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。

利用预训练语言模型实现表格到文本的生成：一种表格结构理解和文本构思的方法

Towards Table-to-Text Generation with Pretrained Language Model: A Table Structure Understanding and Text Deliberating Approach

Question answering (QA) has achieved promising progress recently. However,
answering a question in real-world scenarios like the medical domain is still
challenging, due to the requirement of external knowledge and the insufficient
quantity of high-quality training data. In the light of these challenges, we
study the task of generating medical QA pairs in this paper. With the insight
that each medical question can be considered as a sample from the latent
distribution of questions given answers, we propose an automated medical QA
pair generation framework, consisting of an unsupervised key phrase detector
that explores unstructured material for validity, and a generator that involves
a multi-pass decoder to integrate structural knowledge for diversity. A series
of experiments have been conducted on a real-world dataset collected from the
National Medical Licensing Examination of China. Both automatic evaluation and
human annotation demonstrate the effectiveness of the proposed method. Further
investigation shows that, by incorporating the generated QA pairs for training,
significant improvement in terms of accuracy can be achieved for the
examination QA system.

本文介绍了一种基于自动生成问答对的框架，包含一个无监督关键词检测器发现问题的有效性，一个生成器利用多通道解码器提高结构知识的多样性，用于医学领域的问答任务，能有效提升考试问答系统的准确率。