Telling Stories through Multi-User Dialogue by Modeling Character Relations

• URL: http://arxiv.org/abs/2105.15054v1
• Date: Mon, 31 May 2021 15:39:41 GMT
• Title: Telling Stories through Multi-User Dialogue by Modeling Character Relations
• Authors: Wai Man Si, Prithviraj Ammanabrolu, Mark O. Riedl
• Abstract summary: This paper explores character-driven story continuation, in which the story emerges through characters' first- and second-person narration as well as dialogue. We hypothesize that a multi-task model that trains on character dialogue plus character relationship information improves transformer-based story continuation. 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.
• Score: 14.117921448623342
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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