Towards Zero-Shot Persona Dialogue Generation with In-Context Learning
Xinchao Xu, Zeyang Lei, Wenquan Wu, Zheng-Yu Niu, Hua Wu, Haifeng Wang
Abstract
Much work has been done to improve persona consistency by finetuning a pretrained dialogue model on high-quality human-annoated persona datasets. However, these methods still face the challenges of high cost and poor scalability. To this end, we propose a simple-yet-effective approach to significantly improve zero-shot persona consistency via in-context learning. Specifically, we first pre-train a persona-augmented dialogue generation model and then utilize in-context prompting mechanism to realize zero-shot persona customization. Experimental results demonstrate that our method can dramatically improve persona consistency without compromising coherence and informativeness in zero-shot settings.- Anthology ID:
- 2023.findings-acl.90
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2023
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1387–1398
- Language:
- URL:
- https://aclanthology.org/2023.findings-acl.90
- DOI:
- 10.18653/v1/2023.findings-acl.90
- Cite (ACL):
- Xinchao Xu, Zeyang Lei, Wenquan Wu, Zheng-Yu Niu, Hua Wu, and Haifeng Wang. 2023. Towards Zero-Shot Persona Dialogue Generation with In-Context Learning. In Findings of the Association for Computational Linguistics: ACL 2023, pages 1387–1398, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Towards Zero-Shot Persona Dialogue Generation with In-Context Learning (Xu et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2023.findings-acl.90.pdf