论文标题
我们之间的狼人：一个用于建模社交扣除游戏中说服行为的多模式数据集
Werewolf Among Us: A Multimodal Dataset for Modeling Persuasion Behaviors in Social Deduction Games
论文作者
论文摘要
说服建模是对话代理的关键基础。朝这个方向的现有作品仅限于分析文本对话语料库。我们认为，视觉信号在理解人类说服行为方面也起着重要作用。在本文中，我们介绍了第一个用于建模说服行为的多模式数据集。我们的数据集包括在多玩家社交扣除游戏设置中捕获的199个对话记录和视频，说服策略的26,647个话语水平注释以及扣除游戏结果的游戏水平注释。我们提供广泛的实验，以显示对话上下文和视觉信号如何使说服策略预测受益。我们还探讨了语言模型进行说服建模的概括能力以及说服策略在预测社会扣除游戏结果中的作用。我们的数据集，代码和模型可以在https://persuasion-deductiongame.socialai-data.org上找到。
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.
