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.

Modern visual document retrieval (VDR) models remain strongly English-centric, leaving Arabic substantially under-served in this rapidly advancing field. To address this gap, we introduce DEAM, the first open Arabic visual document retrieval dataset explicitly designed for fine-tuning and evaluating multimodal retrievers on Arabic. DEAM is built through a human-in-the-loop construction pipeline with annotators community annotators, combining Arabic document rendering, query creation, validation, and retrieval-oriented hard-negative mining. The dataset comprises18,275 rendered Arabic documents spanning nine source domains and 82,727 contrastive retrieval triplets, each including five mined hard negatives and one easy negative for contrastive fine-tuning. Zero-shot evaluation of 27 retrievers shows that multilingual text retrievers outperform visual retrievers on Arabic by 13.6 nDCG@10, except on layout-rich news pages. DEAM establishes a public foundation for Arabic visual document retrieval.https://huggingface.co/collections/NAMAA-Space/deam-for-arabic-multimodal-retrieval

Arabic machine translation benchmarks provide limited coverage of variation within Saudi Arabic. We introduce SAFIR, a Saudi-centric benchmark of 610 unique sentence pairs organized into 12 creator specific evaluation splits across five regional groups: Najdi, Hijazi, Southern, Northern, and Eastern. Native speakers authored the Arabic sentences and their English translations; 235 pairs received independent double review for dialect authenticity, translation adequacy, and fluency. We evaluate twelve systems, comprising losed-weight LLMs, open-weight LLMs, and dedicated MT models, in the zero-shot Saudi→English setting using reference-based metrics and GPT-4o judgments. Closed-weight LLMs obtain the strongest aggregate results. Gemini 2.5 Pro achieves the highest average BLEU (34.41), while GPT-5 achieves the highest BERTScore (83.60). Rankings vary across splits, and Southern varieties present particular challenges in judged adequacy. Differences between reference overlap, adequacy, and fluency support reporting performance by sampled variety and evaluation dimension. SAFIR and its leaderboard support more detailed assessment of Saudi Arabic translation.