Domain-Adapted BERT-based Models for Nuanced Arabic Dialect Identification and Tweet Sentiment Analysis
Gıyaseddin Bayrak, Abdul Majeed Issifu · 2022
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 languagespecific 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 1 .