SHAMI-MT: A Syrian Arabic Dialect to Modern Standard Arabic
  Bidirectional Machine Translation System
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.
68922b1a9dcf0e7ac9a66ee9
Serry Sibaee
68922b1a9dcf0e7ac9a66eea
Omer Nacar
68922b1a9dcf0e7ac9a66eeb
Yasser Al-Habashi
68922b1a9dcf0e7ac9a66eec
Adel Ammar
68922b1a9dcf0e7ac9a66eed
Wadii Boulila
SHAMI-MT: A Syrian Arabic Dialect to Modern Standard Arabic
  Bidirectional Machine Translation System
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.
A bidirectional machine translation system, SHAMI-MT, bridges the gap between Modern Standard Arabic and the Syrian dialect using AraT5v2-base-1024 architecture, achieving high-quality translations.
bidirectional machine translation AraT5v2-base-1024 Nabra dataset MADAR corpus GPT-4.1 dialectal Arabic translation