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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 approach, and can be learned with a
simple classifier. We improve on the previous state-of-the-art result for AMR
parsing, boosting end-to-end performance by 3 F on both the LDC2013E117 and
LDC2014T12 datasets.Comment: To appear in ACL 201
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