  Neural models for automated fact verification have achieved promising results
thanks to the availability of large, human-annotated datasets. However, for
each new domain that requires fact verification, creating a dataset by manually
writing claims and linking them to their supporting evidence is expensive. We
develop QACG, a framework for training a robust fact verification model by
using automatically generated claims that can be supported, refuted, or
unverifiable from evidence from Wikipedia. QACG generates question-answer pairs
from the evidence and then converts them into different types of claims.
Experiments on the FEVER dataset show that our QACG framework significantly
reduces the demand for human-annotated training data. In a zero-shot scenario,
QACG improves a RoBERTa model's F1 from 50% to 77%, equivalent in performance
to 2K+ manually-curated examples. Our QACG code is publicly available.
