@inproceedings{nabhani-khatib-2025-transfer,
    title = "Transfer or Translate? Argument Mining in {A}rabic with No Native Annotations",
    author = "Nabhani, Sara  and
      Khatib, Khalid Al",
    editor = "Darwish, Kareem  and
      Ali, Ahmed  and
      Abu Farha, Ibrahim  and
      Touileb, Samia  and
      Zitouni, Imed  and
      Abdelali, Ahmed  and
      Al-Ghamdi, Sharefah  and
      Alkhereyf, Sakhar  and
      Zaghouani, Wajdi  and
      Khalifa, Salam  and
      AlKhamissi, Badr  and
      Almatham, Rawan  and
      Hamed, Injy  and
      Alyafeai, Zaid  and
      Alowisheq, Areeb  and
      Inoue, Go  and
      Mrini, Khalil  and
      Alshammari, Waad",
    booktitle = "Proceedings of The Third Arabic Natural Language Processing Conference",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.arabicnlp-main.33/",
    doi = "10.18653/v1/2025.arabicnlp-main.33",
    pages = "407--416",
    ISBN = "979-8-89176-352-4",
    abstract = "Argument mining for Arabic remains underexplored, largely due to the scarcity of annotated corpora. To address this gap, we examine the effectiveness of cross-lingual transfer from English. Using the English Persuasive Essays (PE) corpus, annotated with argumentative components (Major Claim, Claim, and Premise), we explore several transfer strategies: training encoder-based multilingual and monolingual models on English data, machine-translated Arabic data, and their combination. We further assess the impact of annotation noise introduced during translation by manually correcting portions of the projected training data. In addition, we investigate the potential of prompting large language models (LLMs) for the task. Experiments on a manually corrected Arabic test set show that monolingual models trained on translated data achieve the strongest performance, with further improvements from small-scale manual correction of training examples."
}