Abstract
In most cases, the lack of parallel corpora makes it impossible to directly train supervised models for the text style transfer task. In this paper, we explore training algorithms that instead optimize reward functions that explicitly consider different aspects of the style-transferred outputs. In particular, we leverage semantic similarity metrics originally used for fine-tuning neural machine translation models to explicitly assess the preservation of content between system outputs and input texts. We also investigate the potential weaknesses of the existing automatic metrics and propose efficient strategies of using these metrics for training. The experimental results show that our model provides significant gains in both automatic and human evaluation over strong baselines, indicating the effectiveness of our proposed methods and training strategies.- Anthology ID:
- 2021.naacl-main.337
- Volume:
- Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Month:
- June
- Year:
- 2021
- Address:
- Online
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4262–4273
- Language:
- URL:
- https://aclanthology.org/2021.naacl-main.337
- DOI:
- 10.18653/v1/2021.naacl-main.337
- Cite (ACL):
- Yixin Liu, Graham Neubig, and John Wieting. 2021. On Learning Text Style Transfer with Direct Rewards. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4262–4273, Online. Association for Computational Linguistics.
- Cite (Informal):
- On Learning Text Style Transfer with Direct Rewards (Liu et al., NAACL 2021)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2021.naacl-main.337.pdf
- Code
- yixinL7/Direct-Style-Transfer
- Data
- GYAFC