Performing fact verification based on structured data is important for many
real-life applications and is a challenging research problem, particularly when
it involves both symbolic operations and informal inference based on language
understanding. In this paper, we present a Program-enhanced Verbalization and
Graph Attention Network (ProgVGAT) to integrate programs and execution into
textual inference models. Specifically, a verbalization with program execution
model is proposed to accumulate evidences that are embedded in operations over
the tables. Built on that, we construct the graph attention verification
networks, which are designed to fuse different sources of evidences from
verbalized program execution, program structures, and the original statements
and tables, to make the final verification decision. To support the above
framework, we propose a program selection module optimized with a new training
strategy based on margin loss, to produce more accurate programs, which is
shown to be effective in enhancing the final verification results. Experimental
results show that the proposed framework achieves the new state-of-the-art
performance, a 74.4% accuracy, on the benchmark dataset TABFACT.

本研究提出了 ProgVGAT 框架，结合程序和执行到文本推理模型，通过程序执行的表现和结构、原文陈述和表格等不同证据，来实现基于结构化数据的事实验证，并通过使用基于距离的新训练策略来优化 ProgVGAT 中的程序选择模块，从而提供更精确的程序，检验结果最高达 74.4％的准确率。