SHAMI-MT: A Syrian Arabic Dialect to Modern Standard Arabic Bidirectional Machine Translation System

• URL: http://arxiv.org/abs/2508.02268v1
• Date: Mon, 04 Aug 2025 10:21:11 GMT
• Title: SHAMI-MT: A Syrian Arabic Dialect to Modern Standard Arabic Bidirectional Machine Translation System
• Authors: Serry Sibaee, Omer Nacar, Yasser Al-Habashi, Adel Ammar, Wadii Boulila,
• Abstract summary: This paper introduces textbfSHAMI-MT, a bidirectional machine translation system specifically engineered to bridge the communication gap between Modern Standard Arabic (MSA) and the Syrian dialect.<n>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.<n>Our MSA-to-Shami model achieved an outstanding average quality score of textbf4.01 out of 5.0 when judged by OPENAI model GPT-4.1.
• Score: 0.995313069446686
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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 \textbf{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 \textbf{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.

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