WikiCheck: An end-to-end open source Automatic Fact-Checking API based on Wikipedia

• URL: http://arxiv.org/abs/2109.00835v1
• Date: Thu, 2 Sep 2021 10:45:07 GMT
• Title: WikiCheck: An end-to-end open source Automatic Fact-Checking API based on Wikipedia
• Authors: Mykola Trokhymovych and Diego Saez-Trumper
• Abstract summary: We review the State-of-the-Art datasets and solutions for Automatic Fact-checking. We propose a data filtering method that improves the model's performance and generalization. We present a new fact-checking system, the textitWikiCheck API that automatically performs a facts validation process based on the Wikipedia knowledge base.
• Score: 1.14219428942199
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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