Abstract
Research on causality extraction from text has so far almost entirely neglected counterclaims. Existing causality extraction datasets focus solely on "procausal" claims, i.e., statements that support a relationship. "Concausal" claims, i.e., statements that refute a relationship, are entirely ignored or even accidentally annotated as procausal. We address this shortcoming by developing a new dataset that integrates concausality. Based on an extensive literature review, we first show that concausality is an integral part of causal reasoning on incomplete knowledge. We operationalize this theory in the form of a rigorous guideline for annotation and then augment the Causal News Corpus with concausal statements, obtaining a substantial inter-annotator agreement of Cohen's $\kappa=0.74$. To demonstrate the importance of integrating concausal statements, we show that models trained without concausal relationships tend to misclassify these as procausal instead. Based on our new dataset, this mistake can be mitigated, enabling transformers to effectively distinguish pro- and concausality.
Abstract (translated)
文本中的因果关系提取研究迄今为止几乎完全忽略了反驳观点。现有的因果关系抽取数据集仅关注“正向因果”声明，即支持某种关系的陈述。“反向因果”声明，即否认某关系的陈述，则要么被忽略，甚至被误标为正向因果。我们通过开发一个包含反向因果性的新数据集来解决这一不足。基于广泛的文献回顾，首先展示了反向因果性是不完整知识中因果推理的重要组成部分。我们将此理论以严格的标注指南形式进行操作化，并将反向因果陈述加入到Causal News语料库中，从而实现了高达Cohen's $\kappa=0.74$的显著注释者间一致性。为了展示整合反向因果声明的重要性，我们展示了在没有反向因果关系的数据上训练的模型往往会错误地将其分类为正向因果。基于我们的新数据集，这种误判可以被缓解，使转换器能够有效地区分正向和反向因果性。
URL
https://arxiv.org/abs/2510.08224
