2024/05/12 by А. И. Тихонов, Tikhonov, Alexey
Computer Science · #68T50 #Advanced Text Analysis Techniques #Artificial Intelligence in Games #Computation and Language (cs.CL) #FOS: Computer and information sciences #I.2.7 #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2405.07282
openalex publication_date 2024/05/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.