Mavericks at NADI 2023 Shared Task: Unravelling Regional Nuances through
  Dialect Identification using Transformer-based Approach
Vedant Deshpande Yash Patwardhan Kshitij Deshpande Sudeep Mangalvedhekar Ravindra Murumkar
In this paper, we present our approach for the "Nuanced Arabic Dialect
Identification (NADI) Shared Task 2023". We highlight our methodology for
subtask 1 which deals with country-level dialect identification. Recognizing
dialects plays an instrumental role in enhancing the performance of various
downstream NLP tasks such as speech recognition and translation. The task uses
the Twitter dataset (TWT-2023) that encompasses 18 dialects for the multi-class
classification problem. Numerous transformer-based models, pre-trained on
Arabic language, are employed for identifying country-level dialects. We
fine-tune these state-of-the-art models on the provided dataset. The ensembling
method is leveraged to yield improved performance of the system. We achieved an
F1-score of 76.65 (11th rank on the leaderboard) on the test dataset.