The DOI matches its Crossref record; 202 of the 202 references it cites were found in a registry or an index.
Neural Machine Translation for Low-resource Languages: A Survey
Open accessAbstract
Neural Machine Translation (NMT) has seen tremendous growth in the last ten years since the early 2000s and has already entered a mature phase. While considered the most widely used solution for Machine Translation, its performance on low-resource language pairs remains sub-optimal compared to the high-resource counterparts due to the unavailability of large parallel corpora. Therefore, the implementation of NMT techniques for low-resource language pairs has been receiving the spotlight recently, thus leading to substantial research on this topic. This article presents a detailed survey of research advancements in low-resource language NMT (LRL-NMT) and quantitative analysis to identify the most popular techniques. We provide guidelines to select the possible NMT technique for a given LRL data setting based on our findings. We also present a holistic view of the LRL-NMT research landscape and provide recommendations to enhance the research efforts further.
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Metadata
- Authors
- Ranathunga, S., Lee, E.-S. A., Prifti Skenduli, M., Shekhar, R., Alam, M., & Kaur, R.
- Venue
- ACM Computing Surveys
- Published
- 2023-02-09
- Volume
- 55
- Issue
- 11
- Pages
- 1-37
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W3173162544
- Cited by
- 313
- References
- 202
- FWCI
- 21.33
- Type
- journal article
- Language
- en
- Open access
- green
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- Ranathunga, S., Lee, E.-S. A., Prifti Skenduli, M., Shekhar, R., Alam, M., & Kaur, R. (2023). Neural Machine Translation for Low-resource Languages: A Survey. ACM Computing Surveys. https://doi.org/10.1145/3567592
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