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性信息。近期研究明确地将生成过程分解为内容规划与表层生成两个阶段，并分别使用两个自回归网络实现。然而，由于自回归解码不可并行化的特性以及两个网络冗余的参数，这些方法计算开销较大。本文提出首个完全非自回归的表格到文本模型（先规划再拼接，PTS），该模型通过单一网络并行生成输出。PTS首先利用新型回顾指针预测器编写并校准待生成内容的规划，然后将该规划作为上下文进行拼接以解码描述文本。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捉令牌间的相互依赖关系。在两个公开基准上的实验表明，PTS在推理时间上实现了3.0~5.6倍的加速，减少了50%的参数，同时与强两阶段表格到文本竞争方法相比保持了至少相当的性能。