  We propose a type-controlled framework for inquisitive question generation.
We annotate an inquisitive question dataset with question types, train question
type classifiers, and finetune models for type-controlled question generation.
Empirical results demonstrate that we can generate a variety of questions that
adhere to specific types while drawing from the source texts. We also
investigate strategies for selecting a single question from a generated set,
considering both an informative vs.~inquisitive question classifier and a
pairwise ranker trained from a small set of expert annotations. Question
selection using the pairwise ranker yields strong results in automatic and
manual evaluation. Our human evaluation assesses multiple aspects of the
generated questions, finding that the ranker chooses questions with the best
syntax (4.59), semantics (4.37), and inquisitiveness (3.92) on a scale of 1-5,
even rivaling the performance of human-written questions.
