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 126–150 of 344 papers

TitleStatusHype
Enhancing Chinese Intent Classification by Dynamically Integrating Character Features into Word Embeddings with Ensemble Techniques—0
Chatbot: A Conversational Agent employed with Named Entity Recognition Model using Artificial Neural Network—0
End-to-End Speech to Intent Prediction to improve E-commerce Customer Support Voicebot in Hindi and English—0
End-to-End Natural Language Understanding Pipeline for Bangla Conversational Agents—0
CASA-NLU: Context-Aware Self-Attentive Natural Language Understanding for Task-Oriented Chatbots—0
arXivEdits: Understanding the Human Revision Process in Scientific Writing—0
Alexa Teacher Model: Pretraining and Distilling Multi-Billion-Parameter Encoders for Natural Language Understanding Systems—0
End to End Binarized Neural Networks for Text Classification—0
Can Your Model Tell a Negation from an Implicature? Unravelling Challenges With Intent Encoders—0
Empirical Studies of Institutional Federated Learning For Natural Language Processing—0
Emora: An Inquisitive Social Chatbot Who Cares For You—0
Can ChatGPT Detect Intent? Evaluating Large Language Models for Spoken Language Understanding—0
Are Pre-trained Transformers Robust in Intent Classification? A Missing Ingredient in Evaluation of Out-of-Scope Intent Detection—0
Efficient Intent-Based Filtering for Multi-Party Conversations Using Knowledge Distillation from LLMs—0
Efficacy of Synthetic Data as a Benchmark—0
Building Dialogue Understanding Models for Low-resource Language Indonesian from Scratch—0
A Joint Learning Framework With BERT for Spoken Language Understanding—0
Building a Task-oriented Dialog System for Languages with no Training Data: the Case for Basque—0
Dynamic Label Name Refinement for Few-Shot Dialogue Intent Classification—0
Building an ASR Error Robust Spoken Virtual Patient System in a Highly Class-Imbalanced Scenario Without Speech Data—0
A Preliminary Exploration with GPT-4o Voice Mode—0
Domain- and Task-Adaptation for VaccinChatNL, a Dutch COVID-19 FAQ Answering Corpus and Classification Model—0
Diversity-grounded Channel Prototypical Learning for Out-of-Distribution Intent Detection—0
Differentiable Retrieval Augmentation via Generative Language Modeling for E-commerce Query Intent Classification—0
Bi-directional Joint Neural Networks for Intent Classification and Slot Filling—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