Table-to-text generation aims at automatically generating text to help people conveniently obtain salient information in tables. Recent works explicitly decompose the generation process into content planning and surface generation stages, employing two autoregressive networks for them respectively. However, they are computationally expensive due to the non-parallelizable nature of autoregressive decoding and the redundant parameters of two networks. In this paper, we propose the first totally non-autoregressive table-to-text model (Plan-then-Seam, PTS) that produces its outputs in parallel with one single network. PTS firstly writes and calibrates one plan of the content to be generated with a novel rethinking pointer predictor, and then takes the plan as the context for seaming to decode the description. These two steps share parameters and perform iteratively to capture token inter-dependency while keeping parallel decoding. Experiments on two public benchmarks show that PTS achieves 3.0~5.6 times speedup for inference time, reducing 50% parameters, while maintaining as least comparable performance against strong two-stage table-to-text competitors.

翻译：表格到文本生成旨在自动生成文本，帮助人们便捷地获取表格中的显著信息。近期研究明确将生成过程分解为内容规划与表面生成两个阶段，分别使用两个自回归网络进行处理。然而，由于自回归解码的非并行特性以及两个网络的冗余参数，此类方法计算开销高昂。本文提出首个完全非自回归的表格到文本模型（计划-缝合，PTS），该模型通过单一网络并行生成输出。PTS首先利用新型重思指针预测器编写并校准待生成内容的规划，随后将该规划作为上下文语境进行缝合解码以生成描述。这两个步骤共享参数并迭代执行，在保持并行解码的同时捕获词元间的依赖关系。在两个公开基准上的实验表明，PTS在推理速度上实现3.0~5.6倍加速，参数减少50%，同时在与强基线两阶段表格到文本方法的性能对比中保持至少同等水平。