Detecting polarization in online discourse is important for understanding social fragmentation , yet it remains difficult for Arabic due to dialect variation, informal writing, and implicit framing. In this paper, we study Arabic polarization modeling in the SemEval-2026 Task 9 (POLAR) setting, focusing on polarization detection (ST1) and polarization type classification (ST2). We compare three approaches: encoder fine-tuning, zero-shot prompting, and retrieval-augmented in-context learning (RAG-ICL), across six Arabic encoders and different LLMs. For ST1, RAG-ICL with Gemma-3-27b-it achieves the best result (test macro F1 = 0.83), while remaining competitive with the best fine-tuned encoder (0.82), and substantially outperforming zero-shot prompting. For ST2, a pipeline that first applies the best ST1 encoder as a hard filter and then performs RAG-ICL achieves a macro F1 = 0.62. Prompt-language effects are model-and task-dependent, with some settings doing better with English prompts and others with Arabic prompts. Chain-of-thought, self-refinement, and contrastive prompting do not outperform standard RAG-ICL.

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 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.

Arabic knowledge graph construction is a challenging problem due to the linguistic complexity of Arabic and the need for robust generalization across domains. This paper presents our systems for the KnowledgeGraphEval 2026 shared task on Arabic knowledge graph construction and domain adaptation, covering two subtasks: AdaptNER for cross-domain named entity recognition and relation extraction (RE). For AdaptNER, we employ an entity-span ensemble combining two BIO sequence taggers with constrained conditional random field (CRF) decoding and a GlobalPointer span classifier. Our system achieved a micro-F1 score of 76.00%, ranking first in the official evaluation. For relation extraction, we use a majority-vote ensemble of three LoRA-fine-tuned causal language models. This approach achieved a micro-F1 score of 93.48%, ranking fifth. These results demonstrate the effectiveness of model ensembling and complementary prediction strategies for Arabic named-entity recognition and relation extraction.

Arabic encompasses diverse spoken varieties with limited parallel data, making both translation and automatic evaluation difficult beyond Modern Standard Arabic. We present the NAMAA Community submissions to all three AlexandriaX-2026 subtasks: English-to-dialect dialogue translation, cross-dialect translation, and dialectal MT error detection. Our systems combine few-shot prompting, fine-tuned encoder-decoder and decoder-only models, and encoder- and LLM-based span detection. Held-out ablations show that minimum-Bayes-risk consensus can suffer from model-family imbalance; we therefore use per-dialect routing for translation and 2-of-3 character-level agreement for error-span detection. Our systems rank fourth and fifth of six in the unconstrained and constrained Subtask 1 tracks, third of four on Subtask 2, and second of six on Subtask 3. The Subtask 3 system achieves 0.5448 overlap-F1, exceeding the overall winner on span localization, with category assignment remaining the main weakness. Code and resources are publicly available.[<https://huggingface.co/collections/NAMAA-Space/namaa-at-alexandriax-2026>]

Encoder-only transformer models remain widely used for discriminative NLP tasks, yet recent architectural advances have largely focused on English. In this work, we present AraModernBERT, an adaptation of the ModernBERT encoder architecture to Arabic, and study the impact of transtokenized embedding initialization and native long-context modeling up to 8,192 tokens. We show that transtokenization is essential for Arabic language modeling, yielding dramatic improvements in masked language modeling performance compared to non-transtokenized initialization. We further demonstrate that AraModernBERT supports stable and effective long-context modeling, achieving improved intrinsic language modeling performance at extended sequence lengths. Downstream evaluations on Arabic natural language understanding tasks, including inference, offensive language detection, question-question similarity, and named entity recognition, confirm strong transfer to discriminative and sequence labeling settings. Our results highlight practical considerations for adapting modern encoder architectures to Arabic and other languages written in Arabic-derived scripts.