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

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 me...

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Bibliographic Details
Published in:IEEE access 2023-01, Vol.11, p.1-1
Main Authors: Gong, Hongfang, Wang, Can, Huang, Xiaofei
Format: Article
Language:English
Subjects:
Ablation
Bit error rate
Cognition
Cognition & reasoning
filtering evidence
Filtration
GAT
Graph neural networks
Inference algorithms
Kernel
Natural language inference
Natural language processing
Nodes
Predictive models
program-like evidence
Reasoning
Semantics
table-based fact verification
Task analysis
Verification
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Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3304915