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        <title>The Resistant Word at StanceNakba Shared Task: A Topic-Aware Model for Cross-Topic Stance Detection</title>
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        <namePart type="given">Mohamed</namePart>
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            <title>Proceedings of the 2nd International Workshop on Nakba Narratives as Language Resources @ LREC 2026</title>
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    <abstract>Cross-topic stance detection in Arabic is the task of identifying whether a text expresses a pro, against, or neutral position toward a given issue, and it is particularly challenging under topic shifts and class imbalance. In Subtask B of the StanceNakba 2026 shared task on Arabic cross-topic stance detection, we are given a Levantine Arabic sentence and one of two topics: “Normalization with Israel” or “Refugee/Immigrant Presence in Jordan,” and we must classify the expressed stance. A central difficulty is the systematic failure of standard fine-tuning to recognize the minority neutral class, driven by majority-class dominance in cross-entropy training and accuracy-based checkpoint selection. To address this, we combine random oversampling with class-weighted cross-entropy loss, and we build an ensemble of four Arabic pre-trained transformers MARBERT, AraBERT Large, XLM-RoBERTa Base, and CAMeL-BERT Mix each trained using Stratified 5-Fold cross-validation. Our final system achieves a macro-F1 of 0.9777 and an accuracy of 97.79% on the evaluation set.</abstract>
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%0 Conference Proceedings
%T The Resistant Word at StanceNakba Shared Task: A Topic-Aware Model for Cross-Topic Stance Detection
%A Bahgat, Mohamed
%A Salah, Doaa
%A Yassine, Sarah
%Y Jarrar, Mustafa
%Y El-Haj, Mo
%Y Haddad, Amal
%Y Atiani, Serin
%Y Abudalfa, Shadi
%Y Regier, Terry
%Y Rayson, Paul
%Y Sima’an, Khalil
%Y Mansour, Camille
%S Proceedings of the 2nd International Workshop on Nakba Narratives as Language Resources @ LREC 2026
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F bahgat-etal-2026-resistant
%X Cross-topic stance detection in Arabic is the task of identifying whether a text expresses a pro, against, or neutral position toward a given issue, and it is particularly challenging under topic shifts and class imbalance. In Subtask B of the StanceNakba 2026 shared task on Arabic cross-topic stance detection, we are given a Levantine Arabic sentence and one of two topics: “Normalization with Israel” or “Refugee/Immigrant Presence in Jordan,” and we must classify the expressed stance. A central difficulty is the systematic failure of standard fine-tuning to recognize the minority neutral class, driven by majority-class dominance in cross-entropy training and accuracy-based checkpoint selection. To address this, we combine random oversampling with class-weighted cross-entropy loss, and we build an ensemble of four Arabic pre-trained transformers MARBERT, AraBERT Large, XLM-RoBERTa Base, and CAMeL-BERT Mix each trained using Stratified 5-Fold cross-validation. Our final system achieves a macro-F1 of 0.9777 and an accuracy of 97.79% on the evaluation set.
%R 10.63317/33r5jdoe6avj
%U https://aclanthology.org/2026.nakbanlp-1.24/
%U https://doi.org/10.63317/33r5jdoe6avj
%P 182-186

Markdown (Informal)

[The Resistant Word at StanceNakba Shared Task: A Topic-Aware Model for Cross-Topic Stance Detection](https://aclanthology.org/2026.nakbanlp-1.24/) (Bahgat et al., NakbaNLP 2026)

• The Resistant Word at StanceNakba Shared Task: A Topic-Aware Model for Cross-Topic Stance Detection (Bahgat et al., NakbaNLP 2026)

ACL

• Mohamed Bahgat, Doaa Salah, and Sarah Yassine. 2026. The Resistant Word at StanceNakba Shared Task: A Topic-Aware Model for Cross-Topic Stance Detection. In Proceedings of the 2nd International Workshop on Nakba Narratives as Language Resources @ LREC 2026, pages 182–186, Palma, Mallorca (Spain). ELRA Language Resources Association (ELRA).