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.

Automatic Text Simplification (ATS) is one of the major Natural Language Processing (NLP) tasks, which aims to help people understand text that is above their reading abilities and comprehension. ATS models reconstruct the text into a simpler format by deletion, substitution, addition or splitting, while preserving the original meaning and maintaining correct grammar. Simplified sentences are usually evaluated by human experts based on three main factors: simplicity, adequacy and fluency or by calculating automatic evaluation metrics. In this paper, we conduct a meta-evaluation of reference-based automatic metrics for English sentence simplification using high-quality, human-annotated dataset, NEWSELA-LIKERT. We study the behavior of several evaluation metrics at sentence level across four different sentence simplification models. All the models were trained on the NEWSELA-AUTO dataset. The correlation between the metrics’ scores and human judgements was analyzed and the results used to recommend the most appropriate metrics for this task.