@inproceedings{hassan-etal-2020-alt-semeval,
    title = "{ALT} at {S}em{E}val-2020 Task 12: {A}rabic and {E}nglish Offensive Language Identification in Social Media",
    author = "Hassan, Sabit  and
      Samih, Younes  and
      Mubarak, Hamdy  and
      Abdelali, Ahmed",
    editor = "Herbelot, Aurelie  and
      Zhu, Xiaodan  and
      Palmer, Alexis  and
      Schneider, Nathan  and
      May, Jonathan  and
      Shutova, Ekaterina",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2020.semeval-1.249/",
    doi = "10.18653/v1/2020.semeval-1.249",
    pages = "1891--1897",
    abstract = "This paper describes the systems submitted by the Arabic Language Technology group (ALT) at SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media. We focus on sub-task A (Offensive Language Identification) for two languages: Arabic and English. Our efforts for both languages achieved more than 90{\%} macro-averaged F1-score on the official test set. For Arabic, the best results were obtained by a system combination of Support Vector Machine, Deep Neural Network, and fine-tuned Bidirectional Encoder Representations from Transformers (BERT). For English, the best results were obtained by fine-tuning BERT."
}