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 personalized persuasive dialogue system.

中文翻译：

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劝善：迈向社会公益的个性化劝说对话系统

开发智能的有说服力的对话代理来改变人们的意见和行为以促进社会利益是推进自动对话系统伦理发展的前沿。为此，第一步是了解人类说服对话中采用的战略披露和呼吁的复杂组织。我们设计了一项在线说服任务，要求一名参与者说服另一名参与者向特定慈善机构捐款。我们收集了一个包含 1,017 个对话的大型数据集，并从一个子集中注释了新兴的说服策略。基于注释，我们构建了一个具有上下文信息和句子级特征的基线分类器，以预测语料库中使用的 10 种说服策略。此外，为了了解个性化的说服过程，我们分析了个人的人口统计学和心理背景之间的关系，包括个性、道德、价值体系和他们的捐赠意愿。然后，我们根据个人的个人背景分析了哪些类型的说服策略会导致更多的捐赠。这项工作为开发个性化的说服性对话系统奠定了基础。