论文标题
二元方案中的上下文感知性格推断：介绍Udiva数据集
Context-Aware Personality Inference in Dyadic Scenarios: Introducing the UDIVA Dataset
论文作者
论文摘要
本文介绍了乌迪瓦（Udiva），这是一个面对面二元相互作用的新的非作用数据集，其中对话者以不同的行为启发和认知工作量执行竞争性和协作任务。该数据集由147名参与者之间的90.5小时的二元相互作用组成，该参与者在188个会话中分布，使用多个视听和生理传感器记录。目前，它包括社会人口统计学，自我和同伴报告的个性，内部状态以及参与者的关系分析。作为对Udiva的初步分析，我们提出了一种基于变压器的方法，用于在二元方案中自我报告的人格推断，该方法使用视听数据和各个对话者的不同上下文来源来回归目标人的人格特征。使用所有可用上下文信息时，逐步研究的初步结果显示出一致的改进。
This paper introduces UDIVA, a new non-acted dataset of face-to-face dyadic interactions, where interlocutors perform competitive and collaborative tasks with different behavior elicitation and cognitive workload. The dataset consists of 90.5 hours of dyadic interactions among 147 participants distributed in 188 sessions, recorded using multiple audiovisual and physiological sensors. Currently, it includes sociodemographic, self- and peer-reported personality, internal state, and relationship profiling from participants. As an initial analysis on UDIVA, we propose a transformer-based method for self-reported personality inference in dyadic scenarios, which uses audiovisual data and different sources of context from both interlocutors to regress a target person's personality traits. Preliminary results from an incremental study show consistent improvements when using all available context information.
