Werewolf Among Us: A Multimodal Dataset for Modeling Persuasion Behaviors in Social Deduction Games
This work addresses the need for multimodal data in persuasion modeling for conversational agents, though it is incremental as it extends existing textual analysis to include visual signals.
The authors tackled the problem of modeling persuasion behaviors by introducing the first multimodal dataset, which includes 199 dialogue transcriptions and videos, 26,647 utterance-level annotations, and game-level outcomes from social deduction games, showing that dialogue context and visual signals improve persuasion strategy prediction.
Persuasion modeling is a key building block for conversational agents. Existing works in this direction are limited to analyzing textual dialogue corpus. We argue that visual signals also play an important role in understanding human persuasive behaviors. In this paper, we introduce the first multimodal dataset for modeling persuasion behaviors. Our dataset includes 199 dialogue transcriptions and videos captured in a multi-player social deduction game setting, 26,647 utterance level annotations of persuasion strategy, and game level annotations of deduction game outcomes. We provide extensive experiments to show how dialogue context and visual signals benefit persuasion strategy prediction. We also explore the generalization ability of language models for persuasion modeling and the role of persuasion strategies in predicting social deduction game outcomes. Our dataset, code, and models can be found at https://persuasion-deductiongame.socialai-data.org.