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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
CINS: Comprehensive Instruction for Few-shot Learning in Task-oriented Dialog Systems	Sep 10, 2021	dialog state trackingFew-Shot Learning	—Unverified	0
Joint model for intent and entity recognition	Sep 7, 2021	intent-classificationIntent Classification	—Unverified	0
Integrating Regular Expressions with Neural Networks via DFA	Sep 7, 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
Self-training Improves Pre-training for Few-shot Learning in Task-oriented Dialog Systems	Aug 28, 2021	dialog state trackingFew-Shot Learning	CodeCode Available	0
CAPE: Context-Aware Private Embeddings for Private Language Learning	Aug 27, 2021	intent-classificationIntent Classification	CodeCode Available	0
ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain Detection	Aug 27, 2021	Few-Shot Text Classificationintent-classification	CodeCode Available	0
A Single Example Can Improve Zero-Shot Data Generation	Aug 16, 2021	intent-classificationIntent Classification	—Unverified	0
Single Example Can Improve Zero-Shot Data Generation	Aug 1, 2021	intent-classificationIntent Classification	—Unverified	0
Semi-supervised Meta-learning for Cross-domain Few-shot Intent Classification	Aug 1, 2021	Cross-Domain Few-ShotFew-Shot Learning	—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