  Recent years have seen the proliferation of disinformation and fake news
online. Traditional approaches to mitigate these issues is to use manual or
automatic fact-checking. Recently, another approach has emerged: checking
whether the input claim has previously been fact-checked, which can be done
automatically, and thus fast, while also offering credibility and
explainability, thanks to the human fact-checking and explanations in the
associated fact-checking article. Here, we focus on claims made in a political
debate and we study the impact of modeling the context of the claim: both on
the source side, i.e., in the debate, as well as on the target side, i.e., in
the fact-checking explanation document. We do this by modeling the local
context, the global context, as well as by means of co-reference resolution,
and multi-hop reasoning over the sentences of the document describing the
fact-checked claim. The experimental results show that each of these represents
a valuable information source, but that modeling the source-side context is
most important, and can yield 10+ points of absolute improvement over a
state-of-the-art model.
