  Question Generation (QG), as a challenging Natural Language Processing task,
aims at generating questions based on given answers and context. Existing QG
methods mainly focus on building or training models for specific QG datasets.
These works are subject to two major limitations: (1) They are dedicated to
specific QG formats (e.g., answer-extraction or multi-choice QG), therefore, if
we want to address a new format of QG, a re-design of the QG model is required.
(2) Optimal performance is only achieved on the dataset they were just trained
on. As a result, we have to train and keep various QG models for different QG
datasets, which is resource-intensive and ungeneralizable.
  To solve the problems, we propose a model named Unified-QG based on lifelong
learning techniques, which can continually learn QG tasks across different
datasets and formats. Specifically, we first build a format-convert encoding to
transform different kinds of QG formats into a unified representation. Then, a
method named \emph{STRIDER} (\emph{S}imilari\emph{T}y \emph{R}egular\emph{I}zed
\emph{D}ifficult \emph{E}xample \emph{R}eplay) is built to alleviate
catastrophic forgetting in continual QG learning. Extensive experiments were
conducted on $8$ QG datasets across $4$ QG formats (answer-extraction,
answer-abstraction, multi-choice, and boolean QG) to demonstrate the
effectiveness of our approach. Experimental results demonstrate that our
Unified-QG can effectively and continually adapt to QG tasks when datasets and
formats vary. In addition, we verify the ability of a single trained Unified-QG
model in improving $8$ Question Answering (QA) systems' performance through
generating synthetic QA data.
