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

Hierarchical, definition-guided genre classification places a short Arabic text under a broad genre and, within it, a fine-grained specific genre, using only a natural-language definition of each. We present NAMAA’s submission to AraGenre 2026. Fine-tuned bi-encoder classifiers reached 0.93 hierarchical macro F1 on development via data augmentation and cross-lingual pretraining, but collapsed to as low as 0.22 on the released test set, having specialized to a hand-engineered definition style that missed the test set’s larger, 74-genre taxonomy. We root-caused this gap and switched to zero-shot LLM classification, matching the best fine-tuned ensemble’s test score (0.6899 hierarchical F1) with no training; layering self-consistency sampling and disagreement resolution raised this further to 0.7013, ranking 3rd of 18 teams. Specific Macro F1 (0.5114) lags well behind Broad Macro F1 (0.8913), showing that most remaining errors concentrate in fine-grained genre choices rather than broad-category mistakes. Zero-shot LLM prompting generalizes across this definition and taxonomy shift where fine-tuned, definition-conditioned encoders do not. We release our code and system resources to support reproducibility and further research.[<https://github.com/NAMAA-ORG/Aragenre-Definition-Guided-Genre>].

Arabic sentiment swap aims to reverse the polarity of a dialectal sentence while preserving its underlying meaning. We present NAMAA’s system for DialectSentEval 2026 Subtask 2. Fine-tuning AraT5v2 achieves 79.56 development sentiment-style accuracy, while reranked sampling over eight candidates, using sentiment agreement and content-overlap signals, raises this to 93.57. Cross-backbone ensembling further reaches a development score of 96.78. On the hidden test set, however, this ordering does not transfer: a widened rerank pool with a re-tuned content weight achieves our best official score of 0.7597, whereas the most complex all-source ensemble scores 0.7343. We identify two likely contributors to this development-to-test mismatch: the absence of gold source-polarity labels at test time, which requires classifier-based polarity inference, and train–development sentence duplication that may inflate development performance. These results show that additional generation and ensemble complexity can improve development metrics substantially without guaranteeing better generalization under shifted evaluation conditions.

Arabic dialect sentiment analysis is challenging because regional varieties show considerable lexical, morphological, syntactical, and orthographic variation. This paper presents a transformer-based system developed for DialectSentEval 2026 Subtask 1, a three-class sentiment classification task covering Moroccan, Egyptian, Jordanian, and Saudi Arabic. The system independently fine-tunes three pretrained Arabic language models: AraBERTv2, MARBERTv2, and CAMeLBERT, using a common training protocol. The implementation uses an 80/20 stratified train-validation split, a maximum sequence length of 128, a batch size of 16, a learning rate of 2 × 10^-5, and focal loss with 𝛾=2=2. Model selection was conducted based on validation macro F1. Using the published labeled test set, MARBERTv2 achieves the strongest independent result with 94.07% macro F1, followed by CAMeLBERT at 81.96% and AraBERTv2 at 79.77%. The results demonstrate the importance of Arabic pretraining and dialect-oriented representations and establish MARBERTv2 as the strongest standalone backbone in the current system.