  Conversational systems enable numerous valuable applications, and
question-answering is an important component underlying many of these. However,
conversational question-answering remains challenging due to the lack of
realistic, domain-specific training data. Inspired by this bottleneck, we focus
on conversational question generation as a means to generate synthetic
conversations for training and evaluation purposes. We present a number of
novel strategies to improve conversational flow and accommodate varying
question types and overall fluidity. Specifically, we design ChainCQG as a
two-stage architecture that learns question-answer representations across
multiple dialogue turns using a flow propagation training strategy.ChainCQG
significantly outperforms both answer-aware and answer-unaware SOTA baselines
(e.g., up to 48% BLEU-1 improvement). Additionally, our model is able to
generate different types of questions, with improved fluidity and coreference
alignment.
