2021/04/25 by Lasitha Uyangodage, Uyangodage, Lasitha, Tharindu Ranasinghe +3
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Social and Information Networks (cs.SI) #cs.CL #cs.LG #cs.SI
paper · pdf · doi:10.48550/arxiv.2104.12201
Accepted to Workshop on NLP for Internet Freedom (NLP4IF) at NAACL 2021
arxiv created 2021/04/25 · arxiv updated 2021/04/27
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.