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        <title>Exploring Neural Methods for Parsing Discourse Representation Structures</title>
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    <name type="personal">
        <namePart type="given">Rik</namePart>
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    <name type="personal">
        <namePart type="given">Lasha</namePart>
        <namePart type="family">Abzianidze</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    <name type="personal">
        <namePart type="given">Antonio</namePart>
        <namePart type="family">Toral</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    <name type="personal">
        <namePart type="given">Johan</namePart>
        <namePart type="family">Bos</namePart>
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    <abstract>Neural methods have had several recent successes in semantic parsing, though they have yet to face the challenge of producing meaning representations based on formal semantics. We present a sequence-to-sequence neural semantic parser that is able to produce Discourse Representation Structures (DRSs) for English sentences with high accuracy, outperforming traditional DRS parsers. To facilitate the learning of the output, we represent DRSs as a sequence of flat clauses and introduce a method to verify that produced DRSs are well-formed and interpretable. We compare models using characters and words as input and see (somewhat surprisingly) that the former performs better than the latter. We show that eliminating variable names from the output using De Bruijn indices increases parser performance. Adding silver training data boosts performance even further.</abstract>
    <identifier type="citekey">van-noord-etal-2018-exploring</identifier>
    <identifier type="doi">10.1162/tacl_a_00241</identifier>
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        <url>https://aclanthology.org/Q18-1043</url>
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    <part>
        <date>2018</date>
        <detail type="volume"><number>6</number></detail>
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            <start>619</start>
            <end>633</end>
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%0 Journal Article
%T Exploring Neural Methods for Parsing Discourse Representation Structures
%A van Noord, Rik
%A Abzianidze, Lasha
%A Toral, Antonio
%A Bos, Johan
%J Transactions of the Association for Computational Linguistics
%D 2018
%V 6
%I MIT Press
%C Cambridge, MA
%F van-noord-etal-2018-exploring
%X Neural methods have had several recent successes in semantic parsing, though they have yet to face the challenge of producing meaning representations based on formal semantics. We present a sequence-to-sequence neural semantic parser that is able to produce Discourse Representation Structures (DRSs) for English sentences with high accuracy, outperforming traditional DRS parsers. To facilitate the learning of the output, we represent DRSs as a sequence of flat clauses and introduce a method to verify that produced DRSs are well-formed and interpretable. We compare models using characters and words as input and see (somewhat surprisingly) that the former performs better than the latter. We show that eliminating variable names from the output using De Bruijn indices increases parser performance. Adding silver training data boosts performance even further.
%R 10.1162/tacl_a_00241
%U https://aclanthology.org/Q18-1043
%U https://doi.org/10.1162/tacl_a_00241
%P 619-633

Markdown (Informal)

[Exploring Neural Methods for Parsing Discourse Representation Structures](https://aclanthology.org/Q18-1043) (van Noord et al., TACL 2018)

• Exploring Neural Methods for Parsing Discourse Representation Structures (van Noord et al., TACL 2018)

ACL

• Rik van Noord, Lasha Abzianidze, Antonio Toral, and Johan Bos. 2018. Exploring Neural Methods for Parsing Discourse Representation Structures. Transactions of the Association for Computational Linguistics, 6:619–633.