Multi-Dialect Arabic BERT for Country-Level Dialect Identification
Table 1: Final results on NADI development and testing set
['Model', 'Dev Set Results Accuracy', 'Dev Set Results F1-Score', 'Test Set Results Accuracy', 'Test Set Results F1-Score']
In this section, we present the various approaches employed in our experiments, starting with the winning approach of our Multi-dialect-Arabic-BERT model, followed by the rest of them. As mentioned above, multiple approaches have been investigated in the experiments we conducted, starting with traditional ML techniques then moving to DL approaches, before finally settling on our winning BERT based model. For our traditional ML experiments, we tried various models such as SVM, Logistic Regression (LR) and Naive Bayes (NB), along with features such as TF–IDF. We also tried other ML models that performed well on previous similar tasks such as MADAR Mawdoo3-AI and MADAR Safina models.