StyleDGPT: Stylized Response Generation with Pre-trained Language Models

• URL: http://arxiv.org/abs/2010.02569v1
• Date: Tue, 6 Oct 2020 09:29:50 GMT
• Title: StyleDGPT: Stylized Response Generation with Pre-trained Language Models
• Authors: Ze Yang, Wei Wu, Can Xu, Xinnian Liang, Jiaqi Bai, Liran Wang, Wei Wang, Zhoujun Li
• Abstract summary: We introduce a KL loss and a style classifier to steer response generation towards the target style in both a word-level and a sentence-level. Our model can significantly outperform state-of-the-art methods in terms of both style consistency and contextual coherence.
• Score: 39.526613595499356
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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