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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 191–200 of 344 papers

Title	Date	Tasks	Status	Hype
Generative Adversarial Networks based on Mixed-Attentions for Citation Intent Classification in Scientific Publications	Oct 1, 2021	Citation Intent ClassificationClassification	—Unverified	0
Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System	Sep 29, 2021	Dialogue State TrackingEnd-To-End Dialogue Modelling	CodeCode Available	1
Exploring Teacher-Student Learning Approach for Multi-lingual Speech-to-Intent Classification	Sep 28, 2021	Classificationintent-classification	—Unverified	0
Semi-Supervised Few-Shot Intent Classification and Slot Filling	Sep 17, 2021	ClassificationContrastive Learning	—Unverified	0
Effectiveness of Pre-training for Few-shot Intent Classification	Sep 13, 2021	Classificationintent-classification	CodeCode Available	0
CINS: Comprehensive Instruction for Few-shot Learning in Task-oriented Dialog Systems	Sep 10, 2021	dialog state trackingFew-Shot Learning	—Unverified	0
Integrating Regular Expressions with Neural Networks via DFA	Sep 7, 2021	intent-classificationIntent Classification	—Unverified	0
Joint model for intent and entity recognition	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

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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