RESPER: Computationally Modelling Resisting Strategies in Persuasive Conversations

TL;DR

This paper introduces RESPER, a framework for identifying resisting strategies in persuasive conversations using neural models, revealing their impact on conversation outcomes and highlighting power dynamics.

Contribution

It presents a novel hierarchical neural approach to automatically detect resisting strategies, addressing a gap in prior persuasion modeling research.

Findings

Resisting strategies influence conversation success.

Power asymmetry affects resisting behavior.

Incorporating resisting strategies improves outcome prediction.

Abstract

Modelling persuasion strategies as predictors of task outcome has several real-world applications and has received considerable attention from the computational linguistics community. However, previous research has failed to account for the resisting strategies employed by an individual to foil such persuasion attempts. Grounded in prior literature in cognitive and social psychology, we propose a generalised framework for identifying resisting strategies in persuasive conversations. We instantiate our framework on two distinct datasets comprising persuasion and negotiation conversations. We also leverage a hierarchical sequence-labelling neural architecture to infer the aforementioned resisting strategies automatically. Our experiments reveal the asymmetry of power roles in non-collaborative goal-directed conversations and the benefits accrued from incorporating resisting strategies on the final conversation outcome. We also investigate the role of different resisting strategies on the conversation outcome and glean insights that corroborate with past findings. We also make the code and the dataset of this work publicly available at https://github.com/americast/resper.