RESPER: Computationally Modelling Resisting Strategies in Persuasive Conversations
This work addresses a gap in computational linguistics for applications like persuasion and negotiation, but it is incremental as it builds on existing psychological literature and datasets.
The paper tackles the problem of modeling resisting strategies in persuasive conversations, which previous research had overlooked, by proposing a framework and using a hierarchical neural architecture to automatically identify these strategies, showing benefits for predicting conversation outcomes.
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.