Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification
Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification
Data augmentation aims to enrich training samples for alleviating the overfitting issue in low-resource or class-imbalanced situations. Traditional methods first devise task-specific operations such as Synonym Substitute, then preset the corresponding parameters such as the substitution rate artificially, which require a lot of prior knowledge and are prone to fall …