Machine Trans EN 4 - Revision history

Wang Lifei: /* 4.1.1 Network Integration will Deepen */

2021-12-14T03:07:29Z

4.1.1 Network Integration will Deepen

Wang Lifei: /* References */

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References

Wang Lifei: /* 5. Conclusion */

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5. Conclusion

Wang Lifei: /* 4.2.1 Holding an Appropriate Attitude */

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4.2.1 Holding an Appropriate Attitude

Wang Lifei: /* 4.1.3 Training Methods will be more Varied */

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4.1.3 Training Methods will be more Varied

Wang Lifei: /* 3.2 Standardization—the perfection of logicality */

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3.2 Standardization—the perfection of logicality

Wang Lifei: /* 3.Comparative Analysis of Errors in English-Chinese Translation */

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3.Comparative Analysis of Errors in English-Chinese Translation

Wang Lifei: /* 2. The Development Process of Machine Translation */

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2. The Development Process of Machine Translation

Wang Lifei: /* 1. Introduction */

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1. Introduction

Wang Lifei: /* References */

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References

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 =4.1.1 Network Integration will Deepen=
-In the modern society, data types and modes are diverse. It is difficult to deal with massive complicated data in the future only by the text-oriented circular neural network which based on attention currently. Different networks have different advantages. For example, cyclic neural network is good at processing time series data, convolutional neural network is suitable for processing high latitude data, and recursive neural network is effective in processing tree and graph structure data（侯强 2019:33）. Therefore, how to use parallel or serial methods to optimize the combination of different networks, and develop a multi-mode machine translation model suitable for processing heterogeneous data, so that it can be used in real scenes, and even achieve the degree of intelligence, is a subject to be deepened.
+In the modern society, data types and modes are diverse. It is difficult to deal with massive complicated data in the future only by the text-oriented circular neural network which based on attention currently. Different networks have different advantages. For example, cyclic neural network is good at processing time series data, convolutional neural network is suitable for processing high latitude data, and recursive neural network is effective in processing tree and graph structure data. Therefore, how to use parallel or serial methods to optimize the combination of different networks, and develop a multi-mode machine translation model suitable for processing heterogeneous data, so that it can be used in real scenes, and even achieve the degree of intelligence, is a subject to be deepened.（Hou, 2019:33）
 =4.1.2 Processing Moves toward Parallelism=

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	Liu Yang(2017) 刘洋.神经机器翻译前沿进展 [J]Recent Advances in Neural Machine Translation.计算机研究与发展 Journal of Computer Research and Development, 6: 1144-1149.		Liu Yang(2017) 刘洋.神经机器翻译前沿进展 [J]Recent Advances in Neural Machine Translation.计算机研究与发展 Journal of Computer Research and Development, 6: 1144-1149.
		+
		+	Luo Jimei, Li Mei(2012)罗季美,李梅.机器翻译译文错误分析[J]Error Analysis of Machine Translation.中国翻译 Chinese Translation Journal,5:84-89.

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 Through the above dialectical analysis of some cases in the translation process, it is easy to find that the readability of the version of manual translation is still higher than that of neural machine translation referred to the translation criterion proposed by Liu Zhongde. Though machine translation has an unsurmountable superiority in speed because it can translate a sea of texts in a moment, distinguishing the nuance between different words which may all seem suitable to the original text, and selecting the most appropriate sentence patterns to perfect the logic are the core competitiveness of human translators, so they will not be replaced for a long time, especially in some specific fields which require professional knowledge and life experience.
-However, the advancements of machine translation are obvious to all, the quality of translation texts has been improving. With the development and application of new technology, it has always been an important research topic in the field of artificial intelligence and more and more resources have been poured into this field. After the ups and downs of the past 70 years, it has entered a period of rapid development. How to improve the existing neural machine translation model and build a new model with more accurate translation, so as to make a qualitative leap in the overall translation quality is an urgent task at present. It is believed that with the further development of artificial intelligence, machine translation will grow stronger and play a more important role in overcoming natural language barriers and promoting transnational communication in the future.（李亚超，2018：2750）
+However, the advancements of machine translation are obvious to all, the quality of translation texts has been improving. With the development and application of new technology, it has always been an important research topic in the field of artificial intelligence and more and more resources have been poured into this field. After the ups and downs of the past 70 years, it has entered a period of rapid development. How to improve the existing neural machine translation model and build a new model with more accurate translation, so as to make a qualitative leap in the overall translation quality is an urgent task at present. It is believed that with the further development of artificial intelligence, machine translation will grow stronger and play a more important role in overcoming natural language barriers and promoting transnational communication in the future.（Li，2018：2750）
 ===References===

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 =4.2.1 Holding an Appropriate Attitude=
-Machine translation has been developing for 54 years since its birth, and it has been able to develop independently as a complete artificial intelligence technology. It has now become a discipline and technology comparable to human translation. Therefore, whatever concerns language learning may have about the development of machine translation, it is inexorable the normal development of machine translation and should face it calmly with a proper attitude. As language workers, when machine translation has a certain impact on the employment of the industry, they should constantly "recharge" themselves, improve their ability, and actively treat their translation career.（胡开宝，2016：13）
+Machine translation has been developing for 54 years since its birth, and it has been able to develop independently as a complete artificial intelligence technology. It has now become a discipline and technology comparable to human translation. Therefore, whatever concerns language learning may have about the development of machine translation, it is inexorable the normal development of machine translation and should face it calmly with a proper attitude. As language workers, when machine translation has a certain impact on the employment of the industry, they should constantly "recharge" themselves, improve their ability, and actively treat their translation career.（Hu，2016：13）
 =4.2.2 Cooperating with Machine Translation=

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 =4.1.3 Training Methods will be more Varied=
-Classical neural machine translation model training is highly dependent on bilingual parallel corpora. However, except for some vertical fields in high-resource languages such as Chinese and English, most languages in the world lack large-scale, high-quality and extensive parallel corpora, (刘洋，2017：54）especially low-resource languages. Therefore, how to use reinforcement learning,（Nguyen K，2017：1469） transfer training, dual learning, joint training, confrontation learning, and other training methods to alleviate the lack of corpus resources is also one of the key exploration objects in the future. Training methods have a great influence on the performance of the model, and the training methods vary over time, if all of them can take advantage from each other and avoid the disadvantages, it is expected to further improve the overall performance of the model.
+Classical neural machine translation model training is highly dependent on bilingual parallel corpora. However, except for some vertical fields in high-resource languages such as Chinese and English, most languages in the world lack large-scale, high-quality and extensive parallel corpora, (Liu，2017：54）especially low-resource languages. Therefore, how to use reinforcement learning,（Nguyen K，2017：1469） transfer training, dual learning, joint training, confrontation learning, and other training methods to alleviate the lack of corpus resources is also one of the key exploration objects in the future. Training methods have a great influence on the performance of the model, and the training methods vary over time, if all of them can take advantage from each other and avoid the disadvantages, it is expected to further improve the overall performance of the model.
 ==4.2 Human Translator==

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 Except for vocabulary, some distinctions also exist in the syntax of Chinese and English. The expression used by English speakers and Chinese speakers is quite different although they are delivering the same attitude towards the same thing.
-For example, when they try to write down a passage, English speakers tend to choose more long sentences which are joined together with various tense, conjunctions, and clauses so that they are more logical and coherent. On the contrary, Chinese speakers prefer to use a series of short sentences, the structure of which is usually loose and conjunctions are rarely used. To sum up, English is a hypotactic language, and all the sentences constitute the grape-like structure; Chinese is a paratactic language and mostly a bamboo-like structure.（方梦之，2004）
+For example, when they try to write down a passage, English speakers tend to choose more long sentences which are joined together with various tense, conjunctions, and clauses so that they are more logical and coherent. On the contrary, Chinese speakers prefer to use a series of short sentences, the structure of which is usually loose and conjunctions are rarely used. To sum up, English is a hypotactic language, and all the sentences constitute the grape-like structure; Chinese is a paratactic language and mostly a bamboo-like structure.（Fang，2004）
 Such kind of contrast exists universally in business translation. If the translator fails to notice the syntactic characteristic and logicality of the sentence, the translation will become broken and divert from the original meaning.

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 ===3.Comparative Analysis of Errors in English-Chinese Translation===
-Based on the introduction above, neural machine translation has been widely used in the business field. However, because of the professional characteristics, there are still numerous human translators devoted themselves to business translation, especially in some important international meetings. In order to be faithful to the style of the original text, when translating, it is necessary to use some conventional terms, professional jargon, and fixed sentence patterns, striving to be accurate and standardized, and achieving the authenticity of the style of writing. These kinds of characteristics make the neural machine translation and human translator highly comparable. Guided by the translation criterion— “faithfulness, expressiveness, and closeness” put forward by the prominent translator Liu Zhongde, （方梦之，2004）the detailed comparative analysis of the same texts translated by Netease neural machine translation and human translator respectively will be presented to show their own strengthens and weaknesses.
+Based on the introduction above, neural machine translation has been widely used in the business field. However, because of the professional characteristics, there are still numerous human translators devoted themselves to business translation, especially in some important international meetings. In order to be faithful to the style of the original text, when translating, it is necessary to use some conventional terms, professional jargon, and fixed sentence patterns, striving to be accurate and standardized, and achieving the authenticity of the style of writing. These kinds of characteristics make the neural machine translation and human translator highly comparable. Guided by the translation criterion— “faithfulness, expressiveness, and closeness” put forward by the prominent translator Liu Zhongde, the detailed comparative analysis of the same texts translated by Netease neural machine translation and human translator respectively will be presented to show their own strengthens and weaknesses. （Fang，2004）
 ==3.1 Precision—the selection of vocabulary==

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 ===2. The Development Process of Machine Translation ===
-Since the IBM model was put forward by the researcher Peter Brown in the early 1990s, statistical methods have gradually become the mainstream of machine translation research（杜金华,2013:1）. This method has extensively promoted the development of machine translation technology.  After a variety of statistical machine translation models have emerged, such as the phrase-based translation model, hierarchical phrase translation model, and syntactic translation model, the translation quality has been dramatically improved.
+Since the IBM model was put forward by the researcher Peter Brown in the early 1990s, statistical methods have gradually become the mainstream of machine translation research. This method has extensively promoted the development of machine translation technology.  After a variety of statistical machine translation models have emerged, such as the phrase-based translation model, hierarchical phrase translation model, and syntactic translation model, the translation quality has been dramatically improved.（Du,2013:1）
 Since 2002, with the technical maturity and stability of statistical machine translation, especially phrase-based machine translation, statistical machine translation technology has been making solid strides towards practical and commercial application. Therefore, with the rapid development of technology, people have gradually built-up confidence in machine translation, and the social demand for machine translation has been increasing day by day, with higher and higher expectations.
-However, from the perspective of academic research, both phrase-based translation models and syntactic translation models have experienced a rapid development stage, and the existing theoretical methods and technical models have begun to show "bottlenecks" in the improvement of translation performance. (罗季美，2012：84） In addition, from the perspective of industrialization and utilitarianism, there is an urgent need for a more practical machine translation system, but the gap between the results of machine translation and the requirements of human beings is still very large. Therefore, for the researcher, while excited to see the score of machine translation system evaluation is getting higher and higher, and the performance of online machine translation systems developed by Google, Baidu, Netease, and other enterprises is developing with each passing day, they are facing more and more challenges.
+However, from the perspective of academic research, both phrase-based translation models and syntactic translation models have experienced a rapid development stage, and the existing theoretical methods and technical models have begun to show "bottlenecks" in the improvement of translation performance. In addition, from the perspective of industrialization and utilitarianism, there is an urgent need for a more practical machine translation system, but the gap between the results of machine translation and the requirements of human beings is still very large. Therefore, for the researcher, while excited to see the score of machine translation system evaluation is getting higher and higher, and the performance of online machine translation systems developed by Google, Baidu, Netease, and other enterprises is developing with each passing day, they are facing more and more challenges. (Luo，2012：84）
-Aiming to solve these problems, many technological giants are striving to find a new way to improve both the quality and efficiency of machine translation. There was a breakthrough which bought machine translation to a new level. Since 2014, the sequence-to-sequence neural machine translation has developed rapidly, compared with the statistical machine translation, the translation quality received a significant boost（李亚超，2018:2734）.
+Aiming to solve these problems, many technological giants are striving to find a new way to improve both the quality and efficiency of machine translation. There was a breakthrough which bought machine translation to a new level. Since 2014, the sequence-to-sequence neural machine translation has developed rapidly, compared with the statistical machine translation, the translation quality received a significant boost（Li，2018:2734）.
 The previous statistical machine translation was more like a mechanical system. Each module has its own function and goal and then outputs the translation results through mechanical splicing. Its primary disadvantage is that the model contains low syntactic and semantic components, so it will encounter problems when dealing with languages with significant syntactic differences, such as Chinese-English. Sometimes the result is unreadable even though it is “word-for-word”.
-On the contrary, neural machine translation is consisted of several components, including phrase conditions, partial conditions, sequential conditions, primitive models, and so on. Its core is deep learning of artificial intelligence which can imitate the working mechanism of human brain and adopt a unique neural network structure to model the whole process of translation. The entire model is composed of a large number of “neurons,” and each “neuron” has to complete some simple tasks, and then through the combination of all of them to coordinate the work, a much better translation text appears. （李亚超，2018：2735）
+On the contrary, neural machine translation is consisted of several components, including phrase conditions, partial conditions, sequential conditions, primitive models, and so on. Its core is deep learning of artificial intelligence which can imitate the working mechanism of human brain and adopt a unique neural network structure to model the whole process of translation. The entire model is composed of a large number of “neurons,” and each “neuron” has to complete some simple tasks, and then through the combination of all of them to coordinate the work, a much better translation text appears. （Li，2018：2735）
 Since neural machine translation puts more emphasis on context and the whole text, it produces more coherent and comprehensible content to readers, and be widely accepted and used in various fields in a concise time. In 2017, at the GMIC (Global Mobile Internet Congress), Duan Yitao, the chief scientist of Netease, delivered a keynote speech titled “Machine Translation has Its Own Way” and announced a piece of exciting news: the neural machine translation technology independently developed by Netease has been officially launched. This technology was launched by Youdao (a subsidiary brand of Netease) this time has been jointly developed by it and Netease Hangzhou Research Institute for over two years. It will serve Youdao Dictionary, Youdao Translator, Youdao Web version, Youdao E-reader, and other applications, expecting to bring super-convenient product experience to users. In addition, the Youdao Translation officer also launched photo translation, by which users only need to take pictures of the text, then the results of neural network translation will be shown in real-time. As a pioneer of machine translation in China, the development process of Netease YouDao is precisely a paradigm of the history of machine translation in China. Therefore, in this paper, the neural machine translation technology developed by Netease will be compared with human translators. The same excerpts selected from ''The Economist'' are translated by both of them, then the different versions will be analyzed by the translation criterion so as to figure out their respective strengths and weaknesses, bringing considerations to the current translation situation and references to future development.

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 ===1. Introduction ===
-Nowadays, economic globalization has accelerated overwhelmingly, and considerable resources are poured into the business field. As a branch of global language English, business English is proposed under the theoretical framework of English for Specific Purpose (ESP). It serves international business activities, which is a professional subject requiring specialized English. (Jordan, 1997) As the medium that helps people with different cultural backgrounds to understand each other, business translation is required to be “formal, accurate, standardized and smooth”, which challenges both the machine translation and human translator.
+Nowadays, economic globalization has accelerated overwhelmingly, and considerable resources are poured into the business field. As a branch of global language English, business English is proposed under the theoretical framework of English for Specific Purpose (ESP). It serves international business activities, which is a professional subject requiring specialized English. As the medium that helps people with different cultural backgrounds to understand each other, business translation is required to be “formal, accurate, standardized and smooth”, which challenges both the machine translation and human translator. (Jordan, 1997)
 With the urgent requirement for more precise and higher quality translation, recent years have witnessed the rapid development of neural machine translation (NMT), which has replaced traditional statistical machine translation (SMT) to become a new mainstream technique, playing a crucial part in many fields, like business, academic, and industry. Compared with SMT, the NMT model is more like an organism. The model has many parameters that can be adjusted and optimized for the same goal, making the combination and interaction more organic and the overall translation effect better, which fantastically matched the demands of business translation.

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 ===References===
-[1] Papineni, K., Roukos, S., Ward, T. & Zhu, Weijing. (2002) BLEU: a Method for Automatic Evaluation of Machine Translation, ''Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL)'', Philadelphia,311-18.
+Gehring J，Auli M，Grangier D，et al.  Convolutional sequence to sequence learning[J].ar Xiv：1705.03122v3，2017.
-[2]杜金华,张萌,宗成庆,孙乐.中国机器翻译研究的机遇与挑战——第八届全国机器翻译研讨会总结与展望[J].中文信息学报,2013,27(04):1-8.
+Jordan, R. R. (1997) English for Academic Purposes: A Guide and Resource Book for Teachers. Cambridge: Cambridge University Press．
-[3]方菲. 商务英语机器翻译汉译错误分析[D].广东外语外贸大学,2017.
+Nguyen K，Daume III H，Boyd- Graber J, (2017). Reinforcement learning for bandit neural machine translation with simulated human feedback[C]. Copenhagen, 1464-1474.
-[4]胡开宝,李翼.机器翻译特征及其与人工翻译关系的研究[J].中国翻译,2016,v.37;No.239(05):10-14.
+Papineni, K., Roukos, S., Ward, T. & Zhu, Weijing. (2002) BLEU: a Method for Automatic Evaluation of Machine Translation, Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL), Philadelphia,311-18.
-[5]侯强,侯瑞丽.机器翻译方法研究与发展综述[J].计算机工程与应用,2019,v.55;No.929(10):30-35+66.
+Vaswani A，Shazeer N，Parmar N，(2017). Attention is all you need[C]. Long Beach, 1-11.
-[6]罗季美,李梅.机器翻译译文错误分析[J].中国翻译,2012,v.33;No.215(05):84-89.
+Du Jinhua, Zhang Meng, Zong Chengqing, Sun Le(2013) 杜金华,张萌,宗成庆,孙乐. 中国机器翻译研究的机遇与挑战——第八届全国机器翻译研讨会总结与展望[J]Opportunities and Challenges for Machine Translation Research in China—Summary and Prospects for the English China Workshop on Machine Translation. 中文信息学报 Journal of Chinese Information Processing, 4: 1-8.
-[7]李亚超,熊德意,张民.神经机器翻译综述[J].计算机学报,2018,v.41;No.432(12):2734-2755.
+Fang Mengzhi 方梦之（2004）.译学辞典[M] 译学辞典 A Dictionary of Translation Studies.上海：上海外语教育出版社Shanghai: Shanghai Foreign Language Education Press
-[8]杨南. 基于神经网络学习的统计机器翻译研究[D].中国科学技术大学,2014.
+Hou Qiang, Li Yi(2019) 侯强,侯瑞丽.机器翻译方法研究与发展综述[J]Review of Studies and Development on Machine Translation Methodology.计算机工程与应用 Computer Engineering and Applications,10: 30-35+66.