AMR Parsing as Graph Prediction with Latent Alignment
Table 5: Ablation studies: alignment modeling and relaxation (all on R2). Scores on ablations are averaged over 2 runs.
['Ablation', 'Concepts', 'SRL', 'Smatch']
It is interesting to see the contribution of other modeling decisions we made when modeling and relaxing alignments. First, instead of using Gumbel-Sinkhorn, which encourages mutually-repulsive alignments, we now use a factorized alignment model. A substantial drop in performance indicates that the prior knowledge about the nature of alignments appears beneficial. The performance drop in Smatch score (‘No Sinkhorn reg’) is only moderate. We hypothesize that the softer relaxation favors exploration of alignments and helps to discover better configurations.