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        <title>AMR Parsing via Graph-Sequence Iterative Inference</title>
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    <name type="personal">
        <namePart type="given">Deng</namePart>
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        <name type="personal">
            <namePart type="given">Dan</namePart>
            <namePart type="family">Jurafsky</namePart>
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        <name type="personal">
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        <name type="personal">
            <namePart type="given">Natalie</namePart>
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    <abstract>We propose a new end-to-end model that treats AMR parsing as a series of dual decisions on the input sequence and the incrementally constructed graph. At each time step, our model performs multiple rounds of attention, reasoning, and composition that aim to answer two critical questions: (1) which part of the input sequence to abstract; and (2) where in the output graph to construct the new concept. We show that the answers to these two questions are mutually causalities. We design a model based on iterative inference that helps achieve better answers in both perspectives, leading to greatly improved parsing accuracy. Our experimental results significantly outperform all previously reported Smatch scores by large margins. Remarkably, without the help of any large-scale pre-trained language model (e.g., BERT), our model already surpasses previous state-of-the-art using BERT. With the help of BERT, we can push the state-of-the-art results to 80.2% on LDC2017T10 (AMR 2.0) and 75.4% on LDC2014T12 (AMR 1.0).</abstract>
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    <identifier type="doi">10.18653/v1/2020.acl-main.119</identifier>
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        <date>2020-07</date>
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%0 Conference Proceedings
%T AMR Parsing via Graph-Sequence Iterative Inference
%A Cai, Deng
%A Lam, Wai
%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 cai-lam-2020-amr
%X We propose a new end-to-end model that treats AMR parsing as a series of dual decisions on the input sequence and the incrementally constructed graph. At each time step, our model performs multiple rounds of attention, reasoning, and composition that aim to answer two critical questions: (1) which part of the input sequence to abstract; and (2) where in the output graph to construct the new concept. We show that the answers to these two questions are mutually causalities. We design a model based on iterative inference that helps achieve better answers in both perspectives, leading to greatly improved parsing accuracy. Our experimental results significantly outperform all previously reported Smatch scores by large margins. Remarkably, without the help of any large-scale pre-trained language model (e.g., BERT), our model already surpasses previous state-of-the-art using BERT. With the help of BERT, we can push the state-of-the-art results to 80.2% on LDC2017T10 (AMR 2.0) and 75.4% on LDC2014T12 (AMR 1.0).
%R 10.18653/v1/2020.acl-main.119
%U https://aclanthology.org/2020.acl-main.119
%U https://doi.org/10.18653/v1/2020.acl-main.119
%P 1290-1301

Markdown (Informal)

[AMR Parsing via Graph-Sequence Iterative Inference](https://aclanthology.org/2020.acl-main.119) (Cai & Lam, ACL 2020)

• AMR Parsing via Graph-Sequence Iterative Inference (Cai & Lam, ACL 2020)

ACL

• Deng Cai and Wai Lam. 2020. AMR Parsing via Graph-Sequence Iterative Inference. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1290–1301, Online. Association for Computational Linguistics.