  Table-based fact verification task aims to verify whether the given statement
is supported by the given semi-structured table. Symbolic reasoning with
logical operations plays a crucial role in this task. Existing methods leverage
programs that contain rich logical information to enhance the verification
process. However, due to the lack of fully supervised signals in the program
generation process, spurious programs can be derived and employed, which leads
to the inability of the model to catch helpful logical operations. To address
the aforementioned problems, in this work, we formulate the table-based fact
verification task as an evidence retrieval and reasoning framework, proposing
the Logic-level Evidence Retrieval and Graph-based Verification network
(LERGV). Specifically, we first retrieve logic-level program-like evidence from
the given table and statement as supplementary evidence for the table. After
that, we construct a logic-level graph to capture the logical relations between
entities and functions in the retrieved evidence, and design a graph-based
verification network to perform logic-level graph-based reasoning based on the
constructed graph to classify the final entailment relation. Experimental
results on the large-scale benchmark TABFACT show the effectiveness of the
proposed approach.
