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 241250 of 344 papers

TitleStatusHype
Joint Modelling of Cyber Activities and Physical Context to Improve Prediction of Visitor Behaviors0
Joint Multiple Intent Detection and Slot Labeling for Goal-Oriented Dialog0
Joint Text and Label Generation for Spoken Language Understanding0
KBot: a Knowledge graph based chatBot for natural language understanding over linked data0
KNN-Contrastive Learning for Out-of-Domain Intent Classification0
Knowledge Distillation Meets Few-Shot Learning: An Approach for Few-Shot Intent Classification Within and Across Domains0
Knowledge Distillation Transfer Sets and their Impact on Downstream NLU Tasks0
Label Errors in BANKING770
LARA: Linguistic-Adaptive Retrieval-Augmentation for Multi-Turn Intent Classification0
Learning Better Intent Representations for Financial Open Intent Classification0
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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