Aladdin-FTI @ AMIYA Three Wishes for Arabic NLP: Fidelity, Diglossia, and Multidialectal Generation

Jonathan Mutal, Perla Al Almaoui, Simon Hengchen, Pierrette Bouillon
2/18/2026
cs.CL

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Abstract

Arabic dialects have long been under-represented in Natural Language Processing (NLP) research due to their non-standardization and high variability, which pose challenges for computational modeling. Recent advances in the field, such as Large Language Models (LLMs), offer promising avenues to address this gap by enabling Arabic to be modeled as a pluricentric language rather than a monolithic system. This paper presents Aladdin-FTI, our submission to the AMIYA shared task. The proposed system is designed to both generate and translate dialectal Arabic (DA). Specifically, the model supports text generation in Moroccan, Egyptian, Palestinian, Syrian, and Saudi dialects, as well as bidirectional translation between these dialects, Modern Standard Arabic (MSA), and English. The code and trained model are publicly available.

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@article{mutal2026aladdinfti,
  title  = {Aladdin-FTI @ AMIYA Three Wishes for Arabic NLP: Fidelity, Diglossia, and Multidialectal Generation},
  author = {Jonathan Mutal and Perla Al Almaoui and Simon Hengchen and Pierrette Bouillon},
  year   = {2026},
  eprint = {2602.16290},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2602.16290v1}
}

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