GHAD NLP at AdabEval2026: Transformer-Based Approach for Arabic Politeness and Pragmatic Category Classification

Abstract

This paper presents our submission to the AdabEval 2026 shared task on Arabic politeness classification and pragmatic category prediction. We explored a range of Arabic-specific and multilingual transformer models and integrated their outputs through an ensemble strategy. Our approach achieved state-of-the-art performance in the shared task, ranking first in both subtasks with a macro-F1 score of 0.89 and an accuracy of 0.93 on subtask A, and a macro-F1 score of 0.58 on subtask B. Although our approach delivered high performance on overall politeness classification, pragmatic category prediction remains more challenging. Despite achieving the top ranking in this subtask, the comparatively lower macro-F1 score suggests that modelling fine-grained pragmatic functions requires further methodological refinement and experimentation.

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