2019
EMNLP
EMNLP 2019
Out-of-Domain Detection for Low-Resource Text Classification Tasks
Abstract
AbstractOut-of-domain (OOD) detection for low-resource text classification is a realistic but understudied task. The goal is to detect the OOD cases with limited in-domain (ID) training data, since in machine learning applications we observe that training data is often insufficient. In this work, we propose an OOD-resistant Prototypical Network to tackle this zero-shot OOD detection and few-shot ID classification task. Evaluations on real-world datasets show that the proposed solution outperforms state-of-the-art methods in zero-shot OOD detection task, while maintaining a competitive performance on ID classification task.
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Interdisciplinary Bridge
— Artificial Intelligence and Deep Learning and Machine Learning and Natural Language Processing
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Trend Setter
— Out-of-Distribution Detection
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Hot Topic Early Bird
— prototypical network
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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, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Artificial Intelligence > Learning Paradigms > Few-Shot Learning
Machine Learning > Learning Types > Zero-Shot Learning
Natural Language Processing > Applications > Text Classification
Machine Learning > Learning Paradigms > Few-Shot Learning
Deep Learning > Learning Types > Zero-Shot Learning
Machine Learning > Learning Types > Out-of-Distribution Detection