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 into the
sub-optimum. Besides, the number of editing operations is limited in the
previous methods, which decreases the diversity of the augmented data and thus
restricts the performance gain. To overcome the above limitations, we propose a
framework named Text AutoAugment (TAA) to establish a compositional and
learnable paradigm for data augmentation. We regard a combination of various
operations as an augmentation policy and utilize an efficient Bayesian
Optimization algorithm to automatically search for the best policy, which
substantially improves the generalization capability of models. Experiments on
six benchmark datasets show that TAA boosts classification accuracy in
low-resource and class-imbalanced regimes by an average of 8.8% and 9.7%,
respectively, outperforming strong baselines.

Text AutoAugment 是一种数据增强方法，通过贝叶斯优化算法自动寻找最佳组合操作作为增强策略应用于训练数据。该方法显著提高了模型的泛化能力，并在六个基准数据集上表现突出，特别是在低资源和类不平衡情况下，相应的分类准确率平均分别提高了 8.8% 和 9.7%。