  In this paper, we propose the task of consecutive question generation (CQG),
which generates a set of logically related question-answer pairs to understand
a whole passage, with a comprehensive consideration of the aspects including
accuracy, coverage, and informativeness. To achieve this, we first examine the
four key elements of CQG, i.e., question, answer, rationale, and context
history, and propose a novel dynamic multitask framework with one main task
generating a question-answer pair, and four auxiliary tasks generating other
elements. It directly helps the model generate good questions through both
joint training and self-reranking. At the same time, to fully explore the
worth-asking information in a given passage, we make use of the reranking
losses to sample the rationales and search for the best question series
globally. Finally, we measure our strategy by QA data augmentation and manual
evaluation, as well as a novel application of generated question-answer pairs
on DocNLI. We prove that our strategy can improve question generation
significantly and benefit multiple related NLP tasks.
