A Contrastive Learning Method for Automated Fact-Checking

Seonyeong Song, Jejun An, Kunwoo Park

http://doi.org/10.5626/JOK.2023.50.8.680

As proliferation of online misinformation increases, the importance of automated fact-checking, which enables real-time evaluation, has been emphasized. In this study, we propose a contrastive learning method for automated fact-checking in Korean. The proposed method deems a sentence similar to evidence as a positive sample to determine the authenticity of a given claim. In evaluation experiments, we found that the proposed method was more effective in the sentence selection step of finding evidence sentences for a given claim than previous methods. such as a finetuned pretrained language model and SimCSE. This study shows a potential of contrastive learning for automated fact-checking.