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.

利用大型语言模型 (LLM) 的最新进展，我们提出了首个事件因果识别方法，从 GPT 中设计特定提示以提取事件因果关系。根据 GLUCOSE 数据集中的人工注释事件因果关系，我们的技术表现与监督模型相当，同时易于推广到不同类型和长度的故事。提取的因果关系在故事质量评估上带来了 5.7% 的提升和 8.7% 的故事视频文本对齐改进，我们的发现表明事件因果在计算机故事理解中有巨大的潜力。

计算机故事理解的关键：事件因果关系

Event Causality Is Key to Computational Story Understanding

Transformers have been showing near-human performance on a variety of tasks,
but they are not without their limitations. We discuss the issue of conflating
results of transformers that are instructed to do multiple tasks
simultaneously. In particular, we focus on the domain of commonsense reasoning
within story prose, which we call contextual commonsense inference (CCI). We
look at the GLUCOSE (Mostafazadeh et al. 2020) dataset and task for predicting
implicit commonsense inferences between story sentences. Since the GLUCOSE task
simultaneously generates sentences and predicts the CCI relation, there is a
conflation in the results. Is the model really measuring CCI or is its ability
to generate grammatical text carrying the results? In this paper, we introduce
the task contextual commonsense inference in sentence selection ($\rm{C {\small
IS}}^2$), a simplified task that avoids conflation by eliminating language
generation altogether. Our findings emphasize the necessity of future work to
disentangle language generation from the desired NLP tasks at hand.

通过 CIS2 任务避免语言生成误导，强调解开自然语言处理中的任务与语言生成之间相互混淆的必要性，并探讨了 Transformer 模型在通识推理任务中的表现限制。