Fact verification systems typically rely on neu-
ral network classifiers for veracity prediction,
which lack explainability. This paper proposes
ProoFVer, which uses a seq2seq model to gen-
erate natural logic-based inferences as proofs.
These proofs consist of lexical mutations be-
tween spans in the claim and the evidence
retrieved, each marked with a natural logic
operator. Claim veracity is determined solely
based on the sequence of these operators.
Hence, these proofs are faithful explanations,
and this makes ProoFVer faithful by construc-
tion. Currently, ProoFVer has the highest label
accuracy and the second best score in the
FEVER leaderboard. Furthermore, it improves
by 13.21% points over the next best model
on a dataset with counterfactual
instances,
demonstrating its robustness. As explanations,
the proofs show better overlap with human
rationales than attention-based highlights and
the proofs help humans predict model de-
cisions correctly more often than using the
ProoFVer- Natural Logic Theorem Proving for Fact Verification