Abstract
In this paper, we describe our system for the AAAI 2021 shared task of COVID-19 Fake News Detection in English, where we achieved the 3rd position with the weighted \(F_1\) score of 0.9859 on the test set. Specifically, we proposed an ensemble method of different pre-trained language models such as BERT, Roberta, Ernie, etc. with various training strategies including warm-up, learning rate schedule and k-fold cross-validation. We also conduct an extensive analysis of the samples that are not correctly classified. The code is available at: https://github.com/archersama/3rd-solution-COVID19-Fake-News-Detection-in-English.
X. Li and Y. Xia—Equal contribution.
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Acknowledgements
This work was partially supported by National Key Research and Development Project (2019YFB1704002) and National Natural Science Foundation of China (61876009).
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Li, X., Xia, Y., Long, X., Li, Z., Li, S. (2021). Exploring Text-Transformers in AAAI 2021 Shared Task: COVID-19 Fake News Detection in English. In: Chakraborty, T., Shu, K., Bernard, H.R., Liu, H., Akhtar, M.S. (eds) Combating Online Hostile Posts in Regional Languages during Emergency Situation. CONSTRAINT 2021. Communications in Computer and Information Science, vol 1402. Springer, Cham. https://doi.org/10.1007/978-3-030-73696-5_11
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