SOTAVerified

Intent Discovery

Given a set of labelled and unlabelled utterances, the idea is to identify existing (known) intents and potential (new intents) intents. This method can be utilised in conversational system setting.

Papers

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Showing 11–20 of 42 papers

TitleStatusHype
IntentGPT: Few-shot Intent Discovery with Large Language Models—0
KULCQ: An Unsupervised Keyword-based Utterance Level Clustering Quality Metric—0
Auto-Intent: Automated Intent Discovery and Self-Exploration for Large Language Model Web Agents—0
Pseudo-Label Enhanced Prototypical Contrastive Learning for Uniformed Intent DiscoveryCode0
Assured Automatic Programming via Large Language Models—0
A Survey of Ontology Expansion for Conversational Understanding—0
Controllable Discovery of Intents: Incremental Deep Clustering Using Semi-Supervised Contrastive Learning—0
One Stone, Four Birds: A Comprehensive Solution for QA System Using Supervised Contrastive LearningCode0
Towards Real-world Scenario: Imbalanced New Intent DiscoveryCode0
RoNID: New Intent Discovery with Generated-Reliable Labels and Cluster-friendly Representations—0
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All datasetsATISPersian-ATISSNIPS

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1k-PCA + HDBSCANARI74.94—Unverified
#ModelMetricClaimedVerifiedStatus
1k-PCA + HDBSCANARI11.97—Unverified
#ModelMetricClaimedVerifiedStatus
1k-PCA + HDBSCANARI59.23—Unverified