Neural Machine Translation: A Review of Methods, Resources, and Tools
Computation and Language
2021-01-01 v1
Abstract
Machine translation (MT) is an important sub-field of natural language processing that aims to translate natural languages using computers. In recent years, end-to-end neural machine translation (NMT) has achieved great success and has become the new mainstream method in practical MT systems. In this article, we first provide a broad review of the methods for NMT and focus on methods relating to architectures, decoding, and data augmentation. Then we summarize the resources and tools that are useful for researchers. Finally, we conclude with a discussion of possible future research directions.
Keywords
Cite
@article{arxiv.2012.15515,
title = {Neural Machine Translation: A Review of Methods, Resources, and Tools},
author = {Zhixing Tan and Shuo Wang and Zonghan Yang and Gang Chen and Xuancheng Huang and Maosong Sun and Yang Liu},
journal= {arXiv preprint arXiv:2012.15515},
year = {2021}
}
Comments
Accepted by AI Open