Difference between revisions of "Machine translation"

From China Studies Wiki
Jump to navigation Jump to search
 
(26 intermediate revisions by 5 users not shown)
Line 18: Line 18:
 
[[Machine_Trans_EN_1]]
 
[[Machine_Trans_EN_1]]
  
−
=Chapter 3：On the Realm Advantages And Mutual Development of Machine Translation And Huamn Translation)=
+
=Chapter 2：On the Realm Advantages and Mutual Development of Machine Translation and Huamn Translation)=
−
"'论机器翻译与人工翻译的领域优势及共同发展'"
+
'''论机器翻译与人工翻译的领域优势及共同发展'''
  
 
肖毅瑶 Xiao Yiyao, Hunan Normal University, China
 
肖毅瑶 Xiao Yiyao, Hunan Normal University, China
−
[[Machine_Trans_EN_3]]
+
 
 +
[[Machine_Trans_EN_2]]
 +
 
 +
=Chapter 3: -Missing-=
 +
 
 +
[[Machine_Trans_EN_3]]
  
 
=Chapter 4 : A Comparison Between Machine Translation of Netease and Traditional Human Translation—A Case Study of The Economist Articles)=  
 
=Chapter 4 : A Comparison Between Machine Translation of Netease and Traditional Human Translation—A Case Study of The Economist Articles)=  
Line 52: Line 57:
  
 
=9 谢佳芬（ Machine Translation and Artificial Translation in the Era of Artificial Intelligence）=
 
=9 谢佳芬（ Machine Translation and Artificial Translation in the Era of Artificial Intelligence）=
 +
人工智能时代下的机器翻译与人工翻译
 +
 +
谢佳芬 Xie Jiafen ,Hunan Normal University, China
 +
 
[[Machine_Trans_EN_9]]
 
[[Machine_Trans_EN_9]]
  
−
=10 熊敏(Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts)=
+
=Chapter 10 熊敏 Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts=
 
机器翻译对各类型文本的英汉翻译能力探究
 
机器翻译对各类型文本的英汉翻译能力探究
  
Line 61: Line 70:
 
[[Machine_Trans_EN_10]]
 
[[Machine_Trans_EN_10]]
  
−
 
+
=Chapter 11 Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts=
−
===Abstract===
 
−
The history of machine translation can be traced to 1940s, undergoing germination, recession, revival and flourish. Nowadays, with the rapid development of information technology, machine translation technology emerged and is gradually becoming mature. Whether manual translation can be replaced by machine translation becomes a widely-disputed topic. So in order to explore the ability of machine translation, I adopts two versions of translation, which are manual translation and machine translation(this paper uses Youdao translation) for different types of texts(according to Peter Newmark's types of text：informative text, expressive text and vocative text). The results are quite different in terms of quality and accuracy. The results show that machine translation is more suitable for informative text for its brevity, while the expressive texts especially literary works and vocative texts are difficult to be translated, because there are too many loaded words and cultural images in expressive text and dependence on the readers. To draw a conclusion, the relationship between machine translation and manual translation should be complementary rather than competitive.
 
−
 
 
−
===Key words===
 
−
machine translation; manual translation; Newmark's type of texts
 
−
 
 
−
===题目===
 
−
Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts
 
−
 
 
−
===摘要===
 
−
机器翻译的历史可以追溯到上个世纪40年代，经历了萌芽期、萧条期、复兴期和繁荣期四个阶段。如今，随着信息技术的高速发展，机器翻译技术日益成熟，于是机器翻译能不能替代人工翻译这个话题引起了广泛热议。为了探究机器翻译的能力水平是否足以替代人工翻译，本人根据皮特纽马克的文本类型分类理论（信息类文本，表达文本，呼吁文本），选择了相应的译文类型，并且将其机器翻译的版本以及人工翻译的版本进行对比。结果发现，就质量和准确度而言，译文的水平大相径庭。因为其简洁的特性，机器比较适合翻译信息文本。而就表达文本，尤其是文学作品，因其存在文化负载词及相应的文化现象，所以机器翻译这类文本存在难度。而呼唤文本因其目的性以及对读者的依赖性较强，翻译难度较大。由此得知，机器翻译和人工翻译的关系应该是互补的，而不是竞争的。
 
−
 
 
−
===关键词===
 
−
机器翻译；人工翻译；纽马克文本类型
 
−
 
 
−
===1. Introduction===
 
−
====1.1.Introduction to machine translation====
 
−
Machine translation, also known as computer translation is a technique to translating through machine translator, such as Google translation and Youdao translation. Machine translation is one of the branches of computational linguistics, ranging from computer science, statistics, information science and so on. Machine translation plays an important role in all aspects.
 
−
Machine translation can be traced back to 1940s, when British engineer Booth and American engineer Weaver proposed using computer to translate and started to study machines used for translation. And the first machine translating system launched in 1954, used in English and Russian translation. And this means machine translation becomes a reality. However, the arrival of new things always accompanies barriers and setbacks. In the 1960s, reports from ALPAC (Automated Language Processing Advisory Committee) showed studies on machine translation had stagnated for a decade. At that time, due to the high cost of machine, choosing human translators to finish translating works was more economical for most companies. What’s worse, machine had difficulty in understanding semantic and pragmatic meanings. Fortunately, in the 1970s, with the advance and popularity of computer, machine translation was gradually back on track. With the development of science and technology, machine translation has better technological guarantee, such as artificial intelligence and computer technology. In the last decades, from the late 90s till now, machine translation is becoming more and more mature. The initial rule-oriented machine translation has been updated and Corpus-aided machine translation appears. And nowadays, technology in voice recognition has been further developed. This technique is frequently applied in speech translation products. Users only need to speak source language to the online translator and instantly it will speak out the target version. But machine can’t fully provide precise and accurate translation without any grammatical or semantic problems. Machine can understand the literal meaning of texts, but not the meaning in context. For example, there is polysemy in English, which refers to words having multiple meanings. So when translating these words, translators should take contexts into consideration. But machine has troubles in this way. In addition, machine has no feeling or intention. Hence, it is also difficult to convey the feelings from the source language.(Wei 2021:5)#
 
−
 
 
−
====1.2.Process of machine translation====
 
−
The process of manual translation is different from that of machine translation. Here is the process of the former. (1) Understand source language. (2) Use target language to organize language. (3) Generate translation. Unlike manual translation, machine translation tends to analyze and code source language first, then look for related codes in corpus, and work out the code that represents target language, generating translation. But they share a common feature, which is that Lexicon, grammatical rules and syntactic structure are taken into consideration. This is one of the biggest challenges for machine translation.
 
−
 
 
−
===2.Theorectical framework===
 
−
====2.1Newmark’s text typology====
 
−
Text typology is a theory from linguistics. It undergoes several phrases. Karl Buhler, a German psychologist and linguist defines communicative functions into expressive, information and vocative. Then Roman Jakobson proposes categorization of texts, which are intralingual translation, interlingual translation and intersemiotic translation. Accordingly, he defines six main functions of language, including the referential function, conative function, emotive function, poetic function, metalingual function and the phatic function. Based on the two theories, Peter Newmark, a translation theorist, summarizes the language function into six parts: the informative function, the vocative function, the expressive function, the phatic function, the aesthetic function, and the metalingual function. Later, he further divided texts into informative type, expressive text and vocative type according to the linguistic functions of various texts. (Newmark 2002:2)#
 
−
The core of the informative texts is the truth. It is to convey facts, information, knowledge of certain topics, reality and theories. The language style of the text is objective, logical and standardized. Reports, papers, scientific and technological textbooks are all attributed to informative texts. Translators are required to take format into account for different styles.
 
−
The core of the expressive text is the sender’s emotions and attitudes. It is to express sender’s preferences, feelings, views and so on. The language style of it is subjective. Imaginative literature, including fictions, poems and dramas, autobiography and authoritative statements belong to expressive text. It requires translators to be faithful to the source language and maintain the style.
 
−
The core of the vocative text is readership. It is to call upon readers to act, think, feel and react in the way intended by the text. So it is reader-oriented. Such texts as advertisement, propaganda and notices are of vocative text. Newmark noted that translators need to consider cultural background of the source language and the semantic and pragmatic effect of target language. If readers can comprehend the meaning at once and do as the text says, then the goal of information communication is achieved. (Liu 2021:3)#
 
−
To draw a conclusion, informative texts focus on the information and facts. Expressive texts center on the mind of addressers, including feelings, prejudices and so on. And vocative texts are oriented on readers and call on them to practice as the texts say.
 
−
 
 
−
====2.2Study method====
 
−
Manual translations of the three texts are selected from authoritative versions and universally acknowledged. And machine translations of those come from Youdao Translator. And in this thesis I will compare and evaluate the two methods in word diction, sentence structure, word order and redundancy.
 
−
 
 
−
===3.Comparison and analysis of machine translation and manual translation ===
 
−
====3.1Informative text ====
 
−
（1）English into Chinese
 
−
 
 
−
①Source language:
 
−
 
 
−
Keep the tip of Apple Pencil clean, as dirt and other small particles may cause excessive wear to the tip or damage the screen of i-pad.
 
−
 
 
−
Target language:
 
−
 
 
−
Machine translation: Apple Pencil笔尖应保持清洁，灰尘等小颗粒可能会导致笔尖过度磨损或损坏ipad屏幕。
 
−
 
 
−
Manual translation: 保持Apple Pencil铅笔的笔尖干净，因为灰尘和其他微粒可能会导致笔尖的过度磨损或损坏iPad屏幕。
 
−
 
 
−
Analysis: Here is the instruction of Apple Pencil. And the manual translation is the Chinese version on the instruction.Product instruction tends to be professional, since there are many terms for some concepts. Machine can easily identify these terms and provide related words to translate. The machine version is faithful and expressive to the source language. So it is well-qualified and readable for readers to understand the instruction. So we can use machine to translate informative text.
 
−
 
 
−
②Source language:
 
−
 
 
−
China on Saturday launched a rocket carrying three astronauts-two men and one woman - to the core module of a future space station where they will live and work for six months, the longest orbit for Chinese astronauts.
 
−
 
 
−
Target language:
 
−
 
 
−
Machine translation: 周六，中国发射了一枚运载三名宇航员(两男一女)的火箭，进入未来空间站的核心舱，他们将在那里生活和工作6个月，这是中国宇航员最长的轨道。
 
−
 
 
−
Manual translation: 周六，中国发射了一枚搭载三名宇航员(两男一女)的火箭，进入未来空间站的核心舱，他们将在那里生活和工作6个月，这是中国宇航员最漫长的一次轨道飞行。
 
−
 
 
−
Analysis: This is a news from Reuters, reporting that China has launched a rocket.The meaning of the two translations is almost the same, except for some word diction. But there are some details dealt with different choice. For example, the last sentence of the machine translation is a bit of obscure and direct. There are some ambiguous words and expressions.
 
−
 
 
−
(2)Chinese into English
 
−
 
 
−
Source language:湖南省博物馆是湖南省最大的历史艺术类博物馆，占地面积4.9万平方米，总建筑面积为9.1万平方米，是首批国家一级博物馆，中央地方共建的八个国家级重点博物馆之一、全国文化系统先进集体、文化强省建设有突出贡献先进集体。
 
−
 
 
−
Target language:
 
−
Manual translation: As the largest history and art museum in Hunan province, the Hunan Museum covers an area of 49,000㎡, with the building area reaching 91,000㎡. It is one of the first batch of national first-level museums and one of the first eight national museums co-funded by central and local governments.
 
−
 
 
−
Machine translation: Museum in hunan province is one of the largest historical art museum in hunan province, covers an area of 49000 square meters, a total construction area of 91000 square meters, is the first national museum, the central place to build one of the eight national key museum, national cultural system advanced collectives, strong culture began with outstanding contribution of advanced collective.
 
−
 
 
−
Analysis: Machine translation is not faithful enough in content. For instance, “首批国家一级博物馆” is translated into “first national museum”, which is not the meaning of the source language. And there are some obvious grammar mistakes in the machine translation. For example, machine translates it into just one sentence but there are multiple predicates in it. So it is not grammatically permissible. What’s more, the sentence structure of machine translation is confusing and the focus is not specific enough.
 
−
 
 
−
====3.2Expressive text ====
 
−
(1)English into Chinese
 
−
 
 
−
Source language:
 
−
 
 
−
An individual human existence should be like a river- small at first, narrowly contained within its banks, and rushing passionately past rocks and over waterfalls. Gradually the river grows wider, the banks recede, the waters flow more quietly, and in the end, without any visible breaks, they become merged in the sea, and painlessly lose their individual being.()
 
−
 
 
−
Target language:
 
−
 
 
−
Machine translation: 一个人的存在应该像一条河流——开始很小，被紧紧地夹在两岸中间，然后热情奔放地冲过岩石，飞下瀑布。渐渐地，河面变宽，两岸后退，水流更加平缓，最后，没有任何明显的停顿，它们汇入大海，毫无痛苦地失去了自己的存在。
 
−
 
 
−
Manual translation:人生在世，如若河流；河口初始狭窄，河岸虬曲，而后狂涛击石，飞泻成瀑。河道渐趋开阔，峡岸退去，水流潺缓，终了，一马平川，汇于大海，消逝无影。
 
−
 
 
−
Analysis: Here is a well-known metaphor in the prose How to Grow Old written by Bertrand Russell. The manual translation is written by Tian Rongchang.This is a philosophical prose with graceful language. Literary translation is a most important and difficult branch of translation. Translator should focus on the literal meaning, culture, writing style and so on. It is a combination of beauty and elegance. Therefore, translators find it in a dilemma of beauty and faithfulness, let alone translating machine. Compared with manual translation, machine translation has difficulty in word choice. It is faithful and expressive, but not elegant enough.
 
−
 
 
−
(2)Chinese into English
 
−
 
 
−
Source language:没有一个人将小草叫做“大力士”，但是它的力量之大，的确是世界无比。这种力，是一般人看不见的生命力，只要生命存在，这种力就要显现，上面的石块，丝毫不足以阻挡。因为它是一种“长期抗战”的力，有弹性，能屈能伸的力，有韧性，不达目的不止的力。(Zhang, 2007:186)#
 
−
 
 
−
Target language:
 
−
 
 
−
Machine translation: No one calls the little grass "hercules", but its power is truly matchless in the world. This force is invisible life force. As long as there is life, this force will show itself. The stone above is not strong enough to stop it. Because it is a "long-term resistance" of the force, elastic, can bend and extend force, tenacity, not to achieve the purpose of the force.
 
−
 
 
−
Manual translation: Though nobody describes the little grass as a “husky”, yet its herculean strength is unrivalled. It is the force of life invisible to naked eye. It will display itself so long as there is life. The rock is utterly helpless before this force- a force that will forever remain militant, a force that is resilient and can take temporary setbacks calmly, a force that is tenacity itself and will never give up until the goal is reached. (by Zhang Peiji)
 
−
 
 
−
Analysis:This is the excerpt of a well-known Chinese prose written by Xia Yan. It is written during the war of Resistance Against Japan. So the prose holds symbolic meaning, eulogizing the invisible tenacious vitality so as to encourage Chinese to have confidence in the anti-aggression war. Compared with manual translation, machine translation is much more abstract and confusing, especially for the word diction. For example, “大力士” is translated into “hercules” which is a man of exceptional strength and size in Greek and Roman Mythology, making it difficult to understand if readers of target language have no idea of the allusion. What’s worse, the machine version doesn’t reveal the symbolic meaning of the text, which is the core of this prose.
 
−
 
 
−
====3.3Vocative text ====
 
−
 
 
−
(1)English into Chinese
 
−
 
 
−
①Source language:
 
−
 
 
−
iPhone went to film school, so you don’t have to. (Advertisement of iPhone13)
 
−
 
 
−
Target language:
 
−
 
 
−
Machine translation: iPhone上的是电影学院，所以你不用去。
 
−
 
 
−
Manual translation:电影专业课，iPhone同学替你上完了。
 
−
 
 
−
Analysis：Here are advertisements of iPhone on Apple official website. There is a personification in the source language. It is used to stress the advancement and proficiency in camera, which is an appealing selling point to potential buyers. Compared with manual translation, machine translation is plain and not eye-catching enough for customers.
 
−
 
 
−
②Source language:
 
−
 
 
−
5G speed  OMGGGGG
 
−
 
 
−
Machine language: 5克的速度  OMGGGGG
 
−
 
 
−
Manual translation:
 
−
 
 
−
iPhone的5G    巨巨巨巨巨5G
 
−
 
 
−
Analysis: The “G” in the source language is the unit of speed, standing for generation. However, it is mistaken as a unit of weight, representing gram in the machine translation. So the meaning is not faithful to the source language at all. As for manual translation, it complies with the source in form. Specifically speaking, five “G”s in the former complies with five characters “巨”in the latter. And the pronunciation of the two is similar. There are two layers of meaning for the 5 “G”s. One exclaims the fast speed of 5 generation network and the other new technology. In the manual version, “巨”can be used to show degree, meaning “quite” or “very”.
 
−
 
 
−
③Source language:
 
−
 
 
−
History, faith and reason show the way, the way of unity. We can see each other not as adversaries but as neighbors. We can treat each other with dignity and respect, we can join forces, stop the shouting and lower the temperature. For without unity, there is no peace, only bitterness and fury."
 
−
 
 
−
Target language:
 
−
 
 
−
Machine translation: 历史、信仰和理性指明了团结的道路。我们可以把彼此视为邻居，而不是对手。我们可以尊严地对待彼此，我们可以联合起来，停止大喊大叫，降低温度。因为没有团结，就没有和平，只有痛苦和愤怒。
 
−
 
 
−
Manual translation:历史、信仰和理性为我们指明道路。那是团结之路。我们可以把彼此视为邻居，而不是对手。我们可以有尊严地相互尊重。我们可以联合起来，停止喊叫，减少愤怒。因为没有团结就没有和平，只有痛苦和愤怒
 
−
 
 
−
Analysis: Speech is a way to propagate some activity in public. It is an art to inspire emotion of the audience. The source language is the excerpt of Joe Biden’s inaugural speech. The speech should be inspiring and logic. The machine translation has some misunderstanding. Taking the translation of “lower the temperature” for example, machine only translates its literal meaning, relating to the temperature itself, without considering the context. What’s more, it is less logic than the manual one. Therefore, it adds difficulty to inspire the audience and infect their emotion.
 
−
 
 
−
===4.Common mistakes in machine translation  ===
 
−
 
 
−
====4.1 lexical mistakes  ====
 
−
 
 
−
Common lexical mistakes include misunderstandings in word category, lexical meaning and emotive and evaluative meaning. Misunderstanding in word category shows in the classification of word in the source language. As for misunderstanding in lexical meaning, machine has difficulty in precisely reflecting the meaning of the original texts, due to different cultural background and different language system. And for misunderstanding in emotive meaning, machine has no intention and emotion like human-beings. Therefore, it’s impossible for it to know writers’ feelings and their writing purposes. So sometimes, it may translate something negative into something positive. (Wang 2008:45)#
 
−
 
 
−
====4.2 grammatical mistakes====
 
−
 
 
−
Grammatical analysis plays an important part in translation. Normally speaking, every language has its own unique grammatical rules. So in the process of translation, if translators don’t know the formation rule well, the sentence meaning will be affected. Even though all the lexical meanings are well-known by translators, the lack of consciousness of grammaticality makes it harder to arrange words according to sequential rule. English tends to be hypotactic, while Chinese tends to be paratactic. English sentences are connected through syntactic devices and lexical devices. While Chinese sentences are semantically connected, which means there are limited logical words and connection words in Chinese. So when translating English sentence, we should first analyze its grammaticality and logical structure and then rearrange its sequence. However, online translating machine has troubles in grammatical analysis, which makes its improvement more difficult.
 
−
 
 
−
====4.3 other mistakes====
 
−
 
 
−
The two mistakes above are the internal ones. Apart from mistakes in linguistic system, there are some mistakes in other aspects, such as cultural background.
 
−
 
 
−
===5.Reasons for its common mistakes ===
 
−
 
 
−
====5.1 Difference in two linguistic system====
 
−
 
 
−
With different history, English and Chinese have different ways of expression. Commonly speaking, English is synthetic language which expresses grammatical meaning through inflection such as tense and Chinese is analytic language which expresses grammatical meaning through word order and function word. In addition, English is more compact with full sentences. Subordinate sentence is one of the most important features in modern English. Chinese, on the other hand, is more diffusive with minor sentences.
 
−
 
 
−
====5.2 Difference in thinking patterns and cultural background====
 
−
 
 
−
According to Sapir-Whorf’s Hypothesis, our language helps mould our way of thinking and consequently, different languages may probably express their unique ways of understanding the world. For two different speech communities, the greater their structural differentiations are, the more diverse their conceptualization of the world will be. For example, western culture is more direct and eastern culture more euphemistic. What’s more, English culture tends to be individualism, focusing on detail, through which it reflects the whole, while Chinese culture tends to be collective. Different thinking patterns will add difficulty for machine to translate texts.
 
−
 
 
−
====5.3 Limitation of computer====
 
−
 
 
−
Recently, there are some breakthroughs and innovation in machine translation. However, due to its own limitation, online translation has limitation in some ways. Firstly, compared with machine, human brain is much more complicated, consisting of ten billions of neuron, each of which has different function to affect human’s daily activities and help humans avoid some errors. However, computer can only function according to preset programming has no intention or consciousness. Until now, countless related scholars have invested much time in machine translation. They upload massive language database, which include almost all linguistic rules. But computers still fail to precisely reflect the meaning of source language for many times due to the complexity and flexibility of language.  On the other hand, computers can’t take context into consideration. During translation, it is often the case that machine chooses the most-frequently used meaning of one word. So without the correct and exact meaning, readers are easier to feel confused and even misunderstand the meaning of source language. (Qiu 2021:4)#
 
−
 
 
−
===6.Conclusion===
 
−
From the analysis above, we can draw a conclusion that machine deals with informative text best, followed by non-literary translation of expressive text. What’s more, machine can be a useful tool to get to know the gist and main idea of a specific topic, for the simple sentence structure and numerous terms. And it can improve translating efficiency with high speed. But machine has difficulty in translating literary works, especially proses and poems.
 
−
 
 
−
Machine translation has mixed future. From the perspective of commercial, machine translation boasts a bright future. With the process of globalization, the demand for translation is increasing accordingly. On one hand, if we only depend on human translator to deal with translating works, the quality and accuracy of translation can be greatly affected. On the other hand, if machine is used properly to do some basic work, human translators only need to make preparation before translating, progress, polish and other advanced work, contributing to highly-qualified translation and high working efficiency.
 
−
 
 
−
However, compared with manual translation, machine translation has a bleak future. It is still impossible for machine to replace interpreter or translator in a short term. With intelligence and initiative, humans are able to learn new knowledge constantly, which machine will never accomplish. Besides, machine is not used to replace translators but to assist them in work. In other words, translators and machine carry out their own duties and they are not incompatible.(He 2021:5)#
 
−
 
 
−
To draw a conclusion, although there are certain limitations of machine translation, it can serve as a catalyst for translating works. Therefore, with the rapid development of artificial intelligence and related technology, there are still many opportunities for machine translation.
 
−
 
 
−
===Reference ===
 
−
 
 
−
Chen Cheng陈诚.机器翻译技术的综述[J][Overview of Machine Translation Technology].Electronic Techonology 电子技术,2021,50(11):290-291.
 
−
 
 
−
Cui Zihan 崔子涵.机器翻译译文质量对比——以谷歌翻译和DeepL为例[J] [Comparison among Machine Translation--Taking Google Translation and Deepl for Example].Overseas English 海外英语,2021(15):182-183.
 
−
 
 
−
He Xinyu何馨宇.机器翻译的发展及其对翻译职业化的影响研究[J] [The Development of Machine Translation and its Effect on Professional Transltors].Overseas English 海外英语,2021(20):48-49.
 
−
 
 
−
He Wen 何雯, Wang Xiufeng 王秀峰.信息型文本的在线机器翻译错误研究[J][Research on Errors in Online Machine Translation of Informative text ].Overseas English海外英语,2021(15):188-189.
 
−
 
 
−
Li Deyi 李德毅. (2018). 人工智能导论 [Introduction to Artificial Intelligence]. Beijing: China Science and Technology Press 中国科学技术出版社.
 
−
 
 
−
Liu Qin刘琴.功能目的论对于不同文本类型的翻译解读[J][Analysis of Translations in Different Types of Text based on Functionalist Approaches].Overseas Engliosh 海外英语,2021(17):8-9.
 
−
 
 
−
Li Hanji 李晗佶. (2021). 人工智能时代翻译技术与译者关系演变与重构 [Evolution and reconstruction of the relationship between translation technology and translators in the era of artificial intelligence]. 西华师范大学学报(哲学社会科学版) Journal of West China Normal University (PHILOSOPHY AND SOCIAL SCIENCES EDITION) (2021-12-04) 1-6.
 
−
 
 
−
(英) Peter Newmark A Textbook of Translation[M] Shanghai Foreign Education Press, 2002
 
−
 
 
−
Qiu Quanju 仇全菊.大数据时代背景下机器翻译及其发展趋势[J][Machine Translation and its Development Trend under the Background of Big Data Era]. English Teachers 英语教师,2021,21(16):60-62.
 
−
 
 
−
Wang Hongqi 王红旗. (2008). 语言学概论 [Introduction to Linguistics]. Beijing: Peking University Press 北京大学出版社.
 
−
 
 
−
Wei Guang魏光. 人工翻译与机器翻译译文编辑比较研究[J][Comparative Study of Translation Editing between Manual Translation and Machine Translation]. Overseas English 海外英语,2021(19):18-19+21.
 
−
 
 
−
Zhuo Jianbin 卓键滨,Liu Wenxian 刘文娴,Peng Zili 彭子莉.机器翻译对各类型文本的德汉翻译能力探究[J][Research on the German Chinese Translation Ability of Machine Translation for Various Types of Texts]. Comparative Study of Cultural innovation 文化创新比较研究,2021,5(28):122-125.
 
−
 
 
−
Zhang Peiji 张培基.英译中国现代散文选[M][Selected Modern Chinese Prose Writings]. Shanghai Foreign Languages Education Press 上海外语教育出版社, 2002.
 
−
 
 
−
--[[User:Xiong Min|Xiong Min]] ([[User talk:Xiong Min|talk]]) 01:36, 15 December 2021 (UTC)
 
−
 
 
−
=Chapter 11 陈惠妮=Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts=
 
  
 
机器翻译的译前编辑研究——以医学类文摘为例
 
机器翻译的译前编辑研究——以医学类文摘为例
Line 277: Line 78:
 
[[Machine_Trans_EN_11]]
 
[[Machine_Trans_EN_11]]
  
−
===Abstract===
 
−
At present, globalization is accelerating and the market demand for language services is rapidly increasing . Machine translation, as an important translation method, can greatly improve translation efficiency due to its low cost and high speed. However, because of the limitations of machine translation and the differences between Chinese and English language, machine translation is not accurate enough. In order to balance translation efficiency and translation quality, a great number of manual revisions in translation are required for the machine translating texts. Medical papers are specialized, special and purposeful, so it requires accurate,qualified and professional translation. However, the quality of translations by machine is inefficient to meet the high-quality requirements of medical papers translation. Therefore, the introduction of pre-editing can greatly improve the efficiency and quality of machine translation.
 
−
 
−
===Key words===
 
−
Pre-editing, Machine translation, Medical texts
 
−
 
−
===题目===
 
−
Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts
 
−
 
−
===摘要===
 
−
在全球化加速发展的今天，市场对语言服务的需求迅速增加。机器翻译作为一种重要的翻译途径，由于其成本低、速度快，可以大大提高翻译效率。然而，由于机器翻译的局限性以及中英文语言的差异，机器翻译的准确性不高。为了平衡翻译效率和翻译质量，机器翻译文本需要大量的手工修改。
 
−
医学论文具有专业性、特殊性和目的性，要求其译文准确、合格、专业。然而，机器翻译的质量较低，无法满足医学论文对翻译的高质量要求。因此，译前编辑的引入可以大大提高机器翻译的效率和质量。
 
−
 
−
===关键词===
 
−
译前编辑；机器翻译；医学文本
 
−
 
−
===1. Introduction===
 
−
===1.1 Definition of Machine Translation===
 
−
As Cronin(2013) revealed: "Translation is undergoing a revolutionary upheaval. The influence of digital technology and the Internet on translation is continuous, extensive and profound. From the popularity of automatic online translation applications, translation revolution is everywhere (Cronin,2013) However,the concept of machine translation was firstly proposed in the 1930s. Since 1940s, the machine translation technology has been evolving from rule-based machine translation (RBMT) to statistical machine translation (SMT), and to neural machine translation (NMT). Machine translation refers to the automatic translation of source language into target language by using a computer system. That is, machine translation refers to the automatic translation of text from one language into another natural language by computer software or other online translation webs. Machine translation is also defined as the process of “using a computer system to automatically translate text or speech from one natural language to another” according to the definition by ISO (Cui 2014:68-73).O 'Brien (2002) defines it as "the behavior of modifying errors in machine translation to ensure that the target translation meets certain quality requirements". On the basic level, machine translation performs mechanical substitution of words in one language for words in another language, but that rarely produces a good translation, therefore, recognition of the whole phrases and their closest counterparts in the target language is needed. Not all words in one language have equivalents in another language, and many words have more than one meaning. A huge demand for translation is greatly needed in today’s global world, which creates new opportunities for the development of machine translation, attracts more and more attention and becomes one of the current research focuses.
 
−
 
−
As Cronin(2013) revealed: "Translation is undergoing a revolutionary upheaval. The influence of digital technology and the Internet on translation is continuous, extensive and profound. From the popularity of automatic online translation applications, translation revolution is everywhere (Cronin,2013) However,the concept of machine translation was firstly proposed in the 1930s. Since 1940s, the machine translation technology has been evolving from rule-based machine translation (RBMT) to statistical machine translation (SMT), and to neural machine translation (NMT). Machine translation refers to the automatic translation of source language into target language by using a computer system. That is, machine translation refers to the automatic translation of text from one language into another natural language by computer software or other online translation webs. Machine translation is also defined as the process of “using a computer system to automatically translate text or speech from one natural language to another” according to the definition by ISO (Cui 2014:68-73).O 'Brien (2002) defines it as "the behavior of modifying errors in machine translation to ensure that the target translation meets certain quality requirements". On the basic level, machine translation performs mechanical substitution of words in one language for words in another language, but that rarely produces a good translation, therefore, recognition of the whole phrases and their closest counterparts in the target language is needed. Not all words in one language have equivalents in another language, and many words have more than one meaning. A huge demand for translation is greatly needed in today’s global world, which creates new opportunities for the development of machine translation, attracts more and more attention and becomes one of the current research focuses.
 
−
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:34, 13 December 2021 (UTC)correted by Cai Zhufeng
 
−
 
−
===1.2 Definition of Pre-editing===
 
−
Pre-editing means to adjust and modify the source language to make it fit more with  the characteristics of the machine translation software before putting the source language before the into machine translation, so as to improve the quality of the translation machine translation (Wei Changhong 2008:93-94). Pre-editing is to modify the original text before putting it into machine translation software, in order to improve the recognition rate of machine translation, optimize the output quality of translated text and reduce the workload of post-editing. Because pre-translation editing only needs to be modified in one language, the operation is simpler than post-translation editing, which can realize the double improvement of quality and efficiency. A good pre-editing translation can help machine translation more smoothly, thus improving the machine readability and quality of the output translation. Pre-editing is mostly applied in the following situations: one is when the original text is of poor quality and the machine is difficult to recognize the meaning of the sentence, such as user generated content with poor readability and translatability (Gerlach et al 2003:45-53); Documents that need to be published in multiple languages; Next is when the original text contains a lot of jargon; The last is the original text has a corresponding translation memory bank. If the original text is edited, it can better match the content of the translation memory bank. Pre-editing is a process of identifying problems. It requires to pre-edit the source texts before putting it into machine translation according to the requirements, listing the expressions or sentences that may have trouble in machine translation and then pre-edit it by human. The purpose is to enable the computer to translate better, improve the translatability of machine translation. (Slype G V & Guinet J F & Seitz F 1984:115)
 
−
 
−
Pre-editing means to adjust and modify the source language to make it fit more with  the characteristics of the machine translation software before putting the source language before the into machine translation, so as to improve the quality of the translation machine translation (Wei Changhong 2008:93-94). Pre-editing is to modify the original text before putting it into machine translation software, in order to improve the recognition rate of machine translation, optimize the output quality of translated text and reduce the workload of post-editing. Because pre-translation editing only needs to be modified in one language, the operation is simpler than post-translation editing, which can realize the double improvement of quality and efficiency. A good pre-editing translation can help machine translation more smoothly, thus improving the machine readability and quality of the output translation. Pre-editing is mostly applied in the following situations: one is when the original text is of poor quality and the machine is difficult to recognize the meaning of the sentence, such as user generated content with poor readability and translatability (Gerlach et al 2003:45-53); Documents that need to be published in multiple languages; Next is when the original text contains a lot of jargon; The last is the original text has a corresponding translation memory bank. If the original text is edited, it can better match the content of the translation memory bank. Pre-editing is a process of identifying problems. It requires to pre-edit the source texts before putting it into machine translation according to the requirements, listing the expressions or sentences that may have trouble in machine translation and then pre-edit it by human. The purpose is to enable the computer to translate better, improve the translatability of machine translation. (Slype G V & Guinet J F & Seitz F 1984:115)
 
−
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:36, 13 December 2021 (UTC)correted by Cai Zhufeng
 
−
 
−
===1.3 Machine Translation Mode===
 
−
According to the different knowledge acquisition methods, machine translation modes can be classified as follows: one is rule-based machine translation, which is based on bilingual dictionaries and a library of language rules for each language. The quality of translation depends on whether the source language conforms to the existing rules, but the inexhaustible rules are the hinders of this model. The second mode is machine translation based on statistics. This translation model relies on the principles of mathematics and statistics to find various existing translations corresponding to the translation tasks through the employment of corpus, analyzing the frequency of their occurrence and selecting the translation with the highest frequency for output. The disadvantage of this translation model is that it ignores the flexibility of language and the importance of context. The last one is neural network language model. This model is different from previous translation models in that it uses end-to-end neural network to realize automatic translation between natural languages. At present, the quality of its translation is much higher than that of the previous translation models.
 
−
 
−
According to the different knowledge acquisition methods, machine translation modes can be classified as follows: one is rule-based machine translation, which is based on bilingual dictionaries and a library of language rules for each language. The quality of translation depends on whether the source language conforms to the existing rules, but the inexhaustible rules are the hinders of this model. The second mode is machine translation based on statistics. This translation model relies on the principles of mathematics and statistics to find various existing translations corresponding to the translation tasks through the employment of corpus, analyzing the frequency of their occurrence and selecting the translation with the highest frequency for output. The disadvantage of this translation model is that it ignores the flexibility of language and the importance of context. The last one is neural network language model. This model is different from previous translation models in that it uses end-to-end neural network to realize automatic translation between natural languages. At present, the quality of its translation is much higher than that of the previous translation models.
 
−
 
−
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng
 
−
 
−
===1.4 Source of Study Abstracts===
 
−
The core medical journals from domestic are selected in order to make the paper more representative and authoritative, such as National Medical Journal of China, Journal of Peking University (Health Science), and Journal of Third Military Medical University. All of them include basic medical science, biomedical technology, laboratory medical science and other fields. In this way, the pre-editing approaches included are applicable enough to machine translation of medical abstracts.
 
  
−
The core medical journals from domestic are selected in order to make the paper more representative and authoritative, such as National Medical Journal of China, Journal of Peking University (Health Science), and Journal of Third Military Medical University. All of them include basic medical science, biomedical technology, laboratory medical science and other fields. In this way, the pre-editing approaches included are applicable enough to machine translation of medical abstracts.
 
  
−
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng
+
Written by --[[User:Chen Huini|Chen Huini]] ([[User talk:Chen Huini|talk]]) 04:58, 15 December 2021 (UTC)Chen Huini
  
−
===1.5 Selection of Translation Software===
+
=Chapter 12 The Mistranslation of C-J Machine Translation of Political Statements=
−
Generally, machine translation software includes Google Translation, Youdao Translator and Niu Translation, which has their own special use in translation. Take Google as an example, it is more like neurons in human brain, enabling to learn and collect information to establish connections with its neural machine translator’s neurons. However, it also causes many errors because of the lack of enough information. In this paper, contrastive analysis will be carried on by using Google translation. On the one hand, being a pioneer in the translation of NET, it is inevitably to sue Google as the translation software to translate medical texts in this paper. Comparing the program called “Google brain”, other NMT of translation software are relatively disadvantages. On the other hand, the Google translation enjoys the largest users in the world, with its downloads of more than one billion. The output quality of Google translation is more correct and complete than other machine translation software. With years of development and improvement, Google Translation has been greatly promoted. In this paper, Chinese medical abstract will be automatically translated by Google translation, and then the translation output will be compared with the translation by human and the publication of English abstracts. The main purpose is to prove that the improvement and promotion of quality and accuracy in the medical abstracts will be obtained through the pre-editing approaches.
 
−
 
 
−
Generally, machine translation software includes Google Translation, Youdao Translator and Niu Translation, which has their own special use in translation. Take Google as an example, it is more like neurons in human brain, enabling to learn and collect information to establish connections with its neural machine translator’s neurons. However, it also causes many errors because of the lack of enough information. In this paper, contrastive analysis will be carried on by using Google translation. On the one hand, being a pioneer in the translation of NET, it is inevitably to sue Google as the translation software to translate medical texts in this paper. Comparing the program called “Google brain”, other NMT of translation software are relatively disadvantages. On the other hand, the Google translation enjoys the largest users in the world, with its downloads of more than one billion. The output quality of Google translation is more correct and complete than other machine translation software. With years of development and improvement, Google Translation has been greatly promoted. In this paper, Chinese medical abstract will be automatically translated by Google translation, and then the translation output will be compared with the translation by human and the publication of English abstracts. The main purpose is to prove that the improvement and promotion of quality and accuracy in the medical abstracts will be obtained through the pre-editing approaches.
 
−
 
 
−
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng
 
−
 
 
−
===2.Language Characteristics and Error Division===
 
−
Since Chinese and English are two different languages, it is quite neccessary to identify their own characteristics so as to better analyze and understand the two languages. Tytler (1978: 118-119) argued in his Essay on the Principles of Translation that there are three principles of translation that in all the translation should give readers the same feelings as the source text, except for complete transcript of ideas. There actually also exit some mistakes of these two languages. So the following will make some clarrifications of these two languages to ensure more accurate translation.
 
−
 
 
−
Since Chinese and English are two different languages, it is quite neccessary to identify their own characteristics so as to better analyze and understand the two languages. Tytler (1978: 118-119) argued in his Essay on the Principles of Translation that there are three principles of translation that in all the translation should give readers the same feelings as the source text, except for complete transcript of ideas. There actually also exit some mistakes of these two languages. So the following will make some clarrifications of these two languages to ensure more accurate translation.
 
−
 
 
−
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng
 
−
 
 
−
===2.1 Language Characteristics of Medical Abstracts===
 
−
Chinese and English belong to different language systems, so there are differences in their language structure and the way users think of the languages. When using machine translation from Chinese to English, due to the unequal language levels, there will be many mistakes in the translation process, especially for ESP texts, such as medical papers.Here, these differences mainly refer to the linguistic characteristics of medical abstracts. In medical abstracts, it usually includes structured and unstructured abstracts. Although in different forms, they both describe the purpose, methods and conclusions of the research.
 
−
 
 
−
In the method section of medial abstract, several Chinese sentences can be connected with commas, but each sentence may convey different information. In contrast, English sentences contain a great deal of information, but in order to ensure clarity, some modifiers need to be isolated and then reconstructed. However, in Chinese-English machine translation, a lot of information is put into the sentence because the machine segments the sentence on the basis of the full comma.
 
−
 
 
−
In addition, subjectless sentence is used in the objective and method parts of Chinese medical abstracts. The subjectless sentence means the sentence without or free from subject and is usually employed in two contexts. The first is "needless to say". In Chinese sentences, it is common to omit the subject of the sentence. Chinese sentences can convey meanings by using incomplete sentence structures, so Chinese speakers can understand the meanings of the sentences even though the sentence subject is omitted. The second is "emphasis of action". In this context, subjectless sentences are used to describe behaviors, especially in the study of traditional Chinese medicine abstracts. Comparatively speaking, it should be avoided in English medical abstracts. Abstract sentences are more subjective when describing the learning process, while the essential requirement of English medical abstract is objectivity. From this point, sentences without subject should be avoided in English medical abstracts. Another feature is voice. Few words with passive meanings appear in Chinese abstracts. English sentences are more favoured in using passive voice. When translating from Chinese to English, passive voice should be used to make the contents objective, which is also the basic requirement of medical papers.
 
−
 
 
−
These are the linguistic features of Chinese medical abstracts. There are great differences in sentence structure and expression between Chinese and English medical abstracts. These differences may reduce the accuracy of machine translation, so it is necessary to introduce pre-editing to edit the source text to ensure that the source text can be accurately recognized by the machine and fully translated into English.
 
−
 
 
−
Chinese and English belong to different language systems, so there are differences in their language structure and the way users think of the languages. When using machine translation from Chinese to English, due to the unequal language levels, there will be many mistakes in the translation process, especially for ESP texts, such as medical papers.Here, these differences mainly refer to the linguistic characteristics of medical abstracts. In medical abstracts, it usually includes structured and unstructured abstracts. Although in different forms, they both describe the purpose, methods and conclusions of the research.
 
−
 
 
−
In the method section of medial abstract, several Chinese sentences can be connected with commas, but each sentence may convey different information. In contrast, English sentences contain a great deal of information, but in order to ensure clarity, some modifiers need to be isolated and then reconstructed. However, in Chinese-English machine translation, a lot of information is put into the sentence because the machine segments the sentence on the basis of the full comma.
 
−
 
 
−
In addition, subjectless sentence is used in the objective and method parts of Chinese medical abstracts. The subjectless sentence means the sentence without or free from subject and is usually employed in two contexts. The first is "needless to say". In Chinese sentences, it is common to omit the subject of the sentence. Chinese sentences can convey meanings by using incomplete sentence structures, so Chinese speakers can understand the meanings of the sentences even though the sentence subject is omitted. The second is "emphasis of action". In this context, subjectless sentences are used to describe behaviors, especially in the study of traditional Chinese medicine abstracts. Comparatively speaking, it should be avoided in English medical abstracts. Abstract sentences are more subjective when describing the learning process, while the essential requirement of English medical abstract is objectivity. From this point, sentences without subject should be avoided in English medical abstracts. Another feature is voice. Few words with passive meanings appear in Chinese abstracts. English sentences are more favoured in using passive voice. When translating from Chinese to English, passive voice should be used to make the contents objective, which is also the basic requirement of medical papers.
 
−
 
 
−
These are the linguistic features of Chinese medical abstracts. There are great differences in sentence structure and expression between Chinese and English medical abstracts. These differences may reduce the accuracy of machine translation, so it is necessary to introduce pre-editing to edit the source text to ensure that the source text can be accurately recognized by the machine and fully translated into English.
 
−
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng
 
−
 
 
−
===2.2 Error Division of Machine Translation in Translating Abstracts of Medical Papers from Chinese to English===
 
−
In this paper, errors in machine translation are listed out after analyzing. Some are due to the principles, such as statistical-based MT and NMT, employed by machine translation. Based on the original errors, they can be studied from two levels. One is from the macro-level, referring to mistakes caused by objective factors. These are not human factors, but the disadvantage of machine translation and the limitations of language. The limitations of machine translation are derived from the principles and models adopted and manifests itself as a reliance on reference sources. In this case, semantic ambiguity and textual incoherence may occur in the absence of reference sources.
 
−
 
 
−
However, for ESP texts such as medical papers, the requirements for terms and sentence patterns are far beyond the existing corpora. For unfamiliar texts (that is, the corresponding texts cannot be found in the corpora). The quality of output translation will be relatively low, which is determined by the principles of machine translation. As mentioned above, machine translation has evolved over the past few decades from rules-based to corp-based statistics to NMT. However, there still exist some limitations in machine translation.
 
−
 
 
−
Mistakes on micro-level are mainly caused by the variations and differences of linguistic structure between Chinese and English. Chinese is an implicit language, while English is an explicit one (Lian, 2010) To put it in another way, Chinese expression does not depend on language structures, while English does the opposite. This may result in mismatches between the original and machine translated sentences. This kind of error is mainly divided into two types: component fragment and component missing. For a medical paper, such errors are very serious. As a kind of ESP discourse, medical paper has the characteristics of fixed, objective and accurate language structure. In order to reproduce the characteristics of medical abstracts in translation, it is necessary to avoid errors at the level of words and sentences and pay attention to logic and consistency.
 
−
 
 
−
Lexical errors here refer to the inconsistency of fixed expressions in the translation of terms. Although these terms are machine translation based on the corpus, the corpus may not be perfect for ESP texts such as medical papers. Therefore, fixed expressions are not used in term translation. This is also the most common mistake in machine translation (the term here includes but is not limited to nouns). For ESP texts, medical text in particular, the fixed expressions and the accuracy of terms are of great importance.
 
−
 
 
−
In this paper, errors in machine translation are listed out after analyzing. Some are due to the principles, such as statistical-based MT and NMT, employed by machine translation. Based on the original errors, they can be studied from two levels. One is from the macro-level, referring to mistakes caused by objective factors. These are not human factors, but the disadvantage of machine translation and the limitations of language. The limitations of machine translation are derived from the principles and models adopted and manifests itself as a reliance on reference sources. In this case, semantic ambiguity and textual incoherence may occur in the absence of reference sources.
 
−
 
 
−
However, for ESP texts such as medical papers, the requirements for terms and sentence patterns are far beyond the existing corpora. For unfamiliar texts (that is, the corresponding texts cannot be found in the corpora). The quality of output translation will be relatively low, which is determined by the principles of machine translation. As mentioned above, machine translation has evolved over the past few decades from rules-based to corp-based statistics to NMT. However, there still exist some limitations in machine translation.
 
−
 
 
−
Mistakes on micro-level are mainly caused by the variations and differences of linguistic structure between Chinese and English. Chinese is an implicit language, while English is an explicit one (Lian, 2010) To put it in another way, Chinese expression does not depend on language structures, while English does the opposite. This may result in mismatches between the original and machine translated sentences. This kind of error is mainly divided into two types: component fragment and component missing. For a medical paper, such errors are very serious. As a kind of ESP discourse, medical paper has the characteristics of fixed, objective and accurate language structure. In order to reproduce the characteristics of medical abstracts in translation, it is necessary to avoid errors at the level of words and sentences and pay attention to logic and consistency.
 
−
 
 
−
Lexical errors here refer to the inconsistency of fixed expressions in the translation of terms. Although these terms are machine translation based on the corpus, the corpus may not be perfect for ESP texts such as medical papers. Therefore, fixed expressions are not used in term translation. This is also the most common mistake in machine translation (the term here includes but is not limited to nouns). For ESP texts, medical text in particular, the fixed expressions and the accuracy of terms are of great importance.
 
−
 
 
−
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng
 
−
 
 
−
===3.Approaches Proposed for Pre-editing ===
 
−
Generally speaking, there is a close relationship between pre-editing and post-editing, both of which aim to convey information and ensure high or publishable translation quality. Proper pre-editing can improve the quality of machine translation in terms of adequacy and consistency.
 
−
Complexity of natural language and people use language arbitrarily, bringing many difficulties to Chinese-English machine translation, Hu Qingping (2005:24) has proposed "the research of translation software and the development of the controlled language are the two directions to improve the quality of machine translation : the former aims at the difficulty in the natural language processing, the latter overcomes the arbitrariness of natural language". Feng Quangong and Gao Lin (2017: 63-68) put forward: "The writing principles of controlled language can be applied to pre-editing of machine translation. Pre-editing based on controlled language can effectively reduce the complexity and ambiguity of source text, improve the identifiability of machine translation (the translatability of source text itself), and thus reduce (fully) the workload of post-editing".
 
−
The central task of pre-editing is to transform human-friendly content into machine-friendly content, so words and sentences need to be repositioned or even changed. Based on the analysis of the language characteristics of Chinese and English, the Chinese ideographic group should be split before the Chinese source text is input into the machine software to translate so that the sentence structure is complete  which can be easily recognized by the machine translation software.
 
−
 
 
−
Generally speaking, there is a close relationship between pre-editing and post-editing, both of which aim to convey information and ensure high or publishable translation quality. Proper pre-editing can improve the quality of machine translation in terms of adequacy and consistency.
 
−
 
 
−
Complexity of natural language and people use language arbitrarily, bringing many difficulties to Chinese-English machine translation, Hu Qingping (2005:24) has proposed "the research of translation software and the development of the controlled language are the two directions to improve the quality of machine translation : the former aims at the difficulty in the natural language processing, the latter overcomes the arbitrariness of natural language". Feng Quangong and Gao Lin (2017: 63-68) put forward: "The writing principles of controlled language can be applied to pre-editing of machine translation. Pre-editing based on controlled language can effectively reduce the complexity and ambiguity of source text, improve the identifiability of machine translation (the translatability of source text itself), and thus reduce (fully) the workload of post-editing".
 
−
 
 
−
The central task of pre-editing is to transform human-friendly content into machine-friendly content, so words and sentences need to be repositioned or even changed. Based on the analysis of the language characteristics of Chinese and English, the Chinese ideographic group should be split before the Chinese source text is input into the machine software to translate so that the sentence structure is complete  which can be easily recognized by the machine translation software.
 
−
 
 
−
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufefng
 
−
 
 
−
===3.1 Extraction and Replacement of Terms in the Source Text===
 
−
As a branch of applied English, medical English is the product of the combination of English language knowledge and medical knowledge. The terms in medical papers have the characteristics of fixed and complex language structure. Although Google Translation is based on a corpus, the language structure is not fixed due to the limited size of the corpus. Therefore, the following two steps should be followed before machine translation. The first is to extract terms and create a glossary, and then replace the Chinese terms in the source text with the corresponding English expressions in the glossary. Of course, the terms of extraction should be searched and verified according to the actual situation. All of these words are verified before they can be replaced. This method can not only ensure the accuracy of term translation, but also maintain the consistency of expression, especially when dealing with long texts.
 
−
Example1
 
−
Source abstract: 口腔白斑病癌变相关缺氧应答基因和微小RNA的芯片及表达验证。
 
−
Google translation: Microarray detection/Chip detection and expression verification of hypoxia response genes and microRNAs related to oral leukoplakia canceration.
 
−
Published translation:Transcriptome array screening and verification of oral leukoplakia carcinogenesis-related hypoxia-responsive gene and microRNA.
 
−
Analysis: The expression “ Affymetrix GeneChip” empolyed in this thesis is a human transcription array for transcriptome array. In the source abstract, “芯片检测“ can be meant “screening” but not detection, so the proper and appropriate translation of these expression should be “transcription array screening”, but the Google translates it into “microarry detection of chip detection”. Therefore, term extraction is essential and inevitable before putting the source abstracts into the machine translation software.
 
−
After pre-editing source abstracts: 对 oral leukoplakia carcinogenesis 相关 hypoxia-responsive gene 和微小RNA进行 transcriptome array screening 及表达验证。
 
−
After pre-editing translation: Transcriptome array screening and expression- verification of hypoxia-responsive genes and microRNAs related to oral leukoplakia carcinogenesis.
 
−
 
 
−
As a branch of applied English, medical English is the product of the combination of English language knowledge and medical knowledge. The terms in medical papers have the characteristics of fixed and complex language structure. Although Google Translation is based on a corpus, the language structure is not fixed due to the limited size of the corpus. Therefore, the following two steps should be followed before machine translation. The first is to extract terms and create a glossary, and then replace the Chinese terms in the source text with the corresponding English expressions in the glossary. Of course, the terms of extraction should be searched and verified according to the actual situation. All of these words are verified before they can be replaced. This method can not only ensure the accuracy of term translation, but also maintain the consistency of expression, especially when dealing with long texts.
 
−
 
 
−
Example1
 
−
Source abstract: 口腔白斑病癌变相关缺氧应答基因和微小RNA的芯片及表达验证。
 
−
Google translation: Microarray detection/Chip detection and expression verification of hypoxia response genes and microRNAs related to oral leukoplakia canceration.
 
−
Published translation:Transcriptome array screening and verification of oral leukoplakia carcinogenesis-related hypoxia-responsive gene and microRNA.
 
−
Analysis: The expression “ Affymetrix GeneChip” empolyed in this thesis is a human transcription array for transcriptome array. In the source abstract, “芯片检测“ can be meant “screening” but not detection, so the proper and appropriate translation of these expression should be “transcription array screening”, but the Google translates it into “microarry detection of chip detection”. Therefore, term extraction is essential and inevitable before putting the source abstracts into the machine translation software.
 
−
After pre-editing source abstracts: 对 oral leukoplakia carcinogenesis 相关 hypoxia-responsive gene 和微小RNA进行 transcriptome array screening 及表达验证。
 
−
After pre-editing translation: Transcriptome array screening and expression- verification of hypoxia-responsive genes and microRNAs related to oral leukoplakia carcinogenesis.
 
−
 
 
−
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng
 
−
 
 
−
===3.2 Explication of Subordination===
 
−
As mentioned above, the subordination of Chinese sentences is judged by certain specific words in machine translation . Chinese is a paratactic language, and sentences are often connected by internal logical relationships; while English depends on sentence structure, so sentences are often closely linked by various language forms. In order to improve the accuracy of machine translation, we can adjust the sentence structure and adjust the position of adjectives and their modifiers.
 
−
 
 
−
Example 2
 
−
Source abstracts:回顾性分析南京医科大学附属儿童医院中重度HIE患儿49例及同期就诊的无神经系统症状体征的足月新生儿为对照组31例的头颅磁共振成像（MRI）资料。
 
−
Google translation: A retrospective analysis that the brain magnetic resonance imaging (MRI) data of 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University and full-term neonates with no neurological symptoms and signs during the same period were included in the control group.
 
−
Published translation: A total of 49 children with moderate to severe HIE admitted to the children’s Hospital Affiliated to Nanjing Medical University were retrospectively analyzed. Cranial magnetic resonance imaging (MRI) date of 31 full-term neonates without neurological symptoms and signs who visited the hospital during the same period were recruited as the control group.
 
−
 
 
−
Analysis: As the above example shows that “中重度HIE患儿” and “足月新生儿” in the source abstracts are from the Children’s Hospital Affiliated Nanjing Medical University. The Google translation shows that 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University. There is an error due to the misuse of subordination. There are some advice in order to solve the problem. That is, first to segment the sentence and then reconstruct the sentence. For instance, the Chinese expression “南京医科大学附属儿童医院” carries many modifiers, which requires to cut the modifiers and reconstruct the sentence.
 
−
Therefore, the Chinese expression “南京医科大学附属儿童医院’can be divided into abstractions, leaving two sentences instead of one long sentence with modifiers..
 
−
After pre-editing source abstracts: 对49例中重度HIE患儿以及31例无神经系统症状体征的足月新生儿的MRI资料进行回顾性分析。这些患者收治于南京医科大学儿童附属医院。
 
−
After pre-editing translation: The MRI data of 49 children with moderate to severe HIE and 31 full-term newborns without neurological symptoms and signs were retrospectively analyzed. These patients were admitted to the Children’s Hospital of Nanjing Medical University.
 
−
 
 
−
As mentioned above, the subordination of Chinese sentences is judged by certain specific words in machine translation . Chinese is a paratactic language, and sentences are often connected by internal logical relationships; while English depends on sentence structure, so sentences are often closely linked by various language forms. In order to improve the accuracy of machine translation, we can adjust the sentence structure and adjust the position of adjectives and their modifiers.
 
−
 
 
−
Example 2
 
−
Source abstracts:回顾性分析南京医科大学附属儿童医院中重度HIE患儿49例及同期就诊的无神经系统症状体征的足月新生儿为对照组31例的头颅磁共振成像（MRI）资料。
 
−
Google translation: A retrospective analysis that the brain magnetic resonance imaging (MRI) data of 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University and full-term neonates with no neurological symptoms and signs during the same period were included in the control group.
 
−
Published translation: A total of 49 children with moderate to severe HIE admitted to the children’s Hospital Affiliated to Nanjing Medical University were retrospectively analyzed. Cranial magnetic resonance imaging (MRI) date of 31 full-term neonates without neurological symptoms and signs who visited the hospital during the same period were recruited as the control group.
 
−
 
 
−
Analysis: As the above example shows that “中重度HIE患儿” and “足月新生儿” in the source abstracts are from the Children’s Hospital Affiliated Nanjing Medical University. The Google translation shows that 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University. There is an error due to the misuse of subordination. There are some advice in order to solve the problem. That is, first to segment the sentence and then reconstruct the sentence. For instance, the Chinese expression “南京医科大学附属儿童医院” carries many modifiers, which requires to cut the modifiers and reconstruct the sentence.
 
−
 
 
−
Therefore, the Chinese expression “南京医科大学附属儿童医院’can be divided into abstractions, leaving two sentences instead of one long sentence with modifiers..
 
−
After pre-editing source abstracts: 对49例中重度HIE患儿以及31例无神经系统症状体征的足月新生儿的MRI资料进行回顾性分析。这些患者收治于南京医科大学儿童附属医院。
 
−
After pre-editing translation: The MRI data of 49 children with moderate to severe HIE and 31 full-term newborns without neurological symptoms and signs were retrospectively analyzed. These patients were admitted to the Children’s Hospital of Nanjing Medical University.
 
−
 
 
−
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng
 
−
 
 
−
===3.3 Explication of Subject through Voice Changing===
 
−
The extensive use of subjectless sentences are quite common in the Chinese abstracts, while English prefers to employ passive voice in the sentences so as to make them object and accurate. In order to take the characteristics of English sentences into great and careful consideration, the sentences written in passive voice in particular, it will be helpful for machine translation to find out the subject and reconstruct a sentence in passive voice (Wang Yan: 2008). The first is to discover the “doee” in a sentence and then is to reconstruct the sentence structure and put the “doee” before the verb. The last is to explicate the passive relation between the noun and the verb.
 
−
Example 3
 
−
Source abstract: 回顾性分析2018年1月至2020年12月解放军总医院第七医学中心收治的500例老年髋部骨折患者的资料。
 
−
Google translation: A retrospective analysis of the data of 500 elderly patients with hip fractures admitted to the Seventh Medical Center of the PLA General Hospital from January 2018 to December 2020.
 
−
Published translation: From January 2018 to December 2020, the data of 500 elderly patients with hip fracture treated in the Seventh Medical Center of PLA General Hospital were analyzed retrospectively.
 
−
Analysis: The above example indicates that the “回顾性分析”in the Google Translate is a noun phrase, but the source abstracts actually describes an action or a behavior. The reason for such a mistake is that the machine can’t recognize the Chinese subjetless sentences. To find the subject of the sentence, the source abstracts can be revised and adjusted. Finding the “doee” is the first step, which refers to “500例老年髋部骨折患者的资料”in the source abstracts. And next is to put it in front of the verb, that is to place it in the beginning of the sentence. Last but not least, the passive voice should be explicated in the sentence.
 
−
After pre-editing source abstract: 500例老年髋部骨折患者的资料被回顾性分析，他们在2018年1月之2020年12月期间收治于解放军总医院第七医学中心。
 
−
After pre-editing translation: The data of 500 elderly hip fracture patients were retrospectively analyzed. They were admitted to the Seventh Medical Center of the PLA General Hospital between January 2018 and December 2020.
 
−
 
 
−
The extensive use of subjectless sentences are quite common in the Chinese abstracts, while English prefers to employ passive voice in the sentences so as to make them object and accurate. In order to take the characteristics of English sentences into great and careful consideration, the sentences written in passive voice in particular, it will be helpful for machine translation to find out the subject and reconstruct a sentence in passive voice (Wang Yan: 2008). The first is to discover the “doee” in a sentence and then is to reconstruct the sentence structure and put the “doee” before the verb. The last is to explicate the passive relation between the noun and the verb.
 
−
 
 
−
Example 3
 
−
Source abstract: 回顾性分析2018年1月至2020年12月解放军总医院第七医学中心收治的500例老年髋部骨折患者的资料。
 
−
Google translation: A retrospective analysis of the data of 500 elderly patients with hip fractures admitted to the Seventh Medical Center of the PLA General Hospital from January 2018 to December 2020.
 
−
Published translation: From January 2018 to December 2020, the data of 500 elderly patients with hip fracture treated in the Seventh Medical Center of PLA General Hospital were analyzed retrospectively.
 
−
 
 
−
Analysis: The above example indicates that the “回顾性分析”in the Google Translate is a noun phrase, but the source abstracts actually describes an action or a behavior. The reason for such a mistake is that the machine can’t recognize the Chinese subjetless sentences. To find the subject of the sentence, the source abstracts can be revised and adjusted. Finding the “doee” is the first step, which refers to “500例老年髋部骨折患者的资料”in the source abstracts. And next is to put it in front of the verb, that is to place it in the beginning of the sentence. Last but not least, the passive voice should be explicated in the sentence.
 
−
After pre-editing source abstract: 500例老年髋部骨折患者的资料被回顾性分析，他们在2018年1月之2020年12月期间收治于解放军总医院第七医学中心。
 
−
After pre-editing translation: The data of 500 elderly hip fracture patients were retrospectively analyzed. They were admitted to the Seventh Medical Center of the PLA General Hospital between January 2018 and December 2020.
 
−
 
 
−
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng
 
−
 
 
−
===3.4 Relocation of Modifiers===
 
−
Modifiers, including noun, adverbial and attributive are constantly employed in the Chinese medical papers in order to add information to subject and object in the sentence. By doing this, complicated sentences can be structured, thus causing obstacles to machine translation for recognizing this complex sentence structures when translating Chinese-English sentences. To make the machine translation successfully recognize the sentence structure, simplifying the sentence structure is quite necessary. The key is to moving the location of the modifiers and thus making the modifiers an independent sentence. In other words, the modifiers ought to be put before or behind the main part of the sentence to satisfy the common use of English.
 
−
Usually, the modifiers in Chinese sentence can only be placed in front of the core word, while in English, modifiers are very flexible. It is all right to place the modifiers in front of or behind the major part of the sentences with adjective or a connective noun.
 
−
 
 
−
Example 4
 
−
Source abstracts: 利用DNA重组技术以pET-28a表达系统在E.coli BL21(DE3) 中重组表达Hepl。
 
−
Google Translation: Hepl is recombined in E.coli BL21 (DE3) using DNA recombination technology with pET-28a expression system.
 
−
Published translation: The recombinant Hcpl protein was expressed by using DNA recombination technology through pET-28a expression system in E. coli BL21 (De3).
 
−
Analysis: The Chinese medical text includes two modifiers, “利用DNA”and “以pET-28a)表达系统”. These two modifiers will be translated by machine, which catches more attention to the two constituents. From the Google translation above, one failure is obvious that it misplaces the location of the two modifiers and presents it in not accurate form. To make it translate correctly by machine translation, dividing the sentences is very important so that the source abstracts can be correctly recognized by machine translation.
 
−
 
 
−
After pre-editing source abstract: 利用DNA重组技术，Hcp1重组表达在E.coli BL21 (DE3)中， 通过pET-28a表达系统在E. coli BL21 (DE3)中。
 
−
Google translation: Using DNA recombination technology, Hcp1 recombination is expressed in E.coli BL21 (DE3), via pET-28a expression system in E. coli BL21 (DE3).
 
−
The second is the independence of modifiers, that is, modifiers can also be reconstructed into clauses to modify core words. Compared with English, There is no subordinate clause in Chinese, so redundant Chinese modifiers need to be reconstructed into subordinate clauses in Chinese-English translation to meet the characteristics of English. English, especially sentences with multiple modifiers. Otherwise, sentence structure may be confused, such as scattered modifiers of core words. To avoid this mistake, it is necessary to separate the modifier from the main part of the sentence. These modifiers should be reconstituted into clauses. Also, keep your sentences simple and easy to understand.
 
−
 
 
−
Modifiers, including noun, adverbial and attributive are constantly employed in the Chinese medical papers in order to add information to subject and object in the sentence. By doing this, complicated sentences can be structured, thus causing obstacles to machine translation for recognizing this complex sentence structures when translating Chinese-English sentences. To make the machine translation successfully recognize the sentence structure, simplifying the sentence structure is quite necessary. The key is to moving the location of the modifiers and thus making the modifiers an independent sentence. In other words, the modifiers ought to be put before or behind the main part of the sentence to satisfy the common use of English.
 
−
Usually, the modifiers in Chinese sentence can only be placed in front of the core word, while in English, modifiers are very flexible. It is all right to place the modifiers in front of or behind the major part of the sentences with adjective or a connective noun.
 
−
 
 
−
Example 4
 
−
Source abstracts: 利用DNA重组技术以pET-28a表达系统在E.coli BL21(DE3) 中重组表达Hepl。
 
−
Google Translation: Hepl is recombined in E.coli BL21 (DE3) using DNA recombination technology with pET-28a expression system.
 
−
Published translation: The recombinant Hcpl protein was expressed by using DNA recombination technology through pET-28a expression system in E. coli BL21 (De3).
 
−
Analysis: The Chinese medical text includes two modifiers, “利用DNA”and “以pET-28a)表达系统”. These two modifiers will be translated by machine, which catches more attention to the two constituents. From the Google translation above, one failure is obvious that it misplaces the location of the two modifiers and presents it in not accurate form. To make it translate correctly by machine translation, dividing the sentences is very important so that the source abstracts can be correctly recognized by machine translation.
 
−
 
 
−
After pre-editing source abstract: 利用DNA重组技术，Hcp1重组表达在E.coli BL21 (DE3)中， 通过pET-28a表达系统在E. coli BL21 (DE3)中。
 
−
Google translation: Using DNA recombination technology, Hcp1 recombination is expressed in E.coli BL21 (DE3), via pET-28a expression system in E. coli BL21 (DE3).
 
−
The second is the independence of modifiers, that is, modifiers can also be reconstructed into clauses to modify core words. Compared with English, There is no subordinate clause in Chinese, so redundant Chinese modifiers need to be reconstructed into subordinate clauses in Chinese-English translation to meet the characteristics of English. English, especially sentences with multiple modifiers. Otherwise, sentence structure may be confused, such as scattered modifiers of core words. To avoid this mistake, it is necessary to separate the modifier from the main part of the sentence. These modifiers should be reconstituted into clauses. Also, keep your sentences simple and easy to understand.
 
−
 
 
−
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng
 
−
 
 
−
===3.5 Proper Omission and Deletion of Category Words===
 
−
Category words are commonly used in Chinese. Category words complement the meaning of words, including problems, positions, situations and jobs. Adding this supplementary word is more in line with Chinese custom. In many cases, it has no real meaning, so it can be omitted in translation. In Chinese, category words are frequently used. However, it is rarely used in English, which is one of the differences between Chinese and English. There are many kinds of category words. Considering objectivity and the fixed structure of language, redundant category words should be deleted in Chinese-English translation. In the general, the most commonly used category of words in the Chinese medical abstracts are "process", "behavior", and "situation". These categories of words hinder the language conversion of Chinese and English, resulting in redundancy.
 
−
 
 
−
Example 5
 
−
Source abstract: 探讨口腔白斑病癌变进程中的缺氧应答基因及相关微小RNA (miRNA) 的表达。
 
−
Google translation: The expression of hypoxia response genes and associated microRNAs in the process of oral leukoplakia cancer was discussed.
 
−
Published translation: To study the hypoxia response gene and microRNA (miRNA)expression profiles in the pathogenesis and progression of oral leukoplakia (OLK).
 
−
Analysis: As the above example shows, the Chinese words “进程” belongs to a category word. However, the Chinese expression “癌变”contains the process, so the “进程” expression can be deleted before placing it into the machine translation, because the meaning of it has been overlapping between the expression “癌变”. Therefore, its place can be replaced with preposition “during”.
 
−
 
 
−
After pre-editing source abstract: 探讨口腔白斑病癌变中的缺氧应答基因及相关微小RNA (miRNA) 的表达。
 
−
Google translation: The expression of hypoxia responsive genes and miRNA in oral leukoplakia cancer was investigated.
 
−
 
 
−
Category words are commonly used in Chinese. Category words complement the meaning of words, including problems, positions, situations and jobs. Adding this supplementary word is more in line with Chinese custom. In many cases, it has no real meaning, so it can be omitted in translation. In Chinese, category words are frequently used. However, it is rarely used in English, which is one of the differences between Chinese and English. There are many kinds of category words. Considering objectivity and the fixed structure of language, redundant category words should be deleted in Chinese-English translation. In the general, the most commonly used category of words in the Chinese medical abstracts are "process", "behavior", and "situation". These categories of words hinder the language conversion of Chinese and English, resulting in redundancy.
 
−
 
 
−
Example 5
 
−
Source abstract: 探讨口腔白斑病癌变进程中的缺氧应答基因及相关微小RNA (miRNA) 的表达。
 
−
Google translation: The expression of hypoxia response genes and associated microRNAs in the process of oral leukoplakia cancer was discussed.
 
−
Published translation: To study the hypoxia response gene and microRNA (miRNA)expression profiles in the pathogenesis and progression of oral leukoplakia (OLK).
 
−
Analysis: As the above example shows, the Chinese words “进程” belongs to a category word. However, the Chinese expression “癌变”contains the process, so the “进程” expression can be deleted before placing it into the machine translation, because the meaning of it has been overlapping between the expression “癌变”. Therefore, its place can be replaced with preposition “during”.
 
−
 
 
−
After pre-editing source abstract: 探讨口腔白斑病癌变中的缺氧应答基因及相关微小RNA (miRNA) 的表达。
 
−
Google translation: The expression of hypoxia responsive genes and miRNA in oral leukoplakia cancer was investigated.
 
−
 
 
−
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng
 
−
 
 
−
===Conclusion===
 
−
After years of development, machine translation has made great progress. The accuracy of machine translation has been greatly improved in both text recognition and sentence pattern conversion. However, machine translation has its own limitations. In other words, it needs to rely on the parallel corpus as a reference source for improving its accuracy. ESP text, in particular, is harder to get the high quality by machine translation.
 
−
 
 
−
As one of the research papers, the characteristics of medical abstracts are fixed language structures, objectivity and accuracy (Qin Yi 2004:421-423). Therefore, medical translation must be accurate, object and understandable to follow the specific demands of the medical paper. Being an important field in the human society, medical paper translation is on a great demand, which means that it needs a huge demand for human labor. However, with the machine translation promoting, it will be more efficient to translate medical papers combining the effort by human and machine. The improvement and development of machine translation requires the joint efforts of computer science, information science, statistics, linguistics and other academic circles to achieve more mature human-computer mutual assistance translation (Li Yafei, Zhang Ruihua 2019:38-45).
 
−
 
 
−
However, errors can occur during the process of machine translation of Chinese- English, because of the differences of the Chinese and English and the processing of the machine. Errors from the perspective of linguistic or grammar can affect the machine translation a lot. After division and recognition of errors, some pre-editing approaches are put forward to help the machine translation more accurate and readable, that are, extraction and replacement of terms in the source text, relocation of modifiers, explication of subordination, proper omission, deletion of category words and explication of subject through voice changing.
 
−
 
 
−
The paper mainly focuses on the pre-editing machine translation by using medical papers as a case study. The errors of machine translation occurring in the translation of medical abstracts and pre-editing approaches for machine translation. The quality of machine translation of medical papers is greatly improved after employing the pre-editing methods. However, machine translation is not as flexible and accurate as human brain, so it is of importance to combine pre-editing and post-editing approaches with machine translation in order to produce more accurate, more object machine translation of medical papers.
 
−
 
 
−
After years of development, machine translation has made great progress. The accuracy of machine translation has been greatly improved in both text recognition and sentence pattern conversion. However, machine translation has its own limitations. In other words, it needs to rely on the parallel corpus as a reference source for improving its accuracy. ESP text, in particular, is harder to get the high quality by machine translation.
 
−
 
 
−
As one of the research papers, the characteristics of medical abstracts are fixed language structures, objectivity and accuracy (Qin Yi 2004:421-423). Therefore, medical translation must be accurate, object and understandable to follow the specific demands of the medical paper. Being an important field in the human society, medical paper translation is on a great demand, which means that it needs a huge demand for human labor. However, with the machine translation promoting, it will be more efficient to translate medical papers combining the effort by human and machine. The improvement and development of machine translation requires the joint efforts of computer science, information science, statistics, linguistics and other academic circles to achieve more mature human-computer mutual assistance translation (Li Yafei, Zhang Ruihua 2019:38-45).
 
−
 
 
−
However, errors can occur during the process of machine translation of Chinese- English, because of the differences of the Chinese and English and the processing of the machine. Errors from the perspective of linguistic or grammar can affect the machine translation a lot. After division and recognition of errors, some pre-editing approaches are put forward to help the machine translation more accurate and readable, that are, extraction and replacement of terms in the source text, relocation of modifiers, explication of subordination, proper omission, deletion of category words and explication of subject through voice changing.
 
−
 
 
−
The paper mainly focuses on the pre-editing machine translation by using medical papers as a case study. The errors of machine translation occurring in the translation of medical abstracts and pre-editing approaches for machine translation. The quality of machine translation of medical papers is greatly improved after employing the pre-editing methods. However, machine translation is not as flexible and accurate as human brain, so it is of importance to combine pre-editing and post-editing approaches with machine translation in order to produce more accurate, more object machine translation of medical papers.
 
−
 
 
−
--[[User:Cai Zhufeng|Cai Zhufeng]] ([[User talk:Cai Zhufeng|talk]]) 13:48, 13 December 2021 (UTC)correted by Cai Zhufeng
 
−
 
 
−
===References===
 
−
 
 
−
Cui Qiliang崔启亮(2014).论机器翻译的译后编辑[J] ''On Post-Editing of Machine Translatio''. 中国翻译 Chinese Translators Journal, 035(006):68-73
 
−
 
 
−
Feng Quangong, Gao Lin冯全功,高琳 (2017). 基于受控语言的译前编辑对机器翻译的影响[J] ''Influence of Pre-editing Based on Controlled Language on Machine Translation''. 当代外语研究Contemporary Foreign Language Research,(2): 63-68+87+110.
 
−
 
−
GERLACH J, et al ( 2013). ''Combining Pre-editing and Post-editing to Improve SMT of User-generated Content''[M]// Proceedings of MT Summit XIV Workshop on Post-editing Technology and Practice. 45-53
 
−
 
 
−
Hu Qingping胡清平(2005). 机器翻译中的受控语言[J] ''Controlled Language in Machine Translation''. 中国科技翻译 Chinese Science and Technology Translation, (03): 24-27.
 
−
 
 
−
Lian Shuneng连淑能 (2010). 英汉对比研究增订本[M]''An Updated Version of English-Chinese Contrastive Studies'' . 北京:高等教育出版社Beijing: Higher Education Publishing House. 35-36.
 
−
 
 
−
Li Yafei, Zhang Ruihua黎亚飞,张瑞华 (2019). 机器翻译发展与现状[J]''The Development and Current Situation of Machine Translation''. 中国轻工教育 China Light Industry Education, (5):38-45. 
 
−
 
 
−
Qin Yi秦毅(2004),从翻译基本标准议医学英语的翻译[J] ''On the Translation of Medical English from the Basic Standard of Translation''. 遵义医学院学报 Journal of Zunyi Medical College,27 (4): 421-423.
 
−
 
 
−
Slype G V & Guinet J F & Seitz F (1984). ''Better Translation for Better Communication'' [M] . Oxford: Pergamon Press Ltd (U.K.). 90-93
 
−
 
 
−
O'Brien S (2002). Teaching Post-editing: A Proposal for Course Con-tent [EB/OL]. http://mt-archive. Info/EAMT-2002-0brien. Pdf.
 
−
 
 
−
Tytler, A. F. (1978). ''Essay On The Principles of Translation''[M]. Amsterdam: JohnBenjamins Publishing. 118-119
 
−
 
 
−
Wang Yan王燕 (2008). 医学英语翻译与写作教程[M] ''Medical English Translation and Writing Course''. 重庆:重庆大学出版社 Chongqing: Chongqing University Press. 60-61
 
−
 
 
−
=12 蔡珠凤=(The Mistranslation of C-J Machine Translation of Political Statements)=
 
  
 
机器翻译中政治发言中译日的误译
 
机器翻译中政治发言中译日的误译
Line 561: Line 89:
  
 
[[Machine_Trans_EN_12]]
 
[[Machine_Trans_EN_12]]
−
===Abstract===
 
−
Language is the main way of communication between people. With the continuous development of globalization, the scale of cross-border exchanges is also expanding. However, due to cultural differences and diversity, the languages of different countries and regions are very different, which seriously hinders people's communication. The demand for efficient and convenient translation tools is increasing. At the same time, with the development of network technology and artificial intelligence, recognition technology based on deep learning is more and more widely used in English, Japanese and other fields.
 
  
−
===Key words===
+
=Chapter 13 Study on Post-editing from the Perspective of Functional Equivalence Theory=
−
machine translation; political statements; mistranslation of C-J machine translation
 
−
===题目===
 
−
The Mistranslation of C-J Machine Translation of Political Statements
 
−
===摘要===
 
−
语言是人与人之间交流的主要方式。随着全球化的不断发展，跨境交流的规模也在不断扩大。然而，由于文化的差异和多样性，不同国家和地区的语言差异很大，这严重阻碍了人们的交流。对高效便捷的翻译工具的需求正在增加。同时，随着网络技术和人工智能的发展，基于深度学习的识别技术在英语、日语等领域的应用越来越广泛。
 
  
−
===关键词===
+
陈湘琼, Hunan Normal University
−
机器翻译；政治发言；政治发言中译日的误译
 
−
===1. Introduction===
 
−
===Introduction to machine translation===
 
−
Machine translation, also known as automatic translation, is a process of using computers to convert one natural language (source language) into another natural language (target language). It is a branch of computational linguistics, one of the ultimate goals of artificial intelligence, and has important scientific research value.At the same time, machine translation has important practical value. With the rapid development of economic globalization and the Internet, machine translation technology plays a more and more important role in promoting political, economic and cultural exchanges.The development of machine translation technology has been closely accompanied by the development of computer technology, information theory, linguistics and other disciplines. From the early dictionary matching, to the rule translation of dictionaries combined with linguistic expert knowledge, and then to the statistical machine translation based on corpus, with the improvement of computer computing power and the explosive growth of multilingual information, machine translation technology gradually stepped out of the ivory tower and began to provide real-time and convenient translation services for general users.（Zhang 2019:5-6)
 
  
−
=== C-J machine translation software===
+
[[Machine_Trans_EN_13]]
−
Today's online machine translation software includes Baidu translation, Tencent translation, Google translation, Youdao translation, Bing translation and so on. Google was the first company to launch the machine translation system, and Baidu was the first company to import the machine translation system in China. In addition, Tencent and Youdao have attracted much attention.Machine translation is the process of using computers to convert one natural language into another. It usually refers to sentence and full-text translation between natural languages. In order to continuously improve the translation quality, R & D personnel have added artificial intelligence technologies such as speech recognition, image processing and deep neural network to machine translation on the basis of traditional machine translation based on rules, statistics and examples.With the increase of using machine translation, the joint cooperation between manual translation and machine translation will also increase significantly in the future. What criteria should be used to evaluate the quality of machine translation? In the evolving field of machine translation, there is an urgent need to clarify the unsolvable questions and solved problems.(Lv 1996:3)
+
=Chapter 14 Machine Translation a Challenge for Human Translators=
−
 
 
−
===The history of machine translation===
 
−
The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. alchuni put forward the idea of using machines for translation. In 1933, Soviet inventor П.П. Trojansky designed a machine to translate one language into another, and registered his invention on September 5 of the same year; However, due to the low technical level in the 1930s, his translation machine was not made. In 1946, the first modern electronic computer ENIAC was born. Shortly after that, W. weaver, an American scientist and A. D. booth, a British engineer, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947 when discussing the application scope of electronic computer. In 1949, W. Weaver published the translation memorandum, which formally put forward the idea of machine translation. After 60 years of ups and downs, machine translation has experienced a tortuous and long development path. The academic community generally divides it into the following four stages:
 
−
 
 
−
Pioneering period（1947-1964）
 
−
 
 
−
In 1954, with the cooperation of IBM, Georgetown University completed the English Russian machine translation experiment with ibm-701 computer for the first time, showing the feasibility of machine translation to the public and the scientific community, thus opening the prelude to the study of machine translation.
 
−
It is not too late for China to start this research. As early as 1956, the state included this research in the national scientific work development plan. The topic name is "machine translation, the construction of natural language translation rules and the mathematical theory of natural language". In 1957, the Institute of language and the Institute of computing technology of the Chinese Academy of Sciences cooperated in the Russian Chinese machine translation experiment, translating 9 different types of more complex sentences.From the 1950s to the first half of the 1960s, machine translation research has been on the rise. The United States and the former Soviet Union, two superpowers, have provided a lot of financial support for machine translation projects for military, political and economic purposes, while European countries have also paid considerable attention to machine translation research due to geopolitical and economic needs, and machine translation has become an upsurge for a time. In this period, although machine translation is just in the pioneering stage, it has entered an optimistic period of prosperity.
 
−
 
 
−
Frustrated period（1964-1975）
 
−
 
 
−
In 1964, in order to evaluate the research progress of machine translation, the American Academy of Sciences established the automatic language processing Advisory Committee (Alpac Committee) and began a two-year comprehensive investigation, analysis and test.In November 1966, the committee published a report entitled "language and machine" (Alpac report for short), which comprehensively denied the feasibility of machine translation and suggested stopping the financial support for machine translation projects. The publication of this report has dealt a blow to the booming machine translation, and the research of machine translation has fallen into a standstill. Coincidentally, during this period, China broke out the "ten-year Cultural Revolution", and basically these studies also stagnated. Machine translation has entered a depression.
 
−
 
 
−
convalescence（1975-1989）
 
−
 
 
−
Since the 1970s, with the development of science and technology and the increasingly frequent exchange of scientific and technological information among countries, the language barriers between countries have become more serious. The traditional manual operation mode has been far from meeting the needs, and there is an urgent need for computers to engage in translation. At the same time, the development of computer science and linguistics, especially the substantial improvement of computer hardware technology and the application of artificial intelligence in natural language processing, have promoted the recovery of machine translation research from the technical level. Machine translation projects have begun to develop again, and various practical and experimental systems have been launched successively, such as weinder system Eurpotra multilingual translation system, taum-meteo system, etc.However, after the end of the "ten-year holocaust", China has perked up again, and machine translation research has been put on the agenda again. The "784" project has paid enough attention to machine translation research. After the mid-1980s, the development of machine translation research in China has further accelerated. Firstly, two English Chinese machine translation systems, ky-1 and MT / ec863, have been successfully developed, indicating that China has made great progress in machine translation technology.(Chen 2016:5)
 
−
 
 
−
New period(1990 present)
 
−
 
 
−
With the universal application of the Internet, the acceleration of the process of world economic integration and the increasingly frequent exchanges in the international community, the traditional way of manual operation is far from meeting the rapidly growing needs of translation. People's demand for machine translation has increased unprecedentedly, and machine translation has ushered in a new development opportunity. International conferences on machine translation research have been held frequently, and China has made unprecedented achievements. A series of machine translation software have been launched, such as "Yixing", "Yaxin", "Tongyi", "Huajian", etc. Driven by the market demand, the commercial machine translation system has entered the practical stage, entered the market and came to the users.Since the new century, with the emergence and popularization of the Internet, the amount of data has increased sharply, and statistical methods have been fully applied. Internet companies have set up machine translation research groups and developed machine translation systems based on Internet big data, so as to make machine translation really practical, such as "Baidu translation", "Google translation", etc. In recent years, with the progress of in-depth learning, machine translation technology has further developed, which has promoted the rapid improvement of translation quality, and the translation in oral and other fields is more authentic and fluent.(Liu 2014:6)
 
−
 
 
−
===The problem of machine translation at present===
 
−
Error is inevitable
 
−
Many people have misunderstandings about machine translation. They think that machine translation has great deviation and can't help people solve any problems. In fact, the error is inevitable. The reason is that machine translation uses linguistic principles. The machine automatically recognizes grammar, calls the stored thesaurus and automatically performs corresponding translation. However, errors are inevitable due to changes or irregularities in grammar, morphology and syntax, such as sentences with adverbials after "give me a reason to kill you first" in Dahua journey to the West. After all, a machine is a machine. No one has special feelings for language. How can it feel the lasting charm of "the tenderness of lowering its head, like the shame of a water lotus? After all, the meaning of Chinese is very different due to the changes of morphology, grammar and syntax and the change of context. Even many Chinese people are zhanger monks - they can't touch their heads, let alone machines.(Liu 2014：3）
 
−
 
 
−
Bottleneck
 
−
In fact, no matter which method, the biggest factor affecting the development of machine translation lies in the quality of translation. Judging from the achievements, the quality of machine translation is still far from the ultimate goal.
 
−
Chinese mathematician and linguist Zhou Haizhong once pointed out in his paper "fifty years of machine translation": to improve the quality of machine translation, the first thing to solve is the problem of language itself rather than programming; It is certainly impossible to improve the quality of machine translation by relying on several programs alone. At the same time, he also pointed out that it is impossible for machine translation to achieve the degree of "faithfulness, expressiveness and elegance" when human beings have not yet understood how the brain performs fuzzy recognition and logical judgment of language. This view may reveal the bottleneck restricting the quality of translation.
 
−
It is worth mentioning that American inventor and futurist ray cozwell predicted in an interview with Huffington Post that the quality of machine translation will reach the level of human translation by 2029. There are still many disputes about this thesis in the academic circles.（Cui 2019：4）
 
−
 
 
−
===2.Mistranslation of Chinese Japanese machine translation===
 
−
===2.1Vocabulary mistranslation in Chinese Japanese machine translation===
 
−
===2.1.1Mistranslation of proper nouns===
 
−
In political materials, there are often political dignitaries' names, place names or a large number of proper nouns in the political field. The morphemes of such words are definite and inseparable. Mistranslation will make the source language lose its specific meaning.
 
−
 
 
−
Japanese translation into Chinese                                                Chinese translation into Japanese
 
−
                       
 
−
original text    translation by Youdao reference translation       original text   translation by Youdao       reference translation
 
−
 
 
−
朱鎔基               朱基               朱镕基                    栗战书                 栗戰史書               栗戰書
 
−
           
 
−
労安               劳安                 劳安                    李克强                 李克強                       李克強
 
−
 
 
−
筑紫哲也     筑紫哲也               筑紫哲也                  习近平                 習近平                       習近平
 
−
 
−
山口百惠     山口百惠               山口百惠                   韩正                   韓中                         韓正
 
−
     
 
−
田中角栄     田中角荣               田中角荣                  王沪宁                 王上海氏               王滬寧
 
−
     
 
−
東条英機     东条英社               东条英机                    汪洋                   汪洋                         汪洋
 
−
 
 
−
毛沢东             毛泽东               毛泽东                    赵乐际                   趙樂南               趙樂際
 
−
 
−
トウ・ショウヘイ　　　大酱               邓小平                    江泽民                   江沢民               江沢民
 
−
 
−
周恩来             周恩来                    周恩来
 
−
 
 
−
クリントン     克林顿                    克林顿
 
−
 
 
−
The above table counts 18 special names in the two texts, and 7 machine translation errors. In terms of mistranslation, there are not only "战书" but also "戰史" and "史書" in Chinese. "沪" is the abbreviation of Shanghai. In other words, since "战书" and "沪" are originally common nouns, this disrupts the choice of target language in machine translation. Except Katakana interference (except for トウシゃウヘイ, most of the mistranslations appear in the new Standing Committee. It can be seen that the machine translation system does not update the thesaurus in time. Different from other words, people's names have particularity. Especially as members of the Standing Committee of the Political Bureau of the CPC Central Committee, the translation of their names is officially unified. In this regard, public figures such as political dignitaries, stars, famous hosts and important. In addition, the machine also translates Japanese surnames such as "タ二モト" (谷本), "アンドウ" (安藤) into "塔尼莫特" and "龙胆". It can be found that the language feature that Japanese will be written together with Chinese characters and Hiragana also directly affects the translation quality of Japanese Chinese machine translation. Japanese people often use Katakana pronunciation. For example, when Japanese people talk with Premier Zhu, they often use "二ーハウ" (Hello). However, the machine only recognizes the two pseudonyms "二" and "ハ" and transliterates them into "尼哈" , ignoring the long sound after "二" and "ハ".(Guan 2018:10-12)
 
−
 
 
−
original text                                       Translation by Youdao                         reference translation
 
−
 
 
−
日美安全体制                                         日米の安全体制                                   日米安保体制
 
−
 
 
−
中国共产党第十九次全国代表大会                 中国共産党第19回全国代表大会             中国共産党第19回全国代表大会（第19回党大会）
 
−
 
 
−
十八大                                                     十八大                               第18回党大会中国特色社会主义
 
−
                   
 
−
中国特色社会主義                             中国の特色ある社会主義                                    第18回党大会
 
−
 
 
−
中国共产党中央委员会                             中国共産党中央委員会                           中国共産党中央委員会
 
−
 
 
−
中国共産党中央委員会十八届中共中央政治局常委 第18代中国共產党中央政治局常務委員                      第18期中共中央政治局常務委員
 
−
 
 
−
十八届中共中央政治局委员                   18期の中国共產党中央政治局委員                 第18期中共中央政治局委員
 
−
 
 
−
十九届中共中央政治局常委                 十九回中国共產党中央政治局常務委員                 第19期中央政治局常務委員
 
−
 
 
−
中共十九届一中全会                                中国共產党第十九回一中央委員会               第19期中央委員会第1回全体会議
 
−
 
 
−
The above table is a comparison of the original and translated versions of some proper nouns. As shown in the table, the mistranslation problems are mainly reflected in the mismatch of numerals + quantifiers, the wrong addition of case auxiliary word "の", the lack of connectors, the Mistranslation of abbreviations, dead translation, and the writing errors of Chinese characters. The following is a specific analysis one by one.
 
−
 
 
−
"十八届中共中央政治局常委", "十八届中共中央政治局委员", "十九届中共中央政治局常委" and "中共十九届一中全会" all have quantifiers "届", which are translated into "代", "期" and "回" respectively. The meaning is vague and should be uniformly translated into "期"; Among them, the translation of the last three proper nouns lacks the Chinese conjunction "第". The case auxiliary word "の" was added by mistake in the translation of "十八届中共中央政治局委员" and "日美安全体制". The "中国共产党中央委员会" did not write in the form of Japanese Ming Dynasty characters, but directly used simplified Chinese characters.
 
−
 
 
−
The full name of "中共十九届一中全会" is "中国共产党第十九届中央委员会第一次全体会议", in which "一" stands for "第一次", which is an ordinal word rather than a cardinal word. Machine translation does not produce a correct translation. The fundamental reason is that there are no "规则" for translating such words in machine translation. If we can formulate corresponding rules for such words (for example, 中共m'1届m2 中全会→第m1期中央委员会第m2回全体会议), the translation system must be able to translate well no matter how many plenary sessions of the CPC Central Committee.(Guan 2018:6-7)
 
−
 
 
−
===2.1.2Mistranslation of Polysemy===
 
−
original text                                               Translation by Youdao                             reference translation
 
  
−
スタジオ                                                   摄影棚/工作室                                 直播现场/演播厅
+
Bi bi Nadia, Hunan Normal University
  
−
日中関係の話                                                   中日关系的故事                                 就中日关系(话题)
 
−
 
−
溝                                                                 水沟                                               鸿沟
 
−
 
−
それでは日中の問題について質問のある方。             那么对白天的问题有提问的人。                   关于中日问题的话题，举手提问。
 
−
 
−
私たちのクラスは20人ちょっとですが、                            我们班有20人左右，                              我们班二十多人的意见统一很难，
 
−
 
−
いろいろな意見が出て、まとめるのは大変です。            但是又各种各样的意见，总结起来很困难。                 
 
−
 
−
一体どうやって、13億人もの人をまとめているんですか。     到底是怎么处理13亿的人的呢？                    中国是怎样把13亿人凝聚在一起的？
 
−
 
−
In the original text, the word "スタジオ" appeared four times and was translated into "摄影棚" or "工作室" respectively, but the context is on-site interview, not the production site of photography or film. "話" has many semantics, such as "说话", "事情", "道理", etc. In accordance with the practice of the interview program, Premier Zhu Rongji was invited to answer five questions on the topic of China Japan relations. Obviously, the meaning of the word "故事" is very abrupt. The inherent Japanese word "日中" means "晌午、白天". At the same time, it is also the abbreviation of the names of the two countries. The machine failed to deal with it correctly according to the context. "溝" refers to the gap between China and Japan, not a "水沟". The former "まとめる" appears in the original text with the word "意见", which is intended to describe that it is difficult to unify everyone's opinions. The latter "まとめている" also refers to unifying the thoughts of 1.3 billion people, not "处理". Therefore, it is more appropriate to use "统一" and "处理" in human translation, rather than "总结意见" or "处理意见".(Zhang 2019:5)
 
−
 
−
Mistranslation of polysemy has always been a difficult problem in machine translation research. The translation of each word is correct, but it is often very different from the original expression in the context. Zhang Zhengzeng said that ambiguity is a common phenomenon in natural language. Its essence is that the same language form may have different meanings, which is also one of the differences between natural language and artificial language. Therefore, one of the difficulties faced by machine translation is language disambiguation (Zhang Zheng, 2005:60). In this regard, we can mark all the meanings of polysemous words and judge which meaning to choose by common collocation with other words. At the same time, strengthen the text recognition ability of the machine to avoid the translation inconsistent with the current context. In this way, we can avoid the mistake of "大酱" in the place where famous figures such as Deng Xiaoping should have appeared in the previous political articles.(Wang 2020:7-9)
 
−
 
−
===2.1.3Mistranslation of compound words===
 
−
Multi class words refer to a word with two or more parts of speech, also known as the same word and different classes.
 
−
 
−
original text                                 Translation by Youdao                                   reference translation
 
−
 
−
1998年江泽民主席曾经访问日本,            1998年の江沢民国家主席の日本訪問し、                  1998年、江沢民総書記が日本を訪問し、かつ
 
−
 
−
同已故小渊首相签署了联合宣言。         かつて同じ故小渕首相が署名した共同宣言                 亡くなられた小渕総理と宣言に調印されました
 
−
 
−
Chinese "同" has two parts of speech: prepositions and conjunctions: "故" has many parts of speech, such as nouns, verbs, adjectives and conjunctions. This directly affects the judgment of the machine at the source language level. The word "同" in the above table is used as a conjunction to indicate the other party of a common act. "故" is used as a verb with the semantic meaning of "死亡", which is a modifier of "小渊首相". The machine regards "同" as an adjective and "故" as a noun "原因", which leads to confusion in the structure and unclear semantics of the translation. Different from the polysemy problem, multi category words have at least two parts of speech, and there is often not only one meaning under each part of speech. In this regard, software R & D personnel should fully consider the existence of multi category words, so that the translation machine can distinguish the meaning of words on the basis of marking the part of speech, so as to select the translation through the context and the components of the word in the sentence. Of course, the realization of this function is difficult, and we need to give full play to the wisdom of R & D personnel.(Guan 2018:9-12)
 
−
 
−
===2.2 Syntactic mistranslation in Chinese Japanese machine translation===
 
−
===2.2.1Mistranslation of tenses===
 
−
original text：History is written by the people, and all achievements are attributed to the people.
 
−
 
−
Translation by Youdao：歴史は人民が書いたものであり、すべての成果は人民のためである。
 
−
 
−
reference translation：歴史は人民が綴っていくものであり、すべての成果は人民に帰することとなります。
 
−
 
−
The original sentence meaning is "历史是人民书写的历史" or "历史是人民书写的东西". When translated into Japanese, due to the influence of Japanese language habits, the formal noun "もの" should be supplemented accordingly. In this sentence, both the machine and the interpreter have translated correctly. However, the machine's recognition of "的" is biased, resulting in tense translation errors. The past, present and future will become "history", and this is a continuous action. The form translated as "~ ていく" not only reflects that the action is a continuous action, but also conforms to the tense and semantic information of the original text. In addition, the machine's treatment of the preposition "于" is also inappropriate. In the original text, "人民" is the recipient of action, not the target language. As the most obvious feature of isolated language, function words in Chinese play an important role in semantic expression, and their translation should be regarded as a focus of machine translation research.(Zuo 2021:8)
 
−
 
−
original text：李克强同志是十六届中共中央政治局常委,其他五位同志都是十六届中共中央政治局委员。
 
−
 
−
Translation by Youdao：李克強総理は第16代中国共産党中央政治局常務委員であり、他の５人の同志はいずれも16期の中国共産党中央政治局委員である。
 
−
 
−
reference translation：李克強同志は第16期中国共産党中央政治局常務委員を務め、他の５人は第16期中共中央政治局委員を務めました。
 
−
 
−
Judging the verb "是" in the original text plays its most basic positive role, but due to the complexity of Chinese language, "是" often can not be completely transformed into the form of "だ / である" in the process of translation. This sentence is a judgment of what has happened. Compared with the 19th session, the 18th session has become history. This is an implicit temporal information. It is very difficult for machines without human brain to recognize this implicit information. "中共中央政治局常委" is the abbreviation of "中共中央政治局常务委员会委员" and it is a kind of position. In Japanese, often use it with verbs such as "務める","担当する",etc. The translator adopts the past tense of "務める", which conforms to the expression habit of Japanese and deals with the problem of tense at the same time. However, machine translation only mechanically translates this sentence into judgment sentence, and fails to correctly deal with the past information implied by the word "十八届".(Guan 2018:4)
 
−
 
−
===2.2.2Mistranslation of honorifics===
 
−
Honorific language is a language means to show respect to the listener. Different from Japanese, there is no grammatical category of honorifics in Chinese. There is no specific fixed grammatical form to express honorifics, self modesty and politeness. Instead, specific words such as "您", "请" and "劳驾" are used to express all kinds of respect or self modesty. (Yang 2020:5-9)
 
−
 
−
Original text                              translation by Youdao                                  reference translation
 
−
 
−
女士们，先生们，同志们，朋友们    さんたち、先生たち、同志たち、お友达さん　　                      ご列席の皆さん
 
−
 
−
谢谢大家！                                ありがとうございます！                            ご清聴ありがとうございました。
 
−
 
−
これはどうされますか。                        这是怎么回事呢?                                    您将如何解决这一问题？
 
−
 
−
こうした問題をどうお考えでしょうか。      我们会如何考虑这些问题呢？                              您如何看待这一问题？
 
−
 
−
For example, for the processing of "女士们，先生们，同志们，朋友们", the machine makes the translation correspond to the original one by one based on the principle of unchanged format, but there are errors in semantic communication and pragmatic habits. When speaking on formal occasions, Chinese expression tends to be comprehensive and detailed, as well as the address of the audience. In contrast, Japanese usually uses general terms such as "皆様", "ご臨席の皆さん" or "代表団の方々" as the opening remarks. In terms of Japanese Chinese translation, the machine also failed to recognize the usage of Japanese honorifics such as "さ れ る" and "お考え". It can be seen that Chinese Japanese machine translation has a low ability to deal with honorific expressions. The occasion is more formal. A good translation should not only be fluent in meaning, but also conform to the expression habits of the target language and match the current translation environment.(Che 2021:3-7)
 
−
 
−
===Conclusion===
 
−
In the process of discussing the Mistranslation of machine translation, this paper mainly cites the Mistranslation of Youdao translator. When I studied the problem of machine translation mistranslation, I also used a large number of translation software such as Google and Baidu for parallel comparison, and found that these translation software also had similar problems with Youdao translator. For example, the "新世界新未来" in Chinese ABAC structural phrases is translated by Baidu as "新し世界の新しい未来", which, like Youdao, is not translated in the form of parallel phrases; Google online translation translates the word "“全心全意" into a completely wrong "全面的に"; The original sentence "我吃了很多亏" in the Mistranslation of the unique expression in the language is translated by Google online as "私はたくさんの損失を食べました". Because the "吃" and "亏" in the original text are not closely adjacent, the machine can not recognize this echo and mistakenly treats "亏" as a kind of food, so the machine translates the predicate as "食べました"; Japanese "こうした問題をどう考えでしょうか", Google's online translation is "你如何看待这些问题?", "你" tone can not reflect the tone of Japanese honorific, while Baidu translates as "你怎么想这样的问题呢?" Although the meaning can be understood, it is an irregular spoken language. Google and Baidu also made the same mistakes as Youdao in the translation of proper nouns. For example, they translated "十七届(中共中央政治局常委)”" into "第17回..." .In short, similar mistranslations of Youdao are also common in Baidu and Google. Due to space constraints, they will not be listed one by one here.(Cui 2019:7)
 
−
 
−
Mr. Liu Yongquan, Institute of language, Chinese Academy of Social Sciences (1997) It has been pointed out that machine translation is a linguistic problem in the final analysis. Although corpus based machine translation does not require a lot of linguistic knowledge, language expression is ever-changing. Machine translation based solely on statistical ideas can not avoid the translation quality problems caused by the lack of language rules. The following is based on the analysis of lexical, syntactic and other mistranslations From the perspective of the characteristics of the source language, this paper summarizes some difficulties of Chinese and Japanese in machine translation.(Liu 2014:8)
 
−
 
−
(1) The difficulties of Chinese in machine translation
 
−
 
−
Chinese is a typical isolated language. The relationship between words needs to be reflected by word order and function words. We should pay attention to the transformation of function words such as "的", "在", "向" and "了". Some special verbs, such as "是", "做" and "作", are widely used. How to translate on the basis of conforming to Japanese pragmatic habits and expressions is a difficulty in machine translation research. In terms of word order, Chinese is basically "subject→predicate→object". At the same time, Chinese pays attention to parataxis, and there is no need to be clear in expression with meaningful cohesion, which increases the difficulty of Chinese Japanese machine translation. When there are multiple verbs or modifier components in complex long sentences, machine translation usually can not accurately divide the components of the sentence, resulting in the result that the translation is completely unreadable.(Guan 2018:6-12)
 
−
 
−
(2) Difficulties of Japanese in machine translation
 
−
 
−
Both Chinese and Japanese languages use Chinese characters, and most machines produce the target language through the corresponding translation of Chinese characters. However, pseudonyms in Japanese play an important role in judging the part of speech and meaning, and can not only recognize Chinese characters and judge the structure and semantics of sentences. Japanese vocabulary is composed of Chinese vocabulary, inherent vocabulary and foreign vocabulary. Among them, the first two have a great impact on Chinese Japanese machine translation. For example "提出" is translated as "提出する" or "打ち出す"; and "重视" is translated as "重視する" or "大切にする". Japanese is an adhesive language, which contains a lot of "けど", "が" and "けれども" Some of them are transitional and progressive structures, but there are also some sequential expressions that do not need translation, and these translation software often can not accurately grasp them.(Che 2021:10)
 
−
 
−
===References===
 
−
[1] Navroz Kaur Kahlon,(2021(prepublish));Williamjeet Singh.Machine translation from text to sign language: a systematic review[J].Universal Access in the Information Society,1-35.
 
−
 
−
[2] Cao Qianyu;Hao Hanmei,(2021);Ahmed Syed Hassan.A Chaotic Neural Network Model for English Machine Translation Based on Big Data Analysis[J].Computational Intelligence and Neuroscience,3274326-3274326.
 
−
 
−
[3]Hwang Yongkeun;Kim Yanghoon;Jung Kyomin.(2021)Context-Aware Neural Machine Translation for Korean Honorific Expressions[J].Electronics,10(13):1589-1589.
 
−
 
−
[4]Zakaryia Almahasees.(2021)Analysing English-Arabic Machine Translation:Google Translate, Microsoft Translator and Sakhr.
 
−
 
−
[5](2021)Machine learning in translation[J].Nature Biomedical Engineering,5(6):485-486.
 
−
 
−
[6]Shaimaa Marzouk.(2021(prepublish))An in-depth analysis of the individual impact of controlled language rules on machine translation output: a mixed-methods approach[J].Machine Translation,1-37.
 
−
 
−
[7]Welnitzová Katarína;Munková Daša.(2021)Sentence-structure errors of machine translation into Slovak[J].Topics in Linguistics,22(1):78-92.
 
−
 
−
[8]Xu Xueyuan.(2021).Machine learning-based prediction of urban soil environment and corpus translation teaching[J].Arabian Journal of Geosciences,14(11).
 
−
 
−
[9]Chen Bingchang 陈丙昌(2016).機械翻訳の誤訳分析【D】.Error analysis of mechanical translation.贵州大学.2016(05)
 
−
 
−
[10]Lv Yinqiu 呂寅秋(1996).機械翻訳の言語規則と伝統文法との相違点.【D】The language rules of mechanical translation, the traditional grammar, and the points of contradiction.日本学研究.Japanese Studies.1996(00):21-22
 
−
 
−
[11]Liu Jun 刘君(2014).基于语料库的中日同形词词义用法对比及其日中机器翻译研究【D】.A Corpus-based Comparison of the Meanings of Chinese and Japanese Homographs and Research on Japanese-Chinese Machine Translation.广西大学.(03)
 
−
 
−
[12]Cun Qianqian 崔倩倩(2019).机器翻译错误与译后编辑策略研究【D】.Research on Machine Translation Errors and Post-Editing Strategies.北京外国语大学.(09)
 
−
 
−
[13]Zhang Yi 张义(2019).机器翻译的译文分析【D】.Translation analysis of machine translation.西安外国语大学.(10)
 
−
 
−
[14]Zhang Linqian 张琳婧(2019).在线机器翻译中日翻译错误原因及对策【D】.Causes and countermeasures of online machine translation errors in Chinese-Japanese translation.山西大学.(02)
 
−
 
−
[15]Wang Dan 王丹(2020).基于机器翻译的专利文本译后编辑对策研究【D】.Research on countermeasures for post-translational editing of patent texts based on machine translation.大连理工大学.(06)
 
−
 
−
[16]Yang Xiaokun 杨晓琨(2020).日中机器翻译中的前编辑规则与效果验证【D】.Pre-editing rules and effect verification in Japanese-Chinese machine translation.大连理工大学.(06)
 
−
 
−
[17]Zuo Jia 左嘉(2021). 机器翻译日译汉误译研究【D】. Research on Mistranslation of Machine Translation from Japanese to Chinese.北京第二外国语学院.
 
−
 
−
[18]Guan Biying 关碧莹(2018).关于政治类发言的汉日机器翻译误译分析【D】.Analysis of Chinese-Japanese Machine Translation Mistranslations of Political Speeches.哈尔滨理工大学.
 
−
 
−
[19]Che Tong 车彤(2021).汉译日机器翻译质量评估及译后编辑策略研究【D】.Research on Quality Evaluation of Chinese-Japanese Machine Translation and Post-translation Editing Strategies.北京外国语大学.(09)
 
−
 
−
Networking Linking
 
−
 
−
http://www.elecfans.com/rengongzhineng/692245.html
 
−
 
−
https://baike.baidu.com/item/%E6%9C%BA%E5%99%A8%E7%BF%BB%E8%AF%91/411793
 
−
 
−
=13 陈湘琼Chen Xiangqiong（Study on Post-editing from the Perspective of Functional Equivalence Theory ）=
 
−
[[Machine_Trans_EN_13]]
 
−
 
−
=14 Bi bi Nadia（Machine Translation a Challenge for Human Translators)=
 
 
[[Machine_Trans_EN_14]]
 
[[Machine_Trans_EN_14]]
 +
=Chapter 15 Machine Translation: Advantage or Disadvantage for the Human Translator=
  
−
===Abstract===
+
Mariam Touré, Hunan Normal University
−
Machine translation is a big obstacle in the way of Human translators or interpreters although it is quick and less time consuming.People are trying to get translation of their target language using through source language.For this purpose they are using digital apps like Google translation Google translation does not give accurate and exact interpretation.Google translator is translating word to word but it doesn't clarify it's true and actual meaning.On the other hand human translators can give exact and accurate translation ,they take care of grammatical mistakes , diction and sentence structure.They clarify the purpose of target language through using source language.
 
−
 
 
−
===Keywords===
 
−
Descriptive translation, academic, interdiscipline, comparative literature, localization,Translatology,school of thought,translation , studies, linguistics, corresponding.
 
−
 
 
−
===Body of article===
 
−
Translation is the process of reworking text from one language into another to maintain the original message and communication. But, like anything else, there are different methods of translation, and they vary in form and function. What is translation? The term has become widely used among knowledge transferring researchers and practitioners, especially in the fields of health and health care. In a landmark review, Jonathan Lomas began to argue that 'The tasks… may be defined as to establish and maintain links between researchers and their audience, via the appropriate translation of research findings' (Lomas 1997, p 4). In 2004, World Health Organization's World Report on Knowledge for Better Health suggested that 'One of the key contributions of research to health systems is the translation of knowledge into actions' (WHO 2004, p 33 and p 100). By 2006, special issues of WHO's Bulletin as well as the journals Evaluation and the Health Professions and the Journal of Continuing Education in the Health Professions were dedicated to translation.But what does 'translation' mean? It may be a new word for an old problem, meaning nothing more than 'transfer'. The rapidly emerging field of 'translational medicine' seems to take translation to mean generally what transfer might have meant, that is the transmission of knowledge and evidence 'from bench to bedside'. As one commentator has observed, "Translational medicine" as a fashionable term is being increasingly used to describe the wish of biomedical researchers to ultimately help patients' (Wehling 2008, abstract).
 
−
Semantic uncertainty persists, not least because of the different interests of scientists, clinicians, patients and commercial firms (Littman et al 2007): 'Translational research means different things to different people, but it seems important to almost everyone' (Woolf 2008, p 211).
 
−
In related fields, such as public health (Armstrong et al 2006), 'translation' seems to signify dissatisfaction with 'transfer'. It wants to move away from thinking of knowledge transfer as a form of technology transfer or dissemination, rejecting if only by implication its mechanistic assumptions and its model of linear messaging from A to B. But still, what does it signify?
 
−
 
 
−
===Why translation?===
 
−
'Translation' indicates a closer attention to the problem of shared meaning and how it might be developed. It seems to represent some new epistemological lubricant, facilitating the dissemination of texts and the application and use of the knowledge and information they  in. Simply, translation might be the key to transfer. And yet, when we stop to think, we are more ambivalent. What is translated often seems somehow inferior, not real or original. Note how readily commentators reach for the idea that things might be 'lost in translation'. Knowing at a distance – made in and mediated by translation - makes for incomplete renditions, blurred images, partial truths. So what might 'translation' really mean? The purpose of this paper is to set out, for policy makers and practitioners, the theoretical and conceptual resources translation holds and seems to represent. In doing so, it explores understandings of translation in the fields of literature and linguistics and in the sociology of science and technology. It begins by setting out just why this idea of translation should make immediate, intuitive sense in relation to research, policy and practice.
 
−
1translation in research, policy and practice research as translation
 
−
Research often entails translation from one language to another: where data is collected from more than one ethnic group, for example, or where the language of the researcher is other than that of the research subject. It may draw on a secondary literature or source documents written in different languages, and may be published and disseminated in languages other than the one in which it is first written up.
 
−
2In a different way, to conduct an interview is to ask for an account of experience and its meanings, but it is also to construct and translate that experience in terms defined at least in part by the researcher. In representing what is said, transcripts then select data, usually excluding significant gesture and eye-contact, for example. Often, certain characteristics of speech-acts (such as hesitations) will be edited out. In turn, the format of the transcript shapes the analytic use the researcher may make of it. The basis of research 'findings', then, is an artefact, a transcript or translation, not an original interaction (Ochs 1979, Barnes, Bloor and Henry 1996, Ross 2009).
 
−
3In this way, the researcher recasts aspects of his or her problem or topic in new, scientific form: 'All researchers "translate" the experiences of others' (Temple 1997, p 609). Research is invariably conducted in a sort of 'metalanguage' (Hantrais and Ager 1985): the research process can be conceived as one of successive translations (from theoretical formulation to operationalization, transcription, interpretation and dissemination). Theorization is a process of reciprocal back and forth between theory and fact, in which conceptions of each are revised in order that one fit the other (Baldamus 1974).
 
−
4 It is a kind of translation: a rereading, re-use, re-application or re-representation of what we know in new terms (Turner 1980). Referencing, too, is an act of translation, a form of appropriation and incorporation of one text by another (Gilbert 1977).policy as translation Each of the fields covered by the paper is diverse and ill-defined, and there is no intention here to provide a comprehensive account of any them. Sources have been chosen for their relevance: main references are cited in the text, and additional sources listed in footnotes.
 
−
 
 
−
2For a brief introduction to the technical issues involved in social research in more than one language, see Birbili (2000). For translation issues in survey research and question design in general, see Ervin and Bower (1952) and Deutscher (1968); on translating survey research instruments (in this instance health-related quality of life measures), Bowden and Fox-Rushby (2003). On the use of translators and interpreters, see Temple (1997) and Jentsch (1998).
 
−
3For an interesting discussion of this problem, see Bourdieu (1999), esp pp 621-626, 'The risks of writing'.
 
−
===Difference between machine translation and human translators===
 
−
Few people disagree on the differences between the two, but many argue over the quality of the translations. How accurate are machine translations? How reliable are human translations? Some say machine translation produces near-perfect translations while others are adamant that translations are incomprehensible and cause more problems than they solve. Results will, of course, vary depending on the source and target languages, the machine translation service used (e.g. Google Translate), and the complexity of the original text.
 
−
Machine translation, love it or hate it, is here to stay. In fact, the machine translation market is growing at such a fast pace that it is predicted to reach $980 million by 2022.
 
−
===Machine translation: the pros and cons===
 
−
The advantages of machine translation generally come down to two factors: it’s faster and cheaper. The downside to this is the standard of translation can be anywhere from inaccurate, to incomprehensible, and potentially dangerous (more on that shortly).
 
−
==The advantages of machine translation==
 
−
Many free tools are readily available (Google Translate, Skype Translator, etc.)
 
−
Quick turnaround time                                                                 
 
−
You can translate between multiple languages using one tool
 
−
Translation technology is constantly improving
 
−
The disadvantages of machine translation
 
−
Level of accuracy can be very low                   
 
−
Accuracy is also very inconsistent across different languages
 
−
==Machines can't translate context ==
 
−
Mistakes are sometimes costly                                             
 
−
Sometimes translation simply doesn’t work
 
−
The most important thing to consider with any kind of translation is the cost of potential mistakes. Translating instructions for medical equipment, aviation manuals, legal documents and many other kinds of content require 100% accuracy. In such cases, mistakes can cost lives, huge amounts of money and irreparable damage to your company’s image. So choose carefully!
 
−
Human translation: the pros and cons
 
−
Human translation essentially switches the table in terms of pros and cons. A higher standard of accuracy comes at the price of longer turnaround times and higher costs. What you have to decide is whether that initial investment outweighs the potential cost of mistakes. Alternatively, whether mistakes simply aren’t an option, like the cases we looked at in the previous section.
 
−
 
 
−
==The advantages of human translation  ==
 
−
It’s a translator’s job to ensure the highest accuracy
 
−
Humans can interpret context and capture the same meaning, rather than simply translating words
 
−
Human translators can review their work and provide a quality process
 
−
Humans can interpret the creative use of language, e.g. puns, metaphors, slogans, etc.
 
−
Professional translators understand the idiomatic differences between their languages
 
−
Humans can spot pieces of content where literal translation isn’t possible and find the most suitable alternative
 
−
The disadvantages of human translation
 
−
Turnaround time is longer                                                             
 
−
Translators rarely work for free                                                     
 
−
Unless you use a translation agency, with access to thousands of translators, you’re limited to the languages any one translator can work with
 
−
Simply put, human translation is your best option when accuracy is even remotely important. Other considerations to make are the complexity of your source material and the two languages you’re translating between – both of which can render machines pretty useless.
 
−
 
 
−
When to use machine and human translation
 
−
 
 
−
The truth is, the debate over machine vs human translation is an unnecessary distraction. What we should really be talking about is when to use these two different types of translation services, because they both serve a very valid purpose.
 
−
 
 
−
Examples of when to use machine translation
 
−
 
 
−
When you have a large bulk of content to translate and the general meaning is enough
 
−
When your translation never reaches the final audience, e.g. you’re translating a resource as research for another piece of content
 
−
Translating documents for internal use within a company, provided 100% accuracy isn’t needed
 
−
To partially translate large chunks of content for a human translator to improve upon
 
−
Examples of when to use human translation
 
−
When accuracy is important
 
−
Most cases where your translated content is received by a consumer audience
 
−
When you have a duty of care to provide accurate translations (e.g. legal documents, product instructions, medical guidelines or health and safety content)
 
−
When translating marketing material or other texts for creative language uses.
 
−
 
 
−
types of machine translation.
 
−
 
 
−
What is Machine Translation? Rule Based Machine Translation vs. Statistical Machine Translation. Machine translation (MT) is automated translation. It is the process by which computer software is used to translate a text from one natural language (such as English) to another (such as Spanish).
 
−
 
 
−
To process any translation, human or automated, the meaning of a text in the original (source) language must be fully restored in the target language, i.e. the translation. While on the surface this seems straightforward, it is far more complex. Translation is not a mere word-for-word substitution. A translator must interpret and analyze all of the elements in the text and know how each word may influence another. This requires extensive expertise in grammar, syntax (sentence structure), semantics (meanings), etc., in the source and target languages, as well as familiarity with each local region.
 
−
 
 
−
Human and machine translation each have their share of challenges. For example, no two individual translators can produce identical translations of the same text in the same language pair, and it may take several rounds of revisions to meet customer satisfaction. But the greater challenge lies in how machine translation can produce publishable quality translations.
 
−
 
 
−
Rule-Based Machine Translation Technology
 
−
Rule-based machine translation relies on countless built-in linguistic rules and millions of bilingual dictionaries for each language pair.
 
−
The software parses text and creates a transitional representation from which the text in the target language is generated. This process requires extensive lexicons with morphological, syntactic, and semantic information, and large sets of rules. The software uses these complex rule sets and then transfers the grammatical structure of the source language into the target language.
 
−
Translations are built on gigantic dictionaries and sophisticated linguistic rules. Users can improve the out-of-the-box translation quality by adding their terminology into the translation process. They create user-defined dictionaries which override the system’s default settings.
 
−
In most cases, there are two steps: an initial investment that significantly increases the quality at a limited cost, and an ongoing investment to increase quality incrementally. While rule-based MT brings companies to the quality threshold and beyond, the quality improvement process may be long and expensive.
 
−
 
 
−
Statistical Machine Translation Technology
 
−
Statistical machine translation utilizes statistical translation models whose parameters stem from the analysis of monolingual and bilingual corpora. Building statistical translation models is a quick process, but the technology relies heavily on existing multilingual corpora. A minimum of 2 million words for a specific domain and even more for general language are required. Theoretically it is possible to reach the quality threshold but most companies do not have such large amounts of existing multilingual corpora to build the necessary translation models. Additionally, statistical machine translation is CPU intensive and requires an extensive hardware configuration to run translation models for average performance levels.
 
−
 
 
−
Rule-Based MT vs. Statistical MT
 
−
Rule-based MT provides good out-of-domain quality and is by nature predictable. Dictionary-based customization guarantees improved quality and compliance with corporate terminology. But translation results may lack the fluency readers expect. In terms of investment, the customization cycle needed to reach the quality threshold can be long and costly. The performance is high even on standard hardware.
 
−
 
 
−
Statistical MT provides good quality when large and qualified corpora are available. The translation is fluent, meaning it reads well and therefore meets user expectations. However, the translation is neither predictable nor consistent. Training from good corpora is automated and cheaper. But training on general language corpora, meaning text other than the specified domain, is poor. Furthermore, statistical MT requires significant hardware to build and manage large translation models.
 
−
 
 
−
Rule-Based MT Statistical MT
 
−
+ Consistent and predictable quality – Unpredictable translation quality
 
−
+ Out-of-domain translation quality – Poor out-of-domain quality
 
−
+ Knows grammatical rules – Does not know grammar
 
−
+ High performance and robustness – High CPU and disk space requirements
 
−
+ Consistency between versions – Inconsistency between versions
 
−
– Lack of fluency + Good fluency
 
−
– Hard to handle exceptions to rules + Good for catching exceptions to rules
 
−
– High development and customization costs + Rapid and cost-effective development costs provided the required corpus exists
 
−
Given the overall requirements, there is a clear need for a third approach through which users would reach better translation quality and high performance (similar to rule-based MT), with less investment (similar to statistical MT).
 
−
Post-Edited Machine Translation (PEMT)
 
−
Often, PEMT is used to bridge the gap between the speed of machine translation and the quality of human translation, as translators review, edit and improve machine-translated texts. PEMT services cost more than plain machine translations but less than 100% human translation, especially since the post-editors don’t have to be fluently bilingual—they just have to be skilled proofreaders with some experience in the language and target region.
 
−
Successful translation is about more than just the words, which is why we advocate for not just human translation by skilled linguists, but for translation by people deeply familiar with the cultures they’re writing for. Life experience, study and the knowledge that only comes from living in a geographic region can make the difference between words that are understandable and language that is capable of having real, positive impact.
 
−
 
 
−
PacTranz
 
−
The HUGE list of 51 translation types, methods and techniques
 
−
Upper section of infographic of 51 common types of translation classified in 4 broad categoriesThere are a bewildering number of different types of translation.
 
−
So we’ve identified the 51 types you’re most likely to come across, and explain exactly what each one means.
 
−
This includes all the main translation methods, techniques, strategies, procedures and areas of specialisation.
 
−
It’s our way of helping you make sense of the many different kinds of translation – and deciding which ones are right for you.
 
−
Don’t miss our free summary pdf download later in the article!
 
−
The 51 types of translation we’ve identified fall neatly into four distinct categories.
 
−
Translation Category A: 15 types of translation based on the technical field or subject area of the text
 
−
Icons representing 15 types of translation categorised by the technical field or subject area of the textTranslation companies often define the various kinds of translation they provide according to the subject area of the text.
 
−
This is a useful way of classifying translation types because specialist texts normally require translators with specialist knowledge.
 
−
Here are the most common types you’re like to come across in this category.
 
−
 
 
−
1. General Translation
 
−
What is it?
 
−
The translation of non-specialised text. That is, text that we can all understand without needing specialist knowledge in some area.
 
−
The text may still contain some technical terms and jargon, but these will either be widely understood, or easily researched.
 
−
What this means
 
−
The implication is that you don’t need someone with specialist knowledge for this type of translation – any professional translator can handle them.
 
−
Translators who only do this kind of translation (don’t have a specialist field) are sometimes referred to as ‘generalist’ or ‘general purpose’ translators.
 
−
Examples
 
−
Most business correspondence, website content, company and product/service info, non-technical reports.
 
−
Most of the rest of the translation types in this Category do require specialist translators.
 
−
Check out our video on 13 types of translation requiring special translator expertise:
 
−
 
 
−
2. Technical Translation
 
−
What is it?
 
−
We use the term “technical translation” in two different ways:
 
−
Broad meaning: any translation where the translator needs specialist knowledge in some domain or area.
 
−
This definition would include almost all the translation types described in this section.
 
−
Narrow meaning: limited to the translation of engineering (in all its forms), IT and industrial texts.
 
−
This narrower meaning would exclude legal, financial and medical translations for example, where these would be included in the broader definition.
 
−
What this means
 
−
Technical translations require knowledge of the specialist field or domain of the text.
 
−
That’s because without it translators won’t completely understand the text and its implications. And this is essential if we want a fully accurate and appropriate translation.Good to know Many technical translation projects also have a typesetting/dtp requirement. Be sure your translation provider can handle this component, and that you’ve allowed for it in your project costings and time frames.
 
−
Examples
 
−
Manuals, specialist reports, product brochures
 
−
 
 
−
3. Scientific Translation
 
−
What is it?
 
−
The translation of scientific research or documents relating to it.
 
−
What this means
 
−
These texts invariably contain domain-specific terminology, and often involve cutting edge research.
 
−
So it’s imperative the translator has the necessary knowledge of the field to fully understand the text. That’s why scientific translators are typically either experts in the field who have turned to translation, or professionally qualified translators who also have qualifications and/or experience in that domain.
 
−
On occasion the translator may have to consult either with the author or other domain experts to fully comprehend the material and so translate it appropriately.
 
−
Examples
 
−
Research papers, journal articles, experiment/trial results
 
−
 
 
−
4. Medical Translation
 
−
What is it?
 
−
The translation of healthcare, medical product, pharmaceutical and biotechnology materials.
 
−
Medical translation is a very broad term covering a wide variety of specialist areas and materials – everything from patient information to regulatory, marketing and technical documents.
 
−
As a result, this translation type has numerous potential sub-categories – ‘medical device translations’ and ‘clinical trial translations’, for example.
 
−
What this means
 
−
As with any text, the translators need to fully understand the materials they’re translating. That means sound knowledge of medical terminology and they’ll often also need specific subject-matter expertise.
 
−
Good to know
 
−
Many countries have specific requirements governing the translation of medical device and pharmaceutical documentation. This includes both your client-facing and product-related materials.
 
−
Examples
 
−
Medical reports, product instructions, labeling, clinical trial documentation
 
−
 
 
−
5. Financial Translation
 
−
What is it?
 
−
In broad terms, the translation of banking, stock exchange, forex, financing and financial reporting documents.
 
−
However, the term is generally used only for the more technical of these documents that require translators with knowledge of the field.
 
−
Any competent translator could translate a bank statement, for example, so that wouldn’t typically be considered a financial translation.
 
−
What this means
 
−
You need translators with domain expertise to correctly understand and translate the financial terminology in these texts.
 
−
Examples
 
−
Company accounts, annual reports, fund or product prospectuses, audit reports, IPO documentation
 
−
 
 
−
6. Economic Translations
 
−
What is it?
 
−
1. Sometimes used as a synonym for financial translations.
 
−
2. Other times used somewhat loosely to refer to any area of economic activity – so combining business/commercial, financial and some types of technical translations.
 
−
3. More narrowly, the translation of documents relating specifically to the economy and the field of economics.
 
−
What this means
 
−
As always, you need translators with the relevant expertise and knowledge for this type of translation.
 
−
 
 
−
7. Legal Translation
 
−
What is it?
 
−
The translation of documents relating to the law and legal process.
 
−
What this means
 
−
Legal texts require translators with a legal background.
 
−
That’s because without it, a translator may not:
 
−
– fully understand the legal concepts
 
−
– write in legal style
 
−
– understand the differences between legal systems, and how best to translate concepts that don’t correspond.
 
−
And we need all that to produce professional quality legal translations – translations that are accurate, terminologically correct and stylistically appropriate.
 
−
Examples
 
−
Contracts, legal reports, court judgments, expert opinions, legislation
 
−
 
 
−
8. Juridical Translation
 
−
What is it?
 
−
1. Generally used as a synonym for legal translations.
 
−
2. Alternatively, can refer to translations requiring some form of legal verification, certification or notarization that is common in many jurisdictions.
 
−
 
 
−
9. Judicial Translation
 
−
What is it?
 
−
1. Most commonly a synonym for legal translations.
 
−
2. Rarely, used to refer specifically to the translation of court proceeding documentation – so judgments, minutes, testimonies, etc.
 
−
 
 
−
10. Patent Translation
 
−
What is it?
 
−
The translation of intellectual property and patent-related documents.
 
−
Key features
 
−
Patents have a specific structure, established terminology and a requirement for complete consistency throughout – read more on this here. These are key aspects to patent translations that translators need to get right.
 
−
In addition, subject matter can be highly technical.
 
−
What this means
 
−
You need translators who have been trained in the specific requirements for translating patent documents. And with the domain expertise needed to handle any technical content.
 
−
Examples
 
−
Patent specifications, prior art documents, oppositions, opinions
 
−
 
 
−
11. Literary Translation
 
−
What is it?
 
−
The translation of literary works – novels, short stories, plays, essays, poems.
 
−
Key features
 
−
Literary translation is widely regarded as the most difficult form of translation.
 
−
That’s because it involves much more than simply conveying all meaning in an appropriate style. The translator’s challenge is to also reproduce the character, subtlety and impact of the original – the essence of what makes that work unique.
 
−
This is a monumental task, and why it’s often said that the translation of a literary work should be a literary work in its own right.
 
−
What this means
 
−
Literary translators must be talented wordsmiths with exceptional creative writing skills.
 
−
Because few translators have this skillset, you should only consider dedicated literary translators for this type of translation.
 
−
 
 
−
 
−
12. Commercial Translation
 
−
What is it?
 
−
The translation of documents relating to the world of business.
 
−
This is a very generic, wide-reaching translation type. It includes other more specialised forms of translation – legal, financial and technical, for example. And all types of more general business documentation.
 
−
Also, some documents will require familiarity with business jargon and an ability to write in that style.
 
−
What this means
 
−
Different translators will be required for different document types – specialists should handle materials involving technical and specialist fields, whereas generalist translators can translate non-specialist materials.
 
−
Examples
 
−
Business correspondence, reports, marketing and promotional materials, sales proposals
 
−
 
 
−
13. Business Translations
 
−
What is it?
 
−
A synonym for Commercial Translations.
 
−
 
 
−
14. Administrative Translations
 
−
What is it?
 
−
The translation of business management and administration documents.
 
−
So it’s a subset of business / commercial translations.
 
−
What this means
 
−
The implication is these documents will include business jargon and ‘management speak’, so require a translator familiar with, and practised at, writing in that style.
 
−
Examples
 
−
Management reports and proposals
 
−
 
 
−
15. Marketing Translations
 
−
What is it?
 
−
The translation of advertising, marketing and promotional materials.
 
−
This is a subset of business or commercial translations.
 
−
Key features
 
−
Marketing copy is designed to have a specific impact on the audience – to appeal and persuade.
 
−
So the translated copy must do this too.
 
−
But a direct translation will seldom achieve this – so translators need to adapt their wording to produce the impact the text is seeking.
 
−
And sometimes a completely new message might be needed – see transcreation in our next category of translation types.
 
−
What this means
 
−
Marketing translations require translators who are skilled writers with a flair for producing persuasive, impactful copy.
 
−
As relatively few translators have these skills, engaging the right translator is key.
 
−
Good to know
 
−
This type of translation often comes with a typesetting or dtp requirement – particularly for adverts, posters, brochures, etc.
 
−
Its best for your translation provider to handle this component. That’s because multilingual typesetters understand the design and aesthetic conventions in other languages/cultures. And these are essential to ensure your materials have the desired impact and appeal in your target markets.
 
−
Examples
 
−
Advertising, brochures, some website/social media text.
 
−
Translation Category B: 14 types of translation based on the end product or use of the translation
 
−
This category is all about how the translation is going to be used or the end product that’s produced.
 
−
Most of these types involve either adapting or processing a completed translation in some way, or converting or incorporating it into another program or format.
 
−
You’ll see that some are very specialised, and complex.
 
−
It’s another way translation providers refer to the range of services they provide.
 
−
Check out our video of the most specialised of these types of translation:
 
−
 
 
−
16. Document Translations
 
−
What is it?
 
−
The translation of documents of all sorts.
 
−
Here the translation itself is the end product and needs no further processing beyond standard formatting and layout.
 
−
 
 
−
17. Text Translations
 
−
What is it?
 
−
A synonym for document translation.
 
−
 
 
−
18. Certified Translations
 
−
What is it?
 
−
A translation with some form of certification.
 
−
Key features
 
−
The certification can take many forms. It can be a statement by the translation company, signed and dated, and optionally with their company seal. Or a similar certification by the translator.
 
−
The exact format and wording will depend on what clients and authorities require – here’s an example.
 
−
 
 
−
19. Official Translations
 
−
What is it?
 
−
1. Generally used as a synonym for certified translations.
 
−
2. Can also refer to the translation of ‘official’ documents issued by the authorities in a foreign country. These will almost always need to be certified.
 
−
 
 
−
20. Software Localisation
 
−
What is it?
 
−
Adapting software for another language/culture.
 
−
Key features
 
−
The goal of software localisation is not just to make the program or product available in other languages. It’s also about ensuring the user experience in those languages is as natural and effective as possible.
 
−
Translating the user interface, messaging, documentation, etc is a major part of the process.
 
−
Also key is a customisation process to ensure everything matches the conventions, norms and expectations of the target cultures.
 
−
Adjusting time, date and currency formats are examples of simple customisations. Others might involve adapting symbols, graphics, colours and even concepts and ideas.
 
−
Localisation is often preceded by internationalisation – a review process to ensure the software is optimally designed to handle other languages.
 
−
And it’s almost always followed by thorough testing – to ensure all text is in the correct place and fits the space, and that everything makes sense, functions as intended and is culturally appropriate.
 
−
Localisation is often abbreviated to L10N, internationalisation to i18n.
 
−
What this means
 
−
Software localisation is a specialised kind of translation, and you should always engage a company that specialises in it.
 
−
They’ll have the systems, tools, personnel and experience needed to achieve top quality outcomes for your product.
 
−
 
 
−
21. Game Localisation
 
−
What is it?
 
−
Adapting games for other languages and markets.
 
−
 
 
−
It’s a subset of software localisation.
 
−
Key features
 
−
The goal of game localisation is to provide an engaging and fun gaming experience for speakers of other languages.
 
−
 
 
−
It involves translating all text and recording any required foreign language audio.
 
−
 
 
−
But also adapting anything that would clash with the target culture’s customs, sensibilities and regulations.
 
−
 
 
−
For example, content involving alcohol, violence or gambling may either be censored or inappropriate in the target market.
 
−
 
 
−
And at a more basic level, anything that makes users feel uncomfortable or awkward will detract from their experience and thus the success of the game in that market.
 
−
 
 
−
So portions of the game may have to be removed, added to or re-worked.
 
−
 
 
−
Game localisation involves at least the steps of translation, adaptation, integrating the translations and adaptations into the game, and testing.
 
−
 
 
−
What this means
 
−
Game localisation is a very specialised type of translation best left to those with specific expertise and experience in this area.
 
−
 
 
−
22. Multimedia Localisation
 
−
What is it?
 
−
Adapting multimedia for other languages and cultures.
 
−
 
 
−
Multimedia refers to any material that combines visual, audio and/or interactive elements. So videos and movies, on-line presentations, e-Learning courses, etc.
 
−
Key features
 
−
Anything a user can see or hear may need localising.
 
−
 
 
−
That means the audio and any text appearing on screen or in images and animations.
 
−
 
 
−
Plus it can mean reviewing and adapting the visuals and/or script if these aren’t suitable for the target culture.
 
−
 
 
−
The localisation process will typical involve:
 
−
– Translation
 
−
– Modifying the translation for cultural reasons and/or to meet technical requirements
 
−
– Producing the other language versions
 
−
 
 
−
Audio output may be voice-overs, dubbing or subtitling.
 
−
 
 
−
And output for visuals can involve re-creating elements, or supplying the translated text for the designers/engineers to incorporate.
 
−
 
 
−
What this means
 
−
Multimedia localisation projects vary hugely, and it’s essential your translation providers have the specific expertise needed for your materials.
 
−
 
 
−
 
−
 
 
−
23. Script Translations
 
−
What is it?
 
−
Preparing the text of recorded material for recording in other languages.
 
−
Key features
 
−
There are several issues with script translation.
 
−
 
 
−
One is that translations typically end up longer than the original script. So voicing the translation would take up more space/time on the video than the original language.
 
−
 
 
−
Sometimes that space will be available and this will be OK.
 
−
 
 
−
But generally it won’t be. So the translation has to be edited back until it can be comfortably voiced within the time available on the video.
 
−
 
 
−
Another challenge is the translation may have to synchronise with specific actions, animations or text on screen.
 
−
 
 
−
Also, some scripts also deal with technical subject areas involving specialist technical terminology.
 
−
 
 
−
Finally, some scripts may be very culture-specific – featuring humour, customs or activities that won’t work well in another language. Here the script, and sometimes also the associated visuals, may need to be adjusted before beginning the translation process.
 
−
 
 
−
It goes without saying that a script translation must be done well. If it’s not, there’ll be problems producing a good foreign language audio, which will compromise the effectiveness of the video.
 
−
 
 
−
Translators typically work from a time-coded transcript. This is the original script marked to show the time available for each section of the translation.
 
−
 
 
−
What this means
 
−
There are several potential pitfalls in script translations. So it’s vital your translation provider is practiced at this type of translation and able to handle any technical content.
 
−
 
 
−
 
−
 
 
−
24. Voice-over and Dubbing Projects
 
−
What is it?
 
−
Translation and recording of scripts in other languages.
 
−
 
 
−
Voice-overs vs dubbing
 
−
There is a technical difference.
 
−
A voice-over adds a new track to the production, dubbing replaces an existing one.
 
−
 
−
 
 
−
Key features
 
−
These projects involve two parts:
 
−
– a script translation (as described above), and
 
−
– producing the audio
 
−
 
 
−
So they involve the combined efforts of translators and voice artists.
 
−
The task for the voice artist is to produce a high quality read. That’s one that matches the style, tone and richness of the original.
 
−
 
 
−
Often each section of the new audio will need to be the same length as the original.
 
−
 
 
−
But sometimes the segments will need to be shorter – for example where the voice-over lags the original by a second or two. This is common in interviews etc, where the original voice is heard initially then drops out.
 
−
 
 
−
The most difficult form of dubbing is lip-syncing – where the new audio needs to synchronise with the original speaker’s lip movements, gestures and actions.
 
−
 
 
−
Lip-syncing requires an exceptionally skilled voice talent and considerable time spent rehearsing and fine tuning the translation.
 
−
 
 
−
What this means
 
−
You need to use experienced professionals every step of the way in this type of project.
 
−
 
 
−
That’s to ensure firstly that your foreign-language scripts are first class, then that the voicing is of high professional standard.
 
−
 
 
−
Anything less will mean your foreign language versions will be way less effective and appealing to your target audience.
 
−
 
 
−
 
−
 
 
−
25. Subtitle Translations
 
−
What is it?
 
−
Producing foreign language captions for sub or surtitles.
 
−
Key features
 
−
The goal with subtitling is to produce captions that viewers can comfortably read in the time available and still follow what’s happening on the video.
 
−
 
 
−
To achieve this, languages have “rules” governing the number of characters per line and the minimum time each subtitle should display.
 
−
 
 
−
Sticking to these guidelines is essential if your subtitles are to be effective.
 
−
 
 
−
But this is no easy task – it requires simple language, short words, and a very succinct style. Translators will spend considerable time mulling over and re-working their translation to get it just right.
 
−
 
 
−
Most subtitle translators use specialised software that will output the captions in the format sound engineers need for incorporation into the video.
 
−
 
 
−
What this means
 
−
As with other specialised types of translation, you should only use translators with specific expertise and experience in subtitling.
 
−
 
 
−
 
−
 
 
−
26. Website Localisation
 
−
What is it?
 
−
The translation and adapting of relevant content on a website to best suit the target language and culture.
 
−
 
 
−
Note: Many providers use the term website translation as a synonym for localisation. Strictly speaking though, translation is just one part of localisation.
 
−
Key features
 
−
 
 
−
Not all pages on a website may need to be localised – clients should review their content to identify what’s relevant for the other language versions.
 
−
Some content may need specialist translators – legal and technical pages for example.
 
−
There may also be videos, linked documents, and text or captions in graphics to translate.
 
−
Adaptation can mean changing date, time, currency and number formats, units of measure, etc.
 
−
But also images, colours and even the overall site design and style if these won’t have the desired impact in the target culture.
 
−
Translated files can be supplied in a wide range of formats – translators usually coordinate output with the site webmasters.
 
−
New language versions are normally thoroughly reviewed and tested before going live to confirm everything is displaying correctly, works as intended and is cultural appropriate.
 
−
What this means
 
−
The first step should be to review your content and identify what needs to be translated. This might lead you to modify some pages for the foreign language versions.
 
−
 
 
−
In choosing your translation providers be sure they can:
 
−
– handle any technical or legal content,
 
−
– provide your webmaster with the file types they want.
 
−
 
 
−
And you should always get your translators to systematically review the foreign language versions before going live.
 
−
 
 
−
 
−
 
 
−
27. Transcreation
 
−
What is it?
 
−
Adapting a message to elicit the same emotional response in another language and culture.
 
−
Translation is all about conveying the message or meaning of a text in another language. But sometimes that message or meaning won’t have the desired effect in the target culture.
 
−
 
 
−
This is where transcreation comes in. Transcreation creates a new message that will get the desired emotional response in that culture, while preserving the style and tone of the original.
 
−
 
 
−
So it’s a sort of creative translation – which is where the word comes from, a combination of ‘translation’ and ‘creation’.
 
−
 
 
−
At one level transcreation may be as simple as choosing an appropriate idiom to convey the same intent in the target language – something translators do all the time.
 
−
 
 
−
But mostly the term is used to refer to adapting key advertising and marketing messaging. Which requires copywriting skills, cultural awareness and an excellent knowledge of the target market.
 
−
 
 
−
Who does it?
 
−
Some translation companies have suitably skilled personnel and offer transcreation services.
 
−
 
 
−
Often though it’s done in the target country by specialist copywriters or an advertising or marketing agency – particularly for significant campaigns and to establish a brand in the target marketplace.
 
−
 
 
−
What this means
 
−
Most general marketing and promotional texts won’t need transcreation – they can be handled by a translator with excellent creative writing skills.
 
−
 
 
−
But slogans, by-lines, advertising copy and branding statements often do.
 
−
 
 
−
Whether you should opt for a translation company or an in-market agency will depend on the nature and importance of the material, and of course your budget.
 
−
 
 
−
 
−
 
 
−
28. Audio Translations
 
−
What is it?
 
−
Broad meaning: the translation of any type of recorded material into another language.
 
−
 
 
−
More commonly: the translation of a foreign language video or audio recording into your own language. So this is where you want to know and document what a recording says.
 
−
Key features
 
−
The first challenge with audio translations is it’s often impossible to pick up every word that’s said. That’s because audio quality, speech clarity and speaking speed can all vary enormously.
 
−
 
 
−
It’s also a mentally challenging task to listen to an audio and translate it directly into another language. It’s easy to miss a word or an aspect of meaning.
 
−
 
 
−
So best practice is to first transcribe the audio (type up exactly what is said in the language it is spoken in), then translate that transcription.
 
−
 
 
−
However, this is time consuming and therefore costly, and there are other options if lesser precision is acceptable.
 
−
 
 
−
What this means
 
−
It’s best to discuss your requirements for this kind of translation with your translation provider. They’ll be able to suggest the best translation process for your needs.
 
−
 
 
−
Examples
 
−
Interviews, product videos, police recordings, social media videos.
 
−
 
 
−
 
−
 
 
−
29. Translations with DTP
 
−
What is it?
 
−
Translation incorporated into graphic design files.multilingual dtp example in the form of a Rubik's Cube with foreign text on each square
 
−
Key features
 
−
Graphic design programs are used by professional designers and graphic artists to combine text and images to create brochures, books, posters, packaging, etc.
 
−
 
 
−
Translation plus dtp projects involve 3 steps – translation, typesetting, output.
 
−
 
 
−
The typesetting component requires specific expertise and resources – software and fonts, typesetting know-how, an appreciation of foreign language display conventions and aesthetics.
 
−
 
 
−
What this means
 
−
Make sure your translation company has the required multilingual typesetting/desktop publishing expertise whenever you’re translating a document created in a graphic design program.
 
−
 
 
−
Translation Category C: 13 types of translation based on the translation method employed
 
−
This category has two sub-groups:
 
−
– the practical methods translation providers use to produce their translations, and
 
−
– the translation strategies/methods identified and discussed within academia.
 
−
 
 
−
The translation methods translation providers use
 
−
There are 4 main methods used in the translation industry today. We have an overview of each below, but for more detail, including when to use each one, see our comprehensive blog article.
 
−
 
 
−
Or watch our video.
 
−
 
 
−
Important: If you’re a client you need to understand these 4 methods – choose the wrong one and the translation you end up with may not meet your needs!
 
−
 
 
−
 
 
−
30. Machine Translation (MT)
 
−
What is it?
 
−
A translation produced entirely by a software program with no human intervention.
 
−
 
 
−
A widely used, and free, example is Google Translate. And there are also commercial MT engines, generally tailored to specific domains, languages and/or clients.
 
−
 
 
−
Pros and cons
 
−
There are two limitations to MT:
 
−
– they make mistakes (incorrect translations), and
 
−
– quality of wording is patchy (some parts good, others unnatural or even nonsensical)
 
−
 
 
−
On they positive side they are virtually instantaneous and many are free.
 
−
 
 
−
Best suited for:
 
−
Getting the general idea of what a text says.
 
−
 
 
−
This method should never be relied on when high accuracy and/or good quality wording is needed.
 
−
 
 
−
 
−
 
 
−
31. Machine Translation plus Human Editing (PEMT)
 
−
What is it?
 
−
A machine translation subsequently edited by a human translator or editor (often called Post-editing Machine Translation = PEMT).
 
−
 
 
−
The editing process is designed to rectify some of the deficiencies of a machine translation.
 
−
 
 
−
This process can take different forms, with different desired outcomes. Probably most common is a ‘light editing’ process where the editor ensures the text is understandable, without trying to fix quality of expression.
 
−
 
 
−
Pros and cons
 
−
This method won’t necessarily eliminate all translation mistakes. That’s because the program may have chosen a wrong word (meaning) that wasn’t obvious to the editor.
 
−
 
 
−
And wording won’t generally be as good as a professional human translator would produce.
 
−
 
 
−
Its advantage is it’s generally quicker and a little cheaper than a full translation by a professional translator.
 
−
 
 
−
Best suited for:
 
−
Translations for information purposes only.
 
−
 
 
−
Again, this method shouldn’t be used when full accuracy and/or consistent, natural wording is needed.
 
−
 
 
−
 
−
 
 
−
32. Human Translation
 
−
What is it?
 
−
Translation by a professional human translator.
 
−
Pros and cons
 
−
Professional translators should produce translations that are fully accurate and well-worded.
 
−
 
 
−
That said, there is always the possibility of ‘human error’, which is why translation companies like us typically offer an additional review process – see next method.
 
−
 
 
−
This method will take a little longer and likely cost more than the PEMT method.
 
−
 
 
−
Best suited for:
 
−
Most if not all translation purposes.
 
−
 
 
−
 
−
 
 
−
33. Human Translation + Revision
 
−
What is it?
 
−
A human translation with an additional review by a second translator.
 
−
 
 
−
The review is essentially a safety check – designed to pick up any translation errors and refine wording if need be.
 
−
 
 
−
Pros and cons
 
−
This produces the highest level of translation quality.
 
−
 
 
−
It’s also the most expensive of the 4 methods, and takes the longest.
 
−
 
 
−
Best suited for:
 
−
All translation purposes.
 
−
 
 
−
Gearwheel with 5 practical translation methods written on the teeth
 
−
There’s also one other common term used by practitioners and academics alike to describe a type (method) of translation:
 
−
 
 
−
34. Computer-Assisted Translation (CAT)
 
−
What is it?
 
−
A human translator using computer tools to aid the translation process.
 
−
 
 
−
Key features
 
−
Virtually all translators use such tools these days.
 
−
 
 
−
The most prevalent tool is Translation Memory (TM) software. This creates a database of previous translations that can be accessed for future work.
 
−
 
 
−
TM software is particularly useful when dealing with repeated and closely-matching text, and for ensuring consistency of terminology. For certain projects it can speed up the translation process.
 
−
 
 
−
 
−
 
 
−
The translation methods described by academia
 
−
A great deal has been written within academia analysing how human translators go about their craft.
 
−
 
 
−
Seminal has been the work of Newmark, and the following methods of translation attributed to him are widely discussed in the literature.Gearwheel with Newmark's 8 translation methods written on the teeth
 
−
These methods are approaches and strategies for translating the text as a whole, not techniques for handling smaller text units, which we discuss in our final translation category.
 
−
 
 
−
 
−
 
 
−
35. Word-for-word Translation
 
−
This method translates each word into the other language using its most common meaning and keeping the word order of the original language.
 
−
 
 
−
So the translator deliberately ignores context and target language grammar and syntax.
 
−
 
 
−
Its main purpose is to help understand the source language structure and word use.
 
−
 
 
−
Often the translation will be placed below the original text to aid comparison.
 
−
 
 
−
 
−
 
 
−
36. Literal Translation
 
−
Words are again translated independently using their most common meanings and out of context, but word order changed to the closest acceptable target language grammatical structure to the original.
 
−
 
 
−
Its main suggested purpose is to help someone read the original text.
 
−
 
 
−
 
−
 
 
−
37. Faithful Translation
 
−
Faithful translation focuses on the intention of the author and seeks to convey the precise meaning of the original text.
 
−
 
 
−
It uses correct target language structures, but structure is less important than meaning.
 
−
 
 
−
 
−
 
 
−
38. Semantic Translation
 
−
Semantic translation is also author-focused and seeks to convey the exact meaning.
 
−
 
 
−
Where it differs from faithful translation is that it places equal emphasis on aesthetics, ie the ‘sounds’ of the text – repetition, word play, assonance, etc.
 
−
 
 
−
In this method form is as important as meaning as it seeks to “recreate the precise flavour and tone of the original” (Newmark).slide showing definition of semantic translation as a translation method
 
−
 
−
39. Communicative Translation
 
−
Seeks to communicate the message and meaning of the text in a natural and easily understood way.
 
−
 
 
−
It’s described as reader-focused, seeking to produce the same effect on the reader as the original text.
 
−
 
 
−
A good comparison of Communicative and Semantic translation can be found here.
 
−
 
 
−
40. Free Translation
 
−
Here conveying the meaning and effect of the original are all important.
 
−
 
 
−
There are no constraints on grammatical form or word choice to achieve this.
 
−
 
 
−
Often the translation will paraphrase, so may be of markedly different length to the original.
 
−
 
 
−
41. Adaptation
 
−
Mainly used for poetry and plays, this method involves re-writing the text where the translation would otherwise lack the same resonance and impact on the audience.
 
−
 
 
−
Themes, storylines and characters will generally be retained, but cultural references, acts and situations adapted to relevant target culture ones.
 
−
 
 
−
So this is effectively a re-creation of the work for the target culture.
 
−
 
 
−
42. Idiomatic Translation
 
−
Reproduces the meaning or message of the text using idioms and colloquial expressions and language wherever possible.
 
−
 
 
−
The goal is to produce a translation with language that is as natural as possible.
 
−
 
 
−
Translation Category D: 9 types of translation based on the translation technique used
 
−
These translation types are specific strategies, techniques and procedures for dealing with short chunks of text – generally words or phrases.
 
−
 
 
−
They’re often thought of as techniques for solving translation problems.
 
−
 
 
−
They differ from the translation methods of the previous category which deal with the text as a whole.
 
−
9 translation techniques as titles of books in a bookcase
 
−
 
 
−
43. Borrowing
 
−
What is it?
 
−
Using a word or phrase from the original text unchanged in the translation.
 
−
 
 
−
Key features
 
−
With this procedure we don’t translate the word or phrase at all – we simply ‘borrow’ it from the source language.
 
−
 
 
−
Borrowing is a very common strategy across languages. Initially, borrowed words seem clearly ‘foreign’, but as they become more familiar, they can lose that ‘foreignness’.
 
−
 
 
−
Translators use this technique:
 
−
– when it’s the best word to use – either because it has become the standard, or it’s the most precise term, or
 
−
– for stylist effect – borrowings can add a prestigious or scholarly flavour.
 
−
 
 
−
Borrowed words or phrases are often italicised in English.
 
−
 
 
−
Examples of borrowings in English
 
−
grand prix, kindergarten, tango, perestroika, barista, sampan, karaoke, tofu
 
−
 
 
−
 
 
−
44. Transliteration
 
−
What is it?
 
−
Reproducing the approximate sounds of a name or term from a language with a different writing system.
 
−
 
 
−
Key features
 
−
In English we use the Roman (Latin) alphabet in common with many other languages including almost all European languages.
 
−
 
 
−
Other writing systems include Arabic, Cyrillic, Chinese, Japanese, Korean, Thai, and the Indian languages.
 
−
 
 
−
Transliteration from such systems into the Roman alphabet is also called romanisation.
 
−
 
 
−
There are accepted systems for how individual letters/sounds should be romanised from most other languages – there are three common systems for Chinese, for example.
 
−
 
 
−
English borrowings from languages using non-Roman writing systems also require transliteration – perestroika, sampan, karaoke, tofu are examples from the above list.
 
−
 
 
−
Translators mostly use transliteration as a procedure for translating proper names.
 
−
 
 
−
Examples
 
−
毛泽东                                Mao Tse-tung or Mao Zedong
 
−
Владимир Путин          Vladimir Putin
 
−
서울                                    Seoul
 
−
ភ្នំពេញ                                Phnom Penh
 
−
 
 
−
45. Calque or Loan Translation
 
−
What is it?
 
−
A literal translation of a foreign word or phrase to create a new term with the same meaning in the target language.
 
−
 
 
−
So a calque is a borrowing with translation if you like. The new term may be changed slightly to reflect target language structures.
 
−
 
 
−
Examples
 
−
German ‘Kindergarten’ has been calqued as детский сад in Russian, literally ‘children garden’ in both languages.
 
−
 
 
−
Chinese 洗腦 ‘wash’ + ‘brain’ is the origin of ‘brainwash’ in English.
 
−
 
 
−
English skyscraper is calqued as gratte-ciel in French and rascacielos in Spanish, literally ‘scratches sky’ in both languages.
 
−
 
 
−
46. Word-for-word translation
 
−
What is it?
 
−
A literal translation that is natural and correct in the target language.
 
−
 
 
−
Alternative names are ‘literal translation’ or ‘metaphrase’.
 
−
 
 
−
Note: this technique is different to the translation method of the same name, which does not produce correct and natural text and has a different purpose.
 
−
 
 
−
Key features
 
−
This translation strategy will only work between languages that have very similar grammatical structures.
 
−
 
 
−
And even then, only sometimes.
 
−
 
 
−
For example, standard word order in Turkish is Subject-Object-Verb whereas in English it’s Subject-Verb-Object. So a literal translation between these two will seldom work:
 
−
– Yusuf elmayı yedi is literally ‘Joseph the apple ate’.
 
−
 
 
−
When word-for-word translations don’t produce natural and correct text, translators resort to some of the other techniques described below.
 
−
Examples
 
−
French ‘Quelle heure est-il?’ works into English as ‘What time is it?’.
 
−
 
 
−
Russian ‘Oн хочет что-нибудь поесть’ is ‘He wants something to eat’.
 
−
 
−
47. Transposition
 
−
What is it?
 
−
Translation with a change of grammatical structure.
 
−
 
 
−
This technique gives the translation more natural wording and/or makes it grammatically correct.
 
−
 
 
−
Examples
 
−
A change in word order:
 
−
Our Turkish example Yusuf elmayı yedi (literally ‘Joseph the apple ate’) –> Joseph ate the apple.
 
−
 
 
−
Spanish La Casa Blanca (literally ‘The House White’) –> The White House
 
−
 
 
−
A change in grammatical category:
 
−
German Er hört gerne Musik (literally ‘he listens gladly [to] music’)
 
−
= subject pronoun + verb + adverb + noun
 
−
becomes Spanish Le gusta escuchar música (literally ‘[to] him [it] pleases to listen [to] music’)
 
−
= indirect object pronoun + verb + infinitive + noun
 
−
and English He likes listening to music
 
−
= subject pronoun + verb + gerund + noun.
 
−
 
 
−
48. Modulation
 
−
What is it?
 
−
Translation with a change of focus or point of view in the target language.
 
−
 
 
−
This technique makes the translation more idiomatic – how people would normally say it in the language.
 
−
 
 
−
Examples
 
−
English talks of the ‘top floor’ of a building, French the dernier étage = last floor. ‘Last floor’ would be unnatural in English, so too ‘top floor’ in French.
 
−
 
 
−
German uses the term Lebensgefahr (literally ‘danger to life’) where in English we’d be more likely to say ‘risk of death’.
 
−
In English we’d say ‘I dropped the key’, in Spanish se me cayó la llave, literally ‘the key fell from me’. The English perspective is that I did something (dropped the key), whereas in Spanish something happened to me – I’m the recipient of the action.
 
−
 
 
−
49. Equivalence or Reformulation
 
−
What is it?
 
−
Translating the underlying concept or meaning using a totally different expression.
 
−
 
 
−
This technique is widely used when translating idioms and proverbs.
 
−
 
 
−
And it’s common in titles and advertising slogans.
 
−
 
 
−
It’s a common strategy where a direct translation either wouldn’t make sense or wouldn’t resonate in the same way.
 
−
 
 
−
Examples
 
−
Here are some equivalents of the English saying “Pigs may fly”, meaning something will never happen, or “you’re being unrealistic” (Source):
 
−
– Thai: ชาติหน้าตอนบ่าย ๆ – literally, ‘One afternoon in your next reincarnation’
 
−
– French: Quand les poules auront des dents – literally, ‘When hens have teeth’
 
−
– Russian, Когда рак на горе свистнет – literally, ‘When a lobster whistles on top of a mountain’
 
−
– Dutch, Als de koeien op het ijs dansen – literally, ‘When the cows dance on the ice’
 
−
– Chinese: 除非太陽從西邊出來！– literally, ‘Only if the sun rises in the west’
 
−
 
 
−
50. Adaptation
 
−
What is it?
 
−
A translation that substitutes a culturally-specific reference with something that’s more relevant or meaningful in the target language.
 
−
 
 
−
It’s also known as cultural substitution or cultural equivalence.
 
−
 
 
−
It’s a useful technique when a reference wouldn’t be understood at all, or the associated nuances or connotations would be lost in the target language.
 
−
 
 
−
Note: the translation method of the same name is a similar concept but applied to the text as a whole.
 
−
 
 
−
Examples
 
−
Different cultures celebrate different coming of age birthdays – 21 in many cultures, 20, 15 or 16 in others. A translator might consider changing the age to the target culture custom where the coming of age implications were important in the original text.
 
−
Animals have different connotations across languages and cultures. Owls for example are associated with wisdom in English, but are a bad omen to Vietnamese. A translator might want to remove or amend an animal reference where this would create a different image in the target language.
 
−
 
 
−
51. Compensation
 
−
What is it?
 
−
A meaning or nuance that can’t be directly translated is expressed in another way in the text.
 
−
Example
 
−
Many languages have ways of expressing social status (honorifics) encoded into their grammatical structures.
 
−
 
 
−
So you can convey different levels of respect, politeness, humility, etc simply by choosing different forms of words or grammatical elements.
 
−
But these nuances will be lost when translating into languages that don’t have these structures.
 
−
Then translating into languages that don’t have these structures
 
−
Then translating into languages that don’t have these structures.
 
−
 
 
−
===Conclusion ===
 
  
−
In the light of above mentioned facts and figures here by we would say that machine translation is a challenge for human translators because it can reduce the workload of translation but can't give accurate and exact translation of the target language.It can be less reliable than human translation..
+
[[Machine_Trans_EN_15]]

Latest revision as of 12:25, 25 February 2022

Machine Translation - A challenge or a chance for human translators?

Overview Page of Machine Translation

30 Chapters（0/30)

Machine_Trans_EN_1 Machine_Trans_EN_2 Machine_Trans_EN_3 Machine_Trans_EN_4 Machine_Trans_EN_5 Machine_Trans_EN_6 Machine_Trans_EN_7 Machine_Trans_EN_8 Machine_Trans_EN_9 Machine_Trans_EN_10 Machine_Trans_EN_11 Machine_Trans_EN_12 Machine_Trans_EN_13 Machine_Trans_EN_14 Machine_Trans_EN_15 Machine_Trans_EN_16 Machine_Trans_EN_17 Machine_Trans_EN_18 Machine_Trans_EN_19 Machine_Trans_EN_20 Machine_Trans_EN_21 Machine_Trans_EN_22 Machine_Trans_EN_23 Machine_Trans_EN_24 Machine_Trans_EN_25 Machine_Trans_EN_26 Machine_Trans_EN_27 Machine_Trans_EN_28 Machine_Trans_EN_29 Machine_Trans_EN_30 ...

Back to translation project overview

To the To Do list

Chapter 1:A Comparison Between the Quality of Machine Translation and Human Translation——A Case Study of the Application of artificial intelligence in Sports Events

论机器翻译与人工翻译的质量对比——以人工智能在体育赛事领域的应用为例

卫怡雯 Wei Yiwen, Hunan Normal University, China

Machine_Trans_EN_1

Chapter 2：On the Realm Advantages and Mutual Development of Machine Translation and Huamn Translation)

论机器翻译与人工翻译的领域优势及共同发展

肖毅瑶 Xiao Yiyao, Hunan Normal University, China

Machine_Trans_EN_2

Chapter 3: -Missing-

Machine_Trans_EN_3

Chapter 4 : A Comparison Between Machine Translation of Netease and Traditional Human Translation—A Case Study of The Economist Articles)

网易有道机器翻译与人工翻译的译文对比——以经济学人语料为例

王李菲 Wang Lifei, Hunan Normal University

Machine_Trans_EN_4

Chapter 5: Muhammad Saqib Mehran - Problems in translation studies

Machine_Trans_EN_5

6 徐敏赟(Machine Translation Based on Neural Network --Challenge or Chance?)

Machine_Trans_EN_6

Chapter 7: The Cultivation of artificial intelligence and translation talents in the Belt and Road Initiative

Machine_Trans_EN_7

一带一路背景下人工智能与翻译人才的培养

颜莉莉 Yan Lili, Hunan Normal University, China

Chapter 8: On Machine Translation Under Lanuguage Intelligence——An Option and Opportunity for Human Translators

论语言智能下的机器翻译——译者的选择与机遇

颜静 Yan Jing, Hunan Normal University, China

Machine_Trans_EN_8

9 谢佳芬（ Machine Translation and Artificial Translation in the Era of Artificial Intelligence）

人工智能时代下的机器翻译与人工翻译

谢佳芬 Xie Jiafen ,Hunan Normal University, China

Machine_Trans_EN_9

Chapter 10 熊敏 Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts

机器翻译对各类型文本的英汉翻译能力探究

熊敏, Xiong Min, Hunan Normal University

Machine_Trans_EN_10

Chapter 11 Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts

机器翻译的译前编辑研究——以医学类文摘为例

陈惠妮 Chen Huini, Hunan Normal University

Machine_Trans_EN_11


Written by --Chen Huini (talk) 04:58, 15 December 2021 (UTC)Chen Huini

Chapter 12 The Mistranslation of C-J Machine Translation of Political Statements

机器翻译中政治发言中译日的误译

蔡珠凤 Cai Zhufeng, Hunan Normal University

Machine_Trans_EN_12

Chapter 13 Study on Post-editing from the Perspective of Functional Equivalence Theory

陈湘琼, Hunan Normal University

Machine_Trans_EN_13

Chapter 14 Machine Translation a Challenge for Human Translators

Bi bi Nadia, Hunan Normal University

Machine_Trans_EN_14

Chapter 15 Machine Translation: Advantage or Disadvantage for the Human Translator

Mariam Touré, Hunan Normal University

Machine_Trans_EN_15

Retrieved from "https://bou.de/u/index.php?title=Machine_translation&oldid=135578"