Robust Subgraph Generation Improves Abstract Meaning Representation Parsing

The Abstract Meaning Representation (AMR) is a representation for open-domain rich semantics, with potential use in fields like event extraction and machine translation. Node generation, typically done using a simple dictionary lookup, is currently an important limiting factor in AMR parsing. We propose a small set of actions that derive AMR subgraphs by transformations on spans of text, which allows for more robust learning of this stage. Our set of construction actions generalize better than the previous ap-proach, and can be learned with a sim-ple classifier. We improve on the previ-ous state-of-the-art result for AMR pars-ing, boosting end-to-end performance by 3 F1 on both the LDC2013E117 and LDC2014T12 datasets