Machine Trans EN 8
Machine Translation - A challenge or a chance for human translators?
Overview Page of Machine Translation
30 Chapters（0/30)
Machine_Trans_EN_1 Machine_Trans_EN_2 Machine_Trans_EN_3 Machine_Trans_EN_4 Machine_Trans_EN_5 Machine_Trans_EN_6 Machine_Trans_EN_7 Machine_Trans_EN_8 Machine_Trans_EN_9 Machine_Trans_EN_10 Machine_Trans_EN_11 Machine_Trans_EN_12 Machine_Trans_EN_13 Machine_Trans_EN_14 Machine_Trans_EN_15 Machine_Trans_EN_16 Machine_Trans_EN_17 Machine_Trans_EN_18 Machine_Trans_EN_19 Machine_Trans_EN_20 Machine_Trans_EN_21 Machine_Trans_EN_22 Machine_Trans_EN_23 Machine_Trans_EN_24 Machine_Trans_EN_25 Machine_Trans_EN_26 Machine_Trans_EN_27 Machine_Trans_EN_28 Machine_Trans_EN_29 Machine_Trans_EN_30 ...
Back to translation project overview
8 颜静(On Machine Translation Under Lanuguage Intelligence——An Option and Opportunity for Human Translators) Machine_Trans_EN_8
Abstract
Nowadays the artificial intelligence is sweeping the world, however, the traditional language study and language service industry are 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 study and 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 a reference on the development path for preparatory translators（students majored in language and translation） and full-time and part-time formal translators.
Key words
Language Intelligence; Machine Translation; Interdisciplinarity; Language Service
题目
论语言智能下的机器翻译——译者的选择与机遇
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研究和语言服务行业的发展现状，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，为信息时代包括语言和翻译专业学生的准译员和专、兼职的正式译员提供发展路径上的参考。
关键词
语言智能；机器翻译；学科交叉；语言服务
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. 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.
2.1 The Development of Language Intelligence
According to academician Tan Tieniu, "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 representation, autopilot and so on. So, from these purposes we can see that language plays a vital role in AI. In order to imitate human intelligence, an advanced form of artificial intelligence is to analyze and process human language by using computer and information technology. We call it "language intelligence". Language intelligence is not only the core part of artificial intelligence, but also an important basis and means of human-computer interaction cognition, whose development will contribute to the whole process of AI and further to let AI technologies to practice. Therefore, it is known as the Pearl on the crown of artificial intelligence.
The concept of “language intelligence” was proposed in 2013 at Beijing Academic Forum on Language Intelligence. However, as mentioned above, its research direction in the computer field has always been called natural language processing (NLP). Its history is almost as long as computer and artificial intelligence. After the emergence of computer, there has been the research of artificial intelligence. Natural language processing generally includes two parts: natural language understanding and natural language generation(Chen Yin 2017: 2). The early research of artificial intelligence has involved machine translation and natural language understanding, which is basically divided into three stages.
The first stage is from 1960s to 1980s. In this period, the common method is to establish vocabulary, syntactic and semantic analysis, question and answer, chat and machine translation systems based on rules. The advantage is that rules can make use of human’s own knowledge instead of relying on data, and can start quickly; The problem is on its insufficient coverage, and its rule management and scalability have not been solved.
The second stage starts from 1990s. At this time, statistics-based machine learning (ML) has become popular, and many NLP began to use statistics-based methods. The main idea is to use labeled data to establish a machine learning system based on manually defined features, and to use the data to determine the parameters of the machine learning system through learning. At runtime, by using these learned parameters, the input data is decoded and output. Machine translation and search engines just make use of statistical methods and get success.
The third stage is after 2008, when deep learning functions in voice and image. Subsequently, NLP researchers begin to turn to deep learning. First, they use deep learning for feature calculation or establish a new feature, and then experience the effect under the original statistical learning framework. For example, search engines add in-depth learning to calculate the similarity between search words and documents to improve the relevance of search. Since 2014, people have tried to conduct end-to-end training directly through deep learning modeling. At present, progress has been made in the fields of machine translation, question and answer, reading comprehension and so on.(Li Deyi 2018: 168)
2.2 The Research on Machine Translation
Machine translation is an important research direction in the field of natural language processing. As early as the 17th century, Descartes, a famous French philosopher, put forward the concept of world language in order to convert words that expressing the same meaning in different languages into unified symbols. In 1946, Warren Weaver put forward the idea of using machines to convert words from one language into another, and published the famous memorandum Translation, formally marking the born of the modern concept——machine translation.
Until now, machine translation has experienced four stages according to its translation method: rule-based machine translation, case-based machine translation, statistics-based machine translation and neural machine translation. In the early stage of the development of machine translation, due to the limited computing power and lack of data, people usually input the rules designed by translators and Linguistics experts into the computer. The computer converts the sentences of the source language into the sentences of the target language based on these rules, which is rule-based machine translation. Rule based machine translation is usually divided into three procedures: source language sentence analysis, transformation and target language sentence generation. The source language sentence of the given input will generate a syntax tree after the lexical and syntactic analysis, and then the syntax tree is converted through the conversion rules to generate the syntax tree of the target language. Finally, the target language sentences are obtained by traversing the leaf nodes based on the target language syntax tree.
Rule-based machine translation requires professionals to design rules. When there are too many rules, the dependence between rules will become very complex and it is difficult to build a large-scale translation system. With the development of science and technology, people collect some bilingual and monolingual data, and extract translation templates and translation dictionaries based on these data. In translation process, the computer matches the translation template of the input sentence and generates the translation result based on the successfully matched template fragments and the translation knowledge in the dictionary, which is case-based machine translation.
With the rapid development of the Internet, it is possible to obtain large-scale bilingual and monolingual corpora. Statistical method based on large-scale corpora has become the mainstream of machine translation. Given the source language sentence, the statistical machine translation method models the conditional probability of the target language sentence, which is usually divided into language model and translation model. The translation model describes the meaning consistency between the target language sentence and the source language sentence, while the language model describes the fluency of the target language sentence. The language model uses large-scale monolingual data for training, and the translation model uses large-scale bilingual data for training. Statistical machine translation usually uses a decoding algorithm to generate translation candidates, then uses the language model and translation model to score and sort the translation candidates, and finally selects the best translation candidates as the translation output. Decoding algorithms usually include beam decoding, CKY decoding, etc.
Statistical machine translation uses translation rules (usually extracted from bilingual data based on alignment results) to match the input sentences to obtain the translation candidates of fragments in the input sentences. If there are multiple translation candidates in a segment, the language model and translation model are used to sort these translation candidates, and only some candidates with the highest scores are retained. Translation candidates based on these fragments use translation rules to splice fragments and then form translation candidates of longer fragments. There are two ways of splicing translation fragments: sequential and reverse. Translation model and language model will have different weights when scoring. The weights are usually trained by a development data set.
With the further improvement of computing power, especially the rapid development of parallel training based on GPU, the method based on deep neural network has attracted more and more attention in natural language processing. The method based on deep neural network was first used to train some sub models in statistical machine translation (language model based on deep neural network or translation model based on deep neural network), and significantly improved the performance of statistical machine translation. With the proposal of decoder and encoder framework and attention mechanism, neural machine translation has comprehensively surpassed statistical machine translation, and machine translation has entered the era of neural network.(Li Deyi 2018: 173-174)
3. Language Study in Information Times
The study to language is usually pointed to linguistics. Linguistics is the leading discipline of many humanities, such as literature, which promotes the development and progress of related humanities. Among them, the relationship between linguistics and translation research is particularly close, because in the final analysis, translation is first an operation at the language level, which is the research and application of language. At the same time, we also say that linguistics is a bridge between Humanities and natural sciences. In the information age, because of its own characteristics, language has applied many mathematical methods in research. These characteristics and methods play a very important role in the development and research of application systems such as machine translation and information retrieval. Therefore, in-depth research on language is a unique advantage for preparatory translators to the field of machine translation in language intelligence. Basically, language study can be divided into the following three categories.
3.1 Fundamental Study
Fundamental study is the study of the basic features of language. Linguistics can be divided into specific linguistics and general linguistics from the scope of research objects. Concrete linguistics takes a specific language as the research object. General linguistics takes all human languages as the research object, focusing on the commonness of language and the essence of language, so as to form the universal theory of language. In terms of the time of the research object, linguistics can be divided into diachronic linguistics and synchronic linguistics. Diachronic linguistics, also known as dynamic linguistics, mainly studies the development and evolution of language and its laws. It is a vertical study of language, such as the development history of Chinese and English. Synchronic linguistics, also known as static linguistics, mainly studies the structural system of language. It is a horizontal study of language, such as modern French, modern Chinese and so on. People are used to classifying linguistics from research methods. For example, the study of kinship languages by comparative method is called historical comparative linguistics; Contrastive linguistics is the study of languages without kinship. Structural linguistics and transformational generative linguistics also belong to this category. The basic research introduced above can also take a subsystem or aspect of language as the research object, so as to form idiom phonology, lexicology, grammar, semantics, dialectology and so on.
These basic studies of linguistics play an important leading role in translation. From a macro perspective, with the progress of linguistics and the introduction of language science, translation research has gone through various stages, such as semantics, systemic functional linguistics, pragmatics, stylistics, discourse analysis and typology. From a micro perspective, the birth of each linguistic translation research method is inseparable from a specific linguistic theory. Linguistic translation research is carried out on the basis of linguistics, a science specializing in language, trying to summarize some regular things from the research process to guide translation practice, or analyze the translation process, or evaluate the translation product - translation, or explain the essential characteristics of translation. Linguistic translation research is scientific, because it’s more rigorous, more systematic and closer to the essential characteristics of language (Xu Jun, Mu Lei 2021: 120). In a word, with the guidance of basic linguistic knowledge, translators can not only go further in translation, but also have the opportunity to try the applied research of machine translation and other interdisciplinary research.
3.2 Application Study
The applied study of language is collectively referred to as Applied Linguistics. Applied linguistics uses the theories, methods and basic research results of linguistics to clarify and solve language problems in other fields and transform the basic research results of linguistics into social benefits. The biggest research field of applied linguistics is language teaching, so Applied Linguistics in a narrow sense only refers to language teaching. Language teaching includes native language teaching, foreign language teaching and language diagnosis, treatment and rehabilitation of people with language disabilities. Dictionary compilation, writing creation and reform, the creation and implementation of special language codes used by the disabled, the standardization and promotion of standard language, language translation, social language countermeasures, etc. are also important research contents of Applied Linguistics. In recent decades, with the rapid development of information science and computer science, the fields of information retrieval and management, man-machine dialogue and artificial intelligence have also become important fields of Applied Linguistics. With the development of social science and technology, the field of Applied Linguistics is becoming wider and wider.
One of the major fields of Applied Linguistics involving translation is the study of speech acts. Speech act refers to the analysis of the influence of utterance on the behavior of the speaker and the listener. It studies not only the discourse itself, the so-called locational act, but also the speaker's intention, the illocutionary force, and the role of discourse on the listener, that is, the perlocutionary force. This is a difficult problem for machine translation, because it’s not good at interpreting the meaning outside language or speech. Searle divides speech acts into several types: assertive, directive, committed, expressive and declarative. When understanding the original text, the translator should recognize the illocutionary force, and should not be confused by the literal meaning. For example, when a salesperson sees a customer, he often says, “Is there anything I can do for you?” Or simply say a word, “yes?” The action in this is far greater than its literal meaning. If you don't recognize the action (these two sentences contain the expression of welcome) and literally translate it into "有什么事我可以为您效劳的吗" or "是吗?", it may make misunderstandings. These two sentences with the illocutionary force of expressive seem to be translated into “您要点什么？” and “您来了？” in order to achieve speech act equivalence. Of course, the translator must also consider the perlocutionary force, that is, the possible impact of discourse on the target readers. The translator's recognition of the illocutionary force of the original paragraph is not enough. If perlocutionary force is ignored, the work he has paid may be wasted, and even cause misunderstanding (Xu Jun, Mu Lei 2021: 135). Therefore, when it is difficult for machine translation to correctly translate, it is necessary for translators to show their skills. It is feasible to provide computer with manually labeled data sets for learning, to provide problem-solving ideas for experts in machine translation, or just to study in the field of language intelligence and then study machine translation.
3.3 Interdisciplinarity Study
In October 2018, the Ministry of Education decided to implement the "six excellence and one top-notch" program 2.0, which originally only included the top-notch student training program of basic disciplines such as mathematics and physics, added humanities such as psychology, philosophy, Chinese language and literature, history and so on for the first time. Shortly after that, 13 departments including the Ministry of education and the Ministry of science and technology officially launched the plan to comprehensively promote the construction of new engineering, new medicine, new agriculture and new liberal arts. The cross penetration between disciplines has become a major trend of the current scientific development. The emergence of many interdisciplinarities is a major symbol of contemporary science. Ma Feicheng, a professor at Wuhan University, explained: "on the whole, all disciplines and even the whole science are highly differentiated and constantly moving towards integration." Before that, people were not able to recognize the whole picture of things, and in order to conduct in-depth research, they had to divide science as a whole into relatively narrow disciplines. Therefore, although this improves the research efficiency, it leads to the isolation between disciplines. Ma Feicheng believes that while the mobile Internet has completely changed the way of human production and life, it has also triggered unprecedented legal, ethical and moral problems. "These problems are far from simple technical problems, but deep-seated social and cultural problems that people have never been involved in". The solution of these problems must rely on multi-disciplinary cooperation. As a result, the field of new liberal arts has emerged on the edge of interdisciplinary research. In his opinion, the proposal of the new liberal arts is based on the internal integration of liberal arts and the intersection of arts and science to study, understand and solve the complex problems in the discipline itself, in people and society. In recent years, humanities experimental classes have also appeared in Tsinghua University, Renmin University of China, Zhengzhou University and other universities, and collegiate teaching models have appeared in Xi'an Jiaotong University, Central China Normal University and other universities. These attempts are important experiences in the construction of new liberal arts.
For linguistics, linguistics has many traditional partners, such as literature, sociology, history, philosophy, logic, anthropology, culture, geography, archaeology, psychology and so on. Most of these partners belong to the humanities. Now linguistics has developed some new partners, such as mathematics, computer science, medicine, information science, communication science and so on. Most of these new partners belong to the field of science and technology. The relationship between linguistics and these new and old partners has developed and established many interdisciplinary disciplines of linguistics. The main ones are sociolinguistics, language philosophy, logical linguistics, human linguistics, geographic linguistics, psycholinguistics, neurolinguistics, pathological linguistics, mathematical linguistics, computational linguistics, experimental linguistics, etc. Computational linguistics, which uses computers to process language, is what the field of language intelligence focus on and the important direction for new liberal arts to develop.
Of course, in the face of technological development, the new liberal arts also face challenges. Liberal arts scholars lack the necessary information technology foundation and cannot effectively use technical tools to solve research problems in their own field; The relevant stuffs engaged in computer are often lack of knowledge in relevant fields and cannot effectively capture the real needs of liberal arts scholars, so they cannot compelely play the auxiliary role of technology in research. Moreover, Professor Han Jingtai of Beijing Language and Culture University also reminded that the construction of new liberal arts should not blindly tend to be new, and the essence of "liberal arts" should not be obscured in the process of integrating arts and science. After the intersection of Arts and science, we must pay more attention to and highlight the characteristics of "liberal arts". In any case, interdisciplinary development is indeed the requirement of the development of the times. For pure liberal arts students, an appropriate understanding of knowledge in other fields will also be a valuable asset and make personal development more competitive.
4. Language Service Industry with Machine Translation
Facing the upsurge of artificial intelligence, the traditional translation industry has also been put forward new requirements, and the production mode of translation has gradually changed. The translation industry has always been a result-oriented field, and with the help of computers, it can not only improve the efficiency and quality of translation, but also reduce the cost.
4.1 Translation Mode
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 development of deep learning, machine translation technology has further developed, which has promoted the rapid improvement of translation quality. However, although machine translation has many advantages, such as fast translation speed, large corpus, low cost and easy to control, machine translation is still difficult to be perfect due to the characteristics of language, but it is a feasible strategy to use computer-aided translation to form a man-machine combination mode. Today, with the close combination of computer-aided translation and machine translation, human identity has changed from absolute subject to "MT + cat + PE" mode of man-machine cooperation. We should welcome the arrival of new technology with a positive attitude and clearly identify the convenience it brings to us. It can be predicted that under the background of the development of language intelligence, post-translational editors will become the mainstream of the needs of the translation industry in the future. As Professor Li Sheng, a giant in computational linguistics, said, "Today's artificial intelligence is only weak artificial intelligence, not strong artificial intelligence or super artificial intelligence. Now the role of artificial intelligence is still to use machines to replace simple, repetitive and dangerous labor. If you want to solve the problem that you can't find rules, artificial intelligence can't do it or replace people. People should try to make good use of machines as an assistant to not only improve work efficiency, but also ensure quality." As for the competition between machine translation and human translation, Professor Li Sheng believes, "The best translators must be those who have a deep understanding of artificial intelligence systems and can use them freely. If the artificial intelligence systems are used as auxiliary means, translator’s level will be higher, and the effect be better. It is not the problem of who will be eliminated because machines will always be human’s tools."
4.2 Translators
With the continuous development of machine translation, part-time translators can get great facilitation from the model of "MT + cat + PE". But for full-time translators, the difficulty of translation tasks will gradually increase. Full-time translators need to improve their professional ability in vertical fields that are difficult to reach by machine translation. In addition, they can combine translation ability with other fields. In terms of the definition of language service, Mr. Wang Lifei thinks that language service is based on cross language ability. With the goal of information transformation, knowledge transfer, cultural communication and language education, it is a modern service industry that provides professional services such as translation services, technology R & D, tool application, asset management, marketing trade, investment and M & A, research and consultation, training and examination in the fields of high-tech, international economy and trade, foreign-related law, international communication, government affairs and foreign language training. The definition clearly shows the service basis, service mode and service scope of language service. From the perspective of service basis, it must rely on language ability, and all service activities are language related; from the perspective of service mode, it must provide bilingual or multilingual conversion, information transfer or product marketing and trade, as well as investment and M & A of language service enterprises. Therefore, development , application, management, training, consulting, marketing, trade, etc. must be based on cross language rather than monolingual; from the perspective of service scope, language service industry is an integral part of modern service industry, serving all walks of life of the national economy, including agriculture and industry, as well as other modern service industries, such as transportation and logistics, information service industry, finance and insurance Real estate, leasing and business services, scientific research, technical services, education, culture, sports and entertainment, etc. So, translators do not have to stick to pure language translation but can combine with other fields to tap and give full play to their potential and value.
Conclusion
With the continuous development of artificial intelligence and translation technology, great changes will take place in the language service industry, and translation technology will play a greater role in it. As preparatory translators, students should seize the opportunity to constantly learn new knowledge and make full use of their own language advantages to occupy a place in the field of translation technology, while formal translators need to put aside their prejudices and embrace new technology and its convenience, while grasping the translation mode of man-machine combination, constantly improve their core competitiveness to achieve vertical development, and combine with other fields to achieve horizontal development.
References
Chen Yin 陈鄞. (2017). 自然语言处理基本理论和方法 [Basic Theories and Methods of Natural Language Processing]. Harbin: Harbin Institute of Technology Press 哈尔滨工业大学出版社.
Feng Zhiwei 冯志伟. (2011).语言与数学 [Language and Mathematics].Beijing: World Book Publishing Company 世界图书出版公司.
Han Lintao 韩林涛. (2020). 译者编程入门指南 [An Introduction Guide to Translator Programming]. Beijing: Tsinghua University Press 清华大学出版社.
Hu Kaibao, Tian Xujun 胡开宝,田绪军. (2020). 语言智能背景下的MTI人才培养:挑战、对策与前景 [MTI talent training in the context of language intelligence: challenges, countermeasures and prospects]. 外语界 foreign language 2020(02) 59-64.
Li Deyi 李德毅. (2018). 人工智能导论 [Introduction to Artificial Intelligence]. Beijing: China Science and Technology Press 中国科学技术出版社.
Li Hanji 李晗佶. (2021). 人工智能时代翻译技术与译者关系演变与重构 [Evolution and reconstruction of the relationship between translation technology and translators in the era of artificial intelligence]. 西华师范大学学报(哲学社会科学版) Journal of West China Normal University (PHILOSOPHY AND SOCIAL SCIENCES EDITION) (2021-12-04) 1-6.
Liang Xiaobo, Deng Zhen 梁晓波,邓祯.(2021). 美军语言智能处理技术的发展策略与启示 [Liang Xiaobo, Deng Zhen]. 国防科技 National defense science and technology 42(04) 85-91.
Wang Hongqi 王红旗. (2008). 语言学概论 [Introduction to Linguistics]. Beijing: Peking University Press 北京大学出版社.
Wang Huashu, Ma Shichen, Yang Shaolong 王华树,马世臣,杨绍龙. (2021). 语言服务行业翻译技术发展现状及前瞻 [Development status and Prospect of translation technology in language service industry].河南工业大学学报(社会科学版) Journal of Henan University of Technology (SOCIAL SCIENCE EDITION) 37(04) 1-6.
Wang Lianzhu 王连柱. (2018). 机器学习应用于语言智能的研究综述 [Research review on the application of machine learning to language intelligence]. 现代教育技术 Modern educational technology 28(09) 66-72.
Wang Lifei王立非. (2021). 从语言服务大国迈向语言服务强国 ——再论语言服务、语言服务学科、语言服务人才 [Marching from a Large Country to a Strong One in Language Services —Revisiting Language Services, Language-service Discipline, and Language-service Talents]. 北京第二外国语学院学报 Journal of Beijing International Studies University 43(04) 3-11.
Wang Zonghua 王宗华. (2021). 人工智能时代语言服务业发展对策研究 [Research on the countermeasures of language service industry development in the era of artificial intelligence]. 齐齐哈尔大学学报(哲学社会科学版) Journal of Qiqihar University (PHILOSOPHY AND SOCIAL SCIENCES EDITION) 2021(06) 131-134.
Xu Jun, Mu Lei 许均, 穆雷. (2021). 翻译学概论 [Introduction to Translatology].Beijing: Yilin Publishing House 译林出版社.
Zhang Le, Tang Liang 张乐,唐亮. (2020). 人工智能时代语言学家面临的机遇和挑战 [Opportunities and challenges faced by linguists in the era of artificial intelligence].电脑知识与技术 Computer Knowledge and Technology 16(24) 195-197.