Abstract
In fact-checking applications, a common reason to reject a claim is to detect the presence of erroneous cause-effect relationships between the events at play. However, current automated fact-checking methods lack dedicated causal-based reasoning, potentially missing a valuable opportunity for semantically rich explainability. To address this gap, we propose a methodology that combines event relation extraction, semantic similarity computation, and rule-based reasoning to detect logical inconsistencies between chains of events mentioned in a claim and in an evidence. Evaluated on two fact-checking datasets, this method establishes the first baseline for integrating fine-grained causal event relationships into fact-checking and enhance explainability of verdict prediction.
Abstract (translated)
在事实核查应用中，拒绝一个声明的常见原因之一是检测事件之间是否存在错误的因果关系。然而，当前的自动化事实核查方法缺乏专门基于因果关系的推理能力，这可能错失了提高语义丰富解释性的宝贵机会。为了填补这一空白，我们提出了一种结合事件关系提取、语义相似性计算和规则基础推理的方法，以检测声明中提及的一系列事件与证据中提到的事件之间的逻辑不一致。在两个事实核查数据集上进行评估后，这种方法建立了将细粒度因果事件关系集成到事实核查中的第一个基准，并提高了判决预测的解释性。
URL
https://arxiv.org/abs/2512.13286
