Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good
Xuewei Wang∗1, Weiyan Shi∗2, Richard Kim2, Yoojung Oh2 Sijia Yang3, Jingwen Zhang2 and Zhou Yu2 1 Zhejiang University,2 University of California, Davis,3 University of Pennsylvania khgkim,
Abstract
Developing intelligent persuasive conversational agents to change people’s opinions and actions for social good is the frontier in advancing the ethical development of automated dialogue systems. To do so, the first step is to understand the intricate organization of strategic disclosures and appeals employed in human persuasion conversations. We designed an online persuasion task where one participant was asked to persuade the other to donate to a specific charity. We collected a large dataset with 1,017 dialogues and annotated emerging persuasion strategies from a subset. Based on the annotation, we built a baseline classifier with context information and sentence-level features to predict the 10 persuasion strategies used in the corpus. Furthermore, to develop an understanding of personalized persuasion processes, we analyzed the relationships between individuals’ demographic and psychological backgrounds including personality, morality, value systems, and their willingness for donation. Then, we analyzed which types of persuasion strategies led to a greater amount of donation depending on the individuals’ personal backgrounds. This work lays the ground for developing a personal
中文速览
为了让AI系统学会像人类一样有说服力地劝说他人做好事（如慈善捐款），研究者首先需要弄清楚人类在说服对话中究竟用了哪些策略。研究团队在众包平台上收集了1017段真实的劝捐对话数据集（PersuasionForGood），并对其中300段标注出10种说服策略（如逻辑诉求、情感诉求、以身作则等），同时采集了参与者的人格特征、道德观念、价值观等心理背景信息。在此基础上，他们训练了一个结合上下文和句子特征的分类器来自动识别这些说服策略，并深入分析了哪类人格特质的用户对哪类策略反应更强烈，比如具有某些道德基础的人更容易被情感诉求打动。这项工作为构建"因人施策"的个性化说服对话系统奠定了数据与方法论基础，对推动对话AI在公益、健康等社会场景中的伦理应用具有重要价值。
原文 arXiv:1906.06725;中英对照 + 大白话阅读 https://aha.fim.ai/paper/1906.06725v2