AraFast: Developing and Evaluating a Comprehensive Modern Standard Arabic Corpus for Enhanced Natural Language Processing

Abstract

The research presented in the following paper focuses on the effectiveness of a modern standard Arabic corpus, AraFast, in training transformer models for natural language processing tasks, particularly in Arabic. In the study described herein, four experiments were conducted to evaluate the use of AraFast across different configurations: segmented, unsegmented, and mini versions. The main outcomes of the present study are as follows: Transformer models trained with larger and cleaner versions of AraFast, especially in question-answering, indicate the impact of corpus quality and size on model efficacy. Secondly, a dramatic reduction in training loss was observed with the mini version of AraFast, underscoring the importance of optimizing corpus size for effective training. Moreover, the segmented text format led to a decrease in training loss, highlighting segmentation as a beneficial strategy in Arabic NLP. In addition, using the study findings, challenges in managing noisy data derived from web sources are identified, which were found to significantly hinder model performance. These findings collectively demonstrate the critical role of well-prepared, segmented, and clean corpora in advancing Arabic NLP capabilities. The insights from AraFast’s application can guide the development of more efficient NLP models and suggest directions for future research in enhancing Arabic language processing tools.