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 81–90 of 330 papers

Title	Date	Tasks	Status	Hype
Joint Multiple Intent Detection and Slot Filling with Supervised Contrastive Learning and Self-Distillation	Aug 28, 2023	Contrastive LearningIntent Detection	CodeCode Available	1
ChatGPT as Data Augmentation for Compositional Generalization: A Case Study in Open Intent Detection	Aug 25, 2023	Data AugmentationIntent Detection	CodeCode Available	0
Task Conditioned BERT for Joint Intent Detection and Slot-filling	Aug 11, 2023	Intent Detectionslot-filling	—Unverified	0
Slot Induction via Pre-trained Language Model Probing and Multi-level Contrastive Learning	Aug 9, 2023	Contrastive LearningIntent Detection	CodeCode Available	0
LaDA: Latent Dialogue Action For Zero-shot Cross-lingual Neural Network Language Modeling	Aug 5, 2023	Intent DetectionLanguage Modeling	—Unverified	0
ReCoMIF: Reading comprehension based multi-source information fusion network for Chinese spoken language understanding	Aug 1, 2023	Intent DetectionMultiple-choice	CodeCode Available	0
Utilisation of open intent recognition models for customer support intent detection	Jul 31, 2023	Intent DetectionIntent Discovery	—Unverified	0
A Comparative Analysis of Machine Learning Methods for Lane Change Intention Recognition Using Vehicle Trajectory Data	Jul 28, 2023	Autonomous VehiclesIntent Detection	—Unverified	0
Multi-Intent Detection in User Provided Annotations for Programming by Examples Systems	Jul 8, 2023	Intent DetectionProgram Synthesis	—Unverified	0
Multilingual Few-Shot Learning via Language Model Retrieval	Jun 19, 2023	Few-Shot LearningIn-Context Learning	—Unverified	0

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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