Cross-Document Fact Verification Based on Fine-Grained Graph Neural Network

Cross-Document Fact Verification CDFV aims to retrieve evidence from multiple documents to verify the factuality of a given claim. However, existing CDFV approaches fail to capture complex semantic relationships and fine-grained information in the evidence. To address these issues, we propose a Fine-Grained Graph Neural Network FGGNN for CDFV. FGGNN constructs a sentence-level graph during the evidence selection stage and efficiently propagates information within the graph using Graph Attention Networks GAT , accurately capturing the complex relationships between sentences. This enables FGGNN to select trustworthy and relevant evidence. In the claim verification stage, FGGNN constructs a word-level evidence graph to capture fine-grained relationships at the word level. It then uses a Relational Graph Convolutional Network RGCN to propagate and update information within the graph, fully uncovering the potential logic in the evidence. Additionally, an attention mechanism is introduced to weight the evidence based on its relevance to the claim, emphasizing the importance of key evidence. Finally, FGGNN considers all the evidence and claim information to accurately predict the label of the claim. Experimental results on the CHEF dataset demonstrate the effectiveness of FGGNN in achieving accurate fact verification.