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.

本文提出了第一个完全的非自回归表格到文本模型（Plan-then-Seam，PTS），通过使用一个新颖的重新思考指针预测器编写和校准内容的计划，然后将计划作为上下文进行拼接以解码描述，从而在保持并行解码的同时，通过共享参数并迭代执行这两个步骤来捕获标记间的互相依赖性，这两个步骤共享参数并迭代执行以捕获标记间的互相依赖性，以有效地实现对表格到文本生成任务的性能提升。