  With the growth of fake news and disinformation, the NLP community has been
working to assist humans in fact-checking. However, most academic research has
focused on model accuracy without paying attention to resource efficiency,
which is crucial in real-life scenarios. In this work, we review the
State-of-the-Art datasets and solutions for Automatic Fact-checking and test
their applicability in production environments. We discover overfitting issues
in those models, and we propose a data filtering method that improves the
model's performance and generalization. Then, we design an unsupervised
fine-tuning of the Masked Language models to improve its accuracy working with
Wikipedia. We also propose a novel query enhancing method to improve evidence
discovery using the Wikipedia Search API. Finally, we present a new
fact-checking system, the \textit{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.
