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

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Showing 176–200 of 344 papers

TitleStatusHype
Data Augmentation for Intent Classification—0
A Simple Meta-learning Paradigm for Zero-shot Intent Classification with Mixture Attention Mechanism—0
On Building Spoken Language Understanding Systems for Low Resourced Languages—0
Exploring the Advantages of Dense-Vector to One-Hot Encoding of Intent Classes in Out-of-Scope Detection Tasks—0
Fine-grained Intent Classification in the Legal Domain—0
ORCAS-I: Queries Annotated with Intent using Weak SupervisionCode0
Are Pre-trained Transformers Robust in Intent Classification? A Missing Ingredient in Evaluation of Out-of-Scope Intent Detection—0
Label Errors in BANKING77—0
KNN-Contrastive Learning for Out-of-Domain Intent Classification—0
Evaluating the Practical Utility of Confidence-score based Techniques for Unsupervised Open-world Classification—0
Knowledge Distillation Meets Few-Shot Learning: An Approach for Few-Shot Intent Classification Within and Across Domains—0
Learning to Classify Open Intent via Soft Labeling and Manifold MixupCode0
Redwood: Using Collision Detection to Grow a Large-Scale Intent Classification DatasetCode0
Building an ASR Error Robust Spoken Virtual Patient System in a Highly Class-Imbalanced Scenario Without Speech Data—0
Three-Module Modeling For End-to-End Spoken Language Understanding Using Pre-trained DNN-HMM-Based Acoustic-Phonetic Model—0
Quick Starting Dialog Systems with Paraphrase Generation—0
A Speech Representation Anonymization Framework via Selective Noise PerturbationCode0
Towards Textual Out-of-Domain Detection without In-Domain Labels—0
Bi-directional Joint Neural Networks for Intent Classification and Slot Filling—0
A new data augmentation method for intent classification enhancement and its application on spoken conversation datasets—0
When BERT Meets Quantum Temporal Convolution Learning for Text Classification in Heterogeneous Computing—0
mSLAM: Massively multilingual joint pre-training for speech and text—0
A Deep Learning Approach to Integrate Human-Level Understanding in a Chatbot—0
Few-Shot NLU with Vector Projection Distance and Abstract Triangular CRF—0
Towards Explainable Dialogue System: Explaining Intent Classification using Saliency Techniques—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