从博客和电影中推断细粒度事件因果关系
Inference of Fine-Grained Event Causality from Blogs and Films
摘要：
人类对叙事的理解主要是由对事件之间因果关系的推理驱动的，因此识别它们是语言理解的计算模型的关键能力。这一领域的计算工作通过两种不同的途径来实现这一点：专注于获取事件之间常见因果关系的知识库，或者试图理解特定的故事或宏观事件及其故事情节。在这份立场文件中，我们专注于知识获取的方法，并声称，新闻通讯是一个相对较差的来源，学习日常事件之间的细粒度的因果关系。我们描述的实验使用无监督的方法来学习事件之间的因果关系的第一人称叙事和电影场景描述的叙事类型。我们表明，我们的方法学习细粒度的因果关系，人类判断可能是因果关系超过80%的时间。我们还证明了学习的事件对不存在于公开的事件对数据集提取新闻专线。
英文摘要：
Human understanding of narrative is mainly driven by reasoning about causal relations between events and thus recognizing them is a key capability for computational models of language understanding. Computational work in this area has approached this via two different routes: by focusing on acquiring a knowledge base of common causal relations between events, or by attempting to understand a particular story or macro-event, along with its storyline. In this position paper, we focus on knowledge acquisition approach and claim that newswire is a relatively poor source for learning fine-grained causal relations between everyday events. We describe experiments using an unsupervised method to learn causal relations between events in the narrative genres of first-person narratives and film scene descriptions. We show that our method learns fine-grained causal relations, judged by humans as likely to be causal over 80% of the time. We also demonstrate that the learned event pairs do not exist in publicly available event-pair datasets extracted from newswire.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
