ADAB: Arabic Dataset for Automated Politeness Benchmarking -- A Large-Scale Resource for Computational Sociopragmatics
Abstract
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 under-explored, despite the rich and complex politeness expressions embedded in Arabic communication. In this paper, we introduce ADAB (Arabic Politeness Dataset), a new annotated Arabic dataset collected from four online platforms, including social media, e-commerce, and customer service domains, covering Modern Standard Arabic and multiple dialects (Gulf, Egyptian, Levantine, and Maghrebi). The dataset was annotated based on Arabic linguistic traditions and pragmatic theory, resulting in three classes: polite, impolite, and neutral. It contains 10,000 samples with linguistic feature annotations across 16 politeness categories and achieves substantial inter-annotator agreement (kappa = 0.703). We benchmark 40 model configurations, including traditional machine learning, transformer-based models, and large language models. The dataset aims to support research on politeness-aware Arabic NLP.
Keywords
Cite
@article{arxiv.2602.13870,
title = {ADAB: Arabic Dataset for Automated Politeness Benchmarking -- A Large-Scale Resource for Computational Sociopragmatics},
author = {Hend Al-Khalifa and Nadia Ghezaiel and Maria Bounnit and Hend Hamed Alhazmi and Noof Abdullah Alfear and Reem Fahad Alqifari and Ameera Masoud Almasoud and Sharefah Al-Ghamdi},
journal= {arXiv preprint arXiv:2602.13870},
year = {2026}
}
Comments
Paper accepted @ The Fifteenth biennial Language Resources and Evaluation Conference (LREC2026)