Abstract
Turn-taking modeling is fundamental to spoken dialogue systems, yet its evaluation remains fragmented and often limited to binary boundary detection under narrow interaction settings. Such protocols hinder systematic comparison and obscure model weaknesses across conversational conditions. We present CoDeTT, a context-aware decision benchmark for turn-taking evaluation. CoDeTT formulates turn-taking as a structured decision problem and constructs a multi-scenario dataset with fine-grained decision categories and controlled context variations. Under a unified evaluation protocol, we assess representative existing models and observe substantial performance disparities across decision types and interaction scenarios. CoDeTT provides a standardized benchmark for systematic and context-aware evaluation of turn-taking systems. The benchmark dataset and evaluation toolkit are available at this https URL.
Abstract (translated)
轮次建模是口语对话系统的基础，但其评估方法仍较为零散，且常局限于狭窄交互场景下的二元边界检测。此类评估协议阻碍了系统性对比，并掩盖了模型在不同对话条件下的缺陷。本文提出CoDeTT，一个面向轮次评估的上下文感知决策基准。CoDeTT将轮次转化为结构化决策问题，并构建了包含细粒度决策类别与可控上下文变化的多场景数据集。在统一的评估协议下，我们测试了代表性现有模型，观察到不同决策类型与交互场景间存在显著性能差异。CoDeTT为轮次系统的系统性、上下文感知评估提供了标准化基准。该基准数据集与评估工具包可通过此https URL获取。
URL
https://arxiv.org/abs/2603.25434
