AMR Parsing as Sequence-to-Graph Transduction
Table 4: Ablation studies on components of our model. (Scores are sorted by the delta from the full model.)
['Ablation', 'AMR 1.0', 'AMR 2.0']
Ablation Study Removing target-side copy also leads to a large drop. Specifically, the subtask score of reentrancies drops down to 38.4% when target-side copy is disabled. Coverage loss is useful with regard to discouraging unnecessary repetitive nodes. In addition, our model benefits from input features such as language representations from BERT, index embeddings, POS tags, anonymization indicators, and character-level features from CharCNN. Beam search, commonly used in machine translation, is also helpful in our model. We provide side-by-side examples in the Appendix to further illustrate the contribution from each component, which are largely intuitive, with the exception of BERT embeddings. There the exact contribution of the component (qualitative, before/after ablation) stands out less: future work might consider a probing analysis with manually constructed examples, in the spirit of Linzen et al. ; Conneau et al.