  In order to tell stories in different voices for different audiences,
interactive story systems require: (1) a semantic representation of story
structure, and (2) the ability to automatically generate story and dialogue
from this semantic representation using some form of Natural Language
Generation (NLG). However, there has been limited research on methods for
linking story structures to narrative descriptions of scenes and story events.
In this paper we present an automatic method for converting from Scheherazade's
story intention graph, a semantic representation, to the input required by the
Personage NLG engine. Using 36 Aesop Fables distributed in DramaBank, a
collection of story encodings, we train translation rules on one story and then
test these rules by generating text for the remaining 35. The results are
measured in terms of the string similarity metrics Levenshtein Distance and
BLEU score. The results show that we can generate the 35 stories with correct
content: the test set stories on average are close to the output of the
Scheherazade realizer, which was customized to this semantic representation. We
provide some examples of story variations generated by personage. In future
work, we will experiment with measuring the quality of the same stories
generated in different voices, and with techniques for making storytelling
interactive.
