  Generating some appealing questions in open-domain conversations is an
effective way to improve human-machine interactions and lead the topic to a
broader or deeper direction. To avoid dull or deviated questions, some
researchers tried to utilize answer, the "future" information, to guide
question generation. However, they separate a post-question-answer (PQA) triple
into two parts: post-question (PQ) and question-answer (QA) pairs, which may
hurt the overall coherence. Besides, the QA relationship is modeled as a
one-to-one mapping that is not reasonable in open-domain conversations. To
tackle these problems, we propose a generative triple-wise model with
hierarchical variations for open-domain conversational question generation
(CQG). Latent variables in three hierarchies are used to represent the shared
background of a triple and one-to-many semantic mappings in both PQ and QA
pairs. Experimental results on a large-scale CQG dataset show that our method
significantly improves the quality of questions in terms of fluency, coherence
and diversity over competitive baselines.
