04/07/2020

AMR Parsing with Latent Structural Information

Qiji Zhou, Yue Zhang, Donghong Ji, Hao Tang

Keywords: parsing AMR, AMR Parsing, Abstract Representations, AMRs

Abstract Paper Similar Papers

Abstract: Abstract Meaning Representations (AMRs) capture sentence-level semantics structural representations to broad-coverage natural sentences. We investigate parsing AMR with explicit dependency structures and interpretable latent structures. We generate the latent soft structure without additional annotations, and fuse both dependency and latent structure via an extended graph neural networks. The fused structural information helps our experiments results to achieve the best reported results on both AMR 2.0 (77.5% Smatch F1 on LDC2017T10) and AMR 1.0 ((71.8% Smatch F1 on LDC2014T12).

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