Robust Subgraph Generation Improves Abstract Meaning Representation Parsing
This work addresses a bottleneck in AMR parsing for applications like event extraction and machine translation, but it is incremental as it builds on existing methods with specific gains.
The paper tackled the problem of node generation in Abstract Meaning Representation (AMR) parsing, which was a limiting factor, by proposing a set of actions for robust subgraph generation, resulting in a 3 F1 improvement on two datasets.
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$_1$ on both the LDC2013E117 and LDC2014T12 datasets.