  The success of deep learning models on multi-hop fact verification has
prompted researchers to understand the behavior behind their veracity. One
possible way is erasure search: obtaining the rationale by entirely removing a
subset of input without compromising the veracity prediction. Although
extensively explored, existing approaches fall within the scope of the
single-granular (tokens or sentences) explanation, which inevitably leads to
explanation redundancy and inconsistency. To address such issues, this paper
explores the viability of multi-granular rationale extraction with consistency
and faithfulness for explainable multi-hop fact verification. In particular,
given a pretrained veracity prediction model, both the token-level explainer
and sentence-level explainer are trained simultaneously to obtain
multi-granular rationales via differentiable masking. Meanwhile, three
diagnostic properties (fidelity, consistency, salience) are introduced and
applied to the training process, to ensure that the extracted rationales
satisfy faithfulness and consistency. Experimental results on three multi-hop
fact verification datasets show that the proposed approach outperforms some
state-of-the-art baselines.
