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
Intent Discovery With Or Without Labeled Data Using Dependency Parser—0
A Survey of Ontology Expansion for Conversational Understanding—0
Going beyond research datasets: Novel intent discovery in the industry setting—0
Controllable Discovery of Intents: Incremental Deep Clustering Using Semi-Supervised Contrastive Learning—0
IntenDD: A Unified Contrastive Learning Approach for Intent Detection and Discovery—0
Assured Automatic Programming via Large Language Models—0
Intent Detection and Discovery from User Logs via Deep Semi-Supervised Contrastive Clustering—0
Intent Discovery for Enterprise Virtual Assistants: Applications of Utterance Embedding and Clustering to Intent Mining—0
From Intent Discovery to Recognition with Topic Modeling and Synthetic Data—0
IntentGPT: Few-shot Intent Discovery with Large Language Models—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