Although the existing numerical table-to-text generation models have achieved remarkable progress, the idea of generating an accurate analysis of the input table is not well explored. Most existing table-to-text generation algorithms for generating table related information only copy the table record directly but ignore reasoning or calculating over table records. One of the key steps to achieve this ability is number reasoning, which refers to do logical reasoning about the numbers from table records. In this paper, we attempt to improve the number reasoning capability of neural table-to-text generation by generating additional mathematical equations from numerical table records. We propose a neural architecture called Neural Table Reasoning Generator (NTRG), with an additional switching gate as well as a specifically designed equation decoder for generating mathematical equations adaptively. Moreover, we present a pre-training strategy for NTRG similar to the mask language model. Empirical results show that NTRG yields new state-of-the-art results on ROTOWIRE. Furthermore, in order to give a quantitative evaluation of the ability of number reasoning, we construct a sentence-level number reasoning dataset. Results demonstrate the superiority of our approaches over strong baselines.

尽管现有的数值表格到文本生成模型已经取得了显着的进步，但对生成输入表格的准确分析的想法还没有得到很好的探索。大多数现有的用于生成表相关信息的表到文本生成算法仅直接复制表记录，而忽略对表记录的推理或计算。实现此能力的关键步骤之一是数字推理，即对表记录中的数字进行逻辑推理。在本文中，我们试图通过从数值表格记录生成额外的数学方程来提高神经表格到文本生成的数字推理能力。我们提出了一个叫做神经结构ň eural牛逼能[R easoning g ^生成器 (NTRG)，带有一个额外的开关门以及一个专门设计的方程解码器，用于自适应地生成数学方程。此外，我们提出了一种类似于掩码语言模型的 NTRG 预训练策略。实证结果表明，NTRG 在 ROTOWIRE 上产生了最新的最新结果。此外，为了对数字推理能力进行定量评估，我们构建了一个句子级的数字推理数据集。结果证明了我们的方法在强基线上的优越性。