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    <titleInfo>
        <title>Learning an Executable Neural Semantic Parser</title>
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    <name type="personal">
        <namePart type="given">Jianpeng</namePart>
        <namePart type="family">Cheng</namePart>
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        <namePart type="given">Siva</namePart>
        <namePart type="family">Reddy</namePart>
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    <name type="personal">
        <namePart type="given">Vijay</namePart>
        <namePart type="family">Saraswat</namePart>
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    <name type="personal">
        <namePart type="given">Mirella</namePart>
        <namePart type="family">Lapata</namePart>
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    <abstract>This article describes a neural semantic parser that maps natural language utterances onto logical forms that can be executed against a task-specific environment, such as a knowledge base or a database, to produce a response. The parser generates tree-structured logical forms with a transition-based approach, combining a generic tree-generation algorithm with domain-general grammar defined by the logical language. The generation process is modeled by structured recurrent neural networks, which provide a rich encoding of the sentential context and generation history for making predictions. To tackle mismatches between natural language and logical form tokens, various attention mechanisms are explored. Finally, we consider different training settings for the neural semantic parser, including fully supervised training where annotated logical forms are given, weakly supervised training where denotations are provided, and distant supervision where only unlabeled sentences and a knowledge base are available. Experiments across a wide range of data sets demonstrate the effectiveness of our parser.</abstract>
    <identifier type="citekey">cheng-etal-2019-learning</identifier>
    <identifier type="doi">10.1162/coli_a_00342</identifier>
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        <url>https://aclanthology.org/J19-1002</url>
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        <date>2019-03</date>
        <detail type="volume"><number>45</number></detail>
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%0 Journal Article
%T Learning an Executable Neural Semantic Parser
%A Cheng, Jianpeng
%A Reddy, Siva
%A Saraswat, Vijay
%A Lapata, Mirella
%J Computational Linguistics
%D 2019
%8 March
%V 45
%N 1
%I MIT Press
%C Cambridge, MA
%F cheng-etal-2019-learning
%X This article describes a neural semantic parser that maps natural language utterances onto logical forms that can be executed against a task-specific environment, such as a knowledge base or a database, to produce a response. The parser generates tree-structured logical forms with a transition-based approach, combining a generic tree-generation algorithm with domain-general grammar defined by the logical language. The generation process is modeled by structured recurrent neural networks, which provide a rich encoding of the sentential context and generation history for making predictions. To tackle mismatches between natural language and logical form tokens, various attention mechanisms are explored. Finally, we consider different training settings for the neural semantic parser, including fully supervised training where annotated logical forms are given, weakly supervised training where denotations are provided, and distant supervision where only unlabeled sentences and a knowledge base are available. Experiments across a wide range of data sets demonstrate the effectiveness of our parser.
%R 10.1162/coli_a_00342
%U https://aclanthology.org/J19-1002
%U https://doi.org/10.1162/coli_a_00342
%P 59-94

Markdown (Informal)

[Learning an Executable Neural Semantic Parser](https://aclanthology.org/J19-1002) (Cheng et al., CL 2019)

• Learning an Executable Neural Semantic Parser (Cheng et al., CL 2019)

ACL

• Jianpeng Cheng, Siva Reddy, Vijay Saraswat, and Mirella Lapata. 2019. Learning an Executable Neural Semantic Parser. Computational Linguistics, 45(1):59–94.