  The task of graph-to-text generation aims at producing sentences that
preserve the meaning of input graphs. As a crucial defect, the current
state-of-the-art models may mess up or even drop the core structural
information of input graphs when generating outputs. We propose to tackle this
problem by leveraging richer training signals that can guide our model for
preserving input information. In particular, we introduce two types of
autoencoding losses, each individually focusing on different aspects (a.k.a.
views) of input graphs. The losses are then back-propagated to better calibrate
our model via multi-task training. Experiments on two benchmarks for
graph-to-text generation show the effectiveness of our approach over a
state-of-the-art baseline. Our code is available at
\url{http://github.com/Soistesimmer/AMR-multiview}.
