Adapting Open Domain Fact Extraction and Verification to COVID-FACT through In-Domain Language Modeling - Liu, Zhenghao; Xiong, Chenyan; Dai, Zhuyun; Sun, Si; Sun, Maosong; Liu, Zhiyuan (Empirical Methods in Natural Language Processing (Findings, Short), 2020)
With the epidemic of COVID-19, verifying the scientifically false online information, such as fake news and maliciously fabricated statements, has become crucial. However, the lack of training data in the scientific domain limits the performance of fact verification models. This paper proposes an in-domain language modeling method for fact extraction and verification systems. We come up with SciKGAT to combine the advantages of open-domain literature search, state-of-the-art fact verification systems and in-domain medical knowledge through language modeling. Our experiments on SCIFACT, a dataset of expert-written scientific fact verification, show that SciKGAT achieves 30% absolute improvement on precision. Our analyses show that such improvement thrives from our in-domain language model by picking up more related evidence pieces and accurate fact verification. Our codes and data are released via Github.