Survey of Neural Machine Translation

被引：0
作者：
Zhang, Junjin [1 ]
Tian, Yonghong [1 ]
Song, Zheyu [1 ]
Hao, Yufeng [1 ]
机构：
[1] College of Data Science and Application, Inner Mongolia University of Technology, Hohhot
来源：
Computer Engineering and Applications | 2024年 / 60卷 / 04期
关键词：
data augmentation; document-level machine translation; machine translation; neural machine translation; preprocessing technique;
D O I：
10.3778/j.issn.1002-8331.2305-0102
中图分类号：
学科分类号：
摘要：
Machine translation (MT) mainly studies how to translate the source language into the target language, which is of great significance for promoting the communication between nationalities. At present, neural machine translation (NMT) has become the mainstream MT method by translation speed and quality. In order to better sort out the context, this paper first introduces the history and methods of MT, compares and summarizes three main methods: rule- based machine translation, statistics-based machine translation and deep learning-based machine translation. Then NMT is introduced to explain its common types. Next, six main research fields of NMT are introduced, including multimodal MT, non-autoregressive MT, document- level MT, multilingual MT, data augmentation technology and preprocessing technique. Finally, the future of NMT is prospected from four aspects: low- resource languages, context- sensitive translation, unknown words and large models. This paper provides a systematic introduction to better understand the development status of NMT. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.
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页码：57 / 74
页数：17
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