Seamlessly Integrating Factual Information and Social Content with Persuasive Dialogue
This work addresses the problem of building more effective persuasive dialogue systems for applications like customer service or counseling, though it appears incremental as it builds on existing modular approaches.
The authors tackled the challenge of creating persuasive dialogue systems that address both factual information and social content, developing a modular framework that outperformed an end-to-end baseline in user evaluations across dimensions like competence and friendliness.
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.