The performance of learning models heavily relies on the availability and adequacy of training data. To address the dataset adequacy issue, researchers have extensively explored data augmentation (DA) as a promising approach. DA generates new data instances through transformations applied to the available data, thereby increasing dataset size and variability. This approach has enhanced model performance and accuracy, particularly in addressing class imbalance problems in classification tasks. However, few studies have explored DA for the Arabic language, relying on traditional approaches such as paraphrasing 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: 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% in AraSarcasm, 6% in ASTD, 9% in ATT, and 13% in MOVIE.

翻译：学习模型的性能高度依赖于训练数据的可用性和充分性。为解决数据集充分性问题，研究人员广泛探索了数据增强（DA）作为一种有前景的方法。DA通过对现有数据应用变换生成新数据实例，从而增加数据集的规模和多样性。该方法已提升了模型性能与准确性，尤其在解决分类任务中的类别不平衡问题方面表现突出。然而，针对阿拉伯语的DA研究较少，传统方法多依赖于释义或加噪技术。本文提出一种新的阿拉伯语DA方法，采用近期强大的建模技术AraGPT-2进行增强过程。生成的句子通过欧几里得距离、余弦距离、Jaccard距离和BLEU距离评估其上下文、语义、多样性和新颖性。最后，使用AraBERT Transformer在情感分类任务上评估增强后阿拉伯语数据集的分类性能。实验在四个阿拉伯语情感数据集（AraSarcasm、ASTD、ATT和MOVIE）上进行，所选数据集在规模、标签数量和类别不平衡程度上各不相同。结果表明，所提方法在所有数据集上均提升了阿拉伯语情感文本分类性能，F1分数在AraSarcasm上提高4%，在ASTD上提高6%，在ATT上提高9%，在MOVIE上提高13%。