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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 261–270 of 344 papers

Title	Date	Tasks	Status	Hype
Delexicalized Paraphrase Generation	Dec 4, 2020	Data Augmentationintent-classification	—Unverified	0
Attentively Embracing Noise for Robust Latent Representation in BERT	Dec 1, 2020	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	CodeCode Available	0
Data-Efficient Paraphrase Generation to Bootstrap Intent Classification and Slot Labeling for New Features in Task-Oriented Dialog Systems	Dec 1, 2020	intent-classificationIntent Classification	—Unverified	0
STIL - Simultaneous Slot Filling, Translation, Intent Classification, and Language Identification: Initial Results using mBART on MultiATIS++	Dec 1, 2020	Classificationintent-classification	CodeCode Available	0
Multi-task Learning of Spoken Language Understanding by Integrating N-Best Hypotheses with Hierarchical Attention	Dec 1, 2020	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0
Meta learning to classify intent and slot labels with noisy few shot examples	Nov 30, 2020	Benchmarkingintent-classification	—Unverified	0
Acoustics Based Intent Recognition Using Discovered Phonetic Units for Low Resource Languages	Nov 7, 2020	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0
Recent Neural Methods on Slot Filling and Intent Classification for Task-Oriented Dialogue Systems: A Survey	Nov 1, 2020	intent-classificationIntent Classification	—Unverified	0
Empirical Studies of Institutional Federated Learning For Natural Language Processing	Nov 1, 2020	Federated LearningGPU	—Unverified	0
Iterative Feature Mining for Constraint-Based Data Collection to Increase Data Diversity and Model Robustness	Nov 1, 2020	Diversityintent-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