Although remarkable progress on the neural table-to-text methods has been
made, the generalization issues hinder the applicability of these models due to
the limited source tables. Large-scale pretrained language models sound like a
promising solution to tackle such issues. However, how to effectively bridge
the gap between the structured table and the text input by fully leveraging
table information to fuel the pretrained model is still not well explored.
Besides, another challenge of integrating the deliberation mechanism into the
text-to-text pretrained model for solving the table-to-text task remains seldom
studied. In this paper, to implement the table-to-text generation with
pretrained language model, we propose a table structure understanding and text
deliberating approach, namely TASD. Specifically, we devise a three-layered
multi-head attention network to realize the table-structure-aware text
generation model with the help of the pretrained language model. Furthermore, a
multi-pass decoder framework is adopted to enhance the capability of polishing
generated text for table descriptions. The empirical studies, as well as human
evaluation, on two public datasets, validate that our approach can generate
faithful and fluent descriptive texts for different types of tables.

本文提出了一种名为 TASD 的表结构理解和文本判断方法，采用三层多头自注意力网络实现了以预训练语言模型为基础的文本生成模型，并采用多遍解码器框架增强了表格描述的生成能力，实验证明该方法可以为不同类型的表格生成准确流畅的文本描述。