通过数据增强与过生成-排序方法改进阅读理解问题生成
Improving Reading Comprehension Question Generation with Data Augmentation and Overgenerate-and-rank
AI总结：
针对同一答案对应多类问题、生成结果难匹配教育者偏好的挑战，提出数据增强与过生成-排序结合的阅读理解问题生成方法，在FairytaleQA数据集上ROUGE-L较现有最优方法提升5%，且能有效生成隐含式问题。
摘要：
阅读理解是教育诸多领域的一项关键技能，涵盖语言学习、认知发展以及儿童早期读写能力培养。自动化的答案感知型阅读理解问题生成在教育活动中具备大幅提升学习者支持规模的巨大潜力。该场景下的一项核心技术挑战在于，同一答案可能对应多个问题，且这些问题有时差异极大；经训练的问题生成方法未必能知晓人类教育者更偏好哪类问题。为应对这一挑战，我们提出两项方案：1）一种数据增强方法，针对同一上下文与答案生成多样化问题，以此丰富训练数据集；2）一种过生成-排序方法，用于从候选问题池中筛选最优问题。我们在FairytaleQA数据集上对所提方法进行评估，结果显示其ROUGE-L指标较现有最优方法实现了5%的绝对提升。我们还验证了该方法在生成难度更高的“隐含式”问题方面的有效性，这类问题的答案并非以文本片段形式包含在上下文中。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
