论文标题
神经机器翻译：方法，资源和工具的综述
Neural Machine Translation: A Review of Methods, Resources, and Tools
论文作者
论文摘要
机器翻译（MT）是自然语言处理的重要子场，旨在使用计算机翻译自然语言。近年来，端到端的神经机器翻译（NMT）取得了巨大的成功，并已成为实用MT系统中新的主流方法。在本文中，我们首先对NMT的方法进行了广泛的审查，并着重于与体系结构，解码和数据增强有关的方法。然后，我们总结了对研究人员有用的资源和工具。最后，我们在讨论可能未来的研究方向的讨论中得出结论。
Machine translation (MT) is an important sub-field of natural language processing that aims to translate natural languages using computers. In recent years, end-to-end neural machine translation (NMT) has achieved great success and has become the new mainstream method in practical MT systems. In this article, we first provide a broad review of the methods for NMT and focus on methods relating to architectures, decoding, and data augmentation. Then we summarize the resources and tools that are useful for researchers. Finally, we conclude with a discussion of possible future research directions.
