English(EN) Decoupling Turn-Taking from Semantics: A Decoupled Data Approach for Finite-State-Machine-Based Full-Duplex Dialogue

新方法将轮次切换与语义解耦，以改进对话系统

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

研究人员开发了一种新方法，通过将轮次切换与语义生成解耦来改进全双工对话系统。该方法利用真实的人与人语音对话来训练轮次切换能力，并利用人与代理的文本对话来训练语义行为。通过采用基于规则的数据转换和源感知校准损失函数，该系统显著提高了轮次切换的自然度，同时保持了底层大型语言模型的语义性能。 AI

影响这项研究通过改进对话系统中的轮次切换，有望实现更自然、更高效的人机通信。

排序理由该集群包含一篇学术论文，详细介绍了一种改进对话系统的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CL TIER_1 English(EN) · Yihang Li, Chenhui Chu · 2026-09-04 04:00

解耦轮次切换与语义：一种基于有限状态机的全双工对话解耦数据方法

arXiv:2609.03321v1 Announce Type: new Abstract: The Neural Finite State Machine (NFSM) framework offers a pragmatic path to full-duplex dialogue by serializing turn-taking control and response generation onto a single causal tape under the standard next-token prediction objective…