Abstract

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.

Metadata

publication

arXiv preprint arXiv:2411.09853, 2024

year

2024

publication date

2024/11/15

authors

Pranav Guruprasad, Negar Mokhberian, Nikhil Varghese, Chandra Khatri, Amol Kelkar

link

https://arxiv.org/abs/2411.09853

resource_link

https://arxiv.org/pdf/2411.09853

journal

arXiv preprint arXiv:2411.09853