  How to generate descriptions from structured data organized in tables?
Existing approaches using neural encoder-decoder models often suffer from
lacking diversity. We claim that an open set of templates is crucial for
enriching the phrase constructions and realizing varied generations. Learning
such templates is prohibitive since it often requires a large paired <table,
description> corpus, which is seldom available. This paper explores the problem
of automatically learning reusable "templates" from paired and non-paired data.
We propose the variational template machine (VTM), a novel method to generate
text descriptions from data tables. Our contributions include: a) we carefully
devise a specific model architecture and losses to explicitly disentangle text
template and semantic content information, in the latent spaces, and b)we
utilize both small parallel data and large raw text without aligned tables to
enrich the template learning. Experiments on datasets from a variety of
different domains show that VTM is able to generate more diversely while
keeping a good fluency and quality.
