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

本研究提出了一种用于阅读理解的问答生成模型（HTA-WTA），通过使用 story-based 阅读理解技能（SBRCS）来对阅读能力进行更全面的评估，并使用深度推理问题测试了 HTA-WTA 模型的强大性能。