机器翻译中不同文本的的可译性-以谷歌神经机器翻译为例_英语论文

Translatability of Different Texts in Machining Translation: A Case Study of Google’s Neural Machine Translation System

摘要

近年来，随着经济的发展和科技的进步，全球各国的联系也越来越紧密，而语言也成为连接不同语言文化的国度之间的桥梁。而随着人工智能的兴起，翻译技术也在不断地发展，机器翻译软件也应运而生。它的产生和发展为翻译效率的提高作出了很大贡献，同时，由于技术缺陷等原因，它自身又存在问题。本文通过比较研究的方法，对比了文学和非文学文本在机器翻译中的差异，结合机器翻译的原理，讨论了其针对不同文本的可译性差异。虽然机器翻译还有待提高，但是它在处理某一文本是仍存在其优点。而我们在不断改进其技术的同时也要充分利用其优点，这样就能提高翻译的效率。

关键词：机器翻译；文学文本；非文学文本；可译性

Abstract

Recently, with the development of economy and technology, the communication between different countries is becoming closer and closer and language becomes the bridge connecting different cultures. Meanwhile, with the surging of artificial intelligence, translation technology is increasingly developing. Therefore, machine translation system comes into being. The emergence and development of machine translation system makes a great contribution to the improvement of translation efficiency. However, due to reasons like technological defect, it still has problems. This thesis discusses the difference in translation between literary and non-literary texts from a comparative study perspective. Combined with the basic operating principle of machine translation, this thesis analyzes the translatability of different texts. Although machine translation system still remains to be improved, it has advantages when dealing with certain texts. We should make a good use of its advantages while improving it. In this way, we can complete translation tasks more effectively.

Key words: machine translation, literary text, non-literary text, translatability

Contents

摘 要

Abstract

Acknowledgements

Chapter One  Introduction 1

1.1 Significance of the study 1

1.2 Objectives of the study 1

1.3 Methodologies of the study 1

1.4 Structure of the thesis 2

Chapter Two  Literature Review 3

2.1 Translatability and untranslatability 3

2.2 Classification of different texts 4

  2.2.1 Characteristic of literary text 5

  2.2.2 Characteristic of non-literary text 5

Chapter Three  Introduction to Machine Translation 7

3.1 The developing process of machine translation 7

  3.1.1 Budding period 7

  3.1.2 Depression period 8

  3.1.3 Recovery period 8

  3.1.4 Prosperity period 9

3.2 Classification of machine translation 9

  3.2.1 Rule-based machine translation 10

  3.2.2 Corpus-based machine translation 10

  3.2.3 Neural machine translation 10

Chapter Four  Analysis of Translatability of Different Texts in Machine Translation 12

4.1 The basic operating principle of Google's Neural Machine Translation 12

4.2 Analysis of literary texts in machine translation 13

  4.2.1 Analysis of poetry in machine translation 13

  4.2.2 Analysis of novel in machine translation 15

4.3 Analysis of non-literary texts in machine translation 17

  4.3.1 Analysis of scientific writing in machine translation 17

  4.3.2 Analysis of argumentative writing in machine translation 18

Chapter Five  Conclusion 21

References 22