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 1–10 of 344 papers

TitleStatusHype
MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse LanguagesCode2
The First Evaluation of Chinese Human-Computer Dialogue TechnologyCode2
Are Large Language Models Good Classifiers? A Study on Edit Intent Classification in Scientific Document RevisionsCode1
Simulating Task-Oriented Dialogues with State Transition Graphs and Large Language ModelsCode1
ILLUMINER: Instruction-tuned Large Language Models as Few-shot Intent Classifier and Slot FillerCode1
Learn or Recall? Revisiting Incremental Learning with Pre-trained Language ModelsCode1
InstructTODS: Large Language Models for End-to-End Task-Oriented Dialogue SystemsCode1
ITALIC: An Italian Intent Classification DatasetCode1
Pre-training Intent-Aware Encoders for Zero- and Few-Shot Intent ClassificationCode1
Improving End-to-End SLU performance with Prosodic Attention and DistillationCode1
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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