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。

半结构化表格计数的逻辑推理

Logical Inference for Counting on Semi-structured Tables

The transformer-based pre-trained language models have been tremendously
successful in most of the conventional NLP tasks. But they often struggle in
those tasks where numerical understanding is required. Some possible reasons
can be the tokenizers and pre-training objectives which are not specifically
designed to learn and preserve numeracy. Here we investigate the ability of
text-to-text transfer learning model (T5), which has outperformed its
predecessors in the conventional NLP tasks, to learn numeracy. We consider four
numeracy tasks: numeration, magnitude order prediction, finding minimum and
maximum in a series, and sorting. We find that, although T5 models perform
reasonably well in the interpolation setting, they struggle considerably in the
extrapolation setting across all four tasks.

本文研究了基于转换器的预训练语言模型应用数字理解方面的能力。作者使用文本到文本的转移学习模型 T5，并考虑了数字编码、大小排序、查找数列中的最小和最大值、排序等四个任务。结果表明，虽然 T5 在内插设置下表现良好，但在所有四个任务的外推设置下仍然存在较大难度。