Learning policy scheduling for text augmentation
Abstract
When training deep learning models, data augmentation is an important technique to improve the performance and alleviate overfitting. In natural language processing (NLP), existing augmentation methods often use fixed strategies. However, it might be preferred to use different augmentation policies in different stage of training, and different datasets may require different augmentation policies. In this paper, we take dynamic policy scheduling...
Paper Details
Title
Learning policy scheduling for text augmentation
Published Date
Jan 1, 2022
Journal
Volume
145
Pages
121 - 127
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