with 20 Metallurgical Title examples as the objects and achieved satisfactory results. Subsequently, they developed a JYE-I machine translation system, which based on 200 sentences from metallurgical documents. Its principles and methods were also widely used in the machine translation system developed in the future. In addition, the research achievements of machine translation in China during the 1980s and 1990s also include that Institute of Post and Telecommunication Sciences developed a machine translation system, C Retrieval and automatic typesetting system with good performance and strong practicability in October 1986; In 1988, ISTC launched the ISTIC-I English-Chinese Title System for the translation of applied literature of metallurgy, Information Research Institute of Railway developed an English-Chinese Title Recording machine translation system for railway documents; the Language Institute of the Academy of Social Sciences developed "Tianyu" English-Chinese machine translation system and Matr English-Chinese machine translation system developed by the computer department of National University of Defense Technology. After many explorations and studies, machine translation in China has gradually moved towards application, popularization and commercialization. China Software Technology Corporation launched "Yixing I" in 1988, marking China's machine translation system officially going to the market. After "Yixing", a series of machine translation systems such as Gaoli system in Beijing, Tongyi system in Tianjin and Langwei system in Shaanxi have also entered the public. In the 21st century, the development of a series of apps such as Kingsoft Powerword, Youdao translation and Baidu translation has greatly met the needs of ordinary users for translation. According to the working principle, machine translation has roughly experienced three stages: rule-based machine translation, statistics-based machine translation and deep learning based neural machine translation. [4] These three stages witnessed a leap in the quality of machine translation. Machine translation is more and more used in daily life and even the translation of some texts is almost comparable to artificial translation. In addition to text translation, voice translation, photo translation and other functions have also been listed, which provides great convenience for people's life. It is undeniable that machine translation has become the development trend of translation in the future.<br />
====1.3 The Status Quo of Machine Translation====<br />
In this big data era of information explosion, the prospect of machine translation is also bright. At present, the circular neural network system launched by Google has supported universal translation in more than 60 languages. Many Internet companies such as Microsoft Bing, Sogou, Tencent, Baidu and NetEase Youdao have also launched their own Internet free machine translation systems. [5] Users can obtain translation results free of charge by logging in to the corresponding websites. At present, the circular neural network translation system launched by Google can support real-time translation of more than 60 languages, and the domestic Baidu online machine translation system can also support real-time translation of 28 languages. These Internet online machine translation systems are suitable for a variety of terminal platforms such as mobile phone, PC, tablet and web and its functions are also quite diverse, supporting many translation forms, such as screen word selection, text scanning translation, photo translation, offline translation, web page translation and so on. Although its translation quality needs to be improved, it has been outstanding in the fields of daily dialogue, news translation and so on.<br />
===2. Advantages and Disadvantages of Machine Translation===<br />
Generally speaking, machine translation has the characteristics of high efficiency, low cost, accurate term translation and great development potential and etc. Machine translation is fast and efficient, this is something that artificial translation can’t catch up with. In addition, with the continuous emergence of all kinds of translation software in the market, compared with artificial translation, machine translation is cheap and sometimes even free, which greatly saves the economic cost and time for users with low translation quality requirements. What's more, compared with artificial translation, machine translation has a huge corpus, which makes the translation of some terms, especially the latest scientific and technological terms, more rapid and accurate. The accurate translation of these terms requires the translator to constantly learn, but learning needs a process, which has a certain test on the translator's learning ability and learning speed. In this regard, artificial translation has uncertainty and hysteretic nature. At the same time, with the progress of science and technology and the development of society, the function of machine translation will be more perfect and the quality of translation will be better.Today's machine translation tools and software are easy to carry, all you need to do is just to use the software and electronic dictionary in the mobile phone. There is no need to carry paper dictionaries and books for translation, which saves time and space. At the same time, machine translation covers many fields and is suitable for translation practice in different situations, such as academic, education, commercial trade, social networking, tourism, production technology, etc, it is also easy to deal with various professional terms. However, due to the limitation of translators' own knowledge, artificial translation is often limited to one or a few fields or industries. For example, it is difficult for an interpreter specializing in medical English to translate legal English.<br />
At the same time, machine translation also has its limitations. At first, machine can only operate word to word translation, which only plays the function and role of dictionary. Then, the application of syntax enables the process of sentence translation and it can be solved by using the direct translation method. When the original text and the target language are highly similar, it can be translated directly. For example, the original text "他是个老师." The target language is "he is a teacher ". With the increase of the structural complexity of the original text, the effect of machine translation is greatly reduced. Therefore, at the syntactic level, machine translation still stays in sentences with relatively simple structure. Meanwhile, the original text and the results of machine translation cannot be interchanged equally, indicating that English-Chinese translation has strong randomness, and is not rigorous and scientific enough. <br />
Nowadays, machine translation is highly dependent on parallel corpora, but the construction of parallel corpora is not perfect. At present, the resources of some mainstream languages such as Chinese and English are relatively rich, while the data collection of many small languages is not satisfactory. Moreover, the current corpus is mainly concentrated in the fields of government literature, science and technology, current affairs and news, while there is a serious lack of data in other fields, which can’t reflect the advantages of machine translation. At the same time, corpus construction lags behind. Some informative texts introducing the latest cutting-edge research results often spread the latest academic knowledge and use a large number of new professional terms, such as academic papers and teaching materials while the corpus often lacks the corresponding words of the target language, which makes machine translation powerless<br />
Besides, machine translation is not culturally sensitive. Human may never be able to program machines to understand and experience a particular culture. Different cultures have unique and different language systems, and machines do not have complexity to understand or recognize slang, jargon, puns and idioms. Therefore, their translation may not conform to cultural values and specific norms. This is also one of the challenges that the machine needs to overcome.[6] Artificial intelligence may have human abstract thinking ability in the future, but it is difficult to have image thinking ability including imagination and emotion. [7] Therefore, machine translation is often used in news, science and technology, patents, specifications and other text fields with the purpose of fact description, knowledge and information transmission. These words rarely involve emotional and cultural background. When translating expressive texts, the limitations of machine translation are exposed. The so-called expressive text refers to the text that pays attention to emotional expression and is full of imagination. Its main characteristics are subjectivity, emotion and imagination, such as novels, poetry, prose, art and so on. This kind of text attaches importance to the emotional expression of the author or character image, and uses a lot of metaphors, symbols and other expressions. Machine translation is difficult to catch up with artificial translation in this kind of text, it can only translate the main idea, lack of connotation and literary grace and it cannot have subjective feelings and rational analysis like human beings. In fact, it is not difficult to simulate the human brain, the difficulty is that it is impossible to learn from the rich social experience and life experience of excellent translators. In other words, machine translation lacks the personalization and creativity of human translation. It is this personalization and creativity that promote the development and evolution of language, and what machine translation can only output is mechanical "machine language".<br />
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===3.The Irreplaceability of Artificial Translation ===<br />
====3.1 Translation is Constrained by Context====<br />
At present, machine translation can help people deal with language communication in people's daily life and work, such as clothing, food, housing and transportation, but there is a big gap from the "faithfulness, expressiveness and elegance" emphasized by high-level translation. Language itself is art，which pays more attention to artistry than functionality, and the discipline of art is difficult to quantify and unify. Sometimes it is regular, rigorous, logical and clear, and sometimes it is random, free and logical. If it is translated by machine, it is difficult to grasp this degree. Sometimes, machine translation cannot connect words with contextual meaning. In many languages, the same word may have multiple completely unrelated meanings. In this case, context will have a great impact on word meaning, and the understanding of word meaning depends largely on the meaning read from context. Only human beings can combine words with context, determine their true meaning, and creatively adjust and modify the language to obtain a complete and accurate translation. This is undoubtedly very difficult for machine translation. Artificial translation can get rid of the constraints of the source language and translate the translation in line with the grammar, sentence patterns and word habits of the target language. In the process of translation, translators can use their own knowledge reserves to analyze the differences between the source language and the target language in thinking mode, cultural characteristics, social background, customs and habits, so as to translate a more accurate translation. Artificial translation can also add, delete, domesticate, modify and polish the translation according to the style, make up for the lack of culture, try to maintain the thought, artistic conception and charm of the original text and the style of the source language. In addition, translators can also judge and consider the words with multiple meanings or easy to produce ambiguity according to the context, so as to make the translation more clear and more accurate and improve the quality of the translation.<br />
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===4. Discussion on the Relationship Between Machine Translation and Artificial Translation ===<br />
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===5. Suggestions on the Combined Development of Machine Translation and Artificial Translation===<br />
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===Conclusion===<br />
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===References===<br />
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=10 熊敏(Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts)=<br />
[[Machine_Trans_EN_10]]<br />
===Abstract===<br />
With the rapid development of information technology,machine translation technology emerged and is gradually becoming mature.In order to explore the ability of machine translation, I adopts two versions of translation, which are manual translation and machine translation(this paper uses Youdao translation) for different types of texts(according to Peter Newmark's types of text). The results are quite different in terms of quality and accuracy.<br />
<br />
===Key words===<br />
machine translation; manual translation; Newmark's type of texts<br />
<br />
===题目===<br />
Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts<br />
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===摘要===<br />
随着信息技术的高速发展，机器翻译技术出现了，并且逐渐成熟。为了探究机器翻译的能力水平，本人根据纽马克的文本类型分类，选择了相应的译文类型，并且将其机器翻译的版本以及人工翻译的版本进行对比。就质量和准确度而言，译文的水平大相径庭。<br />
<br />
===关键词===<br />
机器翻译；人工翻译；纽马克文本类型<br />
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===1. Introduction===<br />
====1.1.Introduction to machine translation====<br />
Machine translation, also known as computer translation is a technique to translating through machine translator, such as Google translation and Youdao translation. Machine translation is one of the branches of computational linguistics, ranging from computer science, statistics, information science and so on. Machine translation plays an important role in all aspects.<br />
Machine translation can be traced back to 1940s, when British engineer Booth and American engineer Weaver proposed using computer to translate and started to study machines used for translation. However in the 1960s, reports from ALPAC (Automated Language Processing Advisory Committee) showed studies on machine translation had stagnated for a decade. In the 1970s, with the advancement of computer, machine translation was back to track. In the last decades, machine translation has mainly developed into four stages: rule-based machine translation, statistic machine translation, example-based machine translation and neural machine translation.<br />
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====1.2.Process of machine translation====<br />
The process of manual translation is different from that of machine translation. Here is the process of the former. (1) Understand source language. (2) Use target language to organize language. (3) Generate translation. Unlike manual translation, machine translation tends to analyze and code source language first, then look for related codes in corpus, and work out the code that represents target language, generating translation. But they share a common feature, which is that Lexicon, grammatical rules and syntactic structure are taken into consideration. This is one of the biggest challenges for machine translation.<br />
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===2.Newmark’s type of texts===<br />
Peter Newmark divided texts into informative type, expressive text and vocative type according to the linguistic functions of various texts. <br />
====2.11Informative text====<br />
The core of the informative texts is the truth. It is to convey facts, information, knowledge and the like. The language style of the text is objective and logical. Reports, papers, scientific and technological textbooks are all attributed to informative texts.<br />
====2.2Expressive text====<br />
The core of the expressive text is the emotion. It is to express preferences, feelings, views and so on. The language style of it is subjective. Literary works, including fictions, poems and drama, autobiography and authoritative statements belong to expressive text.<br />
====2.3Vocative text====<br />
The core of the vocative text is readership. It is to call upon readers to act in the way intended by the text. So it is reader-oriented. Such texts advertisement, propaganda and notices are of vocative text.<br />
====2.4Study Method====<br />
Manual translations of the three texts are selected from authoritative versions and universally acknowledged. And machine translations of those come from Youdao Translator. And in this thesis I will compare and evaluate the two methods in word diction, sentence structure, word order and redundancy.<br />
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===7. ===<br />
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===Conclusion===<br />
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===References===<br />
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=11 陈惠妮=(Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts)=<br />
[[Machine_Trans_EN_11]]<br />
===Abstract===<br />
At present, globalization is accelerating and the market demand for language services is rapidly increasing . Machine translation, as an important translation method, can greatly improve translation efficiency due to its low cost and high speed. However, because of the limitations of machine translation and the differences between Chinese and English language, machine translation is not accurate enough. In order to balance translation efficiency and translation quality, a great number of manual revisions in translation are required for the machine translating texts. Medical papers are specialized, special and purposeful, so it requires accurate,qualified and professional translation. However, the quality of translations by machine is inefficient to meet the high-quality requirements of medical papers translation. Therefore, the introduction of pre-editing can greatly improve the efficiency and quality of machine translation.<br />
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===Key words===<br />
Pre-editing, Machine translation, Medical texts<br />
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===题目===<br />
Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts<br />
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===摘要===<br />
在全球化加速发展的今天，市场对语言服务的需求迅速增加。机器翻译作为一种重要的翻译途径，由于其成本低、速度快，可以大大提高翻译效率。然而，由于机器翻译的局限性以及中英文语言的差异，机器翻译的准确性不高。为了平衡翻译效率和翻译质量，机器翻译文本需要大量的手工修改。<br />
医学论文具有专业性、特殊性和目的性，要求其译文准确、合格、专业。然而，机器翻译的质量较低，无法满足医学论文对翻译的高质量要求。因此，译前编辑的引入可以大大提高机器翻译的效率和质量。<br />
<br />
===关键词===<br />
译前编辑；机器翻译；医学文本<br />
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===1. Introduction===<br />
===1.1 Definition of Machine Translation===<br />
The concept of machine translation was firstly proposed in the 1930s. Since 1940s, the machine translation technology has been evolving from rule-based machine translation (RBMT) to statistical machine translation (SMT), and to neural machine translation (NMT). Machine translation refers to the automatic translation of source language into target language by using a computer system. That is, machine translation refers to the automatic translation of text from one language into another natural language by computer software or other online translation webs. Machine translation is also defined as the process of “using a computer system to automatically translate text or speech from one natural language to another” according to the definition by ISO (Cui, 2014). On the basic level, machine translation performs mechanical substitution of words in one language for words in another language, but that rarely produces a good translation, therefore, recognition of the whole phrases and their closest counterparts in the target language is needed. Not all words in one language have equivalents in another language, and many words have more than one meaning. A huge demand for translation is greatly needed in today’s global world, which creates new opportunities for the development of machine translation, attracts more and more attention and becomes one of the current research focuses.<br />
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===1.2 Definition of Pre-editing===<br />
Pre-editing means to adjust and modify the source language to make it fit more with the characteristics of the machine translation software before putting the source language before the into machine translation, so as to improve the quality of the translation machine translation (Wei Changhong, 2008:93-94). Pre-editing is to modify the original text before putting it into machine translation software, in order to improve the recognition rate of machine translation, optimize the output quality of translated text and reduce the workload of post-editing. Because pre-translation editing only needs to be modified in one language, the operation is simpler than post-translation editing, which can realize the double improvement of quality and efficiency. A good pre-editing translation can help machine translation more smoothly, thus improving the machine readability and quality of the output translation. Pre-editing is mostly applied in the following situations: one is when the original text is of poor quality and the machine is difficult to recognize the meaning of the sentence, such as user generated content with poor readability and translatability (Gerlach et al, 2003:45-53); Documents that need to be published in multiple languages; Next is when the original text contains a lot of jargon; The last is the original text has a corresponding translation memory bank. If the original text is edited, it can better match the content of the translation memory bank.<br />
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===1.3 Machine Translation Mode===<br />
According to the different knowledge acquisition methods, machine translation modes can be classified as follows: one is rule-based machine translation, which is based on bilingual dictionaries and a library of language rules for each language. The quality of translation depends on whether the source language conforms to the existing rules, but the inexhaustible rules are the hinders of this model. The second mode is machine translation based on statistics. This translation model relies on the principles of mathematics and statistics to find various existing translations corresponding to the translation tasks through the employment of corpus, analyzing the frequency of their occurrence and selecting the translation with the highest frequency for output. The disadvantage of this translation model is that it ignores the flexibility of language and the importance of context. The last one is neural network language model. This model is different from previous translation models in that it uses end-to-end neural network to realize automatic translation between natural languages. At present, the quality of its translation is much higher than that of the previous translation models.<br />
===1.4 Source of Study Abstracts===<br />
The core medical journals from domestic are selected in order to make the paper more representative and authoritative, such as National Medical Journal of China, Journal of Peking University (Health Science), and Journal of Third Military Medical University. All of them include basic medical science, biomedical technology, laboratory medical science and other fields. In this way, the pre-editing approaches included are applicable enough to machine translation of medical abstracts.<br />
===1.5 Selection of Translation Software===<br />
Generally, machine translation software includes Google Translation, Youdao Translator and Niu Translation, which has their own special use in translation. Take Google as an example, it is more like neurons in human brain, enabling to learn and collect information to establish connections with its neural machine translator’s neurons. However, it also causes many errors because of the lack of enough information. In this paper, contrastive analysis will be carried on by using Google translation. On the one hand, being a pioneer in the translation of NET, it is inevitably to sue Google as the translation software to translate medical texts in this paper. Comparing the program called “Google brain”, other NMT of translation software are relatively disadvantages. On the other hand, the Google translation enjoys the largest users in the world, with its downloads of more than one billion. The output quality of Google translation is more correct and complete than other machine translation software. With years of development and improvement, Google Translation has been greatly promoted. In this paper, Chinese medical abstract will be automatically translated by Google translation, and then the translation output will be compared with the translation by human and the publication of English abstracts. The main purpose is to prove that the improvement and promotion of quality and accuracy in the medical abstracts will be obtained through the pre-editing approaches.<br />
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===2.Language Characteristics and Error Division===<br />
Since Chinese and English are two different languages, it is quite neccessary to identify their own characteristics so as to better analyze and understand the two languages. There also exit some mistakes of these two languages. So the following will make some clarrifications of these two languages.<br />
===2.1 Language Characteristics of Medical Abstracts===<br />
Chinese and English belong to different language systems, so there are differences in their language structure and the way users think of the languages. When using machine translation from Chinese to English, due to the unequal language levels, there will be many mistakes in the translation process, especially for ESP texts, such as medical papers. <br />
Here, these differences mainly refer to the linguistic characteristics of medical abstracts. In medical abstracts, it usually includes structured and unstructured abstracts. Although in different forms, they both describe the purpose, methods and conclusions of the research.<br />
In the method section of medial abstract, several Chinese sentences can be connected with commas, but each sentence may convey different information. In contrast, English sentences contain a great deal of information, but in order to ensure clarity, some modifiers need to be isolated and then reconstructed. However, in Chinese-English machine translation, a lot of information is put into the sentence because the machine segments the sentence on the basis of the full comma.<br />
In addition, subjectless sentence is used in the objective and method parts of Chinese medical abstracts. The subjectless sentence means the sentence without or free from subject and is usually employed in two contexts. The first is "needless to say". In Chinese sentences, it is common to omit the subject of the sentence. Chinese sentences can convey meanings by using incomplete sentence structures, so Chinese speakers can understand the meanings of the sentences even though the sentence subject is omitted. The second is "emphasis of action". In this context, subjectless sentences are used to describe behaviors, especially in the study of traditional Chinese medicine abstracts. Comparatively speaking, it should be avoided in English medical abstracts. Abstract sentences are more subjective when describing the learning process, while the essential requirement of English medical abstract is objectivity. From this point, sentences without subject should be avoided in English medical abstracts. Another feature is voice. Few words with passive meanings appear in Chinese abstracts. English sentences are more favoured in using passive voice. When translating from Chinese to English, passive voice should be used to make the contents objective, which is also the basic requirement of medical papers.<br />
These are the linguistic features of Chinese medical abstracts. There are great differences in sentence structure and expression between Chinese and English medical abstracts. These differences may reduce the accuracy of machine translation, so it is necessary to introduce pre-editing to edit the source text to ensure that the source text can be accurately recognized by the machine and fully translated into English.<br />
===2.2 Error Division of Machine Translation in Translating Abstracts of Medical Papers from Chinese to English===<br />
In this paper, errors in machine translation are listed out after analyzing. Some are due to the principles, such as statistical-based MT and NMT, employed by machine translation. Based on the original errors, they can be studied from two levels. One is from the macro-level, referring to mistakes caused by objective factors. These are not human factors, but the disadvantage of machine translation and the limitations of language. The limitations of machine translation are derived from the principles and models adopted and manifests itself as a reliance on reference sources. In this case, semantic ambiguity and textual incoherence may occur in the absence of reference sources.<br />
However, for ESP texts such as medical papers, the requirements for terms and sentence patterns are far beyond the existing corpora. For unfamiliar texts (that is, the corresponding texts cannot be found in the corpora). The quality of output translation will be relatively low, which is determined by the principles of machine translation. As mentioned above, machine translation has evolved over the past few decades from rules-based to corp-based statistics to NMT. However, there still exist some limitations in machine translation.<br />
Mistakes on micro-level are mainly caused by the variations and differences of linguistic structure between Chinese and English. Chinese is an implicit language, while English is an explicit one (Lian, 2010) To put it in another way, Chinese expression does not depend on language structures, while English does the opposite. This may result in mismatches between the original and machine translated sentences. This kind of error is mainly divided into two types: component fragment and component missing. For a medical paper, such errors are very serious. As a kind of ESP discourse, medical paper has the characteristics of fixed, objective and accurate language structure. In order to reproduce the characteristics of medical abstracts in translation, it is necessary to avoid errors at the level of words and sentences and pay attention to logic and consistency.<br />
Lexical errors here refer to the inconsistency of fixed expressions in the translation of terms. Although these terms are machine translation based on the corpus, the corpus may not be perfect for ESP texts such as medical papers. Therefore, fixed expressions are not used in term translation. This is also the most common mistake in machine translation (the term here includes but is not limited to nouns). For ESP texts, medical text in particular, the fixed expressions and the accuracy of terms are of great importance.<br />
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===Conclusion===<br />
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===References===<br />
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=12 蔡珠凤=(The Mistranslation of C-J Machine Translation of Political Statements)=<br />
[[Machine_Trans_EN_12]]<br />
===Abstract===<br />
Language is the main way of communication between people. With the continuous development of globalization, the scale of cross-border exchanges is also expanding. However, due to cultural differences and diversity, the languages of different countries and regions are very different, which seriously hinders people's communication. The demand for efficient and convenient translation tools is increasing. At the same time, with the development of network technology and artificial intelligence, recognition technology based on deep learning is more and more widely used in English, Japanese and other fields.<br />
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===Key words===<br />
machine translation; political statements; mistranslation of C-J machine translation<br />
===题目===<br />
The Mistranslation of C-J Machine Translation of Political Statements<br />
===摘要===<br />
语言是人与人之间交流的主要方式。随着全球化的不断发展，跨境交流的规模也在不断扩大。然而，由于文化的差异和多样性，不同国家和地区的语言差异很大，这严重阻碍了人们的交流。对高效便捷的翻译工具的需求正在增加。同时，随着网络技术和人工智能的发展，基于深度学习的识别技术在英语、日语等领域的应用越来越广泛。<br />
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===关键词===<br />
机器翻译；政治发言；政治发言中译日的误译<br />
===1. Introduction===<br />
===Introduction to machine translation===<br />
Machine translation, also known as automatic translation, is a process of using computers to convert one natural language (source language) into another natural language (target language). It is a branch of computational linguistics, one of the ultimate goals of artificial intelligence, and has important scientific research value.At the same time, machine translation has important practical value. With the rapid development of economic globalization and the Internet, machine translation technology plays a more and more important role in promoting political, economic and cultural exchanges.The development of machine translation technology has been closely accompanied by the development of computer technology, information theory, linguistics and other disciplines. From the early dictionary matching, to the rule translation of dictionaries combined with linguistic expert knowledge, and then to the statistical machine translation based on corpus, with the improvement of computer computing power and the explosive growth of multilingual information, machine translation technology gradually stepped out of the ivory tower and began to provide real-time and convenient translation services for general users.<br />
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=== C-J machine translation software===<br />
Today's online machine translation software includes Baidu translation, Tencent translation, Google translation, Youdao translation, Bing translation and so on. Google was the first company to launch the machine translation system, and Baidu was the first company to import the machine translation system in China. In addition, Tencent and Youdao have attracted much attention.Machine translation is the process of using computers to convert one natural language into another. It usually refers to sentence and full-text translation between natural languages. In order to continuously improve the translation quality, R & D personnel have added artificial intelligence technologies such as speech recognition, image processing and deep neural network to machine translation on the basis of traditional machine translation based on rules, statistics and examples.With the increase of using machine translation, the joint cooperation between manual translation and machine translation will also increase significantly in the future. What criteria should be used to evaluate the quality of machine translation? In the evolving field of machine translation, there is an urgent need to clarify the unsolvable questions and solved problems.<br />
<br />
===The history of machine translation===<br />
The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. alchuni put forward the idea of using machines for translation. In 1933, Soviet inventor П.П. Trojansky designed a machine to translate one language into another, and registered his invention on September 5 of the same year; However, due to the low technical level in the 1930s, his translation machine was not made. In 1946, the first modern electronic computer ENIAC was born. Shortly after that, W. weaver, an American scientist and A. D. booth, a British engineer, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947 when discussing the application scope of electronic computer. In 1949, W. Weaver published the translation memorandum, which formally put forward the idea of machine translation. After 60 years of ups and downs, machine translation has experienced a tortuous and long development path. The academic community generally divides it into the following four stages:<br />
Pioneering period（1947-1964）<br />
In 1954, with the cooperation of IBM, Georgetown University completed the English Russian machine translation experiment with ibm-701 computer for the first time, showing the feasibility of machine translation to the public and the scientific community, thus opening the prelude to the study of machine translation.<br />
It is not too late for China to start this research. As early as 1956, the state included this research in the national scientific work development plan. The topic name is "machine translation, the construction of natural language translation rules and the mathematical theory of natural language". In 1957, the Institute of language and the Institute of computing technology of the Chinese Academy of Sciences cooperated in the Russian Chinese machine translation experiment, translating 9 different types of more complex sentences.<br />
From the 1950s to the first half of the 1960s, machine translation research has been on the rise. The United States and the former Soviet Union, two superpowers, have provided a lot of financial support for machine translation projects for military, political and economic purposes, while European countries have also paid considerable attention to machine translation research due to geopolitical and economic needs, and machine translation has become an upsurge for a time. In this period, although machine translation is just in the pioneering stage, it has entered an optimistic period of prosperity.<br />
Frustrated period（1964-1975）<br />
In 1964, in order to evaluate the research progress of machine translation, the American Academy of Sciences established the automatic language processing Advisory Committee (Alpac Committee) and began a two-year comprehensive investigation, analysis and test.<br />
In November 1966, the committee published a report entitled "language and machine" (Alpac report for short), which comprehensively denied the feasibility of machine translation and suggested stopping the financial support for machine translation projects. The publication of this report has dealt a blow to the booming machine translation, and the research of machine translation has fallen into a standstill. Coincidentally, during this period, China broke out the "ten-year Cultural Revolution", and basically these studies also stagnated. Machine translation has entered a depression.<br />
convalescence（1975-1989）<br />
Since the 1970s, with the development of science and technology and the increasingly frequent exchange of scientific and technological information among countries, the language barriers between countries have become more serious. The traditional manual operation mode has been far from meeting the needs, and there is an urgent need for computers to engage in translation. At the same time, the development of computer science and linguistics, especially the substantial improvement of computer hardware technology and the application of artificial intelligence in natural language processing, have promoted the recovery of machine translation research from the technical level. Machine translation projects have begun to develop again, and various practical and experimental systems have been launched successively, such as weinder system Eurpotra multilingual translation system, taum-meteo system, etc.<br />
However, after the end of the "ten-year holocaust", China has perked up again, and machine translation research has been put on the agenda again. The "784" project has paid enough attention to machine translation research. After the mid-1980s, the development of machine translation research in China has further accelerated. Firstly, two English Chinese machine translation systems, ky-1 and MT / ec863, have been successfully developed, indicating that China has made great progress in machine translation technology.<br />
New period(1990 present)<br />
With the universal application of the Internet, the acceleration of the process of world economic integration and the increasingly frequent exchanges in the international community, the traditional way of manual operation is far from meeting the rapidly growing needs of translation. People's demand for machine translation has increased unprecedentedly, and machine translation has ushered in a new development opportunity. International conferences on machine translation research have been held frequently, and China has made unprecedented achievements. A series of machine translation software have been launched, such as "Yixing", "Yaxin", "Tongyi", "Huajian", etc. Driven by the market demand, the commercial machine translation system has entered the practical stage, entered the market and came to the users.<br />
Since the new century, with the emergence and popularization of the Internet, the amount of data has increased sharply, and statistical methods have been fully applied. Internet companies have set up machine translation research groups and developed machine translation systems based on Internet big data, so as to make machine translation really practical, such as "Baidu translation", "Google translation", etc. In recent years, with the progress of in-depth learning, machine translation technology has further developed, which has promoted the rapid improvement of translation quality, and the translation in oral and other fields is more authentic and fluent.<br />
<br />
===The problem of machine translation at present===<br />
Error is inevitable<br />
Many people have misunderstandings about machine translation. They think that machine translation has great deviation and can't help people solve any problems. In fact, the error is inevitable. The reason is that machine translation uses linguistic principles. The machine automatically recognizes grammar, calls the stored thesaurus and automatically performs corresponding translation. However, errors are inevitable due to changes or irregularities in grammar, morphology and syntax, such as sentences with adverbials after "give me a reason to kill you first" in Dahua journey to the West. After all, a machine is a machine. No one has special feelings for language. How can it feel the lasting charm of "the tenderness of lowering its head, like the shame of a water lotus? After all, the meaning of Chinese is very different due to the changes of morphology, grammar and syntax and the change of context. Even many Chinese people are zhanger monks - they can't touch their heads, let alone machines.<br />
Bottleneck<br />
In fact, no matter which method, the biggest factor affecting the development of machine translation lies in the quality of translation. Judging from the achievements, the quality of machine translation is still far from the ultimate goal.<br />
Chinese mathematician and linguist Zhou Haizhong once pointed out in his paper "fifty years of machine translation": to improve the quality of machine translation, the first thing to solve is the problem of language itself rather than programming; It is certainly impossible to improve the quality of machine translation by relying on several programs alone. At the same time, he also pointed out that it is impossible for machine translation to achieve the degree of "faithfulness, expressiveness and elegance" when human beings have not yet understood how the brain performs fuzzy recognition and logical judgment of language. This view may reveal the bottleneck restricting the quality of translation. <br />
It is worth mentioning that American inventor and futurist ray cozwell predicted in an interview with Huffington Post that the quality of machine translation will reach the level of human translation by 2029. There are still many disputes about this thesis in the academic circles.<br />
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===2.Mistranslation of Chinese Japanese machine translation===<br />
===2.1Vocabulary mistranslation in Chinese Japanese machine translation===<br />
===2.1.1Mistranslation of proper nouns===<br />
===2.1.2Mistranslation of Polysemy===<br />
===2.1.3Mistranslation of compound words===<br />
===2.2 Syntactic mistranslation in Chinese Japanese machine translation===<br />
===2.2.1Main dynamic Mistranslation===<br />
===2.2.2Dynamic Mistranslation===<br />
===2.2.3Mistranslation of tenses===<br />
===2.2.4Mistranslation of honorifics===<br />
===3.===<br />
===4.===<br />
===Conclusion===<br />
===References===<br />
<br />
=13 陈湘琼Chen Xiangqiong（Study on Post-editing from the Perspective of Functional Equivalence Theory ）=<br />
[[Machine_Trans_EN_13]]<br />
<br />
=14 Bi bi Nadia（Machine Translation a Challenge for Human Translators)=<br />
[[Machine_Trans_EN_14]]<br />
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===Abstract===<br />
Machine translation is a big obstacle in the way of Human translators or interpretors although it is quick and less time consuming.people are trying to get translation of their target language using through source language.For this purpose they are using digital apps like Google translation Google translation does not give accurate and exact interpretation.Google translator is translates word to word translate that doesn't clarifies it's true and actual meaning.On the other hand human translators can give exact and accurate translation ,they take care of grammatical errors , diction and sentence structure.They clarify the purpose of target language through using source language.<br />
===Keywords===<br />
Descriptive translation, academic, interdiscipline, comparative literature, localization,Translatology,school of thought, translation , studies, linguistics, corresponding<br />
===Body of article===<br />
Translation is the process of reworking text from one language into another to maintain the original message and communication. But, like everything else, there are different methods of translation, and they vary in form and function. What is translation? The term has become widely used among knowledge transfer researchers and practitioners, especially in the fields of health and health care. In a landmark review, Jonathan Lomas began to argue that 'The tasks… may be defined as to establish and maintain links between researchers and their audience, via the appropriate translation of research findings' (Lomas 1997, p 4). In 2004, the World Health Organization's World Report on Knowledge for Better Health suggested that 'One of the key contributions of research to health systems is the translation of knowledge into actions' (WHO 2004, p 33 and p 100). By 2006, special issues of WHO's Bulletin as well as the journals Evaluation and the Health Professions and the Journal of Continuing Education in the Health Professions were dedicated to translation.But what does 'translation' mean? It may be a new word for an old problem, meaning nothing more than 'transfer'. The rapidly emerging field of 'translational medicine' seems to take translation to mean generally what transfer might have meant, that is the transmission of knowledge and evidence 'from bench to bedside'. As one commentator has observed, "Translational medicine" as a fashionable term is being increasingly used to describe the wish of biomedical researchers to ultimately help patients' (Wehling 2008, abstract). <br />
Semantic uncertainty persists, not least because of the different interests of scientists, clinicians, patients and commercial firms (Littman et al 2007): 'Translational research means different things to different people, but it seems important to almost everyone' (Woolf 2008, p 211).<br />
In related fields, such as public health (Armstrong et al 2006), 'translation' seems to signify dissatisfaction with 'transfer'. It wants to move away from thinking of knowledge transfer as a form of technology transfer or dissemination, rejecting if only by implication its mechanistic assumptions and its model of linear messaging from A to B. But still, what does it signify?<br />
<br />
===Why translation?===<br />
'Translation' indicates a closer attention to the problem of shared meaning and how it might be developed. It seems to represent some new epistemological lubricant, facilitating the dissemination of texts and the application and use of the knowledge and information they in. Simply, translation might be the key to transfer. And yet, when we stop to think, we are more ambivalent. What is translated often seems somehow inferior, not real or original. Note how readily commentators reach for the idea that things might be 'lost in translation'. Knowing at a distance – made in and mediated by translation - makes for incomplete renditions, blurred images, partial truths. So what might 'translation' really mean? The purpose of this paper is to set out, for policy makers and practitioners, the theoretical and conceptual resources translation holds and seems to represent. In doing so, it explores understandings of translation in the fields of literature and linguistics and in the sociology of science and technology. It begins by setting out just why this idea of translation should make immediate, intuitive sense in relation to research, policy and practice.<br />
1translation in research, policy and practice research as translation<br />
Research often entails translation from one language to another: where data is collected from more than one ethnic group, for example, or where the language of the researcher is other than that of the research subject. It may draw on a secondary literature or source documents written in different languages, and may be published and disseminated in languages other than the one in which it is first written up.<br />
2In a different way, to conduct an interview is to ask for an account of experience and its meanings, but it is also to construct and translate that experience in terms defined at least in part by the researcher. In representing what is said, transcripts then select data, usually excluding significant gesture and eye-contact, for example. Often, certain characteristics of speech-acts (such as hesitations) will be edited out. In turn, the format of the transcript shapes the analytic use the researcher may make of it. The basis of research 'findings', then, is an artefact, a transcript or translation, not an original interaction (Ochs 1979, Barnes, Bloor and Henry 1996, Ross 2009).<br />
3In this way, the researcher recasts aspects of his or her problem or topic in new, scientific form: 'All researchers "translate" the experiences of others' (Temple 1997, p 609). Research is invariably conducted in a sort of 'metalanguage' (Hantrais and Ager 1985): the research process can be conceived as one of successive translations (from theoretical formulation to operationalization, transcription, interpretation and dissemination). Theorization is a process of reciprocal back and forth between theory and fact, in which conceptions of each are revised in order that one fit the other (Baldamus 1974).<br />
4 It is a kind of translation: a rereading, re-use, re-application or re-representation of what we know in new terms (Turner 1980). Referencing, too, is an act of translation, a form of appropriation and incorporation of one text by another (Gilbert 1977).policy as translation Each of the fields covered by the paper is diverse and ill-defined, and there is no intention here to provide a comprehensive account of any them. Sources have been chosen for their relevance: main references are cited in the text, and additional sources listed in footnotes.<br />
<br />
2For a brief introduction to the technical issues involved in social research in more than one language, see Birbili (2000). For translation issues in survey research and question design in general, see Ervin and Bower (1952) and Deutscher (1968); on translating survey research instruments (in this instance health-related quality of life measures), Bowden and Fox-Rushby (2003). On the use of translators and interpreters, see Temple (1997) and Jentsch (1998).<br />
3For an interesting discussion of this problem, see Bourdieu (1999), esp pp 621-626, 'The risks of writing'. <br />
===Difference between machine translation and human translators===<br />
Few people disagree on the differences between the two, but many argue over the quality of the translations. How accurate are machine translations? How reliable are human translations? Some say machine translation produces near-perfect translations while others are adamant that translations are incomprehensible and cause more problems than they solve. Results will, of course, vary depending on the source and target languages, the machine translation service used (e.g. Google Translate), and the complexity of the original text.<br />
Machine translation, love it or hate it, is here to stay. In fact, the machine translation market is growing at such a fast pace that it is predicted to reach $980 million by 2022.<br />
===Machine translation: the pros and cons===<br />
The advantages of machine translation generally come down to two factors: it’s faster and cheaper. The downside to this is the standard of translation can be anywhere from inaccurate, to incomprehensible, and potentially dangerous (more on that shortly).<br />
==The advantages of machine translation==<br />
Many free tools are readily available (Google Translate, Skype Translator, etc.)<br />
Quick turnaround time <br />
You can translate between multiple languages using one tool<br />
Translation technology is constantly improving<br />
The disadvantages of machine translation<br />
Level of accuracy can be very low <br />
Accuracy is also very inconsistent across different languages<br />
==Machines can't translate context ==<br />
Mistakes are sometimes costly <br />
Sometimes translation simply doesn’t work<br />
The most important thing to consider with any kind of translation is the cost of potential mistakes. Translating instructions for medical equipment, aviation manuals, legal documents and many other kinds of content require 100% accuracy. In such cases, mistakes can cost lives, huge amounts of money and irreparable damage to your company’s image. So choose carefully!<br />
Human translation: the pros and cons<br />
Human translation essentially switches the table in terms of pros and cons. A higher standard of accuracy comes at the price of longer turnaround times and higher costs. What you have to decide is whether that initial investment outweighs the potential cost of mistakes. Alternatively, whether mistakes simply aren’t an option, like the cases we looked at in the previous section.<br />
<br />
==The advantages of human translation ==<br />
It’s a translator’s job to ensure the highest accuracy<br />
Humans can interpret context and capture the same meaning, rather than simply translating words<br />
Human translators can review their work and provide a quality process<br />
Humans can interpret the creative use of language, e.g. puns, metaphors, slogans, etc.<br />
Professional translators understand the idiomatic differences between their languages<br />
Humans can spot pieces of content where literal translation isn’t possible and find the most suitable alternative<br />
The disadvantages of human translation<br />
Turnaround time is longer <br />
Translators rarely work for free <br />
Unless you use a translation agency, with access to thousands of translators, you’re limited to the languages any one translator can work with<br />
Simply put, human translation is your best option when accuracy is even remotely important. Other considerations to make are the complexity of your source material and the two languages you’re translating between – both of which can render machines pretty useless.<br />
<br />
When to use machine and human translation<br />
The truth is, the debate over machine vs human translation is an unnecessary distraction. What we should really be talking about is when to use these two different types of translation services, because they both serve a very valid purpose.<br />
<br />
Examples of when to use machine translation<br />
When you have a large bulk of content to translate and the general meaning is enough<br />
When your translation never reaches the final audience, e.g. you’re translating a resource as research for another piece of content<br />
Translating documents for internal use within a company, provided 100% accuracy isn’t needed<br />
To partially translate large chunks of content for a human translator to improve upon<br />
Examples of when to use human translation<br />
When accuracy is important<br />
Most cases where your translated content is received by a consumer audience<br />
When you have a duty of care to provide accurate translations (e.g. legal documents, product instructions, medical guidelines or health and safety content)<br />
When translating marketing material or other texts for creative language uses.<br />
<br />
===Conclusion ===<br />
<br />
In the light of above mentioned facts and figures here by we would say that machine translation is challenge for human translators because it can reduces the wokload of translation but can't give accurate and exact translation of the traget language.It can be less reliable than human translation..</div>Chen Huini