Mavericks at NADI 2023 Shared Task: Unravelling Regional Nuances through Dialect Identification using Transformer-based Approach
This work addresses dialect identification to improve NLP tasks like speech recognition and translation, but it is incremental as it applies existing methods to a new dataset.
The paper tackled country-level Arabic dialect identification using transformer-based models fine-tuned on a Twitter dataset, achieving an F1-score of 76.65 and ranking 11th in the NADI 2023 shared task.
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.