Intent Detection

Intent Detection is a task of determining the underlying purpose or goal behind a user's search query given a context. The task plays a significant role in search and recommendations. A traditional approach for intent detection implies using an intent detector model to classify user search query into predefined intent categories, given a context. One of the key challenges of the task implies identifying user intents for cold-start sessions, i.e., search sessions initiated by a non-logged-in or unrecognized user.

Source: Analyzing and Predicting Purchase Intent in E-commerce: Anonymous vs. Identified Customers

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 161–170 of 330 papers

Title	Date	Tasks	Status
Group is better than individual: Exploiting Label Topologies and Label Relations for Joint Multiple Intent Detection and Slot Filling	Oct 19, 2022	Intent Detectionslot-filling	—Unverified
Explainable Slot Type Attentions to Improve Joint Intent Detection and Slot Filling	Oct 19, 2022	Intent DetectionNatural Language Understanding	—Unverified
Disentangling Confidence Score Distribution for Out-of-Domain Intent Detection with Energy-Based Learning	Oct 17, 2022	Intent DetectionOut of Distribution (OOD) Detection	CodeCode Available
A Unified Framework for Multi-intent Spoken Language Understanding with prompting	Oct 7, 2022	Intent DetectionMulti-Task Learning	CodeCode Available
A Closer Look at Few-Shot Out-of-Distribution Intent Detection	Oct 1, 2022	Intent DetectionTask-Oriented Dialogue Systems	CodeCode Available
Continual Few-shot Intent Detection	Oct 1, 2022	Intent DetectionTask-Oriented Dialogue Systems	—Unverified
HCLD: A Hierarchical Framework for Zero-shot Cross-lingual Dialogue System	Oct 1, 2022	Intent DetectionSentence	—Unverified
A Transformer-based Threshold-Free Framework for Multi-Intent NLU	Oct 1, 2022	Intent DetectionMulti-Task Learning	—Unverified
Incorporating Instructional Prompts into a Unified Generative Framework for Joint Multiple Intent Detection and Slot Filling	Oct 1, 2022	Intent DetectionSemantic Frame Parsing	CodeCode Available
Insurance Question Answering via Single-turn Dialogue Modeling	Oct 1, 2022	Intent DetectionQuestion Answering	—Unverified

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All datasets ATIS MixSNIPS MixATIS SNIPS ASOS.com user intent BANKING77 ATIS (vi)BANKING77 10-shot BANKING77 5-shot CAIS CLINC150 CLINC150 10-shot

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Bi-model with decoder	Accuracy	98.99	—	Unverified
2	Transformer-Capsule	Accuracy	98.89	—	Unverified
3	Attention Encoder-Decoder NN	Accuracy	98.43	—	Unverified
4	Joint model with recurrent slot label context	Accuracy	98.4	—	Unverified
5	CTRAN	Accuracy	98.07	—	Unverified
6	Joint BERT + CRF	Accuracy	97.9	—	Unverified
7	SF-ID	Accuracy	97.76	—	Unverified
8	SF-ID (BLSTM) network	Accuracy	97.76	—	Unverified
9	JointBERT-CAE	Accuracy	97.5	—	Unverified
10	Joint BERT	Accuracy	97.5	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SSRAN	Accuracy	98.4	—	Unverified
2	BiSLU	Accuracy	97.8	—	Unverified
3	DGIF	Accuracy	97.8	—	Unverified
4	Co-guiding Net	Accuracy	97.7	—	Unverified
5	TFMN	Accuracy	97.7	—	Unverified
6	TFMN (PACL)	Accuracy	97.4	—	Unverified
7	MISCA	Accuracy	97.3	—	Unverified
8	Uni-MIS	Accuracy	97.2	—	Unverified
9	SLIM	Accuracy	97.2	—	Unverified
10	UGEN	Accuracy	96.9	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DGIF	Accuracy	83.3	—	Unverified
2	UGEN	Accuracy	83	—	Unverified
3	TFMN (PACL)	Accuracy	82.9	—	Unverified
4	SLIM (PACL)	Accuracy	81.9	—	Unverified
5	BiSLU	Accuracy	81.5	—	Unverified
6	TFMN	Accuracy	79.8	—	Unverified
7	RoBERTa (PACL)	Accuracy	79.1	—	Unverified
8	Co-guiding Net	Accuracy	79.1	—	Unverified
9	Uni-MIS	Accuracy	78.5	—	Unverified
10	SLIM	Accuracy	78.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CTRAN	Accuracy	99.42	—	Unverified
2	Stack-Propagation (+BERT)	Accuracy	99	—	Unverified
3	JointBERT-CAE	Accuracy	98.3	—	Unverified
4	AGIF	Accuracy	98.1	—	Unverified
5	LIDSNet	Accuracy	98	—	Unverified
6	Stack-Propagation	Accuracy	98	—	Unverified
7	SF-ID	Accuracy	97.43	—	Unverified
8	SF-ID (BLSTM) network	Accuracy	97.43	—	Unverified
9	Capsule-NLU	Accuracy	97.3	—	Unverified
10	Slot-Gated BLSTM with Attension	Accuracy	97	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	plain-LSTM	F1	0.89	—	Unverified
2	linear-Ngrams	F1	0.87	—	Unverified
3	glove-LSTM	F1	0.86	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	RoBERTa-Large + ICDA	Accuracy (%)	94.42	—	Unverified
2	OCaTS (kNN-GPT-4)	Accuracy (%)	82.7	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	JointBERT-CAE	Intent Accuracy	97.7	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	RoBERTa-Large + ICDA	Accuracy (%)	89.79	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	RoBERTa-Large + ICDA	Accuracy (%)	84.01	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CM-Net	Acc	94.56	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	RoBERTa-Large + ICDA	Accuracy (%)	97.12	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	RoBERTa-Large + ICDA	Accuracy (%)	94.84	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	RoBERTa-Large + ICDA	Accuracy (%)	92.62	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MIDAS	Accuracy	94.27	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	RoBERTa-Large + ICDA	Accuracy (%)	92.57	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	RoBERTa-Large + ICDA	Accuracy (%)	87.41	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	RoBERTa-Large + ICDA	Accuracy (%)	82.45	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MIDAS	Accuarcy	85.02	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	General SLU Model w/ Profile	Accuracy	0.85	—	Unverified