A comprehensive survey on Arabic text augmentation: approaches, challenges, and applications

Abstract Arabic is a linguistically complex language with a rich structure and valuable syntax that pose unique challenges for natural language processing (NLP), primarily due to the scarcity of large, reliable annotated datasets essential for training models. The varieties of dialects and mixtures of more than one language within a single conversation further complicate the development and efficacy of deep learning models targeting Arabic. Data augmentation (DA) techniques have emerged as a promising solution to tackle data scarcity and improve model performance. However, implementing DA in Arabic NLP presents its challenges, particularly in maintaining semantic integrity and adapting to the language’s intricate morphological structure. This survey comprehensively examines various aspects of Arabic data augmentation techniques, covering strategies for model training, methods for evaluating augmentation performance, understanding the effects and applications of augmentation on data, studying NLP downstream tasks, addressing augmentation problems, proposing solutions, conducting in-depth literature reviews, and drawing conclusions. Through detailed analysis of 75 primary and 9 secondary papers, we categorize DA methods into diversity enhancement, resampling, and secondary approaches, each targeting specific challenges inherent in augmenting Arabic datasets. The goal is to offer insights into DA effectiveness, identify research gaps, and suggest future directions for advancing NLP in Arabic.