论文标题
对神经机器翻译的深度学习技术的调查
A Survey of Deep Learning Techniques for Neural Machine Translation
论文作者
论文摘要
近年来，自然语言处理（NLP）通过深度学习技术取得了巨大的发展。在机器翻译的子场中，一种名为Neural Machine Translation（NMT）的新方法已经出现并引起了学术界和工业的极大关注。但是，在过去几年中提出的大量研究中，研究这种新技术趋势的开发过程几乎没有工作。该文献调查追溯了NMT的起源和主要发展时间表，研究了重要的分支机构，对不同的研究取向进行了分类，并讨论了该领域的一些未来研究趋势。
In recent years, natural language processing (NLP) has got great development with deep learning techniques. In the sub-field of machine translation, a new approach named Neural Machine Translation (NMT) has emerged and got massive attention from both academia and industry. However, with a significant number of researches proposed in the past several years, there is little work in investigating the development process of this new technology trend. This literature survey traces back the origin and principal development timeline of NMT, investigates the important branches, categorizes different research orientations, and discusses some future research trends in this field.
