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

提出了一种基于数据增强和生成 - 排名方法的自动问题生成策略，该策略适用于阅读理解任务，可以有效地提高问题生成的准确率和多样性。