English(EN) Using Semantic Uncertainty to Estimate Transition Relevance in Turn-taking

新方法使用语义不确定性预测对话轮次转换

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-11 04:00

研究人员开发了一种新颖的方法，通过分析语义不确定性来预测口语对话系统中的轮次转换机会。该方法模拟了不断发展的语句如何约束未来含义，并根据语义分散性识别潜在的转换相关点（TRPs）。在真实听众响应数据集上，该方法显著优于现有的仅文本基线，表明语义约束在人类轮次转换中起着至关重要的作用。 AI

影响通过提高对话式AI系统预测用户何时可能停止说话的能力，这项研究可能带来更自然、更及时的响应。

排序理由该集群包含一篇在arXiv上发表的研究论文，详细介绍了一种分析口语对话系统的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CL TIER_1 English(EN) · Muhammad Umair, Jan P. de Ruiter · 2026-09-11 04:00

利用语义不确定性估计轮次转换的相关性

arXiv:2609.10934v1 Announce Type: new Abstract: 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 unscri…