  Commonsense fact verification, as a challenging branch of commonsense
question-answering (QA), aims to verify through facts whether a given
commonsense claim is correct or not. Answering commonsense questions
necessitates a combination of knowledge from various levels. However, existing
studies primarily rest on grasping either unstructured evidence or potential
reasoning paths from structured knowledge bases, yet failing to exploit the
benefits of heterogeneous knowledge simultaneously. In light of this, we
propose Decker, a commonsense fact verification model that is capable of
bridging heterogeneous knowledge by uncovering latent relationships between
structured and unstructured knowledge. Experimental results on two commonsense
fact verification benchmark datasets, CSQA2.0 and CREAK demonstrate the
effectiveness of our Decker and further analysis verifies its capability to
seize more precious information through reasoning.
