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Reading is integral to everyday life, and yet learning to read is a strugglefor many young learners. During lessons, teachers can use comprehensionquestions to increase engagement, test reading skills, and improve retention.Historically such questions were written by skilled teachers, but recentlylanguage models have been used to generate comprehension questions. However,many existing Question Generation (QG) systems focus on generating literalquestions from the text, and have no way to control the type of the generatedquestion. In this paper, we study QG for reading comprehension whereinferential questions are critical and extractive techniques cannot be used. Wepropose a two-step model (HTA-WTA) that takes advantage of previous datasets,and can generate questions for a specific targeted comprehension skill. Wepropose a new reading comprehension dataset that contains questions annotatedwith story-based reading comprehension skills (SBRCS), allowing for a morecomplete reader assessment. Across several experiments, our results show thatHTA-WTA outperforms multiple strong baselines on this new dataset. We show thatthe HTA-WTA model tests for strong SCRS by asking deep inferential questions.

Question Generation for Reading Comprehension Assessment by Modeling How and What to Ask