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 101–125 of 344 papers

TitleStatusHype
Building a Task-oriented Dialog System for Languages with no Training Data: the Case for Basque—0
Forewords—0
Adversarial Training for Multi-task and Multi-lingual Joint Modeling of Utterance Intent Classification—0
Efficient Intent-Based Filtering for Multi-Party Conversations Using Knowledge Distillation from LLMs—0
Building Dialogue Understanding Models for Low-resource Language Indonesian from Scratch—0
Data Augmentation for Intent Classification with Generic Large Language Models—0
Emora: An Inquisitive Social Chatbot Who Cares For You—0
Empirical Studies of Institutional Federated Learning For Natural Language Processing—0
Can Your Model Tell a Negation from an Implicature? Unravelling Challenges With Intent Encoders—0
End to End Binarized Neural Networks for Text Classification—0
DASB -- Discrete Audio and Speech Benchmark—0
Augmented Natural Language for Generative Sequence Labeling—0
End-to-End Speech to Intent Prediction to improve E-commerce Customer Support Voicebot in Hindi and English—0
CASA-NLU: Context-Aware Self-Attentive Natural Language Understanding for Task-Oriented Chatbots—0
Acoustics Based Intent Recognition Using Discovered Phonetic Units for Low Resource Languages—0
Few-shot Intent Classification and Slot Filling with Retrieved Examples—0
Enhancing Pipeline-Based Conversational Agents with Large Language Models—0
Enhancing the Generalization for Intent Classification and Out-of-Domain Detection in SLU—0
Ericson: An Interactive Open-Domain Conversational Search Agent—0
A Simple Meta-learning Paradigm for Zero-shot Intent Classification with Mixture Attention Mechanism—0
Evaluating the Practical Utility of Confidence-score based Techniques for Unsupervised Open-world Classification—0
Finding Task-specific Subnetworks in Multi-task Spoken Language Understanding Model—0
CINS: Comprehensive Instruction for Few-shot Learning in Task-oriented Dialog Systems—0
Audio-to-Intent Using Acoustic-Textual Subword Representations from End-to-End ASR—0
CWCL: Cross-Modal Transfer with Continuously Weighted Contrastive Loss—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