  Misleading or false information has been creating chaos in some places around
the world. To mitigate this issue, many researchers have proposed automated
fact-checking methods to fight the spread of fake news. However, most methods
cannot explain the reasoning behind their decisions, failing to build trust
between machines and humans using such technology. Trust is essential for
fact-checking to be applied in the real world. Here, we address fact-checking
explainability through question answering. In particular, we propose generating
questions and answers from claims and answering the same questions from
evidence. We also propose an answer comparison model with an attention
mechanism attached to each question. Leveraging question answering as a proxy,
we break down automated fact-checking into several steps -- this separation
aids models' explainability as it allows for more detailed analysis of their
decision-making processes. Experimental results show that the proposed model
can achieve state-of-the-art performance while providing reasonable explainable
capabilities.
