用于多轮对话结构标注的代码表注入对话分割：LLM辅助且无需黄金标签的评估
Codebook-Injected Dialogue Segmentation for Multi-Utterance Constructs Annotation: LLM-Assisted and Gold-Label-Free Evaluation
AI总结：
本文提出一种基于LLM的对话分割方法，通过代码表注入提升分割一致性，并在无黄金标签情况下评估不同分割器的性能，发现需根据下游任务优化分割策略。
摘要：
对话行为标注通常将沟通或教学意图视为局部于单个发言或轮次。这导致标注者在底层动作上达成一致，但在段落边界上存在分歧，从而降低显性可靠性。我们提出了代码表注入分割，该方法将边界决策条件于下游标注标准，且评估基于LLM的分割器与标准和检索增强的基线。为无黄金标签评估，我们引入了跨度一致性、独特性和人类-人工智能分布一致性等评估指标。我们发现具有对话行为意识的分割产生比纯文本基线更一致的段落。虽然LLM在创建结构一致的跨度方面表现出色，但基于连贯性的基线在检测对话流程的全局变化方面仍更优。在两个数据集中，没有单一分割器占优。段内连贯性的提升经常以边界独特性和人类-人工智能分布一致性为代价。这些结果突显分割作为关键设计选择，应为下游目标优化，而非单一性能指标。
英文摘要：
Dialogue Act (DA) annotation typically treats communicative or pedagogical intent as localized to individual utterances or turns. This leads annotators to agree on the underlying action while disagreeing on segment boundaries, reducing apparent reliability. We propose codebook-injected segmentation, which conditions boundary decisions on downstream annotation criteria, and evaluate LLM-based segmenters against standard and retrieval-augmented baselines. To assess these without gold labels, we introduce evaluation metrics for span consistency, distinctiveness, and human-AI distributional agreement. We found DA-awareness produces segments that are internally more consistent than text-only baselines. While LLMs excel at creating construct-consistent spans, coherence-based baselines remain superior at detecting global shifts in dialogue flow. Across two datasets, no single segmenter dominates. Improvements in within-segment coherence frequently trade off against boundary distinctiveness and human-AI distributional agreement. These results highlight segmentation as a consequential design choice that should be optimized for downstream objectives rather than a single performance score.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
