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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 301–310 of 344 papers

Title	Date	Tasks	Status	Hype
RNN based Incremental Online Spoken Language Understanding	Oct 23, 2019	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0
Iterative Delexicalization for Improved Spoken Language Understanding	Oct 15, 2019	intent-classificationIntent Classification	—Unverified	0
A Closer Look At Feature Space Data Augmentation For Few-Shot Intent Classification	Oct 9, 2019	Data AugmentationGeneral Classification	—Unverified	0
Controlled Text Generation for Data Augmentation in Intelligent Artificial Agents	Oct 4, 2019	Data AugmentationDecoder	—Unverified	0
CASA-NLU: Context-Aware Self-Attentive Natural Language Understanding for Task-Oriented Chatbots	Sep 18, 2019	Dialogue Managementintent-classification	—Unverified	0
Emu: Enhancing Multilingual Sentence Embeddings with Semantic Specialization	Sep 15, 2019	intent-classificationIntent Classification	CodeCode Available	0
An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction	Sep 4, 2019	BenchmarkingGeneral Classification	CodeCode Available	1
Real-world Conversational AI for Hotel Bookings	Aug 27, 2019	BIG-bench Machine LearningChatbot	—Unverified	0
Active Annotation: bootstrapping annotation lexicon and guidelines for supervised NLU learning	Aug 12, 2019	intent-classificationIntent Classification	—Unverified	0
Submodular Optimization-based Diverse Paraphrasing and its Effectiveness in Data Augmentation	Jun 1, 2019	Data AugmentationDiversity	CodeCode Available	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