Uyangodage, Lasitha, Ranasinghe, Tharindu and Hettiarachchi, Hansi (2021). Transformers to Fight the COVID-19 Infodemic. IN: Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021). Association for Computational Linguistics (ACL).
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.
| Publication DOI: | https://doi.org/10.18653/v1/2021.nlp4if-1.20 |
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| Additional Information: | Copyright © 2021 Association for Computational Linguistics.licensed on a Creative Commons Attribution 4.0 International License. |
| Last Modified: | 29 Oct 2024 16:56 |
| Date Deposited: | 15 May 2023 10:59 |
| Full Text Link: |
https://www.len ... 428-912-739-287 https://arxiv.o ... /abs/2104.12201 |
| Related URLs: | PURE Output Type: | Conference contribution |
| Published Date: | 2021-06 |
| Authors: |
Uyangodage, Lasitha
Ranasinghe, Tharindu ( 
0000-0003-3207-3821)
Hettiarachchi, Hansi |
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)