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Reading comprehension is a crucial skill in many aspects of education,including language learning, cognitive development, and fostering earlyliteracy skills in children. Automated answer-aware reading comprehensionquestion generation has significant potential to scale up learner support ineducational activities. One key technical challenge in this setting is thatthere can be multiple questions, sometimes very different from each other, withthe same answer; a trained question generation method may not necessarily knowwhich question human educators would prefer. To address this challenge, wepropose 1) a data augmentation method that enriches the training dataset withdiverse questions given the same context and answer and 2) anovergenerate-and-rank method to select the best question from a pool ofcandidates. We evaluate our method on the FairytaleQA dataset, showing a 5%absolute improvement in ROUGE-L over the best existing method. We alsodemonstrate the effectiveness of our method in generating harder, "implicit"questions, where the answers are not contained in the context as text spans.

Improving Reading Comprehension Question Generation with Data Augmentation and Overgenerate-and-rank