AraEGI: Arabic Gender Identification Using Transformers for Egyptian Dialect
Ahmed Abdelmaguid, Marwan Torki, Ayman Khalafallah · 2024
With the rise of social media, accurately identifying genders in the text has become crucial for various domains. It is used in a wide range of academic/commercial applications such as personalization of user experiences, analyzing and interpreting text more accurately in Natural Language Understanding (NLU), and identifying and mitigating biases in datasets and models. The importance of this task is reflected in the extensive research and contribution to it, specifically in Arabic text. Existing work addresses this problem as a sub-task of the Author Profiling task, by detecting the author’s gender using multiple texts of the same author. Although current approaches achieve satisfactory results, they still fall behind in the detection of genders based on a single short sentence. In this paper, we provide a newly tailored dataset, AraEGI, to tackle Gender Identification, capable of reaching robust results on the identification of gender in tweets. Our dataset consists of 3 sets, with tweet-level labels showcasing the genders of both the speaker and the listener. We experimented on our dataset using the latest Arabic transformer models to provide a benchmark for future research.