Automated Fact-checking for Wikipedia

Abstract

The incoming flow of information is continuously increasing along with the disinformation piece that can harm society. Filtering unreliable content helps keep Wikipedia as free as possible of disinformation, making it one of the most significant reliable information sources. Consequently, Wikipedia’s knowledge base is widely used for facts verification academic research. The main goal of our work is to transform recent academic achievements into a practical open-source Wikipedia-based fact-checking application that is both accurate and efficient. We review the primary NLI related datasets and study their relevant limitations. As a result, we propose the data filtering method that improves the model’s performance and generalization. We show that transfer learning for NLI models are not working well, and complete model training is needed to achieve the best result on a specific dataset. We come up with an unsupervised fine-tuning of the Masked Language model on field-specific texts for model domain adaptation. Finally, we present the new fact-checking system WikiCheck API that automatically performs a facts validation process based on the Wikipedia knowledge base. It is comparable to SOTA solutions in terms of accuracy and can be used on low memory CPU instances.