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 251–260 of 344 papers

TitleStatusHype
Learning to Classify Intents and Slot Labels Given a Handful of Examples—0
Learning with Weak Supervision for Email Intent Detection—0
Leveraging Acoustic and Linguistic Embeddings from Pretrained speech and language Models for Intent Classification—0
Leveraging Adversarial Training in Self-Learning for Cross-Lingual Text Classification—0
Leveraging Crowdsourcing Data For Deep Active Learning - An Application: Learning Intents in Alexa—0
Leveraging Large Language Models for Exploiting ASR Uncertainty—0
Leveraging Pretrained ASR Encoders for Effective and Efficient End-to-End Speech Intent Classification and Slot Filling—0
Leveraging Unpaired Text Data for Training End-to-End Speech-to-Intent Systems—0
LinguAlchemy: Fusing Typological and Geographical Elements for Unseen Language Generalization—0
LINGUIST: Language Model Instruction Tuning to Generate Annotated Utterances for Intent Classification and Slot Tagging—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