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        <title>Can Multilingual Transformers Fight the COVID-19 Infodemic?</title>
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    <name type="personal">
        <namePart type="given">Lasitha</namePart>
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        <namePart type="given">Tharindu</namePart>
        <namePart type="family">Ranasinghe</namePart>
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        <namePart type="given">Hansi</namePart>
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            <title>Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)</title>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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            <namePart type="given">Galia</namePart>
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    <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.</abstract>
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        <url>https://aclanthology.org/2021.ranlp-1.160</url>
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%0 Conference Proceedings
%T Can Multilingual Transformers Fight the COVID-19 Infodemic?
%A Uyangodage, Lasitha
%A Ranasinghe, Tharindu
%A Hettiarachchi, Hansi
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
%D 2021
%8 September
%I INCOMA Ltd.
%C Held Online
%F uyangodage-etal-2021-multilingual
%X 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.
%U https://aclanthology.org/2021.ranlp-1.160
%P 1432-1437

Markdown (Informal)

[Can Multilingual Transformers Fight the COVID-19 Infodemic?](https://aclanthology.org/2021.ranlp-1.160) (Uyangodage et al., RANLP 2021)

• Can Multilingual Transformers Fight the COVID-19 Infodemic? (Uyangodage et al., RANLP 2021)

ACL

• Lasitha Uyangodage, Tharindu Ranasinghe, and Hansi Hettiarachchi. 2021. Can Multilingual Transformers Fight the COVID-19 Infodemic?. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 1432–1437, Held Online. INCOMA Ltd..