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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 101–110 of 344 papers

Title	Date	Tasks	Status	Hype
Uncertainty-Aware Reward-based Deep Reinforcement Learning for Intent Analysis of Social Media Information	Feb 19, 2023	Decision MakingDeep Reinforcement Learning	—Unverified	0
Conversation Style Transfer using Few-Shot Learning	Feb 16, 2023	Few-Shot LearningIn-Context Learning	—Unverified	0
Skit-S2I: An Indian Accented Speech to Intent dataset	Dec 26, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	CodeCode Available	1
Spoken Language Understanding for Conversational AI: Recent Advances and Future Direction	Dec 21, 2022	Deep Learningintent-classification	—Unverified	0
Effectiveness of Text, Acoustic, and Lattice-based representations in Spoken Language Understanding tasks	Dec 16, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	CodeCode Available	0
The Massively Multilingual Natural Language Understanding 2022 (MMNLU-22) Workshop and Competition	Dec 13, 2022	intent-classificationIntent Classification	—Unverified	0
Zero-Shot Learning for Joint Intent and Slot Labeling	Nov 29, 2022	intent-classificationIntent Classification	—Unverified	0
ESIE-BERT: Enriching Sub-words Information Explicitly with BERT for Joint Intent Classification and SlotFilling	Nov 27, 2022	intent-classificationIntent Classification	—Unverified	0
Multitask Learning for Low Resource Spoken Language Understanding	Nov 24, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0
Introducing Semantics into Speech Encoders	Nov 15, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—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