无需标签，用关键词分析评估对话聚类质量
KULCQ: An Unsupervised Keyword-based Utterance Level Clustering Quality Metric
- 基于关键词分析构建无监督聚类质量评估方法
- 在语义关联捕捉上优于传统几何指标
- 适合对话系统优化与意图发现场景
意图发现对构建和改进对话系统至关重要。尽管已有多种意图发现方法，多数依赖聚类将相似话语归为一类。传统聚类评估需每条话语的意图标签，难以扩展。虽存在无需标签的聚类质量度量，但仅关注簇的几何结构，忽略对话文本中的语言细微差别。本文提出关键词级话语层级聚类质量度量（KULCQ），一种利用关键词分析评估聚类质量的无监督方法。通过与现有无监督度量对比及全面消融实验验证，结果表明KULCQ能更好捕捉对话数据中的语义关系，同时保持与几何聚类原则的一致性。
原文摘要 · Abstract (English)
Intent discovery is crucial for both building new conversational agents and improving existing ones. While several approaches have been proposed for intent discovery, most rely on clustering to group similar utterances together. Traditional evaluation of these utterance clusters requires intent labels for each utterance, limiting scalability. Although some clustering quality metrics exist that do not require labeled data, they focus solely on cluster geometry while ignoring the linguistic nuances present in conversational transcripts. In this paper, we introduce Keyword-based Utterance Level Clustering Quality (KULCQ), an unsupervised metric that leverages keyword analysis to evaluate clustering quality. We demonstrate KULCQ's effectiveness by comparing it with existing unsupervised clustering metrics and validate its performance through comprehensive ablation studies. Our results show that KULCQ better captures semantic relationships in conversational data while maintaining consistency with geometric clustering principles.
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