Recently, the Natural Language Inference (NLI) task has been studied for
semi-structured tables that do not have a strict format. Although neural
approaches have achieved high performance in various types of NLI, including
NLI between semi-structured tables and texts, they still have difficulty in
performing a numerical type of inference, such as counting. To handle a
numerical type of inference, we propose a logical inference system for
reasoning between semi-structured tables and texts. We use logical
representations as meaning representations for tables and texts and use model
checking to handle a numerical type of inference between texts and tables. To
evaluate the extent to which our system can perform inference with numerical
comparatives, we make an evaluation protocol that focuses on numerical
understanding between semi-structured tables and texts in English. We show that
our system can more robustly perform inference between tables and texts that
requires numerical understanding compared with current neural approaches.

本论文提出了一种针对半结构化表格和文本之间进行逻辑推断的系统，使用逻辑表示作为表格和文本的意义表示，并使用模型检查来处理文本和表格之间的数字推断，其可以更稳健地在半结构化表格和文本之间执行需要数值理解的推断，相比于当前的神经网络方法。