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Transformers to Fight the COVID-19 Infodemic

2021-04-25 16:49:23
Lasitha Uyangodage, Tharindu Ranasinghe, Hansi Hettiarachchi
arXiv_CL
       
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. NLP4IF-2021 shared task on fighting the COVID-19 infodemic has been organised to strengthen the research in false information detection where the participants are asked to predict seven different binary labels regarding false information in a tweet. The shared task has been organised in three languages; Arabic, Bulgarian and English. In this paper, we present our approach to tackle the task objective using transformers. Overall, our approach achieves a 0.707 mean F1 score in Arabic, 0.578 mean F1 score in Bulgarian and 0.864 mean F1 score in English ranking 4th place in all the languages.

Abstract (translated)

URL

https://arxiv.org/abs/2104.12201

PDF

https://arxiv.org/pdf/2104.12201.pdf

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