Machine Trans EN 13 - Revision history

Chen Xiangqiong: /* References */

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References

Chen Xiangqiong: /* References */

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References

Chen Xiangqiong: /* References */

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References

Chen Xiangqiong: /* 6. Post-editing Application */

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6. Post-editing Application

Chen Xiangqiong: /* 5. Some Other Problems */

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5. Some Other Problems

Chen Xiangqiong: /* 4.3 Syntactic Errors */

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4.3 Syntactic Errors

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4.3 Syntactic Errors

Chen Xiangqiong: /* 4.1. Preparation */

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4.1. Preparation

Chen Xiangqiong: /* 4. Post-editing */

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4. Post-editing

Chen Xiangqiong: /* 3. Machine Translation Versus Human Translation */

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3. Machine Translation Versus Human Translation

@@ Line 213: / Line 213: @@
 Howe J. The rise of crowdsourcing[J]. Wired magazine, 2006, 14(6): 1-4.
-Cai Xinjie,Wen Bin.Statistical analysis on types of C-E machine translation errors: A case study on C-E translation of publicity text.蔡欣洁,文炳. 汉译英机器翻译错误类型统计分析——以外宣文本汉译英为例[J]. 浙江理工大学学报(社会科学版), 2021, 46(2): 162-169.
+Cai Xinjie,Wen Bin.蔡欣洁,文炳. 汉译英机器翻译错误类型统计分析——以外宣文本汉译英为例[J].Statistical analysis on types of C-E machine translation errors: A case study on C-E translation of publicity text. 浙江理工大学学报(社会科学版)Journal of Zhejiang Sci-tech University (Social Science Edition), 2021, 46(2): 162-169.
-Guo Jianzhong.Contemporary American Translation Theory郭建中. 当代美国翻译理论[M]. 湖北教育出版社, 2000.
+Guo Jianzhong.郭建中. 当代美国翻译理论[M]Contemporary American Translation Theory. 湖北教育出版社Hubei Education Press, 2000.
-Hou Qiang, Hou Ruili. Review of Studied and Developments on Machine Translation Methodology.侯强, 侯瑞丽. 机器翻译方法研究与发展综述 2019年3月12日[J]. 计算机工程与应用, 2019.
+Hou Qiang, Hou Ruili.侯强, 侯瑞丽. 机器翻译方法研究与发展综述 2019年3月12日[J] Review of Studied and Developments on Machine Translation Methodology. 计算机工程与应用Computer Engineering and Applications, 2019.
-Li ShiQi.Application of "MT+PE" Model in Legal Translation Based on A Study on The Cooperation Agreement Translation Progect.李诗琪. "机器翻译+译后编辑"模式在法律翻译中的应用[D]. 上海外国语大学.
+Li ShiQi.李诗琪. "机器翻译+译后编辑"模式在法律翻译中的应用[D].Application of "MT+PE" Model in Legal Translation Based on A Study on The Cooperation Agreement Translation Progect. 上海外国语大学Shanghai International Studies University.
-Luo Jimei,Li Mei.An Analysis of Machine Traslation Errors.罗季美, 李梅. 机器翻译译文错误分析[J]. 中国翻译, 2012, 33(5):6.
+Luo Jimei,Li Mei.罗季美, 李梅. 机器翻译译文错误分析[J].An Analysis of Machine Traslation Errors. 中国翻译Chinese Translators Journal, 2012, 33(5):6.
-Tang Yefan.A Feasibility Analysis of MT+PE in Translation of Different Types of Documents -A Case Study Based on A Lift Assembly Manual and A Video Script Translation Projects.唐叶凡. 机器翻译+译后编辑在不同类型文本中的适用性分析[D]. 上海外国语大学.
+Tang Yefan.唐叶凡. 机器翻译+译后编辑在不同类型文本中的适用性分析[D]. A Feasibility Analysis of MT+PE in Translation of Different Types of Documents -A Case Study Based on A Lift Assembly Manual and A Video Script Translation Projects.上海外国语大学Shanghai International Studies University.
-Wang Huashu,Wang Xin.Translation Technology Research in the Era of Artificial Intelligence: Existing Problems and Prospects of Application Scenarios王华树, 王鑫. 人工智能时代的翻译技术研究:应用场景,现存问题与趋势展望[J]. 外国语文, 2021, 37(1):9.
+Wang Huashu,Wang Xin.王华树, 王鑫. 人工智能时代的翻译技术研究:应用场景,现存问题与趋势展望[J].Translation Technology Research in the Era of Artificial Intelligence: Existing Problems and Prospects of Application Scenarios 外国语文,Foreign Languages 2021, 37(1):9.
-Zhao Tao.Current Situation and Problems of Post-translation Editing in Machine Translation赵涛. 机器翻译译后编辑的现状与问题[J]. 外语教学, 2021, 42(4):5.
+Zhao Tao.赵涛. 机器翻译译后编辑的现状与问题[J].Current Situation and Problems of Post-translation Editing in Machine Translation 外语教学foreign language teaching, 2021, 42(4):5.
-Zhou Bin, Rao Pin.Example-based Machine Translation Evaluation and Post-translation Editing Correction Mode周斌, 饶萍. 基于实例的机器翻译评测及译后编辑修正模式[J]. 浙江理工大学学报：社会科学版, 2020, 44(3):9.
+Zhou Bin, Rao Pin.周斌, 饶萍. 基于实例的机器翻译评测及译后编辑修正模式[J].Example-based Machine Translation Evaluation and Post-translation Editing Correction Mode 浙江理工大学学报：社会科学版Journal of Zhejiang Sci-tech University (Social Science Edition), 2020, 44(3):9.
-Feng Zhiwei.Parallel Development of Machine Translation and Artificial Intelligence.冯志伟. 机器翻译与人工智能的平行发展[J]. 外国语, 2018, 41(6):14.
+Feng Zhiwei.冯志伟. 机器翻译与人工智能的平行发展[J].Parallel Development of Machine Translation and Artificial Intelligence. 外国语Foreign Languages, 2018, 41(6):14.

@@ Line 223: / Line 223: @@
 Luo Jimei,Li Mei.An Analysis of Machine Traslation Errors.罗季美, 李梅. 机器翻译译文错误分析[J]. 中国翻译, 2012, 33(5):6.
-Tang Yefan.唐叶凡. 机器翻译+译后编辑在不同类型文本中的适用性分析[D]. 上海外国语大学.
+Tang Yefan.A Feasibility Analysis of MT+PE in Translation of Different Types of Documents -A Case Study Based on A Lift Assembly Manual and A Video Script Translation Projects.唐叶凡. 机器翻译+译后编辑在不同类型文本中的适用性分析[D]. 上海外国语大学.
-王华树, 王鑫. 人工智能时代的翻译技术研究:应用场景,现存问题与趋势展望[J]. 外国语文, 2021, 37(1):9.
+Wang Huashu,Wang Xin.Translation Technology Research in the Era of Artificial Intelligence: Existing Problems and Prospects of Application Scenarios王华树, 王鑫. 人工智能时代的翻译技术研究:应用场景,现存问题与趋势展望[J]. 外国语文, 2021, 37(1):9.
-赵涛. 机器翻译译后编辑的现状与问题[J]. 外语教学, 2021, 42(4):5.
+Zhao Tao.Current Situation and Problems of Post-translation Editing in Machine Translation赵涛. 机器翻译译后编辑的现状与问题[J]. 外语教学, 2021, 42(4):5.
-周斌, 饶萍. 基于实例的机器翻译评测及译后编辑修正模式[J]. 浙江理工大学学报：社会科学版, 2020, 44(3):9.
+Zhou Bin, Rao Pin.Example-based Machine Translation Evaluation and Post-translation Editing Correction Mode周斌, 饶萍. 基于实例的机器翻译评测及译后编辑修正模式[J]. 浙江理工大学学报：社会科学版, 2020, 44(3):9.
-冯志伟. 机器翻译与人工智能的平行发展[J]. 外国语, 2018, 41(6):14.
+Feng Zhiwei.Parallel Development of Machine Translation and Artificial Intelligence.冯志伟. 机器翻译与人工智能的平行发展[J]. 外国语, 2018, 41(6):14.

@@ Line 213: / Line 213: @@
 Howe J. The rise of crowdsourcing[J]. Wired magazine, 2006, 14(6): 1-4.
-蔡欣洁,文炳. 汉译英机器翻译错误类型统计分析——以外宣文本汉译英为例[J]. 浙江理工大学学报(社会科学版), 2021, 46(2): 162-169.
+Cai Xinjie,Wen Bin.Statistical analysis on types of C-E machine translation errors: A case study on C-E translation of publicity text.蔡欣洁,文炳. 汉译英机器翻译错误类型统计分析——以外宣文本汉译英为例[J]. 浙江理工大学学报(社会科学版), 2021, 46(2): 162-169.
-郭建中. 当代美国翻译理论[M]. 湖北教育出版社, 2000.
+Guo Jianzhong.Contemporary American Translation Theory郭建中. 当代美国翻译理论[M]. 湖北教育出版社, 2000.
-侯强, 侯瑞丽. 机器翻译方法研究与发展综述 2019年3月12日[J]. 计算机工程与应用, 2019.
+Hou Qiang, Hou Ruili. Review of Studied and Developments on Machine Translation Methodology.侯强, 侯瑞丽. 机器翻译方法研究与发展综述 2019年3月12日[J]. 计算机工程与应用, 2019.
-李诗琪. "机器翻译+译后编辑"模式在法律翻译中的应用[D]. 上海外国语大学.
+Li ShiQi.Application of "MT+PE" Model in Legal Translation Based on A Study on The Cooperation Agreement Translation Progect.李诗琪. "机器翻译+译后编辑"模式在法律翻译中的应用[D]. 上海外国语大学.
-罗季美, 李梅. 机器翻译译文错误分析[J]. 中国翻译, 2012, 33(5):6.
+Luo Jimei,Li Mei.An Analysis of Machine Traslation Errors.罗季美, 李梅. 机器翻译译文错误分析[J]. 中国翻译, 2012, 33(5):6.
-唐叶凡. 机器翻译+译后编辑在不同类型文本中的适用性分析[D]. 上海外国语大学.
+Tang Yefan.唐叶凡. 机器翻译+译后编辑在不同类型文本中的适用性分析[D]. 上海外国语大学.
 王华树, 王鑫. 人工智能时代的翻译技术研究:应用场景,现存问题与趋势展望[J]. 外国语文, 2021, 37(1):9.
@@ Line 230: / Line 230: @@
 周斌, 饶萍. 基于实例的机器翻译评测及译后编辑修正模式[J]. 浙江理工大学学报：社会科学版, 2020, 44(3):9.

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 In this part, we will find the application of post-editing in a business situation. People can see that the application of post-editing is going far away from what we can imagine and there is always a mature and dynamic mode around it because the user is trying to make money from it. Now, let's find out something about this new and popular business model.
-To begin with, we want to introduce the concept of" crowdsourcing". Crowdsourcing means that companies entail using many disparate individuals to perform services or to generate ideas or content. (Jeff Howe, 2006) And based on this concept, a new concept rises—Online Collaborative Translation, which is a new kind of translation mode covering most fields and developing rapidly. In recent years, with the cooperation of deep learning, mass data, and high-performance computing, AI has great advancements. The quality of translation is rising because neural machine translation is becoming technology mainstream. The online collaborative translation model will not only be restricted in a human-human relationship, but human-machine, machine-machine becoming possible. (Shao Lu, 2014)
+To begin with, we want to introduce the concept of" crowdsourcing". Crowdsourcing means that companies entail using many disparate individuals to perform services or to generate ideas or content. (Jeff Howe, 2006:4) And based on this concept, a new concept rises—Online Collaborative Translation, which is a new kind of translation mode covering most fields and developing rapidly. In recent years, with the cooperation of deep learning, mass data, and high-performance computing, AI has great advancements. The quality of translation is rising because neural machine translation is becoming technology mainstream. The online collaborative translation model will not only be restricted in a human-human relationship, but human-machine, machine-machine becoming possible.
 What is the process of online collaborative translation? There are 5 steps and every step aims to solve one or more problems: 1. Term marking 2. Term editing 3. Post-editing 4. Checking 5. The item is up a shelf. The first, third, and fourth steps are separately taken control of one person, while the second step needs to be done by all the translators. The purpose of the first step is to mark some special terms and categorize them so that the post-editor can easily recognize those special terms. The second step aims to check and edit terms that can be reused in other articles with the same style or topic. The third step aims to erect grammar errors and semantic errors that make the article influential and readable. The last step will need to be done by professional and skilled translators that problems not so obvious can be found. Then we will explain this mode with a real practice example. However, what most surprised us is that this kind of mode is mostly applied in the translation of online novels. But it doesn't mean that our finding of post-editing is wrong because the online novels are so special that only their plots are important to readers, and expression, text, and style are not so important, because they are only serving for plots, which exactly follow the principle post-editing obey.

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 ===5. Some Other Problems===
-Since we discussed machine translation, post-editing, and their efficiency, some researchers may long have a question: "Is machine translation post-editing worth the effort?" There are so many things that have to be done before and during post-editing, and why not just pick a text and translate it? Some researchers have done a study about this question. Maarit Koponen in his article surveyed post-editing and effort from point views of productivity, quality, monolingual post-editing(Maarit,). For productivity, he argues that the survey can demonstrate a higher rate of productivity when the translators are doing post-editing. For quality, studies show the post-editing texts with even higher quality than manually translated texts. In the condition that readers can't contact the original language, the correct rate of sentences will be below. As for effort, all the aspects above are only some parts of the work that can not easily take a conclusion. And when the researchers try to interview some translators about their feeling, it can be subjective. Everybody has his standard to evaluate his effort in doing post-editing. To solve this problem, researchers use new technologies and methods such as Eye-tracking data, Computerized metrics, translation editing rate. All in all, Maarit answered the question" Is machine translation post-editing worth the effort?" "Yes!" Though there are so many things needed to be explored like "What is the real standard to evaluate post-editing efficiency", "Can machine translation be used in the wider domain, especially those proved can't be translated by machine?" or "Can post-editing finally be done by machine and human finally give way to the AI" Now, the fact we can completely say yes is that post-editing is a new and charming field for more translator to join in.
+Since we discussed machine translation, post-editing, and their efficiency, some researchers may long have a question: "Is machine translation post-editing worth the effort?" There are so many things that have to be done before and during post-editing, and why not just pick a text and translate it? Some researchers have done a study about this question. Maarit Koponen in his article surveyed post-editing and effort from point views of productivity, quality, monolingual post-editing. For productivity, he argues that the survey can demonstrate a higher rate of productivity when the translators are doing post-editing. For quality, studies show the post-editing texts with even higher quality than manually translated texts. In the condition that readers can't contact the original language, the correct rate of sentences will be below. As for effort, all the aspects above are only some parts of the work that can not easily take a conclusion. And when the researchers try to interview some translators about their feeling, it can be subjective. Everybody has his standard to evaluate his effort in doing post-editing. To solve this problem, researchers use new technologies and methods such as Eye-tracking data, Computerized metrics, translation editing rate. All in all, Maarit answered the question" Is machine translation post-editing worth the effort?" "Yes!" Though there are so many things needed to be explored like "What is the real standard to evaluate post-editing efficiency", "Can machine translation be used in the wider domain, especially those proved can't be translated by machine?" or "Can post-editing finally be done by machine and human finally give way to the AI" Now, the fact we can completely say yes is that post-editing is a new and charming field for more translator to join in.
 From Maarit's study, we can also advise translators wanting to join in this new world. Post-editing is worth doing only when translators can use computer software with the complexity of using steps. So, it is necessary to take the pre-education and learn and practice the computer tools, which is not so easy for those who don't have a computer or are not familiar with the computer. It is a relatively narrow space for some people. Then, because the original text is heavily influencing the result of post-editing, translators can't just post-edit based on the machine translation raw material, which has its high requirement to translator's reading comprehension ability and logical thinking ability. They must quickly scan the raw article or use their ears to catch information from the original resource of the text.

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 ===4.3 Syntactic Errors===
-Newmark believes that in syntactical level, sense, signifier, coherent, and natural express response for translation. And the original language and target language should be equivalent in function. (Newmark, 1988:307). Based on this theory, Cai Xinjie found three syntactical errors in machine translation. The first syntactical error is logical confusion. For example:
+Newmark believes that in syntactical level, sense, signifier, coherent, and natural express response for translation. And the original language and target language should be equivalent in function. (Newmark, 1988:56). Based on this theory, Cai Xinjie found three syntactical errors in machine translation. The first syntactical error is logical confusion. For example:
 (3) Create abundant and humanistic urban space

@@ Line 116: / Line 116: @@
 ===4.3 Syntactic Errors===
-Newmark believes that in syntactical level, sense, signifier, coherent, and natural express response for translation. And the original language and target language should be equivalent in function. (Newmark, 1988). Based on this theory, Cai Xinjie found three syntactical errors in machine translation. The first syntactical error is logical confusion. For example:
+Newmark believes that in syntactical level, sense, signifier, coherent, and natural express response for translation. And the original language and target language should be equivalent in function. (Newmark, 1988:307). Based on this theory, Cai Xinjie found three syntactical errors in machine translation. The first syntactical error is logical confusion. For example:
 (3) Create abundant and humanistic urban space
@@ Line 134: / Line 134: @@
 B. 水鼠把缆绳系在码头的缆桩上.
-Then we can find from this sentence that the machine can't even give an understandable sentence without the context. That can be a very tough situation for translators because they can't even do some minor changes according to the original machine-translated text. So two strategies are considered useful for this situation. To avoid the chaos made by an unrecognizable context, the translator should firstly select the right situation that machine translation can be applied. According to Tang Yefan, machine translation and post-editing can be suitable for technical text which has features of professionality, literary meaning, similar sentence model, and simple purpose. (Tang Yefan, 2018) Li Shiqi also said machine translation is efficient for mechanic text which has stable writing type and expressive methods. (Li Shiqi, 2018)But she also declares that machine translation can help her translate the text with a lot of terms which is a litter bit in contrast to what we found, so that can be a new problem we dig deeper.
+Then we can find from this sentence that the machine can't even give an understandable sentence without the context. That can be a very tough situation for translators because they can't even do some minor changes according to the original machine-translated text. So two strategies are considered useful for this situation. To avoid the chaos made by an unrecognizable context, the translator should firstly select the right situation that machine translation can be applied. According to Tang Yefan, machine translation and post-editing can be suitable for technical text which has features of professionality, literary meaning, similar sentence model, and simple purpose. (Tang Yefan, 2018:32) Li Shiqi also said machine translation is efficient for mechanic text which has stable writing type and expressive methods. (Li Shiqi, 2018:24)But she also declares that machine translation can help her translate the text with a lot of terms which is a litter bit in contrast to what we found, so that can be a new problem we dig deeper.
 Another strategy is improving the education of translators themselves. We already certified that no matter how machine translation is being developed, it still can't leave the edition of humans. It is the human who translates and post-edit the text that decides the quality of translation. Errors will be made by machines, and human's job is to realize, find and erect those errors. That is why translators should be sensitive to different error types. Moreover, the translator has to know the purpose of the translation. If the reader or hearer wants information, then the translator gives information. If the participant requires to exchange culture and reach a common view, it is also the translator's responsibility.

← Older revision		Revision as of 12:45, 13 December 2021
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−	We have to admit that the translation quality of machine translation in the general text has become considerably high and is very close to human translation satisfying the information acquisition requirement of readers. (Zhou Bin, Rao Ping, 2020). So, it is more senseful to discuss the text type that should be post-edited. TAUS also gave four situations of professional machine translation: 1. A project without abundant time 2. Project with no need for high quality 3. The first version of machine translation with the need for human post-editing 4. The project as a method to test errors.	+	We have to admit that the translation quality of machine translation in the general text has become considerably high and is very close to human translation satisfying the information acquisition requirement of readers. (Zhou Bin, Rao Ping, 2020:9). So, it is more senseful to discuss the text type that should be post-edited. TAUS also gave four situations of professional machine translation: 1. A project without abundant time 2. Project with no need for high quality 3. The first version of machine translation with the need for human post-editing 4. The project as a method to test errors.

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 ===4. Post-editing===
-If we try to understand post-editing literally, it can be described as “ “the correction of MT output by human translator”(Senez, 1998) or “translator use the machine translation products as the raw material to further editing and control the quality that they can satisfy special client.”[1](Zhao Tao, 2021)
+If we try to understand post-editing literally, it can be described as “ “the correction of MT output by human translator”(Senez, 1998:20) or “translator use the machine translation products as the raw material to further editing and control the quality that they can satisfy special client.”[1](Zhao Tao, 2021:100)
 Generally speaking, post-editing can be divided into two types:light post-editing and full post-editing according to the level of human intervention. The former aims to produce the translation that can be almost understood and the later wants to give the production as good as human translation. But this standard is quite ambiguous. Translation Automation User Society also gave a discrimination that publication quality post-editing mainly needed in some high quality required situation, but keynote translation with high speed is more suitable for normal occasions. Despite the slight different between these two categorizations, the principle to categorize post-editing is identical: purpose.

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 ===3. Machine Translation Versus Human Translation===
-The dream that natural language can be translated by machine come true in the late twentieth. Though not completely perfect, machine translation still fulfil the requirement of translation in technical manuals, scientific documents, commercial prospectuses, administrative memoranda and medical reports.(W.John Hutchins, 1995)
+The dream that natural language can be translated by machine come true in the late twentieth. Though not completely perfect, machine translation still fulfil the requirement of translation in technical manuals, scientific documents, commercial prospectuses, administrative memoranda and medical reports.(W.John Hutchins, 1995:431)
-Researchers divide traditional machine translation method into three categories:Rule-Based, Corpus-Based and Hybrid methods, and all of them have their own merits and demerits. The first one builds the translation knowledge base on dictionaries and grammar rules, but it is not so practical for languages without much correlation and highly rely on human experience. The second one builds the translation knowledge by making full use of the corpus, which is still the mainstream of today’s machine translation. The last one mix both of rule and corpus and successfully raise the efficiency of translation, but it is tough to be managed because of complex system and weak extend ability. (Hou Qiang, 2019)
+Researchers divide traditional machine translation method into three categories:Rule-Based, Corpus-Based and Hybrid methods, and all of them have their own merits and demerits. The first one builds the translation knowledge base on dictionaries and grammar rules, but it is not so practical for languages without much correlation and highly rely on human experience. The second one builds the translation knowledge by making full use of the corpus, which is still the mainstream of today’s machine translation. The last one mix both of rule and corpus and successfully raise the efficiency of translation, but it is tough to be managed because of complex system and weak extend ability. (Hou Qiang, 2019:30)
-According to Martin Woesle, the advantages and disadvantages of machine translation can be obvious. For advantages, machine translation has its speed and availability, low costs, efficiency and welcome of cooperation. However, it can not satisfy some special situation such as: noisy background, ill connectivity, short of electricity, corpus limitation and cultural sensitivity. (Martin Woesle, 2021)
+According to Martin Woesle, the advantages and disadvantages of machine translation can be obvious. For advantages, machine translation has its speed and availability, low costs, efficiency and welcome of cooperation. However, it can not satisfy some special situation such as: noisy background, ill connectivity, short of electricity, corpus limitation and cultural sensitivity. (Martin Woesle, 2021:203)
 ===4. Post-editing===