# Learning Data Augmentation Schedules for Natural Language Processing

@article{Chopard2021LearningDA,
title={Learning Data Augmentation Schedules for Natural Language Processing},
author={Daphn{\'e} Chopard and Matthias Sebastian Treder and Irena Spasi{\'c}},
journal={Proceedings of the Second Workshop on Insights from Negative Results in NLP},
year={2021}
}
• Published 2021
• Computer Science
• Proceedings of the Second Workshop on Insights from Negative Results in NLP
Despite its proven efficiency in other fields, data augmentation is less popular in the context of natural language processing (NLP) due to its complexity and limited results. A recent study (Longpre et al., 2020) showed for example that task-agnostic data augmentations fail to consistently boost the performance of pretrained transformers even in low data regimes. In this paper, we investigate whether data-driven augmentation scheduling and the integration of a wider set of transformations can…

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