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.

本文探讨以人物为驱动的故事延续，引入角色之间的第一人称和第二人称叙述以及对话，需要模型选择与角色个性和关系一致并推动故事发展的语言。我们假设在训练人物对话和关系信息的多任务模型的基础上，改进了基于Transformer的故事延续。为此，我们拓展了Critical Role Dungeons and Dragons数据集，并通过自动提取每对交互角色之间的关系和他们的个性。一系列实验为我们的假设提供了证据，表明我们使用人物关系的多任务模型相对于强基线能够提高故事延续的准确性。

基于角色关系建模的多用户对话故事叙述