  Storytelling serves many different social functions, e.g. stories are used to
persuade, share troubles, establish shared values, learn social behaviors, and
entertain. Moreover, stories are often told conversationally through dialog,
and previous work suggests that information provided dialogically is more
engaging than when provided in monolog. In this paper, we present algorithms
for converting a deep representation of a story into a dialogic storytelling,
that can vary aspects of the telling, including the personality of the
storytellers. We conduct several experiments to test whether dialogic
storytellings are more engaging, and whether automatically generated variants
in linguistic form that correspond to personality differences can be recognized
in an extended storytelling dialog.
