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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Key words

Machine Translation; Human Translation; International Sports Events；Artificial Intelligence

题目

论机器翻译与人工翻译的质量对比——以人工智能在体育赛事领域的应用为例

关键词

机器翻译；人工翻译；国际体育赛事；人工智能

2 吴映红

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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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Key words

Machine Translation; Huamn Translation; Realm Advantages; Symbiotic Development

题目

论机器翻译与人工翻译的领域优势及共生发展

摘要

机器翻译自1947年问世以来不断发展，并逐渐在翻译市场发挥着举足轻重的作用，随之而至的便是人们对于机器翻译与人工翻译之间关系的思考与研究。机器翻译的应运而生给人工翻译市场带来的究竟是巨大的冲击还是无限的机遇呢？二者的关系走向将会如何，是取而代之还是并驾齐驱？译者该如何应对机器翻译的挑战？
   笔者认为随着科学技术的不断完善，人工翻译和机器翻译在不同的领域各自都具备一定主导地位，但机器翻译仍旧存在一定缺陷，永远不可能取代人工翻译。本文立足于机器翻译与人工翻译的不同特点，浅析二者各自的领域优势，探究其共生发展的可能性以及途径。

关键词

人工翻译；机器翻译；领域优势；共生发展

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

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Key words

Machine Translation; Human Translation; Contrastive Analysis

题目

有道神经网络机器翻译与传统人工翻译的译文对比——以经济学人语料为例

关键词

人工翻译；机器翻译；对比分析

5 杨柳青

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

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Abstract

With the acceleration of economic globalization, there is a growing demand for translation services. In recent years, with the rapid development of neural networks and deep learning, the quality of machine translation has been significantly improved. Compared with human translation, machine translation has the advantages of low cost and high speed.

Neural machine translation brings both convenience and pressure to translators. Based on the principles of neural machine translation, this paper will objectively analyze the advantages and disadvantages of neural machine translation, and discuss whether neural machine translation is a chance or a challenge for human translators.

Key words

Neural network; Deep learning; Machine translation; human translation

题目

基于神经网络的机器翻译 --机遇还是挑战？

摘要

随着经济全球化进程的加速，人们对翻译服务的需求也越来越大。近些年来，神经网络和深度学习等技术得到快速发展，机器翻译的质量得到了显著提高，较人工翻译来讲有着成本低，速度快等优势。

基于神经网络的机器翻译给翻译工作者带来便捷的同时，同时也给翻译工作者们带来了一定的压力。本文将会从神经机器翻译的原理出发，客观分析基于神经网络的机器翻译中存在的一些优势与劣势，并以此来探讨机器翻译对于翻译工作者来说到底是机遇还是挑战这一问题。

关键词

神经网络；深度学习；机器翻译；人工翻译；

1.Introduction

Machine Translation, (Li Mu, Liu Shujie, Zhang Dongdong, Zhou Ming 2018, 2) a branch of Natural Language Processing (NLP), refers to the process of using a machine to automatically translate a natural language (source language) sentence into another language (target language) sentence. The natural language here refers to human language used daily (such as English, Chinese, Japanese, etc.), which is different from languages created by humans for specific purposes (such as computer programming languages).

According to statistics, there are about 5600 human languages in existence. In China, as we are a big family composed of 56 ethnic groups, some ethnic minorities also have their own languages and scripts. In other countries, due to the history of colonization, these countries usually have multiple official languages, so some official documents usually need to be written in more than two languages. In the context of the “One Belt, One Road” initiative, communication among different languages has become an important part of building a community with a shared future for mankind. Therefore, the application of machine translation technology can help promote national unity, communication between different languages and cross-cultural communication.

Although the latest machine translation method, neural machine translation, has advantages such as speed and low cost, machine translation is still far from being as effective as human translation. Li Yao (Li Yao 2021, 39) selects Chronicle of a Blood Merchant translated by Andrew F. Jones, a classic work of Yu Hua, and the versions of Baidu Translation, Youdao Translation and Google Translation as corpus to conduct a comparative study on translation quality. Starting from the development of machine translation, Jin Wenlu (Jin Wenlu 2019, 82) analyzed the advantages and disadvantages of machine translation and manual translation, discussed the question of whether machine translation can replace human translation. In order to further explore the impact of machine translation on translators, this paper will take neural machine translation - the latest machine translation as an example to discuss whether machine translation is a chance or a challenge for translators.

2.Comparison of different machine translation methods

Actually, the development of Machine Translation methods is going through four stages: rule-based methods, instance-based methods, statistical machine translation and neural machine translation. At present, thanks to the application of deep learning methods, neural machine translation has become the mainstream. Compared with statistical machine translation, neural machine translation has the following advantages:

1) End-to-end learning does not rely on too many prior assumptions. In the era of statistical machine translation, model design makes more or less assumptions about the process of translation. Phrase-based models, for example, assume that both source and target languages are sliced into sequences of phrases, with some alignment between them. This hypothesis has both advantages and disadvantages. On the one hand, it draws lessons from the relevant concepts of linguistics and helps to integrate the model into human prior knowledge. On the other hand, the more assumptions, the more constrained the model. If the assumptions are correct, the model can describe the problem well. But if the assumptions are wrong, the model can be biased. Deep learning does not rely on prior knowledge, nor does it require manual design of features. The model learns directly from the mapping of input and output (end-to-end learning), which also avoids possible deviations caused by assumptions to a certain extent.

2) The continuous space model of neural network has better representation ability. A basic problem in machine translation is how to represent a sentence. Statistical machine translation regards the process of sentence generation as the derivation of phrases or rules, which is essentially a symbol system in discrete space. Deep learning transforms traditional discrete-based representations into representations of continuous space. For example, a distributed representation of the space of real numbers replaces the discrete lexical representation, and the entire sentence can be described as a vector of real numbers. Therefore, the translation problem can be described in continuous space, which greatly alleviates the dimension disaster of traditional discrete space model. More importantly, continuous space model can be optimized by gradient descent and other methods, which has good mathematical properties and is easy to implement.

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.

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

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Abstract

Nowadays the artificial intelligence is sweeping the world, however, the traditional language research and language service industry is facing new challenges. This paper attempts to comb and analyze the development process of language intelligence in artificial intelligence and the development status of language industry under the background of information age to interpret the feasibility of liberal arts translators to engage in machine translation research and necessity to apply machine translation, thus to provide an option for human translators in information age to develop.

Key words

New Libral Arts; Language Intelligence; Machine Translation; Interdisciplinarity

题目

论语言智能之机器翻译——我们的选择和未来

摘要

当今人工智能的热潮席卷全球，而传统的语言研究和语言服务行业却面临着新的挑战。本文通过梳理分析人工智能中语言智能领域的发展历程和信息时代背景下语言行业的发展现状，对文科译者从事机器翻译研究的可行性和应用机器翻译的必要性进行阐述，为信息时代译者的发展路径提供参考。

关键词

新文科；语言智能；机器翻译；学科交叉

1. Introduction

Obviously, we are now in an era of "explosion" of information and knowledge, which makes us have to find ways to deal with it quickly. Language is the manifestation of information, and the tool that can deal with language with complicated information is just computer. It happens that human beings do not have a special organ to perceive language, but carry the image and sound symbols of language through visual and auditory perception, and then form language information through brain processing and abstraction. Therefore, language intelligence also belongs to the research category of "cognitive intelligence". In view of this, computer has carried out the research on language, among which the common research fields are "natural language processing", "language information processing" and "Computational Linguistics". These three are different, but they all have the same goal, that is, to enable computers to realize and express with language, solve language related problems and simulate human language ability. Among them, machine translation is the integration of language intelligence and technology. The comprehensive research of MT in China starts from the mid-1980s. Especially since the 1990s, a number of MT systems have been published and commercialized systems have been launched. In addition, various universities in China have also carried out MT and computational linguistics research, developed various translation experimental systems and achieved fruitful results. In the research of machine translation, it involves not only translation model and language model, but also alignment method, part of speech tagging, syntactic analysis method, translation evaluation and so on. Therefore, researchers must understand the basic knowledge of translation and be proficient in English, Chinese or other languages. Therefore, we say that compound talents with computer and language related knowledge will be more needed in the language industry or the computer field.

2. Chapter 1 Artificial Intelligence in Rapid Development

At the Dartmouth Conference in 1956, the word "artificial intelligence" appeared in the human world for the first time. In the past 65 years, with the in-depth study of science, artificial intelligence seems to have come out of the original science fiction movies and science fictions, and is closer to human daily life step by step. Nowadays, autopilot, machine translation, chess and E-sports robots, AI synthetic anchor, AI generated portrait and so on have been realized and widely known. Artificial intelligence has also moved from logical intelligence and computational intelligence to today's cognitive intelligence. According to academician Tan Tieniu mentioned in magazine recently, "Artificial intelligence is a technical science that studies and develops theories, methods, technologies and application systems that can simulate, extend and expand human intelligence. Its purpose is to enable intelligent machines to listen, see, speak, think, learn and act, that is, they have the following capabilities——speech recognition and machine translation, image and character recognition, speech synthesis and man-machine dialogue, man-machine games and theorems proving, machine learning and knowledge reprentation, autopilot amd so on. In general, special artificial intelligence has made major breakthroughs. Although general artificial intelligence is in its infancy, it is accelerating its intersection and penetration with other fields and rapidly changing the development of science.

4. Chapter 3 Language Service Industry with Machine Translation

3.2 Translators with More Professional and Diversified Career Path

3.2.1 The Improvement of Tranlation Ability

3.2.2 The Combination with Other Field

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

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

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Abstract

With the rapid development of information technology,machine translation technology emerged and is gradually becoming mature.In order to explore the ability of machine translation, I adopts two versions of translation, which are manual translation and machine translation(this paper uses Youdao translation) for different types of texts(according to Peter Newmark's types of text). The results are quite different in terms of quality and accuracy.

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

随着信息技术的高速发展，机器翻译技术出现了，并且逐渐成熟。为了探究机器翻译的能力水平，本人根据纽马克的文本类型分类，选择了相应的译文类型，并且将其机器翻译的版本以及人工翻译的版本进行对比。就质量和准确度而言，译文的水平大相径庭。

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. However in the 1960s, reports from ALPAC (Automated Language Processing Advisory Committee) showed studies on machine translation had stagnated for a decade. In the 1970s, with the advancement of computer, machine translation was back to track. In the last decades, machine translation has mainly developed into four stages: rule-based machine translation, statistic machine translation, example-based machine translation and neural machine translation.

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.Newmark’s type of texts

Peter Newmark divided texts into informative type, expressive text and vocative type according to the linguistic functions of various texts.

2.11Informative text

The core of the informative texts is the truth. It is to convey facts, information, knowledge and the like. The language style of the text is objective and logical. Reports, papers, scientific and technological textbooks are all attributed to informative texts.

2.2Expressive text

The core of the expressive text is the emotion. It is to express preferences, feelings, views and so on. The language style of it is subjective. Literary works, including fictions, poems and drama, autobiography and authoritative statements belong to expressive text.

2.3Vocative text

The core of the vocative text is readership. It is to call upon readers to act in the way intended by the text. So it is reader-oriented. Such texts advertisement, propaganda and notices are of vocative text.

2.4Study 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

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12 蔡珠凤

Machine_Trans_EN_12

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

Machine_Trans_EN_13

Abstract

With the development of technology，machine translation methods are changing. From rule-based methods to corpus-based methods，and then to neural network translation，every time machine translation become more precise, which means it is not impossible the complete replacement of human translation by machine translation. But machine translation still faces many problems until today such as : fail to translate special terms, incapable to set the right sentence order, unable to understand content and culture background etc. All of these need to be checked out and modified by human translator, so it can be predict that the model Human + Machine will last for a long period. This article will discuss mistakes made in machine translation and describe what translators should do in post-editing based on the skopos theory and functional equivalence theory

Key words

machine translation，post-editing，skopos theory，functional equivalence theory

题目

基于功能对等视角探讨译后编辑问题与对策

摘要

随着科技的不断发展，机器翻译方法也在不断变革，从基于规则的机器翻译，到基于统计的机器翻译，再到今天基于人工神经网络的机器翻译，每一次变化都让机器翻译变得更精确，更高质。这意味着在不远的将来，机器翻译完全代替人工翻译成为一种可能。但是直至今天，机器翻译仍然面临许多的问题如：无法准确翻译术语、无法正确排列句子语序、无法分辨语境等，这些问题依然需要人工检查和修改。机器翻译自有其优点，人工翻译也有无可替代之处，所以在很长一段时间内，翻译都应该是机器+人工的运作方式。本文将基于翻译目的论和功能对等理论，对机器翻译可能出现的错误之处进行探讨，并且旨在描述译者在进行译后编辑时需要注重的方面，为广大译员提供参考。

关键词

机器翻译，译后编辑，翻译目的论，功能对等