Progress in Machine Translation

After more than 70 years of evolution, great achievements have been made in machine translation. Especially in recent years, translation quality has been greatly improved with the emergence of neural machine translation (NMT). In this article, we first review the history of machine translation from rule-based machine translation to example-based machine translation and statistical machine transla-tion. We then introduce NMT in more detail, including the basic framework and the current dominant framework, Transformer, as well as multilingual translation models to deal with the data sparseness problem. In addition, we introduce cutting-edge simultaneous translation methods that achieve a balance between translation quality and latency. We then describe various products and applications of machine translation. At the end of this article, we briefly discuss challenges and future research directions in this field.(c) 2021 THE AUTHORS. Published by Elsevier LTD on behalf of Chinese Academy of Engineering and Higher Education Press Limited Company. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

After more than 70 years of evolution, great achievements have been made in machine translation, especially with the emergence of neural machine translation (NMT) in recent years. This article reviews the history of machine translation and introduces NMT, including its frameworks and multilingual translation models. It also discusses cutting-edge simultaneous translation methods and various machine translation products and applications. Challenges and future research directions in this field are briefly discussed as well.