We proposed reconstructing input graphs as autoencoding processes to encourage preserving the input semantic information for graph-to-text generation.
the auxiliary losses for recovering two complementary views (triple relations and linearized graph) of input graphs are introduced, so that our model is trained to retain input structures for better generation.