This paper investigates a new task named Conversational Question Generation
(CQG) which is to generate a question based on a passage and a conversation
history (i.e., previous turns of question-answer pairs). CQG is a crucial task
for developing intelligent agents that can drive question-answering style
conversations or test user understanding of a given passage. Towards that end,
we propose a new approach named Reinforced Dynamic Reasoning (ReDR) network,
which is based on the general encoder-decoder framework but incorporates a
reasoning procedure in a dynamic manner to better understand what has been
asked and what to ask next about the passage. To encourage producing meaningful
questions, we leverage a popular question answering (QA) model to provide
feedback and fine-tune the question generator using a reinforcement learning
mechanism. Empirical results on the recently released CoQA dataset demonstrate
the effectiveness of our method in comparison with various baselines and model
variants. Moreover, to show the applicability of our method, we also apply it
to create multi-turn question-answering conversations for passages in SQuAD.

本文提出了一个新的任务 —— 对话式问题生成（CQG），研究了如何使用增强动态推理（ReDR）网络来生成针对给定文本和对话历史的问题，使用 QA 模型提供反馈，经过与各种基线和模型变量的比较得出新方法的有效性，并将其应用于 SQuAD 的多轮问答对话中。