SHAMI-MT: A Syrian Arabic Dialect to Modern Standard Arabic
  Bidirectional Machine Translation System
Authors: name: Serry Sibaee, name: Omer Nacar, name: Yasser Al-Habashi, name: Adel Ammar, name: Wadii Boulila

The rich linguistic landscape of the Arab world is characterized by a
significant gap between Modern Standard Arabic (MSA), the language of formal
communication, and the diverse regional dialects used in everyday life. This
diglossia presents a formidable challenge for natural language processing,
particularly machine translation. This paper introduces SHAMI-MT, a
bidirectional machine translation system specifically engineered to bridge the
communication gap between MSA and the Syrian dialect. We present two
specialized models, one for MSA-to-Shami and another for Shami-to-MSA
translation, both built upon the state-of-the-art AraT5v2-base-1024
architecture. The models were fine-tuned on the comprehensive Nabra dataset and
rigorously evaluated on unseen data from the MADAR corpus. Our MSA-to-Shami
model achieved an outstanding average quality score of 4.01 out of 5.0
when judged by OPENAI model GPT-4.1, demonstrating its ability to produce
translations that are not only accurate but also dialectally authentic. This
work provides a crucial, high-fidelity tool for a previously underserved
language pair, advancing the field of dialectal Arabic translation and offering
significant applications in content localization, cultural heritage, and
intercultural communication.