Branching Narratives: Character Decision Points Detection
This addresses narrative analysis for computational linguistics, but is incremental as it introduces a new benchmark task.
The paper tackles the problem of identifying character decision points in narratives that influence story direction, achieving up to 89% accuracy on a new dataset based on CYOA-like games.
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 CYOA-like 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.