  Data-to-text (D2T) and text-to-data (T2D) are dual tasks that convert
structured data, such as graphs or tables into fluent text, and vice versa.
These tasks are usually handled separately and use corpora extracted from a
single source. Current systems leverage pre-trained language models fine-tuned
on D2T or T2D tasks. This approach has two main limitations: first, a separate
system has to be tuned for each task and source; second, learning is limited by
the scarcity of available corpora. This paper considers a more general scenario
where data are available from multiple heterogeneous sources. Each source, with
its specific data format and semantic domain, provides a non-parallel corpus of
text and structured data. We introduce a variational auto-encoder model with
disentangled style and content variables that allows us to represent the
diversity that stems from multiple sources of text and data. Our model is
designed to handle the tasks of D2T and T2D jointly. We evaluate our model on
several datasets, and show that by learning from multiple sources, our model
closes the performance gap with its supervised single-source counterpart and
outperforms it in some cases.
