# A Comprehensive Survey of Multilingual Neural Machine Translation

arXiv: https://arxiv.org/abs/2001.01115 · HTML: https://mlsanity.com/paper/2001.01115/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jan 2020.

## Built on by (most cited first, 12 of 12)

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- [LIMIT: Language Identification, Misidentification, and Translation using Hierarchical Models in 350+ Languages](https://mlsanity.com/paper/2305.14263/) (2023)
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- [Disentangling the Roles of Target-Side Transfer and Regularization in Multilingual Machine Translation](https://mlsanity.com/paper/2402.01772/) (2024)
- [Generalising Multilingual Concept-to-Text NLG with Language Agnostic Delexicalisation](https://mlsanity.com/paper/2105.03432/) (2021)
- [Investigating the potential of Sparse Mixtures-of-Experts for multi-domain neural machine translation](https://mlsanity.com/paper/2407.01126/) (2024)
- [Comparative Analysis of State-of-the-Art Deep Learning Models for Detecting COVID-19 Lung Infection from Chest X-Ray Images](https://mlsanity.com/paper/2208.01637/) (2022)

## Abstract (arXiv)

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.