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In this paper, we propose a novel configurable framework to automatically generate distractive choices for open-domain cloze-style multiple-choice questions, which incorporates a general-purpose knowledge base to effectively create a small distractor candidate set, and a feature-rich learning-to-rank model to select distractors that are both plausible and reliable.
A Preliminary Inquiry Into Using Corpus Word Frequency Data in the Automatic Generation of English Language Cloze Tests
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A preliminary inquiry into using corpus word frequency data in the automatic generation of English language cloze tests
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