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。

建模生成问题策略，实现阅读理解测试中的问题生成

Question Generation for Reading Comprehension Assessment by Modeling How and What to Ask

Existing question answering (QA) techniques are created mainly to answer
questions asked by humans. But in educational applications, teachers often need
to decide what questions they should ask, in order to help students to improve
their narrative understanding capabilities. We design an automated
question-answer generation (QAG) system for this education scenario: given a
story book at the kindergarten to eighth-grade level as input, our system can
automatically generate QA pairs that are capable of testing a variety of
dimensions of a student's comprehension skills. Our proposed QAG model
architecture is demonstrated using a new expert-annotated FairytaleQA dataset,
which has 278 child-friendly storybooks with 10,580 QA pairs. Automatic and
human evaluations show that our model outperforms state-of-the-art QAG baseline
systems. On top of our QAG system, we also start to build an interactive
story-telling application for the future real-world deployment in this
educational scenario.

我们设计了一种教育应用的问答生成自动化系统，可以为幼儿园到八年级的阅读材料生成测试学生理解能力的问题与答案对，并在自动评估和人工评估中证明其优于当前先进的问答生成基线系统。同时，我们还在此基础上开始构建一个交互式的故事讲述应用程序，用于未来在教育领域的实际部署。