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Intent Classification

Intent Classification is the task of correctly labeling a natural language utterance from a predetermined set of intents

Source: Multi-Layer Ensembling Techniques for Multilingual Intent Classification

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 221–230 of 344 papers

Title	Date	Tasks	Status	Hype
Improving the Intent Classification accuracy in Noisy Environment	Mar 12, 2023	Automatic Speech RecognitionClassification	—Unverified	0
In a Few Words: Comparing Weak Supervision and LLMs for Short Query Intent Classification	Apr 30, 2025	ClassificationIn-Context Learning	—Unverified	0
In-Context Learning for Text Classification with Many Labels	Sep 19, 2023	ClassificationIn-Context Learning	—Unverified	0
RNN based Incremental Online Spoken Language Understanding	Oct 23, 2019	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0
Industry Scale Semi-Supervised Learning for Natural Language Understanding	Mar 29, 2021	intent-classificationIntent Classification	—Unverified	0
InFoBERT: Zero-Shot Approach to Natural Language Understanding Using Contextualized Word Embedding	Sep 1, 2021	intent-classificationIntent Classification	—Unverified	0
Integrating Regular Expressions with Neural Networks via DFA	Sep 7, 2021	intent-classificationIntent Classification	—Unverified	0
Integration of Pre-trained Networks with Continuous Token Interface for End-to-End Spoken Language Understanding	Apr 15, 2021	intent-classificationIntent Classification	—Unverified	0
IntenDD: A Unified Contrastive Learning Approach for Intent Detection and Discovery	Oct 25, 2023	Contrastive Learningintent-classification	—Unverified	0
Intent-Aware Dialogue Generation and Multi-Task Contrastive Learning for Multi-Turn Intent Classification	Nov 21, 2024	ChatbotClassification	—Unverified	0

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All datasets SLURP MASSIVE KUAKE-QIC ORCAS-I

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	TDT 0-8	Accuracy (%)	90.07	—	Unverified
2	Partially Fine-tuned HuBERT	Accuracy (%)	87.51	—	Unverified
3	Multi-SLURP	Accuracy (%)	78.33	—	Unverified
4	Finstreder (Conformer)	Accuracy (%)	53.11	—	Unverified
5	Finstreder (Quartznet)	Accuracy (%)	43.15	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	mT5 Base (encoder-only)	Intent Accuracy	86.1	—	Unverified
2	mT5 Base (text-to-text)	Intent Accuracy	85.3	—	Unverified
3	XLM-R Base	Intent Accuracy	85.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	RoBERTa-wwm-ext-base	Accuracy	85.5	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BERT (query + URL)	F1-score	0.77	—	Unverified