Werewolf Among Us: A Multimodal Dataset for Modeling Persuasion Behaviors in Social Deduction Games

Autor:	Lai, Bolin, Zhang, Hongxin, Liu, Miao, Pariani, Aryan, Ryan, Fiona, Jia, Wenqi, Hayati, Shirley Anugrah, Rehg, James M., Yang, Diyi
Rok vydání:	2022
Předmět:	Computer Science - Machine Learning Computer Science - Computation and Language Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: 17 pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2212.08279 Zobrazit plný text záznamu View this record from Arxiv