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We present a novel method for obtaining high-quality, domain-targeted multiple choice questions from crowd workers. Generating these questions can be difficult without trading away originality, relevance or diversity in the answer options. Our method addresses these problems by leveraging a large corpus of domain-specific text and a small set of existing questions. It produces model suggestions for document selection and answer distractor choice which aid the human question generation process.doi:10.18653/v1/w17-4413 dblp:conf/aclnut/WelblLG17 fatcat:djxqvrctrzcsxnfhtlg3o75bn4