On The Arabic Dialects' Identification: Overcoming Challenges of Geographical Similarities Between Arabic dialects and Imbalanced Datasets

Arabic is one of the world's richest languages, with a diverse range of dialects based on geographical origin. In this paper, we present a solution to tackle subtask 1 (Country-level dialect identification) of the Nuanced Arabic Dialect Identification (NADI) shared task 2022 achieving third place with an average macro F1 score between the two test sets of 26.44%. In the preprocessing stage, we removed the most common frequent terms from all sentences across all dialects, and in the modeling step, we employed a hybrid loss function approach that includes Weighted cross entropy loss and Vector Scaling(VS) Loss. On test sets A and B, our model achieved 35.68% and 17.192% Macro F1 scores, respectively. © 2022 Association for Computational Linguistics.