@inproceedings{abdulsattar-ross-2026-arabic,
    title = "{A}rabic Dialect Translation with Small {LLM}s: Enhancing through Reasoning-Oriented Reinforcement Learning",
    author = "Abdulsattar, Sohaila  and
      Ross, Keith",
    booktitle = "Proceedings of the 2nd Workshop on {NLP} for Languages Using {A}rabic Script",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/manual-author-scripts/2026.abjadnlp-1.11/",
    pages = "84--99",
    abstract = "Arabic dialect{\ensuremath{\leftrightarrow}}English machine translation remains difficult due to extreme dialect variation, inconsistent orthography, and limited parallel data. Moreover, dialect translation is often needed in remote regions or by economically-disadvantaged communities, which often operate in compute-constrained or offline settings. Motivated by these concerns, in this paper we explore optimizing Arabic dialect{\ensuremath{\leftrightarrow}}English translators that run over small LLMs, which could be implemented on small offline devices. We show that reasoning-oriented reinforcement learning can substantially improve small multilingual LLMs for Arabic dialect translation. Using the MADAR corpus, small Qwen-2.5 models trained with a think-then-translate template and optimized with Group-Relative Policy Optimization using a SacreBLEU reward outperform a much larger 7B baseline trained with supervised fine-tuning. The dialect-to-English BLEU score more than doubles from 17.4 to 34.9, while the English-to-dialect COMET score improves from 0.57 to 0.73."
}