Fact checking at scale is difficult -- while the number of active fact
checking websites is growing, it remains too small for the needs of the
contemporary media ecosystem. However, despite good intentions, contributions
from volunteers are often error-prone, and thus in practice restricted to claim
detection. We investigate how to increase the accuracy and efficiency of fact
checking by providing information about the claim before performing the check,
in the form of natural language briefs. We investigate passage-based briefs,
containing a relevant passage from Wikipedia, entity-centric ones consisting of
Wikipedia pages of mentioned entities, and Question-Answering Briefs, with
questions decomposing the claim, and their answers. To produce QABriefs, we
develop QABriefer, a model that generates a set of questions conditioned on the
claim, searches the web for evidence, and generates answers. To train its
components, we introduce QABriefDataset which we collected via crowdsourcing.
We show that fact checking with briefs -- in particular QABriefs -- increases
the accuracy of crowdworkers by 10% while slightly decreasing the time taken.
For volunteer (unpaid) fact checkers, QABriefs slightly increase accuracy and
reduce the time required by around 20%.

通过提供自然语言简报，尤其是 QABriefs 来提高事实核查的准确性和效率，可以使众包者的准确率提高 10％，而无偿事实核查者的准确率略有提高，所需时间减少约 20％。