# Branching Narratives: Character Decision Points Detection

arXiv: https://arxiv.org/abs/2405.07282 · HTML: https://mlsanity.com/paper/2405.07282/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 26 references that are in the map.
- First posted on arXiv 12 May 2024.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the MACHIAVELLI Benchmark](https://mlsanity.com/paper/2304.03279/) (2023)
- [Strategies for Structuring Story Generation](https://mlsanity.com/paper/1902.01109/) (2019)
- [Text Segmentation as a Supervised Learning Task](https://mlsanity.com/paper/1803.09337/) (2018)
- [Movie Plot Analysis via Turning Point Identification](https://mlsanity.com/paper/1908.10328/) (2019)
- [Experimental Narratives: A Comparison of Human Crowdsourced Storytelling and AI Storytelling](https://mlsanity.com/paper/2310.12902/) (2023)
- [MPST: A Corpus of Movie Plot Synopses with Tags](https://mlsanity.com/paper/1802.07858/) (2018)
- [Dialogue Shaping: Empowering Agents through NPC Interaction](https://mlsanity.com/paper/2307.15833/) (2023)
- [Procedural Adherence and Interpretability Through Neuro-Symbolic Generative Agents](https://mlsanity.com/paper/2402.16905/) (2024)
- [DYPLODOC: Dynamic Plots for Document Classification](https://mlsanity.com/paper/2107.12226/) (2021)
- [What is Wrong with Language Models that Can Not Tell a Story?](https://mlsanity.com/paper/2211.05044/) (2022)
- [Automated Action Model Acquisition from Narrative Texts](https://mlsanity.com/paper/2307.10247/) (2023)
- [Actionable Entities Recognition Benchmark for Interactive Fiction](https://mlsanity.com/paper/2109.13855/) (2021)

## Built on by (most cited first, 1 of 1)

- [Hanging in the Balance: Pivotal Moments in Crisis Counseling Conversations](https://mlsanity.com/paper/2506.03941/) (2025)

## Abstract (arXiv)

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.