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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 171–180 of 344 papers

Title	Date	Tasks	Status	Hype	Score
OmniDialog: An Omnipotent Pre-training Model for Task-Oriented Dialogue System	Dec 28, 2023	Dialogue GenerationDialogue Management	—Unverified	0	0
On Building Spoken Language Understanding Systems for Low Resourced Languages	May 25, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0	0
On Spoken Language Understanding Systems for Low Resourced Languages	Nov 16, 2021	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0	0
Open-Domain Neural Dialogue Systems	Nov 1, 2017	Deep LearningDialogue Management	—Unverified	0	0
Optimizing NLU Reranking Using Entity Resolution Signals in Multi-domain Dialog Systems	Jun 1, 2021	Entity Resolutionintent-classification	—Unverified	0	0
Outlier Detection for Improved Data Quality and Diversity in Dialog Systems	Apr 5, 2019	Diversityintent-classification	—Unverified	0	0
Paraphrase and Aggregate with Large Language Models for Minimizing Intent Classification Errors	Jun 24, 2024	ClassificationDecision Making	—Unverified	0	0
PerSHOP -- A Persian dataset for shopping dialogue systems modeling	Jan 1, 2024	intent-classificationIntent Classification	—Unverified	0	0
Practical Application of Domain Dependent Confidence Measurement for Spoken Language Understanding Systems	Jun 1, 2018	Automatic Speech Recognition (ASR)Feature Engineering	—Unverified	0	0
Practical token pruning for foundation models in few-shot conversational virtual assistant systems	Aug 21, 2024	ClassificationContrastive Learning	—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