Fine-Grained Arabic Dialect Identification: Investigating Various Approaches Across Multiple Datasets

Arabic, as a language, encompasses numerous dialectical variations. Despite sharing a common vocabulary, these variants exhibit significant differences across territories, even within the same country. The Arabic dialects identification (ADI) is important for many NLP tasks and can be conducted as a preliminary step. In this work, we focus on the fine-grained ADI task, investigating the effectiveness of various feature extraction techniques and classification algorithms through a comparative study across three datasets: MADAR, NADI and NADAR. The first two are available online and well known in the literature. While we construct the third one by merging the first two datasets. We experimented with three groups of models: machine learning models, deep learning models and pre-trained language models. Our goal is to analyze the efficiency of these models for a fine-grained ADI task from different perspectives. Specifically, we analyzed the variation in classifier performance based on various criteria: the training set size, the considered dialect, and the dataset type (whether it is parallel, balanced or not). We employed the Explainable AI (XAI) technique LIME to investigate the interpretability of the models. Our analysis of the results yielded several key insights that guided our conclusions : (1) classical ML models have competitive performances compared to sophisticated neuronal models in terms of training time and F1-score. (2) The size of the dataset has a positive impact on the performance of the classifiers but in some experiments, larger datasets resulted in decreased performance. (3) The confusion errors mainly concern the dialects of geographically close regions. (4) Parallel corpora are more adapted to training ADI models.