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.
@article{arxiv.2010.02569,
title = {StyleDGPT: Stylized Response Generation with Pre-trained Language Models},
author = {Ze Yang and Wei Wu and Can Xu and Xinnian Liang and Jiaqi Bai and Liran Wang and Wei Wang and Zhoujun Li},
journal= {arXiv preprint arXiv:2010.02569},
year = {2020}
}