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Using Semantic Uncertainty to Estimate Transition Relevance in Turn-taking

arxiv.org/abs/2609.10934

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Updated 5 h ago · first seen 11 Sept 2026

paper_01M294G4EFGTTWV7MQ9TGY7CJ6

Published
11 Sept 2026
T1 · 5 h ago
arXiv
2609.10934
T1 · 5 h ago
Category
cs.CL
T1 · 5 h ago

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Abstractabstract1

Claim history for Abstract
ValueValid from → toStatusSourceConfidenceExtractor
Turn-taking is a fundamental mechanism that governs when interlocutors speak and listen. Although Spoken Dialogue Systems (SDS) exploit a range of linguistic, acoustic, and non-verbal cues, they produce ill-timed responses in unscripted interaction. A central challenge is anticipating Transition Relevance Places (TRPs), or opportunities, not obligations, for a listener to take the floor. Human listeners do not wait for turn endings; as an utterance unfolds, they use expectations about its developing meaning to anticipate TRPs and decide whether to take the floor. We examine whether these evolving expectations can be modeled through semantic uncertainty -- an LLM-derived measure of how strongly a turn so far constrains what may plausibly come next. To do so, we sample possible continuations of an ongoing turn and use changes in semantic dispersion to identify TRPs within turns. We evaluate this account on a dataset with TRP labels derived from real-time listener responses, rather than retrospective annotation. Our approach substantially outperforms prompt-based and fine-tuned text-only baselines, providing empirical support for the view that evolving semantic constraints inform perceived turn-taking opportunities in unscripted interaction.currentcurrentarXiv (Atom API + RSS)T1highdeterministic

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