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A Survey of Multilingual Neural Machine Translation

Authors:

Anoop KunchukuttanAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 53, Issue 5

Article No.: 99, Pages 1 - 38

https://doi.org/10.1145/3406095

Published: 28 September 2020 Publication History

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Abstract

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.

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Index Terms

A Survey of Multilingual Neural Machine Translation
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Machine translation

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 53, Issue 5

September 2021

782 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3426973

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Albert Zomaya
University of Sydney, Austraila

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Published: 28 September 2020

Accepted: 01 June 2020

Revised: 01 June 2020

Received: 01 July 2019

Published in CSUR Volume 53, Issue 5

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