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    <titleInfo>
        <title>Learning compositional structures for semantic graph parsing</title>
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    <name type="personal">
        <namePart type="given">Jonas</namePart>
        <namePart type="family">Groschwitz</namePart>
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    <name type="personal">
        <namePart type="given">Meaghan</namePart>
        <namePart type="family">Fowlie</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    <name type="personal">
        <namePart type="given">Alexander</namePart>
        <namePart type="family">Koller</namePart>
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        <dateIssued>2021-08</dateIssued>
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            <title>Proceedings of the 5th Workshop on Structured Prediction for NLP (SPNLP 2021)</title>
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        <name type="personal">
            <namePart type="given">Zornitsa</namePart>
            <namePart type="family">Kozareva</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Sujith</namePart>
            <namePart type="family">Ravi</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Andreas</namePart>
            <namePart type="family">Vlachos</namePart>
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        <name type="personal">
            <namePart type="given">Priyanka</namePart>
            <namePart type="family">Agrawal</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">André</namePart>
            <namePart type="family">Martins</namePart>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>AM dependency parsing is a method for neural semantic graph parsing that exploits the principle of compositionality. While AM dependency parsers have been shown to be fast and accurate across several graphbanks, they require explicit annotations of the compositional tree structures for training. In the past, these were obtained using complex graphbank-specific heuristics written by experts. Here we show how they can instead be trained directly on the graphs with a neural latent-variable model, drastically reducing the amount and complexity of manual heuristics. We demonstrate that our model picks up on several linguistic phenomena on its own and achieves comparable accuracy to supervised training, greatly facilitating the use of AM dependency parsing for new sembanks.</abstract>
    <identifier type="citekey">groschwitz-etal-2021-learning</identifier>
    <identifier type="doi">10.18653/v1/2021.spnlp-1.3</identifier>
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        <url>https://aclanthology.org/2021.spnlp-1.3</url>
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        <date>2021-08</date>
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            <start>22</start>
            <end>36</end>
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%0 Conference Proceedings
%T Learning compositional structures for semantic graph parsing
%A Groschwitz, Jonas
%A Fowlie, Meaghan
%A Koller, Alexander
%Y Kozareva, Zornitsa
%Y Ravi, Sujith
%Y Vlachos, Andreas
%Y Agrawal, Priyanka
%Y Martins, André
%S Proceedings of the 5th Workshop on Structured Prediction for NLP (SPNLP 2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F groschwitz-etal-2021-learning
%X AM dependency parsing is a method for neural semantic graph parsing that exploits the principle of compositionality. While AM dependency parsers have been shown to be fast and accurate across several graphbanks, they require explicit annotations of the compositional tree structures for training. In the past, these were obtained using complex graphbank-specific heuristics written by experts. Here we show how they can instead be trained directly on the graphs with a neural latent-variable model, drastically reducing the amount and complexity of manual heuristics. We demonstrate that our model picks up on several linguistic phenomena on its own and achieves comparable accuracy to supervised training, greatly facilitating the use of AM dependency parsing for new sembanks.
%R 10.18653/v1/2021.spnlp-1.3
%U https://aclanthology.org/2021.spnlp-1.3
%U https://doi.org/10.18653/v1/2021.spnlp-1.3
%P 22-36

Markdown (Informal)

[Learning compositional structures for semantic graph parsing](https://aclanthology.org/2021.spnlp-1.3) (Groschwitz et al., spnlp 2021)

• Learning compositional structures for semantic graph parsing (Groschwitz et al., spnlp 2021)

ACL

• Jonas Groschwitz, Meaghan Fowlie, and Alexander Koller. 2021. Learning compositional structures for semantic graph parsing. In Proceedings of the 5th Workshop on Structured Prediction for NLP (SPNLP 2021), pages 22–36, Online. Association for Computational Linguistics.