Using Syntax-Based Machine Translation to Parse English into Abstract Meaning Representation
Table 2: AMR parsing Smatch scores for the experiments in this work. We provide a cross-reference to the section of this paper that describes each of the evaluated systems. Entries in bold are improvements over the previous state of the art. Human inter-annotator Smatch performance is in the 79-83 range [Cai and Knight2013].
['System', 'Section', 'Tune', 'Test']
We progressively show the incremental improvements and compare to the current state of the art system of \newciteflanigan-etal:ACL2014. ACL2014. Adding an AMR LM and semantic resources increases scores further. Rule-based alignments are an improvement upon unsupervised alignments, but concatenating the two alignments yields the best results.