  AMR-to-text generation aims to recover a text containing the same meaning as
an input AMR graph. Current research develops increasingly powerful graph
encoders to better represent AMR graphs, with decoders based on standard
language modeling being used to generate outputs. We propose a decoder that
back predicts projected AMR graphs on the target sentence during text
generation. As the result, our outputs can better preserve the input meaning
than standard decoders. Experiments on two AMR benchmarks show the superiority
of our model over the previous state-of-the-art system based on graph
Transformer.
