Question Generation via Overgenerating Transformations and Ranking

@inproceedings{Heilman2009QuestionGV,
  title={Question Generation via Overgenerating Transformations and Ranking},
  author={Michael Heilman},
  year={2009}
}
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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Generating reading comprehension look-back strategy questions from expository texts

  • D. M. Gates.
  • Master’s thesis, Carnegie Mellon University.
  • 2008
Highly Influential
3 Excerpts

Overview of the TREC 2007 question answering track

  • H. T. Dang, D. Kelly, J. Lin.
  • Proc. of TREC.
  • 2008
2 Excerpts

Constructions at Work: The Nature of Generalization in Language

  • A. Goldberg.
  • Oxford University Press, New York.
  • 2006

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