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        <title>ShanghaiTech at MRP 2019: Sequence-to-Graph Transduction with Second-Order Edge Inference for Cross-Framework Meaning Representation Parsing</title>
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    <name type="personal">
        <namePart type="given">Xinyu</namePart>
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    <name type="personal">
        <namePart type="given">Yixian</namePart>
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    <name type="personal">
        <namePart type="given">Zixia</namePart>
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    <name type="personal">
        <namePart type="given">Chengyue</namePart>
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    <name type="personal">
        <namePart type="given">Kewei</namePart>
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        <dateIssued>2019-nov</dateIssued>
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            <title>Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning</title>
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    <abstract>This paper presents the system used in our submission to the CoNLL 2019 shared task: Cross-Framework Meaning Representation Parsing. Our system is a graph-based parser which combines an extended pointer-generator network that generates nodes and a second-order mean field variational inference module that predicts edges. Our system achieved 1st and 2nd place for the DM and PSD frameworks respectively on the in-framework ranks and achieved 3rd place for the DM framework on the cross-framework ranks.</abstract>
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    <identifier type="doi">10.18653/v1/K19-2005</identifier>
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%0 Conference Proceedings
%T ShanghaiTech at MRP 2019: Sequence-to-Graph Transduction with Second-Order Edge Inference for Cross-Framework Meaning Representation Parsing
%A Wang, Xinyu
%A Liu, Yixian
%A Jia, Zixia
%A Jiang, Chengyue
%A Tu, Kewei
%S Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning
%D 2019
%8 nov
%I Association for Computational Linguistics
%C Hong Kong
%F wang-etal-2019-shanghaitech
%X This paper presents the system used in our submission to the CoNLL 2019 shared task: Cross-Framework Meaning Representation Parsing. Our system is a graph-based parser which combines an extended pointer-generator network that generates nodes and a second-order mean field variational inference module that predicts edges. Our system achieved 1st and 2nd place for the DM and PSD frameworks respectively on the in-framework ranks and achieved 3rd place for the DM framework on the cross-framework ranks.
%R 10.18653/v1/K19-2005
%U https://aclanthology.org/K19-2005
%U https://doi.org/10.18653/v1/K19-2005
%P 55-65

Markdown (Informal)

[ShanghaiTech at MRP 2019: Sequence-to-Graph Transduction with Second-Order Edge Inference for Cross-Framework Meaning Representation Parsing](https://aclanthology.org/K19-2005) (Wang et al., CoNLL 2019)

• ShanghaiTech at MRP 2019: Sequence-to-Graph Transduction with Second-Order Edge Inference for Cross-Framework Meaning Representation Parsing (Wang et al., CoNLL 2019)

ACL

• Xinyu Wang, Yixian Liu, Zixia Jia, Chengyue Jiang, and Kewei Tu. 2019. ShanghaiTech at MRP 2019: Sequence-to-Graph Transduction with Second-Order Edge Inference for Cross-Framework Meaning Representation Parsing. In Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning, pages 55–65, Hong Kong. Association for Computational Linguistics.