Abstract
This paper describes the TOKOFOU system, an ensemble model for misinformation detection tasks based on six different transformer-based pre-trained encoders, implemented in the context of the COVID-19 Infodemic Shared Task for English. We fine tune each model on each of the task’s questions and aggregate their prediction scores using a majority voting approach. TOKOFOU obtains an overall F1 score of 89.7%, ranking first.- Anthology ID:
- 2021.nlp4if-1.18
- 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:
- 119–124
- Language:
- URL:
- https://aclanthology.org/2021.nlp4if-1.18
- DOI:
- 10.18653/v1/2021.nlp4if-1.18
- Cite (ACL):
- Georgios Tziafas, Konstantinos Kogkalidis, and Tommaso Caselli. 2021. Fighting the COVID-19 Infodemic with a Holistic BERT Ensemble. In Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda, pages 119–124, Online. Association for Computational Linguistics.
- Cite (Informal):
- Fighting the COVID-19 Infodemic with a Holistic BERT Ensemble (Tziafas et al., NLP4IF 2021)
- PDF:
- https://preview.aclanthology.org/corrections-2024-05/2021.nlp4if-1.18.pdf
- Code
- gtziafas/nlp4ifchallenge
- Data
- TweetEval