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.

翻译：阅读理解是教育领域中的关键技能，涉及语言学习、认知发展及培养儿童早期读写能力。自动化答案感知的阅读理解问题生成在扩展教育活动中学习者支持方面具有巨大潜力。该场景的一个关键技术挑战是：同一答案可能对应多个问题（有时彼此差异显著），而训练生成方法可能无法预知人类教育者更偏好哪种问题。为应对这一挑战，我们提出：(1) 一种数据增强方法，通过为相同上下文和答案生成多样化问题来丰富训练数据集；(2) 一种过生成-排序方法，从候选问题池中筛选最优问题。在 FairytaleQA 数据集上的评估表明，我们的方法在 ROUGE-L 指标上较现有最佳方法绝对提升 5%。此外，我们验证了该方法在生成更难、更"隐含"的问题（即答案不直接以文本片段形式存在于上下文中）上的有效性。