  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.
