Alexandria: A Multi-Domain Dialectal Arabic Machine Translation Dataset for Culturally Inclusive and Linguistically Diverse LLMs

• URL: http://arxiv.org/abs/2601.13099v1
• Date: Mon, 19 Jan 2026 14:38:28 GMT
• Title: Alexandria: A Multi-Domain Dialectal Arabic Machine Translation Dataset for Culturally Inclusive and Linguistically Diverse LLMs
• Authors: Abdellah El Mekki, Samar M. Magdy, Houdaifa Atou, Ruwa AbuHweidi, Baraah Qawasmeh, Omer Nacar, Thikra Al-hibiri, Razan Saadie, Hamzah Alsayadi, Nadia Ghezaiel Hammouda, Alshima Alkhazimi, Aya Hamod, Al-Yas Al-Ghafri, Wesam El-Sayed, Asila Al sharji, Mohamad Ballout, Anas Belfathi, Karim Ghaddar, Serry Sibaee, Alaa Aoun, Areej Asiri, Lina Abureesh, Ahlam Bashiti, Majdal Yousef, Abdulaziz Hafiz, Yehdih Mohamed, Emira Hamedtou, Brakehe Brahim, Rahaf Alhamouri, Youssef Nafea, Aya El Aatar, Walid Al-Dhabyani, Emhemed Hamed, Sara Shatnawi, Fakhraddin Alwajih, Khalid Elkhidir, Ashwag Alasmari, Abdurrahman Gerrio, Omar Alshahri, AbdelRahim A. Elmadany, Ismail Berrada, Amir Azad Adli Alkathiri, Fadi A Zaraket, Mustafa Jarrar, Yahya Mohamed El Hadj, Hassan Alhuzali, Muhammad Abdul-Mageed,
• Abstract summary: We introduce textbfAlexandria, a large-scale, community-driven, human-translated dataset.<n>Alexandria covers 13 Arab countries and 11 high-impact domains, including health, education, and agriculture.<n>The dataset consists of multi-turn conversational scenarios annotated with speaker-addressee gender configurations.
• Score: 21.956278976240196
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Arabic is a highly diglossic language where most daily communication occurs in regional dialects rather than Modern Standard Arabic. Despite this, machine translation (MT) systems often generalize poorly to dialectal input, limiting their utility for millions of speakers. We introduce \textbf{Alexandria}, a large-scale, community-driven, human-translated dataset designed to bridge this gap. Alexandria covers 13 Arab countries and 11 high-impact domains, including health, education, and agriculture. Unlike previous resources, Alexandria provides unprecedented granularity by associating contributions with city-of-origin metadata, capturing authentic local varieties beyond coarse regional labels. The dataset consists of multi-turn conversational scenarios annotated with speaker-addressee gender configurations, enabling the study of gender-conditioned variation in dialectal use. Comprising 107K total samples, Alexandria serves as both a training resource and a rigorous benchmark for evaluating MT and Large Language Models (LLMs). Our automatic and human evaluation of Arabic-aware LLMs benchmarks current capabilities in translating across diverse Arabic dialects and sub-dialects, while exposing significant persistent challenges.

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