用对话握手识别实现精准话题分割，提升航海通信理解能力
DASH: Dialogue-Aware Similarity and Handshake Recognition for Topic Segmentation in Public-Channel Conversations
- 通过对话握手检测识别话题切换点
- 在多个数据集上达到当前最优准确率
- 适合需要可解释性的实时对话监控场景
对话话题分割（DTS）对于理解任务导向的公共频道通信（如航海VHF对话）至关重要，这类对话具有非正式表达和隐式转折的特点。为解决传统方法的局限性，我们提出基于大语言模型的DASH-DTS框架。其核心贡献包括：(1) 通过对话握手识别实现话题切换检测；(2) 借助相似性引导的样例选择进行上下文增强；(3) 生成选择性正负样本以提升模型判别力与鲁棒性。此外，我们发布了首个真实航海VHF通信公开数据集VHF-Dial，推动该领域研究。DASH-DTS提供每个片段的可解释推理过程与置信度评分。实验表明，该框架在VHF-Dial及标准基准上均取得多个领先分割准确率，为操作对话的稳定监控与决策支持奠定坚实基础。
原文摘要 · Abstract (English)
Dialogue Topic Segmentation (DTS) is crucial for understanding task-oriented public-channel communications, such as maritime VHF dialogues, which feature informal speech and implicit transitions. To address the limitations of traditional methods, we propose DASH-DTS, a novel LLM-based framework. Its core contributions are: (1) topic shift detection via dialogue handshake recognition; (2) contextual enhancement through similarity-guided example selection; and (3) the generation of selective positive and negative samples to improve model discrimination and robustness. Additionally, we release VHF-Dial, the first public dataset of real-world maritime VHF communications, to advance research in this domain. DASH-DTS provides interpretable reasoning and confidence scores for each segment. Experimental results demonstrate that our framework achieves several sota segmentation trusted accuracy on both VHF-Dial and standard benchmarks, establishing a strong foundation for stable monitoring and decision support in operational dialogues.
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