SOTAVerified

Intent Classification

Intent Classification is the task of correctly labeling a natural language utterance from a predetermined set of intents

Source: Multi-Layer Ensembling Techniques for Multilingual Intent Classification

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 201–225 of 344 papers

TitleStatusHype
SNOiC: Soft Labeling and Noisy Mixup based Open Intent Classification Model—0
Sparse Multitask Learning for Efficient Neural Representation of Motor Imagery and Execution—0
Spoken Language Understanding for Conversational AI: Recent Advances and Future Direction—0
Strategies to Improve Few-shot Learning for Intent Classification and Slot-Filling—0
TaDSE: Template-aware Dialogue Sentence Embeddings—0
TaskMix: Data Augmentation for Meta-Learning of Spoken Intent Understanding—0
The Devil is in the Details: On Models and Training Regimes for Few-Shot Intent Classification—0
The impact of domain-specific representations on BERT-based multi-domain spoken language understanding—0
The Massively Multilingual Natural Language Understanding 2022 (MMNLU-22) Workshop and Competition—0
Three-Module Modeling For End-to-End Spoken Language Understanding Using Pre-trained DNN-HMM-Based Acoustic-Phonetic Model—0
token2vec: A Joint Self-Supervised Pre-training Framework Using Unpaired Speech and Text—0
Towards ASR Robust Spoken Language Understanding Through In-Context Learning With Word Confusion Networks—0
Towards Better Citation Intent Classification—0
Towards Explainable Dialogue System: Explaining Intent Classification using Saliency Techniques—0
Towards Textual Out-of-Domain Detection without In-Domain Labels—0
Training data reduction for multilingual Spoken Language Understanding systems—0
Uncertainty-Aware Reward-based Deep Reinforcement Learning for Intent Analysis of Social Media Information—0
User Intent Classification using Memory Networks: A Comparative Analysis for a Limited Data Scenario—0
User Intent Inference for Web Search and Conversational Agents—0
Using multiple ASR hypotheses to boost i18n NLU performance—0
Utterance Intent Classification of a Spoken Dialogue System with Efficiently Untied Recursive Autoencoders—0
Weakly Supervised Data Augmentation Through Prompting for Dialogue Understanding—0
Adapting Long Context NLM for ASR Rescoring in Conversational Agents—0
When BERT Meets Quantum Temporal Convolution Learning for Text Classification in Heterogeneous Computing—0
Why do you cite? An investigation on citation intents and decision-making classification processes—0
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All datasetsSLURPMASSIVEKUAKE-QICORCAS-I

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TDT 0-8Accuracy (%)90.07—Unverified
2Partially Fine-tuned HuBERTAccuracy (%)87.51—Unverified
3Multi-SLURPAccuracy (%)78.33—Unverified
4Finstreder (Conformer)Accuracy (%)53.11—Unverified
5Finstreder (Quartznet)Accuracy (%)43.15—Unverified
#ModelMetricClaimedVerifiedStatus
1mT5 Base (encoder-only)Intent Accuracy86.1—Unverified
2mT5 Base (text-to-text)Intent Accuracy85.3—Unverified
3XLM-R BaseIntent Accuracy85.1—Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-wwm-ext-baseAccuracy85.5—Unverified
#ModelMetricClaimedVerifiedStatus
1BERT (query + URL)F1-score0.77—Unverified