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 downstream task in hand. ARBERT, MARBERT,AraBERT, Arabic ALBERT and BERT-base-arabic were used. According to the results, BERT-base-arabic had the highest 0.784 F1 score on the test set.- Anthology ID:
- 2021.nlp4if-1.17
- Volume:
- Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda
- Month:
- June
- Year:
- 2021
- Address:
- Online
- Editors:
- Anna Feldman, Giovanni Da San Martino, Chris Leberknight, Preslav Nakov
- Venue:
- NLP4IF
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 115–118
- Language:
- URL:
- https://aclanthology.org/2021.nlp4if-1.17
- DOI:
- 10.18653/v1/2021.nlp4if-1.17
- Cite (ACL):
- Wassim Henia, Oumayma Rjab, Hatem Haddad, and Chayma Fourati. 2021. iCompass at NLP4IF-2021–Fighting the COVID-19 Infodemic. In Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda, pages 115–118, Online. Association for Computational Linguistics.
- Cite (Informal):
- iCompass at NLP4IF-2021–Fighting the COVID-19 Infodemic (Henia et al., NLP4IF 2021)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2021.nlp4if-1.17.pdf