Title: Neural Machine Translation: Challenges, Progress and Future
Authors: Jiajun Zhang, Chengqing Zong
Published: 13th April 2020 (Monday) @ 07:53:57
Link: http://arxiv.org/abs/2004.05809v1
Abstract
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. In addition, we maintain the state-of-the-art methods for various NMT tasks at the website https://github.com/ZNLP/SOTA-MT.