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.

翻译：深度学习模型在多跳事实验证上的成功促使研究者探索其判决行为背后的机制。一种可行的方法是擦除搜索：通过完全移除输入中的子集而不影响真实性预测来获取理由。尽管已有广泛研究，现有方法均局限于单粒度（词元或句子）解释，这不可避免地导致解释冗余与不一致。为解决这些问题，本文探索了在可解释多跳事实验证中实现具有一致性与忠实性的多粒度理由提取的可行性。具体而言，给定一个预训练的真实性预测模型，通过可微分掩码同时训练词元级解释器与句子级解释器以获取多粒度理由。与此同时，引入三种诊断属性（忠实性、一致性、显著性）并应用于训练过程，确保提取的理由满足忠实性与一致性要求。在三个多跳事实验证数据集上的实验结果表明，所提方法优于若干最先进的基线模型。