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    <titleInfo>
        <title>Transition-based DRS Parsing Using Stack-LSTMs</title>
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    <name type="personal">
        <namePart type="given">Kilian</namePart>
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            <title>Proceedings of the IWCS Shared Task on Semantic Parsing</title>
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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">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Rik</namePart>
            <namePart type="family">van Noord</namePart>
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        <name type="personal">
            <namePart type="given">Hessel</namePart>
            <namePart type="family">Haagsma</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Johan</namePart>
            <namePart type="family">Bos</namePart>
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    <abstract>We present our submission to the IWCS 2019 shared task on semantic parsing, a transition-based parser that uses explicit word-meaning pairings, but no explicit representation of syntax. Parsing decisions are made based on vector representations of parser states, encoded via stack-LSTMs (Ballesteros et al., 2017), as well as some heuristic rules. Our system reaches 70.88% f-score in the competition.</abstract>
    <identifier type="citekey">evang-2019-transition</identifier>
    <identifier type="doi">10.18653/v1/W19-1202</identifier>
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%0 Conference Proceedings
%T Transition-based DRS Parsing Using Stack-LSTMs
%A Evang, Kilian
%Y Abzianidze, Lasha
%Y van Noord, Rik
%Y Haagsma, Hessel
%Y Bos, Johan
%S Proceedings of the IWCS Shared Task on Semantic Parsing
%D 2019
%8 May
%I Association for Computational Linguistics
%C Gothenburg, Sweden
%F evang-2019-transition
%X We present our submission to the IWCS 2019 shared task on semantic parsing, a transition-based parser that uses explicit word-meaning pairings, but no explicit representation of syntax. Parsing decisions are made based on vector representations of parser states, encoded via stack-LSTMs (Ballesteros et al., 2017), as well as some heuristic rules. Our system reaches 70.88% f-score in the competition.
%R 10.18653/v1/W19-1202
%U https://aclanthology.org/W19-1202
%U https://doi.org/10.18653/v1/W19-1202

Markdown (Informal)

[Transition-based DRS Parsing Using Stack-LSTMs](https://aclanthology.org/W19-1202) (Evang, IWCS 2019)

• Transition-based DRS Parsing Using Stack-LSTMs (Evang, IWCS 2019)

ACL

• Kilian Evang. 2019. Transition-based DRS Parsing Using Stack-LSTMs. In Proceedings of the IWCS Shared Task on Semantic Parsing, Gothenburg, Sweden. Association for Computational Linguistics.