Abstract
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 multiturn question-answering conversations for passages in SQuAD