融合多学科策略，让对话机器人更会说服人。
Enhancing Persuasive Dialogue Agents by Synthesizing Cross-Disciplinary Communication Strategies
- 从社会心理学等三领域提取有效说服策略
- 在两个数据集上提升说服成功率，尤其对抵触者效果显著
- 适合需要高说服力的智能客服、心理辅导等场景
当前说服性对话代理多依赖有限预设策略，难以应对真实互动复杂性。本文提出一种跨学科框架，整合社会心理学、行为经济学与传播学中的有效说服策略。在两个不同数据集上验证：一是聚焦特定领域的「Persuasion for Good」，二是涵盖广泛情境的「DailyPersuasion」。结果表明，该框架在两组数据上均表现优异，显著提升说服成功率，并展现出良好泛化能力。尤其在面对初始意愿低的用户时仍具强说服力，解决了说服型对话系统的核心挑战。
原文摘要 · Abstract (English)
Current approaches to developing persuasive dialogue agents often rely on a limited set of predefined persuasive strategies that fail to capture the complexity of real-world interactions. We applied a cross-disciplinary approach to develop a framework for designing persuasive dialogue agents that draws on proven strategies from social psychology, behavioral economics, and communication theory. We validated our proposed framework through experiments on two distinct datasets: the Persuasion for Good dataset, which represents a specific in-domain scenario, and the DailyPersuasion dataset, which encompasses a wide range of scenarios. The proposed framework achieved strong results for both datasets and demonstrated notable improvement in the persuasion success rate as well as promising generalizability. Notably, the proposed framework also excelled at persuading individuals with initially low intent, which addresses a critical challenge for persuasive dialogue agents.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。