Structural Neural Encoders for AMR-to-text Generation
Table 1: BLEU and Meteor (%) scores on the development split of LDC2015E86.
['[BOLD] Input', '[BOLD] Model', '[BOLD] BLEU', '[BOLD] Meteor']
We first evaluate the overall performance of the models, after which we focus on two phenomena that we expect to benefit most from structural encoders: reentrancies and long-range dependencies. The sequential encoder (Seq) is a re-implementation of \newcitekonstas2017neural. We test both approaches of stacking structural and sequential components: structure on top of sequence (SeqTreeLSTM and SeqGCN), and sequence on top of structure (TreeLSTMSeq and GCNSeq). To inspect the effect of the sequential component, we run ablation tests by removing the RNNs altogether (TreeLSTM and GCN). GCN-based models are used both as tree encoders (reentrancies are removed) and graph encoders (reentrancies are maintained).