AMR dependency parsing with a typed semantic algebra

ACL 2018

AMR dependency parsing with a typed semantic algebra

Jan 28, 2021
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Abstract: We present a semantic parser for Abstract Meaning Representations which learns to parse strings into tree repre-sentations of the compositional structure of an AMR graph. This allows us to use standard neural techniques for supertagging and dependency tree parsing, constrained by a linguistically principled type system. We present two approximative decoding algorithms, which achieve state-of-the-art accuracy and outperform strong baselines. Authors: Jonas Groschwitz, Matthias Lindemann, Meaghan Fowlie, Mark Johnson, Alexander Koller (Saarland University, Macquarie University)

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