  Automated fact verification plays an essential role in fostering trust in the
digital space. Despite the growing interest, the verification of temporal facts
has not received much attention in the community. Temporal fact verification
brings new challenges where cues of the temporal information need to be
extracted and temporal reasoning involving various temporal aspects of the text
must be applied. In this work, we propose an end-to-end solution for temporal
fact verification that considers the temporal information in claims to obtain
relevant evidence sentences and harness the power of large language model for
temporal reasoning. Recognizing that temporal facts often involve events, we
model these events in the claim and evidence sentences. We curate two temporal
fact datasets to learn time-sensitive representations that encapsulate not only
the semantic relationships among the events, but also their chronological
proximity. This allows us to retrieve the top-k relevant evidence sentences and
provide the context for a large language model to perform temporal reasoning
and outputs whether a claim is supported or refuted by the retrieved evidence
sentences. Experiment results demonstrate that the proposed approach
significantly enhances the accuracy of temporal claim verification, thereby
advancing current state-of-the-art in automated fact verification.
