Abstract
This paper provides a detailed overview of the system and its outcomes, which were produced as part of the NLP4IF Shared Task on Fighting the COVID-19 Infodemic at NAACL 2021. This task is accomplished using a variety of techniques. We used state-of-the-art contextualized text representation models that were fine-tuned for the down-stream task in hand. ARBERT, MARBERT,AraBERT, Arabic ALBERT and BERT-base-arabic were used. According to the results, BERT-base-arabic had the highest 0.748 F1 score on the test set.
Cite
CITATION STYLE
Henia, W., Rjab, O., Haddad, H., & Fourati, C. (2021). iCompass at NLP4IF-2021–Fighting the COVID-19 Infodemic. In NLP4IF 2021 - NLP for Internet Freedom: Censorship, Disinformation, and Propaganda, Proceedings of the 4th Workshop (pp. 115–118). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2021.nlp4if-1.17
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