  The opaqueness of the multi-hop fact verification model imposes imperative
requirements for explainability. One feasible way is to extract rationales, a
subset of inputs, where the performance of prediction drops dramatically when
being removed. Though being explainable, most rationale extraction methods for
multi-hop fact verification explore the semantic information within each piece
of evidence individually, while ignoring the topological information
interaction among different pieces of evidence. Intuitively, a faithful
rationale bears complementary information being able to extract other
rationales through the multi-hop reasoning process. To tackle such
disadvantages, we cast explainable multi-hop fact verification as subgraph
extraction, which can be solved based on graph convolutional network (GCN) with
salience-aware graph learning. In specific, GCN is utilized to incorporate the
topological interaction information among multiple pieces of evidence for
learning evidence representation. Meanwhile, to alleviate the influence of
noisy evidence, the salience-aware graph perturbation is induced into the
message passing of GCN. Moreover, the multi-task model with three diagnostic
properties of rationale is elaborately designed to improve the quality of an
explanation without any explicit annotations. Experimental results on the
FEVEROUS benchmark show significant gains over previous state-of-the-art
methods for both rationale extraction and fact verification.
