We present an overview of the AdabEval 2026 shared task, organized as part of the OSACT7 workshop (co-located with LREC 2026). This task introduces the first benchmark suite for politeness detection. It includes two subtasks: Politeness Classification (Subtask A) and Category Prediction (Subtask B). The task focuses on evaluating models’ ability to recognize and categorize politeness phenomena in Arabic text. Evaluation was conducted using an automatic metric (macro F1-score). A total of 28 unique teams participated in the shared task. Of these, 13 teams submitted final system predictions across the two subtasks. The top-performing systems relied primarily on transformer-based architectures. The winning systems achieved macro F1-scores of 0.89 for Subtask A and 0.58 for Subtask B.

Humor generation remains one of the most challenging tasks in natural language processing, particularly in Arabic, where cultural context, dialectal variation, and linguistic nuances are central to comedic effect. In this paper, we present the ARHAHA 2026 shared task on constrained Arabic humor generation. The task requires systems to generate jokes that incorporate a given pair of words while adhering to safety and cultural constraints. We describe the task design, dataset construction, and evaluation framework, which combines automatic validation with human evaluation. Nine teams registered for the shared task; among them, three submitted final system outputs and two provided system description papers. Each participating system generated 1,200 Arabic jokes. For each system, a subset of 300 jokes was selected for evaluation by three independent annotators. The evaluation considered humor quality, originality, lexical constraint compliance, and safety. The results show that participating systems can produce safe and original content. However, generating genuinely humorous outputs remains difficult. The top-performing system was judged humorous in only 5.01% of outputs, highlighting the inherent difficulty of computational humor generation. All three systems maintained very low rates of policy violations and stereotyping, demonstrating the effectiveness of constrained generation for safe content production. However, the very low humor rates indicate a substantial gap between generating fluent, constraint-compliant text and producing genuinely funny content. The top-performing system achieves stronger performance across originality, lexical compliance, and safety, resulting in a final score of 49.25, compared to 44.62 for the second-ranked system and 35.99 for the third-ranked system. These results reveal that humor generation, rather than safety or constraint adherence, is the dominant bottleneck in constrained Arabic humor generation.

The growing importance of culturally-aware natural language processing systems has led to an increasing demand for resources that capture sociopragmatic phenomena across diverse languages. Nevertheless, Arabic-language resources for politeness detection remain severely under-explored, despite the rich and complex politeness expressions deeply embedded in Arabic communication. In this paper, a new annotated Arabic dataset, called ADAB/أدب (Arabic Politeness Dataset), was generated and carefully collected from four diverse online platforms including social media, e-commerce, and customer service domains, encompassing both Modern Standard Arabic (MSA) and multiple dialectal varieties (Gulf, Egyptian, Levantine, and Maghrebi). This dataset has undergone a thorough annotation process guided by Arabic linguistic traditions and contemporary pragmatic theory, resulting in three-way politeness classifications: polite, impolite, and neutral. The generated dataset contains 10,000 samples with detailed linguistic feature annotations across 16 politeness categories, achieving substantial inter-annotator agreement (κ = 0.703). A comprehensive benchmarking of this dataset was conducted utilizing 40 model configurations spanning traditional machine learning (12 models), transformer-based architecture (10 models), and large language models (18 configurations), thereby effectively demonstrating its practical utility and inherent challenges. This generated resource aims to bridge the gap in Arabic sociopragmatic NLP and encourage further research into politeness-aware applications for the Arabic language.