神经网络机器翻译技术背景下机器翻译效果对比及其发展困境

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英文篇名：Translation Effect of Neural Machine Translation and the Trouble It Face 作者：杨渊艺 ; 张洋睿 英文作者：YANG Yuan-yi;ZHANG Yang-rui;School of Foreign Languages, Pingxiang University; 关键词：神经网络机器翻译技术 ; 机器翻译 ; 翻译软件 英文关键词：NMT;;machine translation;;translation software 中文刊名：PXJY 英文刊名：Journal of Pingxiang University 机构：萍乡学院外国语学院; 出版日期：2019-02-28 出版单位：萍乡学院学报 年：2019 期：v.36;No.182 语种：中文; 页：PXJY201901019 页数：5 CN：01 ISSN：36-1339/G4 分类号：93-97

摘要

神经网络机器翻译技术模拟人脑神经系统,以深度学习技术为基础,将整个句子作为翻译的基本单元,使得机器翻译的准确率大大提升。谷歌、百度、腾讯三家公司推出的翻译软件都采用了神经网络翻译技术,通过比较可知三者的长句翻译能力突出,译文风格各具特色,但在语言方面都仍面临困境。首先,因语言差异导致的漏译误译;其次,因词义多义引起的选词障碍;第三,因语境因素带来的隐性含义的理解。本文认为只有不断完善深度学习的算法,建立纠错数据库,进行跨学科交流,才能进一步提升机器翻译质量。
        Neural Machine Translation(NMT) technology, based on deep learning technology, simulates the human brain nerve system, and takes the whole sentence as the basic unit of translation, which greatly improves the accuracy of machine translation. The translation softwares launched by Google, Baidu, and Tencent all use neural network translation technology. By comparing the three softwares, all of them are excellent in translating long sentence and each has distinctive translation styles. However, they still are faced with troble,such as mistranslations and omission due to language differences, obstacles caused by polysemy, and the difficulty in understanding of implicit meanings caused by contextual factors. To further improve the quality of machine translation, the algorithm of deep learning should be perfected, the error correction database should be established, and interdisciplinary communication should be carried out.

引文

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