Verifying the correctness of a textual statement requires not only semantic
reasoning about the meaning of words, but also symbolic reasoning about logical
operations like count, superlative, aggregation, etc. In this work, we propose
LogicalFactChecker, a neural network approach capable of leveraging logical
operations for fact checking. It achieves the state-of-the-art performance on
TABFACT, a large-scale, benchmark dataset built for verifying a textual
statement with semi-structured tables. This is achieved by a graph module
network built upon the Transformer-based architecture. With a textual statement
and a table as the input, LogicalFactChecker automatically derives a program
(a.k.a. logical form) of the statement in a semantic parsing manner. A
heterogeneous graph is then constructed to capture not only the structures of
the table and the program, but also the connections between inputs with
different modalities. Such a graph reveals the related contexts of each word in
the statement, the table and the program. The graph is used to obtain
graph-enhanced contextual representations of words in Transformer-based
architecture. After that, a program-driven module network is further introduced
to exploit the hierarchical structure of the program, where semantic
compositionality is dynamically modeled along the program structure with a set
of function-specific modules. Ablation experiments suggest that both the
heterogeneous graph and the module network are important to obtain strong
results.

本论文提出了 LogicalFactChecker，这是一种基于神经网络的方法，能够利用逻辑操作来进行事实检查，并且在 TABFACT 数据集上实现了最先进的性能，通过 Transformer-based 架构构建基于图形模块网络的异构图使得每个单词在语句、表格和程序中的相关上下文都能被观察到，并通过程序驱动的模块网络进一步利用了程序的层次结构。