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.
BibTeX
@inproceedings{xu-etal-2023-towards-zero,
title = "Towards Zero-Shot Persona Dialogue Generation with In-Context Learning",
author = "Xu, Xinchao and
Lei, Zeyang and
Wu, Wenquan and
Niu, Zheng-Yu and
Wu, Hua and
Wang, Haifeng",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.90/",
doi = "10.18653/v1/2023.findings-acl.90",
pages = "1387--1398"
}