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.
The system developed by REF automatically identified question topics (i.e., the part of a sentence about which a question should be asked), and accordingly generated cloze (fillin-the-blank) questions.
Mind the Gap: Learning to Choose Gaps for Question Generation
NAACL
Computer Science