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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 151–160 of 344 papers

Title	Date	Tasks	Status	Hype	Score
Active Annotation: bootstrapping annotation lexicon and guidelines for supervised NLU learning	Aug 12, 2019	intent-classificationIntent Classification	—Unverified	0	0
A Closer Look At Feature Space Data Augmentation For Few-Shot Intent Classification	Oct 9, 2019	Data AugmentationGeneral Classification	—Unverified	0	0
Developing Production-Level Conversational Interfaces with Shallow Semantic Parsing	Nov 1, 2018	Dialogue ManagementGeneral Classification	—Unverified	0	0
Delexicalized Paraphrase Generation	Dec 4, 2020	Data Augmentationintent-classification	—Unverified	0	0
DeepPavlov: Open-Source Library for Dialogue Systems	Jul 1, 2018	General Classificationintent-classification	—Unverified	0	0
An Exploration into the Performance of Unsupervised Cross-Task Speech Representations for "In the Wild'' Edge Applications	May 9, 2023	Emotion Recognitionintent-classification	—Unverified	0	0
Decoupling Representation and Knowledge for Few-Shot Intent Classification and Slot Filling	Dec 21, 2023	intent-classificationIntent Classification	—Unverified	0	0
Data Query Language and Corpus Tools for Slot-Filling and Intent Classification Data	May 1, 2020	ClassificationGeneral Classification	—Unverified	0	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	0
Balancing Accuracy and Efficiency in Multi-Turn Intent Classification for LLM-Powered Dialog Systems in Production	Nov 19, 2024	ClassificationData Augmentation	—Unverified	0	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