Penalizing Divergence: Multi-Parallel Translation for Low-Resource Languages of North America

Garrett Nicolai, Changbing Yang, Miikka Silfverberg


Abstract
This paper explores a special case in multilingual machine translation: so called multi-parallel translation, where the target data for all language pairs are identical. While multi-parallelism offers benefits which are not available in a standard translation setting, translation models can easily overfit when training data are limited. We introduce a regularizer, the divergence penalty, which penalizes the translation model when it represents source sentences with identical target translations in divergent ways. Experiments on very low-resourced Indigenous North American languages show that an initially deficient multilingual translator can improve by 4.9 BLEU through mBART pre-training, and 5.5 BLEU points with the strategic addition of monolingual data, and that a divergence penalty leads to further increases of 0.4 BLEU. Further experiments on Germanic languages demonstrate a improvement of 0.5 BLEU when applying the divergence penalty. An investigation of the neural encoder representations learned by our translation models shows that the divergence penalty encourages models to learn a unified neural interlingua.
Anthology ID:
2022.coling-1.378
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
4292–4298
Language:
URL:
https://aclanthology.org/2022.coling-1.378
DOI:
Bibkey:
Cite (ACL):
Garrett Nicolai, Changbing Yang, and Miikka Silfverberg. 2022. Penalizing Divergence: Multi-Parallel Translation for Low-Resource Languages of North America. In Proceedings of the 29th International Conference on Computational Linguistics, pages 4292–4298, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Penalizing Divergence: Multi-Parallel Translation for Low-Resource Languages of North America (Nicolai et al., COLING 2022)
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PDF:
https://aclanthology.org/2022.coling-1.378.pdf