Difference between revisions of "Machine translation"

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Written by --[[User:Chen Huini|Chen Huini]] ([[User talk:Chen Huini|talk]]) 04:58, 15 December 2021 (UTC)Chen Huini
 
Written by --[[User:Chen Huini|Chen Huini]] ([[User talk:Chen Huini|talk]]) 04:58, 15 December 2021 (UTC)Chen Huini
  
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=12 蔡珠凤 The Mistranslation of C-J Machine Translation of Political Statements=
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=Chapter 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
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[[Machine_Trans_EN_12]]
 
[[Machine_Trans_EN_12]]
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===Abstract===
 
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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.
 
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===Key words===
 
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machine translation; political statements; mistranslation of C-J machine translation
 
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===题目===
 
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The Mistranslation of C-J Machine Translation of Political Statements
 
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===摘要===
 
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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。
 
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===关键词===
 
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机器翻译；政治发言；政治发言中译日的误译
 
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===1. Introduction===
 
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===Introduction to machine translation===
 
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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)
 
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=== C-J machine translation software===
 
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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)
 
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===The history of machine translation===
 
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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:
 
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Pioneering period（1947-1964）
 
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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.
 
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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.
 
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Frustrated period（1964-1975）
 
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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.
 
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convalescence（1975-1989）
 
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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)
 
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New period(1990 present)
 
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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)
 
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===The problem of machine translation at present===
 
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Error is inevitable
 
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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）
 
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Bottleneck
 
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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.
 
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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.
 
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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）
 
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===2.Mistranslation of Chinese Japanese machine translation===
 
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===2.1Vocabulary mistranslation in Chinese Japanese machine translation===
 
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===2.1.1Mistranslation of proper nouns===
 
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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.
 
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Japanese translation into Chinese                                                Chinese translation into Japanese
 
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original text    translation by Youdao reference translation       original text   translation by Youdao       reference translation
 
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朱鎔基               朱基               朱镕基                    栗战书                 栗戰史書               栗戰書
 
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労安               劳安                 劳安                    李克强                 李克強                       李克強
 
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筑紫哲也     筑紫哲也               筑紫哲也                  习近平                 習近平                       習近平
 
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山口百惠     山口百惠               山口百惠                   韩正                   韓中                         韓正
 
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田中角栄     田中角荣               田中角荣                  王沪宁                 王上海氏               王滬寧
 
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東条英機     东条英社               东条英机                    汪洋                   汪洋                         汪洋
 
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毛沢东             毛泽东               毛泽东                    赵乐际                   趙樂南               趙樂際
 
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トウ・ショウヘイ　　　大酱               邓小平                    江泽民                   江沢民               江沢民
 
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周恩来             周恩来                    周恩来
 
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クリントン     克林顿                    克林顿
 
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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)
 
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original text                                       Translation by Youdao                         reference translation
 
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日美安全体制                                         日米の安全体制                                   日米安保体制
 
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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）
 
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十八大                                                     十八大                               第18回党大会中国特色社会主义
 
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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
 
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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
 
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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
 
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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
 
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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
 
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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
 
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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.
 
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"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.
 
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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)
 
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===2.1.2Mistranslation of Polysemy===
 
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original text                                               Translation by Youdao                             reference translation
 
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スタジオ                                                   摄影棚/工作室                                 直播现场/演播厅
 
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日中関係の話                                                   中日关系的故事                                 就中日关系(话题)
 
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溝                                                                 水沟                                               鸿沟
 
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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。
 
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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，
 
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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。                 
 
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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？
 
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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)
 
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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)
 
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===2.1.3Mistranslation of compound words===
 
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Multi class words refer to a word with two or more parts of speech, also known as the same word and different classes.
 
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original text                                 Translation by Youdao                                   reference translation
 
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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
 
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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
 
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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)
 
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===2.2 Syntactic mistranslation in Chinese Japanese machine translation===
 
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===2.2.1Mistranslation of tenses===
 
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original text：History is written by the people, and all achievements are attributed to the people.
 
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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。
 
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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。
 
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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)
 
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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。
 
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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。
 
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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。
 
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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)
 
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===2.2.2Mistranslation of honorifics===
 
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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)
 
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Original text                              translation by Youdao                                  reference translation
 
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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
 
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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。
 
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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？
 
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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？
 
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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)
 
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===Conclusion===
 
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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)
 
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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)
 
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(1) The difficulties of Chinese in machine translation
 
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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)
 
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(2) Difficulties of Japanese in machine translation
 
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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)
 
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===References===
 
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[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.
 
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[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.
 
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[4]Zakaryia Almahasees.(2021)Analysing English-Arabic Machine Translation:Google Translate, Microsoft Translator and Sakhr.
 
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[5](2021)Machine learning in translation[J].Nature Biomedical Engineering,5(6):485-486.
 
−
 
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[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.
 
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[7]Welnitzová Katarína;Munková Daša.(2021)Sentence-structure errors of machine translation into Slovak[J].Topics in Linguistics,22(1):78-92.
 
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[8]Xu Xueyuan.(2021).Machine learning-based prediction of urban soil environment and corpus translation teaching[J].Arabian Journal of Geosciences,14(11).
 
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[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)
 
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[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
 
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[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)
 
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[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)
 
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[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)
 
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[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)
 
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[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)
 
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[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)
 
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[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.
 
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[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.
 
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[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)
 
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Networking Linking
 
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http://www.elecfans.com/rengongzhineng/692245.html
 
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https://baike.baidu.com/item/%E6%9C%BA%E5%99%A8%E7%BF%BB%E8%AF%91/411793
 
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=13 陈湘琼Chen Xiangqiong（Study on Post-editing from the Perspective of Functional Equivalence Theory ）=
 
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[[Machine_Trans_EN_13]]
 
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=Chapter 14 Bi bi Nadia（Machine Translation a Challenge for Human Translators)=
 
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[[Machine_Trans_EN_14]]
 
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Bi bi Nadia, Hunan Normal University, China
 

Revision as of 12:56, 15 December 2021

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

Overview Page of Machine Translation

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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 ...

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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

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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

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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

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Chapter 5: Muhammad Saqib Mehran - Problems in translation studies

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

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

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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

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

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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

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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

陈惠妮 Chen Huini, Hunan Normal University

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

Chapter 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

蔡珠凤 Cai Zhufeng, Hunan Normal University

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