Smelting Gold and Silver for Improved Multilingual AMR-to-Text Generation
This addresses the challenge of generating text from AMR graphs in multiple languages, offering improvements for NLP applications in those languages, though it is incremental as it builds on existing data augmentation strategies.
The paper tackled the problem of multilingual AMR-to-text generation by investigating techniques for automatically generating AMR annotations, finding that models trained on gold AMR with silver sentences outperform those using generated silver AMR, and combining both sources improves results, surpassing previous state-of-the-art for German, Italian, Spanish, and Chinese by a large margin.
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.