This paper describes the Northwestern University in Qatar submission to the AraGenre 2026 Shared Task on hierarchical Arabic genre classification. We formulate the task as a definition-guided dense retrieval problem by encoding Arabic documents and English genre definitions into a shared semantic space using the multilingual BGE-M3 model. Genre labels are assigned by retrieving the most semantically similar definition without task specific fine tuning. On the development set, the proposed approach achieved a Broad Macro F1 of 0.6125, a Specific Macro F1 of 0.6125, and a Hierarchical Macro F1 of 0.6125. These results show that a lightweight retrieval based framework is an effective approach for hierarchical Arabic genre classification.

This paper presents the Northwestern University in Qatar (NU-Q) submission to Task 1 of the BAREC Shared Task 2026 on fine-grained Arabic readability assessment. The task requires predicting the readability level of Arabic sentences across 19 ordered readability levels. Our system fine-tunes CAMeLBERT-Mix as a 19-class classifier using the official BAREC training data and cross-entropy optimization. We evaluate the proposed system using Quadratic Weighted Kappa (QWK), the official shared-task evaluation metric, together with classification accuracy. Our submitted system achieves a validation QWK of 0.7393 and an accuracy of 45.96%, indicating that pretrained Arabic language models provide a strong baseline for fine-grained Arabic readability assessment.

This paper describes our submission to Subtask 1 of DialectSentEval 2026, which focuses on three class sentiment classification for Arabic dialectal text. The task aims to classify sentences written in five Arabic dialects into positive, negative, or neutral sentiment categories. We investigate the effectiveness of transformer-based Arabic language models through a series of controlled experiments. Specifically, we compare MARBERTv2 and AraBERT while maintaining identical pre processing, training, and evaluation settings to ensure a fair comparison. In addition, we examine whether explicitly providing dialect information as part of the input improves classification performance.

Experimental results on an internal validation split show that MARBERTv2 consistently outperforms AraBERT on this task. Furthermore, removing the explicit dialect tag from the input slightly improves performance, suggesting that MARBERTv2 can effectively capture dialect-specific linguistic patterns directly from the sentence. Our best configuration, which uses MARBERTv2 with sentence-only input, achieves a Macro F1-score of 0.9394 and an accuracy of 94.24%. These findings demonstrate the effectiveness of dialect-aware pre-trained language models for Arabic sentiment analysis and establish a strong baseline for the official shared task evaluation.

This paper presents our submission to Task 1 (Argumentative Discourse Unit Classification, Closed Track) of the Daleel 2026 Shared Task. The task addresses Arabic multi-label argumentative discourse unit classification at the paragraph level. Our system fine-tunes CAMeLBERT-Mix using a class-weighted Binary Cross-Entropy loss, paragraph-type-aware input representation, head-tail tokenization for long paragraphs, and label-specific threshold optimization. On the official development set, the proposed system achieves a Macro F1-score of 0.6121, a Micro F1-score of 0.7509, and a Samples F1-score of 0.6387. These results suggest that a carefully optimized CAMeLBERT-Mix framework provides a competitive and reproducible approach for Arabic argumentative discourse unit classification under the Closed Track setting.

This paper describes our submission to the IslamicEval 2026 Task 1 (Span Detection) shared task, which aims to identify and classify evidence spans in AI-generated Arabic Islamic answers. We formulate the task as token-level sequence labeling and fine-tune CAMeLBERT-Mix using BIO tagging, overlapping-window inference, and prediction reconciliation to detect four evidence categories: Ayah, matn, isnad, and claimed_source. On the official development set, our approach achieves a Precision of 95.55%, Recall of 97.06%, F1-score of 96.30%, and Token Accuracy of 98.66%. Multi-seed and qualitative analyses demonstrate the robustness of the proposed framework for Arabic evidence span detection.

How do Arabic-speaking communities express and engage with psychological stress on social media? We introduce AraStress, the first large-scale Arabic corpus dedicated to psychological stress research, comprising 175,862 public social media posts from 2020 to 2024, covering pandemic and post-pandemic periods.It fills a significant gap in Arabic mental-health NLP resources focused on stress, enabling large-scale analysis of related expressions.Unlike prior work focusing primarily on Twitter and depression or suicidality, AraStress addresses the critical gap in stress-focused resources. Our lexicon-based analysis reveals that stress-related posts elicit predominantly affective engagement and exhibit a hybrid lexical framing that integrates religious and therapeutic language. AraStress provides a foundational resource for culturally grounded computational models of stress detection and digital wellbeing in Arabic-speaking communities.