Structural Neural Encoders for AMR-to-text Generation
Table 2: Scores on the test split of LDC2015E86 and LDC2017T10. Tree is the tree-based GCNSeq and Graph is the graph-based GCNSeq.
['[BOLD] Model', '[BOLD] BLEU', '[BOLD] Meteor']
In order to mitigate the effects of random seeds, we train five models with different random seeds and report the results of the median model, according to their BLEU score on the development set Beck et al. We achieve state-of-the-art results with both tree and graph encoders, demonstrating the efficacy of our GCNSeq approach. The graph encoder outperforms the other systems and previous work on both datasets. These results demonstrate the benefit of structural encoders over purely sequential ones as well as the advantage of explicitly including reentrancies. The differences between our graph encoder and that of \newcitesong