Leveraging contextual knowledge has become standard practice in automated claim verification, yet the impact of temporal reasoning has been largely overlooked. Our study demonstrates that time positively influences the claim verification process of evidence-based fact-checking. The temporal aspects and relations between claims and evidence are first established through grounding on shared timelines, which are constructed using publication dates and time expressions extracted from their text. Temporal information is then provided to RNN-based and Transformer-based classifiers before or after claim and evidence encoding. Our time-aware fact-checking models surpass base models by up to 9% Micro F1 (64.17%) and 15% Macro F1 (47.43%) on the MultiFC dataset. They also outperform prior methods that explicitly model temporal relations between evidence. Our findings show that the presence of temporal information and the manner in which timelines are constructed greatly influence how fact-checking models determine the relevance and supporting or refuting character of evidence documents.

翻译：利用上下文知识已成为自动化声明验证的标准做法，但时序推理的影响在很大程度上被忽视了。我们的研究表明，时间因素对基于证据的事实核查中的声明验证过程具有积极影响。首先，通过基于共享时间线（利用发表日期及从文本中提取的时间表达式构建）的锚定，确定了声明与证据之间的时序方面及其关系。随后，在声明和证据编码之前或之后，将时序信息提供给基于RNN和Transformer的分类器。在MultiFC数据集上，我们提出的时间感知事实核查模型相较于基础模型在Micro F1（64.17%）上提升了9%，在Macro F1（47.43%）上提升了15%。这些模型也优于先前显式建模证据间时序关系的方法。我们的研究结果表明，时序信息的存在以及时间线的构建方式，会显著影响事实核查模型如何判定证据文档的相关性及其支持或反驳特性。