  Many genres of natural language text are narratively structured, a testament
to our predilection for organizing our experiences as narratives. There is
broad consensus that understanding a narrative requires identifying and
tracking the goals and desires of the characters and their narrative outcomes.
However, to date, there has been limited work on computational models for this
problem. We introduce a new dataset, DesireDB, which includes gold-standard
labels for identifying statements of desire, textual evidence for desire
fulfillment, and annotations for whether the stated desire is fulfilled given
the evidence in the narrative context. We report experiments on tracking desire
fulfillment using different methods, and show that LSTM Skip-Thought model
achieves F-measure of 0.7 on our corpus.
