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- Title
Cross-lingual Transfer from Large Multilingual Translation Models to Unseen Under-resourced Languages.
- Authors
TARS, Maali; TĀTTAR, Andre; FISHEL, Mark
- Abstract
Low-resource machine translation has been a challenging problem to solve, with the lack of data being a big obstacle in producing good quality neural machine translation (NMT) systems. However, recent work on developing large multilingual translation models gives a platform for attempts to create NMT systems for extremely low-resource languages that can achieve reasonable and usable quality. We leverage the information in large multilingual translation models by performing cross-lingual transfer learning to extremely low-resource Finno-Ugric languages. Our experiments include seven languages with limited resources that are unseen by the original pre-trained translation model and five high-resource languages that have the potential to help during training, previously seen by the model during training. We report state-of-the-art results on multiple test sets and translation directions as well as analyze the low-resource languages in smaller language groups in order to track the source of our higher translation quality.
- Subjects
MACHINE translating; TRANSLATING &; interpreting; LANGUAGE &; languages; GENETIC translation
- Publication
Baltic Journal of Modern Computing, 2022, Vol 10, Issue 3, p435
- ISSN
2255-8942
- Publication type
Article
- DOI
10.22364/bjmc.2022.10.3.16