  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.
