@inproceedings{elmallah-etal-2024-arabic,
    title = "{A}rabic Diacritization Using Morphologically Informed Character-Level Model",
    author = "Elmallah, Muhammad Morsy  and
      Reda, Mahmoud  and
      Darwish, Kareem  and
      El-Sheikh, Abdelrahman  and
      Elneima, Ashraf Hatim  and
      Aljubran, Murtadha  and
      Alsaeed, Nouf  and
      Mohammed, Reem  and
      Al-Badrashiny, Mohamed",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.128",
    pages = "1446--1454",
    abstract = "Arabic diacritic recovery i.e. diacritization is necessary for proper vocalization and an enabler for downstream applications such as language learning and text to speech. Diacritics come in two varieties, namely: core-word diacritics and case endings. In this paper we introduce a highly effective morphologically informed character-level model that can recover both types of diacritics simultaneously. The model uses a Recurrent Neural Network (RNN) based architecture that takes in text as a sequence of characters, with markers for morphological segmentation, and outputs a sequence of diacritics. We also introduce a character-based morphological segmentation model that we train for Modern Standard Arabic (MSA) and dialectal Arabic. We demonstrate the efficacy of our diacritization model on Classical Arabic, MSA, and two dialectal (Moroccan and Tunisian) texts. We achieve the lowest reported word-level diacritization error rate for MSA (3.4{\%}), match the best results for Classical Arabic (5.4{\%}), and report competitive results for dialectal Arabic.",
}

Arabic diacritic recovery i.e. diacritization is necessary for proper vocalization and an enabler for downstream applications such as language learning and text to speech. Diacritics come in two varieties, namely: core-word diacritics and case endings. In this paper we introduce a highly effective morphologically informed character-level model that can recover both types of diacritics simultaneously. The model uses a Recurrent Neural Network (RNN) based architecture that takes in text as a sequence of characters, with markers for morphological segmentation, and outputs a sequence of diacritics. We also introduce a character-based morphological segmentation model that we train for Modern Standard Arabic (MSA) and dialectal Arabic. We demonstrate the efficacy of our diacritization model on Classical Arabic, MSA, and two dialectal (Moroccan and Tunisian) texts. We achieve the lowest reported word-level diacritization error rate for MSA (3.4{\%}), match the best results for Classical Arabic (5.4{\%}), and report competitive results for dialectal Arabic.
Elmallah, Muhammad Morsy Reda, Mahmoud Darwish, Kareem El-Sheikh, Abdelrahman Elneima, Ashraf Hatim Aljubran, Murtadha Alsaeed, Nouf Mohammed, Reem Al-Badrashiny, Mohamed
Arabic Diacritization Using Morphologically Informed Character-Level Model