@inproceedings{dahou-cheragui-2026-helpful,
    title = "Helpful or Harmful? The Dual Role of Linguistic Features in {LLM}-Based Dialectal Machine Translation",
    author = "Dahou, Abdelhalim Hafedh  and
      Cheragui, Mohamed Amine",
    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-1.8/",
    doi = "10.63317/5dpmbr8bbedw",
    pages = "66--75",
    abstract = "Large Language Models (LLMs) have shown promising results in dialectal machine translation, yet the impact of explicit linguistic features remains underexplored. This paper examines whether part-of-speech (POS) tags and diacritization help or hinder LLM-based translation between Algerian dialect (Darija) and Modern Standard Arabic (MSA). Using a linguistically enriched subset of the PADIC dataset, we conduct bidirectional experiments across several frontier and open-weight LLMs, evaluated with automatic metrics and human judgments of adequacy and fluency. Results reveal a dual and asymmetric effect: diacritics can improve adequacy in the MSA {\textrightarrow} Algerian dialect direction, while POS tags and forced diacritization often introduce noise, especially for Algerian dialect {\textrightarrow} MSA translation. We further observe a mismatch between traditional overlap-based metrics and human evaluation, suggesting limitations in current evaluation practices. Overall, explicit linguistic augmentation does not consistently benefit LLM-based dialectal translation and must be applied cautiously."
}