Abstract
Generating natural language requires conveying content in an appropriate style. We explore two related tasks on generating text of varying formality: monolingual formality transfer and formality-sensitive machine translation. We propose to solve these tasks jointly using multi-task learning, and show that our models achieve state-of-the-art performance for formality transfer and are able to perform formality-sensitive translation without being explicitly trained on style-annotated translation examples.- Anthology ID:
- C18-1086
- Volume:
- Proceedings of the 27th International Conference on Computational Linguistics
- Month:
- August
- Year:
- 2018
- Address:
- Santa Fe, New Mexico, USA
- Editors:
- Emily M. Bender, Leon Derczynski, Pierre Isabelle
- Venue:
- COLING
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1008–1021
- Language:
- URL:
- https://aclanthology.org/C18-1086
- DOI:
- Cite (ACL):
- Xing Niu, Sudha Rao, and Marine Carpuat. 2018. Multi-Task Neural Models for Translating Between Styles Within and Across Languages. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1008–1021, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
- Cite (Informal):
- Multi-Task Neural Models for Translating Between Styles Within and Across Languages (Niu et al., COLING 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/C18-1086.pdf
- Code
- xingniu/multitask-ft-fsmt
- Data
- GYAFC