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 176–200 of 330 papers

Title	Date	Tasks	Status
Adapting Task-Oriented Dialogue Models for Email Conversations	Aug 19, 2022	Intent DetectionNatural Language Understanding	—Unverified
Real-time Caller Intent Detection In Human-Human Customer Support Spoken Conversations	Aug 14, 2022	Intent Detection	—Unverified
Intent Detection and Discovery from User Logs via Deep Semi-Supervised Contrastive Clustering	Jul 1, 2022	ClusteringIntent Detection	—Unverified
Prompt Augmented Generative Replay via Supervised Contrastive Learning for Lifelong Intent Detection	Jul 1, 2022	Continual LearningContrastive Learning	—Unverified
Lightweight Transformers for Conversational AI	Jul 1, 2022	GPUIntent Detection	—Unverified
The Spoken Language Understanding MEDIA Benchmark Dataset in the Era of Deep Learning: data updates, training and evaluation tools	Jun 1, 2022	Intent DetectionSpoken Language Understanding	—Unverified
Calibrate and Refine! A Novel and Agile Framework for ASR-error Robust Intent Detection	May 23, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Enhancing Slot Tagging with Intent Features for Task Oriented Natural Language Understanding using BERT	May 19, 2022	Intent DetectionNatural Language Understanding	—Unverified
A Fast Attention Network for Joint Intent Detection and Slot Filling on Edge Devices	May 16, 2022	Intent DetectionNatural Language Understanding	—Unverified
A Framework to Generate High-Quality Datapoints for Multiple Novel Intent Detection	May 4, 2022	Intent Detection	CodeCode Available
Are Pre-trained Transformers Robust in Intent Classification? A Missing Ingredient in Evaluation of Out-of-Scope Intent Detection	May 1, 2022	intent-classificationIntent Classification	—Unverified
Incremental Intent Detection for Medical Domain with Contrast Replay Networks	May 1, 2022	Incremental LearningIntent Detection	—Unverified
UniDU: Towards A Unified Generative Dialogue Understanding Framework	Apr 10, 2022	Dialogue State Trackingdialogue summary	—Unverified
Confidence Calibration for Intent Detection via Hyperspherical Space and Rebalanced Accuracy-Uncertainty Loss	Mar 17, 2022	Intent Detection	—Unverified
Learning Discriminative Representations and Decision Boundaries for Open Intent Detection	Mar 11, 2022	Intent DetectionNatural Language Understanding	—Unverified
Exploiting Topic Information for Joint Intent Detection and Slot Filling	Jan 16, 2022	Intent DetectionNatural Language Understanding	—Unverified
Learn to Discover Dialog Intents via Self-supervised Context Pretraining	Jan 16, 2022	Contrastive LearningIntent Detection	—Unverified
Exploring the Limits of Natural Language Inference Based Setup for Few-Shot Intent Detection	Dec 14, 2021	Few-Shot LearningGeneralized Few-Shot Learning	CodeCode Available
Intention Recognition for Multiple Agents	Dec 5, 2021	ClusteringDescriptive	—Unverified
Towards Explainable Dialogue System: Explaining Intent Classification using Saliency Techniques	Dec 1, 2021	intent-classificationIntent Classification	—Unverified
A Generative Language Model for Few-shot Aspect-Based Sentiment Analysis	Nov 16, 2021	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	—Unverified
A Graph-to-Sequence Model for Joint Intent Detection and Slot Filling in Task-Oriented Dialogue Systems	Nov 16, 2021	Decoderglobal-optimization	—Unverified
NATURE: Natural Auxiliary Text Utterances for Realistic Spoken Language Evaluation	Nov 9, 2021	Intent Detectionslot-filling	—Unverified
User Centered Design (VI): Human Factors Approaches for Intelligent Human-Computer Interaction	Nov 8, 2021	Intent DetectionPhilosophy	—Unverified
LIDSNet: A Lightweight on-device Intent Detection model using Deep Siamese Network	Oct 6, 2021	Intent DetectionNatural Language Understanding	—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