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 101125 of 344 papers

TitleStatusHype
Uncertainty-Aware Reward-based Deep Reinforcement Learning for Intent Analysis of Social Media Information0
Conversation Style Transfer using Few-Shot Learning0
Skit-S2I: An Indian Accented Speech to Intent datasetCode1
Spoken Language Understanding for Conversational AI: Recent Advances and Future Direction0
Effectiveness of Text, Acoustic, and Lattice-based representations in Spoken Language Understanding tasksCode0
The Massively Multilingual Natural Language Understanding 2022 (MMNLU-22) Workshop and Competition0
Zero-Shot Learning for Joint Intent and Slot Labeling0
ESIE-BERT: Enriching Sub-words Information Explicitly with BERT for Joint Intent Classification and SlotFilling0
Multitask Learning for Low Resource Spoken Language Understanding0
Introducing Semantics into Speech Encoders0
Prompt Learning for Domain Adaptation in Task-Oriented Dialogue0
Multilingual Name Entity Recognition and Intent Classification Employing Deep Learning Architectures0
token2vec: A Joint Self-Supervised Pre-training Framework Using Unpaired Speech and Text0
End-to-end Spoken Language Understanding with Tree-constrained Pointer GeneratorCode0
End-to-End Speech to Intent Prediction to improve E-commerce Customer Support Voicebot in Hindi and English0
arXivEdits: Understanding the Human Revision Process in Scientific Writing0
Learning Better Intent Representations for Financial Open Intent Classification0
Weakly Supervised Data Augmentation Through Prompting for Dialogue Understanding0
Augmenting Task-Oriented Dialogue Systems with Relation Extraction0
Audio-to-Intent Using Acoustic-Textual Subword Representations from End-to-End ASR0
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 Classification0
Knowledge Distillation Transfer Sets and their Impact on Downstream NLU Tasks0
Explainable Abuse Detection as Intent Classification and Slot FillingCode0
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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