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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 161–170 of 344 papers

Title	Date	Tasks	Status	Hype
Improving Out-of-Scope Detection in Intent Classification by Using Embeddings of the Word Graph Space of the Classes	Nov 1, 2020	ClassificationGeneral Classification	—Unverified	0
Improving Spoken Language Understanding By Exploiting ASR N-best Hypotheses	Jan 11, 2020	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0
Controlled Data Generation via Insertion Operations for NLU	Jul 1, 2022	intent-classificationIntent Classification	—Unverified	0
In a Few Words: Comparing Weak Supervision and LLMs for Short Query Intent Classification	Apr 30, 2025	ClassificationIn-Context Learning	—Unverified	0
In-Context Learning for Text Classification with Many Labels	Sep 19, 2023	ClassificationIn-Context Learning	—Unverified	0
Fine-grained Intent Classification in the Legal Domain	May 6, 2022	Classificationintent-classification	—Unverified	0
RNN based Incremental Online Spoken Language Understanding	Oct 23, 2019	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0
Industry Scale Semi-Supervised Learning for Natural Language Understanding	Mar 29, 2021	intent-classificationIntent Classification	—Unverified	0
InFoBERT: Zero-Shot Approach to Natural Language Understanding Using Contextualized Word Embedding	Sep 1, 2021	intent-classificationIntent Classification	—Unverified	0
An Adapter-Based Unified Model for Multiple Spoken Language Processing Tasks	Jun 20, 2024	Automatic Speech RecognitionDecoder	—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