Focused Prefix Tuning for Controllable Text Generation
Congda Ma, Tianyu Zhao, Makoto Shing, Kei Sawada, Manabu Okumura
Abstract
In a controllable text generation dataset, there exist unannotated attributes that could provide irrelevant learning signals to models that use it for training and thus degrade their performance. We propose focused prefix tuning (FPT) to mitigate the problem and to enable the control to focus on the desired attribute. Experimental results show that FPT can achieve better control accuracy and text fluency than baseline models in single-attribute control tasks. In multi-attribute control tasks, FPT achieves comparable control accuracy with the state-of-the-art approach while keeping the flexibility to control new attributes without retraining existing models.- Anthology ID:
- 2023.acl-short.96
- Volume:
- Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short 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:
- 1116–1127
- Language:
- URL:
- https://aclanthology.org/2023.acl-short.96
- DOI:
- 10.18653/v1/2023.acl-short.96
- Cite (ACL):
- Congda Ma, Tianyu Zhao, Makoto Shing, Kei Sawada, and Manabu Okumura. 2023. Focused Prefix Tuning for Controllable Text Generation. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 1116–1127, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Focused Prefix Tuning for Controllable Text Generation (Ma et al., ACL 2023)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2023.acl-short.96.pdf