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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
The impact of domain-specific representations on BERT-based multi-domain spoken language understanding	Apr 1, 2021	Classificationdomain classification	—Unverified	0
Industry Scale Semi-Supervised Learning for Natural Language Understanding	Mar 29, 2021	intent-classificationIntent Classification	—Unverified	0
NUBOT: Embedded Knowledge Graph With RASA Framework for Generating Semantic Intents Responses in Roman Urdu	Feb 20, 2021	Chatbotintent-classification	—Unverified	0
Leveraging Acoustic and Linguistic Embeddings from Pretrained speech and language Models for Intent Classification	Feb 15, 2021	ClassificationGeneral Classification	—Unverified	0
Neural Data Augmentation via Example Extrapolation	Feb 2, 2021	Data AugmentationDiversity	CodeCode Available	0
Phoneme-BERT: Joint Language Modelling of Phoneme Sequence and ASR Transcript	Feb 1, 2021	intent-classificationIntent Classification	CodeCode Available	1
ProtoDA: Efficient Transfer Learning for Few-Shot Intent Classification	Jan 28, 2021	ClassificationData Augmentation	—Unverified	0
A survey of joint intent detection and slot-filling models in natural language understanding	Jan 20, 2021	ClassificationGeneral Classification	—Unverified	0
A character representation enhanced on-device Intent Classification	Jan 12, 2021	ClassificationGeneral Classification	—Unverified	0
Revisiting Mahalanobis Distance for Transformer-Based Out-of-Domain Detection	Jan 11, 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