论文标题
tortify 2022的UOFA-TRUTH：基于变压器和转移学习的多模式事实检查
UofA-Truth at Factify 2022 : Transformer And Transfer Learning Based Multi-Modal Fact-Checking
论文作者
论文摘要
识别假新闻是一项非常艰巨的任务，尤其是在考虑通过文本，图像，视频和/或音频传达信息的多种模式时。我们试图通过我们简单而有效的方法在de-factify@aaai2022中的分类任务中解决多模式新闻源（包括文本和图像）中自动错误信息/虚假信息检测的问题。我们的模型产生的F1加权分数为74.807％，这是所有提交中第四大的。在本文中，我们将解释我们执行共同任务的方法。
Identifying fake news is a very difficult task, especially when considering the multiple modes of conveying information through text, image, video and/or audio. We attempted to tackle the problem of automated misinformation/disinformation detection in multi-modal news sources (including text and images) through our simple, yet effective, approach in the FACTIFY shared task at De-Factify@AAAI2022. Our model produced an F1-weighted score of 74.807%, which was the fourth best out of all the submissions. In this paper we will explain our approach to undertake the shared task.
