  Given a textual passage and an answer, humans are able to ask questions with
various expressions, but this ability is still challenging for most question
generation (QG) systems. Existing solutions mainly focus on the internal
knowledge within the given passage or the semantic word space for diverse
content planning. These methods, however, have not considered the potential of
external knowledge for expression diversity. To bridge this gap, we propose
RAST, a framework for Retrieval-Augmented Style Transfer, where the objective
is to utilize the style of diverse templates for question generation. For
training RAST, we develop a novel Reinforcement Learning (RL) based approach
that maximizes a weighted combination of diversity reward and consistency
reward. Here, the consistency reward is computed by a Question-Answering (QA)
model, whereas the diversity reward measures how much the final output mimics
the retrieved template. Experimental results show that our method outperforms
previous diversity-driven baselines on diversity while being comparable in
terms of consistency scores. Our code is available at
https://github.com/gouqi666/RAST.
