@inproceedings{henia-etal-2021-icompass,
    title = "i{C}ompass at {NLP}4{IF}-2021{--}Fighting the {COVID}-19 Infodemic",
    author = "Henia, Wassim  and
      Rjab, Oumayma  and
      Haddad, Hatem  and
      Fourati, Chayma",
    editor = "Feldman, Anna  and
      Da San Martino, Giovanni  and
      Leberknight, Chris  and
      Nakov, Preslav",
    booktitle = "Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2021.nlp4if-1.17/",
    doi = "10.18653/v1/2021.nlp4if-1.17",
    pages = "115--118",
    abstract = "This paper provides a detailed overview of the system and its outcomes, which were produced as part of the NLP4IF Shared Task on Fighting the COVID-19 Infodemic at NAACL 2021. This task is accomplished using a variety of techniques. We used state-of-the-art contextualized text representation models that were fine-tuned for the downstream task in hand. ARBERT, MARBERT,AraBERT, Arabic ALBERT and BERT-base-arabic were used. According to the results, BERT-base-arabic had the highest 0.784 F1 score on the test set."
}