StyleDGPT: Stylized Response Generation with Pre-trained Language Models
Ze Yang, Wei Wu, Can Xu, Xinnian Liang, Jiaqi Bai, Liran Wang, Wei Wang, Zhoujun Li
Abstract
Generating responses following a desired style has great potentials to extend applications of open-domain dialogue systems, yet is refrained by lacking of parallel data for training. In this work, we explore the challenging task with pre-trained language models that have brought breakthrough to various natural language tasks. To this end, we introduce a KL loss and a style classifier to the fine-tuning step in order to steer response generation towards the target style in both a word-level and a sentence-level. Comprehensive empirical studies with two public datasets indicate that our model can significantly outperform state-of-the-art methods in terms of both style consistency and contextual coherence.- Anthology ID:
- 2020.findings-emnlp.140
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2020
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Trevor Cohn, Yulan He, Yang Liu
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1548–1559
- Language:
- URL:
- https://aclanthology.org/2020.findings-emnlp.140
- DOI:
- 10.18653/v1/2020.findings-emnlp.140
- Cite (ACL):
- Ze Yang, Wei Wu, Can Xu, Xinnian Liang, Jiaqi Bai, Liran Wang, Wei Wang, and Zhoujun Li. 2020. StyleDGPT: Stylized Response Generation with Pre-trained Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1548–1559, Online. Association for Computational Linguistics.
- Cite (Informal):
- StyleDGPT: Stylized Response Generation with Pre-trained Language Models (Yang et al., Findings 2020)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2020.findings-emnlp.140.pdf
- Code
- TobeyYang/StyleDGPT