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

Smelting Gold and Silver for Improved Multilingual AMR-to-Text Generation