Sentiment swap is the task of rewriting a sentence so that it carries the opposite polarity while its topic stays the same. We present a system for Arabic sentiment swap built on two observations about the data. First, the swap is a local edit rather than a paraphrase: 91% of reference rewrites stay within two words of their source. Second, the polarity to swap away from has to be predicted, because the test data does not provide it. We therefore fine-tune mT5 and refine it with Direct Preference Optimization under a reward that balances target-polarity probability against content preservation. At inference we pool candidates from four generators, one of them a minimal-edit tagger, together with a retrieval index, rerank them with the same balance, and repair them with polarity-flip lexicons mined from the training set. Source polarity is supplied by a dedicated classifier that reaches 97.3% accuracy, against 74.3% for the off-the-shelf sentiment model that the evaluation itself uses. Every component was scored on the official leaderboard: generation-side work contributes +0.044 Sentiment Style Accuracy, and the dedicated polarity classifier a further +0.155. The final system reaches 0.9581 Sentiment Style Accuracy, 56.91 BLEU and 72.18 chrF, ranking 1st of 15 submissions.

Dialect sentiment analysis asks for the polarity of a sentence written in a regional Arabic variety rather than Modern Standard Arabic. We present a system for Subtask 1 of DialectSentEval built on two observations about the data. First, the task is one of zero-shot transfer rather than classification: the test set introduces Lebanese, a dialect with no labeled training data and the highest out-of-vocabulary rate in the task (52.0% of word types, against 19.5–41.0% for the seen dialects). Second, k-fold cross-validation cannot see this, since it measures generalization to unseen sentences and never to an unseen dialect. We therefore validate with a leave-one-dialect-out diagnostic and score every component on the official leaderboard. Three dialectally diverse encoders (MARBERTv2, CAMeLBERT-DA, AraBERTv2) are trained with Fast Gradient Method adversarial training, then refined by confidence-filtered self-training on the unlabeled test set and character-level noise augmentation. Adversarial training contributes +0.040 Macro F1 over a gold-only ensemble, self-training a further +0.027, and per-class pseudo-label filtering +0.018. The final system reaches 0.8667 Macro F1, ranking 1st of 12 submissions, 4.6 points above the runner-up.

Cross-dialect Arabic machine translation asks for translation between Modern Standard Arabic and regional dialects with almost no parallel text. We present a system for Subtask 2 of AlexandriaX-2026, built on two findings about where the gains come from. First, data was not the binding constraint: once our mined-and-synthetic recipe had decelerated to +1.8% relative spBLEU per round, a single increase in LoRA capacity beat three further data-tuning rounds combined (+8.9%). Second, planned epoch budgets were consistently too short, and training past them was the largest single lever at every stage. We fine-tune NLLB-200-distilled-600M on a three-tier corpus of real, mined and synthetic pairs, then refine it with Direct Preference Optimization. Ablating three reward designs isolates a third finding: rewarding agreement with self-distilled references regresses real quality despite healthy training dynamics. Restricting the reward to references we trust, with a reference-free substitute for the two zero-resource dialects, yields a real gain that a further iteration gives back. Every configuration was scored on the official leaderboard. Our submission reached spBLEU 25.84 and chrF++ 39.02, ranking 2nd of 4 teams; post-evaluation experimentation reached 28.04.