Bibliographic description:
Trokhymovych, Mykola. Automated Fact-checking for Wikipedia / Mykola Trokhymovych; Supervisor: Diego Saez-Trumper; Ukrainian Catholic University, Department of Computer Sciences. – Lviv : [s.n.], 2021. – 52 p.: ill.
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.