Exploring Text-transformers in AAAI 2021 Shared Task: COVID-19 Fake News Detection in English

By Xiangyang Li, Yu Xia, Xiang Long, Zheng Li, Sujian Li. 2021

43 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Sociology and Political Science, Natural Language Processing, AI Ethics, Fairness & Society

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jie Tang
2. Yuxiao Dong
3. Xiao Liu
4. Ming Ding
5. Zhengxiao Du
6. W. Tam
7. Wenguang Chen
8. Hanyu Lai

In Sociology and Political Science

1. Ming Ding
2. Yuxiao Dong
3. Jie Tang
4. Wendi Zheng

In Natural Language Processing

1. Jie Tang
2. Ming Ding
3. Yuxiao Dong
4. W. Tam
5. Wenguang Chen
6. Aohan Zeng
7. Xiao Liu
8. P. Zhang

Citations in the corpus

• GLM-130B: An Open Bilingual Pre-trained Model (2022, background): “Conversely, releasing an open LLM can provide access and transparency to all the researchers and promote the research to reduce the potential harm of LLMs, like algorithms to identify the synthetic text Gehrmann et al. (2019) or detect fake news Li et al. (2021).”