1 research outputs found
Generating Literal and Implied Subquestions to Fact-check Complex Claims
Verifying complex political claims is a challenging task, especially when
politicians use various tactics to subtly misrepresent the facts. Automatic
fact-checking systems fall short here, and their predictions like "half-true"
are not very useful in isolation, since we have no idea which parts of the
claim are true and which are not. In this work, we focus on decomposing a
complex claim into a comprehensive set of yes-no subquestions whose answers
influence the veracity of the claim. We present ClaimDecomp, a dataset of
decompositions for over 1000 claims. Given a claim and its verification
paragraph written by fact-checkers, our trained annotators write subquestions
covering both explicit propositions of the original claim and its implicit
facets, such as asking about additional political context that changes our view
of the claim's veracity. We study whether state-of-the-art models can generate
such subquestions, showing that these models generate reasonable questions to
ask, but predicting the comprehensive set of subquestions from the original
claim without evidence remains challenging. We further show that these
subquestions can help identify relevant evidence to fact-check the full claim
and derive the veracity through their answers, suggesting that they can be
useful pieces of a fact-checking pipeline