M2D: Monolog to Dialog Generation for Conversational Story Telling

  • Kevin K. Bowden
  • Grace I. Lin
  • Lena I. Reed
  • Jean E. Fox Tree
  • Marilyn A. Walker
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10045)

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.

Keywords

Analysis and evaluation of systems ICIDS Dialog Natural language generation Personality Conversational storytelling 

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Kevin K. Bowden
    • 1
  • Grace I. Lin
    • 1
  • Lena I. Reed
    • 1
  • Jean E. Fox Tree
    • 1
  • Marilyn A. Walker
    • 1
  1. 1.Natural Language and Dialog Systems LabUniversity of CaliforniaSanta CruzUSA

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