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 this https URL.

本文介绍了第一个模拟说服行为的多模态数据集，并使用对话背景和视觉信号提供了广泛实验证明，展示了说服策略预测的益处和语言模型对说服建模的泛化能力。

社交推理游戏中说服行为建模的多模态数据集：我们中的狼人

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

Modeling what makes an advertisement persuasive, i.e., eliciting the desired
response from consumer, is critical to the study of propaganda, social
psychology, and marketing. Despite its importance, computational modeling of
persuasion in computer vision is still in its infancy, primarily due to the
lack of benchmark datasets that can provide persuasion-strategy labels
associated with ads. Motivated by persuasion literature in social psychology
and marketing, we introduce an extensive vocabulary of persuasion strategies
and build the first ad image corpus annotated with persuasion strategies. We
then formulate the task of persuasion strategy prediction with multi-modal
learning, where we design a multi-task attention fusion model that can leverage
other ad-understanding tasks to predict persuasion strategies. Further, we
conduct a real-world case study on 1600 advertising campaigns of 30 Fortune-500
companies where we use our model's predictions to analyze which strategies work
with different demographics (age and gender). The dataset also provides image
segmentation masks, which labels persuasion strategies in the corresponding ad
images on the test split. We publicly release our code and dataset
this https URL.

本文介绍了一种使用多模态学习的注意力融合模型来预测广告中运用的说服策略，并使用构建的广告图像语料库对模型的效果进行了测试。测试数据中的图像分割掩模标注了对应广告中的说服策略。通过对 30 所财富 500 强企业的 1600 个广告活动的实际案例研究，可以分析对不同人口统计学数据（年龄和性别）采用哪种策略更有效。