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 26–42 of 42 papers

TitleStatusHype
Controllable Discovery of Intents: Incremental Deep Clustering Using Semi-Supervised Contrastive Learning—0
Dial-In LLM: Human-Aligned LLM-in-the-loop Intent Clustering for Customer Service Dialogues—0
Going beyond research datasets: Novel intent discovery in the industry setting—0
IntenDD: A Unified Contrastive Learning Approach for Intent Detection and Discovery—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
Intent Discovery With Or Without Labeled Data Using Dependency Parser—0
IntentGPT: Few-shot Intent Discovery with Large Language Models—0
KULCQ: An Unsupervised Keyword-based Utterance Level Clustering Quality Metric—0
Multimodal Intent Discovery from Livestream Videos—0
New Intent Discovery with Attracting and Dispersing Prototype—0
RoNID: New Intent Discovery with Generated-Reliable Labels and Cluster-friendly Representations—0
Semi-supervised Intent Discovery with Contrastive Learning—0
Towards Open Intent Discovery for Conversational Text—0
From Intent Discovery to Recognition with Topic Modeling and Synthetic Data—0
Unknown Intent Detection Using Multi-Objective Optimization on Deep Learning Classifiers—0
Utilisation of open intent recognition models for customer support intent detection—0
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Benchmark Results

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