  Given the recent proliferation of false claims online, there has been a lot
of manual fact-checking effort. As this is very time-consuming, human
fact-checkers can benefit from tools that can support them and make them more
efficient. Here, we focus on building a system that could provide such support.
Given an input document, it aims to detect all sentences that contain a claim
that can be verified by some previously fact-checked claims (from a given
database). The output is a re-ranked list of the document sentences, so that
those that can be verified are ranked as high as possible, together with
corresponding evidence. Unlike previous work, which has looked into claim
retrieval, here we take a document-level perspective. We create a new manually
annotated dataset for this task, and we propose suitable evaluation measures.
We further experiment with a learning-to-rank approach, achieving sizable
performance gains over several strong baselines. Our analysis demonstrates the
importance of modeling text similarity and stance, while also taking into
account the veracity of the retrieved previously fact-checked claims. We
believe that this research would be of interest to fact-checkers, journalists,
media, and regulatory authorities.
