SOTAVerified

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

Showing 151200 of 330 papers

TitleStatusHype
Fine-tuning Pre-trained Language Models for Few-shot Intent Detection: Supervised Pre-training and IsotropizationCode1
A Framework to Generate High-Quality Datapoints for Multiple Novel Intent DetectionCode0
Incremental Intent Detection for Medical Domain with Contrast Replay Networks0
Are Pre-trained Transformers Robust in Intent Classification? A Missing Ingredient in Evaluation of Out-of-Scope Intent Detection0
A Generative Language Model for Few-shot Aspect-Based Sentiment AnalysisCode1
UniDU: Towards A Unified Generative Dialogue Understanding Framework0
Confidence Calibration for Intent Detection via Hyperspherical Space and Rebalanced Accuracy-Uncertainty Loss0
Learning Discriminative Representations and Decision Boundaries for Open Intent DetectionCode0
Learn to Discover Dialog Intents via Self-supervised Context Pretraining0
Exploiting Topic Information for Joint Intent Detection and Slot Filling0
Text is no more Enough! A Benchmark for Profile-based Spoken Language UnderstandingCode1
Exploring the Limits of Natural Language Inference Based Setup for Few-Shot Intent DetectionCode0
Intention Recognition for Multiple Agents0
Towards Explainable Dialogue System: Explaining Intent Classification using Saliency Techniques0
A Graph-to-Sequence Model for Joint Intent Detection and Slot Filling in Task-Oriented Dialogue Systems0
A Generative Language Model for Few-shot Aspect-Based Sentiment Analysis0
NATURE: Natural Auxiliary Text Utterances for Realistic Spoken Language Evaluation0
User Centered Design (VI): Human Factors Approaches for Intelligent Human-Computer Interaction0
A Label-Aware BERT Attention Network for Zero-Shot Multi-Intent Detection in Spoken Language UnderstandingCode1
LIDSNet: A Lightweight on-device Intent Detection model using Deep Siamese Network0
Robot Intent Recognition Method Based on State Grid Business Office0
ConvFiT: Conversational Fine-Tuning of Pretrained Language Models0
Towards Joint Intent Detection and Slot Filling via Higher-order Attention0
TEXTOIR: An Integrated and Visualized Platform for Text Open Intent RecognitionCode0
Effectiveness of Pre-training for Few-shot Intent ClassificationCode0
Few-Shot Intent Detection via Contrastive Pre-Training and Fine-TuningCode1
ConQX: Semantic Expansion of Spoken Queries for Intent Detection based on Conditioned Text Generation0
Ontology Population Reusing Resources for Dialogue Intent Detection: Generic and Multilingual Approach0
Unknown Intent Detection Using Multi-Objective Optimization on Deep Learning Classifiers0
SLIM: Explicit Slot-Intent Mapping with BERT for Joint Multi-Intent Detection and Slot FillingCode1
HAN: Higher-order Attention Network for Spoken Language Understanding0
Density-Based Dynamic Curriculum Learning for Intent Detection0
Joint Multiple Intent Detection and Slot Filling via Self-distillation0
Lifelong Intent Detection via Multi-Strategy Rebalancing0
OutFlip: Generating Examples for Unknown Intent Detection with Natural Language Attack0
ProtAugment: Intent Detection Meta-Learning through Unsupervised Diverse ParaphrasingCode1
Real-Time Activity Recognition and Intention Recognition Using a Vision-based Embedded System0
Energy-based Unknown Intent Detection with Data ManipulationCode1
A Joint and Domain-Adaptive Approach to Spoken Language Understanding0
Token-Level Supervised Contrastive Learning for Punctuation RestorationCode1
Representation based meta-learning for few-shot spoken intent recognitionCode0
Out-of-Scope Intent Detection with Self-Supervision and Discriminative TrainingCode0
A Simple Fix to Mahalanobis Distance for Improving Near-OOD DetectionCode1
Generative Conversational Networks0
Are Pretrained Transformers Robust in Intent Classification? A Missing Ingredient in Evaluation of Out-of-Scope Intent DetectionCode1
GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot FillingCode1
Spoken Language Understanding for Task-oriented Dialogue Systems with Augmented Memory Networks0
ProtAugment: Unsupervised diverse short-texts paraphrasing for intent detection meta-learningCode1
Learning to Bridge Metric Spaces: Few-shot Joint Learning of Intent Detection and Slot Filling0
A Streaming End-to-End Framework For Spoken Language Understanding0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Bi-model with decoderAccuracy98.99Unverified
2Transformer-CapsuleAccuracy98.89Unverified
3Attention Encoder-Decoder NNAccuracy98.43Unverified
4Joint model with recurrent slot label contextAccuracy98.4Unverified
5CTRANAccuracy98.07Unverified
6Joint BERT + CRFAccuracy97.9Unverified
7SF-ID (BLSTM) networkAccuracy97.76Unverified
8SF-IDAccuracy97.76Unverified
9JointBERT-CAEAccuracy97.5Unverified
10Stack-Propagation (+BERT)Accuracy97.5Unverified
#ModelMetricClaimedVerifiedStatus
1SSRANAccuracy98.4Unverified
2BiSLUAccuracy97.8Unverified
3DGIFAccuracy97.8Unverified
4TFMNAccuracy97.7Unverified
5Co-guiding NetAccuracy97.7Unverified
6TFMN (PACL)Accuracy97.4Unverified
7MISCAAccuracy97.3Unverified
8Uni-MISAccuracy97.2Unverified
9SLIMAccuracy97.2Unverified
10SLIM (PACL)Accuracy96.9Unverified
#ModelMetricClaimedVerifiedStatus
1DGIFAccuracy83.3Unverified
2UGENAccuracy83Unverified
3TFMN (PACL)Accuracy82.9Unverified
4SLIM (PACL)Accuracy81.9Unverified
5BiSLUAccuracy81.5Unverified
6TFMNAccuracy79.8Unverified
7Co-guiding NetAccuracy79.1Unverified
8RoBERTa (PACL)Accuracy79.1Unverified
9Uni-MISAccuracy78.5Unverified
10SLIMAccuracy78.3Unverified
#ModelMetricClaimedVerifiedStatus
1CTRANAccuracy99.42Unverified
2Stack-Propagation (+BERT)Accuracy99Unverified
3JointBERT-CAEAccuracy98.3Unverified
4AGIFAccuracy98.1Unverified
5LIDSNetAccuracy98Unverified
6Stack-PropagationAccuracy98Unverified
7SF-IDAccuracy97.43Unverified
8SF-ID (BLSTM) networkAccuracy97.43Unverified
9Capsule-NLUAccuracy97.3Unverified
10Slot-Gated BLSTM with AttensionAccuracy97Unverified
#ModelMetricClaimedVerifiedStatus
1plain-LSTMF10.89Unverified
2linear-NgramsF10.87Unverified
3glove-LSTMF10.86Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-Large + ICDAAccuracy (%)94.42Unverified
2OCaTS (kNN-GPT-4)Accuracy (%)82.7Unverified
#ModelMetricClaimedVerifiedStatus
1JointBERT-CAEIntent Accuracy97.7Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-Large + ICDAAccuracy (%)89.79Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-Large + ICDAAccuracy (%)84.01Unverified
#ModelMetricClaimedVerifiedStatus
1CM-NetAcc94.56Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-Large + ICDAAccuracy (%)97.12Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-Large + ICDAAccuracy (%)94.84Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-Large + ICDAAccuracy (%)92.62Unverified
#ModelMetricClaimedVerifiedStatus
1MIDASAccuracy94.27Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-Large + ICDAAccuracy (%)92.57Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-Large + ICDAAccuracy (%)87.41Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-Large + ICDAAccuracy (%)82.45Unverified
#ModelMetricClaimedVerifiedStatus
1MIDASAccuarcy85.02Unverified
#ModelMetricClaimedVerifiedStatus
1General SLU Model w/ ProfileAccuracy0.85Unverified