  The neural seq2seq based question generation (QG) is prone to generating
generic and undiversified questions that are poorly relevant to the given
passage and target answer. In this paper, we propose two methods to address the
issue. (1) By a partial copy mechanism, we prioritize words that are
morphologically close to words in the input passage when generating questions;
(2) By a QA-based reranker, from the n-best list of question candidates, we
select questions that are preferred by both the QA and QG model. Experiments
and analyses demonstrate that the proposed two methods substantially improve
the relevance of generated questions to passages and answers.
