We present NU-SentEval’s system for DialectSentEval 2026 Subtask 2 (Arabic Dialect Sentiment Swap), which rewrites Arabic dialectal sentences into the opposite sentiment polarity while preserving their core content. Our approach fine-tunes AraT5v2-base-1024, a pretrained Arabic sequence-to-sequence model, directly on the shared task’s source-target sentence pairs, and generates outputs using standard beam search decoding. On the official Final Evaluation test leaderboard, our system achieves 54.63 BLEU, 72.61 chrF, and 68.98% sentiment preservation. Our manual error analysis shows the model reliably inverts sentiment for short, single-clause inputs, often matching the human reference exactly, though it occasionally fails to propagate the sentiment flip to secondary cues such as emojis, resulting in mixed-signal outputs.