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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 301–310 of 344 papers

Title	Date	Tasks	Status	Hype
A Transfer Learning Method for Goal Recognition Exploiting Cross-Domain Spatial Features	Nov 22, 2019	Activity Recognitionintent-classification	—Unverified	0
A Joint Learning Framework With BERT for Spoken Language Understanding	Nov 20, 2019	Decoderintent-classification	—Unverified	0
Multi-task Sentence Encoding Model for Semantic Retrieval in Question Answering Systems	Nov 18, 2019	intent-classificationIntent Classification	—Unverified	0
Metric Learning for Dynamic Text Classification	Nov 4, 2019	ClassificationGeneral Classification	CodeCode Available	0
Cross-lingual intent classification in a low resource industrial setting	Nov 1, 2019	ClassificationCross-Lingual Transfer	—Unverified	0
RNN based Incremental Online Spoken Language Understanding	Oct 23, 2019	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0
Iterative Delexicalization for Improved Spoken Language Understanding	Oct 15, 2019	intent-classificationIntent Classification	—Unverified	0
A Closer Look At Feature Space Data Augmentation For Few-Shot Intent Classification	Oct 9, 2019	Data AugmentationGeneral Classification	—Unverified	0
Controlled Text Generation for Data Augmentation in Intelligent Artificial Agents	Oct 4, 2019	Data AugmentationDecoder	—Unverified	0
CASA-NLU: Context-Aware Self-Attentive Natural Language Understanding for Task-Oriented Chatbots	Sep 18, 2019	Dialogue Managementintent-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