Psychological research suggests the central role of event causality in human story understanding. Further, event causality has been heavily utilized in symbolic story generation. However, few machine learning systems for story understanding employ event causality, partially due to the lack of reliable methods for identifying open-world causal event relations. Leveraging recent progress in large language models (LLMs), we present the first method for event causality identification that leads to material improvements in computational story understanding. We design specific prompts for extracting event causal relations from GPT. Against human-annotated event causal relations in the GLUCOSE dataset, our technique performs on par with supervised models, while being easily generalizable to stories of different types and lengths. The extracted causal relations lead to 5.7\% improvements on story quality evaluation and 8.7\% on story video-text alignment. Our findings indicate enormous untapped potential for event causality in computational story understanding.

翻译：心理学研究表明，事件因果关系在人类故事理解中起核心作用。此外，事件因果关系已在符号故事生成中被广泛运用。然而，大多数面向故事理解的机器学习系统并未采用事件因果关系，部分原因在于缺乏可靠的开放世界因果事件关系识别方法。借助大语言模型的最新进展，我们首次提出一种能显著提升计算故事理解性能的事件因果关系识别方法。我们设计了从GPT中提取事件因果关系的特定提示词。在GLUCOSE数据集的人工标注事件因果关系测试中，我们的技术性能与监督模型相当，且易于泛化至不同长度与类型的故事。提取的因果关系使故事质量评估指标提升5.7%，故事视频-文本对齐指标提升8.7%。研究结果表明事件因果关系在计算故事理解中具有巨大的未开发潜力。