@inproceedings{si-etal-2021-telling,
    title = "Telling Stories through Multi-User Dialogue by Modeling Character Relations",
    author = "Si, Wai Man  and
      Ammanabrolu, Prithviraj  and
      Riedl, Mark",
    editor = "Li, Haizhou  and
      Levow, Gina-Anne  and
      Yu, Zhou  and
      Gupta, Chitralekha  and
      Sisman, Berrak  and
      Cai, Siqi  and
      Vandyke, David  and
      Dethlefs, Nina  and
      Wu, Yan  and
      Li, Junyi Jessy",
    booktitle = "Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = jul,
    year = "2021",
    address = "Singapore and Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.sigdial-1.30/",
    doi = "10.18653/v1/2021.sigdial-1.30",
    pages = "269--275",
    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."
}