Seamlessly Integrating Factual Information and Social Content with Persuasive Dialogue

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 fact...

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Bibliographic Details
Main Authors Chen, Maximillian, Shi, Weiyan, Yan, Feifan, Hou, Ryan, Zhang, Jingwen, Sahay, Saurav, Yu, Zhou
Format Journal Article
LanguageEnglish
Published 15.03.2022
Subjects
Computer Science - Artificial Intelligence
Computer Science - Computation and Language
Computer Science - Computers and Society
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Summary: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.
DOI:10.48550/arxiv.2203.07657