构建了叙利亚方言与标准阿拉伯语双向翻译系统，提升本地化沟通效率。
SHAMI-MT: A Syrian Arabic Dialect to Modern Standard Arabic Bidirectional Machine Translation System
- 基于AraT5v2架构，分别训练双向翻译模型。
- 在MADAR数据集上，翻译质量达4.01分（满分5分）。
- 适合文化传承、跨区域交流等实际应用场景。
阿拉伯世界语言生态丰富，但现代标准阿拉伯语（MSA）与日常使用的地区方言之间存在显著差距，造成自然语言处理尤其是机器翻译的挑战。本文提出SHAMI-MT，一种专门用于弥合标准阿拉伯语与叙利亚方言之间沟通鸿沟的双向机器翻译系统。设计了两个专用模型：一个用于从标准阿拉伯语到叙利亚方言，另一个反之，均基于先进的AraT5v2-base-1024架构。模型在Nabra数据集上进行微调，并在未见的MADAR语料库数据上严格评估。其标准阿拉伯语转叙利亚方言模型在OPENAI GPT-4.1评分中获得平均4.01分（满分5分），表明翻译结果不仅准确，且具有高度方言真实性。该工作为此前被忽视的语言对提供了高保真工具，推动了方言阿拉伯语翻译的发展，在内容本地化、文化遗产保护和跨文化沟通方面具有重要意义。
原文摘要 · Abstract (English)
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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