A Survey of Multilingual Neural Machine Translation
A Survey of Multilingual Neural Machine Translation
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DOI：
10.1145/3406095
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发表时间：
2020-10-01
影响因子：
16.6
通讯作者：
Kunchukuttan, Anoop
中科院分区：
文献类型：
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作者：
Dabre, Raj;Chu, Chenhui;Kunchukuttan, Anoop
我们对多语言神经机器翻译（MNMT）进行了一次调查，近年来该领域获得了很大的吸引力。由于翻译知识转移（迁移学习），MNMT在提高翻译质量方面非常有用。MNMT比它的统计机器翻译同行更有前途和有趣，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的途径。为了提高翻译质量，人们提出了许多方法来利用多语言平行语料库。然而，由于缺乏全面调查，很难确定哪些方法是有希望的，因此值得进一步探讨。在这篇文章中，我们提出了一个深入的调查现有的文献MNMT。我们首先根据它们的中心用例对各种方法进行分类，然后根据资源场景、底层建模原则、核心问题和挑战对它们进行进一步分类。只要有可能，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的未来发展方向。本文面向NMT的初学者和专家。我们希望这篇文章将作为一个起点，以及对MNMT感兴趣的研究人员和工程师的新想法的来源。
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.