2020
ACL
ACL 2020
Dynamic Memory Induction Networks for Few-Shot Text Classification
Abstract
AbstractThis paper proposes Dynamic Memory Induction Networks (DMIN) for few-short text classification. The model develops a dynamic routing mechanism over static memory, enabling it to better adapt to unseen classes, a critical capability for few-short classification. The model also expands the induction process with supervised learning weights and query information to enhance the generalization ability of meta-learning. The proposed model brings forward the state-of-the-art performance significantly by 2 4% improvement on the miniRCV1 and ODIC datasets. Detailed analysis is further performed to show how the proposed network achieves the new performance.
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Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning and Natural Language Processing
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Hot Topic Early Bird
— few-shot learning
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio
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Keyword Pioneer
— induction process
Authors
Topics
Artificial Intelligence > Learning Paradigms > Few-Shot Learning
Natural Language Processing > Applications > Text Classification
Machine Learning > Learning Paradigms > Few-Shot Learning
Machine Learning > Learning Paradigms > Meta-Learning
Machine Learning > Learning Types > Meta-Learning
Deep Learning > Learning Types > Few-Shot Learning