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Erschienen in:
IVRUX: A Tool for Analyzing Immersive Narratives in Virtual Reality Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

M2D: Monolog to Dialog Generation for Conversational Story Telling

verfasst von : Kevin K. Bowden, Grace I. Lin, Lena I. Reed, Jean E. Fox Tree, Marilyn A. Walker

Erschienen in: Interactive Storytelling

Verlag: Springer International Publishing

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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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Vorheriges Kapitel IVRUX: A Tool for Analyzing Immersive Narratives in Virtual Reality
Nächstes Kapitel Exit 53: Physiological Data for Improving Non-player Character Interaction
Fußnoten
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Metadaten
Titel
M2D: Monolog to Dialog Generation for Conversational Story Telling
verfasst von
Kevin K. Bowden
Grace I. Lin
Lena I. Reed
Jean E. Fox Tree
Marilyn A. Walker
Copyright-Jahr
2016
Verlag
Springer International Publishing
Buch
Interactive Storytelling

Print ISBN: 978-3-319-48278-1

Electronic ISBN: 978-3-319-48279-8

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-48279-8_2