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.

翻译：我们为难问问题的生成建议了一种类型控制框架。 我们用问题类型、 培训问题分类器和类型控制问题生成的微调模型来批注一个疑惑问题数据集。 经验性结果显示,我们可以产生一系列符合特定类型的问题,同时从源文本中提取。 我们还调查从生成的一组中选择单一问题的策略, 同时考虑到信息化与~ 疑问分类器和从小组专家说明中培训的对称排名。 使用对称排名选择问题在自动和手动评估中产生强有力的结果。 我们的人类评估评估评估对产生问题的多个方面进行评估,发现排名者在1-5的尺度上选择了最佳语法(4.59)、语义(4.37)和好奇性(3.92)的问题,甚至与人写问题的表现相匹配。