A Survey of Multilingual Neural Machine Translation

By Raj Dabre, Chenhui Chu, Anoop Kunchukuttan. ACM Computing Surveys, 2019

361 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/b4693a93b033d6ec6c5c98a22797e43928ac7470

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Citations in the corpus

Neural Machine Translation: A Review (2019, background): “[528] provide an overview of recent trends in multilingual NMT.”
Structured information extraction from scientific text with large language models (2024, background): “Usecases for seq2seq are broad 35 and include machine translation 36 , answering generalfactualknowledgequestions 33,37 ,performingsimplearithmetic 33 , translating between languages 36,38 , summarizing text 28,39 , and chatbot applications 26,40 .”
Neural Machine Translation for Low-resource Languages: A Survey (2021, background): “Multi-NMT systems handle translation between more than one language pair [37, 73].”
The Political Biases of ChatGPT (2023, background): “The 2012–2022 decade has witnessed spectacular improvements in AI, from computer vision (O’Mahony et al. 2020), to machine translation (Dabre et al. 2020), to generative models for images ( Wikipedia 2023c) and text ( Wikipedia 2022).”
A Primer on Pretrained Multilingual Language Models (2021, neutral): “Our survey is thus different from existing surveys on cross-lingual word embedding models (Ruder et al., 2019), multilingual NMT models (Dabre et al., 2020) and pre-trained language models (Qiu et al., 2020; Kalyan et al., 2021) which do not focus on pretrained multi-lingual language models.”
Orca: A Distributed Serving System for Transformer-Based Generative Models (2022, background): “AI21 Labs [37], DeepMind [26,48], Google [15,21,63], Meta Platforms [10,67], Microsoft [50], Microsoft & NVIDIA [59], and OpenAI [12] have reported that every language processing task, including translation [11, 17], classification [20, 53], question-answering [32, 33, 40] and more, can be cast as a”
Pre-Trained Language Models for Text Generation: A Survey (2022, methodology): “method in both academic research and commercial use [32].”
Multilingual training for Software Engineering (2021, background): “Third,we find that quite often a model trained in one programming language achieves surprisingly good performance on a test set in a different language, sometimes even surpassing a model trained on the same language as the test set! Taken together, these findings suggest that pooling data across lan”