Dialectal Arabic remains significantly underrepresented in parallel resources for direct machine translation with English, particularly for regional varieties such as Saudi Najdi Arabic. In this work, we introduce NAJD-MT, a systematically constructed Saudi Najdi-English parallel corpus designed for training bidirectional neural machine translation models. Starting from the Saudi Arabic Dialectal Annotated (SADA) dataset, we generate English translations using GPT-4.1 and subsequently apply cross-lingual embedding-based cosine similarity filtering to improve semantic alignment and reduce translation noise. We analyze the impact of varying semantic similarity thresholds on corpus size and downstream translation performance. Using the constructed datasets, we train and evaluate multiple Transformer-based models, including NLLB-200, OPUS-MT, mBART, and AraT5v2, in both Najdi→English and English→Najdi directions. Experimental results demonstrate that stricter semantic filtering (cosine ≥ 0.7) consistently improves translation quality despite reducing dataset size, highlighting that data purity plays a critical role in dialectal machine translation training. Our findings provide a reproducible framework for constructing high-fidelity dialect English parallel corpora and emphasize the importance of semantic alignment filtering in low-resource dialectal settings.

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