Joint Semantic and Strategy Matching for Persuasive Dialogue

Chuhao Jin, Yutao Zhu, Lingzhen Kong, Shijie Li, Xiao Zhang, Ruihua Song, Xu Chen, huan chen, Yuchong Sun, Yu Chen, Jun Xu

Submission Type: Regular Long Paper

Submission Track: Dialogue and Interactive Systems

Submission Track 2: NLP Applications

Keywords: Persuasive dialogue, Application of dialogue, Retrieval-based dialogue

Abstract:

Persuasive dialogue aims to persuade users to achieve some targets by conversations.
While previous persuasion models have achieved notable successes, they mostly base themselves on utterance semantic matching, and an important aspect has been ignored, that is, the strategy of the conversations, for example, the agent can choose an \textit{emotional-appeal} strategy to impress users.
Compared with utterance semantics, conversation strategies are high-level concepts, which can be informative and provide complementary information to achieve effective persuasions.
In this paper, we propose to build a persuasion model by jointly modeling the conversation semantics and strategies, where we design a BERT-like module and an auto-regressive predictor to match the semantics and strategies, respectively.
Experimental results indicate that our proposed approach can significantly improve the state-of-the-art baseline by 5\% on a small dataset and 37\% on a large dataset in terms of Recall@1. Detailed analyses show that the auto-regressive predictor contributes most to the final performance.

Submission Number: 3870