  Recent developments in neural networks have led to the advance in
data-to-text generation. However, the lack of ability of neural models to
control the structure of generated output can be limiting in certain real-world
applications. In this study, we propose a novel Plan-then-Generate (PlanGen)
framework to improve the controllability of neural data-to-text models.
Extensive experiments and analyses are conducted on two benchmark datasets,
ToTTo and WebNLG. The results show that our model is able to control both the
intra-sentence and inter-sentence structure of the generated output.
Furthermore, empirical comparisons against previous state-of-the-art methods
show that our model improves the generation quality as well as the output
diversity as judged by human and automatic evaluations.
