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 176–200 of 344 papers

TitleStatusHype
Towards Explainable Dialogue System: Explaining Intent Classification using Saliency Techniques—0
Multi-modal Intent Classification for Assistive Robots with Large-scale Naturalistic Datasets—0
Data Augmentation for Intent Classification with Generic Large Language Models—0
Class Embeddings for Improved Out-of-Scope Detection in Intent Classification—0
Towards Better Citation Intent Classification—0
Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System—0
On Spoken Language Understanding Systems for Low Resourced Languages—0
A Fine-tuned Wav2vec 2.0/HuBERT Benchmark For Speech Emotion Recognition, Speaker Verification and Spoken Language Understanding—0
Multilingual Paraphrase Generation For Bootstrapping New Features in Task-Oriented Dialog Systems—0
Not So Fast, Classifier – Accuracy and Entropy Reduction in Incremental Intent Classification—0
Few-Shot Intent Classification by Gauging Entailment Relationship Between Utterance and Semantic Label—0
An Explicit-Joint and Supervised-Contrastive Learning Framework for Few-Shot Intent Classification and Slot Filling—0
Intent Classification Using Pre-trained Language Agnostic Embeddings For Low Resource Languages—0
MMIU: Dataset for Visual Intent Understanding in Multimodal Assistants—0
NaRLE: Natural Language Models using Reinforcement Learning with Emotion Feedback—0
Generative Adversarial Networks based on Mixed-Attentions for Citation Intent Classification in Scientific Publications—0
Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue SystemCode1
Exploring Teacher-Student Learning Approach for Multi-lingual Speech-to-Intent Classification—0
Semi-Supervised Few-Shot Intent Classification and Slot Filling—0
Effectiveness of Pre-training for Few-shot Intent ClassificationCode0
CINS: Comprehensive Instruction for Few-shot Learning in Task-oriented Dialog Systems—0
Integrating Regular Expressions with Neural Networks via DFA—0
Joint model for intent and entity recognition—0
InFoBERT: Zero-Shot Approach to Natural Language Understanding Using Contextualized Word Embedding—0
Self-training Improves Pre-training for Few-shot Learning in Task-oriented Dialog SystemsCode0
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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