For the sake of practical implementation, we describe tools that facilitate Data Augmentation such as the use of consistency regularization, controllers, and offline and online augmentation pipelines, to preview a few. We highlight the key differences and promising ideas that have yet to be tested in NLP. NLP is at an early stage in applying Data Augmentation compared to Computer Vision. We highlight studies that cover how augmentations can construct test sets for generalization. Deep Learning generally struggles with the measurement of generalization and characterization of overfitting. We follow these motifs with a concrete list of augmentation frameworks that have been developed for text data. We begin with the major motifs of Data Augmentation summarized into strengthening local decision boundaries, brute force training, causality and counterfactual examples, and the distinction between meaning and form. In this survey, we consider how the Data Augmentation training strategy can aid in its development. Natural Language Processing (NLP) is one of the most captivating applications of Deep Learning.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |