M2D: Monolog to Dialog Generation for Conversational Story Telling
This work addresses the need for more engaging conversational storytelling in social and entertainment applications, though it appears incremental as it builds on prior work in dialog generation and personality modeling.
The paper tackled the problem of converting monologic story representations into dialogic storytelling with customizable storyteller personalities, and found that dialogic versions were more engaging and that personality-based linguistic variations could be recognized in extended dialogs.
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.