  We propose a novel text generation task, namely Curiosity-driven Question
Generation. We start from the observation that the Question Generation task has
traditionally been considered as the dual problem of Question Answering, hence
tackling the problem of generating a question given the text that contains its
answer. Such questions can be used to evaluate machine reading comprehension.
However, in real life, and especially in conversational settings, humans tend
to ask questions with the goal of enriching their knowledge and/or clarifying
aspects of previously gathered information. We refer to these inquisitive
questions as Curiosity-driven: these questions are generated with the goal of
obtaining new information (the answer) which is not present in the input text.
In this work, we experiment on this new task using a conversational Question
Answering (QA) dataset; further, since the majority of QA dataset are not built
in a conversational manner, we describe a methodology to derive data for this
novel task from non-conversational QA data. We investigate several automated
metrics to measure the different properties of Curious Questions, and
experiment different approaches on the Curiosity-driven Question Generation
task, including model pre-training and reinforcement learning. Finally, we
report a qualitative evaluation of the generated outputs.
