Abstract
We demonstrate Scrutinizer, a system that supports human fact checkers in translating text claims into SQL queries on an associated database. Scrutinizer coordinates teams of human fact checkers and reduces their verification time by proposing queries or query fragments over relevant data. Those proposals are based on claim text classifiers, that gradually improve during the verification of multiple claims. In addition, Scrutinizer uses tentative execution of query candidates to narrow down the set of alternatives. The verification process is controlled by a cost-based optimizer that plans effective question sequences to verify specific claims, and prioritizes claims for verification. In this demonstration, we first show how our system can assist users in verifying statistical claims. We then let users come up with new, unseen claims and show how the system effectively learns new queries with little user feedback.
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