Abstract
We exploit the pre-trained seq2seq model mBART for multilingual text style transfer. Using machine translated data as well as gold aligned English sentences yields state-of-the-art results in the three target languages we consider. Besides, in view of the general scarcity of parallel data, we propose a modular approach for multilingual formality transfer, which consists of two training strategies that target adaptation to both language and task. Our approach achieves competitive performance without monolingual task-specific parallel data and can be applied to other style transfer tasks as well as to other languages.- Anthology ID:
- 2022.acl-short.29
- Volume:
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Smaranda Muresan, Preslav Nakov, Aline Villavicencio
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 262–271
- Language:
- URL:
- https://aclanthology.org/2022.acl-short.29
- DOI:
- 10.18653/v1/2022.acl-short.29
- Cite (ACL):
- Huiyuan Lai, Antonio Toral, and Malvina Nissim. 2022. Multilingual Pre-training with Language and Task Adaptation for Multilingual Text Style Transfer. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 262–271, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- Multilingual Pre-training with Language and Task Adaptation for Multilingual Text Style Transfer (Lai et al., ACL 2022)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.acl-short.29.pdf
- Code
- laihuiyuan/multilingual-tst
- Data
- GYAFC, XFORMAL