基于个体特质和历史发言，动态预测多人对话中的发言顺序。
Learning Time-Varying Turn-Taking Behavior in Group Conversations
- 用性格特征和过往发言记录建模发言倾向变化
- 发现发言间隔时间影响发言概率，传统模型常忽略此点
- 适合研究社交互动、群体行为或人机对话系统设计
我们提出一种灵活的概率模型，仅基于个体特征和过往发言行为，预测多人对话中的发言轮次。现有对话模型往往无法泛化到不同群体，且常采用统一公式刻画发言行为，难以适配所有群体。为此，我们构建了更通用的对话模型，能够根据个体的性格特质及过去发言记录，预测任意群体中成员的发言顺序。关键创新在于可学习个体发言倾向随上次发言时间变化的动态规律。我们在合成数据和真实对话数据上验证了该方法，结果表明，传统行为模型可能不具现实性，凸显了本数据驱动且理论严谨方法的价值。
原文摘要 · Abstract (English)
We propose a flexible probabilistic model for predicting turn-taking patterns in group conversations based solely on individual characteristics and past speaking behavior. Many models of conversation dynamics cannot yield insights that generalize beyond a single group. Moreover, past works often aim to characterize speaking behavior through a universal formulation that may not be suitable for all groups. We thus develop a generalization of prior conversation models that predicts speaking turns among individuals in any group based on their individual characteristics, that is, personality traits, and prior speaking behavior. Importantly, our approach provides the novel ability to learn how speaking inclination varies based on when individuals last spoke. We apply our model to synthetic and real-world conversation data to verify the proposed approach and characterize real group interactions. Our results demonstrate that previous behavioral models may not always be realistic, motivating our data-driven yet theoretically grounded approach.
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