The DOI matches its Crossref record; 72 of the 72 references it cites were found in a registry or an index.
A Survey of Multilingual Neural Machine Translation
Open accessAbstract
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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Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
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Convolutional Sequence to Sequence Learning
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“Recurrent neural network (RNN) [8], convolutional neural network (CNN) [52] and self-attention/feed-forward network (SA/FFN) [148] architectures are most commonly used approaches based on this paradigm.”
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Overview and challenges of machine translation for contextually appropriate translations
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Mirsky, Y., Demontis, A., Kotak, J., Shankar, R., Gelei, D., Yang, L., Zhang, X., Pintor, M., Lee, W., Elovici, Y., et al. (2023) · Computers & Security · doi:10.1016/j.cose.2022.103006 · Cited by 97
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Metadata
- Authors
- Dabre, R., Chu, C., & Kunchukuttan, A.
- Venue
- ACM Computing Surveys
- Published
- 2020-09-28
- Volume
- 53
- Issue
- 5
- Pages
- 1-38
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W2945415576
- Cited by
- 294
- References
- 72
- FWCI
- 14.49
- Type
- journal article
- Language
- en
- Open access
- hybrid
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