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We present $\textbf{$\texttt{SkillQG}$}$: a question generation frameworkwith controllable comprehension types for assessing and improving machinereading comprehension models. Existing question generation systems widelydifferentiate questions by $\textit{literal}$ information such as questionwords and answer types to generate semantically relevant questions for a givencontext. However, they rarely consider the $\textit{comprehension}$ nature ofquestions, i.e. the different comprehension capabilities embodied by differentquestions. In comparison, our $\texttt{SkillQG}$ is able to tailor afine-grained assessment and improvement to the capabilities of questionanswering models built on it. Specifically, we first frame the comprehensiontype of questions based on a hierarchical skill-based schema, then formulate$\texttt{SkillQG}$ as a skill-conditioned question generator. Furthermore, toimprove the controllability of generation, we augment the input text withquestion focus and skill-specific knowledge, which are constructed byiteratively prompting the pre-trained language models. Empirical resultsdemonstrate that $\texttt{SkillQG}$ outperforms baselines in terms of quality,relevance, and skill-controllability while showing a promising performanceboost in downstream question answering task.

SkillQG: Learning to Generate Question for Reading Comprehension Assessment