Text Style Transfer with Contrastive Transfer Pattern Mining
Jingxuan Han, Quan Wang, Licheng Zhang, Weidong Chen, Yan Song, Zhendong Mao
Abstract
Text style transfer (TST) is an important task in natural language generation, which aims to alter the stylistic attributes (e.g., sentiment) of a sentence and keep its semantic meaning unchanged. Most existing studies mainly focus on the transformation between styles, yet ignore that this transformation can be actually carried out via different hidden transfer patterns. To address this problem, we propose a novel approach, contrastive transfer pattern mining (CTPM), which automatically mines and utilizes inherent latent transfer patterns to improve the performance of TST. Specifically, we design an adaptive clustering module to automatically discover hidden transfer patterns from the data, and introduce contrastive learning based on the discovered patterns to obtain more accurate sentence representations, and thereby benefit the TST task. To the best of our knowledge, this is the first work that proposes the concept of transfer patterns in TST, and our approach can be applied in a plug-and-play manner to enhance other TST methods to further improve their performance. Extensive experiments on benchmark datasets verify the effectiveness and generality of our approach.- Anthology ID:
- 2023.acl-long.439
- Volume:
- Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 7914–7927
- Language:
- URL:
- https://aclanthology.org/2023.acl-long.439
- DOI:
- 10.18653/v1/2023.acl-long.439
- Cite (ACL):
- Jingxuan Han, Quan Wang, Licheng Zhang, Weidong Chen, Yan Song, and Zhendong Mao. 2023. Text Style Transfer with Contrastive Transfer Pattern Mining. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7914–7927, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Text Style Transfer with Contrastive Transfer Pattern Mining (Han et al., ACL 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2023.acl-long.439.pdf