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 251–300 of 344 papers

TitleStatusHype
Learning to Classify Intents and Slot Labels Given a Handful of Examples—0
Learning with Weak Supervision for Email Intent Detection—0
Leveraging Acoustic and Linguistic Embeddings from Pretrained speech and language Models for Intent Classification—0
Leveraging Adversarial Training in Self-Learning for Cross-Lingual Text Classification—0
Leveraging Crowdsourcing Data For Deep Active Learning - An Application: Learning Intents in Alexa—0
Leveraging Large Language Models for Exploiting ASR Uncertainty—0
Leveraging Pretrained ASR Encoders for Effective and Efficient End-to-End Speech Intent Classification and Slot Filling—0
Leveraging Unpaired Text Data for Training End-to-End Speech-to-Intent Systems—0
LinguAlchemy: Fusing Typological and Geographical Elements for Unseen Language Generalization—0
LINGUIST: Language Model Instruction Tuning to Generate Annotated Utterances for Intent Classification and Slot Tagging—0
LLM-based Weak Supervision Framework for Query Intent Classification in Video Search—0
LLMs Will Always Hallucinate, and We Need to Live With This—0
Local-to-global learning for iterative training of production SLU models on new features—0
Luganda Speech Intent Recognition for IoT Applications—0
Meta-Inductive Node Classification across Graphs—0
Meta learning to classify intent and slot labels with noisy few shot examples—0
MMIU: Dataset for Visual Intent Understanding in Multimodal Assistants—0
Modeling Temporality of Human Intentions by Domain Adaptation—0
mSLAM: Massively multilingual joint pre-training for speech and text—0
Multi-Layer Ensembling Techniques for Multilingual Intent Classification—0
Multilingual Name Entity Recognition and Intent Classification Employing Deep Learning Architectures—0
Multilingual Paraphrase Generation For Bootstrapping New Features in Task-Oriented Dialog Systems—0
Multi-modal Intent Classification for Assistive Robots with Large-scale Naturalistic Datasets—0
Multitask Learning for Low Resource Spoken Language Understanding—0
Fast Intent Classification for Spoken Language UnderstandingCode0
Effectiveness of Text, Acoustic, and Lattice-based representations in Spoken Language Understanding tasksCode0
A Domain Knowledge Enhanced Pre-Trained Language Model for Vertical Search: Case Study on Medicinal ProductsCode0
Learning to Classify Open Intent via Soft Labeling and Manifold MixupCode0
Exploring Robustness of Multilingual LLMs on Real-World Noisy DataCode0
On the Robustness of Intent Classification and Slot Labeling in Goal-oriented Dialog Systems to Real-world NoiseCode0
Effectiveness of Pre-training for Few-shot Intent ClassificationCode0
Finstreder: Simple and fast Spoken Language Understanding with Finite State Transducers using modern Speech-to-Text modelsCode0
Fleurs-SLU: A Massively Multilingual Benchmark for Spoken Language UnderstandingCode0
Self-Supervised Speech Representations are More Phonetic than SemanticCode0
From Masked Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-shot Spoken Language UnderstandingCode0
Self-training Improves Pre-training for Few-shot Learning in Task-oriented Dialog SystemsCode0
Generalized Intent Discovery: Learning from Open World Dialogue SystemCode0
ORCAS-I: Queries Annotated with Intent using Weak SupervisionCode0
Generating Hard-Negative Out-of-Scope Data with ChatGPT for Intent ClassificationCode0
Leveraging GANs for citation intent classification and its impact on citation network analysisCode0
Contrastive and Consistency Learning for Neural Noisy-Channel Model in Spoken Language UnderstandingCode0
Out-of-Scope Domain and Intent Classification through Hierarchical Joint ModelingCode0
Uddessho: An Extensive Benchmark Dataset for Multimodal Author Intent Classification in Low-Resource Bangla LanguageCode0
TestNUC: Enhancing Test-Time Computing Approaches through Neighboring Unlabeled Data ConsistencyCode0
Exploring Description-Augmented Dataless Intent ClassificationCode0
ImpactCite: An XLNet-based method for Citation Impact AnalysisCode0
Diverse Few-Shot Text Classification with Multiple MetricsCode0
Improved Out-of-Scope Intent Classification with Dual Encoding and Threshold-based Re-ClassificationCode0
Adaptive Open-Set Active Learning with Distance-Based Out-of-Distribution Detection for Robust Task-Oriented Dialog SystemCode0
Improving Dialectal Slot and Intent Detection with Auxiliary Tasks: A Multi-Dialectal Bavarian Case StudyCode0
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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