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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 101–110 of 344 papers

Title	Date	Tasks	Status	Hype	Score
Effectiveness of Text, Acoustic, and Lattice-based representations in Spoken Language Understanding tasks	Dec 16, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	CodeCode Available	0	5
Effective Open Intent Classification with K-center Contrastive Learning and Adjustable Decision Boundary	Apr 20, 2023	Contrastive Learningintent-classification	CodeCode Available	0	5
Explainable Abuse Detection as Intent Classification and Slot Filling	Oct 6, 2022	Abuse DetectionGeneral Classification	CodeCode Available	0	5
Contrastive and Consistency Learning for Neural Noisy-Channel Model in Spoken Language Understanding	May 23, 2024	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	CodeCode Available	0	5
Efficiently Aligned Cross-Lingual Transfer Learning for Conversational Tasks using Prompt-Tuning	Apr 3, 2023	Cross-Lingual Transferintent-classification	CodeCode Available	0	5
Exploring Description-Augmented Dataless Intent Classification	Jul 25, 2024	AvgClassification	CodeCode Available	0	5
Fast Intent Classification for Spoken Language Understanding	Dec 3, 2019	ClassificationDecision Making	CodeCode Available	0	5
Enhancing Out-of-Distribution Detection in Natural Language Understanding via Implicit Layer Ensemble	Oct 20, 2022	Contrastive Learningintent-classification	CodeCode Available	0	5
End-to-end Spoken Language Understanding with Tree-constrained Pointer Generator	Oct 29, 2022	intent-classificationIntent Classification	CodeCode Available	0	5
Evaluating N-best Calibration of Natural Language Understanding for Dialogue Systems	Sep 1, 2022	Classificationintent-classification	CodeCode Available	0	5

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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