Abstract
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.
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Notes
- 1.
Available from nlds.soe.ucsc.edu/personabank.
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Acknowledgments
We would like to thank Chung-Ning Chang and Diego Pedro for their roles as collaborators in the early inception of our system. This research was supported by NSF IIS CHS #1115742 and award #SC-14-74 from the Nuance Foundation.
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Bowden, K.K., Lin, G.I., Reed, L.I., Fox Tree, J.E., Walker, M.A. (2016). M2D: Monolog to Dialog Generation for Conversational Story Telling. In: Nack, F., Gordon, A. (eds) Interactive Storytelling. ICIDS 2016. Lecture Notes in Computer Science(), vol 10045. Springer, Cham. https://doi.org/10.1007/978-3-319-48279-8_2
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