  Story composition is a challenging problem for machines and even for humans.
We present a neural narrative generation system that interacts with humans to
generate stories. Our system has different levels of human interaction, which
enables us to understand at what stage of story-writing human collaboration is
most productive, both to improving story quality and human engagement in the
writing process. We compare different varieties of interaction in
story-writing, story-planning, and diversity controls under time constraints,
and show that increased types of human collaboration at both planning and
writing stages results in a 10-50% improvement in story quality as compared to
less interactive baselines. We also show an accompanying increase in user
engagement and satisfaction with stories as compared to our own less
interactive systems and to previous turn-taking approaches to interaction.
Finally, we find that humans tasked with collaboratively improving a particular
characteristic of a story are in fact able to do so, which has implications for
future uses of human-in-the-loop systems.
