Learning Latent Variable for Logical Reasoning in Table-Based Fact Verification

Table-based fact verification (TFV) aims to classify whether a statement is entailed or refuted by a given table, which necessitates adept comprehension and logical inference skills among both tabular and textual data. The complexity of TFV is attributed to the involvement of both soft linguistic reasoning and hard symbolic reasoning. Existing studies tend to rely exclusively on table pre-trained models, lacking sufficient reasoning ability and treating various types of reasoning without distinction. In this paper, we propose a novel approach that transforms TFV task into a latent variable learning problem, employing a set of task-specific functions. Specifically, we leverage the hard Expectation-Maximization (EM) algorithm to ascertain the latent logic type underlying statements, then channel each statement through a specialized network designed for unique logical reasoning. Furthermore, we conduct a comprehensive exploration of the practical implementations of our proposed method in TFV task. Our approach diverges from the prevalent reliance on table-based pre-trained models, yet manages to surpass performance of various baseline models, exemplifying its efficacy and innovation.