The Resistant Word at StanceNakba Shared Task: A Topic-Aware Model for Cross-Topic Stance Detection
Proceedings of the 2nd International Workshop on Nakba Narratives as Language Resources @ LREC 2026
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.