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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 241–250 of 344 papers

Title	Date	Tasks	Status	Hype
Out-of-Scope Domain and Intent Classification through Hierarchical Joint Modeling	Apr 30, 2021	ClassificationGeneral Classification	CodeCode Available	0
Fuzzy Classification of Multi-intent Utterances	Apr 22, 2021	ClassificationGeneral Classification	—Unverified	0
Adapting Long Context NLM for ASR Rescoring in Conversational Agents	Apr 21, 2021	intent-classificationIntent Classification	—Unverified	0
Data Augmentation for Voice-Assistant NLU using BERT-based Interchangeable Rephrase	Apr 16, 2021	Data Augmentationintent-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
On the Robustness of Intent Classification and Slot Labeling in Goal-oriented Dialog Systems to Real-world Noise	Apr 14, 2021	Data AugmentationGoal-Oriented Dialog	CodeCode Available	0
Few-shot Intent Classification and Slot Filling with Retrieved Examples	Apr 12, 2021	ClassificationFew-Shot Learning	—Unverified	0
Intent Recognition and Unsupervised Slot Identification for Low Resourced Spoken Dialog Systems	Apr 3, 2021	Data AugmentationGeneral Classification	—Unverified	0
The impact of domain-specific representations on BERT-based multi-domain spoken language understanding	Apr 1, 2021	Classificationdomain classification	—Unverified	0
Industry Scale Semi-Supervised Learning for Natural Language Understanding	Mar 29, 2021	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