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 76100 of 344 papers

TitleStatusHype
A new approach for fine-tuning sentence transformers for intent classification and out-of-scope detection tasksCode0
Out-of-Scope Domain and Intent Classification through Hierarchical Joint ModelingCode0
ImpactCite: An XLNet-based method for Citation Impact AnalysisCode0
DarijaBanking: A New Resource for Overcoming Language Barriers in Banking Intent Detection for Moroccan Arabic SpeakersCode0
Attentively Embracing Noise for Robust Latent Representation in BERTCode0
Finstreder: Simple and fast Spoken Language Understanding with Finite State Transducers using modern Speech-to-Text modelsCode0
Fast Intent Classification for Spoken Language UnderstandingCode0
Fleurs-SLU: A Massively Multilingual Benchmark for Spoken Language UnderstandingCode0
Improving Dialectal Slot and Intent Detection with Auxiliary Tasks: A Multi-Dialectal Bavarian Case StudyCode0
Exploring Description-Augmented Dataless Intent ClassificationCode0
Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot FillingCode0
Exploring Robustness of Multilingual LLMs on Real-World Noisy DataCode0
A Domain Knowledge Enhanced Pre-Trained Language Model for Vertical Search: Case Study on Medicinal ProductsCode0
Explainable Abuse Detection as Intent Classification and Slot FillingCode0
From Masked Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-shot Spoken Language UnderstandingCode0
Conversational Factor Information Retrieval Model (ConFIRM)Code0
End-to-end Spoken Language Understanding with Tree-constrained Pointer GeneratorCode0
Enhancing Out-of-Distribution Detection in Natural Language Understanding via Implicit Layer EnsembleCode0
Bengali Intent Classification with Generative Adversarial BERTCode0
Emu: Enhancing Multilingual Sentence Embeddings with Semantic SpecializationCode0
Contrastive and Consistency Learning for Neural Noisy-Channel Model in Spoken Language UnderstandingCode0
Evaluating N-best Calibration of Natural Language Understanding for Dialogue SystemsCode0
Neural Data Augmentation via Example ExtrapolationCode0
Effective Open Intent Classification with K-center Contrastive Learning and Adjustable Decision BoundaryCode0
Effectiveness of Text, Acoustic, and Lattice-based representations in Spoken Language Understanding tasksCode0
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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