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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 251–260 of 344 papers

Title	Date	Tasks	Status	Hype
Meta learning to classify intent and slot labels with noisy few shot examples	Nov 30, 2020	Benchmarkingintent-classification	—Unverified	0
SLURP: A Spoken Language Understanding Resource Package	Nov 26, 2020	Intent ClassificationSlot Filling	CodeCode Available	1
Search4Code: Code Search Intent Classification Using Weak Supervision	Nov 24, 2020	ClassificationCode Search	CodeCode Available	1
Acoustics Based Intent Recognition Using Discovered Phonetic Units for Low Resource Languages	Nov 7, 2020	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0
SciWING– A Software Toolkit for Scientific Document Processing	Nov 1, 2020	Citation 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
Improving Out-of-Scope Detection in Intent Classification by Using Embeddings of the Word Graph Space of the Classes	Nov 1, 2020	ClassificationGeneral Classification	—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
Example-Driven Intent Prediction with Observers	Oct 17, 2020	Attributeintent-classification	CodeCode Available	1

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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