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 101150 of 330 papers

TitleStatusHype
CTRAN: CNN-Transformer-based Network for Natural Language UnderstandingCode1
A Hybrid Architecture for Out of Domain Intent Detection and Intent DiscoveryCode1
QAID: Question Answering Inspired Few-shot Intent Detection0
A Persian Benchmark for Joint Intent Detection and Slot FillingCode1
Selective In-Context Data Augmentation for Intent Detection using Pointwise V-Information0
Gaze-based intention estimation: principles, methodologies, and applications in HRI0
Multi-Tenant Optimization For Few-Shot Task-Oriented FAQ RetrievalCode0
HIT-SCIR at MMNLU-22: Consistency Regularization for Multilingual Spoken Language UnderstandingCode0
Discovering Customer-Service Dialog System with Semi-Supervised Learning and Coarse-to-Fine Intent Detection0
Spoken Language Understanding for Conversational AI: Recent Advances and Future Direction0
MULTI3NLU++: A Multilingual, Multi-Intent, Multi-Domain Dataset for Natural Language Understanding in Task-Oriented Dialogue0
Effectiveness of Text, Acoustic, and Lattice-based representations in Spoken Language Understanding tasksCode0
Enhancing Joint Multiple Intent Detection and Slot Filling with Global Intent-Slot Co-occurrenceCode0
Learning to Select from Multiple OptionsCode0
A Scope Sensitive and Result Attentive Model for Multi-Intent Spoken Language Understanding0
Deep Smart Contract Intent DetectionCode1
Estimating Soft Labels for Out-of-Domain Intent Detection0
A Dynamic Graph Interactive Framework with Label-Semantic Injection for Spoken Language Understanding0
DialogUSR: Complex Dialogue Utterance Splitting and Reformulation for Multiple Intent DetectionCode1
Group is better than individual: Exploiting Label Topologies and Label Relations for Joint Multiple Intent Detection and Slot Filling0
Co-guiding Net: Achieving Mutual Guidances between Multiple Intent Detection and Slot Filling via Heterogeneous Semantics-Label GraphsCode1
Explainable Slot Type Attentions to Improve Joint Intent Detection and Slot Filling0
Disentangling Confidence Score Distribution for Out-of-Domain Intent Detection with Energy-Based LearningCode0
A Unified Framework for Multi-intent Spoken Language Understanding with promptingCode0
A Closer Look at Few-Shot Out-of-Distribution Intent DetectionCode0
Incorporating Instructional Prompts into a Unified Generative Framework for Joint Multiple Intent Detection and Slot FillingCode0
Insurance Question Answering via Single-turn Dialogue Modeling0
A Transformer-based Threshold-Free Framework for Multi-Intent NLU0
Continual Few-shot Intent Detection0
Towards Multi-label Unknown Intent DetectionCode0
HCLD: A Hierarchical Framework for Zero-shot Cross-lingual Dialogue System0
CAE: Mechanism to Diminish the Class Imbalanced in SLU Slot Filling TaskCode0
From Disfluency Detection to Intent Detection and Slot FillingCode0
Multi-grained Label Refinement Network with Dependency Structures for Joint Intent Detection and Slot FillingCode0
Entity Aware Syntax Tree Based Data Augmentation for Natural Language Understanding0
Adapting Task-Oriented Dialogue Models for Email Conversations0
Z-BERT-A: a zero-shot Pipeline for Unknown Intent detectionCode1
Real-time Caller Intent Detection In Human-Human Customer Support Spoken Conversations0
Pre-training Tasks for User Intent Detection and Embedding Retrieval in E-commerce SearchCode1
Lightweight Transformers for Conversational AI0
On the Effectiveness of Sentence Encoding for Intent Detection Meta-LearningCode1
Prompt Augmented Generative Replay via Supervised Contrastive Learning for Lifelong Intent Detection0
Intent Detection and Discovery from User Logs via Deep Semi-Supervised Contrastive Clustering0
The Spoken Language Understanding MEDIA Benchmark Dataset in the Era of Deep Learning: data updates, training and evaluation tools0
Learning Dialogue Representations from Consecutive UtterancesCode1
InstructDial: Improving Zero and Few-shot Generalization in Dialogue through Instruction TuningCode1
Calibrate and Refine! A Novel and Agile Framework for ASR-error Robust Intent Detection0
DeepStruct: Pretraining of Language Models for Structure PredictionCode1
Enhancing Slot Tagging with Intent Features for Task Oriented Natural Language Understanding using BERT0
A Fast Attention Network for Joint Intent Detection and Slot Filling on Edge Devices0
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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