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

Showing 191200 of 344 papers

TitleStatusHype
Revisiting Mahalanobis Distance for Transformer-Based Out-of-Domain Detection0
Reward-Driven Interaction: Enhancing Proactive Dialogue Agents through User Satisfaction Prediction0
Robust Task Clustering for Deep Many-Task Learning0
Scalable Semi-Supervised Query Classification Using Matrix Sketching0
SciWING– A Software Toolkit for Scientific Document Processing0
Semi-Supervised Few-Shot Intent Classification and Slot Filling0
Semi-supervised Meta-learning for Cross-domain Few-shot Intent Classification0
Simple, Fast, Accurate Intent Classification and Slot Labeling for Goal-Oriented Dialogue Systems0
Simple is Better! Lightweight Data Augmentation for Low Resource Slot Filling and Intent Classification0
Single Example Can Improve Zero-Shot Data Generation0
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Benchmark Results

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