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This paper proposes AEDA (An Easier Data Augmentation) technique to helpimprove the performance on text classification tasks. AEDA includes only randominsertion of punctuation marks into the original text. This is an easiertechnique 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 thewords while changing their positions in the sentence leading to a bettergeneralized performance. Furthermore, the deletion operation in EDA can causeloss of information which, in turn, misleads the network, whereas AEDApreserves all the input information. Following the baseline, we performexperiments on five different datasets for text classification. We show thatusing the AEDA-augmented data for training, the models show superiorperformance compared to using the EDA-augmented data in all five datasets. Thesource code is available for further study and reproduction of the results.

AEDA: An Easier Data Augmentation Technique for Text Classification