  Recent years have seen the proliferation of disinformation and misinformation
online, thanks to the freedom of expression on the Internet and to the rise of
social media. Two solutions were proposed to address the problem: (i) manual
fact-checking, which is accurate and credible, but slow and non-scalable, and
(ii) automatic fact-checking, which is fast and scalable, but lacks
explainability and credibility. With the accumulation of enough manually
fact-checked claims, a middle-ground approach has emerged: checking whether a
given claim has previously been fact-checked. This can be made automatically,
and thus fast, while also offering credibility and explainability, thanks to
the human fact-checking and explanations in the associated fact-checking
article. This is a relatively new and understudied research direction, and here
we focus on claims made in a political debate, where context really matters.
Thus, 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
reasoning over the target text using Transformer-XH. The experimental results
show that each of these represents a valuable information source, but that
modeling the source-side context is more important, and can yield 10+ points of
absolute improvement.
