Question Generation via Overgenerating Transformations and Ranking

  title={Question Generation via Overgenerating Transformations and Ranking},
  author={Michael Heilman},
We describe an extensible approach to generating questions for the purpose of reading comprehension assessment and practice. Our framework for question generation composes general-purpose rules to transform declarative sentences into questions, is modular in that existing NLP tools can be leveraged, and includes a statistical component for scoring questions based on features of the input, output, and transformations performed. In an evaluation in which humans rated questions according to… CONTINUE READING
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