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 translation. 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.

经过70多年的演进，机器翻译取得了巨大成就。特别是近年来，随着神经机器翻译（NMT）的出现，翻译质量有了很大的提高。在这篇文章中，我们首先回顾了机器翻译的历史，从基于规则的机器翻译到基于示例的机器翻译和统计机器翻译。然后我们更详细地介绍了 NMT，包括基本框架和当前占主导地位的框架 Transformer，以及处理数据稀疏问题的多语言翻译模型。此外，我们还介绍了在翻译质量和延迟之间取得平衡的尖端同步翻译方法。然后我们描述了机器翻译的各种产品和应用。在这篇文章的最后，