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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 331–340 of 344 papers

Title	Date	Tasks	Status	Hype
Diverse Few-Shot Text Classification with Multiple Metrics	May 19, 2018	ClassificationFew-Shot Learning	CodeCode Available	0
Leveraging Crowdsourcing Data For Deep Active Learning - An Application: Learning Intents in Alexa	Mar 12, 2018	Active LearningDeep Learning	—Unverified	0
Forewords	Dec 1, 2017	Emotion RecognitionIntent Classification	—Unverified	0
Open-Domain Neural Dialogue Systems	Nov 1, 2017	Deep LearningDialogue Management	—Unverified	0
A Telecom-Domain Online Customer Service Assistant Based on Question Answering with Word Embedding and Intent Classification	Nov 1, 2017	General ClassificationInformation Retrieval	—Unverified	0
The First Evaluation of Chinese Human-Computer Dialogue Technology	Sep 29, 2017	intent-classificationIntent Classification	CodeCode Available	2
Jointly Trained Sequential Labeling and Classification by Sparse Attention Neural Networks	Sep 28, 2017	ClassificationGeneral Classification	—Unverified	0
Robust Task Clustering for Deep Many-Task Learning	Aug 26, 2017	ClusteringFew-Shot Learning	—Unverified	0
Utterance Intent Classification of a Spoken Dialogue System with Efficiently Untied Recursive Autoencoders	Aug 1, 2017	Automatic Speech Recognition (ASR)Classification	—Unverified	0
User Intent Classification using Memory Networks: A Comparative Analysis for a Limited Data Scenario	Jun 19, 2017	General Classificationintent-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