@inproceedings{mostafa-etal-2022-gof,
    title = "{GOF} at {A}rabic Hate Speech 2022: Breaking The Loss Function Convention For Data-Imbalanced {A}rabic Offensive Text Detection",
    author = "Mostafa, Ali  and
      Mohamed, Omar  and
      Ashraf, Ali",
    editor = "Al-Khalifa, Hend  and
      Elsayed, Tamer  and
      Mubarak, Hamdy  and
      Al-Thubaity, Abdulmohsen  and
      Magdy, Walid  and
      Darwish, Kareem",
    booktitle = "Proceedinsg of the 5th Workshop on Open-Source Arabic Corpora and Processing Tools with Shared Tasks on Qur`an QA and Fine-Grained Hate Speech Detection",
    month = jun,
    year = "2022",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.osact-1.21/",
    pages = "167--175",
    abstract = "With the rise of social media platforms, we need to ensure that all users have a secure online experience by eliminating and identifying offensive language and hate speech. Furthermore, detecting such content is challenging, particularly in the Arabic language, due to a number of challenges and limitations. In general, one of the most challenging issues in real-world datasets is long-tailed data distribution. We report our submission to the Offensive Language and hate-speech Detection shared task organized with the 5th Workshop on Open-Source Arabic Corpora and Processing Tools Arabic (OSACT5); in our approach, we focused on how to overcome such a problem by experimenting with alternative loss functions rather than using the traditional weighted cross-entropy loss. Finally, we evaluated various pre-trained deep learning models using the suggested loss functions to determine the optimal model. On the development and test sets, our final model achieved 86.97{\%} and 85.17{\%}, respectively."
}