Machine translation

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

Overview Page of Machine Translation

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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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2 吴映红（The Introduction of Machine Translation)

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3 肖毅瑶(On the Realm Advantages And Symbiotic Development of Machine Translation And Huamn Translation)

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4 王李菲 （Comparison Between Neural Machine Translation of Netease and Traditional Human Translation—A Case Study of The Economist Articles)

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

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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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Abstract

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.

Key words

Artificial intelligence,Machine translation,cultivation of translation talents,"The Belt and Road initiative"

题目

一带一路背景下人工智能与翻译人才的培养

摘要

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。

关键词

人工智能；机器翻译；翻译人才培养；一带一路

1. Introduction

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.

2.Development status of machine translation in the era of artificial intelligence

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. 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.

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.

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.

3.Challenges in the training of translation talents in universities

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.

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.

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.

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.

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.

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.

4.The Language environment and opportunities and challenges of the Belt and Road initiative

During visits to Central and Southeast Asian countries in September and October 2013, Chinese President Xi Jinping put forward the major initiative of jointly building the Silk Road Economic Belt and the 21st Century Maritime Silk Road. And began to be abbreviated as the Belt and Road Initiative.

According to the Vision and Actions for Jointly Building silk Road Economic Belt and 21st Century Maritime Silk Road, the Silk Road Economic Belt focuses on connecting China, Central Asia, Russia and Europe (the Baltic Sea). From China to the Persian Gulf and the Mediterranean Sea via Central and West Asia; China to Southeast Asia, South Asia, Indian Ocean. The focus of the 21st Century Maritime Silk Road is to stretch from China's coastal ports to Europe, through the South China Sea and the Indian Ocean. From China's coastal ports across the South China Sea to the South Pacific.

The Belt and Road "construction is comply with the world multi-polarization and economic globalization, cultural diversity, the initiative of social informatization tide, drive along the countries achieve economic policy coordination, to carry out a wider range, higher level, the deeper regional cooperation and jointly create open, inclusive and balanced, pratt &whitney regional economic cooperation framework.

4.1The language environment of the Belt and Road

The "Belt and Road" involves a wide range of countries and regions, and their languages and cultures are very complex. How to make good use of language, do a good job in translation services, actively spread Chinese culture to the world, strengthen the ability of discourse, and tell Chinese stories well, the first thing to do is to understand the language situation of the countries along the "Belt and Road".

4.1.1The most common language in countries along the "Belt and Road"

There are a wide variety of languages spoken in 65 countries along the Belt and Road, involving nine language families. However, The status of English as the first language in the world is undeniable. Most of the countries participating in the Belt and Road are developing countries, and many of them speak English as their first foreign language. Especially in southeast Asian and South Asian countries, English plays an important role in foreign communication, whether as the official language or the first foreign language. Besides English, more than 100 million people speak Russian, Hindi, Bengali, Arabic and other major languages in the "Belt and Road" countries. It can also be seen that a common feature of languages in countries along the "Belt and Road" is the popularization of English education. English is widely used in international politics, economy, culture, education, science and technology, playing the role of the most important language in the world.

4.1.2The complex language conditions of countries along the "Belt and Road"

The languages spoken in countries along the Belt and Road involve nine major language families and almost all the world's religious types. Differences in religious beliefs also result in differences in culture, customs and social values behind languages. The languages of some countries along the belt and Road have also been influenced by historical and realistic factors, such as colonization, internal division and immigration.

India, for example, has no national language, but more than 20 official languages. India is a multi-ethnic country, a total of more than 100 people, one of the most obvious difference between nation and nation is the language problem. Therefore, according to the difference of language, India divides different ethnic groups into different states, big and small. Ethnic groups that use the same language are divided into one state. If there are two languages in a state, the state is divided into two parts. And Indian languages differ not only in word order but also in the way they are written. In India, for example, Hindi is spoken by the largest number of people in the north, with about 700 million speakers and 530 million as their first language. It is written in The Hindu language and belongs to the Indo-European language family. Telugu in the east is spoken by about 95 million people and 81.13 million as their first language. It is written in Telugu, which belongs to the Dravidian language family and is quite different from Hindi. As a result, a parliamentary session in India requires dozens of interpreters.

These factors cannot be ignored in the process of translation, from language communication to cultural understanding, from text to thought exchange, through the bridge of language to truly connect the people, so as to avoid misreading and misunderstanding caused by differences in language and national conditions.

4.2 Opportunities and challenges of the "Belt and Road"

With the promotion of the Belt and Road Initiative, there has been an unprecedented boom in translation. In the previous translation boom in China, most of the foreign languages were translated into Chinese, and most of the foreign cultures were imported into China. However, this time, in the context of the "Belt and Road" initiative, translating Chinese into foreign languages has become an important task for translators. As is known to all, there are many different kinds of "One Belt And One Road" along the national language and culture is complex, the service "area" construction has become a factor in Chinese translation talents training mode reform, one of the foreign language universities have action, many colleges and universities to establish the "area" all the way along the country's small language major, as a result, "One Belt And One Road" initiative to promote, It has brought unprecedented opportunities for human translation. The cultivation of diversified translation talents and the cultivation of translation talents in small languages is an urgent problem to be solved in China. The cultivation of translation talents cannot be completed overnight, and the state needs to reform the training mode of translation talents from the perspective of language strategic development. Only in this way can we meet the new demand for human translation under the new situation of the belt and Road Initiative.

For a long time, the traditional orientation of translation curriculum and training goal in colleges and universities is to train translation teachers and translators in need of society through translation theory and practice and literary translation practice, which cannot meet the needs of society. Since 2007, in order to meet the needs of the socialist market economy for application-oriented high-level professionals, the Academic Degrees Committee of The State Council approved the establishment of Master of Translation and Interpreting (MTI for short). After joining the pilot program of MTI, more and more universities are reforming the curriculum and training mode of master of Translation in order to cultivate translators who meet the needs of the society.

Language is an important carrier of culture, and translation is an important link for exporting culture. The quality of translation output also reflects the cultural soft power of a country. With the rise of China, more and more people are interested in Chinese culture, and the number of Chinese learners keeps increasing. Under the background of "One Belt and One Road", excellent translators are urgently needed to spread Chinese culture. With the promotion of "One Belt and One Road" Initiative, the number of other countries learning mutual learning and cultural exchanges with China has increased unprecedeningly, bringing vigorous opportunities for the spread of Chinese culture. Translation talents who understand small languages and multi-lingual translators are needed. They should not only use language to convey information, but also use language as a lubricant for communication.

5.Training translation talents from the perspective of machine translation

Under the prevailing environment of machine translation, it poses a great challenge to the cultivation of translation talents. According to the current situation, translation needs and the shortage of translation talents, colleges and universities should reform and innovate the existing training programs for translation talents in terms of the quality of translation talents, the reform of training mode and the use of artificial intelligence. Based on the obtained data and literature, the author discusses how to train translation talents in the perspective of machine translation from the following aspects.

5.1 Quality requirements for translation talents

Zhong Weihe and Murray made a more detailed and profound discussion on translator's literacy, believing that "translators should not only be proficient in two languages, but also have extensive cultural and encyclopedic knowledge and relevant professional knowledge; Master a variety of translation skills, a lot of translation practice; Have a clear translator role awareness, good professional ethics, practical and enterprising style of work, conscious team spirit and calm psychological quality ". According to the collected data, the author will elaborate the requirements for translation talents from four aspects: language literacy, humanistic literacy, translation ability and innovation ability.

The first is language literacy, which is the most basic and important requirement. MAO Dun pointed out that "mastery of mother tongue and target language are the foundation of translation". A solid foundation of bilingual skills is the basic skills of translators. Poor language proficiency seems to be a common problem among students majoring in translation and interpreting. Many translation diseases are caused by poor Chinese foundation. As part of going global, the belt and Road initiative is to tell Chinese culture and Chinese stories, which requires translators to be able to use both languages flexibly. Therefore, the first problem that colleges and universities face to solve is to improve the language level of foreign language learners.

The second is humanistic literacy. Humanistic literacy is mainly manifested by a good command of politics, economy, history, literature and other knowledge, which is particularly important for interpreters. In addition, cross-cultural communication cannot be ignored. In the process of communicating with foreigners or translating, translators often encounter the first cross-cultural contradiction. Cross-culture refers to having a full and correct understanding of cultural phenomena, customs and habits that differ or conflict with the national culture, and accepting and adapting to them in an inclusive manner on this basis. So the interpreter can first fully understand and master the national conditions and culture of the target country, which is particularly important in the "Belt and Road". There are more than 60 countries along the "Belt and Road", and it takes a lot of energy to master their national conditions and culture.

The third is translation ability. We should distinguish between translation ability and language ability. Translation ability is actually a system of knowledge and skills necessary for translation, the core of which is conversion ability. First of all, it reflects the ability to use tools to assist translation, such as computer application, translation technology and so on. In addition, interpreters should have enough healthy psychological quality and good professional quality. In terms of translation ability, the current training model of translation talents is inadequate.

The last one is innovation. The cultivation of learners' thinking ability is the key to translation teaching and the cultivation of thoughtful translators should be the connotation of translation teaching. Therefore, the interpreter is not only a translation tool, which is no different from machine translation. More importantly, it is necessary to explore translation with thoughts, have a sense of lifelong learning and innovation consciousness. Translators must constantly innovate themselves, learn new knowledge, and strive to seek reform and innovation. Many colleges and universities should also consciously cultivate students' innovation ability and broaden their thinking and vision.

5.2 The reform of college curriculum setting

First, we will further reform the curriculum of colleges and universities. Add economics, law and engineering to the curriculum, these contents in the "belt and Road". "One Road" is very important in the construction. According to the author's personal experience, the most typical problem of foreign language majors in colleges and universities is the single learning of foreign languages. More professional foreign language colleges and universities will add some literature courses and national conditions courses of the language target countries. Obviously, whether foreign language graduates are engaged in translation work or not, these knowledge is not enough. Of course, great reforms have been carried out in foreign language teaching, such as combining foreign language with finance, law, diplomacy and so on, and taking the way of minor training foreign language majors.

Domestic enterprises with a high degree of internationalization attach great importance to translation. Their translation research includes cutting-edge theoretical and applied research, involving machine translation, natural language processing and AI theory, algorithm and model. With such a foundation, enterprises can solve problems by themselves, such as embedding automatic translation functions in mobile phones. International enterprises not only do technical translation, but also deal with all forms of translation and localization in society. At present, translation teaching in most colleges and universities is still in the early mode, and it is an objective fact that it is divorced from the workplace and has a gap with the needs of enterprises.

Second, we should adjust and strengthen the construction of second foreign language teaching for foreign language majors. In the 1980s, our country was in urgent need of Russian translation. At that time, students majoring in English could translate microelectronic product manuals and related business documents in English and Russian at the same time after learning Russian for half a year. The mutual conversion between English and Russian played a great role in practice. According to the author, in the Graduate Institute of Interpretation and Translation of Beijing Foreign Studies University a very few students majored in multiple languages at the graduate level, that is, they majored in minor languages at the undergraduate level and were admitted to the Graduate Institute of Interpretation and Translation in English. Their training mode is to study English in the Graduate Institute of Interpretation and Translation for two years and the third year in the corresponding department of the undergraduate major. Such training mode in my opinion is a bigger model, cost It's more difficult for students.

In addition, there is a great disparity in the development of second foreign language teaching in colleges and universities, and the overall level is not high enough. Part of the second foreign language university foreign language professional may still be too much focus in languages such as German, French and Japanese, should as far as possible, considering the need of the construction of the "region", like Croatia, Serbia, Turkish, Hungarian, Italian, Indonesian, Albanian, these are the countries along the "area" the language of the two countries, Colleges and universities should encourage the teaching of a second foreign language.

Third, the teaching of translation technology should be strengthened. Traditional translation teaching teaches translation skills, such as the translation of words, sentences, texts and figures of speech. Translation technology refers to a series of practical workplace technologies with computer-aided translation software and translation project management as the core, which can greatly improve translation efficiency. However, due to the relative lack of translation technology teachers and equipment in colleges and universities, there is a disconnect between talent training and the requirements of translation technology in the translation field.

5.3 Application of artificial intelligence to translation teaching practice

In order to improve the teaching quality and train students' English translation ability, it is necessary to realize the effective integration of ARTIFICIAL intelligence and translation activity courses, which should not only reflect the effectiveness of artificial intelligence translation technology, but also help students establish a healthy concept of English communication. Through the application of artificial intelligence technology, students can strengthen their flexible translation skills through close communication with "AI program" during the learning stage of English translation activity class. For example, teachers can ask students to translate directly against the translation content provided on the translation screen of the ARTIFICIAL intelligence system. After that, the system can collect the translation answers with the help of speech recognition function, and then judge the accuracy of the translation content, thus providing important feedback to students.

China has used such artificial intelligence technology in the Putonghua test to ensure that every student can find a suitable translation method in practical communication. The so-called artificial intelligence technology is a new kind of technology modeled after the characteristics of human neural network thinking, can combine the human mind to respond. If it can be integrated into English translation activity teaching, it can not only improve the teaching efficiency, but also enhance students' enthusiasm in learning the course.

At the same time, during the training of translation talents, teachers also need to take into account the importance of influencing education factors, so that students can form a higher disciplinary quality in translation, so as to fit the concept of quality education in the new era. Only when artificial intelligence translation content is fully integrated into college English translation activity courses can the overall translation ability of college students be maximized.

5.4The improvement of translator's technical ability

In the previous part, the author roughly mentioned that translation teaching should be improved, which will be elaborated here. At present, only a few universities can make full use of the advantages of translation technology in translation teaching and focus on cultivating professional translation talents. Most universities still cannot get rid of the traditional teaching mode of "language + relevant professional knowledge" in translation teaching, and generally lack a correct understanding of COMPUTER-aided translation teaching.

According to Wang Huashu et al., the courses that can be offered around the composition of translators' technical literacy include computer-assisted translation, translation and corpus, machine translation and post-translation editing, localization and internationalization, film and television translation (subtitle), technical communication and technical writing, and computer programming. The course modules involved are: Fundamentals of COMPUTER-aided Translation, CAT tool application, corpus alignment and processing, term management, QA technology for translation quality assurance, OFFICE fundamentals, translation management technology, basic computer knowledge, desktop typesetting, localization and internationalization, project management system and content management system, technical writing, basic knowledge of computer programming, basic knowledge of web code, etc.

8 颜静(On Machine Translation Under Lanuguage Intelligence——An Option and Opportunity for Human Translators)

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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）

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Abstract

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.

Key Words

Machine Translation; Artificial Translation; Artificial Intelligence

题目

人工智能时代下的机器翻译与人工翻译

摘要

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。

关键词

机器翻译;人工翻译;人工智能

1. Introduction

1.1 The History of Machine Translation Aborad

The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. Alchuni put forward the idea of using machines for translation. In 1933, the Soviet inventor Troyansky designed a machine to translate one language into another. [1]In 1946, the world's first modern electronic computer ENIAC was born. Soon after, American scientist Warren Weaver, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947. In 1949, Warren Weaver published a memorandum entitled Translation, which formally raised the issue of machine translation. In 1954, Georgetown University, with the cooperation of IBM, completed the English-Russian machine translation experiment with IBM-701 computer for the first time, which opened the prelude of machine translation research. [2] In 2006, Google translation was officially released as a free service software, bringing a big upsurge of statistical machine translation research. It was Franz Och who joined Google in 2004 and led Google translation. What’s more, it is precisely because of the unremitting efforts of generations of scientists that science fiction has been brought into reality step by step.

1.2 The History of Machine Translation in China

In 1956, the research and development of machine translation has been named in the scientific and technological work and made little achievements in China. On the eve of the tenth anniversary of the National Day in 1959, our country successfully carried out experiments, which translated nine different types of complicated sentences on large general-purpose electronic computers. The dictionary includes 2030 entries, and the grammar rule system consists of 29 circuit diagrams. [3]. After a period of stagnation, China's machine translation ushered in a high-speed development stage after the 1980s in the wave of the third scientific and technological revolution. With the rapid development of economy and science and technology, China has made a qualitative leap in the field of machine translation research with the pace of reform and opening up. In 1978, Institute of Scientific and Technological Information of China, Institute of Computing Technology and Institute of Linguistics carried out an English-Chinese translation experiment with 20 Metallurgical Title examples as the objects and achieved satisfactory results. Subsequently, they developed a JYE-I machine translation system, which based on 200 sentences from metallurgical documents. Its principles and methods were also widely used in the machine translation system developed in the future. In addition, the research achievements of machine translation in China during the 1980s and 1990s also include that Institute of Post and Telecommunication Sciences developed a machine translation system, C Retrieval and automatic typesetting system with good performance and strong practicability in October 1986; In 1988, ISTC launched the ISTIC-I English-Chinese Title System for the translation of applied literature of metallurgy, Information Research Institute of Railway developed an English-Chinese Title Recording machine translation system for railway documents; the Language Institute of the Academy of Social Sciences developed "Tianyu" English-Chinese machine translation system and Matr English-Chinese machine translation system developed by the computer department of National University of Defense Technology. After many explorations and studies, machine translation in China has gradually moved towards application, popularization and commercialization. China Software Technology Corporation launched "Yixing I" in 1988, marking China's machine translation system officially going to the market. After "Yixing", a series of machine translation systems such as Gaoli system in Beijing, Tongyi system in Tianjin and Langwei system in Shaanxi have also entered the public. In the 21st century, the development of a series of apps such as Kingsoft Powerword, Youdao translation and Baidu translation has greatly met the needs of ordinary users for translation. According to the working principle, machine translation has roughly experienced three stages: rule-based machine translation, statistics-based machine translation and deep learning based neural machine translation. [4] These three stages witnessed a leap in the quality of machine translation. Machine translation is more and more used in daily life and even the translation of some texts is almost comparable to artificial translation. In addition to text translation, voice translation, photo translation and other functions have also been listed, which provides great convenience for people's life. It is undeniable that machine translation has become the development trend of translation in the future.

1.3 The Status Quo of Machine Translation

In this big data era of information explosion, the prospect of machine translation is also bright. At present, the circular neural network system launched by Google has supported universal translation in more than 60 languages. Many Internet companies such as Microsoft Bing, Sogou, Tencent, Baidu and NetEase Youdao have also launched their own Internet free machine translation systems. [5] Users can obtain translation results free of charge by logging in to the corresponding websites. At present, the circular neural network translation system launched by Google can support real-time translation of more than 60 languages, and the domestic Baidu online machine translation system can also support real-time translation of 28 languages. These Internet online machine translation systems are suitable for a variety of terminal platforms such as mobile phone, PC, tablet and web and its functions are also quite diverse, supporting many translation forms, such as screen word selection, text scanning translation, photo translation, offline translation, web page translation and so on. Although its translation quality needs to be improved, it has been outstanding in the fields of daily dialogue, news translation and so on.

2. Advantages and Disadvantages of Machine Translation

Generally speaking, machine translation has the characteristics of high efficiency, low cost, accurate term translation and great development potential and etc. Machine translation is fast and efficient, this is something that artificial translation can’t catch up with. In addition, with the continuous emergence of all kinds of translation software in the market, compared with artificial translation, machine translation is cheap and sometimes even free, which greatly saves the economic cost and time for users with low translation quality requirements. What's more, compared with artificial translation, machine translation has a huge corpus, which makes the translation of some terms, especially the latest scientific and technological terms, more rapid and accurate. The accurate translation of these terms requires the translator to constantly learn, but learning needs a process, which has a certain test on the translator's learning ability and learning speed. In this regard, artificial translation has uncertainty and hysteretic nature. At the same time, with the progress of science and technology and the development of society, the function of machine translation will be more perfect and the quality of translation will be better.Today's machine translation tools and software are easy to carry, all you need to do is just to use the software and electronic dictionary in the mobile phone. There is no need to carry paper dictionaries and books for translation, which saves time and space. At the same time, machine translation covers many fields and is suitable for translation practice in different situations, such as academic, education, commercial trade, social networking, tourism, production technology, etc, it is also easy to deal with various professional terms. However, due to the limitation of translators' own knowledge, artificial translation is often limited to one or a few fields or industries. For example, it is difficult for an interpreter specializing in medical English to translate legal English. At the same time, machine translation also has its limitations. At first, machine can only operate word to word translation, which only plays the function and role of dictionary. Then, the application of syntax enables the process of sentence translation and it can be solved by using the direct translation method. When the original text and the target language are highly similar, it can be translated directly. For example, the original text "他是个老师." The target language is "he is a teacher ". With the increase of the structural complexity of the original text, the effect of machine translation is greatly reduced. Therefore, at the syntactic level, machine translation still stays in sentences with relatively simple structure. Meanwhile, the original text and the results of machine translation cannot be interchanged equally, indicating that English-Chinese translation has strong randomness, and is not rigorous and scientific enough. Nowadays, machine translation is highly dependent on parallel corpora, but the construction of parallel corpora is not perfect. At present, the resources of some mainstream languages such as Chinese and English are relatively rich, while the data collection of many small languages is not satisfactory. Moreover, the current corpus is mainly concentrated in the fields of government literature, science and technology, current affairs and news, while there is a serious lack of data in other fields, which can’t reflect the advantages of machine translation. At the same time, corpus construction lags behind. Some informative texts introducing the latest cutting-edge research results often spread the latest academic knowledge and use a large number of new professional terms, such as academic papers and teaching materials while the corpus often lacks the corresponding words of the target language, which makes machine translation powerless Besides, machine translation is not culturally sensitive. Human may never be able to program machines to understand and experience a particular culture. Different cultures have unique and different language systems, and machines do not have complexity to understand or recognize slang, jargon, puns and idioms. Therefore, their translation may not conform to cultural values and specific norms. This is also one of the challenges that the machine needs to overcome.[6] Artificial intelligence may have human abstract thinking ability in the future, but it is difficult to have image thinking ability including imagination and emotion. [7] Therefore, machine translation is often used in news, science and technology, patents, specifications and other text fields with the purpose of fact description, knowledge and information transmission. These words rarely involve emotional and cultural background. When translating expressive texts, the limitations of machine translation are exposed. The so-called expressive text refers to the text that pays attention to emotional expression and is full of imagination. Its main characteristics are subjectivity, emotion and imagination, such as novels, poetry, prose, art and so on. This kind of text attaches importance to the emotional expression of the author or character image, and uses a lot of metaphors, symbols and other expressions. Machine translation is difficult to catch up with artificial translation in this kind of text, it can only translate the main idea, lack of connotation and literary grace and it cannot have subjective feelings and rational analysis like human beings. In fact, it is not difficult to simulate the human brain, the difficulty is that it is impossible to learn from the rich social experience and life experience of excellent translators. In other words, machine translation lacks the personalization and creativity of human translation. It is this personalization and creativity that promote the development and evolution of language, and what machine translation can only output is mechanical "machine language".

3.The Irreplaceability of Artificial Translation

3.1 Translation is Constrained by Context

At present, machine translation can help people deal with language communication in people's daily life and work, such as clothing, food, housing and transportation, but there is a big gap from the "faithfulness, expressiveness and elegance" emphasized by high-level translation. Language itself is art，which pays more attention to artistry than functionality, and the discipline of art is difficult to quantify and unify. Sometimes it is regular, rigorous, logical and clear, and sometimes it is random, free and logical. If it is translated by machine, it is difficult to grasp this degree. Sometimes, machine translation cannot connect words with contextual meaning. In many languages, the same word may have multiple completely unrelated meanings. In this case, context will have a great impact on word meaning, and the understanding of word meaning depends largely on the meaning read from context. Only human beings can combine words with context, determine their true meaning, and creatively adjust and modify the language to obtain a complete and accurate translation. This is undoubtedly very difficult for machine translation. Artificial translation can get rid of the constraints of the source language and translate the translation in line with the grammar, sentence patterns and word habits of the target language. In the process of translation, translators can use their own knowledge reserves to analyze the differences between the source language and the target language in thinking mode, cultural characteristics, social background, customs and habits, so as to translate a more accurate translation. Artificial translation can also add, delete, domesticate, modify and polish the translation according to the style, make up for the lack of culture, try to maintain the thought, artistic conception and charm of the original text and the style of the source language. In addition, translators can also judge and consider the words with multiple meanings or easy to produce ambiguity according to the context, so as to make the translation more clear and more accurate and improve the quality of the translation.

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

Machine_Trans_EN_10

Abstract

The history of machine translation can be traced to 1940s, undergoing germination, recession, revival and flourish. Nowadays, with the rapid development of information technology, machine translation technology emerged and is gradually becoming mature. Whether manual translation can be replaced by machine translation becomes a widely-disputed topic. So 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：informative text, expressive text and vocative text). The results are quite different in terms of quality and accuracy. The results show that machine translation is more suitable for informative text for its brevity, while the expressive texts especially literary works and vocative texts are difficult to be translated, because there are too many loaded words and cultural images in expressive text and dependence on the readers. To draw a conclusion, the relationship between machine translation and manual translation should be complementary rather than competitive.

Key words

machine translation; manual translation; Newmark's type of texts

题目

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

摘要

机器翻译的历史可以追溯到上个世纪40年代，经历了萌芽期、萧条期、复兴期和繁荣期四个阶段。如今，随着信息技术的高速发展，机器翻译技术日益成熟，于是机器翻译能不能替代人工翻译这个话题引起了广泛热议。为了探究机器翻译的能力水平是否足以替代人工翻译，本人根据皮特纽马克的文本类型分类理论（信息类文本，表达文本，呼吁文本），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。结果发现，就质量和准确度而言，译文的水平大相径庭。因为其简洁的特性，机器比较适合翻译信息文本。而就表达文本，尤其是文学作品，因其存在文化负载词及相应的文化现象，所以机器翻译这类文本存在难度。而呼唤文本因其目的性以及对读者的依赖性较强，翻译难度较大。由此得知，机器翻译和人工翻译的关系应该是互补的，而不是竞争的。

关键词

机器翻译；人工翻译；纽马克文本类型

1. Introduction

1.1.Introduction to machine translation

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. 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. And the first machine translating system launched in 1954, used in English and Russian translation. And this means machine translation becomes a reality. However, the arrival of new things always accompanies barriers and setbacks. In the 1960s, reports from ALPAC (Automated Language Processing Advisory Committee) showed studies on machine translation had stagnated for a decade. At that time, due to the high cost of machine, choosing human translators to finish translating works was more economical for most companies. What’s worse, machine had difficulty in understanding semantic and pragmatic meanings. Fortunately, in the 1970s, with the advance and popularity of computer, machine translation was gradually back on track. With the development of science and technology, machine translation has better technological guarantee, such as artificial intelligence and computer technology. In the last decades, from the late 90s till now, machine translation is becoming more and more mature. The initial rule-oriented machine translation has been updated and Corpus-aided machine translation appears. And nowadays, technology in voice recognition has been further developed. This technique is frequently applied in speech translation products. Users only need to speak source language to the online translator and instantly it will speak out the target version. But machine can’t fully provide precise and accurate translation without any grammatical or semantic problems. Machine can understand the literal meaning of texts, but not the meaning in context. For example, there is polysemy in English, which refers to words having multiple meanings. So when translating these words, translators should take contexts into consideration. But machine has troubles in this way. In addition, machine has no feeling or intention. Hence, it is also difficult to convey the feelings from the source language.

1.2.Process of machine translation

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.

2.Theorectical framework

2.1Newmark’s text typology

Text typology is a theory from linguistics. It undergoes several phrases. Karl Buhler, a German psychologist and linguist defines communicative functions into expressive, information and vocative. Then Roman Jakobson proposes categorization of texts, which are intralingual translation, interlingual translation and intersemiotic translation. Accordingly, he defines six main functions of language, including the referential function, conative function, emotive function, poetic function, metalingual function and the phatic function. Based on the two theories, Peter Newmark, a translation theorist, summarizes the language function into six parts: the informative function, the vocative function, the expressive function, the phatic function, the aesthetic function, and the metalingual function. Later, he further divided texts into informative type, expressive text and vocative type according to the linguistic functions of various texts. The core of the informative texts is the truth. It is to convey facts, information, knowledge of certain topics, reality and theories. The language style of the text is objective, logical and standardized. Reports, papers, scientific and technological textbooks are all attributed to informative texts. Translators are required to take format into account for different styles. The core of the expressive text is the sender’s emotions and attitudes. It is to express sender’s preferences, feelings, views and so on. The language style of it is subjective. Imaginative literature, including fictions, poems and dramas, autobiography and authoritative statements belong to expressive text. It requires translators to be faithful to the source language and maintain the style. The core of the vocative text is readership. It is to call upon readers to act, think, feel and react in the way intended by the text. So it is reader-oriented. Such texts as advertisement, propaganda and notices are of vocative text. Newmark noted that translators need to consider cultural background of the source language and the semantic and pragmatic effect of target language. If readers can comprehend the meaning at once and do as the text says, then the goal of information communication is achieved. To draw a conclusion, informative texts focus on the information and facts. Expressive texts center on the mind of addressers, including feelings, prejudices and so on. And vocative texts are oriented on readers and call on them to practice as the texts say.

2.2Study method

Manual translations of the three texts are selected from authoritative versions and universally acknowledged. And machine translations of those come from Youdao Translator. And in this thesis I will compare and evaluate the two methods in word diction, sentence structure, word order and redundancy.

11 陈惠妮=(Study on Pre- editing of Machine Translation - A Case Study of Medical Abstracts)

Machine_Trans_EN_11

Abstract

At present, globalization is accelerating and the market demand for language services is rapidly increasing . Machine translation, as an important translation method, can greatly improve translation efficiency due to its low cost and high speed. However, because of the limitations of machine translation and the differences between Chinese and English language, machine translation is not accurate enough. In order to balance translation efficiency and translation quality, a great number of manual revisions in translation are required for the machine translating texts. Medical papers are specialized, special and purposeful, so it requires accurate,qualified and professional translation. However, the quality of translations by machine is inefficient to meet the high-quality requirements of medical papers translation. Therefore, the introduction of pre-editing can greatly improve the efficiency and quality of machine translation.

Key words

Pre-editing, Machine translation, Medical texts

题目

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

摘要

在全球化加速发展的今天，市场对语言服务的需求迅速增加。机器翻译作为一种重要的翻译途径，由于其成本低、速度快，可以大大提高翻译效率。然而，由于机器翻译的局限性以及中英文语言的差异，机器翻译的准确性不高。为了平衡翻译效率和翻译质量，机器翻译文本需要大量的手工修改。 医学论文具有专业性、特殊性和目的性，要求其译文准确、合格、专业。然而，机器翻译的质量较低，无法满足医学论文对翻译的高质量要求。因此，译前编辑的引入可以大大提高机器翻译的效率和质量。

关键词

译前编辑；机器翻译；医学文本

1.1 Definition of Machine Translation

As Cronin(2013) revealed: "Translation is undergoing a revolutionary upheaval. The influence of digital technology and the Internet on translation is continuous, extensive and profound. From the popularity of automatic online translation applications, translation revolution is everywhere (Cronin,2013) However,the concept of machine translation was firstly proposed in the 1930s. Since 1940s, the machine translation technology has been evolving from rule-based machine translation (RBMT) to statistical machine translation (SMT), and to neural machine translation (NMT). Machine translation refers to the automatic translation of source language into target language by using a computer system. That is, machine translation refers to the automatic translation of text from one language into another natural language by computer software or other online translation webs. Machine translation is also defined as the process of “using a computer system to automatically translate text or speech from one natural language to another” according to the definition by ISO (Cui, 2014).O 'Brien (2002) defines it as "the behavior of modifying errors in machine translation to ensure that the target translation meets certain quality requirements". On the basic level, machine translation performs mechanical substitution of words in one language for words in another language, but that rarely produces a good translation, therefore, recognition of the whole phrases and their closest counterparts in the target language is needed. Not all words in one language have equivalents in another language, and many words have more than one meaning. A huge demand for translation is greatly needed in today’s global world, which creates new opportunities for the development of machine translation, attracts more and more attention and becomes one of the current research focuses.

1.2 Definition of Pre-editing

Pre-editing means to adjust and modify the source language to make it fit more with the characteristics of the machine translation software before putting the source language before the into machine translation, so as to improve the quality of the translation machine translation (Wei Changhong, 2008:93-94). Pre-editing is to modify the original text before putting it into machine translation software, in order to improve the recognition rate of machine translation, optimize the output quality of translated text and reduce the workload of post-editing. Because pre-translation editing only needs to be modified in one language, the operation is simpler than post-translation editing, which can realize the double improvement of quality and efficiency. A good pre-editing translation can help machine translation more smoothly, thus improving the machine readability and quality of the output translation. Pre-editing is mostly applied in the following situations: one is when the original text is of poor quality and the machine is difficult to recognize the meaning of the sentence, such as user generated content with poor readability and translatability (Gerlach et al, 2003:45-53); Documents that need to be published in multiple languages; Next is when the original text contains a lot of jargon; The last is the original text has a corresponding translation memory bank. If the original text is edited, it can better match the content of the translation memory bank. Pre-editing is a process of identifying problems. It requires to pre-edit the source texts before putting it into machine translation according to the requirements, listing the expressions or sentences that may have trouble in machine translation and then pre-edit it by human. The purpose is to enable the computer to translate better, improve the translatability of machine translation. (Slype G V & Guinet J F & Seitz F,1984:115)

1.3 Machine Translation Mode

According to the different knowledge acquisition methods, machine translation modes can be classified as follows: one is rule-based machine translation, which is based on bilingual dictionaries and a library of language rules for each language. The quality of translation depends on whether the source language conforms to the existing rules, but the inexhaustible rules are the hinders of this model. The second mode is machine translation based on statistics. This translation model relies on the principles of mathematics and statistics to find various existing translations corresponding to the translation tasks through the employment of corpus, analyzing the frequency of their occurrence and selecting the translation with the highest frequency for output. The disadvantage of this translation model is that it ignores the flexibility of language and the importance of context. The last one is neural network language model. This model is different from previous translation models in that it uses end-to-end neural network to realize automatic translation between natural languages. At present, the quality of its translation is much higher than that of the previous translation models.

1.4 Source of Study Abstracts

The core medical journals from domestic are selected in order to make the paper more representative and authoritative, such as National Medical Journal of China, Journal of Peking University (Health Science), and Journal of Third Military Medical University. All of them include basic medical science, biomedical technology, laboratory medical science and other fields. In this way, the pre-editing approaches included are applicable enough to machine translation of medical abstracts.

1.5 Selection of Translation Software

Generally, machine translation software includes Google Translation, Youdao Translator and Niu Translation, which has their own special use in translation. Take Google as an example, it is more like neurons in human brain, enabling to learn and collect information to establish connections with its neural machine translator’s neurons. However, it also causes many errors because of the lack of enough information. In this paper, contrastive analysis will be carried on by using Google translation. On the one hand, being a pioneer in the translation of NET, it is inevitably to sue Google as the translation software to translate medical texts in this paper. Comparing the program called “Google brain”, other NMT of translation software are relatively disadvantages. On the other hand, the Google translation enjoys the largest users in the world, with its downloads of more than one billion. The output quality of Google translation is more correct and complete than other machine translation software. With years of development and improvement, Google Translation has been greatly promoted. In this paper, Chinese medical abstract will be automatically translated by Google translation, and then the translation output will be compared with the translation by human and the publication of English abstracts. The main purpose is to prove that the improvement and promotion of quality and accuracy in the medical abstracts will be obtained through the pre-editing approaches.

2.Language Characteristics and Error Division

Since Chinese and English are two different languages, it is quite neccessary to identify their own characteristics so as to better analyze and understand the two languages. Tytler (1978) argued in his Essay on the Principles of Translation that there are three principles of translation that in all the translation should give readers the same feelings as the source text, except for complete transcript of ideas. There actually also exit some mistakes of these two languages. So the following will make some clarrifications of these two languages to ensure more accurate translation.

2.1 Language Characteristics of Medical Abstracts

Chinese and English belong to different language systems, so there are differences in their language structure and the way users think of the languages. When using machine translation from Chinese to English, due to the unequal language levels, there will be many mistakes in the translation process, especially for ESP texts, such as medical papers. Here, these differences mainly refer to the linguistic characteristics of medical abstracts. In medical abstracts, it usually includes structured and unstructured abstracts. Although in different forms, they both describe the purpose, methods and conclusions of the research. In the method section of medial abstract, several Chinese sentences can be connected with commas, but each sentence may convey different information. In contrast, English sentences contain a great deal of information, but in order to ensure clarity, some modifiers need to be isolated and then reconstructed. However, in Chinese-English machine translation, a lot of information is put into the sentence because the machine segments the sentence on the basis of the full comma. In addition, subjectless sentence is used in the objective and method parts of Chinese medical abstracts. The subjectless sentence means the sentence without or free from subject and is usually employed in two contexts. The first is "needless to say". In Chinese sentences, it is common to omit the subject of the sentence. Chinese sentences can convey meanings by using incomplete sentence structures, so Chinese speakers can understand the meanings of the sentences even though the sentence subject is omitted. The second is "emphasis of action". In this context, subjectless sentences are used to describe behaviors, especially in the study of traditional Chinese medicine abstracts. Comparatively speaking, it should be avoided in English medical abstracts. Abstract sentences are more subjective when describing the learning process, while the essential requirement of English medical abstract is objectivity. From this point, sentences without subject should be avoided in English medical abstracts. Another feature is voice. Few words with passive meanings appear in Chinese abstracts. English sentences are more favoured in using passive voice. When translating from Chinese to English, passive voice should be used to make the contents objective, which is also the basic requirement of medical papers. These are the linguistic features of Chinese medical abstracts. There are great differences in sentence structure and expression between Chinese and English medical abstracts. These differences may reduce the accuracy of machine translation, so it is necessary to introduce pre-editing to edit the source text to ensure that the source text can be accurately recognized by the machine and fully translated into English.

2.2 Error Division of Machine Translation in Translating Abstracts of Medical Papers from Chinese to English

In this paper, errors in machine translation are listed out after analyzing. Some are due to the principles, such as statistical-based MT and NMT, employed by machine translation. Based on the original errors, they can be studied from two levels. One is from the macro-level, referring to mistakes caused by objective factors. These are not human factors, but the disadvantage of machine translation and the limitations of language. The limitations of machine translation are derived from the principles and models adopted and manifests itself as a reliance on reference sources. In this case, semantic ambiguity and textual incoherence may occur in the absence of reference sources. However, for ESP texts such as medical papers, the requirements for terms and sentence patterns are far beyond the existing corpora. For unfamiliar texts (that is, the corresponding texts cannot be found in the corpora). The quality of output translation will be relatively low, which is determined by the principles of machine translation. As mentioned above, machine translation has evolved over the past few decades from rules-based to corp-based statistics to NMT. However, there still exist some limitations in machine translation. Mistakes on micro-level are mainly caused by the variations and differences of linguistic structure between Chinese and English. Chinese is an implicit language, while English is an explicit one (Lian, 2010) To put it in another way, Chinese expression does not depend on language structures, while English does the opposite. This may result in mismatches between the original and machine translated sentences. This kind of error is mainly divided into two types: component fragment and component missing. For a medical paper, such errors are very serious. As a kind of ESP discourse, medical paper has the characteristics of fixed, objective and accurate language structure. In order to reproduce the characteristics of medical abstracts in translation, it is necessary to avoid errors at the level of words and sentences and pay attention to logic and consistency. Lexical errors here refer to the inconsistency of fixed expressions in the translation of terms. Although these terms are machine translation based on the corpus, the corpus may not be perfect for ESP texts such as medical papers. Therefore, fixed expressions are not used in term translation. This is also the most common mistake in machine translation (the term here includes but is not limited to nouns). For ESP texts, medical text in particular, the fixed expressions and the accuracy of terms are of great importance.

3.Approaches Proposed for Pre-editing

Generally speaking, there is a close relationship between pre-editing and post-editing, both of which aim to convey information and ensure high or publishable translation quality. Proper pre-editing can improve the quality of machine translation in terms of adequacy and consistency. Complexity of natural language and people use language arbitrarily, bringing many difficulties to Chinese-English machine translation, Hu Qingping (2005:24) has proposed "the research of translation software and the development of the controlled language are the two directions to improve the quality of machine translation : the former aims at the difficulty in the natural language processing, the latter overcomes the arbitrariness of natural language". Feng Quangong and Gao Lin (2017: 63-68) put forward: "The writing principles of controlled language can be applied to pre-editing of machine translation. Pre-editing based on controlled language can effectively reduce the complexity and ambiguity of source text, improve the identifiability of machine translation (the translatability of source text itself), and thus reduce (fully) the workload of post-editing". The central task of pre-editing is to transform human-friendly content into machine-friendly content, so words and sentences need to be repositioned or even changed. Based on the analysis of the language characteristics of Chinese and English, the Chinese ideographic group should be split before the Chinese source text is input into the machine software to translate so that the sentence structure is complete which can be easily recognized by the machine translation software.

12 蔡珠凤=(The Mistranslation of C-J Machine Translation of Political Statements)

Machine_Trans_EN_12

Abstract

Language is the main way of communication between people. With the continuous development of globalization, the scale of cross-border exchanges is also expanding. However, due to cultural differences and diversity, the languages of different countries and regions are very different, which seriously hinders people's communication. The demand for efficient and convenient translation tools is increasing. At the same time, with the development of network technology and artificial intelligence, recognition technology based on deep learning is more and more widely used in English, Japanese and other fields.

Key words

machine translation; political statements; mistranslation of C-J machine translation

题目

The Mistranslation of C-J Machine Translation of Political Statements

摘要

语言是人与人之间交流的主要方式。随着全球化的不断发展，跨境交流的规模也在不断扩大。然而，由于文化的差异和多样性，不同国家和地区的语言差异很大，这严重阻碍了人们的交流。对高效便捷的翻译工具的需求正在增加。同时，随着网络技术和人工智能的发展，基于深度学习的识别技术在英语、日语等领域的应用越来越广泛。

关键词

机器翻译；政治发言；政治发言中译日的误译

Introduction to machine translation

Machine translation, also known as automatic translation, is a process of using computers to convert one natural language (source language) into another natural language (target language). It is a branch of computational linguistics, one of the ultimate goals of artificial intelligence, and has important scientific research value.At the same time, machine translation has important practical value. With the rapid development of economic globalization and the Internet, machine translation technology plays a more and more important role in promoting political, economic and cultural exchanges.The development of machine translation technology has been closely accompanied by the development of computer technology, information theory, linguistics and other disciplines. From the early dictionary matching, to the rule translation of dictionaries combined with linguistic expert knowledge, and then to the statistical machine translation based on corpus, with the improvement of computer computing power and the explosive growth of multilingual information, machine translation technology gradually stepped out of the ivory tower and began to provide real-time and convenient translation services for general users.

C-J machine translation software

Today's online machine translation software includes Baidu translation, Tencent translation, Google translation, Youdao translation, Bing translation and so on. Google was the first company to launch the machine translation system, and Baidu was the first company to import the machine translation system in China. In addition, Tencent and Youdao have attracted much attention.Machine translation is the process of using computers to convert one natural language into another. It usually refers to sentence and full-text translation between natural languages. In order to continuously improve the translation quality, R & D personnel have added artificial intelligence technologies such as speech recognition, image processing and deep neural network to machine translation on the basis of traditional machine translation based on rules, statistics and examples.With the increase of using machine translation, the joint cooperation between manual translation and machine translation will also increase significantly in the future. What criteria should be used to evaluate the quality of machine translation? In the evolving field of machine translation, there is an urgent need to clarify the unsolvable questions and solved problems.

The history of machine translation

The research history of machine translation can be traced back to the 1930s and 1940s. In the early 1930s, the French scientist G.B. alchuni put forward the idea of using machines for translation. In 1933, Soviet inventor П.П. Trojansky designed a machine to translate one language into another, and registered his invention on September 5 of the same year; However, due to the low technical level in the 1930s, his translation machine was not made. In 1946, the first modern electronic computer ENIAC was born. Shortly after that, W. weaver, an American scientist and A. D. booth, a British engineer, a pioneer of information theory, put forward the idea of automatic language translation by computer in 1947 when discussing the application scope of electronic computer. In 1949, W. Weaver published the translation memorandum, which formally put forward the idea of machine translation. After 60 years of ups and downs, machine translation has experienced a tortuous and long development path. The academic community generally divides it into the following four stages: Pioneering period（1947-1964） In 1954, with the cooperation of IBM, Georgetown University completed the English Russian machine translation experiment with ibm-701 computer for the first time, showing the feasibility of machine translation to the public and the scientific community, thus opening the prelude to the study of machine translation. It is not too late for China to start this research. As early as 1956, the state included this research in the national scientific work development plan. The topic name is "machine translation, the construction of natural language translation rules and the mathematical theory of natural language". In 1957, the Institute of language and the Institute of computing technology of the Chinese Academy of Sciences cooperated in the Russian Chinese machine translation experiment, translating 9 different types of more complex sentences. From the 1950s to the first half of the 1960s, machine translation research has been on the rise. The United States and the former Soviet Union, two superpowers, have provided a lot of financial support for machine translation projects for military, political and economic purposes, while European countries have also paid considerable attention to machine translation research due to geopolitical and economic needs, and machine translation has become an upsurge for a time. In this period, although machine translation is just in the pioneering stage, it has entered an optimistic period of prosperity. Frustrated period（1964-1975） In 1964, in order to evaluate the research progress of machine translation, the American Academy of Sciences established the automatic language processing Advisory Committee (Alpac Committee) and began a two-year comprehensive investigation, analysis and test. In November 1966, the committee published a report entitled "language and machine" (Alpac report for short), which comprehensively denied the feasibility of machine translation and suggested stopping the financial support for machine translation projects. The publication of this report has dealt a blow to the booming machine translation, and the research of machine translation has fallen into a standstill. Coincidentally, during this period, China broke out the "ten-year Cultural Revolution", and basically these studies also stagnated. Machine translation has entered a depression. convalescence（1975-1989） Since the 1970s, with the development of science and technology and the increasingly frequent exchange of scientific and technological information among countries, the language barriers between countries have become more serious. The traditional manual operation mode has been far from meeting the needs, and there is an urgent need for computers to engage in translation. At the same time, the development of computer science and linguistics, especially the substantial improvement of computer hardware technology and the application of artificial intelligence in natural language processing, have promoted the recovery of machine translation research from the technical level. Machine translation projects have begun to develop again, and various practical and experimental systems have been launched successively, such as weinder system Eurpotra multilingual translation system, taum-meteo system, etc. However, after the end of the "ten-year holocaust", China has perked up again, and machine translation research has been put on the agenda again. The "784" project has paid enough attention to machine translation research. After the mid-1980s, the development of machine translation research in China has further accelerated. Firstly, two English Chinese machine translation systems, ky-1 and MT / ec863, have been successfully developed, indicating that China has made great progress in machine translation technology. New period(1990 present) With the universal application of the Internet, the acceleration of the process of world economic integration and the increasingly frequent exchanges in the international community, the traditional way of manual operation is far from meeting the rapidly growing needs of translation. People's demand for machine translation has increased unprecedentedly, and machine translation has ushered in a new development opportunity. International conferences on machine translation research have been held frequently, and China has made unprecedented achievements. A series of machine translation software have been launched, such as "Yixing", "Yaxin", "Tongyi", "Huajian", etc. Driven by the market demand, the commercial machine translation system has entered the practical stage, entered the market and came to the users. Since the new century, with the emergence and popularization of the Internet, the amount of data has increased sharply, and statistical methods have been fully applied. Internet companies have set up machine translation research groups and developed machine translation systems based on Internet big data, so as to make machine translation really practical, such as "Baidu translation", "Google translation", etc. In recent years, with the progress of in-depth learning, machine translation technology has further developed, which has promoted the rapid improvement of translation quality, and the translation in oral and other fields is more authentic and fluent.

The problem of machine translation at present

Error is inevitable Many people have misunderstandings about machine translation. They think that machine translation has great deviation and can't help people solve any problems. In fact, the error is inevitable. The reason is that machine translation uses linguistic principles. The machine automatically recognizes grammar, calls the stored thesaurus and automatically performs corresponding translation. However, errors are inevitable due to changes or irregularities in grammar, morphology and syntax, such as sentences with adverbials after "give me a reason to kill you first" in Dahua journey to the West. After all, a machine is a machine. No one has special feelings for language. How can it feel the lasting charm of "the tenderness of lowering its head, like the shame of a water lotus? After all, the meaning of Chinese is very different due to the changes of morphology, grammar and syntax and the change of context. Even many Chinese people are zhanger monks - they can't touch their heads, let alone machines. Bottleneck In fact, no matter which method, the biggest factor affecting the development of machine translation lies in the quality of translation. Judging from the achievements, the quality of machine translation is still far from the ultimate goal. Chinese mathematician and linguist Zhou Haizhong once pointed out in his paper "fifty years of machine translation": to improve the quality of machine translation, the first thing to solve is the problem of language itself rather than programming; It is certainly impossible to improve the quality of machine translation by relying on several programs alone. At the same time, he also pointed out that it is impossible for machine translation to achieve the degree of "faithfulness, expressiveness and elegance" when human beings have not yet understood how the brain performs fuzzy recognition and logical judgment of language. This view may reveal the bottleneck restricting the quality of translation. It is worth mentioning that American inventor and futurist ray cozwell predicted in an interview with Huffington Post that the quality of machine translation will reach the level of human translation by 2029. There are still many disputes about this thesis in the academic circles.

2.1.1Mistranslation of proper nouns

 In political materials, there are often political dignitaries' names, place names or a large number of proper nouns in the political field. The morphemes of such words are definite and inseparable. Mistranslation will make the source language lose its specific meaning. 

         Chinese translation into Japanese	                          Japanese translation into Chinese

original text translation by Youdao reference translation original text translation by Youdao reference translation

  栗战书	       栗戰史書	               栗戰書	             労安	         劳安	                劳安
  李克强	        李克強	               李克強	            朱鎔基	         朱基	               朱镕基
  习近平	        習近平	               習近平	           筑紫哲也	       筑紫哲也	               筑紫哲也
   韩正	         韓中	                韓正	           山口百惠	       山口百惠	               山口百惠
  王沪宁	       王上海氏	               王滬寧	           田中角栄	       田中角荣	               田中角荣
   汪洋	         汪洋	                汪洋	           東条英機	       东条英社	               东条英机
  赵乐际	        趙樂南	               趙樂際	            毛沢东	        毛泽东	                毛泽东
  江泽民	        江沢民	               江沢民	        トウ・ショウヘイ	 大酱	                邓小平
                                                                   周恩来	        周恩来                  周恩来

クリントン 克林顿 克林顿

 The above table counts 18 special names in the two texts, and 7 machine translation errors. In terms of mistranslation, there are not only "战书" but also "戰史" and "史書" in Chinese. "沪" is the abbreviation of Shanghai. In other words, since "战书" and "沪" are originally common nouns, this disrupts the choice of target language in machine translation. Except Katakana interference (except for トウシゃウヘイ, most of the mistranslations appear in the new Standing Committee. It can be seen that the machine translation system does not update the thesaurus in time. Different from other words, people's names have particularity. Especially as members of the Standing Committee of the Political Bureau of the CPC Central Committee, the translation of their names is officially unified. In this regard, public figures such as political dignitaries, stars, famous hosts and important. In addition, the machine also translates Japanese surnames such as "タ二モト" (谷本), "アンドウ" (安藤) into "塔尼莫特" and "龙胆". It can be found that the language feature that Japanese will be written together with Chinese characters and Hiragana also directly affects the translation quality of Japanese Chinese machine translation. Japanese people often use Katakana pronunciation. For example, when Japanese people talk with Premier Zhu, they often use "二ーハウ" (Hello). However, the machine only recognizes the two pseudonyms "二" and "ハ" and transliterates them into "尼哈" , ignoring the long sound after "二" and "ハ".

　 original text Translation by Youdao reference translation

      日美安全体制	                      日米の安全体制	                           日米安保体制

中国共产党第十九次全国代表大会 中国共産党第19回全国代表大会 中国共産党第19回全国代表大会（第19回党大会）

         十八大	                         十八大	                                   第18回党大会
    中国特色社会主义	                     中国特色社会主義	                     中国の特色ある社会主義
  中国共产党中央委员会	                   中国共産党中央委員会	                      中国共産党中央委員会
十八届中共中央政治局常委	    第18代中国共產党中央政治局常務委員	          第18期中共中央政治局常務委員
十八届中共中央政治局委员	      18期の中国共產党中央政治局委員	            第18期中共中央政治局委員
十九届中共中央政治局常委	    十九回中国共產党中央政治局常務委員	            第19期中央政治局常務委員
   中共十九届一中全会                中国共產党第十九回一中央委員会	          第19期中央委員会第1回全体会議
 The above table is a comparison of the original and translated versions of some proper nouns. As shown in the table, the mistranslation problems are mainly reflected in the mismatch of numerals + quantifiers, the wrong addition of case auxiliary word "の", the lack of connectors, the Mistranslation of abbreviations, dead translation, and the writing errors of Chinese characters. The following is a specific analysis one by one.
 "十八届中共中央政治局常委", "十八届中共中央政治局委员", "十九届中共中央政治局常委" and "中共十九届一中全会" all have quantifiers "届", which are translated into "代", "期" and "回" respectively. The meaning is vague and should be uniformly translated into "期"; Among them, the translation of the last three proper nouns lacks the Chinese conjunction "第". The case auxiliary word "の" was added by mistake in the translation of "十八届中共中央政治局委员" and "日美安全体制". The "中国共产党中央委员会" did not write in the form of Japanese Ming Dynasty characters, but directly used simplified Chinese characters.
 The full name of "中共十九届一中全会" is "中国共产党第十九届中央委员会第一次全体会议", in which "一" stands for "第一次", which is an ordinal word rather than a cardinal word. Machine translation does not produce a correct translation. The fundamental reason is that there are no "规则" for translating such words in machine translation. If we can formulate corresponding rules for such words (for example, 中共m'1届m2 中全会→第m1期中央委员会第m2回全体会议), the translation system must be able to translate well no matter how many plenary sessions of the CPC Central Committee.

2.1.2Mistranslation of Polysemy

　original text Translation by Youdao reference translation

   スタジオ	                                                   摄影棚/工作室	                                 直播现场/演播厅
  日中関係の話	                                                   中日关系的故事	                                就中日关系(话题)
      溝	                                                       水沟	                                              鸿沟

それでは日中の問題について質問のある方。 那么对白天的问题有提问的人。 关于中日问题的话题，举手提问。 私たちのクラスは20人ちょっとですが、 我们班有20人左右， 我们班二十多人的意见统一很难， いろいろな意見が出て、まとめるのは大変です。 但是又各种各样的意见，总结起来很困难。 中国是怎样把13亿人凝聚在一起的？ 一体どうやって、13億人もの人をまとめているんですか。 到底是怎么处理13亿的人的呢？

 In the original text, the word "スタジオ" appeared four times and was translated into "摄影棚" or "工作室" respectively, but the context is on-site interview, not the production site of photography or film. "話" has many semantics, such as "说话", "事情", "道理", etc. In accordance with the practice of the interview program, Premier Zhu Rongji was invited to answer five questions on the topic of China Japan relations. Obviously, the meaning of the word "故事" is very abrupt. The inherent Japanese word "日中" means "晌午、白天". At the same time, it is also the abbreviation of the names of the two countries. The machine failed to deal with it correctly according to the context. "溝" refers to the gap between China and Japan, not a "水沟". The former "まとめる" appears in the original text with the word "意见", which is intended to describe that it is difficult to unify everyone's opinions. The latter "まとめている" also refers to unifying the thoughts of 1.3 billion people, not "处理". Therefore, it is more appropriate to use "统一" and "处理" in human translation, rather than "总结意见" or "处理意见".
 Mistranslation of polysemy has always been a difficult problem in machine translation research. The translation of each word is correct, but it is often very different from the original expression in the context. Zhang Zhengzeng said that ambiguity is a common phenomenon in natural language. Its essence is that the same language form may have different meanings, which is also one of the differences between natural language and artificial language. Therefore, one of the difficulties faced by machine translation is language disambiguation (Zhang Zheng, 2005:60). In this regard, we can mark all the meanings of polysemous words and judge which meaning to choose by common collocation with other words. At the same time, strengthen the text recognition ability of the machine to avoid the translation inconsistent with the current context. In this way, we can avoid the mistake of "大酱" in the place where famous figures such as Deng Xiaoping should have appeared in the previous political articles.

2.1.3Mistranslation of compound words

 Multi class words refer to a word with two or more parts of speech, also known as the same word and different classes.

　 original text Translation by Youdao reference translation 1998年江泽民主席曾经访问日本, 1998年の江沢民国家主席の日本訪問し、 1998年、江沢民総書記が日本を訪問し、かつ、 同已故小渊首相签署了联合宣言。 かつて同じ故小渕首相が署名した共同宣言 亡くなられた小渕総理と宣言に調印されました

 Chinese "同" has two parts of speech: prepositions and conjunctions: "故" has many parts of speech, such as nouns, verbs, adjectives and conjunctions. This directly affects the judgment of the machine at the source language level. The word "同" in the above table is used as a conjunction to indicate the other party of a common act. "故" is used as a verb with the semantic meaning of "死亡", which is a modifier of "小渊首相". The machine regards "同" as an adjective and "故" as a noun "原因", which leads to confusion in the structure and unclear semantics of the translation. Different from the polysemy problem, multi category words have at least two parts of speech, and there is often not only one meaning under each part of speech. In this regard, software R & D personnel should fully consider the existence of multi category words, so that the translation machine can distinguish the meaning of words on the basis of marking the part of speech, so as to select the translation through the context and the components of the word in the sentence. Of course, the realization of this function is difficult, and we need to give full play to the wisdom of R & D personnel.

2.2.1Mistranslation of tenses

original text：History is written by the people, and all achievements are attributed to the people. Translation by Youdao：歴史は人民が書いたものであり、すべての成果は人民のためである。 reference translation：歴史は人民が綴っていくものであり、すべての成果は人民に帰することとなります。

 The original sentence meaning is "历史是人民书写的历史" or "历史是人民书写的东西". When translated into Japanese, due to the influence of Japanese language habits, the formal noun "もの" should be supplemented accordingly. In this sentence, both the machine and the interpreter have translated correctly. However, the machine's recognition of "的" is biased, resulting in tense translation errors. The past, present and future will become "history", and this is a continuous action. The form translated as "~ ていく" not only reflects that the action is a continuous action, but also conforms to the tense and semantic information of the original text. In addition, the machine's treatment of the preposition "于" is also inappropriate. In the original text, "人民" is the recipient of action, not the target language. As the most obvious feature of isolated language, function words in Chinese play an important role in semantic expression, and their translation should be regarded as a focus of machine translation research.

original text：李克强同志是十六届中共中央政治局常委,其他五位同志都是十六届中共中央政治局委员。 Translation by Youdao：李克強総理は第16代中国共産党中央政治局常務委員であり、他の５人の同志はいずれも16期の中国共産党中央政治局委員である。 reference translation：李克強同志は第16期中国共産党中央政治局常務委員を務め、他の５人は第16期中共中央政治局委員を務めました。

 Judging the verb "是" in the original text plays its most basic positive role, but due to the complexity of Chinese language, "是" often can not be completely transformed into the form of "だ / である" in the process of translation. This sentence is a judgment of what has happened. Compared with the 19th session, the 18th session has become history. This is an implicit temporal information. It is very difficult for machines without human brain to recognize this implicit information. "中共中央政治局常委" is the abbreviation of "中共中央政治局常务委员会委员" and it is a kind of position. In Japanese, often use it with verbs such as "務める","担当する",etc. The translator adopts the past tense of "務める", which conforms to the expression habit of Japanese and deals with the problem of tense at the same time. However, machine translation only mechanically translates this sentence into judgment sentence, and fails to correctly deal with the past information implied by the word "十八届".

2.2.2Mistranslation of honorifics

 Honorific language is a language means to show respect to the listener. Different from Japanese, there is no grammatical category of honorifics in Chinese. There is no specific fixed grammatical form to express honorifics, self modesty and politeness. Instead, specific words such as "您", "请" and "劳驾" are used to express all kinds of respect or self modesty. 
        Original text                       translation by Youdao                                  reference translation

女士们，先生们，同志们，朋友们 さんたち、先生たち、同志たち、お友达さん　　 ご列席の皆さん

          谢谢大家！                       ありがとうございます！                             ご清聴ありがとうございました。

これはどうされますか。 这是怎么回事呢? 您将如何解决这一问题？ こうした問題をどうお考えでしょうか。 我们会如何考虑这些问题呢？ 您如何看待这一问题？

 For example, for the processing of "女士们，先生们，同志们，朋友们", the machine makes the translation correspond to the original one by one based on the principle of unchanged format, but there are errors in semantic communication and pragmatic habits. When speaking on formal occasions, Chinese expression tends to be comprehensive and detailed, as well as the address of the audience. In contrast, Japanese usually uses general terms such as "皆様", "ご臨席の皆さん" or "代表団の方々" as the opening remarks. In terms of Japanese Chinese translation, the machine also failed to recognize the usage of Japanese honorifics such as "さ れ る" and "お考え". It can be seen that Chinese Japanese machine translation has a low ability to deal with honorific expressions. The occasion is more formal. A good translation should not only be fluent in meaning, but also conform to the expression habits of the target language and match the current translation environment.

Conclusion

 In the process of discussing the Mistranslation of machine translation, this paper mainly cites the Mistranslation of Youdao translator. When I studied the problem of machine translation mistranslation, I also used a large number of translation software such as Google and Baidu for parallel comparison, and found that these translation software also had similar problems with Youdao translator. For example, the "新世界新未来" in Chinese ABAC structural phrases is translated by Baidu as "新し世界の新しい未来", which, like Youdao, is not translated in the form of parallel phrases; Google online translation translates the word "“全心全意" into a completely wrong "全面的に"; The original sentence "我吃了很多亏" in the Mistranslation of the unique expression in the language is translated by Google online as "私はたくさんの損失を食べました". Because the "吃" and "亏" in the original text are not closely adjacent, the machine can not recognize this echo and mistakenly treats "亏" as a kind of food, so the machine translates the predicate as "食べました"; Japanese "こうした問題をどう考えでしょうか", Google's online translation is "你如何看待这些问题?", "你" tone can not reflect the tone of Japanese honorific, while Baidu translates as "你怎么想这样的问题呢?" Although the meaning can be understood, it is an irregular spoken language. Google and Baidu also made the same mistakes as Youdao in the translation of proper nouns. For example, they translated "十七届(中共中央政治局常委)”" into "第17回..." .In short, similar mistranslations of Youdao are also common in Baidu and Google. Due to space constraints, they will not be listed one by one here. 
 Mr. Liu Yongquan, Institute of language, Chinese Academy of Social Sciences (1997) It has been pointed out that machine translation is a linguistic problem in the final analysis. Although corpus based machine translation does not require a lot of linguistic knowledge, language expression is ever-changing. Machine translation based solely on statistical ideas can not avoid the translation quality problems caused by the lack of language rules. The following is based on the analysis of lexical, syntactic and other mistranslations From the perspective of the characteristics of the source language, this paper summarizes some difficulties of Chinese and Japanese in machine translation. 
(1) The difficulties of Chinese in machine translation 

Chinese is a typical isolated language. The relationship between words needs to be reflected by word order and function words. We should pay attention to the transformation of function words such as "的", "在", "向" and "了". Some special verbs, such as "是", "做" and "作", are widely used. How to translate on the basis of conforming to Japanese pragmatic habits and expressions is a difficulty in machine translation research. In terms of word order, Chinese is basically "subject→predicate→object". At the same time, Chinese pays attention to parataxis, and there is no need to be clear in expression with meaningful cohesion, which increases the difficulty of Chinese Japanese machine translation. When there are multiple verbs or modifier components in complex long sentences, machine translation usually can not accurately divide the components of the sentence, resulting in the result that the translation is completely unreadable. (2) Difficulties of Japanese in machine translation Both Chinese and Japanese languages use Chinese characters, and most machines produce the target language through the corresponding translation of Chinese characters. However, pseudonyms in Japanese play an important role in judging the part of speech and meaning, and can not only recognize Chinese characters and judge the structure and semantics of sentences. Japanese vocabulary is composed of Chinese vocabulary, inherent vocabulary and foreign vocabulary. Among them, the first two have a great impact on Chinese Japanese machine translation. For example "提出" is translated as "提出する" or "打ち出す"; and "重视" is translated as "重視する" or "大切にする". Japanese is an adhesive language, which contains a lot of "けど", "が" and "けれども" Some of them are transitional and progressive structures, but there are also some sequential expressions that do not need translation, and these translation software often can not accurately grasp them.

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Networking Linking

http://www.elecfans.com/rengongzhineng/692245.html

https://baike.baidu.com/item/%E6%9C%BA%E5%99%A8%E7%BF%BB%E8%AF%91/411793

13 陈湘琼Chen Xiangqiong（Study on Post-editing from the Perspective of Functional Equivalence Theory ）

Machine_Trans_EN_13

14 Bi bi Nadia（Machine Translation a Challenge for Human Translators)

Machine_Trans_EN_14

Abstract

Machine translation is a big obstacle in the way of Human translators or interpretors although it is quick and less time consuming.people are trying to get translation of their target language using through source language.For this purpose they are using digital apps like Google translation Google translation does not give accurate and exact interpretation.Google translator is translates word to word translate that doesn't clarifies it's true and actual meaning.On the other hand human translators can give exact and accurate translation ,they take care of grammatical errors , diction and sentence structure.They clarify the purpose of target language through using source language.

Keywords

Descriptive translation, academic, interdiscipline, comparative literature, localization,Translatology,school of thought, translation , studies, linguistics, corresponding

Body of article

Translation is the process of reworking text from one language into another to maintain the original message and communication. But, like everything else, there are different methods of translation, and they vary in form and function. What is translation? The term has become widely used among knowledge transfer researchers and practitioners, especially in the fields of health and health care. In a landmark review, Jonathan Lomas began to argue that 'The tasks… may be defined as to establish and maintain links between researchers and their audience, via the appropriate translation of research findings' (Lomas 1997, p 4). In 2004, the World Health Organization's World Report on Knowledge for Better Health suggested that 'One of the key contributions of research to health systems is the translation of knowledge into actions' (WHO 2004, p 33 and p 100). By 2006, special issues of WHO's Bulletin as well as the journals Evaluation and the Health Professions and the Journal of Continuing Education in the Health Professions were dedicated to translation.But what does 'translation' mean? It may be a new word for an old problem, meaning nothing more than 'transfer'. The rapidly emerging field of 'translational medicine' seems to take translation to mean generally what transfer might have meant, that is the transmission of knowledge and evidence 'from bench to bedside'. As one commentator has observed, "Translational medicine" as a fashionable term is being increasingly used to describe the wish of biomedical researchers to ultimately help patients' (Wehling 2008, abstract). Semantic uncertainty persists, not least because of the different interests of scientists, clinicians, patients and commercial firms (Littman et al 2007): 'Translational research means different things to different people, but it seems important to almost everyone' (Woolf 2008, p 211). In related fields, such as public health (Armstrong et al 2006), 'translation' seems to signify dissatisfaction with 'transfer'. It wants to move away from thinking of knowledge transfer as a form of technology transfer or dissemination, rejecting if only by implication its mechanistic assumptions and its model of linear messaging from A to B. But still, what does it signify?

Why translation?

'Translation' indicates a closer attention to the problem of shared meaning and how it might be developed. It seems to represent some new epistemological lubricant, facilitating the dissemination of texts and the application and use of the knowledge and information they in. Simply, translation might be the key to transfer. And yet, when we stop to think, we are more ambivalent. What is translated often seems somehow inferior, not real or original. Note how readily commentators reach for the idea that things might be 'lost in translation'. Knowing at a distance – made in and mediated by translation - makes for incomplete renditions, blurred images, partial truths. So what might 'translation' really mean? The purpose of this paper is to set out, for policy makers and practitioners, the theoretical and conceptual resources translation holds and seems to represent. In doing so, it explores understandings of translation in the fields of literature and linguistics and in the sociology of science and technology. It begins by setting out just why this idea of translation should make immediate, intuitive sense in relation to research, policy and practice. 1translation in research, policy and practice research as translation Research often entails translation from one language to another: where data is collected from more than one ethnic group, for example, or where the language of the researcher is other than that of the research subject. It may draw on a secondary literature or source documents written in different languages, and may be published and disseminated in languages other than the one in which it is first written up. 2In a different way, to conduct an interview is to ask for an account of experience and its meanings, but it is also to construct and translate that experience in terms defined at least in part by the researcher. In representing what is said, transcripts then select data, usually excluding significant gesture and eye-contact, for example. Often, certain characteristics of speech-acts (such as hesitations) will be edited out. In turn, the format of the transcript shapes the analytic use the researcher may make of it. The basis of research 'findings', then, is an artefact, a transcript or translation, not an original interaction (Ochs 1979, Barnes, Bloor and Henry 1996, Ross 2009). 3In this way, the researcher recasts aspects of his or her problem or topic in new, scientific form: 'All researchers "translate" the experiences of others' (Temple 1997, p 609). Research is invariably conducted in a sort of 'metalanguage' (Hantrais and Ager 1985): the research process can be conceived as one of successive translations (from theoretical formulation to operationalization, transcription, interpretation and dissemination). Theorization is a process of reciprocal back and forth between theory and fact, in which conceptions of each are revised in order that one fit the other (Baldamus 1974). 4 It is a kind of translation: a rereading, re-use, re-application or re-representation of what we know in new terms (Turner 1980). Referencing, too, is an act of translation, a form of appropriation and incorporation of one text by another (Gilbert 1977).policy as translation Each of the fields covered by the paper is diverse and ill-defined, and there is no intention here to provide a comprehensive account of any them. Sources have been chosen for their relevance: main references are cited in the text, and additional sources listed in footnotes.

2For a brief introduction to the technical issues involved in social research in more than one language, see Birbili (2000). For translation issues in survey research and question design in general, see Ervin and Bower (1952) and Deutscher (1968); on translating survey research instruments (in this instance health-related quality of life measures), Bowden and Fox-Rushby (2003). On the use of translators and interpreters, see Temple (1997) and Jentsch (1998). 3For an interesting discussion of this problem, see Bourdieu (1999), esp pp 621-626, 'The risks of writing'.

Difference between machine translation and human translators

Few people disagree on the differences between the two, but many argue over the quality of the translations. How accurate are machine translations? How reliable are human translations? Some say machine translation produces near-perfect translations while others are adamant that translations are incomprehensible and cause more problems than they solve. Results will, of course, vary depending on the source and target languages, the machine translation service used (e.g. Google Translate), and the complexity of the original text. Machine translation, love it or hate it, is here to stay. In fact, the machine translation market is growing at such a fast pace that it is predicted to reach $980 million by 2022.

Machine translation: the pros and cons

The advantages of machine translation generally come down to two factors: it’s faster and cheaper. The downside to this is the standard of translation can be anywhere from inaccurate, to incomprehensible, and potentially dangerous (more on that shortly).

The advantages of machine translation

Many free tools are readily available (Google Translate, Skype Translator, etc.) Quick turnaround time You can translate between multiple languages using one tool Translation technology is constantly improving The disadvantages of machine translation Level of accuracy can be very low Accuracy is also very inconsistent across different languages

Machines can't translate context

Mistakes are sometimes costly Sometimes translation simply doesn’t work The most important thing to consider with any kind of translation is the cost of potential mistakes. Translating instructions for medical equipment, aviation manuals, legal documents and many other kinds of content require 100% accuracy. In such cases, mistakes can cost lives, huge amounts of money and irreparable damage to your company’s image. So choose carefully! Human translation: the pros and cons Human translation essentially switches the table in terms of pros and cons. A higher standard of accuracy comes at the price of longer turnaround times and higher costs. What you have to decide is whether that initial investment outweighs the potential cost of mistakes. Alternatively, whether mistakes simply aren’t an option, like the cases we looked at in the previous section.

The advantages of human translation

It’s a translator’s job to ensure the highest accuracy Humans can interpret context and capture the same meaning, rather than simply translating words Human translators can review their work and provide a quality process Humans can interpret the creative use of language, e.g. puns, metaphors, slogans, etc. Professional translators understand the idiomatic differences between their languages Humans can spot pieces of content where literal translation isn’t possible and find the most suitable alternative The disadvantages of human translation Turnaround time is longer Translators rarely work for free Unless you use a translation agency, with access to thousands of translators, you’re limited to the languages any one translator can work with Simply put, human translation is your best option when accuracy is even remotely important. Other considerations to make are the complexity of your source material and the two languages you’re translating between – both of which can render machines pretty useless.

When to use machine and human translation The truth is, the debate over machine vs human translation is an unnecessary distraction. What we should really be talking about is when to use these two different types of translation services, because they both serve a very valid purpose.

Examples of when to use machine translation When you have a large bulk of content to translate and the general meaning is enough When your translation never reaches the final audience, e.g. you’re translating a resource as research for another piece of content Translating documents for internal use within a company, provided 100% accuracy isn’t needed To partially translate large chunks of content for a human translator to improve upon Examples of when to use human translation When accuracy is important Most cases where your translated content is received by a consumer audience When you have a duty of care to provide accurate translations (e.g. legal documents, product instructions, medical guidelines or health and safety content) When translating marketing material or other texts for creative language uses.

Conclusion

In the light of above mentioned facts and figures here by we would say that machine translation is challenge for human translators because it can reduces the wokload of translation but can't give accurate and exact translation of the traget language.It can be less reliable than human translation..