论文标题
神经机器翻译：挑战，进步和未来
Neural Machine Translation: Challenges, Progress and Future
论文作者
论文摘要
机器翻译（MT）是一种利用计算机自动翻译人类语言的技术。如今，神经机器翻译（NMT）模拟具有深层神经网络的源和目标语言之间的直接映射，在翻译性能方面取得了很大的突破，并成为MT的事实上的范式。本文回顾了NMT框架，讨论了NMT的挑战，引入了一些令人兴奋的进展，并最终期待着一些潜在的未来研究趋势。此外，我们在网站https://github.com/znlp/sota-mt上维护各种NMT任务的最新方法。
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.
