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        <title>Generating Logical Forms from Graph Representations of Text and Entities</title>
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    <name type="personal">
        <namePart type="given">Peter</namePart>
        <namePart type="family">Shaw</namePart>
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    <name type="personal">
        <namePart type="given">Philip</namePart>
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    <name type="personal">
        <namePart type="given">Angelica</namePart>
        <namePart type="family">Chen</namePart>
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    <name type="personal">
        <namePart type="given">Francesco</namePart>
        <namePart type="family">Piccinno</namePart>
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    <name type="personal">
        <namePart type="given">Yasemin</namePart>
        <namePart type="family">Altun</namePart>
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            <namePart type="family">Korhonen</namePart>
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            <namePart type="given">David</namePart>
            <namePart type="family">Traum</namePart>
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            <namePart type="given">Lluís</namePart>
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    <abstract>Structured information about entities is critical for many semantic parsing tasks. We present an approach that uses a Graph Neural Network (GNN) architecture to incorporate information about relevant entities and their relations during parsing. Combined with a decoder copy mechanism, this approach provides a conceptually simple mechanism to generate logical forms with entities. We demonstrate that this approach is competitive with the state-of-the-art across several tasks without pre-training, and outperforms existing approaches when combined with BERT pre-training.</abstract>
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    <identifier type="doi">10.18653/v1/P19-1010</identifier>
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%0 Conference Proceedings
%T Generating Logical Forms from Graph Representations of Text and Entities
%A Shaw, Peter
%A Massey, Philip
%A Chen, Angelica
%A Piccinno, Francesco
%A Altun, Yasemin
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F shaw-etal-2019-generating
%X Structured information about entities is critical for many semantic parsing tasks. We present an approach that uses a Graph Neural Network (GNN) architecture to incorporate information about relevant entities and their relations during parsing. Combined with a decoder copy mechanism, this approach provides a conceptually simple mechanism to generate logical forms with entities. We demonstrate that this approach is competitive with the state-of-the-art across several tasks without pre-training, and outperforms existing approaches when combined with BERT pre-training.
%R 10.18653/v1/P19-1010
%U https://aclanthology.org/P19-1010
%U https://doi.org/10.18653/v1/P19-1010
%P 95-106

Markdown (Informal)

[Generating Logical Forms from Graph Representations of Text and Entities](https://aclanthology.org/P19-1010) (Shaw et al., ACL 2019)

• Generating Logical Forms from Graph Representations of Text and Entities (Shaw et al., ACL 2019)

ACL

• Peter Shaw, Philip Massey, Angelica Chen, Francesco Piccinno, and Yasemin Altun. 2019. Generating Logical Forms from Graph Representations of Text and Entities. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 95–106, Florence, Italy. Association for Computational Linguistics.