Inquisitive probing questions come naturally to humans in a variety of
settings, but is a challenging task for automatic systems. One natural type of
question to ask tries to fill a gap in knowledge during text comprehension,
like reading a news article: we might ask about background information, deeper
reasons behind things occurring, or more. Despite recent progress with
data-driven approaches, generating such questions is beyond the range of models
trained on existing datasets.
We introduce INQUISITIVE, a dataset of ~19K questions that are elicited while
a person is reading through a document. Compared to existing datasets,
INQUISITIVE questions target more towards high-level (semantic and discourse)
comprehension of text. We show that readers engage in a series of pragmatic
strategies to seek information. Finally, we evaluate question generation models
based on GPT-2 and show that our model is able to generate reasonable questions
although the task is challenging, and highlight the importance of context to
generate INQUISITIVE questions.

介绍了 INQUISITIVE 数据集，该数据集包含～19,000 个问题，是读者在阅读文档时 elicited 的。与现有数据集相比，INQUISITIVE 的问题更加注重文本的高层（语义和话语）理解，并根据基于 GPT-2 的问题生成模型评估了该任务的挑战性，强调了上下文对生成 INQUISITIVE 问题的重要性。