Monolingual Adapters for Zero-Shot Neural Machine Translation
Jerin Philip, Alexandre Berard, Matthias Gallé, Laurent Besacier
Abstract
We propose a novel adapter layer formalism for adapting multilingual models. They are more parameter-efficient than existing adapter layers while obtaining as good or better performance. The layers are specific to one language (as opposed to bilingual adapters) allowing to compose them and generalize to unseen language-pairs. In this zero-shot setting, they obtain a median improvement of +2.77 BLEU points over a strong 20-language multilingual Transformer baseline trained on TED talks.- Anthology ID:
- 2020.emnlp-main.361
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4465–4470
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.361
- DOI:
- 10.18653/v1/2020.emnlp-main.361
- Cite (ACL):
- Jerin Philip, Alexandre Berard, Matthias Gallé, and Laurent Besacier. 2020. Monolingual Adapters for Zero-Shot Neural Machine Translation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 4465–4470, Online. Association for Computational Linguistics.
- Cite (Informal):
- Monolingual Adapters for Zero-Shot Neural Machine Translation (Philip et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/2020.emnlp-main.361.pdf