Neural Machine Translation: Challenges, Progress and Future
It provides a comprehensive overview for researchers and practitioners in machine translation, but is incremental as it synthesizes existing knowledge.
This paper reviews the neural machine translation (NMT) framework, discussing its challenges, recent progress, and future trends, while maintaining a website for state-of-the-art methods.
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.