Fighting the COVID-19 Infodemic with a Holistic BERT Ensemble
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 10 of its 20 references that are in the map. First posted on arXiv 12 Apr 2021.
Builds on
10 of 20 references.
References · 10
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 1.
3 papers.
- 2021Findings of the NLP4IF-2021 Shared Tasks on Fighting the COVID-19 Infodemic and Censorship Detectioncited by 14
- 2022BERT-Deep CNN: State-of-the-Art for Sentiment Analysis of COVID-19 Tweetscited by 2
- 2024A hybrid transformer and attention based recurrent neural network for robust and interpretable sentiment analysis of tweetscited by 2
- 2024A hybrid transformer and attention based recurrent neural network for robust and interpretable sentiment analysis of tweets30 Mar 2024
- 2022BERT-Deep CNN: State-of-the-Art for Sentiment Analysis of COVID-19 Tweets4 Nov 2022
- 2021Findings of the NLP4IF-2021 Shared Tasks on Fighting the COVID-19 Infodemic and Censorship Detection23 Sep 2021