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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 121–130 of 344 papers

Title	Date	Tasks	Status	Hype	Score
A Simple Meta-learning Paradigm for Zero-shot Intent Classification with Mixture Attention Mechanism	Jun 5, 2022	Classificationintent-classification	—Unverified	0	0
A Multi-Granularity Matching Attention Network for Query Intent Classification in E-commerce Retrieval	Mar 28, 2023	intent-classificationIntent Classification	—Unverified	0	0
Enhancing the Generalization for Intent Classification and Out-of-Domain Detection in SLU	Jun 28, 2021	intent-classificationIntent Classification	—Unverified	0	0
Enhancing Pipeline-Based Conversational Agents with Large Language Models	Sep 7, 2023	intent-classificationIntent Classification	—Unverified	0	0
Enhancing Chinese Intent Classification by Dynamically Integrating Character Features into Word Embeddings with Ensemble Techniques	May 23, 2018	General Classificationintent-classification	—Unverified	0	0
Chatbot: A Conversational Agent employed with Named Entity Recognition Model using Artificial Neural Network	Jun 19, 2020	ChatbotInformation Retrieval	—Unverified	0	0
End-to-End Speech to Intent Prediction to improve E-commerce Customer Support Voicebot in Hindi and English	Oct 26, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0	0
End-to-End Natural Language Understanding Pipeline for Bangla Conversational Agents	Jul 12, 2021	BIG-bench Machine LearningChatbot	—Unverified	0	0
CASA-NLU: Context-Aware Self-Attentive Natural Language Understanding for Task-Oriented Chatbots	Sep 18, 2019	Dialogue Managementintent-classification	—Unverified	0	0
arXivEdits: Understanding the Human Revision Process in Scientific Writing	Oct 26, 2022	intent-classificationIntent Classification	—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