Seamlessly Integrating Factual Information and Social Content with Persuasive Dialogue

• DOI: 10.18653/v1/2022.aacl-main.31
• Publication Date: 2024-11-27T22:28:01Z
• Authors: Maximillian Chen; Weiyan Shi; Feifan Yan; Ryan Hou; Jingwen Zhang; Saurav Sahay; Zhou Yu
• Keywords: FOS: Computer and information sciences; Computer Science - Computers and Society; Computer Science - Computation and Language; Artificial Intelligence (cs.AI); Computer Science - Artificial Intelligence; Computers and Society (cs.CY); 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology; Computation and Language (cs.CL)

Complex conversation settings such as persuasion involve communicating changes in attitude or behavior, so users' perspectives need to be addressed, even when not directly related to the topic. In this work, we contribute a novel modular dialogue system framework that seamlessly integrates factual information and social content into persuasive dialogue. Our framework is generalizable to any dialogue tasks that have mixed social and task contents. We conducted a study that compared user evaluations of our framework versus a baseline end-to-end generation model. We found our framework was evaluated more favorably in all dimensions including competence and friendliness, compared to the end-to-end model which does not explicitly handle social content or factual questions.<br/>To appear in Proceedings of AACL-IJCNLP 2022; 16 pages, 4 figures, 7 tables<br/>