This paper proposes AEDA (An Easier Data Augmentation) technique to help
improve the performance on text classification tasks. AEDA includes only random
insertion of punctuation marks into the original text. This is an easier
technique to implement for data augmentation than EDA method (Wei and Zou,
2019) with which we compare our results. In addition, it keeps the order of the
words while changing their positions in the sentence leading to a better
generalized performance. Furthermore, the deletion operation in EDA can cause
loss of information which, in turn, misleads the network, whereas AEDA
preserves all the input information. Following the baseline, we perform
experiments on five different datasets for text classification. We show that
using the AEDA-augmented data for training, the models show superior
performance compared to using the EDA-augmented data in all five datasets. The
source code is available for further study and reproduction of the results.

本文提出了一种名为 AEDA 的数据增强技术，通过在原始文本中随机插入标点符号来改善文本分类任务的性能，相比于 EDA 方法，AEDA 更易于实现，而且在句子中改变单词位置时保留词序，从而导致更好的泛化性能。实验结果表明，在使用 AEDA 增强数据训练的情况下，该模型在五个数据集中表现出优异的性能。