Low-resource Machine Translation
Low-Resource Machine Translation is a subfield of machine translation that focuses on developing methods for language pairs with limited parallel corpora, where the scarcity of training data poses significant challenges to achieving high translation quality.
472
Publications
24.7K
Citations
1.3K
Authors
343
Institutions
Publications per year
2017–2026
384
472
Understanding Back-Translation at Scale
Sergey Edunov, Myle Ott, Michael Auli et al. · 2018 · 1K citations · Full text
Multilingual Denoising Pre-training for Neural Machine Translation
Yinhan Liu, Jiatao Gu, Naman Goyal et al. · arXiv (Cornell University) · 2020 · 608 citations · Full text
Computing, Computer Science, Corpus-based Translation Studies +14
XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale
Arun Babu, Changhan Wang, Andros Tjandra et al. · Interspeech 2022 · 2022 · 519 citations
1–5 of 472
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343
Carnegie Mellon University
Pittsburgh, United States
19 h-index
88 publications
6.4K citations

Johns Hopkins University
Baltimore, United States
19 h-index
39 publications
1.9K citations

University of Edinburgh
Edinburgh, United Kingdom
16 h-index
53 publications
3.7K citations
Google (United States)
Mountain View, United States
14 h-index
60 publications
7.4K citations
Meta (Israel)
Tel Aviv, Israel
13 h-index
59 publications
12.1K citations
Dublin City University
Dublin, Ireland
12 h-index
32 publications
627 citations

Chinese Academy of Sciences
Beijing, China
8 h-index
26 publications
600 citations

University of Hong Kong
Pok Fu Lam, Hong Kong
7 h-index
15 publications
2.4K citations
1–8 of 343
Repository · 92 publications · 5.8K citations
35
h-index
15 publications · 340 citations
13
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9 publications · 281 citations
9
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Conference · 9 publications · 276 citations
9
h-index
TACL · 5 publications · 1.5K citations
5
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