Double Graph Attention Network Reasoning Method Based on Filtering and Program-Like Evidence for Table-Based Fact Verification

Abstract

Table-based fact verification requests parsing table and statement structure and performing numerical and logical reasoning. Previous methods may select erroneous programs and ignore the interpretability of table-based fact verification. Thus, we propose a double graph attention network reasoning method based on filtering and program-like evidence (DGMFP). In detail, we initially obtain the filtering evidence based on tables and the program-like evidence based on logical forms to incorporate the semantic and symbolic information of evidence. Then, we construct an evidence graph with statement-evidence pairs as nodes and use the kernel in graph neural network to conduct more fine-grained joint reasoning and improve the interpretability of table-based fact verification. We also construct a connected graph with all entities and functions in the program-like evidence as nodes and use the graph attention network (GAT) to capture more fine-grained relationships within the program-like evidence. Finally, we connect the outputs of two GAT models and BERT model to predict labels. Experimental results on TABFACT show that DGMFP outperforms all baselines with 76.1% accuracy. Ablation studies further indicate that constructed two graphs, filtering evidence, and program-like evidence play an important role in better understanding the semi-structured table.

Keywords

• Natural language inference
• table-based fact verification
• GAT
• filtering evidence
• program-like evidence