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 176–200 of 344 papers

TitleStatusHype
Outlier Detection for Improved Data Quality and Diversity in Dialog Systems—0
Paraphrase and Aggregate with Large Language Models for Minimizing Intent Classification Errors—0
PerSHOP -- A Persian dataset for shopping dialogue systems modeling—0
Practical Application of Domain Dependent Confidence Measurement for Spoken Language Understanding Systems—0
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
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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