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arXiv 2203.07657cs.CLcs.AIcs.CY

Seamlessly Integrating Factual Information and Social Content with Persuasive Dialogue

Columbia University(哥伦比亚大学);University of California, Davis(加州大学戴维斯分校);Intel Labs(英特尔实验室)

Maximillian Chen, Weiyan Shi, Feifan Yan, Ryan Hou, Jingwen Zhang, Saurav Sahay, Zhou Yu

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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