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    <titleInfo>
        <title>Parsing into Variable-in-situ Logico-Semantic Graphs</title>
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    <name type="personal">
        <namePart type="given">Yufei</namePart>
        <namePart type="family">Chen</namePart>
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    <name type="personal">
        <namePart type="given">Weiwei</namePart>
        <namePart type="family">Sun</namePart>
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        <name type="personal">
            <namePart type="given">Dan</namePart>
            <namePart type="family">Jurafsky</namePart>
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    <abstract>We propose variable-in-situ logico-semantic graphs to bridge the gap between semantic graph and logical form parsing. The new type of graph-based meaning representation allows us to include analysis for scope-related phenomena, such as quantification, negation and modality, in a way that is consistent with the state-of-the-art underspecification approach. Moreover, the well-formedness of such a graph is clear, since model-theoretic interpretation is available. We demonstrate the effectiveness of this new perspective by developing a new state-of-the-art semantic parser for English Resource Semantics. At the core of this parser is a novel neural graph rewriting system which combines the strengths of Hyperedge Replacement Grammar, a knowledge-intensive model, and Graph Neural Networks, a data-intensive model. Our parser achieves an accuracy of 92.39% in terms of elementary dependency match, which is a 2.88 point improvement over the best data-driven model in the literature. The output of our parser is highly coherent: at least 91% graphs are valid, in that they allow at least one sound scope-resolved logical form.</abstract>
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    <identifier type="doi">10.18653/v1/2020.acl-main.605</identifier>
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%0 Conference Proceedings
%T Parsing into Variable-in-situ Logico-Semantic Graphs
%A Chen, Yufei
%A Sun, Weiwei
%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 chen-sun-2020-parsing
%X We propose variable-in-situ logico-semantic graphs to bridge the gap between semantic graph and logical form parsing. The new type of graph-based meaning representation allows us to include analysis for scope-related phenomena, such as quantification, negation and modality, in a way that is consistent with the state-of-the-art underspecification approach. Moreover, the well-formedness of such a graph is clear, since model-theoretic interpretation is available. We demonstrate the effectiveness of this new perspective by developing a new state-of-the-art semantic parser for English Resource Semantics. At the core of this parser is a novel neural graph rewriting system which combines the strengths of Hyperedge Replacement Grammar, a knowledge-intensive model, and Graph Neural Networks, a data-intensive model. Our parser achieves an accuracy of 92.39% in terms of elementary dependency match, which is a 2.88 point improvement over the best data-driven model in the literature. The output of our parser is highly coherent: at least 91% graphs are valid, in that they allow at least one sound scope-resolved logical form.
%R 10.18653/v1/2020.acl-main.605
%U https://aclanthology.org/2020.acl-main.605
%U https://doi.org/10.18653/v1/2020.acl-main.605
%P 6772-6782

Markdown (Informal)

[Parsing into Variable-in-situ Logico-Semantic Graphs](https://aclanthology.org/2020.acl-main.605) (Chen & Sun, ACL 2020)

• Parsing into Variable-in-situ Logico-Semantic Graphs (Chen & Sun, ACL 2020)

ACL

• Yufei Chen and Weiwei Sun. 2020. Parsing into Variable-in-situ Logico-Semantic Graphs. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6772–6782, Online. Association for Computational Linguistics.