2021
EMNLP
EMNLP 2021
[RETRACTED] DMix: Distance Constrained Interpolative Mixup
Abstract
AbstractInterpolation-based regularisation methods have proven to be effective for various tasks and modalities. Mixup is a data augmentation method that generates virtual training samples from convex combinations of individual inputs and labels. We extend Mixup and propose DMix, distance-constrained interpolative Mixup for sentence classification leveraging the hyperbolic space. DMix achieves state-of-the-art results on sentence classification over existing data augmentation methods across datasets in four languages.
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Interdisciplinary Bridge
— Deep Learning and Machine Learning and Natural Language Processing
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Keyword Pioneer
— interpolative regularization
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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
Machine Learning > Application Areas > Data Augmentation
Natural Language Processing > Applications > Text Classification
Machine Learning > Learning Types > Supervised Learning
Machine Learning > Learning Types > Data Augmentation
Deep Learning > Learning Types > Representation Learning
Deep Learning > Learning Types > Data Augmentation