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 125 of 344 papers

TitleStatusHype
The First Evaluation of Chinese Human-Computer Dialogue TechnologyCode2
MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse LanguagesCode2
Knowledge Distillation from BERT Transformer to Speech Transformer for Intent ClassificationCode1
Exploring the Role of Context in Utterance-level Emotion, Act and Intent Classification in Conversations: An Empirical StudyCode1
InstructTODS: Large Language Models for End-to-End Task-Oriented Dialogue SystemsCode1
ITALIC: An Italian Intent Classification DatasetCode1
Search4Code: Code Search Intent Classification Using Weak SupervisionCode1
Benchmarking Natural Language Understanding Services for building Conversational AgentsCode1
Deep Open Intent Classification with Adaptive Decision BoundaryCode1
Example-Driven Intent Prediction with ObserversCode1
Improving End-to-End SLU performance with Prosodic Attention and DistillationCode1
Incremental Few-shot Text Classification with Multi-round New Classes: Formulation, Dataset and SystemCode1
Intent Classification and Slot Filling for Privacy PoliciesCode1
Interactive Classification by Asking Informative QuestionsCode1
A Multi-Task BERT Model for Schema-Guided Dialogue State TrackingCode1
BERT for Joint Intent Classification and Slot FillingCode1
CBLUE: A Chinese Biomedical Language Understanding Evaluation BenchmarkCode1
An Evaluation Dataset for Intent Classification and Out-of-Scope PredictionCode1
Are Large Language Models Good Classifiers? A Study on Edit Intent Classification in Scientific Document RevisionsCode1
Data Augmentation for Intent Classification with Off-the-shelf Large Language ModelsCode1
Efficient Sequence Transduction by Jointly Predicting Tokens and DurationsCode1
End-to-End Slot Alignment and Recognition for Cross-Lingual NLUCode1
Are Pretrained Transformers Robust in Intent Classification? A Missing Ingredient in Evaluation of Out-of-Scope Intent DetectionCode1
ILLUMINER: Instruction-tuned Large Language Models as Few-shot Intent Classifier and Slot FillerCode1
ConveRT: Efficient and Accurate Conversational Representations from TransformersCode1
Show:102550
← PrevPage 1 of 14Next →

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