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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 41–50 of 344 papers

Title	Date	Tasks	Status	Hype
An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction	Sep 4, 2019	BenchmarkingGeneral Classification	CodeCode Available	1
Benchmarking Natural Language Understanding Services for building Conversational Agents	Mar 13, 2019	BenchmarkingGeneral Classification	CodeCode Available	1
BERT for Joint Intent Classification and Slot Filling	Feb 28, 2019	General Classificationintent-classification	CodeCode Available	1
Induction Networks for Few-Shot Text Classification	Feb 27, 2019	Few-Shot Text ClassificationGeneral Classification	CodeCode Available	1
Leveraging GANs for citation intent classification and its impact on citation network analysis	May 27, 2025	Citation Intent Classificationintent-classification	CodeCode Available	0
Reward-Driven Interaction: Enhancing Proactive Dialogue Agents through User Satisfaction Prediction	May 24, 2025	intent-classificationIntent Classification	—Unverified	0
Intent Classification on Low-Resource Languages with Query Similarity Search	May 23, 2025	ClassificationInformation Retrieval	—Unverified	0
In a Few Words: Comparing Weak Supervision and LLMs for Short Query Intent Classification	Apr 30, 2025	ClassificationIn-Context Learning	—Unverified	0
ConvoGen: Enhancing Conversational AI with Synthetic Data: A Multi-Agent Approach	Mar 21, 2025	Conversation SummarizationFew-Shot Learning	—Unverified	0
Efficient Intent-Based Filtering for Multi-Party Conversations Using Knowledge Distillation from LLMs	Mar 21, 2025	intent-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