通过合成跨学科沟通策略增强说服对话代理
Enhancing Persuasive Dialogue Agents by Synthesizing Cross-Disciplinary Communication Strategies
AI总结：
本文提出一种跨学科方法，通过合成策略提升说服对话代理的说服效果和泛化能力，尤其在说服低意图个体方面表现突出。
摘要：
当前开发说服对话代理的方法往往依赖有限的预定义说服策略，无法捕捉真实世界互动的复杂性。我们采用跨学科方法，开发了一个框架，该框架借鉴了社会心理学、行为经济学和沟通理论中已验证的策略。我们通过两个不同的数据集验证了所提出的框架：Persuasion for Good数据集代表特定领域场景，DailyPersuasion数据集涵盖广泛场景。所提出的框架在两个数据集上均取得优异成果，并在说服成功率和可推广性方面表现出显著提升。值得注意的是，该框架在说服初始意图较低的个体方面也表现出色，这解决了说服对话代理的关键挑战。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
