  Recent work on multilingual AMR-to-text generation has exclusively focused on
data augmentation strategies that utilize silver AMR. However, this assumes a
high quality of generated AMRs, potentially limiting the transferability to the
target task. In this paper, we investigate different techniques for
automatically generating AMR annotations, where we aim to study which source of
information yields better multilingual results. Our models trained on gold AMR
with silver (machine translated) sentences outperform approaches which leverage
generated silver AMR. We find that combining both complementary sources of
information further improves multilingual AMR-to-text generation. Our models
surpass the previous state of the art for German, Italian, Spanish, and Chinese
by a large margin.
