Question Generation (QG) receives increasing research attention in NLP
community. One motivation for QG is that QG significantly facilitates the
preparation of educational reading practice and assessments. While the
significant advancement of QG techniques was reported, current QG results are
not ideal for educational reading practice assessment in terms of
\textit{controllability} and \textit{question difficulty}. This paper reports
our results toward the two issues. First, we report a state-of-the-art
exam-like QG model by advancing the current best model from 11.96 to 20.19 (in
terms of BLEU 4 score). Second, we propose to investigate a variant of QG
setting by allowing users to provide keywords for guiding QG direction. We also
present a simple but effective model toward the QG controllability task.
Experiments are also performed and the results demonstrate the feasibility and
potentials of improving QG diversity and controllability by the proposed
keyword provision QG model.

本文研究了问题生成中的控制性和难度问题，提出了一种基于关键词的问题生成模型，并通过实验证明了该模型能够显著提高问题生成的多样性和可控性。