Arabic sentiment analysis is challenged by morphological complexity and lexical variation across Arabic dialects, compounded by subjectivity in how speakers and writers express sentiment. In this paper, we present our submission for the AraSentEval 2026 Shared Task on Arabic Dialect Sentiment Analysis. We propose SARF (صرف) a multi-view architectural framework that integrates surface-level context with stemmed and rooted morphological perspectives using a shared MARBERTv2 encoder. Our system employs a hybrid BERT-CNN-BiLSTM-Attention architecture to capture both local sentiment n-grams and global sequential dependencies. Experimental results show that while individual morphological normalization strategies (stemming or rooting) may degrade performance, their joint integration via cross-morphological attention provides robust features across diverse dialects. Our final system achieved a competitive macro-F1-score of 0.9263, ranking 2nd out of 15 participating teams.

We present TTLab’s submission to the AlexandriaX-2026 Subtask 3 on Arabic MT error span detection and classification. Our system frames the task as token-level classification over surface forms, preserving character offsets to ensure exact alignment with the evaluation metric. To handle severe label imbalance, we employ a focal loss with class weighting and dialect-specific decoding thresholds. Among six Arabic pre-trained encoders, MARBERTv2 achieves the best overall performance of 40.8 and 40.91 on the development and test set, respectively, ranking 3rd out of all participating teams. While our system localizes error spans effectively, classification of rare error types remains challenging, highlighting the need for data augmentation for tail categories.

Arabic-language stance detection remains challenging, and previous shared-task systems have largely relied on multitask learning and ensembles. While these systems achieve state-of-the-art performance, their applicability and transferability are limited by the additional complexity introduced by multitask learning. To reduce this complexity, we introduce CLASP-Ar, which reformulates the task as cloze-style masked language modeling. In this approach, the target, predicted sentiment, and text are combined into a single prompt whose [MASK] prediction is restricted to a verbalizer-constrained label vocabulary. When evaluated on the StanceEval-2026: Arabic Stance Detection Shared Task, our proposed approach achieves an F_avg2 score of 71.36% on Track 1 and 74.14% on Track 2 test sets.

Argument Mining (AM) is a critical NLP task that remains significantly under-resourced in Arabic. This paper presents STAR-Ar, a BERT-BiLSTM-CRF architecture for argument discourse detection and classification, as our system for Daleel 2026, the inaugural Arabic argument mining shared task. The task requires the identification and classification of argumentative discourse units (ADUs) in debate and editorial texts. We jointly model these two objectives as a token-level sequence labeling task using a BERT-BiLSTM-CRF architecture that combines contextual transformer embeddings with structural transition constraints to support accurate span detection. STAR-Ar achieves an F1-score of 72.69 on validation and 73.7 on test data. Our domain-specific analysis shows that models trained exclusively on editorials underperform those trained on debates, a disparity we primarily attribute to the smaller size of the editorial dataset.