Neural machine translation: Challenges, progress and future

被引:25
作者
Zhang, JiaJun [1 ,2 ]
Zong, ChengQing [1 ,2 ,3 ]
机构
[1] CASIA, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
[2] Univ Chinese Acad Sci, Beijing 100190, Peoples R China
[3] CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai 200031, Peoples R China
基金
中国国家自然科学基金;
关键词
neural machine translation; Transformer; multimodal translation; low-resource translation; document translation; KNOWLEDGE; SEQUENCE; MODELS;
D O I
10.1007/s11431-020-1632-x
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Machine translation (MT) is a technique that leverages computers to translate human languages automatically. Nowadays, neural machine translation (NMT) which models direct mapping between source and target languages with deep neural networks has achieved a big breakthrough in translation performance and become the de facto paradigm of MT. This article makes a review of NMT framework, discusses the challenges in NMT, introduces some exciting recent progresses and finally looks forward to some potential future research trends.
引用
收藏
页码:2028 / 2050
页数:23
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