A Comprehensive Survey of Multilingual Neural Machine Translation

TL;DR

This survey comprehensively reviews recent advances in multilingual neural machine translation, highlighting approaches, challenges, and future directions to guide researchers and practitioners in the field.

Contribution

It provides an in-depth categorization and comparison of MNMT techniques, addressing gaps in existing literature and outlining promising research avenues.

Findings

Various approaches differ in resource efficiency and translation quality

Identification of key challenges like data scarcity and model scalability

Future research directions include low-resource settings and model interpretability

Abstract

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.