@inproceedings{el-mekki-etal-2026-alexandria,
    title = "Alexandria: A Multi-Domain Dialectal {A}rabic Machine Translation Dataset for Culturally Inclusive and Linguistically Diverse {LLM}s",
    author = "EL Mekki, Abdellah  and
      Magdy, Samar M.  and
      Atou, Houdaifa  and
      AbuHweidi, Ruwa  and
      Qawasmeh, Baraah  and
      Nacar, Omer  and
      Al-hibiri, Thikra  and
      Saadie, Razan  and
      Alsayadi, Hamzah A.  and
      Hammouda, Nadia Ghezaiel  and
      Alkhazimi, Alshima Mohammed  and
      Hamod, Aya  and
      Al-Ghafri, Al-Yas Yaqoob  and
      El-Sayed, Wesam  and
      al Sharji, Asila Ismail  and
      Ballout, Mohamad  and
      Belfathi, Anas  and
      Ghaddar, Karim  and
      Sibaee, Serry  and
      Aoun, Alaa  and
      Aseri, Aeej Mohammed  and
      Abureesh, Lina  and
      Bashiti, Ahlam  and
      Yousef, Majdal  and
      Hafiz, Abdulaziz  and
      Mohamed, Yehdih  and
      Hamedtou, Emira  and
      Emehah, Brakehe  and
      Alhamouri, Rahaf  and
      Nafea, Youssef  and
      El Aatar, Aya  and
      Al-Dhabyani, Walid  and
      Hamed, Emhemed S.  and
      Shatnawi, Sara  and
      Alwajih, Fakhraddin  and
      Elkhidir, Khalid  and
      Alasmari, Ashwag  and
      Gerrio, Abdurrahman  and
      Alshahri, Omar Said  and
      Elmadany, AbdelRahim A.  and
      Berrada, Ismail  and
      Al-kathiri, Amir Azad Adli  and
      Zaraket, Fadi  and
      Jarrar, Mustafa  and
      EL Hadj, Yahya Mohamed  and
      Alhuzali, Hassan  and
      Abdul-Mageed, Muhammad",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1503/",
    pages = "32567--32592",
    ISBN = "979-8-89176-390-6",
    abstract = "Arabic is a highly diglossic language where most daily communication occurs in regional dialects rather than Modern Standard Arabic (MSA). Despite this, machine translation (MT) systems often generalize poorly to dialectal input, limiting their utility for millions of speakers. We introduce 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 parallel English-Dialectal Arabic multi-turn conversational scenarios annotated with speaker-addressee gender configurations, enabling the study of gender-conditioned variation in dialectal use. Comprising 107K total turns, Alexandria serves as both a training resource and as a rigorous benchmark for evaluating MT and Large Language Models (LLMs). Our automatic and human evaluation benchmarks the current capabilities of Arabic-aware LLMs in translating across diverse Arabic dialects and sub-dialects while exposing significant persistent challenges.The Alexandria dataset, the creation prompts, the translation and revision guidelines, and the evaluation code are publicly available in the following repository: https://github.com/UBC-NLP/Alexandria"
}