  Knowledge graphs (KGs) can vary greatly from one domain to another. Therefore
supervised approaches to both graph-to-text generation and text-to-graph
knowledge extraction (semantic parsing) will always suffer from a shortage of
domain-specific parallel graph-text data; at the same time, adapting a model
trained on a different domain is often impossible due to little or no overlap
in entities and relations. This situation calls for an approach that (1) does
not need large amounts of annotated data and thus (2) does not need to rely on
domain adaptation techniques to work well in different domains. To this end, we
present the first approach to unsupervised text generation from KGs and show
simultaneously how it can be used for unsupervised semantic parsing. We
evaluate our approach on WebNLG v2.1 and a new benchmark leveraging scene
graphs from Visual Genome. Our system outperforms strong baselines for both
text$\leftrightarrow$graph conversion tasks without any manual adaptation from
one dataset to the other. In additional experiments, we investigate the impact
of using different unsupervised objectives.
