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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 261–270 of 344 papers

Title	Date	Tasks	Status	Hype
End to End Binarized Neural Networks for Text Classification	Oct 11, 2020	BinarizationClassification	—Unverified	0
Leveraging Unpaired Text Data for Training End-to-End Speech-to-Intent Systems	Oct 8, 2020	Data Augmentationintent-classification	—Unverified	0
STIL -- Simultaneous Slot Filling, Translation, Intent Classification, and Language Identification: Initial Results using mBART on MultiATIS++	Oct 2, 2020	ClassificationGeneral Classification	CodeCode Available	0
一种结合话语伪标签注意力的人机对话意图分类方法(A Human-machine Dialogue Intent Classification Method using Utterance Pseudo Label Attention)	Oct 1, 2020	intent-classificationIntent Classification	—Unverified	0
Zero-Shot Learning with Common Sense Knowledge Graphs	Sep 28, 2020	Common Sense ReasoningEntity Typing	—Unverified	0
Augmented Natural Language for Generative Sequence Labeling	Sep 15, 2020	intent-classificationIntent Classification	—Unverified	0
A Comparison of LSTM and BERT for Small Corpus	Sep 11, 2020	intent-classificationIntent Classification	—Unverified	0
Emora: An Inquisitive Social Chatbot Who Cares For You	Sep 10, 2020	Chatbotintent-classification	—Unverified	0
Simple is Better! Lightweight Data Augmentation for Low Resource Slot Filling and Intent Classification	Sep 8, 2020	Data AugmentationGeneral Classification	—Unverified	0
Joint Modelling of Cyber Activities and Physical Context to Improve Prediction of Visitor Behaviors	Aug 26, 2020	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