Machine Trans EN 11

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

To the To Do list

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 2014:68-73).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: 118-119) 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.

3.1 Extraction and Replacement of Terms in the Source Text

As a branch of applied English, medical English is the product of the combination of English language knowledge and medical knowledge. The terms in medical papers have the characteristics of fixed and complex language structure. Although Google Translation is based on a corpus, the language structure is not fixed due to the limited size of the corpus. Therefore, the following two steps should be followed before machine translation. The first is to extract terms and create a glossary, and then replace the Chinese terms in the source text with the corresponding English expressions in the glossary. Of course, the terms of extraction should be searched and verified according to the actual situation. All of these words are verified before they can be replaced. This method can not only ensure the accuracy of term translation, but also maintain the consistency of expression, especially when dealing with long texts. Example1 Source abstract: 口腔白斑病癌变相关缺氧应答基因和微小RNA的芯片及表达验证。 Google translation: Microarray detection/Chip detection and expression verification of hypoxia response genes and microRNAs related to oral leukoplakia canceration. Published translation:Transcriptome array screening and verification of oral leukoplakia carcinogenesis-related hypoxia-responsive gene and microRNA. Analysis: The expression “ Affymetrix GeneChip” empolyed in this thesis is a human transcription array for transcriptome array. In the source abstract, “芯片检测“ can be meant “screening” but not detection, so the proper and appropriate translation of these expression should be “transcription array screening”, but the Google translates it into “microarry detection of chip detection”. Therefore, term extraction is essential and inevitable before putting the source abstracts into the machine translation software. After pre-editing source abstracts: 对 oral leukoplakia carcinogenesis 相关 hypoxia-responsive gene 和微小RNA进行 transcriptome array screening 及表达验证。 After pre-editing translation: Transcriptome array screening and expression- verification of hypoxia-responsive genes and microRNAs related to oral leukoplakia carcinogenesis.

3.2 Explication of Subordination

As mentioned above, the subordination of Chinese sentences is judged by certain specific words in machine translation . Chinese is a paratactic language, and sentences are often connected by internal logical relationships; while English depends on sentence structure, so sentences are often closely linked by various language forms. In order to improve the accuracy of machine translation, we can adjust the sentence structure and adjust the position of adjectives and their modifiers.

Example 2 Source abstracts:回顾性分析南京医科大学附属儿童医院中重度HIE患儿49例及同期就诊的无神经系统症状体征的足月新生儿为对照组31例的头颅磁共振成像（MRI）资料。 Google translation: A retrospective analysis that the brain magnetic resonance imaging (MRI) data of 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University and full-term neonates with no neurological symptoms and signs during the same period were included in the control group. Published translation: A total of 49 children with moderate to severe HIE admitted to the children’s Hospital Affiliated to Nanjing Medical University were retrospectively analyzed. Cranial magnetic resonance imaging (MRI) date of 31 full-term neonates without neurological symptoms and signs who visited the hospital during the same period were recruited as the control group.

Analysis: As the above example shows that “中重度HIE患儿” and “足月新生儿” in the source abstracts are from the Children’s Hospital Affiliated Nanjing Medical University. The Google translation shows that 49 children with moderate to severe HIE in the Children's Hospital of Nanjing Medical University. There is an error due to the misuse of subordination. There are some advice in order to solve the problem. That is, first to segment the sentence and then reconstruct the sentence. For instance, the Chinese expression “南京医科大学附属儿童医院” carries many modifiers, which requires to cut the modifiers and reconstruct the sentence. Therefore, the Chinese expression “南京医科大学附属儿童医院’can be divided into abstractions, leaving two sentences instead of one long sentence with modifiers.. After pre-editing source abstracts: 对49例中重度HIE患儿以及31例无神经系统症状体征的足月新生儿的MRI资料进行回顾性分析。这些患者收治于南京医科大学儿童附属医院。 After pre-editing translation: The MRI data of 49 children with moderate to severe HIE and 31 full-term newborns without neurological symptoms and signs were retrospectively analyzed. These patients were admitted to the Children’s Hospital of Nanjing Medical University.

3.3 Explication of Subject through Voice Changing

The extensive use of subjectless sentences are quite common in the Chinese abstracts, while English prefers to employ passive voice in the sentences so as to make them object and accurate. In order to take the characteristics of English sentences into great and careful consideration, the sentences written in passive voice in particular, it will be helpful for machine translation to find out the subject and reconstruct a sentence in passive voice (Wang Yan: 2008). The first is to discover the “doee” in a sentence and then is to reconstruct the sentence structure and put the “doee” before the verb. The last is to explicate the passive relation between the noun and the verb. Example 3 Source abstract: 回顾性分析2018年1月至2020年12月解放军总医院第七医学中心收治的500例老年髋部骨折患者的资料。 Google translation: A retrospective analysis of the data of 500 elderly patients with hip fractures admitted to the Seventh Medical Center of the PLA General Hospital from January 2018 to December 2020. Published translation: From January 2018 to December 2020, the data of 500 elderly patients with hip fracture treated in the Seventh Medical Center of PLA General Hospital were analyzed retrospectively. Analysis: The above example indicates that the “回顾性分析”in the Google Translate is a noun phrase, but the source abstracts actually describes an action or a behavior. The reason for such a mistake is that the machine can’t recognize the Chinese subjetless sentences. To find the subject of the sentence, the source abstracts can be revised and adjusted. Finding the “doee” is the first step, which refers to “500例老年髋部骨折患者的资料”in the source abstracts. And next is to put it in front of the verb, that is to place it in the beginning of the sentence. Last but not least, the passive voice should be explicated in the sentence. After pre-editing source abstract: 500例老年髋部骨折患者的资料被回顾性分析，他们在2018年1月之2020年12月期间收治于解放军总医院第七医学中心。 After pre-editing translation: The data of 500 elderly hip fracture patients were retrospectively analyzed. They were admitted to the Seventh Medical Center of the PLA General Hospital between January 2018 and December 2020.

3.4 Relocation of Modifiers

Modifiers, including noun, adverbial and attributive are constantly employed in the Chinese medical papers in order to add information to subject and object in the sentence. By doing this, complicated sentences can be structured, thus causing obstacles to machine translation for recognizing this complex sentence structures when translating Chinese-English sentences. To make the machine translation successfully recognize the sentence structure, simplifying the sentence structure is quite necessary. The key is to moving the location of the modifiers and thus making the modifiers an independent sentence. In other words, the modifiers ought to be put before or behind the main part of the sentence to satisfy the common use of English. Usually, the modifiers in Chinese sentence can only be placed in front of the core word, while in English, modifiers are very flexible. It is all right to place the modifiers in front of or behind the major part of the sentences with adjective or a connective noun.

Example 4 Source abstracts: 利用DNA重组技术以pET-28a表达系统在E.coli BL21(DE3) 中重组表达Hepl。 Google Translation: Hepl is recombined in E.coli BL21 (DE3) using DNA recombination technology with pET-28a expression system. Published translation: The recombinant Hcpl protein was expressed by using DNA recombination technology through pET-28a expression system in E. coli BL21 (De3). Analysis: The Chinese medical text includes two modifiers, “利用DNA”and “以pET-28a)表达系统”. These two modifiers will be translated by machine, which catches more attention to the two constituents. From the Google translation above, one failure is obvious that it misplaces the location of the two modifiers and presents it in not accurate form. To make it translate correctly by machine translation, dividing the sentences is very important so that the source abstracts can be correctly recognized by machine translation.

After pre-editing source abstract: 利用DNA重组技术，Hcp1重组表达在E.coli BL21 (DE3)中， 通过pET-28a表达系统在E. coli BL21 (DE3)中。 Google translation: Using DNA recombination technology, Hcp1 recombination is expressed in E.coli BL21 (DE3), via pET-28a expression system in E. coli BL21 (DE3). The second is the independence of modifiers, that is, modifiers can also be reconstructed into clauses to modify core words. Compared with English, There is no subordinate clause in Chinese, so redundant Chinese modifiers need to be reconstructed into subordinate clauses in Chinese-English translation to meet the characteristics of English. English, especially sentences with multiple modifiers. Otherwise, sentence structure may be confused, such as scattered modifiers of core words. To avoid this mistake, it is necessary to separate the modifier from the main part of the sentence. These modifiers should be reconstituted into clauses. Also, keep your sentences simple and easy to understand.

3.5 Proper Omission and Deletion of Category Words

Category words are commonly used in Chinese. Category words complement the meaning of words, including problems, positions, situations and jobs. Adding this supplementary word is more in line with Chinese custom. In many cases, it has no real meaning, so it can be omitted in translation. In Chinese, category words are frequently used. However, it is rarely used in English, which is one of the differences between Chinese and English. There are many kinds of category words. Considering objectivity and the fixed structure of language, redundant category words should be deleted in Chinese-English translation. In the general, the most commonly used category of words in the Chinese medical abstracts are "process", "behavior", and "situation". These categories of words hinder the language conversion of Chinese and English, resulting in redundancy.

Example 5 Source abstract: 探讨口腔白斑病癌变进程中的缺氧应答基因及相关微小RNA (miRNA) 的表达。 Google translation: The expression of hypoxia response genes and associated microRNAs in the process of oral leukoplakia cancer was discussed. Published translation: To study the hypoxia response gene and microRNA (miRNA)expression profiles in the pathogenesis and progression of oral leukoplakia (OLK). Analysis: As the above example shows, the Chinese words “进程” belongs to a category word. However, the Chinese expression “癌变”contains the process, so the “进程” expression can be deleted before placing it into the machine translation, because the meaning of it has been overlapping between the expression “癌变”. Therefore, its place can be replaced with preposition “during”.

After pre-editing source abstract: 探讨口腔白斑病癌变中的缺氧应答基因及相关微小RNA (miRNA) 的表达。 Google translation: The expression of hypoxia responsive genes and miRNA in oral leukoplakia cancer was investigated.

Conclusion

After years of development, machine translation has made great progress. The accuracy of machine translation has been greatly improved in both text recognition and sentence pattern conversion. However, machine translation has its own limitations. In other words, it needs to rely on the parallel corpus as a reference source for improving its accuracy. ESP text, in particular, is harder to get the high quality by machine translation.

As one of the research papers, the characteristics of medical abstracts are fixed language structures, objectivity and accuracy (Qin Yi 2004:421-423). Therefore, medical translation must be accurate, object and understandable to follow the specific demands of the medical paper. Being an important field in the human society, medical paper translation is on a great demand, which means that it needs a huge demand for human labor. However, with the machine translation promoting, it will be more efficient to translate medical papers combining the effort by human and machine. The improvement and development of machine translation requires the joint efforts of computer science, information science, statistics, linguistics and other academic circles to achieve more mature human-computer mutual assistance translation (Li Yafei, Zhang Ruihua 2019:38-45).

However, errors can occur during the process of machine translation of Chinese- English, because of the differences of the Chinese and English and the processing of the machine. Errors from the perspective of linguistic or grammar can affect the machine translation a lot. After division and recognition of errors, some pre-editing approaches are put forward to help the machine translation more accurate and readable, that are, extraction and replacement of terms in the source text, relocation of modifiers, explication of subordination, proper omission, deletion of category words and explication of subject through voice changing.

The paper mainly focuses on the pre-editing machine translation by using medical papers as a case study. The errors of machine translation occurring in the translation of medical abstracts and pre-editing approaches for machine translation. The quality of machine translation of medical papers is greatly improved after employing the pre-editing methods. However, machine translation is not as flexible and accurate as human brain, so it is of importance to combine pre-editing and post-editing approaches with machine translation in order to produce more accurate, more object machine translation of medical papers.

As for as I am concerned, it would be better if there are more examples to show the differences caused by machine translation and how effective it is if the pre-editing methods are adopted. Corrected by--Cai Zhufeng (talk) 11:01, 15 December 2021 (UTC)Cai zhufeng

References

Cui Qiliang崔启亮(2014).论机器翻译的译后编辑[J] On Post-Editing of Machine Translatio. 中国翻译 Chinese Translators Journal, 035(006):68-73

Feng Quangong, Gao Lin冯全功,高琳 (2017). 基于受控语言的译前编辑对机器翻译的影响[J] Influence of Pre-editing Based on Controlled Language on Machine Translation. 当代外语研究Contemporary Foreign Language Research,(2): 63-68+87+110.

GERLACH J, et al ( 2013). Combining Pre-editing and Post-editing to Improve SMT of User-generated Content[M]// Proceedings of MT Summit XIV Workshop on Post-editing Technology and Practice. 45-53

Hu Qingping胡清平(2005). 机器翻译中的受控语言[J] Controlled Language in Machine Translation. 中国科技翻译 Chinese Science and Technology Translation, (03): 24-27.

Lian Shuneng连淑能 (2010). 英汉对比研究增订本[M]An Updated Version of English-Chinese Contrastive Studies . 北京:高等教育出版社Beijing: Higher Education Publishing House. 35-36.

Li Yafei, Zhang Ruihua黎亚飞,张瑞华 (2019). 机器翻译发展与现状[J]The Development and Current Situation of Machine Translation. 中国轻工教育 China Light Industry Education, (5):38-45. 

Qin Yi秦毅(2004),从翻译基本标准议医学英语的翻译[J] On the Translation of Medical English from the Basic Standard of Translation. 遵义医学院学报 Journal of Zunyi Medical College,27 (4): 421-423.

Slype G V & Guinet J F & Seitz F (1984). Better Translation for Better Communication [M] . Oxford: Pergamon Press Ltd (U.K.). 90-93

O'Brien S (2002). Teaching Post-editing: A Proposal for Course Con-tent [EB/OL]. http://mt-archive. Info/EAMT-2002-0brien. Pdf.

Tytler, A. F. (1978). Essay On The Principles of Translation[M]. Amsterdam: JohnBenjamins Publishing. 118-119

Wang Yan王燕 (2008). 医学英语翻译与写作教程[M] Medical English Translation and Writing Course. 重庆:重庆大学出版社 Chongqing: Chongqing University Press. 60-61

Written by --Chen Huini (talk) 10:50, 15 December 2021 (UTC)Chen Huini