2025
EMNLP
EMNLP 2025
AutoIntent: AutoML for Text Classification
Abstract
AbstractAutoIntent is an automated machine learning tool for text classification tasks. Unlike existing solutions, AutoIntent offers end-to-end automation with embedding model selection, classifier optimization, and decision threshold tuning, all within a modular, sklearn-like interface. The framework is designed to support multi-label classification and out-of-scope detection. AutoIntent demonstrates superior performance compared to existing AutoML tools on standard intent classification datasets and enables users to balance effectiveness and resource consumption.
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Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning and Natural Language Processing
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Keyword Pioneer
— embedding model selection
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio
Authors
Topics
Machine Learning > Application Areas > Efficient Computing
Natural Language Processing > Applications > Intent Classification
Natural Language Processing > Applications > Text Classification
Machine Learning > Application Areas > Model Compression
Machine Learning > Learning Types > Deep Learning
Machine Learning > Learning Types > Hyperparameter Optimization
Artificial Intelligence > Core AI > Natural Language Processing
Machine Learning > Application Areas > Text Classification
Machine Learning > Optimization & Theory > Hyperparameter Optimization