@inproceedings{zhou-etal-2020-amr,
title = "{AMR} Parsing with Latent Structural Information",
author = "Zhou, Qiji and
Zhang, Yue and
Ji, Donghong and
Tang, Hao",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.397",
doi = "10.18653/v1/2020.acl-main.397",
pages = "4306--4319",
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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<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).</abstract>
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%0 Conference Proceedings
%T AMR Parsing with Latent Structural Information
%A Zhou, Qiji
%A Zhang, Yue
%A Ji, Donghong
%A Tang, Hao
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F zhou-etal-2020-amr
%X 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).
%R 10.18653/v1/2020.acl-main.397
%U https://aclanthology.org/2020.acl-main.397
%U https://doi.org/10.18653/v1/2020.acl-main.397
%P 4306-4319
Markdown (Informal)
[AMR Parsing with Latent Structural Information](https://aclanthology.org/2020.acl-main.397) (Zhou et al., ACL 2020)
ACL
- Qiji Zhou, Yue Zhang, Donghong Ji, and Hao Tang. 2020. AMR Parsing with Latent Structural Information. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4306–4319, Online. Association for Computational Linguistics.