A Multi-Task Learning Approach to Dialectal Arabic Identification and Translation to Modern Standard Arabic
Abdullah Khered, Youcef Benkhedda, Riza Batista-Navarro · 2025
Translating Dialectal Arabic (DA) into Modern Standard Arabic (MSA) is a complex task due to the linguistic diversity and informal nature of dialects, particularly in social media texts.To improve translation quality, we propose a Multi-Task Learning (MTL) framework that combines DA-MSA translation as the primary task and dialect identification as an auxiliary task.Additionally, we introduce LahjaTube, a new corpus containing DA transcripts and corresponding MSA and English translations, covering four major Arabic dialects: Egyptian (EGY), Gulf (GLF), Levantine (LEV), and Maghrebi (MGR), collected from YouTube.We evaluate AraT5 and AraBART on the Dial2MSA-Verified dataset under Single-Task Learning (STL) and MTL setups.Our results show that adopting the MTL framework and incorporating LahjaTube into the training data improve the translation performance, leading to a BLEU score improvement of 2.65 points over baseline models.