@inproceedings{hamdy-etal-2026-comparative,
    title = "A Comparative Study of {A}rabic Sentiment Swap Models for {A}ra{S}ent{E}val 2026",
    author = "Hamdy, Yumna  and
      ElDamhougy, Mohab  and
      Eid, Yomna  and
      Hussein, Ensaf",
    editor = "Al-Khalifa, Hend  and
      El-Haj, Mo  and
      Ezzini, Saad",
    booktitle = "The 7th Workshop on Open-Source {A}rabic Corpora and Processing Tools ({OSACT}7) with 5 Shared Tasks",
    month = may,
    year = "2026",
    address = "Palma, Mallorca (Spain)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/revision-workflow/2026.osact-1.36/",
    doi = "10.63317/5effrrzp6ew2",
    pages = "269--273",
    abstract = "Sentiment swap is a controlled text generation task that rewrites a sentence by inverting its sentiment polarity while preserving semantic content and fluency. In this paper, we present our system for AraSentEval 2026 Subtask 2 on Arabic sentiment swap, a particularly challenging problem due to Arabic{'}s rich morphology and dialectal variation. We investigate multiple modeling paradigms, including encoder{--}decoder and multilingual approaches, and propose an enhanced system that combines targeted data augmentation and ensemble learning. Specifically, we augment underrepresented dialectal patterns to improve robustness and ensemble two Arabic-focused sequence-to-sequence models, AraBART and AraT5v2. Experiments are conducted on the MA{'}aks parallel dataset under fine-tuned settings. Our system ranked first in AraSentEval 2026 Subtask 2, achieving a BLEU score of 43.0, chrF of 65.36, and sentiment preservation accuracy of 0.7554. The results demonstrate that dialect-aware augmentation together with model ensembling substantially improves sentiment-controlled generation in Arabic and establishes strong baselines for future research in low-resource sentiment manipulation. Keywords: Arabic NLP, sentiment swap, style transfer, AraSentEval, text generation"
}