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        <title>Branching Narratives: Character Decision Points Detection</title>
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            <namePart type="given">Chris</namePart>
            <namePart type="family">Madge</namePart>
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            <namePart type="given">Jon</namePart>
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            <namePart type="given">Karen</namePart>
            <namePart type="family">Fort</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Branching Narratives: Character Decision Points Detection
%A Tikhonov, Alexey
%Y Madge, Chris
%Y Chamberlain, Jon
%Y Fort, Karen
%Y Kruschwitz, Udo
%Y Lukin, Stephanie
%S Proceedings of the 10th Workshop on Games and Natural Language Processing @ LREC-COLING 2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F tikhonov-2024-branching
%X 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.
%U https://aclanthology.org/2024.games-1.8
%P 70-75

Markdown (Informal)

[Branching Narratives: Character Decision Points Detection](https://aclanthology.org/2024.games-1.8) (Tikhonov, games-WS 2024)

• Branching Narratives: Character Decision Points Detection (Tikhonov, games-WS 2024)

ACL

• Alexey Tikhonov. 2024. Branching Narratives: Character Decision Points Detection. In Proceedings of the 10th Workshop on Games and Natural Language Processing @ LREC-COLING 2024, pages 70–75, Torino, Italia. ELRA and ICCL.