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 151–175 of 344 papers

TitleStatusHype
Weakly Supervised Data Augmentation Through Prompting for Dialogue Understanding—0
Augmenting Task-Oriented Dialogue Systems with Relation Extraction—0
Audio-to-Intent Using Acoustic-Textual Subword Representations from End-to-End ASR—0
Enhancing Out-of-Distribution Detection in Natural Language Understanding via Implicit Layer EnsembleCode0
The Open-World Lottery Ticket Hypothesis for OOD Intent ClassificationCode0
The Devil is in the Details: On Models and Training Regimes for Few-Shot Intent Classification—0
Knowledge Distillation Transfer Sets and their Impact on Downstream NLU Tasks—0
Explainable Abuse Detection as Intent Classification and Slot FillingCode0
A Domain Knowledge Enhanced Pre-Trained Language Model for Vertical Search: Case Study on Medicinal ProductsCode0
Wizard of Tasks: A Novel Conversational Dataset for Solving Real-World Tasks in Conversational Settings—0
Domain- and Task-Adaptation for VaccinChatNL, a Dutch COVID-19 FAQ Answering Corpus and Classification Model—0
TaskMix: Data Augmentation for Meta-Learning of Spoken Intent Understanding—0
CAE: Mechanism to Diminish the Class Imbalanced in SLU Slot Filling TaskCode0
LINGUIST: Language Model Instruction Tuning to Generate Annotated Utterances for Intent Classification and Slot Tagging—0
Generalized Intent Discovery: Learning from Open World Dialogue SystemCode0
Analyzing the Impact of Varied Window Hyper-parameters on Deep CNN for sEMG based Motion Intent Classification—0
Evaluating N-best Calibration of Natural Language Understanding for Dialogue SystemsCode0
Data Augmentation for Intent Classification of German Conversational Agents in the Finance Domain—0
A Survey of Intent Classification and Slot-Filling Datasets for Task-Oriented Dialog—0
Local-to-global learning for iterative training of production SLU models on new features—0
Benchmarking Language-agnostic Intent Classification for Virtual Assistant PlatformsCode0
Controlled Data Generation via Insertion Operations for NLU—0
Strategies to Improve Few-shot Learning for Intent Classification and Slot-Filling—0
Finstreder: Simple and fast Spoken Language Understanding with Finite State Transducers using modern Speech-to-Text modelsCode0
Alexa Teacher Model: Pretraining and Distilling Multi-Billion-Parameter Encoders for Natural Language Understanding Systems—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