This paper presents the Saudi Arabian Dialects Song Lyrics Corpus (SADSLyC), the first dataset featuring song lyrics from the five major Saudi dialects: Najdi (Central Region), Hijazi (Western Region), Shamali (Northern Region), Janoubi (Southern Region), and Shargawi (Eastern Region). The dataset consists of 31,358 sentences, with each sentence representing a self-contained verse in a song, totaling 151,841 words. Additionally, we present a baseline experiment using the SaudiBERT model to classify the fine-grained dialects in the SADSLyC Corpus. The model achieved an overall accuracy of 73% on the test dataset.

This paper presents the findings from our participation in the 6th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT6) in 2024. Our specific focus was on the second task (Task 2), which involved translating text at the sentence level from five distinct Dialectal Arabic (DA) (Gulf, Egyptian, Levantine, Iraqi, and Maghrebi) into Modern Standard Arabic (MSA). Our team, Sirius_Translators, fine-tuned four AraT5 models namely; AraT5 base, AraT5v2-base-1024, AraT5-MSA-Small, and AraT5-MSA-Base for the Arabic machine translation (MT) task. These models were fine-tuned using a variety of parallel corpora containing Dialectal Arabic and Modern Standard Arabic. Based on the evaluation results of OSACT6 2024 Shared Task2, our fine-tuned AraT5v2-base-1024 model achieved an overall BLEU score of 21.0 on the development (Dev) set and 9.57 on the test set, respectively.