  Understanding a narrative requires reading between the lines and reasoning
about the unspoken but obvious implications about events and people's mental
states - a capability that is trivial for humans but remarkably hard for
machines. To facilitate research addressing this challenge, we introduce a new
annotation framework to explain naive psychology of story characters as
fully-specified chains of mental states with respect to motivations and
emotional reactions. Our work presents a new large-scale dataset with rich
low-level annotations and establishes baseline performance on several new
tasks, suggesting avenues for future research.
