将对话中的指向性从离散标签转向连续水平，更精准捕捉多人群体对话中的互动关系。
On the Structure of Address in Multi-Party Dialogue: From Discrete Labels to Continuous Levels
- 把对话指向性视为连续变量，而非单一标签
- 连续表示比离散标签更能预测轮次切换和听众反应
- 适用于需要精细理解互动关系的对话系统研究
在对话系统与多名用户之间的多人群体对话中，识别话语指向谁是一个关键挑战。以往工作通常将说话对象检测当作多分类任务，仅选择一个代表个体或群体的离散标签。该设定假设指向性本质为离散，并主要用于预测轮次切换。本文通过分析一个多参与者人工标注语料库（由多位标注者标注），构建了基于多数投票的二元指向标签，以及利用潜在变量模型从标注者判断中推断出的连续指向水平。我们考察了这些表示与轮次切换及听众行为（包括视线和回应信号）的关系。结果表明，除了轮次切换外，视线和回应信号也与指向性相关。使用连续指向水平的模型在预测拟合度上优于离散标签模型，提示指向性可能具有等级结构。最后，基于研究发现讨论了未来指向性检测的研究方向。
原文摘要 · Abstract (English)
In multi-party dialogues between a dialogue system and multiple users, identifying to whom an utterance is addressed is a key challenge. Prior work has typically treated addressee detection as a multi-class classification task, selecting a single label representing an individual participant or the group. This formulation assumes that address is inherently discrete and has primarily been used for predicting turn-taking. In this paper, we revisit this assumption by analyzing address as a continuous phenomenon. Using a multi-party human dialogue corpus annotated by multiple annotators, we construct both binary address labels derived from majority-vote addressee labels and continuous address levels inferred from annotator judgments using a latent-variable model. We then examine how these representations relate to turn-taking as well as listener behaviors, including gaze and backchannels. Our results show that, in addition to turn-taking, both gaze and backchannels are associated with address. Furthermore, models using continuous address levels achieve better predictive fit than those using discrete labels, suggesting that address may exhibit graded structure. Finally, we discuss the future directions of addressee detection research based on the findings of this study.
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