Alexandria: A Multi-Domain Dialectal Arabic Machine Translation Dataset for Culturally Inclusive and Linguistically Diverse LLMs
University of British Columbia · University Mohammed VI Polytechnic · Ibb University · Minia University · prince sultan university · American University of Beirut · Birzeit University · Jordan University of Science and Technology · University of Waterloo · Cairo University · King Khaled University · Misurata University · Dhofar University · Mohammed VI Polytechnic University · University of Technology and Applied Sciences · Arab Center for Research and Policy Studies and American University of Beirut · Hamad Bin Khalifa University and Birzeit University · Institut Supérieur du Numérique · Umm Al-Qura University
PDF 由论文原始站点提供，PaperCompass 不保存论文文件。DOI 10.18653/v1/2026.acl-long.1503 ↗
摘要
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