KULCQ: An Unsupervised Keyword-based Utterance Level Clustering Quality Metric
This addresses the scalability issue in evaluating intent discovery for conversational agents, though it appears incremental as it builds on existing unsupervised metrics.
The paper tackles the problem of evaluating utterance clusters in intent discovery without requiring labeled data, by introducing KULCQ, an unsupervised metric that uses keyword analysis. The results show that KULCQ better captures semantic relationships in conversational data while maintaining consistency with geometric principles.
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.