脚本知识的因果推理（CS CL）
- 2020 年 4 月 6 日
- 筆記
什么时候一系列事件能定义一个日常场景，以及如何从文本中引出这些知识？ 在先前的工作中归纳这样的脚本时依赖一个或另一种形式，以及一个语料库事件的实例之间的相关性措施。我们从概念和实际意义上都认为，纯粹基于相关性的方法是不够的，相反，通过干预正式定义，我们提出了一种基于事件之间的因果效应的脚本归纳方法。通过人工评估和自动评估，我们发现我们基于因果效应的方法的输出更符合脚本所代表的直觉。
原文题目：Causal Inference of Script Knowledge
原文：When does a sequence of events define an everyday scenario and how can this knowledge be induced from text? Prior works in inducing such scripts have relied on, in one form or another, measures of correlation between instances of events in a corpus. We argue from both a conceptual and practical sense that a purely correlation-based approach is insufficient, and instead propose an approach to script induction based on the causal effect between events, formally defined via interventions. Through both human and automatic evaluations, we show that the output of our method based on causal effects better matches the intuition of what a script represents
原文作者：Noah Weber
原文地址：https://arxiv.org/abs/2004.01174
