@inproceedings{alfattni-kurdi-2026-ghad,
title = "{GHAD} {NLP} at {A}dab{E}val2026: Transformer-Based Approach for {A}rabic Politeness and Pragmatic Category Classification",
author = "Alfattni, Ghada and
Kurdi, Ghader",
editor = "Al-Khalifa, Hend and
El-Haj, Mo and
Ezzini, Saad",
booktitle = "The 7th Workshop on Open-Source {A}rabic Corpora and Processing Tools ({OSACT}7) with 5 Shared Tasks",
month = may,
year = "2026",
address = "Palma, Mallorca (Spain)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.osact-1.18/",
doi = "10.63317/4k9uztg6j9jr",
pages = "157--164",
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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%0 Conference Proceedings
%T GHAD NLP at AdabEval2026: Transformer-Based Approach for Arabic Politeness and Pragmatic Category Classification
%A Alfattni, Ghada
%A Kurdi, Ghader
%Y Al-Khalifa, Hend
%Y El-Haj, Mo
%Y Ezzini, Saad
%S The 7th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT7) with 5 Shared Tasks
%D 2026
%8 May
%I Association for Computational Linguistics
%C Palma, Mallorca (Spain)
%F alfattni-kurdi-2026-ghad
%X 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.
%R 10.63317/4k9uztg6j9jr
%U https://aclanthology.org/2026.osact-1.18/
%U https://doi.org/10.63317/4k9uztg6j9jr
%P 157-164
Markdown (Informal)
[GHAD NLP at AdabEval2026: Transformer-Based Approach for Arabic Politeness and Pragmatic Category Classification](https://aclanthology.org/2026.osact-1.18/) (Alfattni & Kurdi, OSACT 2026)
ACL