机器翻译近十年发展综述——基于CiteSpace可视化分析
Overview of the Development of Machine Translation in the Past Ten Years—Based on CiteSpace Visual Analysis
DOI: 10.12677/ML.2023.113159, PDF,   
作者: 张 玥:上海海事大学外国语学院,上海
关键词: 机器翻译研究热点发展趋势Machine Translation Research Hotspots Development Trend
摘要: 机器翻译是利用计算机将源语言转换为目标语言的过程,是计算语言学的一个分支,是人工智能在翻译领域的一大进步,具有重要的科学研究价值。随着科技的迅猛发展,机器翻译从“机械大脑”到基于规则的机器翻译和基于统计的机器翻译,再到基于深度学习的神经网络机器翻译,技术上不断推陈出新,算法上不断升级优化。同时,在经济全球化的大背景下,机器翻译技术在促进政治、经济、文化交流等方面起到越来越重要的作用。本文将借助CiteSpace可视化分析来探讨2012年至2022年机器翻译的发展,发现围绕机器翻译主题所引出的研究热点有译后编辑、人工智能等方面,并且机器翻译逐渐与其他学科相结合,形成跨学科研究模式。因此,在未来的发展中,我们要多领域地培养人才,以此促进机器翻译技术创新。
Abstract: Machine translation is the process of converting the source language into the target language by using computers. It is a branch of computational linguistics and a great progress of artificial intelligence in the field of translation. It has important scientific research value. With the rapid development of science and technology, machine translation has evolved from “mechanical brain” to rule-based machine translation and statistic-based machine translation, and then to neural network machine translation based on deep learning. In terms of technology, it continues to bring forth new ideas, and in terms of algorithms, it continues to upgrade and optimize. At the same time, in the background of economic globalization, machine translation technology plays an increasingly important role in promoting political, economic and cultural exchanges. This paper will explore the development of machine translation from 2012 to 2022 with the help of CiteSpace visual analysis. It is found that the research hotspots around the topic of machine translation include post-translation editing, artificial intelligence and other aspects, and machine translation is gradually combined with other disciplines to form an interdisciplinary research model. Therefore, in the future development, we should cultivate talents in multiple fields to promote the technological innovation of machine translation.
文章引用:张玥. 机器翻译近十年发展综述——基于CiteSpace可视化分析[J]. 现代语言学, 2023, 11(3): 1171-1176. https://doi.org/10.12677/ML.2023.113159

参考文献

[1] Hutchins, W.J. (1986) Machine Translation: Past, Present, and Future. Ellis Horwood Limited, Chichester.
[2] Bar-Hillel, Y. (1958) Some Linguistic Obstacles to Machine Translation. Proceedings of the Second International Congress on Cybernetics, Belgium, 3-10 September 1958, 197-207.
[3] Bar-Hillel, Y. (1959) Report on the State of Machine Translation in the United States and Great Britain.
[4] Hutchins, J. (2010) Machine Translation: A Concise History. Journal of Translation Studies, 13, 29-70.
[5] Poibeau, T. (2017) Machine Translation. The MIT Press, Boston. [Google Scholar] [CrossRef
[6] 徐一灿, 刘继斌. 机器翻译的现状和前景[J]. 海外英语, 2017(21): 117-118+148.
[7] 冯志伟. 机器翻译与人工智能的平行发展[J]. 外国语(上海外国语大学学报), 2018, 41(6): 35-48.
[8] 侯强, 侯瑞丽. 神经机器翻译研究——洞见与前景[J]. 外语学刊, 2021(5): 54-59.
[9] Bahdanau, D., Cho, K. and Bengio, Y. (2014) Neural Machine Translation by Jointly Learning to Align and Translate. ArXiv: 1409.0473.
[10] Chung, J., Gulecehre, C., Cho, K. and Bengio, Y. (2014) Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. ArXiv: 1412.3555.
[11] 冯全功, 刘明. 译后编辑能力三维模型构建[J]. 外语界, 2018(3): 55-61.
[12] Konttinen, K., Salmi, L. and Koponen, M. (2020) Revision and Post-Editing Competences in Translator Education. In Koponen, M., Mossop, B., Robert, I.S. and Scocchera, G., Eds., Translation Revision and Post-Editing: Industry Practices and Cognitive Processes, Rutledge, London & New York, 187-202. [Google Scholar] [CrossRef