There are three problems existing in the popular data-to-text datasets. First, the large-scale datasets either contain noise or lack real application scenarios. Second, the datasets close to real applications are relatively small in size. Last, current datasets bias in the English language while leaving other languages underexplored. To alleviate these limitations, in this paper, we present CATS, a pragmatic Chinese answer-to-sequence dataset with large scale and high quality. The dataset aims to generate textual descriptions for the answer in the practical TableQA system. Further, to bridge the structural gap between the input SQL and table and establish better semantic alignments, we propose a Unified Graph Transformation approach to establish a joint encoding space for the two hybrid knowledge resources and convert this task to a graph-to-text problem. The experiment results demonstrate the effectiveness of our proposed method. Further analysis on CATS attests to both the high quality and challenges of the dataset.

本研究提出了CATS数据集，它是一个实用的基于表格问答系统的大规模高质量的答案到描述的中文数据集，并通过提出统一图转换方法，将这一任务转化为图到文本问题，以建立输入SQL和表之间的语义对齐。实验结果表明了我们提出的方法的有效性，并进一步分析了CATS数据集的质量和挑战。

CATS：一个实用的中文问答序列数据集，具有大规模且高质量