Maximillian Chen


2022

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Seamlessly Integrating Factual Information and Social Content with Persuasive Dialogue
Maximillian Chen | Weiyan Shi | Feifan Yan | Ryan Hou | Jingwen Zhang | Saurav Sahay | Zhou Yu
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

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 model was evaluated to be more favorable in all dimensions including competence and friendliness compared to the baseline model which does not explicitly handle social content or factual questions.