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 201–210 of 344 papers

TitleStatusHype
Multi-modal Intent Classification for Assistive Robots with Large-scale Naturalistic Datasets—0
Training data reduction for multilingual Spoken Language Understanding systems—0
Multi-task pre-finetuning for zero-shot cross lingual transfer—0
On Spoken Language Understanding Systems for Low Resourced Languages—0
Towards Better Citation Intent Classification—0
Data Augmentation for Intent Classification with Generic Large Language Models—0
Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System—0
Class Embeddings for Improved Out-of-Scope Detection in Intent Classification—0
A Fine-tuned Wav2vec 2.0/HuBERT Benchmark For Speech Emotion Recognition, Speaker Verification and Spoken Language Understanding—0
Few-Shot Intent Classification by Gauging Entailment Relationship Between Utterance and Semantic Label—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