Data Augmentation using Transformers and Similarity Measures for Improving Arabic Text Classification
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Learning models are highly dependent on data to work effectively, and they give a better performance upon training on big datasets. Massive research exists in the literature to address the dataset adequacy issue. One promising approach for solving dataset adequacy issues is the data augmentation (DA) approach. In DA, the amount of training data instances is increased by making different transformations on the available data instances to generate new correct and representative data instances. DA increases the dataset size and its variability, which enhances the model performance and its prediction accuracy. DA also solves the class imbalance problem in the classification learning techniques. Few studies have recently considered DA in the Arabic language. These studies rely on traditional augmentation approaches, such as paraphrasing by using rules or noising-based techniques. In this paper, we propose a new Arabic DA method that employs the recent powerful modeling technique, namely the AraGPT-2, for the augmentation process. The generated sentences are evaluated in terms of context, semantics, diversity, and novelty using the Euclidean, cosine, Jaccard, and BLEU distances. Finally, the AraBERT transformer is used on sentiment classification tasks to evaluate the classification performance of the augmented Arabic dataset. The experiments were conducted on four sentiment Arabic datasets, namely AraSarcasm, ASTD, ATT, and MOVIE. The selected datasets vary in size, label number, and unbalanced classes. The results show that the proposed methodology enhanced the Arabic sentiment text classification on all datasets with an increase in F1 score by 4
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