SOTAVerified|Agents Browse Leaderboard About

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 311–320 of 344 papers

Title	Date	Tasks	Status	Hype	Score
Enhancing the Generalization for Intent Classification and Out-of-Domain Detection in SLU	Jun 28, 2021	intent-classificationIntent Classification	—Unverified	0	0
Ericson: An Interactive Open-Domain Conversational Search Agent	Apr 5, 2023	Conversational SearchDialogue Management	—Unverified	0	0
Evaluating the Practical Utility of Confidence-score based Techniques for Unsupervised Open-world Classification	May 1, 2022	intent-classificationIntent Classification	—Unverified	0	0
Exploring Fluent Query Reformulations with Text-to-Text Transformers and Reinforcement Learning	Dec 18, 2020	intent-classificationIntent Classification	—Unverified	0	0
Exploring Teacher-Student Learning Approach for Multi-lingual Speech-to-Intent Classification	Sep 28, 2021	Classificationintent-classification	—Unverified	0	0
Exploring the Advantages of Dense-Vector to One-Hot Encoding of Intent Classes in Out-of-Scope Detection Tasks	May 18, 2022	intent-classificationIntent Classification	—Unverified	0	0
Exploring Zero and Few-shot Techniques for Intent Classification	May 11, 2023	ClassificationData Augmentation	—Unverified	0	0
Few-shot Intent Classification and Slot Filling with Retrieved Examples	Apr 12, 2021	ClassificationFew-Shot Learning	—Unverified	0	0
Few-Shot Intent Classification by Gauging Entailment Relationship Between Utterance and Semantic Label	Nov 1, 2021	Classificationintent-classification	—Unverified	0	0
Few-Shot NLU with Vector Projection Distance and Abstract Triangular CRF	Dec 9, 2021	intent-classificationIntent Classification	—Unverified	0	0

Show:10 25 50

← PrevPage 32 of 35Next →

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