Generating Variations in a Virtual Storyteller
This dissertation introduces the Expressive-Story Translator (EST) content planner and Fabula Tales sentence planner in a storytelling natural language generation framework. Both planners operate in a domain independent manner, abstractly modeling a variety of stories regardless of story vocabulary. The EST captures story semantics from a narrative representation and constructs text plans to maintain semantic content through rhetorical relations. Content planning is performed using these relations to enhance narrative effects, such as modeling emotions and temporal reordering. The EST transforms the story into semantic-syntactic structures interpreted by the parameterizable sentence planner, Fabula Tales. The semantic-syntactic integration allows Fabula Tales to employ narrative sentence planning devices to change narrator point of view, insert direct speech acts, and supplement character voice using operations for lexical selection, aggregation, and pragmatic marker insertion. The frameworks are evaluated using traditional machine translation metrics, narrative metrics, and overgenerate and rank to holistically test the effectiveness of each generated retelling. This work shows how different framings affect reader perception of stories and its characters, and uses statistical analysis of reader feedback to build story models tailored for specific narration preferences.