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 281–290 of 344 papers

TitleStatusHype
Effectiveness of Pre-training for Few-shot Intent ClassificationCode0
Finstreder: Simple and fast Spoken Language Understanding with Finite State Transducers using modern Speech-to-Text modelsCode0
Fleurs-SLU: A Massively Multilingual Benchmark for Spoken Language UnderstandingCode0
Self-Supervised Speech Representations are More Phonetic than SemanticCode0
From Masked Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-shot Spoken Language UnderstandingCode0
Self-training Improves Pre-training for Few-shot Learning in Task-oriented Dialog SystemsCode0
Generalized Intent Discovery: Learning from Open World Dialogue SystemCode0
ORCAS-I: Queries Annotated with Intent using Weak SupervisionCode0
Generating Hard-Negative Out-of-Scope Data with ChatGPT for Intent ClassificationCode0
Leveraging GANs for citation intent classification and its impact on citation network analysisCode0
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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