@inproceedings{uyangodage-etal-2021-multilingual,
    title = "Can Multilingual Transformers Fight the {COVID}-19 Infodemic?",
    author = "Uyangodage, Lasitha  and
      Ranasinghe, Tharindu  and
      Hettiarachchi, Hansi",
    editor = "Mitkov, Ruslan  and
      Angelova, Galia",
    booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)",
    month = sep,
    year = "2021",
    address = "Held Online",
    publisher = "INCOMA Ltd.",
    url = "https://aclanthology.org/2021.ranlp-1.160",
    pages = "1432--1437",
    abstract = "The massive spread of false information on social media has become a global risk especially in a global pandemic situation like COVID-19. False information detection has thus become a surging research topic in recent months. In recent years, supervised machine learning models have been used to automatically identify false information in social media. However, most of these machine learning models focus only on the language they were trained on. Given the fact that social media platforms are being used in different languages, managing machine learning models for each and every language separately would be chaotic. In this research, we experiment with multilingual models to identify false information in social media by using two recently released multilingual false information detection datasets. We show that multilingual models perform on par with the monolingual models and sometimes even better than the monolingual models to detect false information in social media making them more useful in real-world scenarios.",
}