We present a survey on multilingual neural machine translation (MNMT), which has gained a lot of traction in 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 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 article, 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 for MNMT. This article is aimed towards both beginners and experts in NMT. We hope this article will serve as a starting point as well as a source of new ideas for researchers and engineers interested in MNMT.

中文翻译：

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多语言神经机器翻译综述

我们提出了一项关于多语言神经机器翻译 (MNMT) 的调查，该调查近年来获得了很大的关注。由于翻译知识转移（迁移学习），MNMT 在提高翻译质量方面很有用。MNMT 比它的统计机器翻译对应物更有前途和更有趣，因为端到端建模和分布式表示为机器翻译的研究开辟了新途径。已经提出了许多方法来利用多语言并行语料库来提高翻译质量。然而，由于缺乏全面的调查，很难确定哪些方法是有前途的，因此值得进一步探索。在本文中，我们对现有的 MNMT 文献进行了深入调查。我们首先根据中心用例对各种方法进行分类，然后根据资源场景、基础建模原则、核心问题和挑战进一步对它们进行分类。在可能的情况下，我们通过相互比较来解决几种技术的优点和缺点。我们还讨论了 MNMT 的未来方向。本文面向 NMT 的初学者和专家。我们希望这篇文章可以作为对 MNMT 感兴趣的研究人员和工程师的起点和新想法的来源。本文面向 NMT 的初学者和专家。我们希望这篇文章可以作为对 MNMT 感兴趣的研究人员和工程师的起点和新想法的来源。本文面向 NMT 的初学者和专家。我们希望这篇文章可以作为对 MNMT 感兴趣的研究人员和工程师的起点和新想法的来源。