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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.</abstract>
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%0 Conference Proceedings
%T Telling Stories through Multi-User Dialogue by Modeling Character Relations
%A Si, Wai Man
%A Ammanabrolu, Prithviraj
%A Riedl, Mark
%Y Li, Haizhou
%Y Levow, Gina-Anne
%Y Yu, Zhou
%Y Gupta, Chitralekha
%Y Sisman, Berrak
%Y Cai, Siqi
%Y Vandyke, David
%Y Dethlefs, Nina
%Y Wu, Yan
%Y Li, Junyi Jessy
%S Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2021
%8 July
%I Association for Computational Linguistics
%C Singapore and Online
%F si-etal-2021-telling
%X 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.
%R 10.18653/v1/2021.sigdial-1.30
%U https://aclanthology.org/2021.sigdial-1.30
%U https://doi.org/10.18653/v1/2021.sigdial-1.30
%P 269-275

Markdown (Informal)

[Telling Stories through Multi-User Dialogue by Modeling Character Relations](https://aclanthology.org/2021.sigdial-1.30) (Si et al., SIGDIAL 2021)

• Telling Stories through Multi-User Dialogue by Modeling Character Relations (Si et al., SIGDIAL 2021)

ACL

• Wai Man Si, Prithviraj Ammanabrolu, and Mark Riedl. 2021. Telling Stories through Multi-User Dialogue by Modeling Character Relations. In Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 269–275, Singapore and Online. Association for Computational Linguistics.