Recently, the text-to-table generation task has attracted increasing
attention due to its wide applications. In this aspect, the dominant model
formalizes this task as a sequence-to-sequence generation task and serializes
each table into a token sequence during training by concatenating all rows in a
top-down order. However, it suffers from two serious defects: 1) the predefined
order introduces a wrong bias during training, which highly penalizes shifts in
the order between rows; 2) the error propagation problem becomes serious when
the model outputs a long token sequence. In this paper, we first conduct a
preliminary study to demonstrate the generation of most rows is
order-insensitive. Furthermore, we propose a novel sequence-to-sequence&set
text-to-table generation model. Specifically, in addition to a text encoder
encoding the input text, our model is equipped with a table header generator to
first output a table header, i.e., the first row of the table, in the manner of
sequence generation. Then we use a table body generator with learnable row
embeddings and column embeddings to generate a set of table body rows in
parallel. Particularly, to deal with the issue that there is no correspondence
between each generated table body row and target during training, we propose a
target assignment strategy based on the bipartite matching between the first
cells of generated table body rows and targets. Experiment results show that
our model significantly surpasses the baselines, achieving state-of-the-art
performance on commonly-used datasets.

本文介绍了一种用于文本到表格生成的新模型，该模型在考虑到多行中的大多数组合不敏感的情况下，首先采用文本编码器编码输入文本，再加入了一种表头生成器来以序列生成的方式输出表头，之后再使用具有可学习的行和列嵌入的表体生成器并行生成一组表身行，实验结果表明，此模型优于基线，达到了公认的数据集上的最佳性能。