@inproceedings{qin-etal-2021-improving,
    title = "Improving {A}rabic Diacritization with Regularized Decoding and Adversarial Training",
    author = "Qin, Han  and
      Chen, Guimin  and
      Tian, Yuanhe  and
      Song, Yan",
    editor = "Zong, Chengqing  and
      Xia, Fei  and
      Li, Wenjie  and
      Navigli, Roberto",
    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2021.acl-short.68/",
    doi = "10.18653/v1/2021.acl-short.68",
    pages = "534--542",
    abstract = "Arabic diacritization is a fundamental task for Arabic language processing. Previous studies have demonstrated that automatically generated knowledge can be helpful to this task. However, these studies regard the auto-generated knowledge instances as gold references, which limits their effectiveness since such knowledge is not always accurate and inferior instances can lead to incorrect predictions. In this paper, we propose to use regularized decoding and adversarial training to appropriately learn from such noisy knowledge for diacritization. Experimental results on two benchmark datasets show that, even with quite flawed auto-generated knowledge, our model can still learn adequate diacritics and outperform all previous studies, on both datasets."
}