Factify: An Automated Fact-Checker for Web Information

Automated fact-checking has received widespread attention in recent years. In this paper, we present Factify, a transformer-based fact-checking approach that retrieves textual evidence from a trusted source for a given claim and subsequently uses the retrieved evidence to judge the validity of the claim. Our retrieval model is based on extractive question-answering and to train the model, we fine-tune various pre-trained BERT models using the FEVER fact-checking dataset. Our claim verification model is also BERT-based and is again trained using the FEVER dataset. Our evaluation using the test data of the FEVER dataset demonstrates the effectiveness of our approach in retrieving relevant textual evidences for given claims from trusted sources, and for determining whether claims are true or false based on the retrieved evidences, compared to various state-of-the-art automated fact-checking approaches.