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 · 2020
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 stateof-the-art methods in terms of both style consistency and contextual coherence.