Difference between revisions of "Machine translation"

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[[DCG-To-Do|To the To Do list]]
 
[[DCG-To-Do|To the To Do list]]
  
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=1 卫怡雯(A Comparison Between the Quality of Machine Translation and Human Translation——A Case Study of the Application of artificial intelligence in Sports Events)=
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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=
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[[Machine_Trans_EN_1]]
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'''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'''
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===Abstract===
 
  
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===Key words===
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卫怡雯 Wei Yiwen, Hunan Normal University, China
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Machine Translation; Human Translation; International Sports Events；Artificial Intelligence
 
  
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===题目===
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[[Machine_Trans_EN_1]]
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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
 
  
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===摘要===
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=Chapter 2：On the Realm Advantages and Mutual Development of Machine Translation and Huamn Translation)=
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'''论机器翻译与人工翻译的领域优势及共同发展'''
  
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===关键词===
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肖毅瑶 Xiao Yiyao, Hunan Normal University, China
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机器翻译；人工翻译；国际体育赛事；人工智能
 
  
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===1. Introduction===
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[[Machine_Trans_EN_2]]
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===2. ===
 
  
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===3.===
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=Chapter 3: -Missing-=
  
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===4.===
 
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===5. ===
 
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===Conclusion===
 
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===References===
 
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=2 吴映红=
 
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[[Machine_Trans_EN_2]]
 
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=3 肖毅瑶(On the Realm Advantages And Symbiotic Development of Machine Translation And Huamn Translation)=
 
 
[[Machine_Trans_EN_3]]
 
[[Machine_Trans_EN_3]]
  
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===Abstract===
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=Chapter 4 : A Comparison Between Machine Translation of Netease and Traditional Human Translation—A Case Study of The Economist Articles)=  
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'''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'''
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===Key words===
 
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Machine Translation; Huamn Translation; Realm Advantages; Symbiotic Development
 
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===题目===
 
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论机器翻译与人工翻译的领域优势及共生发展
 
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===摘要===
 
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机器翻译自1947年问世以来不断发展，并逐渐在翻译市场发挥着举足轻重的作用，随之而至的便是人们对于机器翻译与人工翻译之间关系的思考与研究。机器翻译的应运而生给人工翻译市场带来的究竟是巨大的冲击还是无限的机遇呢？二者的关系走向将会如何，是取而代之还是并驾齐驱？译者该如何应对机器翻译的挑战？
 
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    笔者认为随着科学技术的不断完善，人工翻译和机器翻译在不同的领域各自都具备一定主导地位，但机器翻译仍旧存在一定缺陷，永远不可能取代人工翻译。本文立足于机器翻译与人工翻译的不同特点，浅析二者各自的领域优势，探究其共生发展的可能性以及途径。
 
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===关键词===
 
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人工翻译；机器翻译；领域优势；共生发展
 
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===1. Introduction===
 
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===2. ===
 
  
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===3.===
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王李菲 Wang Lifei, Hunan Normal University
  
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===4.===
 
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===5. ===
 
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===Conclusion===
 
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===References===
 
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=4 王李菲 （Comparison Between Machine Translation of Netease and Traditional Human Translation—A Case Study of The Economist Articles)=
 
 
[[Machine_Trans_EN_4]]
 
[[Machine_Trans_EN_4]]
  
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===Abstract===
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=Chapter 5: Muhammad Saqib Mehran - Problems in translation studies=
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[[Machine_Trans_EN_5]]
  
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===Key words===
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=6 徐敏赟(Machine Translation Based on Neural Network --Challenge or Chance?)=
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Machine Translation; Human Translation; Contrastive Analysis
 
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===题目===
 
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有道神经网络机器翻译与传统人工翻译的译文对比——以经济学人语料为例
 
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===摘要===
 
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===关键词===
 
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人工翻译；机器翻译；对比分析
 
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===1. Introduction===
 
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===2. ===
 
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===3.===
 
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===4.===
 
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===5. ===
 
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===Conclusion===
 
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===References===
 
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=5 杨柳青=
 
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[[Machine_Trans_EN_5]]
 
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=6 徐敏赟(Machine Translation Based on Neural Network --Challenge or Chance)=
 
 
[[Machine_Trans_EN_6]]
 
[[Machine_Trans_EN_6]]
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= = = Key Words = = =
 
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Egg, Hen
 
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= = = 题目 = = =
 
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= = = 摘要 = = =
 
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= = = 关键词 = = =
 
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= = = Introduction = = =
 
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Here starts the normal text of the chapter. Please remember to indicate the source of EACH PARAGRAPH, sometimes even of single sentences. You can indicate it like this. (Woesler 2020, 345) And don't forget to mention the full bibliographical entry beneath under References.
 
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= = = The Egg = = =
 
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Bla, bla, bla
 
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= = = The Hen = = =
 
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Bla, bla, bla
 
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= = = Conclusion = = =
 
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Bla, bla, bla
 
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= = = References = = =
 
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Woesler, Martin. (2020). Responsibility and Ethics in Times of Corona. Woesler, Martin and Hans-Martin Sass eds. Medicine and Ethics in Times of Corona Muenster: LIT
 
  
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=7 颜莉莉(一带一路背景下人工智能与翻译人才的培养)=
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=Chapter 7: The Cultivation of artificial intelligence and translation talents in the Belt and Road Initiative=
 
[[Machine_Trans_EN_7]]
 
[[Machine_Trans_EN_7]]
  
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===Abstract===
 
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In the era of artificial intelligence, artificial intelligence has been applied to various fields. In the field of translation, traditional translation models can no longer meet the rapid development and updating of the information age. The development of machine translation has brought structural changes to the language service industry, which poses challenges to the cultivation of translation talents. Under the background of "The Belt and Road initiative", translation talents have higher and higher requirements on translation literacy. Artificial intelligence and translation technology are used to reform the training mode of translation talents, so as to better serve the development of The Times. This paper mainly explores the cultivation of artificial intelligence and translation talents under the background of the Belt and Road Initiative. The cultivation of translation talents is moving towards comprehensive cultivation of talents. On the contrary, artificial intelligence and machine translation can also be used to improve the teaching mode and teaching content, so as to win together in cooperation.
 
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===Key words===
 
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Artificial intelligence,Machine translation,cultivation of translation talents,"The Belt and Road initiative"
 
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===题目===
 
 
一带一路背景下人工智能与翻译人才的培养
 
一带一路背景下人工智能与翻译人才的培养
  
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===摘要===
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颜莉莉 Yan Lili, Hunan Normal University, China
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进入人工智能时代，人工智能被应用于各个领域。在翻译领域，传统的翻译模式已无法满足信息化时代的飞速发展和更新，机器翻译的发展给语言服务行业带来了结构性改变，这对翻译人才的培养提出了挑战。“一带一路”背景下，对翻译人才的翻译素养要求越来越高，利用人工智能和翻译技术对翻译人才培养模式进行革新，更好为时代发展服务。本文主要探究在一带一路背景下人工智能和翻译人才培养，翻译人才的培养过程中正向对人才的综合性培养，反之也可以利用人工智能和机器翻译完善教学模式和教学内容，在合作中共赢。
 
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===关键词===
 
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人工智能；机器翻译；翻译人才培养；一带一路
 
  
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===1. Introduction===
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=Chapter 8: On Machine Translation Under Lanuguage Intelligence——An Option and Opportunity for Human Translators=
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With the development of science and technology in China, artificial intelligence has also been greatly improved, and related technologies have been applied to various fields, such as the use of intelligent robots to deliver food to quarantined people during the epidemic, which has made people's lives more convenient. The most controversial and widely discussed issue is machine translation. Before the emergence of machine translation, translation was generally dominated by human translation, including translation and interpretation, which was divided into simultaneous interpretation and hand transmission, etc. It takes a lot of time and energy to cultivate a translation talent. However, nowadays, the era is developing rapidly and information is updated rapidly. As a translation talent, it is necessary to constantly update its knowledge reserve to keep up with the pace of The Times. The emergence of machine translation has also posed challenges to translation talents and the training of translation talents. Although machine translation had some problems in the early stage, it is now constantly improving its functions. In the context of the belt and Road Initiative, both machine translation and human translation are facing difficulties. Regardless of whether human translation is still needed, what is more important at present is how to train translators to adapt to difficulties and promote the cooperation between human translation and machine translation.
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'''论语言智能下的机器翻译——译者的选择与机遇'''
  
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===2.Development status of machine translation in the era of artificial intelligence ===
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颜静 Yan Jing, Hunan Normal University, China
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With the development of AI technology, machine translation has made great progress and has been applied to people's lives. For example, more and more tourists choose to download translation software when traveling abroad, which makes machine translation take an absolute advantage in daily email reply and other translation activities that do not require high accuracy. The translation software commonly used by netizens include Google Translation, Baidu Translation, Youdao Translation, IFly.com Translation, etc. Even wechat and other chat software can also carry out instant Translation into English. Some companies have also launched translation pens, translation machines and other equipment, which enables even native speakers to rely on machine translation to carry out basic communication with other Chinese people.
 
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But so far, machine translation still faces huge problems. Although machine translation has made great progress, it is highly dependent on corpus and other big data matching. It does not reach the thinking level of human brain, and cannot deal with the problem of translation differences caused by culture and religion. In addition, many minor languages cannot be translated by machine due to lack of corpus.
 
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What's more, most of the corpus is about developed countries such as Britain and France, and most of the corpus is about diplomacy, politics, science and technology, etc., while there are very few about nationality, culture, religion, etc.
 
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In addition, machine translation can only be used for daily communication at present. If it involves important occasions such as large conferences and international affairs, it is impossible to risk using machine translation for translation work. Professional translators are required to carry out translation work. So machine translation still has a long way to go.
 
  
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===3.Challenges in the training of translation talents in universities===
 
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The cultivation of translators is targeted at the market. Professors Zhu Yifan and Guan Xinchao from the School of Foreign Languages at Shanghai Jiao Tong University believe that the cultivation of translators can be divided into four types: high-end translators and interpreters, senior translators and researchers, compound translators and applied translators.
 
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From their names, it can be seen that high-end translators and interpreters and senior translators and researchers talents have high requirements on the knowledge and quality of interpreters, because they have to face the changing international situation, and have to deal with all kinds of sensitive relations and political related content, they should have flexible cross-cultural communication skills. In addition, for literature, sociology and humanities academic works, it is not only necessary to translate their content, but also to understand their essence. Therefore, translators should not only have humanistic feelings, but also need to have a deep understanding of Chinese and western culture.
 
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However, there is not much demand for this kind of translation in the society. Such high-level translation requirements are not needed in daily life and work. The greatest demand is for compound translators, which means that they should master knowledge in a specific field while mastering a foreign language. For example, compound translators in the financial field should not only be good at foreign languages, but also master financial knowledge, including professional terms, special expressions and sentence patterns.
 
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Now we say that machine translation can replace human translation should refer to the field of compound translation talents. Although AI technology has enabled machine translation to participate in creation, it does not mean that compound translation talents will be replaced by machines. The complexity of language and the flexible cross-cultural awareness required in communication make it impossible for machine translation to completely replace human translation.
 
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The last type of applied translation talents are mostly involved in the general text without too much technical content and few professional terms, so it is easy to be replaced by machine translation.
 
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Therefore, the author thinks that what universities are facing at present is not only how to train translation talents to cope with the development of machine translation, but to consider the application of machine translation in the process of training translation talents to achieve human-machine integration, so as to better complete the translation work.
 
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===4.一带一路语言环境和人才需要===
 
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===5.在机器翻译视域下如何培养翻译人才 ===
 
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===5.1 对翻译人才的素养要求 ===
 
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===5.2 利用人工智能进行翻译实践活动===
 
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===5.3 大数据、术语库和语料库的应用===
 
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===5.2 针对一带一路的机器翻译与翻译人才的合作===
 
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===Conclusion===
 
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===References===
 
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=8 颜静(On Machine Translation Under Lanuguage Intelligence——An Option and Opportunity for Human Translators)=
 
 
[[Machine_Trans_EN_8]]
 
[[Machine_Trans_EN_8]]
  
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===Abstract===
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=9 谢佳芬（ Machine Translation and Artificial Translation in the Era of Artificial Intelligence）=
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Nowadays the artificial intelligence is sweeping the world, however, the traditional language research and language service industry is facing new challenges.  This paper attempts to comb and analyze the development process of language intelligence in artificial intelligence and the development status of language industry under the background of information age to interpret the feasibility of liberal arts translators to engage in machine translation research and necessity to apply machine translation, thus to provide an option for human translators in information age to develop.
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人工智能时代下的机器翻译与人工翻译
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===Key words===
 
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New Libral Arts; Language Intelligence; Machine Translation; Interdisciplinarity
 
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===题目===
 
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论语言智能之机器翻译——我们的选择和未来
 
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===摘要===
 
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当今人工智能的热潮席卷全球，而传统的语言研究和语言服务行业却面临着新的挑战。本文通过梳理分析人工智能中语言智能领域的发展历程和信息时代背景下语言行业的发展现状，对文科译者从事机器翻译研究的可行性和应用机器翻译的必要性进行阐述，为信息时代译者的发展路径提供参考。
 
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===关键词===
 
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新文科；语言智能；机器翻译；学科交叉
 
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===1. Introduction===
 
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Obviously, we are now in an era of "explosion" of information and knowledge, which makes us have to find ways to deal with it quickly. Language is the manifestation of information, and the tool that can deal with language with complicated information is just computer. It happens that human beings do not have a special organ to perceive language, but carry the image and sound symbols of language through visual and auditory perception, and then form language information through brain processing and abstraction. Therefore, language intelligence also belongs to the research category of "cognitive intelligence". In view of this, computer has carried out the research on language, among which the common research fields are "natural language processing", "language information processing" and "Computational Linguistics". These three are different, but they all have the same goal, that is, to enable computers to realize and express with language, solve language related problems and simulate human language ability. Among them, machine translation is the integration of language intelligence and technology. The comprehensive research of MT in China starts from the mid-1980s. Especially since the 1990s, a number of MT systems have been published and commercialized systems have been launched. In addition, various universities in China have also carried out MT and computational linguistics research, developed various translation experimental systems and achieved fruitful results. In the research of machine translation, it involves not only translation model and language model, but also alignment method, part of speech tagging, syntactic analysis method, translation evaluation and so on. Therefore, researchers must understand the basic knowledge of translation and be proficient in English, Chinese or other languages. Therefore, we say that compound talents with computer and language related knowledge will be more needed in the language industry or the computer field.
 
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===2. Chapter 1 Artificial Intelligence in Rapid Development===
 
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At the Dartmouth Conference in 1956, the word "artificial intelligence" appeared in the human world for the first time. In the past 65 years, with the in-depth study of science, artificial intelligence seems to have come out of the original science fiction movies and science fictions, and is closer to human daily life step by step. Nowadays, autopilot, machine translation, chess and E-sports robots, AI synthetic anchor, AI generated portrait and so on have been realized and widely known. Artificial intelligence has also moved from logical intelligence and computational intelligence to today's cognitive intelligence. According to academician Tan Tieniu mentioned in magazine recently, "Artificial intelligence is a technical science that studies and develops theories, methods, technologies and application systems that can simulate, extend and expand human intelligence. Its purpose is to enable intelligent machines to listen, see, speak, think, learn and act, that is, they have the following capabilities——speech recognition and machine translation, image and character recognition, speech synthesis and man-machine dialogue, man-machine games and theorems proving, machine learning and knowledge reprentation, autopilot amd so on. In general, special artificial intelligence has made major breakthroughs. Although general artificial intelligence is in its infancy, it is accelerating its intersection and penetration with other fields and rapidly changing the development of science.
 
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====1.1 The Development of Language Intelligence====
 
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====1.2 The Research on Machine Translation====
 
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===3. Chapter 2 Interdisciplinarity in Irresistible Trend===
 
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====2.1 The Construction of New Liberal Arts====
 
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====2.1 The Current Status of New Liberal Arts====
 
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===4. Chapter 3 Language Service Industry with Machine Translation===
 
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====3.1 Translation Mode of Man-machine Cooperation====
 
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====3.2 Translators with More Professional and Diversified Career Path====
 
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3.2.1 The Improvement of Tranlation Ability
 
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3.2.2 The Combination with Other Field
 
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===Conclusion===
 
  
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===References===
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谢佳芬 Xie Jiafen ,Hunan Normal University, China
  
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=9 谢佳芬（人工智能时代下的机器翻译与人工翻译）=
 
 
[[Machine_Trans_EN_9]]
 
[[Machine_Trans_EN_9]]
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===Abstract===
 
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With the continuous development of information technology, many industries are facing the competitive pressure of artificial intelligence, and so is the field of translation. Artificial intelligence technology has developed rapidly and combined with the field of translation，which has brought great impact and changes to traditional translation, but artificial intelligence translation and artificial translation have their own advantages and disadvantages. Artificial translation is in the leading position in adapting to human language logical habits and understanding characteristics, but in terms of translation threshold and economic value, the efficiency of artificial intelligence translation is even better. In a word, we need to know that machine translation and human translation are complementary rather than antagonistic.
 
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===Key Words===
 
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Machine Translation; Artificial Translation; Artificial Intelligence
 
  
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===题目===
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=Chapter 10 熊敏 Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts=
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人工智能时代下的机器翻译与人工翻译
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机器翻译对各类型文本的英汉翻译能力探究
  
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===摘要===
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熊敏, Xiong Min, Hunan Normal University
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伴随着信息技术的不断发展，多个行业面临着人工智能的竞争压力，翻译领域也是如此。人工智能技术快速发展并与翻译领域结合，人工智能翻译给传统翻译带来了巨大的冲击和变革，但人工智能翻译与人工翻译存在着各自的优劣特点和发展空间，在适应人类语言逻辑习惯和理解特点的翻译效果上，人工翻译处于领先地位，但在翻译门槛和经济价值上，人工智能翻译的效率则更胜一筹。总的来说，我们要知道机器翻译与人工翻译是互补而非对立的关系。
 
  
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===关键词===
 
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机器翻译;人工翻译;人工智能
 
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===1. Introduction===
 
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===2. Advantages and Disadvantages of Machine Translation===
 
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===3.The Irreplaceability of Artificial Translation ===
 
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===4. Discussion on the Relationship Between Machine Translation and Artificial Translation ===
 
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===5.  Suggestions on the Combined Development of Machine Translation and Artificial Translation===
 
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===6. ===
 
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===7. ===
 
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===Conclusion===
 
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===References===
 
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=10 熊敏(Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts)=
 
 
[[Machine_Trans_EN_10]]
 
[[Machine_Trans_EN_10]]
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===Abstract===
 
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With the rapid development of information technology,machine translation technology emerged and is gradually becoming mature.In order to explore the ability of machine translation, I adopts two versions of translation, which are manual translation and machine translation(this paper uses Youdao translation) for different types of texts(according to Peter Newmark's types of text). The results are quite different in terms of quality and accuracy.
 
  
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===Key words===
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=Chapter 11 Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts=
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machine translation; manual translation; Newmark's type of texts
 
  
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===题目===
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机器翻译的译前编辑研究——以医学类文摘为例
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Research on the English Chinese Translation Ability of Machine Translation for Various Types of Texts
 
  
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===摘要===
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陈惠妮 Chen Huini, Hunan Normal University
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随着信息技术的高速发展，机器翻译技术出现了，并且逐渐成熟。为了探究机器翻译的能力水平，本人根据纽马克的文本类型分类，选择了相应的译文类型，并且将其机器翻译的版本以及人工翻译的版本进行对比。就质量和准确度而言，译文的水平大相径庭。
 
  
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===关键词===
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[[Machine_Trans_EN_11]]
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机器翻译；人工翻译；纽马克文本类型
 
  
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===1. Introduction===
 
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====1.1.Introduction to machine translation====
 
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Machine translation, also known as computer translation is a technique to translating through machine translator, such as Google translation and Youdao translation. Machine translation is one of the branches of computational linguistics, ranging from computer science, statistics, information science and so on. Machine translation plays an important role in all aspects.
 
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Machine translation can be traced back to 1940s, when British engineer Booth and American engineer Weaver proposed using computer to translate and started to study machines used for translation. However in the 1960s, reports from ALPAC (Automated Language Processing Advisory Committee) showed studies on machine translation had stagnated for a decade. In the 1970s, with the advancement of computer, machine translation was back to track. In the last decades, machine translation has mainly developed into four stages: rule-based machine translation, statistic machine translation, example-based machine translation and neural machine translation.
 
  
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====1.2.Process of machine translation====
 
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The process of manual translation is different from that of machine translation. Here is the process of the former. (1) Understand source language. (2) Use target language to organize language. (3) Generate translation. Unlike manual translation, machine translation tends to analyze and code source language first, then look for related codes in corpus, and work out the code that represents target language, generating translation. But they share a common feature, which is that Lexicon, grammatical rules and syntactic structure are taken into consideration. This is one of the biggest challenges for machine translation.
 
  
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===2. ===
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Written by --[[User:Chen Huini|Chen Huini]] ([[User talk:Chen Huini|talk]]) 04:58, 15 December 2021 (UTC)Chen Huini
  
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===3. ===
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=Chapter 12 The Mistranslation of C-J Machine Translation of Political Statements=
  
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===4.  ===
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机器翻译中政治发言中译日的误译
  
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===5. ===
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蔡珠凤 Cai Zhufeng, Hunan Normal University
  
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===6. ===
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[[Machine_Trans_EN_12]]
  
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===7. ===
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=Chapter 13 Study on Post-editing from the Perspective of Functional Equivalence Theory=
  
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===Conclusion===
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陈湘琼, Hunan Normal University
  
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===References===
 
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=11 陈惠妮=
 
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[[Machine_Trans_EN_11]]
 
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=12 蔡珠凤=
 
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[[Machine_Trans_EN_12]]
 
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=13 陈湘琼Chen Xiangqiong（Study on Post-editing from the Perspective of Functional Equivalence Theory ）=
 
 
[[Machine_Trans_EN_13]]
 
[[Machine_Trans_EN_13]]
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=Chapter 14 Machine Translation a Challenge for Human Translators=
  
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===Abstract===
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Bi bi Nadia, Hunan Normal University
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With the development of technology，machine translation methods are changing. From rule-based methods to corpus-based methods，and then to neural network translation，every time machine translation become more precise, which means it is not impossible the complete  replacement of human translation by machine translation. But machine translation still faces many problems until today such as : fail to translate special terms, incapable to set the right sentence order, unable to understand content and culture background etc. All of these need to be checked out and modified by human translator, so it can be predict that the model ''Human + Machine''  will last for a long period. This article will discuss mistakes made in machine translation and describe what translators should do in post-editing based on the skopos theory and functional equivalence theory
 
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===Key words===
 
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machine translation，post-editing，skopos theory，functional equivalence theory
 
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===题目===
 
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基于功能对等视角探讨译后编辑问题与对策
 
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===摘要===
 
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随着科技的不断发展，机器翻译方法也在不断变革，从基于规则的机器翻译，到基于统计的机器翻译，再到今天基于人工神经网络的机器翻译，每一次变化都让机器翻译变得更精确，更高质。这意味着在不远的将来，机器翻译完全代替人工翻译成为一种可能。但是直至今天，机器翻译仍然面临许多的问题如：无法准确翻译术语、无法正确排列句子语序、无法分辨语境等，这些问题依然需要人工检查和修改。机器翻译自有其优点，人工翻译也有无可替代之处，所以在很长一段时间内，翻译都应该是机器+人工的运作方式。本文将基于翻译目的论和功能对等理论，对机器翻译可能出现的错误之处进行探讨，并且旨在描述译者在进行译后编辑时需要注重的方面，为广大译员提供参考。
 
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===关键词===
 
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机器翻译，译后编辑，翻译目的论，功能对等
 
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===1. Introduction===
 
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===2. Machine Translation Versus Human Translation===
 
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===3. Skopos Theory and Translation Equivalent===
 
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===4. The Relationship between MT and HT ===
 
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===5. Post-editing On Words===
 
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===6. Post-editing On Sentences===
 
  
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===7. Post-editing On Style and Culture Background===
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[[Machine_Trans_EN_14]]
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=Chapter 15 Machine Translation: Advantage or Disadvantage for the Human Translator=
  
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===Conclusion===
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Mariam Touré, Hunan Normal University
  
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===References===
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[[Machine_Trans_EN_15]]

Latest revision as of 12:25, 25 February 2022

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

Overview Page of Machine Translation

30 Chapters（0/30)

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

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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

Machine_Trans_EN_1

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

论机器翻译与人工翻译的领域优势及共同发展

肖毅瑶 Xiao Yiyao, Hunan Normal University, China

Machine_Trans_EN_2

Chapter 3: -Missing-

Machine_Trans_EN_3

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

网易有道机器翻译与人工翻译的译文对比——以经济学人语料为例

王李菲 Wang Lifei, Hunan Normal University

Machine_Trans_EN_4

Chapter 5: Muhammad Saqib Mehran - Problems in translation studies

Machine_Trans_EN_5

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

Machine_Trans_EN_6

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

Machine_Trans_EN_7

一带一路背景下人工智能与翻译人才的培养

颜莉莉 Yan Lili, Hunan Normal University, China

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

论语言智能下的机器翻译——译者的选择与机遇

颜静 Yan Jing, Hunan Normal University, China

Machine_Trans_EN_8

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

人工智能时代下的机器翻译与人工翻译

谢佳芬 Xie Jiafen ,Hunan Normal University, China

Machine_Trans_EN_9

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

机器翻译对各类型文本的英汉翻译能力探究

熊敏, Xiong Min, Hunan Normal University

Machine_Trans_EN_10

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

机器翻译的译前编辑研究——以医学类文摘为例

陈惠妮 Chen Huini, Hunan Normal University

Machine_Trans_EN_11


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

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

机器翻译中政治发言中译日的误译

蔡珠凤 Cai Zhufeng, Hunan Normal University

Machine_Trans_EN_12

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

陈湘琼, Hunan Normal University

Machine_Trans_EN_13

Chapter 14 Machine Translation a Challenge for Human Translators

Bi bi Nadia, Hunan Normal University

Machine_Trans_EN_14

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

Mariam Touré, Hunan Normal University

Machine_Trans_EN_15

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