@inproceedings{tikhonov-2024-branching,
    title = "Branching Narratives: Character Decision Points Detection",
    author = "Tikhonov, Alexey",
    editor = "Madge, Chris  and
      Chamberlain, Jon  and
      Fort, Karen  and
      Kruschwitz, Udo  and
      Lukin, Stephanie",
    booktitle = "Proceedings of the 10th Workshop on Games and Natural Language Processing @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.games-1.8",
    pages = "70--75",
    abstract = "This paper presents the Character Decision Points Detection (CHADPOD) task, a task of identification of points within narratives where characters make decisions that may significantly influence the story{'}s direction. We propose a novel dataset based on Choose Your Own Adventure (a registered trademark of Chooseco LLC) games graphs to be used as a benchmark for such a task. We provide a comparative analysis of different models{'} performance on this task, including a couple of LLMs and several MLMs as baselines, achieving up to 89{\%} accuracy. This underscores the complexity of narrative analysis, showing the challenges associated with understanding character-driven story dynamics. Additionally, we show how such a model can be applied to the existing text to produce linear segments divided by potential branching points, demonstrating the practical application of our findings in narrative analysis.",
}

This paper presents the Character Decision Points Detection (CHADPOD) task, a task of identification of points within narratives where characters make decisions that may significantly influence the story{'}s direction. We propose a novel dataset based on Choose Your Own Adventure (a registered trademark of Chooseco LLC) games graphs to be used as a benchmark for such a task. We provide a comparative analysis of different models{'} performance on this task, including a couple of LLMs and several MLMs as baselines, achieving up to 89{\%} accuracy. This underscores the complexity of narrative analysis, showing the challenges associated with understanding character-driven story dynamics. Additionally, we show how such a model can be applied to the existing text to produce linear segments divided by potential branching points, demonstrating the practical application of our findings in narrative analysis.
Tikhonov, Alexey
Branching Narratives: Character Decision Points Detection