Fact verification aims to retrieve related evidence from raw text to verify the correctness of a given claim. Existing works mainly leverage the single-granularity features for the representation learning of evidences, i.e., sentence features, ignoring other features like entity-level and context-level features. In addition, they usually focus on improving the prediction accuracy while lacking the interpretability of the inference process, which leads to unreliable results. Thus, in this paper, to investigate how to utilize multi-granularity semantic units for evidence representation as well as to improve the explainability of evidence reasoning, we propose a Hierarchical Reasoning-based Heterogeneous Graph Neural Network for fact verification (HHGN). HHGN combines multiple features of entity, sentence as well as context for evidence representation, and employs a heterogeneous graph to capture their semantic relations. Inspired by the human inference process, we design a hierarchical reasoning-based node updating strategy to propagate the evidence features. Then, we extract the potential reasoning paths from the graph to predict the label, which aggregates the results of different paths weighted by their relevance to the claim. We evaluate our proposal on FEVER, a large-scale benchmark dataset for fact verification. Our experimental results demonstrate the superiority of HHGN over the competitive baselines in both single evidence and multiple evidences settings. In addition, HHGN presents reasonable interpretability in the form of aggregating the features of relevant entity units and selecting the evidence sentences with high confidence.

事实验证旨在从原始文本中检索相关证据，以验证给定声明的正确性。现有工作主要利用单粒度特征进行证据的表征学习，即句子特征，而忽略了其他特征，如实体级和上下文级特征。此外，他们通常专注于提高预测精度，而缺乏推理过程的可解释性，导致结果不可靠。因此，在本文中，为了研究如何利用多粒度语义单元证据表示以及改善的证据推理的explainability，我们提出了一个ħ基于推理ierarchical ħ eterogeneous ģ拍摄和神经Ñ事实验证网络（HHGN）。HHGN 结合实体、句子和上下文的多个特征进行证据表示，并采用异构图来捕获它们的语义关系。受人类推理过程的启发，我们设计了一种基于层次推理的节点更新策略来传播证据特征。然后，我们从图中提取潜在的推理路径来预测标签，该标签根据与索赔的相关性加权不同路径的结果。我们在 FEVER 上评估我们的提议，这是一个用于事实验证的大规模基准数据集。我们的实验结果证明了 HHGN 在单证据和多证据设置中优于竞争基线。此外，