发表机构
Tufts University（塔夫茨大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用LLM语义不确定性建模话轮中不断演变的语义约束，通过采样延续并监测语义离散度变化来预测转换关联位置，在实时听者反应数据集上显著优于文本基线。
AI 中文摘要
话轮转换是控制对话者何时说话和倾听的基本机制。尽管口语对话系统（SDS）利用了多种语言、声学和副语言线索，但在无脚本交互中仍会产生不合时宜的回应。一个核心挑战是预测转换关联位置（TRPs），即听者接话的机会而非义务。人类听者不会等待话轮结束；随着话语展开，他们利用对其发展意义的预期来预测TRPs并决定是否接话。我们研究这些不断演变的预期是否可以通过语义不确定性来建模——这是一种由大语言模型（LLM）衍生的度量，衡量当前话轮对可能合理出现的后续内容的约束强度。为此，我们对进行中的话轮采样可能的延续，并利用语义离散度的变化来识别话轮内的TRPs。我们在一个基于实时听者反应（而非回顾性标注）的TRP标签数据集上评估了这一方法。我们的方法显著优于基于提示和微调的纯文本基线，为以下观点提供了实证支持：不断演变的语义约束信息在无脚本交互中影响感知到的话轮转换机会。
英文摘要
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.
CommentsAccepted to Findings of the Association for Computational Linguistics: EMNLP 2026. 21 pages, 4 figures, 9 tables
