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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 151–200 of 344 papers

TitleStatusHype
Weakly Supervised Data Augmentation Through Prompting for Dialogue Understanding—0
Augmenting Task-Oriented Dialogue Systems with Relation Extraction—0
Audio-to-Intent Using Acoustic-Textual Subword Representations from End-to-End ASR—0
Enhancing Out-of-Distribution Detection in Natural Language Understanding via Implicit Layer EnsembleCode0
The Open-World Lottery Ticket Hypothesis for OOD Intent ClassificationCode0
The Devil is in the Details: On Models and Training Regimes for Few-Shot Intent Classification—0
Knowledge Distillation Transfer Sets and their Impact on Downstream NLU Tasks—0
Explainable Abuse Detection as Intent Classification and Slot FillingCode0
A Domain Knowledge Enhanced Pre-Trained Language Model for Vertical Search: Case Study on Medicinal ProductsCode0
Wizard of Tasks: A Novel Conversational Dataset for Solving Real-World Tasks in Conversational Settings—0
Domain- and Task-Adaptation for VaccinChatNL, a Dutch COVID-19 FAQ Answering Corpus and Classification Model—0
TaskMix: Data Augmentation for Meta-Learning of Spoken Intent Understanding—0
CAE: Mechanism to Diminish the Class Imbalanced in SLU Slot Filling TaskCode0
LINGUIST: Language Model Instruction Tuning to Generate Annotated Utterances for Intent Classification and Slot Tagging—0
Generalized Intent Discovery: Learning from Open World Dialogue SystemCode0
Analyzing the Impact of Varied Window Hyper-parameters on Deep CNN for sEMG based Motion Intent Classification—0
Evaluating N-best Calibration of Natural Language Understanding for Dialogue SystemsCode0
Data Augmentation for Intent Classification of German Conversational Agents in the Finance Domain—0
A Survey of Intent Classification and Slot-Filling Datasets for Task-Oriented Dialog—0
Local-to-global learning for iterative training of production SLU models on new features—0
Benchmarking Language-agnostic Intent Classification for Virtual Assistant PlatformsCode0
Controlled Data Generation via Insertion Operations for NLU—0
Strategies to Improve Few-shot Learning for Intent Classification and Slot-Filling—0
Finstreder: Simple and fast Spoken Language Understanding with Finite State Transducers using modern Speech-to-Text modelsCode0
Alexa Teacher Model: Pretraining and Distilling Multi-Billion-Parameter Encoders for Natural Language Understanding Systems—0
Data Augmentation for Intent Classification—0
A Simple Meta-learning Paradigm for Zero-shot Intent Classification with Mixture Attention Mechanism—0
On Building Spoken Language Understanding Systems for Low Resourced Languages—0
Exploring the Advantages of Dense-Vector to One-Hot Encoding of Intent Classes in Out-of-Scope Detection Tasks—0
Fine-grained Intent Classification in the Legal Domain—0
ORCAS-I: Queries Annotated with Intent using Weak SupervisionCode0
Are Pre-trained Transformers Robust in Intent Classification? A Missing Ingredient in Evaluation of Out-of-Scope Intent Detection—0
Label Errors in BANKING77—0
KNN-Contrastive Learning for Out-of-Domain Intent Classification—0
Evaluating the Practical Utility of Confidence-score based Techniques for Unsupervised Open-world Classification—0
Knowledge Distillation Meets Few-Shot Learning: An Approach for Few-Shot Intent Classification Within and Across Domains—0
Learning to Classify Open Intent via Soft Labeling and Manifold MixupCode0
Redwood: Using Collision Detection to Grow a Large-Scale Intent Classification DatasetCode0
Building an ASR Error Robust Spoken Virtual Patient System in a Highly Class-Imbalanced Scenario Without Speech Data—0
Three-Module Modeling For End-to-End Spoken Language Understanding Using Pre-trained DNN-HMM-Based Acoustic-Phonetic Model—0
Quick Starting Dialog Systems with Paraphrase Generation—0
A Speech Representation Anonymization Framework via Selective Noise PerturbationCode0
Towards Textual Out-of-Domain Detection without In-Domain Labels—0
Bi-directional Joint Neural Networks for Intent Classification and Slot Filling—0
A new data augmentation method for intent classification enhancement and its application on spoken conversation datasets—0
When BERT Meets Quantum Temporal Convolution Learning for Text Classification in Heterogeneous Computing—0
mSLAM: Massively multilingual joint pre-training for speech and text—0
A Deep Learning Approach to Integrate Human-Level Understanding in a Chatbot—0
Few-Shot NLU with Vector Projection Distance and Abstract Triangular CRF—0
Towards Explainable Dialogue System: Explaining Intent Classification using Saliency Techniques—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