Double Graph Attention Network Reasoning Method Based on Filtering and Program-Like Evidence for Table-Based Fact Verification
Hongfang Gong, Can Wang, Xiaofei Huang · IEEE Access · 2023
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.