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 101–150 of 344 papers

TitleStatusHype
Uncertainty-Aware Reward-based Deep Reinforcement Learning for Intent Analysis of Social Media Information—0
Conversation Style Transfer using Few-Shot Learning—0
Skit-S2I: An Indian Accented Speech to Intent datasetCode1
Spoken Language Understanding for Conversational AI: Recent Advances and Future Direction—0
Effectiveness of Text, Acoustic, and Lattice-based representations in Spoken Language Understanding tasksCode0
The Massively Multilingual Natural Language Understanding 2022 (MMNLU-22) Workshop and Competition—0
Zero-Shot Learning for Joint Intent and Slot Labeling—0
ESIE-BERT: Enriching Sub-words Information Explicitly with BERT for Joint Intent Classification and SlotFilling—0
Multitask Learning for Low Resource Spoken Language Understanding—0
Introducing Semantics into Speech Encoders—0
Prompt Learning for Domain Adaptation in Task-Oriented Dialogue—0
Multilingual Name Entity Recognition and Intent Classification Employing Deep Learning Architectures—0
token2vec: A Joint Self-Supervised Pre-training Framework Using Unpaired Speech and Text—0
End-to-end Spoken Language Understanding with Tree-constrained Pointer GeneratorCode0
End-to-End Speech to Intent Prediction to improve E-commerce Customer Support Voicebot in Hindi and English—0
arXivEdits: Understanding the Human Revision Process in Scientific Writing—0
Learning Better Intent Representations for Financial Open Intent Classification—0
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
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
A Domain Knowledge Enhanced Pre-Trained Language Model for Vertical Search: Case Study on Medicinal ProductsCode0
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
Analyzing the Impact of Varied Window Hyper-parameters on Deep CNN for sEMG based Motion Intent Classification—0
Generalized Intent Discovery: Learning from Open World Dialogue SystemCode0
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
Z-BERT-A: a zero-shot Pipeline for Unknown Intent detectionCode1
A Survey of Intent Classification and Slot-Filling Datasets for Task-Oriented Dialog—0
A Multi-Task BERT Model for Schema-Guided Dialogue State TrackingCode1
Local-to-global learning for iterative training of production SLU models on new features—0
Controlled Data Generation via Insertion Operations for NLU—0
Strategies to Improve Few-shot Learning for Intent Classification and Slot-Filling—0
Benchmarking Language-agnostic Intent Classification for Virtual Assistant PlatformsCode0
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
Learning Dialogue Representations from Consecutive UtterancesCode1
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
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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