Automatic question generation from text - an aid to independent study
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EdAppsNLP 05 Proceedings of the second workshop on Building Educational Applications Using NLP
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CSLDAMT '10 Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk
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Automatic gap-fill question generation from text books
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UCNLG+EVAL '11 Proceedings of the UCNLG+Eval: Language Generation and Evaluation Workshop
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NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstration Session
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Not all learning takes place in an educational setting: more and more self-motivated learners are turning to on-line text to learn about new topics. Our goal is to provide such learners with the well-known benefits of testing by automatically generating quiz questions for online text. Prior work on question generation has focused on the grammaticality of generated questions and generating effective multiple-choice distractors for individual question targets, both key parts of this problem. Our work focuses on the complementary aspect of determining what part of a sentence we should be asking about in the first place; we call this "gap selection." We address this problem by asking human judges about the quality of questions generated from a Wikipedia-based corpus, and then training a model to effectively replicate these judgments. Our data shows that good gaps are of variable length and span all semantic roles, i.e., nouns as well as verbs, and that a majority of good questions do not focus on named entities. Our resulting system can generate fill-in-the-blank (cloze) questions from generic source materials.