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arXiv · 2105.15054

Telling Stories through Multi-User Dialogue by Modeling Character Relations

Wai Man Si·Prithviraj Ammanabrolu·Mark O. Riedl
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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.

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Wai Man Si, Prithviraj Ammanabrolu, Mark O. Riedl. 2021-05-31. Telling Stories through Multi-User Dialogue by Modeling Character Relations. https://arxiv.org/abs/2105.15054

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