  We consider the task of data-to-text generation, which aims to create textual
output from non-linguistic input. We focus on generating long-form text, i.e.,
documents with multiple paragraphs, and propose a neural model enhanced with a
planning component responsible for organizing high-level information in a
coherent and meaningful way. We infer latent plans sequentially with a
structured variational model, while interleaving the steps of planning and
generation. Text is generated by conditioning on previous variational decisions
and previously generated text. Experiments on two data-to-text benchmarks
(RotoWire and MLB) show that our model outperforms strong baselines and is
sample efficient in the face of limited training data (e.g., a few hundred
instances).
