EXPLAIN, EDIT, GENERATE: Rationale-Sensitive Counterfactual Data
  Augmentation for Multi-hop Fact Verification
Yingjie Zhu Jiasheng Si Yibo Zhao Haiyang Zhu Deyu Zhou Yulan He
Automatic multi-hop fact verification task has gained significant attention
in recent years. Despite impressive results, these well-designed models perform
poorly on out-of-domain data. One possible solution is to augment the training
data with counterfactuals, which are generated by minimally altering the causal
features of the original data. However, current counterfactual data
augmentation techniques fail to handle multi-hop fact verification due to their
incapability to preserve the complex logical relationships within multiple
correlated texts. In this paper, we overcome this limitation by developing a
rationale-sensitive method to generate linguistically diverse and
label-flipping counterfactuals while preserving logical relationships. In
specific, the diverse and fluent counterfactuals are generated via an
Explain-Edit-Generate architecture. Moreover, the checking and filtering
modules are proposed to regularize the counterfactual data with logical
relations and flipped labels. Experimental results show that the proposed
approach outperforms the SOTA baselines and can generate linguistically diverse
counterfactual data without disrupting their logical relationships.