Stylized Knowledge-Grounded Dialogue Generation via Disentangled Template Rewriting

• URL: http://arxiv.org/abs/2204.05610v1
• Date: Tue, 12 Apr 2022 08:17:21 GMT
• Title: Stylized Knowledge-Grounded Dialogue Generation via Disentangled Template Rewriting
• Authors: Qingfeng Sun, Can Xu, Huang Hu, Yujing Wang, Jian Miao, Xiubo Geng, Yining Chen, Fei Xu, Daxin Jiang
• Abstract summary: We study a new problem: Stylized Knowledge-Grounded Dialogue Generation. It presents two challenges: How to train a SKDG model where no context, knowledge, stylized response> triples are available. We propose a novel disentangled template rewriting (DTR) method which generates responses via combing disentangled style templates and content templates.
• Score: 55.10977824136768
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current Knowledge-Grounded Dialogue Generation (KDG) models specialize in producing rational and factual responses. However, to establish long-term relationships with users, the KDG model needs the capability to generate responses in a desired style or attribute. Thus, we study a new problem: Stylized Knowledge-Grounded Dialogue Generation (SKDG). It presents two challenges: (1) How to train a SKDG model where no <context, knowledge, stylized response> triples are available. (2) How to cohere with context and preserve the knowledge when generating a stylized response. In this paper, we propose a novel disentangled template rewriting (DTR) method which generates responses via combing disentangled style templates (from monolingual stylized corpus) and content templates (from KDG corpus). The entire framework is end-to-end differentiable and learned without supervision. Extensive experiments on two benchmarks indicate that DTR achieves a significant improvement on all evaluation metrics compared with previous state-of-the-art stylized dialogue generation methods. Besides, DTR achieves comparable performance with the state-of-the-art KDG methods in standard KDG evaluation setting.

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