We present a survey on multilingual neural machine translation (MNMT), which has gained a lot of traction in the recent years. MNMT has been useful in improving translation quality as a result of translation knowledge transfer (transfer learning). MNMT is more promising and interesting than its statistical machine translation counterpart because end-to-end modeling and distributed representations open new avenues for research on machine translation. Many approaches have been proposed in order to exploit multilingual parallel corpora for improving translation quality. However, the lack of a comprehensive survey makes it difficult to determine which approaches are promising and hence deserve further exploration. In this paper, we present an in-depth survey of existing literature on MNMT. We first categorize various approaches based on their central use-case and then further categorize them based on resource scenarios, underlying modeling principles, core-issues and challenges. Wherever possible we address the strengths and weaknesses of several techniques by comparing them with each other. We also discuss the future directions that MNMT research might take. This paper is aimed towards both, beginners and experts in NMT. We hope this paper will serve as a starting point as well as a source of new ideas for researchers and engineers interested in MNMT.

本文旨在介绍多语言神经网络机器翻译（MNMT）的概述，重点关注其经验转移的传输学习方法，对相关研究领域、建模原理和挑战进行了细致分类，讨论了不同技术的优劣，并探讨了未来的研究方向。

多语言神经机器翻译综述