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
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
ViMQ: A Vietnamese Medical Question Dataset for Healthcare Dialogue System DevelopmentCode1
Efficient Sequence Transduction by Jointly Predicting Tokens and DurationsCode1
Skit-S2I: An Indian Accented Speech to Intent datasetCode1
Z-BERT-A: a zero-shot Pipeline for Unknown Intent detectionCode1
A Multi-Task BERT Model for Schema-Guided Dialogue State TrackingCode1
Learning Dialogue Representations from Consecutive UtterancesCode1
Data Augmentation for Intent Classification with Off-the-shelf Large Language ModelsCode1
LightHuBERT: Lightweight and Configurable Speech Representation Learning with Once-for-All Hidden-Unit BERTCode1
pNLP-Mixer: an Efficient all-MLP Architecture for LanguageCode1
Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue SystemCode1
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
CBLUE: A Chinese Biomedical Language Understanding Evaluation BenchmarkCode1
Are Pretrained Transformers Robust in Intent Classification? A Missing Ingredient in Evaluation of Out-of-Scope Intent DetectionCode1
OutFlip: Generating Out-of-Domain Samples for Unknown Intent Detection with Natural Language AttackCode1
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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