Exploiting Multilingualism through Multistage Fine-Tuning for Low-Resource Neural Machine Translation

Raj Dabre, Atsushi Fujita, Chenhui Chu


Abstract
This paper highlights the impressive utility of multi-parallel corpora for transfer learning in a one-to-many low-resource neural machine translation (NMT) setting. We report on a systematic comparison of multistage fine-tuning configurations, consisting of (1) pre-training on an external large (209k–440k) parallel corpus for English and a helping target language, (2) mixed pre-training or fine-tuning on a mixture of the external and low-resource (18k) target parallel corpora, and (3) pure fine-tuning on the target parallel corpora. Our experiments confirm that multi-parallel corpora are extremely useful despite their scarcity and content-wise redundancy thus exhibiting the true power of multilingualism. Even when the helping target language is not one of the target languages of our concern, our multistage fine-tuning can give 3–9 BLEU score gains over a simple one-to-one model.
Anthology ID:
D19-1146
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1410–1416
Language:
URL:
https://aclanthology.org/D19-1146
DOI:
10.18653/v1/D19-1146
Bibkey:
Cite (ACL):
Raj Dabre, Atsushi Fujita, and Chenhui Chu. 2019. Exploiting Multilingualism through Multistage Fine-Tuning for Low-Resource Neural Machine Translation. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 1410–1416, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Exploiting Multilingualism through Multistage Fine-Tuning for Low-Resource Neural Machine Translation (Dabre et al., EMNLP-IJCNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/D19-1146.pdf