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 . References (main Chinese and foreign references to carry out this research)
-W, Weaver. Machine Translation of Languages. Cambridge: MIT Press,(Reprinted from a memorandum written by Weaver in 1949)1949;1955.
+W, Weaver. Machine Translation of Languages. Cambridge: MIT Press, (Reprinted from a memorandum written by Weaver in 1949)1949;1955.
 Yngve, Victor H. “About mechanical Translation.” Mechanical Translation 1.1 (1954a): 1-2.
@@ Line 717: / Line 717: @@
 Yu K. Cognitive computation in natural language processing [J].  Interface Intelligent Machine Series Event, 2009.
-陈丹. 浅析语料库对于翻译研究的意义. 科技视界[J], 2013(34): 22.
+罗季美, 李梅. 机器翻译译文错误分析[J]. 中国翻译. 2012, 33(05): 84-89.
-Chen Dan. On the significance of corpus to translation studies. Science and Technology Vision [J], 2013(34): 22.
+Luo Jimei, Li Mei.Error Analysis of Machine Translation[J].Chinese Translation,2012,33(05):84-89.
 李梅, 朱锡明. 译后编辑自动化的英汉机器翻译新探索[J]. 中国翻译, 2013(4).
@@ Line 728: / Line 732: @@
 On Post-Translation Editing in Machine Translation. China Translation, 2014, 35(6): 68-73.
 冯全功, 张慧玉. 全球语言服务行业背景下译后编辑者培养研究[J]. 外语界, 2015(1).
@@ Line 766: / Line 774: @@
 丁亮、何彦青. 融合领域知识与深度学习的机器翻译领域自适应研究[J]. 情报科学, 2017(10): 125-132.
 Ding Liang, He Yanqing. Adaptive Research of Machine Translation Domain integrating Domain Knowledge and Deep Learning [J]. Information Science, 2017(10): 125-132.
@@ Line 820: / Line 832: @@
 Chen Wei. Deconstruction of Translator's Subjectivity by Machine Translation -- Also on the Future Foothold of Human Translation [J]. Foreign Language Studies, 2020(2): 76-83.
 赵涛. 机器翻译译后编辑的现状与问题[J]. 外语教学, 2021(4).
@@ Line 836: / Line 853: @@
 Journal of Henan University of Science and Technology (Natural Science Edition), 2021, 42(3): 97-104.
-王湘玲、赖思、贾艳芳. 人工翻译与神经网络机器翻译译后编辑比较研究 ——基于对隐喻翻译的眼动追踪与键盘记录数据[J]. 外语教学理论与实践, 2021(4): 115-126.
+王湘玲，赖思，贾艳芳. 人工翻译与神经网络机器翻译译后编辑比较研究 ——基于对隐喻翻译的眼动追踪与键盘记录数据[J]. 外语教学理论与实践, 2021(4): 115-126.
 Wang Xiangling, Lai Si, Jia Yanfang. A Comparative Study on Post-translation Editing between Artificial Translation and Neural Network Machine Translation: Based on Eye Tracking and Keystroke Data for Metaphorical Translation [J]. Foreign Language Teaching Theory and Practice, 2021(4): 115-126.

@@ Line 63: / Line 63: @@
 In 1990, the International Congress of Computational Linguistics was held in Finland. Hinton believed that this conference opened the era of statistical machine translation based on large-scale parallel corpora (Hinton et al., 2012). Previously, MACHINE translation has experienced rule-based translation system based on dictionaries and manual rules, example-based translation system, To develop statistical machine Translation system based on statistics.
-，Geoffery Hinton提出了深层网络模型，预示着人工智能深度学习研究的开端；2011年各大公司逐步开展大规模深度学习的设计和部署，力图开发最先进的神经网络；2012年Geoffery Hinton带领的团队研发的计算机视觉识别率骤然升至80%，深度模型逐步取代人工特征工程，同时强化学习技术也取得了卓越的进展，如2016年Google子公司DeepMind研发的基于深度强化学习网络的AlphaGo打败了人类顶尖棋手李世石。此外，神经网络机器翻译逐渐成为翻译领域的主力军，成为翻译领域最强大的算法。这种最先进的算法是深度学习的一项应用，其中大量翻译完成的句子数据集被用于训练能够在任意语言对之间的翻译模型。
+年，Geoffery Hinton提出了深层网络模型，预示着人工智能深度学习研究的开端；2011年各大公司逐步开展大规模深度学习的设计和部署，力图开发最先进的神经网络；2012年Geoffery Hinton带领的团队研发的计算机视觉识别率骤然升至80%，深度模型逐步取代人工特征工程，同时强化学习技术也取得了卓越的进展，如2016年Google子公司DeepMind研发的基于深度强化学习网络的AlphaGo打败了人类顶尖棋手李世石。此外，神经网络机器翻译逐渐成为翻译领域的主力军，成为翻译领域最强大的算法。这种最先进的算法是深度学习的一项应用，其中大量翻译完成的句子数据集被用于训练能够在任意语言对之间的翻译模型。
 With the continuous development of deep learning, neural network machine translation has gradually become the main force and the most powerful algorithm in the field of translation. This state-of-the-art algorithm is an application of deep learning, in which a large data set of translated sentences is used to train a translation model capable of between arbitrary language pairs.
@@ Line 559: / Line 559: @@
 . References (main Chinese and foreign references to carry out this research)
-W. Weaver.Machine Translation of Languages. Cambridge: MIT Press,(Reprinted from a memorandum written by Weaver in 1949)1949;1955.
+W, Weaver. Machine Translation of Languages. Cambridge: MIT Press,(Reprinted from a memorandum written by Weaver in 1949)1949;1955.
 Yngve, Victor H. “About mechanical Translation.” Mechanical Translation 1.1 (1954a): 1-2.
@@ Line 645: / Line 645: @@
 Midori Tatsumi.  Post-Editing Machine Translated Text in A Commercial Setting: Observation and Statistical Analysis[D].  Ireland: Dublin City University, 2010.
-O’ Brien, S. Introduction to Post-Editing: Who, What, How and Where to Next? 2010. http://amta2010.amtaweb.org/AMTA/papers/6-01-ObrienPostEdit.pdf (consulted 28. 3. 2022).
+O' Brien, S. Introduction to Post-Editing: Who, What, How and Where to Next? 2010. http://amta2010.amtaweb.org/AMTA/papers/6-01-ObrienPostEdit.pdf (consulted 28. 3. 2022).
-O 'Brien, S. Introduction to post-editing: Who, What, How and Where to Next?  2010. http://amta2010.amtaweb.org/AMTA/papers/6-01-ObrienPostEdit.pdf (consulted 28. 3. 2022).
+O' Brien, S. Introduction to post-editing: Who, What, How and Where to Next?  2010. http://amta2010.amtaweb.org/AMTA/papers/6-01-ObrienPostEdit.pdf (consulted 28. 3. 2022).
 TAUS. (2010). MT Post-editing Guidelines, available at https://www.taus.net/academy/best-practices/postedit-best-practices/machine-translation-post-editing-guidelines

@@ Line 63: / Line 63: @@
 In 1990, the International Congress of Computational Linguistics was held in Finland. Hinton believed that this conference opened the era of statistical machine translation based on large-scale parallel corpora (Hinton et al., 2012). Previously, MACHINE translation has experienced rule-based translation system based on dictionaries and manual rules, example-based translation system, To develop statistical machine Translation system based on statistics.
-随着深度学习的不断发展，神经网络机器翻译逐渐成为翻译领域的主力军，成为翻译领域最强大的算法。这种最先进的算法是深度学习的一项应用，其中大量翻译完成的句子数据集被用于训练能够在任意语言对之间的翻译模型。
+，Geoffery Hinton提出了深层网络模型，预示着人工智能深度学习研究的开端；2011年各大公司逐步开展大规模深度学习的设计和部署，力图开发最先进的神经网络；2012年Geoffery Hinton带领的团队研发的计算机视觉识别率骤然升至80%，深度模型逐步取代人工特征工程，同时强化学习技术也取得了卓越的进展，如2016年Google子公司DeepMind研发的基于深度强化学习网络的AlphaGo打败了人类顶尖棋手李世石。此外，神经网络机器翻译逐渐成为翻译领域的主力军，成为翻译领域最强大的算法。这种最先进的算法是深度学习的一项应用，其中大量翻译完成的句子数据集被用于训练能够在任意语言对之间的翻译模型。
 With the continuous development of deep learning, neural network machine translation has gradually become the main force and the most powerful algorithm in the field of translation. This state-of-the-art algorithm is an application of deep learning, in which a large data set of translated sentences is used to train a translation model capable of between arbitrary language pairs.

@@ Line 67: / Line 67: @@
 With the continuous development of deep learning, neural network machine translation has gradually become the main force and the most powerful algorithm in the field of translation. This state-of-the-art algorithm is an application of deep learning, in which a large data set of translated sentences is used to train a translation model capable of between arbitrary language pairs.
-而国内机器翻译的发展较晚，机器翻译研究则是从1956年开始的；1959年，中科院语言所和计算所共同研制了俄-汉翻译系统；20世纪70年代，我国开始重视机器翻译；20世纪80年代中期到90年代初期，是我国机器翻译发展的三个重要时期，分别于1987年和1992年发明了KY-1英汉翻译系统以及IMT/EC863英汉机译系统；从90年代初到现在，我国机器翻译研究蓬勃发展，推出了“译星”、“通译”等系列机器翻译软件。机器翻译作为一种新兴产业，逐步专业化和市场化。21世纪以来，机器翻译发展速度加快，各大IT公司都先后推出自己的机器翻译系统（谷歌、百度、科大讯飞、阿里巴巴、腾讯、搜狗、网易、小牛、微软等),同时神经翻译技术和深度学习技术作为新的翻译范式，在诸多语种及应用场景中的翻译质量已经超越了统计机器翻译技术，并成为目前学术界和工业界研究的热点。机器翻译发成呈现出新的广度和深度，具有深刻的时代意义和现实价值。
+而国内机器翻译的发展较晚，机器翻译研究则是从1956年开始的；1959年，中科院语言所和计算所共同研制了俄-汉翻译系统；20世纪70年代，我国开始重视机器翻译；20世纪80年代中期到90年代初期，是我国机器翻译发展的三个重要时期，分别于1987年和1992年发明了KY-1英汉翻译系统以及IMT/EC863英汉机译系统；从90年代初到现在，我国机器翻译研究蓬勃发展，推出了“译星”、“通译”等系列机器翻译软件。机器翻译作为一种新兴产业，逐步专业化和市场化。21世纪以来，机器翻译发展速度加快，各大IT公司都先后推出自己的机器翻译系统（谷歌、百度、科大讯飞、阿里巴巴、腾讯、搜狗、网易、小牛、微软等),同时神经翻译技术和深度学习技术作为新的翻译范式，在诸多语种及应用场景中的翻译质量已经超越了统计机器翻译技术，并成为目前学术界和工业界研究的热点。机器翻译发展呈现出新的广度和深度，具有深刻的时代意义和现实价值。
 However, the development of machine translation in China is relatively late, and the research on machine translation started in 1956. In 1959, the Russian-Chinese translation system was jointly developed by the Institute of Language and Computer Science of the Chinese Academy of Sciences. In the 1970s, China began to attach importance to machine translation. From the mid-1980s to the early 1990s, there were three important periods in the development of Machine translation in China. In 1987 and 1992, KY-1 english-Chinese translation system and IMT/EC863 English-Chinese machine translation system were invented respectively. From the early 1990s to the present, machine translation research in China has developed vigorously.
@@ Line 619: / Line 619: @@
 Chesterman A. ed. Readings in Translation Theory [C].  Helsinki: Oy Finn Lectura Ab, 1989.
-Allen, J. Post-editing. In: H. Somers (ed.) Computers and Translation: A  Translator’s Guide[M]. John Benjamins Publishing, Amsterdam, 2003.
+Hutchines, W.J.， H. I. Sommers. An Introduction to Machine Translation[M]. San Diego: Academic Press Inc., 1992.
 Senez, Dorothy. “Post-editing service for machine translation users at the European Commission.” Translating and the Computer 20. Proceedings from Aslib conference, 12 - 13 November 1998.

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 .1.1 Domestic and foreign academic trends of MACHINE translation
-机器翻译在翻译界是一门新兴学科，也是一项新兴技术。依据ISO/DIS 17100: 2013标准的定义，机器翻译是“使用计算机系统将文本或语音从一种自然语言自动翻译为另一种语言”（MT, automated translation of text or speech from one natural language to another using a computer system）。20世纪30年代，法国科学家G・B・Artsouni最早提出机器翻译的设想；1946年John Presper和John Mauchly设计并制造了世界上第一台电子计算机ENICA；1947年，Warren・Weaver与A・D・Booth讨论了计算机可能的应用范围，并首次提出了机器翻译的思想；1948年同Andrew D. Booth进行探讨，Warren Weaver（1949）正式提出机器翻译概念以及四种可能实现的策略，包括基于简单的词语替换方法，其核心是解决词义消岐问题、假设语言是一种逻辑表达、假设语言间的自动翻译实际上可以看作通信过程和假设所有语言之间存在相同的逻辑特征，可以视为一种通用语言或者中间语言。
+机器翻译在翻译界是一门新兴学科，也是一项新兴技术。依据ISO/DIS 17100: 2013标准的定义，机器翻译是“使用计算机系统将文本或语音从一种自然语言自动翻译为另一种语言”（MT, automated translation of text or speech from one natural language to another using a computer system）。20世纪30年代，法国科学家G・B・Artsouni最早提出机器翻译的设想；1946年John Presper和John Mauchly设计并制造了世界上第一台电子计算机ENICA；1947年，Warren Weaver与Andrew D.Booth讨论了计算机可能的应用范围，并首次提出了机器翻译的思想；1948年同Andrew D.Booth进行探讨，Warren Weaver（1949）正式提出机器翻译概念以及四种可能实现的策略，包括基于简单的词语替换方法，其核心是解决词义消岐问题、假设语言是一种逻辑表达、假设语言间的自动翻译实际上可以看作通信过程和假设所有语言之间存在相同的逻辑特征，可以视为一种通用语言或者中间语言,Weaver认为源语言到目标语言的自动翻译可以先将源语言转换为中间语言，再从中间语言转换为目标语言；Weaver于1952年在麻省理工学院召开了第一届机器翻译会议，Yehoshua Bar Hillel组织了18位专家参与本次大会；1954年，美国乔治敦大学和IBM公司开展了世界第一次计算机翻译；Locke William N., Booth A. Donald(1955)在总结机器翻译大会与会人员14篇论文的基础上，编纂了历史上第一本有关机器翻译的书籍。
 Machine translation is a new subject and technology in the field of translation. According to the ISO/DIS 17100: Definition of 2013 Standard, Machine translation is "the automatic translation of text or speech from one natural language into another using a computer system" (MT, Automated translation of text or speech from one natural language to another using a computer system). In the 1930s, the French scientist G. B. Artsouni first proposed the idea of machine translation; In 1946, two British and American engineers A. D. Booth and Warren Weaver proposed the use of computer for language translation when discussing the possible application range of computer. In 1949, Weaver first put forward the idea of using computers for translation, and put forward four specific principles to avoid "word-for-word" translation. Bar Hillel organized and held the first Machine translation Conference in 1952. Machine translation is an interdisciplinary study, and countries have joined the queue of machine translation research.
-年，美国乔治敦大学和IBM公司开展了世界第一次计算机翻译；1950至1960年间，机器翻译的研究主要针对理论语言学中的句法分析，建立了多种基于句法分析的机器翻译模型，研究发现基于句法分析的机器翻译并不能生成高质量的译文（Bar Hillel, 1958; 1959）。为了进一步推动机器翻译的发展，人们建立了自动语言处理咨询委员会（ALPAC，The Automatic Language Processing Advisory Committee）。该机构成员较少关注机器翻译的学术价值及研究发展的潜力，仅关注英、俄双语互译，得出关于译文质量的结论不全面，未对报告进行更深刻的研究，因此ALPAC出示的报告信用力度与有效度存在不足。（Hinton et al, 2012）
+至1960年间，机器翻译的研究主要针对理论语言学中的句法分析，建立了多种基于句法分析的机器翻译模型，研究发现基于句法分析的机器翻译并不能生成高质量的译文（Bar Hillel, 1958; 1959）。为了进一步推动机器翻译的发展，人们建立了自动语言处理咨询委员会（ALPAC，The Automatic Language Processing Advisory Committee）。该机构成员较少关注机器翻译的学术价值及研究发展的潜力，仅关注英、俄双语互译，得出关于译文质量的结论不全面，未对报告进行更深刻的研究，因此ALPAC出示的报告信用力度与有效度存在不足。（Hinton et al, 2012）
 In 1954, Georgetown University and IBM launched the world's first computer translation; From 1950 to 1960, researches on MACHINE translation mainly focused on syntactic analysis in theoretical linguistics, and various models of machine translation based on syntactic analysis were established. However, researches found that syntactic machine translation based on syntactic analysis could not produce high-quality translation (Bar Hillel, 1958; 1959). In order to further promote The development of machine translation, people set up The Automatic Language Processing Advisory Committee (ALPAC). However, the members of this organization paid little attention to the academic value and research development potential of machine translation, and only paid attention to the bilingual translation between English and Russian. The conclusion of translation quality was not comprehensive, and they did not conduct a deeper study on the report. Therefore, the credibility and validity of the report presented by ALPAC were insufficient. (Hinton et al., 2012)
-年，自动语言处理咨询委员会发布题为《语言与机器》的报告，给机器翻译带来了很大的影响：一是导致机器翻译的应用投资以及技术研究大范围停滞，但在加拿大、法国、欧盟等国家和地区的机器翻译研究却表现突出。加拿大于1965年在蒙特利尔成立了机器翻译研究中心，建立了TAUM机器翻译系统，主要从事英、法双语机器翻译研究与实践（Poibeau, 2017）；20世纪70年代中期，Vauquois将之前应用的翻译系统进行了一定程度的改造。由于各个国家的语言翻译需求不一，欧盟与在美国诞生的第一个商业机器翻译系统开发商Systran合作，开展欧盟成员国语言的机器自动翻译。
+年，自动语言处理咨询委员会发布题为《语言与机器》的报告，否定机器翻译的可行性，给机器翻译带来了很大的影响：一是导致机器翻译的应用投资以及技术研究大范围停滞，但在加拿大、法国、欧盟等国家和地区的机器翻译研究却表现突出。加拿大于1965年在蒙特利尔成立了机器翻译研究中心，建立了TAUM机器翻译系统，主要从事英、法双语机器翻译研究与实践（Poibeau, 2017）；20世纪70年代中期，Vauquois将之前应用的翻译系统进行了一定程度的改造。由于各个国家的语言翻译需求不一，欧盟与在美国诞生的第一个商业机器翻译系统开发商Systran合作，开展欧盟成员国语言的机器自动翻译。
 In 1966, the Automatic Language Processing Advisory Committee issued a report entitled "Language and Machine", which brought great influence to machine translation. First, the application investment and technical research of machine translation largely stopped, but the research of machine translation in Canada, France, the European Union and other countries and regions showed outstanding performance. Canada established the Machine Translation Research Center in Montreal in 1965 and established TAUM Machine Translation System, which is mainly engaged in the research and practice of English-French bilingual machine translation (Poibeau, 2017). In the mid-1970s, Vauquois partially adapted the translation system previously used. The European Union has teamed up with Systran, the first commercial machine translation system developed in the United States, to automate machine translation of eu member states' languages, as countries have different needs for language translation.
-年国际计算语言学大会在芬兰召开，辛顿认为这次会议开启了基于大规模平行语料库的统计机器翻译时代（Hinton et al，2012）。此前，机器翻译经历了基于词典和手工规则的机器翻译系统（rule-based translation），基于实例的机器翻译系统（example-based translation），发展到基于统计的机器翻译系统（statistical machine translation）。
+年国际计算语言学大会在芬兰召开，Geoffrey Hinton认为这次会议开启了基于大规模平行语料库的统计机器翻译时代（Geoffrey Hinton et al，2012）。此前，机器翻译经历了基于词典和手工规则的机器翻译系统（rule-based translation），基于实例的机器翻译系统（example-based translation），发展到基于统计的机器翻译系统（statistical machine translation）。
 In 1990, the International Congress of Computational Linguistics was held in Finland. Hinton believed that this conference opened the era of statistical machine translation based on large-scale parallel corpora (Hinton et al., 2012). Previously, MACHINE translation has experienced rule-based translation system based on dictionaries and manual rules, example-based translation system, To develop statistical machine Translation system based on statistics.
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 With the continuous development of deep learning, neural network machine translation has gradually become the main force and the most powerful algorithm in the field of translation. This state-of-the-art algorithm is an application of deep learning, in which a large data set of translated sentences is used to train a translation model capable of between arbitrary language pairs.
-而国内机器翻译的发展较晚，机器翻译研究则是从1956年开始的；1959年，中科院语言所和计算所共同研制了俄-汉翻译系统；20世纪70年代，我国开始重视机器翻译；20世纪80年代中期到90年代初期，是我国机器翻译发展的三个重要时期，分别于1987年和1992年发明了KY-1英汉翻译系统以及IMT/EC863英汉机译系统；从90年代初到现在，我国机器翻译研究蓬勃发展。
+而国内机器翻译的发展较晚，机器翻译研究则是从1956年开始的；1959年，中科院语言所和计算所共同研制了俄-汉翻译系统；20世纪70年代，我国开始重视机器翻译；20世纪80年代中期到90年代初期，是我国机器翻译发展的三个重要时期，分别于1987年和1992年发明了KY-1英汉翻译系统以及IMT/EC863英汉机译系统；从90年代初到现在，我国机器翻译研究蓬勃发展，推出了“译星”、“通译”等系列机器翻译软件。机器翻译作为一种新兴产业，逐步专业化和市场化。21世纪以来，机器翻译发展速度加快，各大IT公司都先后推出自己的机器翻译系统（谷歌、百度、科大讯飞、阿里巴巴、腾讯、搜狗、网易、小牛、微软等),同时神经翻译技术和深度学习技术作为新的翻译范式，在诸多语种及应用场景中的翻译质量已经超越了统计机器翻译技术，并成为目前学术界和工业界研究的热点。机器翻译发成呈现出新的广度和深度，具有深刻的时代意义和现实价值。
 However, the development of machine translation in China is relatively late, and the research on machine translation started in 1956. In 1959, the Russian-Chinese translation system was jointly developed by the Institute of Language and Computer Science of the Chinese Academy of Sciences. In the 1970s, China began to attach importance to machine translation. From the mid-1980s to the early 1990s, there were three important periods in the development of Machine translation in China. In 1987 and 1992, KY-1 english-Chinese translation system and IMT/EC863 English-Chinese machine translation system were invented respectively. From the early 1990s to the present, machine translation research in China has developed vigorously.
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 Yngve, Victor H.  "The machine and The man."  Mechanical Translation 1.2 (1954B): 21-22.
 Miller, George A. and J. G. Beebe-Center. “Some psychological methods for evaluating the quality of translations.” Mechanical Translation 3.3 (1956): 73-80.

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	Koponen, M. et al. Translation Revision and Post-Editing: Industry Practices and Cognitive Processes [M]. London: Routledge, 2021.		Koponen, M. et al. Translation Revision and Post-Editing: Industry Practices and Cognitive Processes [M]. London: Routledge, 2021.
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−	~~孙玉石. 中日文化交流史上的又一座丰碑 —— 日本新译二十卷本《鲁迅全集》陆续出版[J]. 鲁迅研究月刊, 1985(7).~~
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−	Sun Yushi. Another monument in the History of Sino-Japanese cultural exchange -- The new Japanese translation of the complete Works of Lu Xun published in Succession [J]. Lu Xun Studies Monthly, 1985(7).

	黄河燕, 陈肇雄. 一种智能译后编辑器的设计及其实现算法[J]. 软件学报, 1994(3)．		黄河燕, 陈肇雄. 一种智能译后编辑器的设计及其实现算法[J]. 软件学报, 1994(3)．

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 .Basis of selected topic (academic history and research trends of relevant research at home and abroad; The unique academic value and application value of this subject compared with the existing research)
-机器翻译（Machine Translation）是使用计算机将一种语言符号转换成另一种语言符号，自20世纪30年代问世开始，经历了基于规则（rule-based）、基于实例（example-based）、基于统计（statistic-based）、和基于不同方法应用（multiple methods application-based）的四个发展阶段，基于规则和基于统计的混合式机译系统是如今机器翻译主流模式之一，能很好地规避单一模式可能引发的弊病。随着AI技术的不断发展，机器翻译也在不断完善和发展，能够帮助人们快速、准确地搜索并记忆专业术语（相对于人工翻译而言此项工作既耗时耗力，反复比对后结果还不尽如人意）；机器翻译耗时少、处理速度快、工作环境要求低（只需电脑和翻译软件即可，而人工翻译需要一个安静无干扰的工作环境，而译者的身体和精神状态无疑也会影响翻译的质量），能够帮助减少甚至消除人工翻译的体力和脑力负荷也能大大降低翻译的成本；机器翻译都是多语“人才”，只要人们不断丰富平行语料库文本的语种，所有的语言互译都是可以实现的，而从事翻译的人们能熟练掌握3 - 4种语言互换就已经是凤毛菱角了，更不用说所有的语言互换了。在认识机器翻译的强大优势的同时，我们也不能忽视现有机器翻译，因为自身的不足呈现出一些通用的弊病：各种误译、错译、漏译、语序错误、句法结构误译、句法不同等晦涩难懂的译文；机器翻译对于词语的处理仍停留在字面和浅层意义对应，无法实现词语深层意义和语用意义对应；机器翻译只能解读程式化和信息性文本的语义内容，还无法解读表情性文本中蕴含的情感和态度等等。
+机器翻译（Machine Translation）是使用计算机将一种语言符号转换成另一种语言符号，自20世纪30年代问世开始，经历了基于规则（rule-based）、基于实例（example-based）、基于统计（statistic-based）、和基于不同方法应用（multiple methods application-based）的四个发展阶段，基于规则和基于统计的混合式机译系统是如今机器翻译主流模式之一，能很好地规避单一模式可能引发的弊病。随着AI技术的不断发展，机器翻译也在不断完善和发展，能够帮助人们快速、准确地搜索并记忆专业术语（相对于人工翻译而言此项工作既耗时耗力，反复比对后结果还不尽如人意）；机器翻译耗时少、处理速度快、工作环境要求低（只需电脑和翻译软件即可，而人工翻译需要一个安静无干扰的工作环境，而译者的身体和精神状态无疑也会影响翻译的质量），能够帮助减少甚至消除人工翻译的体力和脑力负荷也能大大降低翻译的成本；机器翻译都是多语“人才”，只要人们不断丰富平行语料库文本的语种，所有的语言互译都是可以实现的，而从事翻译的人们能熟练掌握3-4种语言互换就已经是凤毛菱角了，更不用说所有的语言互换了。在认识机器翻译的强大优势的同时，我们也不能忽视现有机器翻译，因为自身的不足呈现出一些通用的弊病：各种误译、错译、漏译、语序错误、句法结构误译、句法不同等晦涩难懂的译文；机器翻译对于词语的处理仍停留在字面和浅层意义对应，无法实现词语深层意义和语用意义对应；机器翻译只能解读程式化和信息性文本的语义内容，还无法解读表情性文本中蕴含的情感和态度等等。
 Machine Translation is the use of computers to convert symbols of one language into symbols of another language. Since its inception in 1930s, It has experienced four development stages of rule-based, example-based, statistic-based and multiple methods application-based. Rule-based and statistics-based hybrid machine translation system is one of the mainstream models of machine translation, which can well avoid the disadvantages caused by a single model. With the continuous development of AI technology, machine translation is also improving and developing, which can help people search and remember professional terms quickly and accurately (compared with human translation, this work is time-consuming and time-consuming, and the results after repeated comparison are not satisfactory). Machine translation work less time consuming, fast processing speed, low environmental requirements (by computer and translation software, and human translation requires a quiet working environment without interference, and the physical and mental state of the translator will definitely affect the quality of the translation), can help to reduce or even eliminate human translation of physical and mental load can also greatly reduce the cost of translation; Machine translation is a multilingual "talent", as long as people continue to enrich the language of the parallel corpus text, all language translation can be realized, and the translator can master 3-4 language exchange is already a chicken's head, let alone all the language exchange. While recognizing the powerful advantages of machine translation, we should not ignore the existing machine translation, because its own shortcomings present some common shortcomings: various mistranslations, mistranslations, omissions, word order errors, syntactic structure mistranslations, syntax differences and other obscure translation; Machine translation still deals with the correspondence between literal and superficial meanings, and fails to realize the correspondence between deep meanings and pragmatic meanings. Machine translation can only interpret the semantic content of stylized and informative texts, but not the emotions and attitudes contained in expressive texts.
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 Koponen, M. et al.  Translation Revision and Post-Editing: Industry Practices and Cognitive Processes [M].  London: Routledge, 2021.
 孙玉石. 中日文化交流史上的又一座丰碑 —— 日本新译二十卷本《鲁迅全集》陆续出版[J]. 鲁迅研究月刊, 1985(7).
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 Huanghe Yan, Chen Zhaoxiong. Design and Implementation algorithm of an intelligent post-translation editor [J]. Journal of Software, 1994(3).
 詹卫东、常宝宝、俞士汶. 机器翻译与语言研究[J]. 语言科学, 2002(1): 47-61.
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 Wei Changhong, Zhang Chunbai. Journal of Machine Translation, 2007(3).
 俞凯. 自然语言处理中的认知计算[J]. Interface智能机器系列活动, 2009.
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 She Xiaoling. Research on Lu Xun's Novels translated into Russian [J]. Russian Literature and Art, 2016(1).
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 Weng Yi-ming, Wang Jin-ping. Machine Translation of Literary Discourse [J]. Contemporary Foreign Language Studies, 2020(6).
 陈伟. 机器翻译对译者主体性的解构 —— 兼论人工翻译的未来落脚点[J]. 外语研究, 2020(2): 76-83.