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
Practical token pruning for foundation models in few-shot conversational virtual assistant systems—0
Privacy-preserving Active Learning on Sensitive Data for User Intent Classification—0
Privacy-preserving Representation Learning for Speech Understanding—0
Prompt Learning for Domain Adaptation in Task-Oriented Dialogue—0
Prompt Perturbation Consistency Learning for Robust Language Models—0
ProtoDA: Efficient Transfer Learning for Few-Shot Intent Classification—0
PythonPal: Enhancing Online Programming Education through Chatbot-Driven Personalized Feedback—0
Quick Starting Dialog Systems with Paraphrase Generation—0
Real-world Conversational AI for Hotel Bookings—0
Recent Neural Methods on Slot Filling and Intent Classification for Task-Oriented Dialogue Systems: A Survey—0
Reliable and Interpretable Drift Detection in Streams of Short Texts—0
Revisiting Mahalanobis Distance for Transformer-Based Out-of-Domain Detection—0
Reward-Driven Interaction: Enhancing Proactive Dialogue Agents through User Satisfaction Prediction—0
Robust Task Clustering for Deep Many-Task Learning—0
Scalable Semi-Supervised Query Classification Using Matrix Sketching—0
SciWING– A Software Toolkit for Scientific Document Processing—0
Semi-Supervised Few-Shot Intent Classification and Slot Filling—0
Semi-supervised Meta-learning for Cross-domain Few-shot Intent Classification—0
Simple, Fast, Accurate Intent Classification and Slot Labeling for Goal-Oriented Dialogue Systems—0
Simple is Better! Lightweight Data Augmentation for Low Resource Slot Filling and Intent Classification—0
Single Example Can Improve Zero-Shot Data Generation—0
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
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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