On the Structure of Address in Multi-Party Dialogue: From Discrete Labels to Continuous Levels
This paper reexamines addressee detection in multi-party dialogue, proposing that address is a continuous phenomenon rather than discrete. Using a multi-annotator corpus, they construct continuous address levels that relate to turn-taking, gaze, and backchannels. Continuous models outperform discrete ones, suggesting a graded structure of address.
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[Submitted on 17 Jul 2026]
Title:On the Structure of Address in Multi-Party Dialogue: From Discrete Labels to Continuous Levels
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Abstract: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.
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
Cite as: arXiv:2607.15648 [cs.CL]
(or arXiv:2607.15648v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.15648
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Taiga Mori [view email] [v1] Fri, 17 Jul 2026 05:45:58 UTC (3,192 KB)
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