Researchers reframe addressee detection as continuous spectrum rather than discrete labels
Researchers challenge the standard framing of addressee detection in multi-party dialogue systems by reconceptualizing it as a continuous rather than discrete problem. Using annotator disagreement as a signal, they develop methods to capture graded levels of address intent instead of forcing binary speaker-target assignments. This shift matters for dialogue systems handling group conversations, where utterances often target multiple participants with varying degrees of directness. The work suggests that treating address as probabilistic rather than categorical could improve turn-taking prediction and make dialogue agents more nuanced in understanding conversational flow.
Modelwire context
ExplainerThe paper's core contribution isn't just proposing a new model, but arguing that the task itself has been misframed. By treating annotator disagreement as meaningful signal rather than noise, the authors suggest addressee detection has inherent ambiguity that discrete labels erase.
This is largely disconnected from recent activity in the space, which has focused on scaling dialogue models and improving instruction-following. This work belongs to the narrower subfield of dialogue structure and turn-taking, where the question isn't how big or capable a model is, but how precisely it understands conversational mechanics. The shift from categorical to probabilistic framing echoes broader moves in NLP toward uncertainty quantification, though applied here to a specific structural problem rather than general language understanding.
If dialogue systems trained with continuous address scores show measurable improvements on held-out multi-party conversation benchmarks (especially those with genuine group dynamics rather than synthetic data), that validates the framing. If the method remains confined to academic papers without adoption in commercial dialogue platforms by mid-2027, it likely signals the practical gains don't justify the added complexity.
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MentionsMulti-party dialogue systems · Addressee detection · Latent-variable model
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Modelwire summarizes, we don’t republish. arXiv cs.CL originally reported this story as “On the Structure of Address in Multi-Party Dialogue: From Discrete Labels to Continuous Levels”. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.