A Sequence-to-Sequence&Set Model for Text-to-Table Generation
Tong Li, Zhihao Wang, Liangying Shao, Xuling Zheng, Xiaoli Wang, Jinsong Su · 2023
Recently, the text-to-table generation task has attracted increasing attention due to its wide applications.In this aspect, the dominant model (Wu et al., 2022) 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 topdown 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 commonlyused datasets.1