Telling Stories through Multi-User Dialogue by Modeling Character Relations (2021)

Abstract: This paper explores character-driven story continuation, in which the story emerges through characters’ first- and second-person narration as well as dialogue—requiring models to select language that is consistent with a character’s persona and their relationships with other characters while following and advancing the story. We hypothesize that a multi-task model that trains on character dialogue plus character relationship information improves transformer-based story continuation. To this end, we extend the Critical Role Dungeons and Dragons Dataset (Rameshkumar and Bailey, 2020)—consisting of dialogue transcripts of people collaboratively telling a story while playing the role-playing game Dungeons and Dragons—with automatically extracted relationships between each pair of interacting characters as well as their personas. A series of ablations lend evidence to our hypothesis, showing that our multi-task model using character relationships improves story continuation accuracy over strong baselines.

## introduction automated storytelling can be thought of as creative, long-from text generation and understanding-requiring explicit long-term memory, consistency, and creativity among other pre-requisites. most modern (neural) automated storytellers are plot-driven and frame the task in terms of sequentially generating plot points that narrate the story in third-person @xcite @xcite . this approach does not generally place much weight on individual characters or their interactions-information known to be critical for creating stories @xcite . we are inspired by the idea of characterdriven and emergent storytelling wherein narrative emerges through characters' interactions as seen scanlan: okay. how will we know when it's time for me to release? we have to wait for tooma to go report. ## character-driven storytelling this section first describes the automated extensions to the crd3 dataset, specifically information on character relationships, followed by the multitask learning setup and transformer architecture that leverage the new data for story continuation. ## evaluation we conduct two ablations studies that analyze: our base transformer model that we build off of in each of these is the bi-encoder ranker described in section 3.2. the transformer encoder is a similar architecture as bert @xcite , with ## conclusions we hypothesized that injecting models with information on relationships between characters would improve their ability to complete character-driven stories. a series of ablation studies support this, with a key insight being that a particularly efficient way of giving story continuation models this information is by multi-task training them on both character dialogue and relationship information automatically extracted from online sources.