We present $\textbf{$\texttt{SkillQG}$}$: a question generation framework
with controllable comprehension types for assessing and improving machine
reading comprehension models. Existing question generation systems widely
differentiate questions by $\textit{literal}$ information such as question
words and answer types to generate semantically relevant questions for a given
context. However, they rarely consider the $\textit{comprehension}$ nature of
questions, i.e. the different comprehension capabilities embodied by different
questions. In comparison, our $\texttt{SkillQG}$ is able to tailor a
fine-grained assessment and improvement to the capabilities of question
answering models built on it. Specifically, we first frame the comprehension
type of questions based on a hierarchical skill-based schema, then formulate
$\texttt{SkillQG}$ as a skill-conditioned question generator. Furthermore, to
improve the controllability of generation, we augment the input text with
question focus and skill-specific knowledge, which are constructed by
iteratively prompting the pre-trained language models. Empirical results
demonstrate that $\texttt{SkillQG}$ outperforms baselines in terms of quality,
relevance, and skill-controllability while showing a promising performance
boost in downstream question answering task.

通过控制问题的理解类型提高机器阅读理解质量，本文提出了 $	extbf {$	exttt {SkillQG}$}$ 框架。在一个基于技能的层级模式中，根据不同能力的问题，通过迭代调用预训练语言模型来生成针对不同能力问题的精细化评估和提高模型的生成问题。实证结果证明，$	exttt {SkillQG}$ 相对于基准线在质量、相关性和技能控制方面性能更优，在下游问答任务中也有很好的性能提升。