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.

使用 Wikipedia 自动产生问题 - 答案对，生成不同类型的声明的 QACG 框架可以训练一个强健的事实验证模型，实验结果表明，在零样本情况下，QACG 能够将 RoBERTa 模型的 F1 从 50％提高到 77％，并且等效于 2000 多个手动筛选的例子。