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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 171–180 of 344 papers

Title	Date	Tasks	Status	Hype
Single Example Can Improve Zero-Shot Data Generation	Aug 1, 2021	intent-classificationIntent Classification	—Unverified	0
SNOiC: Soft Labeling and Noisy Mixup based Open Intent Classification Model	Oct 11, 2023	intent-classificationIntent Classification	—Unverified	0
Sparse Multitask Learning for Efficient Neural Representation of Motor Imagery and Execution	Dec 10, 2023	Efficient Neural Networkintent-classification	—Unverified	0
Spoken Language Understanding for Conversational AI: Recent Advances and Future Direction	Dec 21, 2022	Deep Learningintent-classification	—Unverified	0
Strategies to Improve Few-shot Learning for Intent Classification and Slot-Filling	Jul 1, 2022	Contrastive LearningData Augmentation	—Unverified	0
TaDSE: Template-aware Dialogue Sentence Embeddings	May 23, 2023	Contrastive Learningintent-classification	—Unverified	0
TaskMix: Data Augmentation for Meta-Learning of Spoken Intent Understanding	Sep 26, 2022	Data AugmentationDiversity	—Unverified	0
The Devil is in the Details: On Models and Training Regimes for Few-Shot Intent Classification	Oct 12, 2022	intent-classificationIntent Classification	—Unverified	0
The impact of domain-specific representations on BERT-based multi-domain spoken language understanding	Apr 1, 2021	Classificationdomain classification	—Unverified	0
The Massively Multilingual Natural Language Understanding 2022 (MMNLU-22) Workshop and Competition	Dec 13, 2022	intent-classificationIntent Classification	—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