Domain-Adapted BERT-based Models for Nuanced Arabic Dialect Identification and Tweet Sentiment Analysis

Abstract

This paper summarizes the solution of the Nuanced Arabic Dialect Identification (NADI) 2022 shared task. It consists of two subtasks: a country-level Arabic Dialect Identification (ADID) and an Arabic Sentiment Analysis (ASA). Our work shows the importance of using domain-adapted models and language-specific pre-processing in NLP task solutions. We implement a simple but strong baseline technique to increase the stability of fine-tuning settings to obtain a good generalization of models. Our best model for the Dialect Identification subtask achieves a Macro F-1 score of 25.54% as an average of both Test-A (33.89%) and Test-B (19.19%) F-1 scores. We also obtained a Macro F-1 score of 74.29% of positive and negative sentiments only, in the Sentiment Analysis task.