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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 231–240 of 344 papers

Title	Date	Tasks	Status	Hype
Intent Classification for Bank Chatbots through LLM Fine-Tuning	Oct 7, 2024	Chatbotintent-classification	—Unverified	0
Intent Classification on Low-Resource Languages with Query Similarity Search	May 23, 2025	ClassificationInformation Retrieval	—Unverified	0
Intent Classification Using Pre-trained Language Agnostic Embeddings For Low Resource Languages	Oct 18, 2021	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0
Intent Detection and Slots Prompt in a Closed-Domain Chatbot	Dec 27, 2018	ChatbotClassification	—Unverified	0
Intent Recognition and Unsupervised Slot Identification for Low Resourced Spoken Dialog Systems	Apr 3, 2021	Data AugmentationGeneral Classification	—Unverified	0
Introducing Semantics into Speech Encoders	Nov 15, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0
Iterative Delexicalization for Improved Spoken Language Understanding	Oct 15, 2019	intent-classificationIntent Classification	—Unverified	0
Iterative Feature Mining for Constraint-Based Data Collection to Increase Data Diversity and Model Robustness	Nov 1, 2020	Diversityintent-classification	—Unverified	0
Jointly Trained Sequential Labeling and Classification by Sparse Attention Neural Networks	Sep 28, 2017	ClassificationGeneral Classification	—Unverified	0
Joint model for intent and entity recognition	Sep 7, 2021	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