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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 71–80 of 344 papers

Title	Date	Tasks	Status	Hype
Improving End-to-End Speech Processing by Efficient Text Data Utilization with Latent Synthesis	Oct 9, 2023	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0
Conversational Factor Information Retrieval Model (ConFIRM)	Oct 6, 2023	GPUInformation Retrieval	CodeCode Available	0
CWCL: Cross-Modal Transfer with Continuously Weighted Contrastive Loss	Sep 26, 2023	image-classificationImage Classification	—Unverified	0
In-Context Learning for Text Classification with Many Labels	Sep 19, 2023	ClassificationIn-Context Learning	—Unverified	0
Leveraging Large Language Models for Exploiting ASR Uncertainty	Sep 9, 2023	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0
Enhancing Pipeline-Based Conversational Agents with Large Language Models	Sep 7, 2023	intent-classificationIntent Classification	—Unverified	0
Differentiable Retrieval Augmentation via Generative Language Modeling for E-commerce Query Intent Classification	Aug 18, 2023	intent-classificationIntent Classification	—Unverified	0
Leveraging Pretrained ASR Encoders for Effective and Efficient End-to-End Speech Intent Classification and Slot Filling	Jul 13, 2023	intent-classificationIntent Classification	—Unverified	0
ITALIC: An Italian Intent Classification Dataset	Jun 14, 2023	Classificationintent-classification	CodeCode Available	1
Revisit Few-shot Intent Classification with PLMs: Direct Fine-tuning vs. Continual Pre-training	Jun 8, 2023	intent-classificationIntent Classification	CodeCode Available	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