Enhancing Persuasive Dialogue Agents by Synthesizing Cross-Disciplinary Communication Strategies

TL;DR

This paper introduces a cross-disciplinary framework for persuasive dialogue agents, integrating strategies from social psychology, economics, and communication theory, leading to improved success rates and better persuasion of low-intent individuals.

Contribution

It presents a novel, cross-disciplinary approach to designing persuasive dialogue agents, enhancing their effectiveness and generalizability across diverse scenarios.

Findings

Improved persuasion success rate across datasets

Effective persuasion of individuals with low initial intent

Strong generalizability demonstrated in experiments

Abstract

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.