Data Augmentation is Dead, Long Live Data Augmentation

arXiv:2402.14895v1 Announce Type: cross
Abstract: Textual data augmentation (DA) is a prolific field of study where novel techniques to create artificial data are regularly proposed, and that has demonstrated great efficiency on small data settings, at least for text classification tasks. In this paper, we challenge those results, showing that classical data augmentation is simply a way of performing better fine-tuning, and that spending more time fine-tuning before applying data augmentation negates its effect. This is a significant contribution as …

abstract artificial arxiv augmentation challenge classification cs.ai cs.cl cs.lg data efficiency least live data novel paper prolific results small small data study tasks text text classification textual type