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 226–250 of 344 papers

TitleStatusHype
CAPE: Context-Aware Private Embeddings for Private Language LearningCode0
ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain DetectionCode0
A Single Example Can Improve Zero-Shot Data Generation—0
Single Example Can Improve Zero-Shot Data Generation—0
Semi-supervised Meta-learning for Cross-domain Few-shot Intent Classification—0
Improved Text Classification via Contrastive Adversarial Training—0
End-to-End Natural Language Understanding Pipeline for Bangla Conversational Agents—0
Word-Free Spoken Language Understanding for Mandarin-Chinese—0
Representation based meta-learning for few-shot spoken intent recognitionCode0
Enhancing the Generalization for Intent Classification and Out-of-Domain Detection in SLU—0
CONDA: a CONtextual Dual-Annotated dataset for in-game toxicity understanding and detection—0
Optimizing NLU Reranking Using Entity Resolution Signals in Multi-domain Dialog Systems—0
From Masked Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-shot Spoken Language UnderstandingCode0
Meta-Inductive Node Classification across Graphs—0
Joint Text and Label Generation for Spoken Language Understanding—0
Out-of-Scope Domain and Intent Classification through Hierarchical Joint ModelingCode0
Fuzzy Classification of Multi-intent Utterances—0
Adapting Long Context NLM for ASR Rescoring in Conversational Agents—0
Data Augmentation for Voice-Assistant NLU using BERT-based Interchangeable Rephrase—0
Integration of Pre-trained Networks with Continuous Token Interface for End-to-End Spoken Language Understanding—0
On the Robustness of Intent Classification and Slot Labeling in Goal-oriented Dialog Systems to Real-world NoiseCode0
Few-shot Intent Classification and Slot Filling with Retrieved Examples—0
Intent Recognition and Unsupervised Slot Identification for Low Resourced Spoken Dialog Systems—0
The impact of domain-specific representations on BERT-based multi-domain spoken language understanding—0
Industry Scale Semi-Supervised Learning for Natural Language Understanding—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