@inproceedings{keleg-etal-2020-asu,
    title = "{ASU}{\_}{OPTO} at {OSACT}4 - Offensive Language Detection for {A}rabic text",
    author = "Keleg, Amr  and
      El-Beltagy, Samhaa R.  and
      Khalil, Mahmoud",
    editor = "Al-Khalifa, Hend  and
      Magdy, Walid  and
      Darwish, Kareem  and
      Elsayed, Tamer  and
      Mubarak, Hamdy",
    booktitle = "Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resource Association",
    url = "https://preview.aclanthology.org/ingest-emnlp/2020.osact-1.10/",
    pages = "66--70",
    language = "eng",
    ISBN = "979-10-95546-51-1",
    abstract = "In the past years, toxic comments and offensive speech are polluting the internet and manual inspection of these comments is becoming a tiresome task to manage. Having a machine learning based model that is able to filter offensive Arabic content is of high need nowadays. In this paper, we describe the model that was submitted to the Shared Task on Offensive Language Detection that is organized by (The 4th Workshop on Open-Source Arabic Corpora and Processing Tools). Our model makes use transformer based model (BERT) to detect offensive content. We came in the fourth place in subtask A (detecting Offensive Speech) and in the third place in subtask B (detecting Hate Speech)."
}