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 201–250 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
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
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
CBLUE: A Chinese Biomedical Language Understanding Evaluation BenchmarkCode1
CONDA: a CONtextual Dual-Annotated dataset for in-game toxicity understanding and detection—0
Are Pretrained Transformers Robust in Intent Classification? A Missing Ingredient in Evaluation of Out-of-Scope Intent DetectionCode1
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
OutFlip: Generating Out-of-Domain Samples for Unknown Intent Detection with Natural Language AttackCode1
Joint Text and Label Generation for Spoken Language Understanding—0
Out-of-Scope Domain and Intent Classification through Hierarchical Joint ModelingCode0
Incremental Few-shot Text Classification with Multi-round New Classes: Formulation, Dataset and SystemCode1
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
Speak or Chat with Me: End-to-End Spoken Language Understanding System with Flexible InputsCode1
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
NUBOT: Embedded Knowledge Graph With RASA Framework for Generating Semantic Intents Responses in Roman Urdu—0
Leveraging Acoustic and Linguistic Embeddings from Pretrained speech and language Models for Intent Classification—0
Neural Data Augmentation via Example ExtrapolationCode0
Phoneme-BERT: Joint Language Modelling of Phoneme Sequence and ASR TranscriptCode1
ProtoDA: Efficient Transfer Learning for Few-Shot Intent Classification—0
A survey of joint intent detection and slot-filling models in natural language understanding—0
A character representation enhanced on-device Intent Classification—0
Revisiting Mahalanobis Distance for Transformer-Based Out-of-Domain DetectionCode0
Intent Classification and Slot Filling for Privacy PoliciesCode1
Exploring Fluent Query Reformulations with Text-to-Text Transformers and Reinforcement Learning—0
Deep Open Intent Classification with Adaptive Decision BoundaryCode1
Generation of complex database queries and API calls from natural language utterances—0
Using multiple ASR hypotheses to boost i18n NLU performance—0
Delexicalized Paraphrase Generation—0
Attentively Embracing Noise for Robust Latent Representation in BERTCode0
Data-Efficient Paraphrase Generation to Bootstrap Intent Classification and Slot Labeling for New Features in Task-Oriented Dialog Systems—0
Multi-task Learning of Spoken Language Understanding by Integrating N-Best Hypotheses with Hierarchical Attention—0
STIL - Simultaneous Slot Filling, Translation, Intent Classification, and Language Identification: Initial Results using mBART on MultiATIS++Code0
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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