Improving Evidence Retrieval for Automated Explainable Fact-Checking

Abstract

Automated fact-checking on a large-scale is a challenging task that has not been studied systematically until recently. Large noisy document collections like the web or news articles make the task more difficult. In this paper, we describe the components of a three-stage automated fact-checking system, named Quin+. We demonstrate that using dense passage representations increases the evidence recall in a noisy setting. We experiment with two sentence selection approaches, an embedding-based selection using a dense retrieval model, and a sequence labeling approach for context-aware selection. Quin+ is able to verify open-domain claims using a large-scale corpus or web search results.