Summary of Kulcq: An Unsupervised Keyword-based Utterance Level Clustering Quality Metric, by Pranav Guruprasad et al.
KULCQ: An Unsupervised Keyword-based Utterance Level Clustering Quality Metric
by Pranav Guruprasad, Negar Mokhberian, Nikhil Varghese, Chandra Khatri, Amol Kelkar
First submitted to arxiv on: 15 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces Keyword-based Utterance Level Clustering Quality (KULCQ), an unsupervised metric for evaluating intent discovery in conversational agents. Existing methods rely on clustering and traditional evaluation requires labeled data, limiting scalability. KULCQ leverages keyword analysis to capture semantic relationships in conversational data while maintaining consistency with geometric clustering principles. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Intent discovery is important for building new or improving existing conversational agents. The paper proposes a new way to evaluate the quality of clustered utterances without needing labels. This approach uses keywords to analyze conversations and looks at how well the clusters capture what’s being said. The results show that this method works better than others in capturing the meaning of conversations. |
Keywords
» Artificial intelligence » Clustering » Unsupervised
