Structural Neural Encoders for AMR-to-text Generation
Table 3: Counts of reentrancies for the development and test split of LDC2017T10
['[BOLD] # reentrancies', '[BOLD] # dev sents.', '[BOLD] # test sents.']
Overall scores show an advantage of graph encoder over tree and sequential encoders, but they do not shed light into how this is achieved. Because graph encoders are the only ones to model reentrancies explicitly, we expect them to deal better with these structures. It is, however, possible that the other models are capable of handling these structures implicitly. It is expected that the benefit of the graph models will be more evident for those examples containing more reentrancies. To test this hypothesis, we evaluate the various scenarios as a function of the number of reentrancies in each example, using the Meteor score as a metric.