How to Ask for Donations? Learning User-Specific Persuasive Dialogue Policies through Online Interactions

Abstract

Persuasive conversations are more effective when they are custom-tailored for the intended audience. Current persuasive dialogue systems rely heavily on advice-giving or focus on different framing policies in a constrained and less dynamic/flexible manner. In this paper, we argue for a new approach, in which the system can identify optimal persuasive strategies in context and persuade users through online interactions. We study two main questions (1) can a reinforcement-learning-based dialogue framework learn to exercise user-specific communicative strategies for persuading users? (2) How can we leverage the crowd-sourcing platforms to collect data for training, and evaluating such frameworks for human-AI(/machine) conversations? We describe a prototype system that interacts with users with the goal of persuading them to donate to a charity and use experiments with crowd workers and analyses of our learned policies to document that our approach leads to learning context-sensitive persuasive strategies that focus on user's reactions towards donation and contribute to increasing dialogue success.