@inproceedings{qarqaz-etal-2021-r00,
    title = "R00 at {NLP}4{IF}-2021 Fighting {COVID}-19 Infodemic with Transformers and More Transformers",
    author = "Qarqaz, Ahmed  and
      Abujaber, Dia  and
      Abdullah, Malak",
    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/jlcl-multiple-ingestion/2021.nlp4if-1.15/",
    doi = "10.18653/v1/2021.nlp4if-1.15",
    pages = "104--109",
    abstract = "This paper describes the winning model in the Arabic NLP4IF shared task for fighting the COVID-19 infodemic. The goal of the shared task is to check disinformation about COVID-19 in Arabic tweets. Our proposed model has been ranked 1st with an F1-Score of 0.780 and an Accuracy score of 0.762. A variety of transformer-based pre-trained language models have been experimented with through this study. The best-scored model is an ensemble of AraBERT-Base, Asafya-BERT, and ARBERT models. One of the study`s key findings is showing the effect the pre-processing can have on every model`s score. In addition to describing the winning model, the current study shows the error analysis."
}