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 F1 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.
翻译:在本文中,我们用英文描述了我们的AAAI 2021年COVID-19假消息探测共同任务系统,我们在该测试组中达到了第三位位置,在测试组中加权F1分为0.9859,具体地说,我们提出了不同预先培训语言模型(如BERT、罗伯塔、厄尼等)的混合方法,包括各种培训战略,包括暖气、学习进度表和k倍交叉校验,我们还对未正确分类的样本进行了广泛分析,代码见:https://github.com/archersama/3rd-solution-COVID19-Fake-News-Servement-in-English。