Abstract We launched ASBN-MT5, the system for Arabic Sentiment Swap, which performs the task of inverting the sentiment of a sentence while keeping the meaning intact. This is a sequence-to-sequence task. We demonstrate ASBN-MT5: mT5, which is a MultiLingual T5 model, fine-tuned on the provided dataset of the AraSentEval 2026 Shared Task. We describe the data as the first of its kind for the Arabic language, as MAAKS is the first manually composed, parallel, cross-linguistic corpus for the Arabic language. With the preliminary results of Sentiment Flip for the task of Sentiment Inversion, we have recorded a rate of 59.5% for positive to negative conversions and 58.5% for negative to positive conversions, while maintaining an average similarity to the original sentences of 0.955. We present the Arabic prompts and a neuro-developmental (Deep Learning) recipe. Due to the evaluation criteria which include Exact Match, Flip Success, Surface Similarity, and Quality of Output, we restrict the use of Prepended Negation as the main technique and recommend the use of LLMs designed for the Arabic language in the near future. Keywords: mT5, sequence-to-sequence, AraSentEval 2026, Arabic NLP, Text Style Transfer, Sentiment Swap

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.