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
Meta-Inductive Node Classification across Graphs—0
Meta learning to classify intent and slot labels with noisy few shot examples—0
MMIU: Dataset for Visual Intent Understanding in Multimodal Assistants—0
Modeling Temporality of Human Intentions by Domain Adaptation—0
mSLAM: Massively multilingual joint pre-training for speech and text—0
Multi-Layer Ensembling Techniques for Multilingual Intent Classification—0
Multilingual Name Entity Recognition and Intent Classification Employing Deep Learning Architectures—0
Multilingual Paraphrase Generation For Bootstrapping New Features in Task-Oriented Dialog Systems—0
Multi-modal Intent Classification for Assistive Robots with Large-scale Naturalistic Datasets—0
Multitask Learning for Low Resource Spoken Language Understanding—0
Multi-task Learning of Spoken Language Understanding by Integrating N-Best Hypotheses with Hierarchical Attention—0
Multi-task pre-finetuning for zero-shot cross lingual transfer—0
Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System—0
Multi-task Sentence Encoding Model for Semantic Retrieval in Question Answering Systems—0
NaRLE: Natural Language Models using Reinforcement Learning with Emotion Feedback—0
Natural language understanding for task oriented dialog in the biomedical domain in a low resources context—0
Neural Graph Machines: Learning Neural Networks Using Graphs—0
Not So Fast, Classifier – Accuracy and Entropy Reduction in Incremental Intent Classification—0
NUBOT: Embedded Knowledge Graph With RASA Framework for Generating Semantic Intents Responses in Roman Urdu—0
OmniActions: Predicting Digital Actions in Response to Real-World Multimodal Sensory Inputs with LLMs—0
OmniDialog: An Omnipotent Pre-training Model for Task-Oriented Dialogue System—0
On Building Spoken Language Understanding Systems for Low Resourced Languages—0
On Spoken Language Understanding Systems for Low Resourced Languages—0
Open-Domain Neural Dialogue Systems—0
Optimizing NLU Reranking Using Entity Resolution Signals in Multi-domain Dialog 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