  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.
