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We introduce an automatic system that achieves state-of-the-art results onthe Winograd Schema Challenge (WSC), a common sense reasoning task thatrequires diverse, complex forms of inference and knowledge. Our method uses aknowledge hunting module to gather text from the web, which serves as evidencefor candidate problem resolutions. Given an input problem, our system generatesrelevant queries to send to a search engine, then extracts and classifiesknowledge from the returned results and weighs them to make a resolution. Ourapproach improves F1 performance on the full WSC by 0.21 over the previous bestand represents the first system to exceed 0.5 F1. We further demonstrate thatthe approach is competitive on the Choice of Plausible Alternatives (COPA)task, which suggests that it is generally applicable.

A Knowledge Hunting Framework for Common Sense Reasoning