用因果推理提升事实核查的可解释性，识别事件链中的逻辑矛盾。
Integrating Causal Reasoning into Automated Fact-Checking
- 结合事件关系抽取与规则推理，检测声明与证据间因果链条不一致
- 在两个数据集上建立首个细粒度因果关系核查基线
- 适合关注可解释性与逻辑严谨性的事实核查研究者
在事实核查应用中，判断一个声明为假的常见原因是发现其中事件间的因果关系错误。然而，当前自动化事实核查方法缺乏专门的因果推理能力，可能错失语义丰富的可解释性机会。为此，我们提出一种结合事件关系抽取、语义相似性计算与基于规则推理的方法，用于检测声明中事件链与证据中事件链之间的逻辑不一致。该方法在两个事实核查数据集上进行了评估，首次建立了将细粒度因果事件关系融入事实核查的基准，显著提升了判定结果的可解释性。
原文摘要 · Abstract (English)
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.
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