Yuelu Mountain Universities Science City 2022

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国家社会科学基金项目课题论证活页

Leaflet of project demonstration of National Social Science Foundation

课题名称：软件的译后编辑与研究

Project title: Enhancing artificially intelligent machine translation by feedback from Post-translation editing

商务伙伴 1：网易有道

Business Partner 1: NetEase Youdao

商务伙伴 2：......

Business Partner 2:......

0. 题解

0. Answer key

1. 选题依据（国内外相关研究的学术史梳理及研究动态；本课题相对于已有研究的独到学术价值和应用价值等）

1. Basis of topic selection (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)

1.1国内外学术动态

1.1 Academic trends at home and abroad

1.1.1机器翻译国内外学术动态

1.1.1 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（G.B.阿尔楚尼）最早提出机器翻译的设想；1946年，英美两位工程师A.D.Booth（布思）和Warren Weaver（沃伦・韦弗）在讨论计算机的可能应用范围之时，提出利用计算机进行语言翻译；1949年，Weaver首次提出使用计算机进行翻译的思想，提出了避免出现“字对字”翻译的四条具体原则。Bar-Hillel（巴・希勒）于1952年组织召开了第一次机器翻译大会，机器翻译是一门跨学科的研究，因此各国纷纷加入机器翻译研究的队列。

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, French scientist G.B. Artsouni (G.B. Alchuni) first proposed the idea of machine translation; In 1946, two British and American engineers A.D. Ooth (Booth) and Warren Weaver (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, so countries have joined the queue of machine translation research.

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；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，建立起多种基于句法分析的机器翻译模型，然而研究现基于句法分析的机器翻译并不能产出高质量的译文（Bar-Hillel，1958；1959）。为了更好地发展机器翻译，人们建立了自动语言处理咨询委员会，（ALPAC，The Automatic Language Processing Advisory Committee）。由于该机构成员较少关注机器翻译的学术价值以及研究发展的潜力，且仅关注英俄双语互译，得出的译文质量的结论不全面，没有对报告进行更深刻的研究，因此ALPAC 出示的报告的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 on machine translation based on syntactic analysis failed to produce high-quality translation (Bar-Hillel, 1958; 1959). In order to better develop machine translation, people set up The Automatic Language Processing Advisory Committee (ALPAC). Since the members of this organization paid little attention to the academic value and research development potential of machine translation, and only paid attention to english-Russian bilingual translation, the conclusion of translation quality was not comprehensive, and they did not conduct a deeper study on the report. Therefore, the credit strength and validity of the report presented by ALPAC were insufficient. (Hinton et al., 2012)

自动语言处理咨询委员会发布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则表现突出。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，因此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。

The report issued by the Automatic Language Processing Advisory Committee has brought great influence to machine translation. First, the application investment and technical research of machine translation have largely stopped, but the research of machine translation in Canada, France, the European Union and other countries and regions has performed well. 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. Since language translation needs vary from country to country, the European Union has teamed up with Systran, the developer of the first commercial machine translation system in the United States, to develop automatic machine translation for eu member states' languages.

1990年国际计算语言学大会在芬兰召开。辛顿认为这次会议了开启基于大规模平行语料库的统计机器翻译时代（Hinton et al，2012）。机器翻译的发展经历了基于词典和手工规则的规则系统（Rule-based translation），基于实例的翻译系统（Example-based translation），发展到基于统计的机器翻译系统（Statistical machine translation）。

The 1990 International Congress of Computational Linguistics was held in Finland. Hinton believes that this conference opened the era of statistical machine translation based on large-scale parallel corpora (Hinton et al., 2012). The development of machine translation has experienced rule-based translation 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，神经机器翻译逐渐成为翻译领域的主力军。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。

With the continuous development of deep learning, neural machine translation has gradually become the main force in the field of translation. 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.

1.1.2译后编辑国内外学术动态

1.1.2 Post-editing domestic and foreign academic developments

了解译后编辑（PE, post-editing），译后编辑则是“检查和修正机器翻译的输出（to check and correct MT output）”（ISO, 2014）。Allan（2003）指出从1947年英国工程师A. D. Booth和美国科学家W. Weaver首次进行俄英机器翻译到现代翻译技术高速发展的今天，机器翻译的质量大大提高，但鉴于语言结构、修辞手法、逻辑思维、文化差异、一词多义等现象，我们仍需对机器翻译的译文进行修改和加工，即译后编辑来提高机器翻译质量，如修改语言错误、提高表达准确性与译文可读性等。

Understanding PE, post-editing is "to check and correct MT output" (ISO, 2014). Allan (2003) pointed out that from 1947, British engineer A. D. Booth and W. Weaver maiden, Russia, Britain, machine translation to modern technology rapid development today, greatly improving the quality of machine translation, but given the structure of language, rhetoric and logic thinking, cultural differences, the phenomenon such as polysemy, we still need to modify for the machine translation and processing, namely the editor to improve the quality of machine translation, translation Such as correcting language errors, improving expression accuracy and translation readability.

20世纪30年代初法国工程师G. B. Artsouni最早提出机器翻译的设想，1947年英国工程师A. D. Booth和美国科学家W. Weaver首次提出利用计算机进行翻译。W. Weaver于1949年发表《翻译》备忘录，并提出机器翻译的思想。1954年，美国乔治敦大学和IBM公司进行世界第一次计算机俄英翻译实验，随后前苏联及欧洲国家因为军事或经济发展需要，非常重视机器翻译研究，机器翻译呈现热潮。同年，Victor H. Yngve（1954）简要介绍译后编辑的概念、任务和作用，指出机器翻译与人工合作将成为未来机器翻译行业发展的特点。Miller和Beebe-Center（1956）介绍五种机器翻译质量评价方法，即主观评判、单词评判（单词数及顺序）、信息传译评判、阅读理解评判和语义评判，并分析各评价方法的优劣，同时指出并非所有文学作品都可以通过机器成功翻译。1966年由美国政府资助的语言自动处理咨询委员会（Automatic Language Processing Advisory Committee, ALPAC）通过调查研究发布了报告——《语言与机器：翻译和语言学中的计算机》（Language and Machines: Computers in Translation and Linguistics），指出机器翻译质量粗糙，未达到预期效果，建议专注字典开发以及译后编辑，使机器翻译资助锐减。（ALPAC, 1966）

In the early 1930s, French engineer G. B. Artsouni first proposed the idea of machine translation. In 1947, British engineer A. D. Booth and W. Weaver first proposed the use of computers for translation. W. Weaver published the memorandum "Translation" in 1949 and put forward the idea of machine translation. In 1954, Georgetown University and IBM conducted the world's first computer Russian-English translation experiment. Later, the former Soviet Union and European countries attached great importance to machine translation research due to the needs of military or economic development, and machine translation became a boom. In the same year, Victor H. Yngve (1954) briefly introduced the concept, task and role of post-translation editing, and pointed out that machine translation and human cooperation would become the characteristics of the future development of machine translation industry. Miller and Beebe-Center (1956) introduced five quality evaluation methods of machine translation, namely subjective evaluation, word evaluation (word number and order), information translation evaluation, reading comprehension evaluation and semantic evaluation, analyzed the advantages and disadvantages of each evaluation method, and pointed out that not all literary works can be successfully translated by machine. In 1966, the Automatic Language Processing Advisory Committee (ALPAC), funded by the U.S. government, published a report "Language and Machines: Language and Machines: translation and Linguistics Computers in Translation and Linguistics, pointed out that the quality of machine Translation is rough and does not achieve the expected effect, and suggested that focus on dictionary development and post-translation editing, so that the funding for machine Translation decreased sharply. (ALPAC, 1966)

Orr和Small（1967）对比人工翻译、机器翻译和机器翻译译后编辑模式下俄英翻译速度、效率和准确度方面的差异，发现人工翻译效果优于译后编辑，在三个方面都超越了机器翻译，速度与效率的差异尤甚，而机器翻译与译后编辑的差异相较于人工翻译与译后编辑的差异更大。鉴于译后编辑压力大、费用高，二者仍建议发展机器翻译。Van Slype（1978）在欧盟引进Systran机器翻译后，写了一份报告分析机器翻译、人工翻译和译后编辑译文的可读性，分别为78%，98%和98%，大多数机器翻译用户认为某些情况下的机器翻译译文质量是可以被接受的。来自加拿大通用汽车有限公司的Sereda（1982）指出译后编辑是机器翻译系统中一个非常重要的因素，译后编辑的成功与否直接关系到机器翻译产生的经济价值大小，还谈到影响译后编辑功能的因素包括机器翻译系统的语言能力、源语质量、专业术语存储、个人及机器翻译能力等。Green（1982）将机器翻译错误分成简单的小型错误、耗时的大型错误以及“灰色”错误，并建议译后编辑在面对之前出现过的文本时，可以提前编辑那些有代表性的数据以避免犯相同的错误，从而降低译者的烦恼指数。Laurian（1984）逐渐侧重快译后编辑，将其按照错误修正优先等级分成“必须修改错误”、“可能需要修改错误”和“不该出现的错误”三类，并指出各自的区别，提出译后编辑既不是修改，也不是更正，更不是改写。作者一共收集了11种错误类型，帮助那些没有经过太多语言培训的人群判断文本是否适合译后编辑，这就是后来的机器翻译可行性研究。Elizabeth Wagner（1985）从1981年开始收集数据了解当时使用快译后编辑服务的人群和目的，并分析翻译软件Systran的弊端在于译“词”而非译“意”。Vasconcellos（1985）认为理想的译后编辑应该是一个职业译员，而非某个主题专家，但她也指出译者对于机器翻译的态度是决定译文质量的最关键因素。Schneider（1989）针对西门子公司对译后编辑的培训谈到译后编辑起始阶段翻译效率会降低，经过几个月到一年不等的时间，使用者反馈效率大大提高，语言转换时间逐渐减少。

Orr and Small (1967) compared the differences in speed, efficiency and accuracy of Russian-English translation in the mode of manual translation, machine translation and machine translation post-editing, and found that the effect of manual translation was better than that of post-editing, and exceeded that of machine translation in all three aspects, especially in speed and efficiency. The differences between machine translation and post-translation editing are greater than those between human translation and post-translation editing. In view of the high pressure and cost of post-translation editing, they still recommend the development of machine translation. Van Slype (1978), after the introduction of Systran machine translation in the EU, wrote a report analyzing the readability of machine translation, human translation and post-edited translation, with results of 78%, 98% and 98% respectively. Most machine translation users believe that the quality of machine translation in some cases is acceptable. Sereda (1982) from General Motors Of Canada pointed out that post-editing is a very important factor in machine translation system, and the success of post-editing is directly related to the economic value generated by machine translation. The factors that affect the post-translation editing function include the language ability of the machine translation system, the quality of the source language, the storage of professional terms, and the ability of individual and machine translation. Green (1982) divided machine translation errors into simple small errors, time-consuming large errors and "gray" errors, and suggested that post-translation editors should edit representative data in advance to avoid making the same mistakes when faced with previously appeared texts, so as to reduce the translator's annoyance index. Laurian (1984) gradually focused on post-fast translation editing and divided it into three categories according to the error correction priority: "errors that must be modified", "errors that may need to be modified" and "errors that should not occur", and pointed out their differences. Post-translation editing was neither modification, correction nor rewriting. The author collected a total of 11 error types to help those without much language training judge whether a text is suitable for post-translation editing, which became the feasibility study of machine translation. Elizabeth Wagner (1985) began to collect data in 1981 to understand the people and purposes of quick translation and post-editing services at that time, and analyzed that the drawback of translation software Systran lies in the translation of "words" rather than "meanings". Vasconcellos (1985) believed that the ideal post-translation editor should be a professional translator rather than a subject matter expert, but she also pointed out that the translator's attitude towards machine translation is the most critical factor to determine the quality of translation. Schneider (1989) pointed out that the translation efficiency of post-editing would decrease in the initial stage of post-editing. After several months to a year, users' feedback efficiency would be greatly improved, and the time of language conversion would gradually decrease.

Dorothy Senez（1998）分析译后编辑与翻译的区别，指出前者在增加译文数量、有效利用现有工具和降低翻译成本方面前景广阔。Krings, Hans P.（2001）采用实证研究方法完成博士后研究报告，说明译后编辑即文本修改的过程。同年，该报告被Geoffrey S. Koby从德文译成英文并出版，成为译后编辑领域中最早的一本专著。Allen（2003）讨论机器翻译译后编辑本地化进程、译后编辑原则以及新开发的译后自动编辑模型。Guerberof（2009）通过实验研究机器翻译片段与翻译记忆系统中模糊匹配片段在效率和质量上的关系，发现使用机器翻译的译者工作更高效、译文质量更高，也指出具有翻译技术经验的译者更高效，但译文质量不受影响。Midori Tatsumi（2010）采用定性和定量研究分析发现句子结构、文本类型、术语使用、译后编辑模式与译后编辑行为等因素共同影响译后编辑人员的编辑数量，该研究有助于业界更好地使用机器翻译系统，也能帮助译后编辑人员提高译后编辑技巧和策略。翻译自动化用户协会（TAUS, Translation Automation User Society）（TAUS, 2010）指出译后编辑指导原则的内容涵盖未做译后编辑的原始机器译文和人们对翻译内容的最终预期质量，个人可以根据顾客需要和机器翻译文本质量来选择相应的指导原则。Garcia（2011）指出当下翻译记忆工具能帮助译员将数据库不能匹配的词条存入记忆库，通过实验对比译后编辑与人工翻译发现译后编辑在效率方面优势较小，在翻译质量方面优势非常明显，并从双语方向、文本难度和译者水平层面分析其对译文质量的影响，讨论译者是否应该考虑使用译后编辑替代传统翻译模式。

Dorothy Senez (1998) analyzed the difference between post-translation editing and translation and pointed out that the former has a broad prospect in increasing the number of translations, effectively utilizing existing tools and reducing translation costs. Krings, Hans P. (2001) used empirical research methods to complete the post-doctoral research report, explaining that post-translation editing is the process of text modification. In the same year, the report was translated from German into English by Geoffrey S. Koby and published, making it the first monograph in the field of post-translation editing. Allen (2003) discussed the localization process of post-translation editing in machine translation, post-translation editing principles and the newly developed automatic post-translation editing model. Guerberof (2009) conducted an experimental study on the relationship between the efficiency and quality of machine translated fragments and fuzzy matched fragments in translation memory system, and found that translators using machine translation work more efficiently and translation quality is higher. It also pointed out that translators with translation technology experience are more efficient, but the quality of translation is not affected. Midori Tatsumi (2010) used qualitative and quantitative research to analyze and find that sentence structure, text type, use of terms, post-editing mode and post-editing behavior and other factors jointly affect the number of post-editing staff. This research is conducive to better use of machine translation system in the industry. It can also help post-editors to improve their post-editing skills and strategies. Translation Automation User Society (TAUS, TAUS, 2010) points out that the post-editing guidelines cover the original machine Translation without post-editing and the final expected quality of the translated content. Individuals can choose appropriate guidelines based on customer needs and machine-translated text quality. Garcia (2011) pointed out that the present translation memory tools can help the interpreter in memory, the database can't match the entry through experiment contrast translation editing and artificial translation found after editing in efficiency advantage is small, very obvious advantages in terms of quality of translation, and from the bilingual direction, difficulty of text and translator level level to analyze its impact on the quality of the translation, Discuss whether translators should consider using post-editing to replace traditional translation models.

2013年第十四届机器翻译峰会（Machine Translation Summit XIV）在法国召开，期间举行了“第二届译后编辑技术与实践研讨会”（The 2nd Workshop on Post-editing Technologies and Practice）重点探讨译后编辑界面和功能设置、译后编辑效率、译后编辑评测工具、在线译后编辑环境、机器翻译与译后编辑技术推广、单双语译后编辑效果对比与编辑能力差异、译前编辑对译后编辑的推动作用、人工翻译与机器翻译译后编辑模式的译文质量对比、译后编辑处理等议题。（Sharon O’Brien, 2013） 2014年颁布并实施了关于机器翻译译后编辑的草案标准ISO18587，指出译后编辑三个阶段具体要求，即准备阶段、译后编辑阶段和译后编辑后处理阶段，描述译后编辑人员需具备的能力和资质，将译后编辑输出分为快译后编辑（Light post-editing）和完全译后编辑（Full post-editing）两个级别，指出对应特点，该标准得到各行业的广泛认可。ISO 18587（2017）区分了完全译后编辑和快译后编辑的差异，探讨快译后编辑方式是否也能满足顾客需求，并指出必须对译后编辑从业人员的机器翻译译后编辑能力进行培训。Koponen（2017）首次对比分析英芬语机器翻译后必须修改的地方以及译后编辑人员自己所做的修改，发现大部分编辑都是正确的，但也有34%的编辑是多余的。研究表明这两种语言译后编辑采取的调整词序、删除人称代词是多余的，这对于将来译后编辑实践和培训颇有指导意义。Lommel（2018）指出单靠语言学家无法满足当代人们对快速和低价翻译的需求，拥抱机器翻译的语言服务商已经取得了飞速的发展，介绍机器增强翻译（神经机器翻译）的定义和组成内容，详细探讨适应性机器翻译和内容充实自动化、该领域的技术供应商以及机器增强翻译的未来。Sakamoto（2019）用定性和定量研究方法引用布迪厄文化资本理论“资本”、“场域”和“惯习”的概念，分析译者为何不愿从事译后编辑的原因，该研究为机器翻译股东和翻译工作教育者理解译员与译后编辑所处的社会机构提供了概念化的工具。Seung（2020）旨在探寻指导机器翻译译后编辑的方法论思想以改进译文风格，尤其是针对句法和语义上有巨大差异的英韩等语对。第一步整理出双语写作中语言和文化的不同之处，第二步了解译员对于写作风格的不同命名，第三步将人工翻译中使用的策略引入到机器翻译译后编辑，研究如何将这些策略植入到英韩机器翻译译后编辑中，从而改善译文风格。该研究为改善译后编辑风格指导标准奠定基础，为改善译文风格积累人工译后编辑数据，从而帮助打造能满足不同顾客需求的译后编辑自动化系统。Koponen（2021）指出人脑修改译文与机器修改译文之间的界限正在逐渐消失，政府或公司翻译部门、翻译公司、文学出版部门、志愿者部门以及人工和机器修改培训领域研究，具体包括基于调查、访谈、击键记录的实证研究，更侧重从理论层面探寻翻译与译后编辑的传统区别。错误修订和译后编辑研究涉及8种语言，研究话题涉及译者和修订者关系、非专业译员的修订行为与译后编辑。

The 14th Machine Translation Summit XIV was held in France in 2013, The 2nd Workshop on post-Editing Technologies and Editing was held Practice), the paper mainly discusses the translation after editing interface and feature set, the translation editor efficiency, translation evaluation tools, online editing environment after translation, machine translation and post-translation editing technology popularization, single and double language translation editing effect after contrast and editing ability difference, former editor of the translation editor role after the translation and human translation and machine translation quality of edit mode after contrast , post-translation editing processing and other issues. (Sharon O 'Brien, 2013) In 2014, the draft standard ISO18587 on post-translation editing of machine translation was issued and implemented, which pointed out the specific requirements of three stages of post-translation editing, namely, the preparation stage, the post-translation editing stage and the post-translation editing post-processing stage, and described the abilities and qualifications of post-translation editing personnel. The output of post-editing is divided into two levels, Light post-editing and Full post-editing, pointing out the corresponding characteristics, the standard has been widely recognized by various industries. ISO 18587 (2017) distinguishes between full post-translation editing and fast post-translation editing, discusses whether fast post-translation editing can also meet customer needs, and points out that post-translation editing practitioners must be trained in machine translation post-translation editing ability. Koponen (2017) made a comparative analysis for the first time on the necessary modifications in English-Finnish machine translation and the modifications made by the editors themselves, and found that most of the edits were correct, but 34% of the edits were redundant. The research shows that the adjustment of word order and the deletion of personal pronouns are unnecessary, which is of great significance to the practice and training of post-translation editing in the future. Lommel (2018) points out that linguists alone cannot meet the needs of contemporary people for fast and cheap translation. Language service providers embracing machine translation have achieved rapid development. The definition and components of machine enhanced translation (NMT) are introduced. Explore in detail adaptive machine translation and content enrichment automation, technology providers in the field, and the future of machine enhanced translation. Sakamoto (2019) using qualitative and quantitative research methods refer to bourdieu cultural capital theory "capital" and "field" and "habitus" concept, analyze why the translator's reluctance to engage in translation after editing, the research of machine translation after shareholders and translation work educators understand the interpreter and edit social organization provides a conceptual tools. Seung (2020) aims to explore the methodological ideas guiding post-translation editing of machine translation in order to improve the translation style, especially for English and Korean language pairs with huge differences in syntax and semantics. The first step is to sort out the differences in language and culture in bilingual writing. The second step is to understand the different naming styles of writing by translators. The third step is to introduce the strategies used in human translation into machine translation and post-editing, and to study how to implant these strategies into English-Korean machine translation and post-editing so as to improve the translation style. This study lays the foundation for improving the guidance standards of post-translation editing style, and accumulates the data of post-translation editing for improving the style of translation, so as to help build an automatic post-translation editing system that can meet the needs of different customers. Koponen (2021) points out that the boundary between human brain modified translation and machine modified translation is gradually disappearing. Studies on government or company translation departments, translation companies, literary publishing departments, volunteer departments and human and machine modified training fields, specifically including empirical studies based on surveys, interviews and keystroke records. The traditional differences between translation and post-translation editing are explored from the theoretical level. The research on incorrect revision and post-editing involves eight languages, including the relationship between translator and reviser, non-professional translators' revision behavior and post-editing.

国内机器翻译研究于1956年开始，第二年，中国科学院语言研究所与计算机技术研究所合作开展俄汉机器翻译研究，译后编辑的研究主要围绕译后编辑工具的设计与开发。黄河燕（1994）提出新的智能译后编辑器的设计原理和算法以提高译后编辑效率。魏长虹（2007）指出机器翻译的各组成部分，译后编辑能帮助译员提高译文质量和人工校译效率，分析译后编辑的基本概念、译后编辑的必要性、译后编辑的手段、译后编辑需要修正的错误和译后编辑实施者五个方面的内容。2008年，格微公司设计“格微协同翻译平台”（GE-CCT），将机器翻译与译后编辑结合，并在商务翻译领域加以实践。李梅（2013）对汽车专业英汉翻译平行语料库英汉机器翻译中出现的规律性、典型性译文错误进行机器翻译二次加工，即通过译后编辑自动化过滤这部分错误来提高机器翻译速度和机器翻译质量。崔启亮（2014）探讨译后编辑的概念，分析译后编辑的应用与研究现状、推动译后编辑发展的动力、适合译后编辑研究的题材，并提出提高译后编辑质量和效率的行为准则。冯全功（2015）分别从译后编辑的行业需求、课程设置、编辑能力、教学及工具选择方面讨论译后编辑的培养方法，并指出在高校开设译后编辑课程能够增强学生职业竞争力，更好地满足语言服务业对译后编辑的需求。崔启亮（2015）针对英语科技文本译后编辑的案例分析机器翻译各种错误类型，包括过译、欠译、术语翻译、形式、格式、多译和漏译、冗余、词性判断、从句翻译、短语顺序、原文句子结构束缚，并总结译后编辑的特点。冯全功（2016）分析目前译后编辑研究的聚焦点，并指出将来研究发展趋势，如开发集成翻译环境、研发特定机器翻译系统、同译后编辑、应用人才培养以及产学研层面进行深度合作。冯全功（2018）尝试从认知、知识和技能三个维度构建译后编辑能力模型，论述每个维度的构成、表征和关联以及该模型对于译后编辑教学的启示作用。王湘玲（2018）通过对“翻译研究文献目录”与“机器翻译档案馆”两大数据库语料分析译后编辑过程及产品评估、影响效率因素、工具及人才培养的研究进程与研究方法，探索其研究趋势，为相关研究及翻译人才培养和学科建设提供新的研究视角和研究方法。王湘玲（2019）通过击键记录和问卷调查法，对比实验者在人工翻译和机器翻译译后编辑的翻译速度、译文质量、译者态度三方面的差异，从而指导未来机器翻译译后编辑人才培养。陆强（2019）研究依据翻译的准确性、完整性、术语一致等要求制定通用译后编辑质量规范指导机器翻译译后编辑项目，也指出可以根据具体项目特点调整项目质量规范，更好指导译后编辑工作。朱慧芬（2020）从纽马克翻译理论出发分析“八八战略”谷歌英译出现的错误，探索译者在文本、所指、衔接和自然层面的译后编辑原则，并指出隐含信息补充和“名从源主”的特色专有名词翻译原则。蔡强（2020）使用Google汉英机器翻译中国知网数据库中200篇科技论文摘要，发现机器翻译主要错误包括词汇、句法、逻辑和其他错误等，且错误频次依次递减，提出在词汇、句法、逻辑和标点使用方面进行译后编辑，丰富了科技文本机器翻译的译后编辑研究。赵涛（2021）指出人们还未能很好区分机器翻译和译后编辑的差异，有必要综合从多个角度去认识机器翻译的相对优势与固有缺点。机器翻译译后编辑可以根据文本类型和客户需求分类，适应不同的使用环境，还提到今后需要加大机器翻译译后编辑的教学研究工作，分别从知识、实践与技能层面培养和提高机器翻译译后编辑核心能力。李梅（2021）通过分析职业译员参加英汉机器翻译译后编辑测试结果研究原文对译后编辑时间与译文质量的关系，帮助人们理解人机协作、了解原文与译后编辑的关系以及提高译后编辑效力。陈胜（2021）通过对比分析汉语石油地质文献在7大主要国内外线上翻译平台英译，发现主要问题集中在词义、词性、词序、句子结构、断句、名词语法标记、搭配、标点符号、字母大小写、信息完整性等方面，提出采用“信达切”的原则开展译后编辑，寻找翻译处理速度与译文准确性之间的最佳平衡点。

Domestic machine translation research began in 1956. In the second year, the Institute of Linguistics, Chinese Academy of Sciences and the Institute of Computer Technology jointly carried out russian-Chinese machine translation research, and the research on post-translation editing mainly focused on the design and development of post-translation editing tools. Huang Heyan (1994) proposed the design principle and algorithm of a new intelligent post-translation editor to improve the efficiency of post-translation editing. Wei Changhong (2007) pointed out the various components of machine translation, post-editing can help translators improve the quality of translation and the efficiency of manual translation, and analyzed the basic concepts of post-editing, the necessity of post-editing, the means of post-editing, the errors that need to be corrected after editing, and the practitioners of post-editing. In 2008, Ge-CCT designed the ge-CCT collaborative Translation Platform (GE-CCT), which combines machine translation with post-translation editing and puts it into practice in the field of business translation. Li Mei (2013) carried out machine translation secondary processing of regular and typical translation errors in parallel corpus of Chinese-English machine translation for automotive majors, namely, automatic filtering of these errors through post-translation editing to improve machine translation speed and machine translation quality. Cui Qiliang (2014) discussed the concept of post-editing, analyzed the application and research status of post-editing, the driving force for the development of post-editing, the subjects suitable for post-editing research, and put forward the code of conduct to improve the quality and efficiency of post-editing. Feng Quangong (2015) discussed the cultivation methods of post-editing from the aspects of the industry demand for post-editing, curriculum setting, editing ability, teaching and tool selection, and pointed out that offering post-editing courses in universities can enhance students' professional competitiveness and better meet the demand of post-editing in the language service industry. Cui Qiliang (2015) analyzed various types of errors in machine translation, including over-translation, under-translation, term translation, form, format, multiple translation and omission, redundancy, part of speech judgment, clause translation, phrase sequence and original sentence structure constraint, and summarized the characteristics of post-translation editing. Feng Quangong (2016) analyzed the current focus of post-translation editing research and pointed out the development trend of future research, such as the development of integrated translation environment, research and development of specific machine translation system, cooperation with post-translation editing, application personnel training and in-depth cooperation at the industry-university-research level. Feng Quangong (2018) attempted to construct a model of post-translation editing ability from three dimensions of cognition, knowledge and skills, and discussed the composition, representation and correlation of each dimension as well as the enlightenment of this model to post-translation editing teaching. Wang Xiangling (2018) by means of "translation studies bibliography" and "machine translation archives" two big database editing process after translation corpus analysis and product evaluation, the factors affecting efficiency, tools, and the research process of personnel training and research methods, explore its research trend, for related research and translation talent training and discipline construction provides a new research perspective and research methods. Wang Xiangling (2019) compared the subjects' differences in translation speed, translation quality and translator's attitude between manual translation and machine translation through keystroke records and questionnaire survey, so as to guide the training of post-translation editing talents in machine translation in the future. Lu Qiang (2019) studied the formulation of general post-editing quality specifications to guide post-editing projects in machine translation based on translation accuracy, completeness and terminology consistency, and also pointed out that the project quality specifications can be adjusted according to specific project characteristics to better guide post-editing work. Zhu Huifen (2020), based on Newmark translation theory, analyzed the errors in Google English translation of the "eight-eight Strategy", explored the translator's post-translation editing principles in the aspects of text, signification, cohesion and nature, and pointed out the translation principles of implicit information supplement and "name from the source". CAI Qiang (2020) used Google Chinese-English machine translation of abstracts of 200 scientific and technological papers in the CNQI database, and found that the main errors in machine translation included vocabulary, syntax, logic and other errors, and the frequency of errors decreased in descending order. He proposed post-translation editing in terms of vocabulary, syntax, logic and punctuation. It has enriched the research of post-translation editing in machine translation of sci-tech texts. Zhao Tao (2021) points out that people have not distinguished the differences between machine translation and post-translation editing well, and it is necessary to comprehensively understand the relative advantages and inherent disadvantages of machine translation from multiple perspectives. Machine translation and post-editing can be classified according to text types and customer needs to adapt to different use environments. It is also mentioned that in the future, it is necessary to increase the teaching and research work of machine translation and post-editing, and to cultivate and improve the core abilities of machine translation and post-editing from the aspects of knowledge, practice and skills. Li Mei (2021) studied the relationship between post-editing time and translation quality of original text by analyzing the results of post-editing test for professional translators in English-Chinese machine translation, so as to help people understand human-computer collaboration, understand the relationship between original text and post-editing, and improve the effectiveness of post-editing. Chen she (2021) through the contrastive analysis of Chinese petroleum geological literature translation in seven major online translation platform at home and abroad, found the main problem is focused on the meaning, part of speech, word order, sentence structure, pausing, noun markers, collocation, punctuation, grammar letter case, information integrity and so on, is put forward based on the principle of "cinda cut" to carry out the translation after editing, To find the best balance between translation processing speed and translation accuracy.

Laurian（1984）在讨论机器翻译文本是否需要译后编辑时，指出如果文本中的信息要求人去感知，那么这类文本翻译译文的可信度和准确度就会有所缺失，不适合进行机器翻译，需要进行译后编辑。Reiss（1989）根据功能语言学家K. Bühler的语言功能三分法，将文本分成信息型、表情型和操作型三种类型，并说明文本类型与翻译策略的整体关系。Chesterman（1989）指出表情型文本是一种“创作类产品”，兼具美学特征，信息发出者可以围绕主题自由创造，并有意识地“使用语言的表情和联想意义”，将个人的情感、情绪、态度通过“创造性作品”及对事实“艺术性塑造”加以表达。研究还指出诗歌是最具表情功能的文本，在翻译过程中也是译者参与“再创造”的过程。

Laurian (1984), when discussing whether machine-translated texts need post-translation editing, pointed out that if the information in the texts requires human perception, the credibility and accuracy of the translated texts of such texts will be deficient, which is not suitable for machine translation and post-translation editing is required. Reiss (1989) based on functional linguist K. Buhler's tripartite method of language functions divides text into three types: information type, expression type and operation type, and explains the overall relationship between text type and translation strategy. Chesterman (1989) points out that the expression type of text is a kind of "creative class" products, both aesthetic characteristics, message originators can free created around the theme, and consciously "the use of language expression and meaning", the personal emotion, mood, attitude through the works of the "creative" and "artistic shape" to express the facts. The study also points out that poetry is the most expressive text and the translator participates in the process of "re-creation" in the process of translation.

卓键滨（2018）对比三种不同文本类型采用人工翻译和机器翻译（谷歌翻译）的译文质量和准确度差异，发现机器翻译最擅长处理信息型文本，其次是表达型文本中的非文学翻译，并指出机器翻译必须与人工翻译合作，才能共同促进彼此和翻译事业的发展而不会被时代淘汰。2018年，通过实验法对比韩国文学作品人工翻译与机器翻译译后编辑质量，发现人工翻译所需时长是译后编辑的3倍，译文准确度与流利性都不如译后编辑，指出人工翻译与译后编辑译文产生类似的错误类型，如误译、省略和语法错误等。熊璨（2020）从语言、文化和话语三个层面分析人工智能翻译文学作品和非文学作品的可行性和存在的问题，指出文化和话语层面存在发展障碍，文学类作品单纯依靠人工智能翻译是无法实现的，只有采用人机结合的机器翻译译后编辑模式才能保证人工智能翻译文学作品的质量。翁义明（2020）运用语料库研究方法，对比文学作品中汉语流水句人工翻译和机器翻译英译文的句法和语篇，发现二者在目标语小句主语、动词类型、句子类别、关联词语与译文语序存在巨大差异，并指出机器翻译需要丰富译文动词类型、句法结构和语篇内关联方式，缩小与人工翻译的差距，达到增强文学翻译语言的生动性、地道性和欣赏性。

Zhuo Jianbin (2018) compared the translation quality and accuracy differences between human translation and machine translation (Google Translate) in three different text types, and found that machine translation is best at processing informational text, followed by non-literary translation in expressive text, and pointed out that machine translation must cooperate with human translation. Only in this way can we promote the development of each other and the cause of translation and not be eliminated by The Times. In 2018, through experiment contrast Korean literature human translation and machine translation after editing quality, found after artificial translation required length is 3 times of editing, translation accuracy and fluency are behind after editing, pointed out that after the artificial translation and editing a similar error type, such as mistranslation, ellipsis and grammatical errors. Xiong Can (2020) analyzed the feasibility and existing problems of ai translation of literary and non-literary works from three aspects of language, culture and discourse, and pointed out that there were development barriers in culture and discourse, and literary works could not be translated solely by artificial intelligence. The quality of literary works translated by artificial intelligence can only be guaranteed by using machine translation and post-editing mode combined with human-machine translation. WengYiMing corpus (2020) research methods, comparative literature in Chinese run-on sentence human translation and English translation machine translation, syntactic and discourse found both in the target language clause subject, verb types, sentence category, associated disparity in the word and the word order, and points out that translation machine translation needs to enrich verb types, syntactic structure and text inside connection way, To narrow the gap with artificial translation and enhance the vividness, idiomatic and appreciation of literary translation language.

1.1.3机器翻译以及译后编辑的问题研究

1.1.3 Research on machine translation and post-translation editing

众所周知，信息技术的发展和应用给翻译带来了深刻的影响与变革（孔令然、崔亮，2018；司显柱、郭小洁，2018）。近年来，机器翻译+译前/译后编辑模式成为机器翻译领域的一项重要变革，已经广泛应用在人们的工作和生活中，成为未来翻译工作的一种重要模式。人们对于译后编辑的研究也呈现井喷态势，包括理论探讨，如译后编辑的概念、发展现状、人才培养、能力建设及未来趋势等（魏长宏、张春柏，2007；崔启亮，2014；冯全功、崔启亮，2016；冯全功、刘明，2018），还包括各种实证研究，研究者往往从某个译后编辑实践出发，探讨译后编辑的方法（陈齐祖，2014）和作用（王萍，2016；郭高攀、王宗英，2017）等，同时越来越多年轻的硕士研究生会选择机器翻译的译后编辑作为硕士论文研究主题。目前人们对于机器翻译背后的机制了解不够，因此未能提出具有针对性的译后编辑策略，且现有的机器翻译策略和方法大都停留在句子层面，未涉及段落和篇章翻译的策略和方法，还有待提高。

As we all know, the development and application of information technology has brought profound influence and reform to translation (Kong Lingran, Cui Liang, 2018; Si Xianzhu, Guo Xiaojie, 2018). In recent years, machine translation + pre-translation/post-translation editing mode has become an important revolution in the field of machine translation. It has been widely used in people's work and life and has become an important mode of translation work in the future. People's research on post-translation editing also showed a trend of explosion, including theoretical discussions, such as the concept of post-translation editing, development status, talent training, capacity building and future trends (Wei Changhong, Zhang Chunbai, 2007; Cui Qiliang, 2014; Feng Quangong, Cui Qiliang, 2016; Feng Quangong, Liu Ming, 2018), including various empirical studies, researchers often start from the practice of post-translation editing to discuss the methods of post-translation editing (Chen Qizu, 2014) and the role of post-translation editing (Wang Ping, 2016; Guo Gaopan, Wang Zongying, 2017). Meanwhile, more and more young postgraduate students will choose post-translation editing of machine translation as the research topic of their master's thesis. At present, people do not know enough about the mechanism behind machine translation, so they fail to put forward targeted post-translation editing strategies. Moreover, the existing strategies and methods of machine translation mostly stay at the sentence level, and do not involve the strategies and methods of paragraph and text translation, which need to be improved.

2012年，学者罗季美、李梅通过对比分析机器译文和人工译文，发现前者在词汇层面的错误率高达84.13%，在句法层面的错误也占到总句数的42.45%。

In 2012, scholars Luo Jimei and Li Mei made a comparative analysis of machine translation and artificial translation, and found that the former made up 84.13% of lexical errors and 42.45% of syntactic errors in total sentences.

2013年，百度开始研究神经网络机器翻译，2015年率先在机器翻译系统中采用深度神经网络。2014年学者杨南在《基于神经网络学习的统计机器翻译研究》中指出基于规则的机器翻译系统难以有效地利用新的资源自动提高翻译系统的性能，而基于统计的机器翻译系统的稳定性和拓展性明显优于其他两种方法，能够自然地处理语言的歧义性，从现有语料库中快速构建高性能的翻译系统，并在增加语料的同时能够自动提升翻译性能。2016年，谷歌发布神经网络机器翻译（GNMT），宣称该系统将翻译质量提高到了接近人工翻译的水平。2018年学者徐雪惠在《机器翻译汉英质量评价》中指出目前的技术还不成熟，不能处理复杂的句子结构或理解文字的深层次含义，并且受到的语料训练比较有限，同时语料库容量不足，难以形成较为系统、完善的训练结果。机器与语言学研究的结合尚不完全，无法从语言学层面科学地处理翻译，比如语篇层面来看机器翻译的句子是不连贯的，其对于文化负载信息和比喻的处理也不尽如人意。

In 2013, Baidu started researching neural network machine translation, and in 2015, it was the first to adopt deep neural network in its machine translation system. Yang Na 2014 scholars in the study of statistical machine translation research based on neural network, "said in a machine translation system based on rules is difficult to effectively use new resources to improve the performance of the translation system automatically, based on statistical machine translation system stability and expansibility is superior to other two methods, can deal with the ambiguity of language, naturally A high performance translation system can be rapidly constructed from the existing corpus, and the translation performance can be automatically improved while the corpus is added. In 2016, Google released Neural Network Machine Translation (GNMT), claiming that the system had improved the quality of translation to a level close to that of human translation. In 2018, xu Xuehui, a scholar, pointed out in Chinese-English Quality Evaluation of Machine Translation that the current technology is not mature enough to process complex sentence structures or understand the deep meaning of words, and the corpus training is relatively limited. Meanwhile, the corpus capacity is insufficient, which makes it difficult to form systematic and perfect training results. The combination of machine and linguistic research is not complete, so it cannot scientifically process translation from the linguistic level. For example, from the textual level, the sentences of machine translation are incoherent, and the processing of culture-loaded information and metaphor is not satisfactory.

2022年3月学者董振恒、任维平、游新冬、吕学强在《融入新能源领域术语知识的机器翻译方法》一文中指出已有的神经机器翻译模型在通用领域取得了较高的翻译质量，但对于涉及专业术语的特定领域，其翻译结果存在着大量漏译、错译现象，术语翻译仍存在较大提升空间。

Scholars Dong Zhenheng, Ren Weiping in March 2022, new winter swimming, Lv Xueqiang in terms of knowledge "into the new energy field machine translation method, the article points out that the existing neural machine translation in the general field obtained higher translation quality, but for specific areas involved in professional terms, the translation results there are a large number of leakage, mistranslation phenomenon, There is still much room for improvement in terminology translation.

2022年4月学者张超轶、陈媛、张聚伟在《融合术语信息的神经机器翻译参数初始化研究》一文中提到电气工程领域英汉机器翻译平行语料稀缺。由于电气领域资源低，有限的双语语料限制了此项量对词本身所包含信息的学习，因而电气工程领域的机器翻译译文准确率也不高。

In April 2022, scholars Zhang Chaoyi, Chen Yuan and Zhang Juwei mentioned the scarcity of parallel corpus for English-Chinese machine translation in the field of electrical engineering in the paper "Research on Parameter Initialization of Neuromachine Translation Integrating Terminology Information". Due to the low resources in the field of electrical engineering and the limited bilingual corpus, the accuracy of machine translation in the field of electrical engineering is not high.

2022年9月学者黄永中在《翻译转换理论视角下机器翻译与人工翻译的对比分析》中提到机器翻译可以胜任那些对于表达形式、风格和文化内涵、意境不做特定要求的翻译工作，而对于散发着强烈艺术个性、独特人文风格与魅力的文本，比如小说、诗歌、散文等文艺作品还不能够胜任。

In September 2022 scholars yong-zhong huang in the perspective of translation theory analysis in machine translation and human translation "mentioned in machine translation can be competent for the for expression form, style and cultural connotation, the specific requirements of the artistic conception is not to do translation work, and for sending out the strong artistic personality, unique cultural style and charm of the text, For example, novels, poems, prose and other literary works are not competent.

研究者郭望皓在《神经机器翻译译文测评及译后编辑研究》中提到，机器翻译错误类型主要分为拼写错误、词汇短语错误、句子句法错误、语义错误四大类；在进行中英翻译时，主要是对词汇与句子进行研究，发现常常出现词汇层面的词性误译，并且没有规律可循，即使同一神经机器翻译在翻译不同句子的同一短语结构时，也会出现不同的译法。因此通过对文学文本进行译后编辑，则可以帮助机器翻译提供更大、更准确的语料资源。其次，由于神经机器翻译模型中缺乏“硬对齐”模块，导致译文中经常会出现漏译，即“该翻译的没有翻译”，最后造成意义的不完整。

Guo Wanghao, a researcher, mentioned in his research on Translation Evaluation and Post-translation Editing in Neural machine Translation that machine translation errors are mainly divided into four categories: spelling errors, vocabulary and phrase errors, sentence syntax errors and semantic errors. When translating from Chinese to English, we mainly study words and sentences, and find that pos mistranslations often occur at the lexical level, and there are no rules to follow. Even when translating the same phrase structure in different sentences with the same neural machine translation, there will be different translation methods. Therefore, post-translation editing of literary texts can help machine translation provide larger and more accurate corpus resources. Secondly, due to the lack of hard-aligned modules in the NEURAL machine translation model, translation omissions often occur in the translation, that is, "the translation is not translated", resulting in incomplete meaning.

基于以上学者对于机器翻译的研究，尽管机器翻译能给我们带来极大的便利，有效处理较多层次较低的翻译任务，但神经网络机器翻译仍存在着诸多问题，包括：1. 机器翻译在某些特定领域受现有语料质量的影响，导致译文准确率不高，如电气领域、新能源领域等；2. 在通用领域下，机器翻译在处理结构复杂的句子以及句子的深层含义还不成熟；3. 机器翻译在处理小说、诗歌、散文等创造性程度高的文本或表现性文本时，由于这些文本重视作者或人物形象的情感表现，词汇语义表达往往不稳定，较为模糊，而机器翻译受自身先天不足的限制，只能表达浅层的语言情境，无法表达深层语言情境、语篇语境和人际语境，因此我们需要提升机器翻译对于不同类别文本的兼容性。

Based on the above researches on machine translation, although machine translation can bring us great convenience and effectively handle many lower-level translation tasks, there are still many problems in neural network machine translation, including: 1. Machine translation is affected by the quality of existing corpus in some specific fields, leading to low accuracy of translation, such as electrical field, new energy field, etc. 2. In the general field, machine translation is still immature in dealing with complex sentences and their deep meanings; 3. When machine translation is dealing with highly creative texts or expressive texts such as novels, poems and essays, the semantic expression of vocabulary is often unstable and fuzzy because these texts attach importance to the emotional expression of the author or the image of the characters. However, machine translation is limited by its inherent deficiencies and can only express shallow linguistic situations. Therefore, we need to improve the compatibility of machine translation for different types of texts.

1.1 研究价值

1.2 Research value

根据Chesterman对文本类型和翻译的研究，我们了解到文学作品等表情功能文本单纯依靠机器翻译无法实现“艺术再创造”，因而必须依靠人工参与机器翻译译后编辑模式，进行“艺术再创造”才能保证翻译质量。

According to Chesterman's research on text types and translation, we know that emotion-functional texts such as literary works cannot be "recreated artistically" by machine translation alone. Therefore, human participation in post-translation editing mode of machine translation must be relied on for "recreated artistically" to ensure translation quality.

1. 提高机器翻译的质量

2. Improve the quality of machine translation

减少现有机器翻译具有的误译、错译、漏译以及在长、难句处理时出现的句法不通顺、佶屈聱牙译文的数量

Reduce the number of mistranslations, mistranslations, omissions and syntactically awkward and suffocating translations that are difficult and difficult in existing machine translation

3. 减少甚至最终取缔机器翻译中的人工参与程度

4. Reduce and eventually eliminate human involvement in machine translation

5. 加速不同语际间的文化传播

6. Accelerate cultural communication between different languages

机器翻译处理速度远远大于人类，大概5-6倍，不眠不休

Machine translation processing is much faster than human beings, probably 5-6 times, never sleeps

7. 完善机器翻译软件开发

8. Perfect machine translation software development

提高语料库现有文本与待译文本的匹配度，精确搜索算法、扩建双语平行对齐语料库、充实不同文本类型的语料库资源、

Improve the matching degree of the existing texts in the corpus and the texts to be translated, accurately search algorithms, expand the bilingual parallel alignment corpus, enrich the corpus resources of different text types,

9. 促进AI智能的发展

10. Promote the development of AI intelligence

使计算机在具有抽象思维能力的基础上，开发与人类媲美的形象思维能力

On the basis of abstract thinking ability, computer can develop image thinking ability comparable with human beings

因而我们研究针对现有《鲁迅全集》英、德译本内容不全，且此文本属于文学翻译，作品翻译难度大、耗时耗力。通过机器翻译与译后编辑相结合的方式，首先我们可以通过网络在线机器翻译对语料进行翻译，其后，对语料进行整理，将重复性较高的语料进行统一处理，其次在进行以后编辑的过程中对具有修辞以及深意的词句进行译后编辑，因此我们就能够通过译后编辑构建一个文学领域高质量的语料，这则在对机器翻译引擎进行了文学领域的训练，则为机器翻译提供了深度学习的机会和提供了高质量的语料。同时这对中华文化的传播与中华典籍外译起到了推动作用。

Therefore, the existing English and German versions of The Complete Works of Lu Xun are not complete, and this text belongs to literary translation, which is difficult and time-consuming. Through the machine translation and editing a combination of translation, first of all, we can through the network online machine translation to translate corpus, followed by the corpus, the higher repeatability of corpora are unified handling, second in the process of edit later with rhetoric and meaning after the translation of the words of the editor, Therefore, we can construct a high-quality corpus in the field of literature through post-translation editing, which provides deep learning opportunities and high-quality corpus for machine translation by training the machine translation engine in the field of literature. At the same time, it has promoted the spread of Chinese culture and the translation of Chinese classics.

2. 研究内容（本课题的研究对象、总体框架、重点难点、主要目标等）

2. Research content (research object, overall framework, key and difficult points, main objectives, etc.)

2.1研究对象

2.1 Research Objects

研究对象包括《鲁迅全集》的英译本，以及《鲁迅全集》代表的文学作品在机器翻译过程中出现的问题，对比机器翻译在译前进行预测能力的翻译质量与译后使用平行语料的翻译质量，从而进一步提高机器翻译在文学领域的准确度。

Research objects include English translation of "lu xun complete works", and "lu xun complete works" on behalf of the literary works of the problem in the process of machine translation, and contrast before translation machine translation translation quality and prediction ability after using parallel corpus translation quality, further improve the accuracy of machine translation in the field of literature.

2.2 总体框架

2.2 General Framework

I. 文献综述

I. Literature review

II. 《鲁迅全集》

II. The Complete Works of Lu Xun

II.I 鲁迅

II. I lu xun

II.I.I 鲁迅简介

I.I. Introduction of Lu Xun

II.I.II 鲁迅的影响力

The influence of Lu Xun

II.II 《鲁迅全集》及译本

II.II The Complete Works of Lu Xun and its translation

II.II.I 《鲁迅全集》简介

II.II.I Introduction to The Complete Works of Lu Xun

II.II.I.I 《鲁迅全集》内容

II.II.I. CONTENTS of The Complete Works of Lu Xun

II.II.I.II 《鲁迅全集》的文本特点

II.II.I text features of the Complete Works of Lu Xun

II.II.II 《鲁迅全集》译本简介

II.II. Introduction to the translation of The Complete Works of Lu Xun

II.II.II.I 《鲁迅全集》现有译本

Ii.ii.i Current translation of The Complete Works of Lu Xun

II.II.II.II 《鲁迅全集》译入语的文本特点

Ii.ii.ii. Text features of the Translation of The Complete Works of Lu Xun

III. 《鲁迅全集》翻译 —— 机器翻译译后编辑

III. Translation of The Complete Works of Lu Xun -- machine translation and editing

III.I 机器翻译

III.I Machine translation

III.I.I 机器翻译的优点

Iii.i Advantages of machine translation

III.I.II 机器翻译的不足

Iii.i. Shortcomings of machine translation

III.I.III **翻译软件

Iii.ii ** Translation software

III.II 译后编辑

III.II Post-translation and editing

III.II.I 译后编辑的优点

III.II.I Advantages of post-translation editing

III.II.II 译后编辑的不足

Iii.ii. Deficiencies in post-translation editing

III.II.III **翻译软件译后编辑

Iii.ii.III ** Translation software for editing after translation

III.III 机器翻译译后编辑在《鲁迅全集》翻译的应用

III.III Application of post-translation editing in the translation of The Complete Works of Lu Xun

III.III.I 文本类型

Iii.iii.i Text type

III.III.II 翻译评估标准

Iii.iii.ii Translation assessment criteria

III.III.III 机器翻译错误类型及应对方法

III.III.III Machine translation error types and solutions

III.III.IV 译后编辑类型及策略

Iii.iii.iv Types and strategies of post-translation editing

IV. **翻译软件对于表情型文本的包容性

IV. The inclusiveness of ** translation software for emoticons

IV.I 深层语言规则描写

IV.I Description of deep language rules

IV.II 翻译单位多样化

IV.II Variety of translation units

IV.III 语篇语境制约

IV.III Textual context constraints

IV.IV 人际语境制约

IV. Interpersonal context constraints

IV.V 语料库建设

Iv. V Corpus construction

2.3重点难点

2.3 Key points and Difficulties

2.3.1研究重点

2.3.1 Research Focus

采用合理的机器翻译译后编辑方法和策略完成《鲁迅全集》英、德译本。

To complete the English and German translations of The Complete Works of Lu Xun by using reasonable post-translation methods and strategies.

2.3.2研究难点

2.3.2 Research difficulties

（1）《鲁迅全集》翻译任务艰巨（内容多、主题杂、耗时长）。

（2）The translation of The Complete Works of Lu Xun is a difficult task (with many contents, miscellaneous topics and long time consuming).

（2）如何提升**机器翻译软件对于表情型文本《鲁迅全集》的包容性。

(2) How to improve the inclusiveness of ** machine translation software for the emotion-type text "The Complete Works of Lu Xun".

2.4主要目标

2.4 Main Objectives

（1）高效保质使用机器翻译译后编辑完成《鲁迅全集》英、德全译本.

（2）Complete English and German translations of the Complete Works of Lu Xun with high efficiency and high quality.

（3）提升**翻译软件对于表情型文本的包容性。

（4）Improve the inclusion of emoticons in translation software.

3. 思路方法（本课题研究的基本思路、具体研究方法、研究计划及其可行性等）

3. Ideas and methods (basic ideas, specific research methods, research plan and feasibility of this research, etc.)

3.1基本思路

3.1 Basic Ideas

梳理现有《鲁迅全集》和译本，了解**机器翻译软件的特点以及机器翻译译后编辑的方法、策略和原则的基础上，结合文本类型分类说，对照《鲁迅全集》原译本，采用机器翻译与译后编辑相结合，分析译文错误类型并加以修正，总结错误规律和常规解决办法，建立译后自动化编辑程序，提升机器翻译对于诸如《鲁迅全集》表情型文本的包容性，更好地促进机器翻译的发展，推动《鲁迅全集》等中华典籍外译，传扬中国文化，弘扬中国精神。

Combing the existing "lu xun complete works" and translation, understanding of the features of * * machine translation software and machine translation methods, strategies and principles of editing translation on the basis of the combination of text type classification, contrast "lu xun complete works" the original translation, machine translation and translation after editing, the combination of analysis of translation error types and amended, summarizes the law of error and conventional solution, The establishment of post-translation automatic editing procedures, improve the machine translation for the expression of text such as the Complete Works of Lu Xun, better promote the development of machine translation, promote the complete Works of Lu Xun and other Chinese classics translated, spread Chinese culture, carry forward the Chinese spirit.

3.2研究方法

3.2 Research methods

文献研究法、实验法

Literature research method, experimental method

3.3研究计划

3.3 Research Plan

2022年3月 - 2022年7月《鲁迅全集》语料输入

March 2022 -- July 2022 Corpus input of The Complete Works of Lu Xun

2022年8月 - 2023年8月《鲁迅全集》英文、德文机器翻译及译后编辑

Aug. 2022 -- Aug. 2023: English and German machine translation and post-translation editing of The Complete Works of Lu Xun

2023年9月 - 2023年12月对《鲁迅全集》英文、德文译本校勘

From September 2023 to December 2023, collated the English and German versions of the Complete Works of Lu Xun

3.4可行性

3.4 the feasibility

（1）符合国家中华文化“走出去”战略方针，有利于推动中国文化继承、发展、延续和传播，使中华文化永葆青春。

(1) In line with the national Strategy of "going global" of Chinese culture, it is conducive to promoting the inheritance, development, continuation and dissemination of Chinese culture and keeping Chinese culture young forever.

（2）项目主持人精通中、德语，资深汉学家，翻译了曹雪芹的《红楼梦》及鲁迅、周作人、许地山、郁达夫、朱自清、冰心、巴金、钱钟书、贾平凹等众多著名国内作家的经典著作。

(2) The project host, a senior Sinologist proficient in Chinese and German, translated Cao Xueqin's A Dream of Red Mansions and the classic works of lu Xun, Zhou Zuoren, Xu Dishan, Yu Dafu, Zhu Ziqing, Bing Xin, Ba Jin, Qian Zhongshu, Jia Pingwa and many other famous Chinese writers.

（3）项目执行者包括MTI及翻译方向的教师、研究生，具有合格的资质和能力开展《鲁迅全集》英译和德译机器翻译译后编辑工作。

(3) Project implementers include teachers and postgraduates from MTI and translation, who are qualified and capable of translating the Complete Works of Lu Xun into English and German machine translation.

（4）项目与课程内容（中华典籍外译）紧密结合，理论联系实际，线上线下结合。

(4) The project is closely combined with the course content (Translation of Chinese classics), theory is connected with practice, and online and offline are combined.

（5）**翻译软件公司的智力支持。

(5) Intellectual support from ** translation software company.

4. 创新之处（在学术思想、学术观点、研究方法等方面的特色和创新）

4. Innovation (characteristics and innovation in academic thoughts, viewpoints, research methods, etc.)

（1）填补当下《鲁迅全集》无全译本的空白。

(1) Fill in the gap that there is no complete translation of the Complete Works of Lu Xun.

（2）《鲁迅全集》等表情型文本机器翻译的可行性

(2) The feasibility of machine translation of emotion-type texts such as The Complete Works of Lu Xun

5. 预期成果（成果形式、使用去向及预期社会效益等）

5. Expected results (results form, use direction and expected social benefits, etc.)

（1）《鲁迅全集》英文、德文全译本出版和发行，将鲁迅的作品完整地展示给英文和德文读者，让更多地人认识鲁迅和《鲁迅全集》，了解中华文化，促进中华文化在全球的发展和传播。

(1) To publish and distribute the Complete Works of Lu Xun in English and German versions, so as to show the complete works of Lu Xun to English and German readers, so that more people can get to know Lu Xun and the Complete Works of Lu Xun, understand Chinese culture, and promote the development and dissemination of Chinese culture in the world.

（2）兼容表情型文本的**机器翻译自动化编辑软件开发，可以高效保质地完成诸如《鲁迅全集》类表情型文本的机器翻译，促进**机器翻译软件的开发和本地化发展，给公司带来巨大的经济收益，给使用者带来极大地便利。

(2) The development of automatic machine translation and editing software compatible with emotion-type texts can efficiently and effectively complete the machine translation of emotion-type texts such as The Complete Works of Lu Xun, and promote the development and localization of machine translation software, bringing huge economic benefits to the company and great convenience to users.

5.1成果形式与使用去向

5.1 Form and use of results

（1）《鲁迅全集》英、德译本在国内外发行使用，版权属项目负责人。

(1) The English and German versions of The Complete Works of Lu Xun are published and used at home and abroad. The copyright belongs to the project leader.

（2）兼容表情型文本的**机器翻译自动化编辑软件，可供项目负责人和教学研究团队使用。

(2) ** machine translation automatic editing software compatible with emotile-type text, which can be used by project leaders and teaching and research teams.

5.2 预期效益

5.2 Expected Benefits

（1）《鲁迅全集》英、德译本发行使用

(1) The Complete Works of Lu Xun published in English and German

（2）兼容表情型文本的**机器翻译自动化编辑软件成功研发

(2) The automatic editing software for machine translation compatible with emoticon text was successfully developed

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6. References (main Chinese and foreign references to carry out this research)

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Hou Gui-xin. Proofreading of Huagai Collection Continuation -- Also On The Collation of the Third Volume of The complete Works of Lu Xun (2005) [J]. Modern Chinese Literature Studies Series, 2020, 04: 85-112.

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Zhao Tao. Current Situation and Problems of Post-translation editing in Machine Translation [J]. Foreign Language Teaching, 2021(4).

李梅. 机器翻译译后编辑过程中原文对译员影响研究[J]. 外语教学, 2021(4).

Li Mei. Research on the Influence of Original Text on Translators in Machine Translation and Post-editing [J]. Foreign Language Teaching, 2021(4).

陈胜, 田传茂. 在线翻译平台汉英翻译的问题及译后编辑 —— 以石油地质文献为例[J]. 中国科技翻译, 2021, 34(1): 31-34, 49.

Chen Sheng, TIAN Chuanmao. Problems and Post-translation Editing of Chinese-English translation on online translation platform -- A Case study of petroleum geology Literature [J]. China Science & Technology Translation, 2021, 34(1): 31-34, 49.

Yuelu Mountain Universities Science City 2022

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