AMR Parsing with Latent Structural Information
Qiji Zhou, Yue Zhang, Donghong Ji, Hao Tang
Semantics: Sentence Level Long Paper
Session 7B: Jul 7
(09:00-10:00 GMT)
Session 8A: Jul 7
(12:00-13:00 GMT)
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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