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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 51–60 of 344 papers

Title	Date	Tasks	Status	Hype	Score
ImpactCite: An XLNet-based method for Citation Impact Analysis	May 5, 2020	Citation Intent ClassificationClassification	CodeCode Available	0	5
Fleurs-SLU: A Massively Multilingual Benchmark for Spoken Language Understanding	Jan 10, 2025	Automatic Speech RecognitionClassification	CodeCode Available	0	5
Finstreder: Simple and fast Spoken Language Understanding with Finite State Transducers using modern Speech-to-Text models	Jun 29, 2022	Intent ClassificationSlot Filling	CodeCode Available	0	5
From Masked Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-shot Spoken Language Understanding	May 15, 2021	intent-classificationIntent Classification	CodeCode Available	0	5
CAE: Mechanism to Diminish the Class Imbalanced in SLU Slot Filling Task	Sep 21, 2022	intent-classificationIntent Classification	CodeCode Available	0	5
Generalized Intent Discovery: Learning from Open World Dialogue System	Sep 13, 2022	intent-classificationIntent Classification	CodeCode Available	0	5
Exploring Robustness of Multilingual LLMs on Real-World Noisy Data	Jan 14, 2025	intent-classificationIntent Classification	CodeCode Available	0	5
Exploring Description-Augmented Dataless Intent Classification	Jul 25, 2024	AvgClassification	CodeCode Available	0	5
The Open-World Lottery Ticket Hypothesis for OOD Intent Classification	Oct 13, 2022	intent-classificationIntent Classification	CodeCode Available	0	5
Explainable Abuse Detection as Intent Classification and Slot Filling	Oct 6, 2022	Abuse DetectionGeneral 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