ADA: An Attention-Based Data Augmentation Approach to Handle Imbalanced Textual Datasets

Abstract

This paper presents an Attention-based Data Augmentation (ADA) approach that extracts keywords from minority class data points using a vector similarity-based mechanism, uses the extracted keywords to extract significant contextual words from minority class documents using an attention mechanism, and uses the significant contextual words to enrich the minority class dataset. By creating new documents based on significant contextual words and adding them to the minority class dataset, we oversample the dataset for the minority class. On the classification job, we compare the original and oversampled versions of the datasets. We also compare ADA over the augmented datasets with two popular state-of-the-art text data augmentation methods. According to the experimental findings, classification algorithms perform better when used to augmented datasets produced by any data augmentation technique than when applied to the datasets’ original versions. Additionally, the classifiers trained over the augmented datasets generated by ADA are more effective than those generated by state-of-the-art data augmentation techniques.