@inproceedings{weisberg-mitelman-etal-2024-code,
    title = "Code-Switching and Back-Transliteration Using a Bilingual Model",
    author = "Weisberg Mitelman, Daniel  and
      Dershowitz, Nachum  and
      Bar, Kfir",
    editor = "Graham, Yvette  and
      Purver, Matthew",
    booktitle = "Findings of the Association for Computational Linguistics: EACL 2024",
    month = mar,
    year = "2024",
    address = "St. Julian{'}s, Malta",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2024.findings-eacl.102/",
    pages = "1501--1511",
    abstract = "The challenges of automated transliteration and code-switching{--}detection in Judeo-Arabic texts are addressed. We introduce two novel machine-learning models, one focused on transliterating Judeo-Arabic into Arabic, and another aimed at identifying non-Arabic words, predominantly Hebrew and Aramaic. Unlike prior work, our models are based on a bilingual Arabic-Hebrew language model, providing a unique advantage in capturing shared linguistic nuances. Evaluation results show that our models outperform prior solutions for the same tasks. As a practical contribution, we present a comprehensive pipeline capable of taking Judeo-Arabic text, identifying non-Arabic words, and then transliterating the Arabic portions into Arabic script. This work not only advances the state of the art but also offers a valuable toolset for making Judeo-Arabic texts more accessible to a broader Arabic-speaking audience."
}