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    <titleInfo>
        <title>Error Exploration for Automatic Abstract Meaning Representation Parsing</title>
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    <name type="personal">
        <namePart type="given">Maria</namePart>
        <namePart type="family">Boritchev</namePart>
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        <namePart type="given">Johannes</namePart>
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            <title>Proceedings of the 15th International Conference on Computational Semantics</title>
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            <namePart type="given">Maxime</namePart>
            <namePart type="family">Amblard</namePart>
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        <name type="personal">
            <namePart type="given">Ellen</namePart>
            <namePart type="family">Breitholtz</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>Following the data-driven methods of evaluation and error analysis in meaning representation parsing presented in (Buljan et al., 2022), we performed an error exploration of an Abstract Meaning Representation (AMR) parser. Our aim is to perform a diagnosis of the types of errors found in the output of the tool in order to implement adaptation and correction strategies to accommodate these errors. This article presents the exploration, its results, the strategies we implemented and the effect of these strategies on the performances of the tool. Though we did not observe a significative rise on average in the performances of the tool, we got much better results in some cases using our adaptation techniques.</abstract>
    <identifier type="citekey">boritchev-heinecke-2023-error</identifier>
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        <url>https://aclanthology.org/2023.iwcs-1.25</url>
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        <date>2023-06</date>
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%0 Conference Proceedings
%T Error Exploration for Automatic Abstract Meaning Representation Parsing
%A Boritchev, Maria
%A Heinecke, Johannes
%Y Amblard, Maxime
%Y Breitholtz, Ellen
%S Proceedings of the 15th International Conference on Computational Semantics
%D 2023
%8 June
%I Association for Computational Linguistics
%C Nancy, France
%F boritchev-heinecke-2023-error
%X Following the data-driven methods of evaluation and error analysis in meaning representation parsing presented in (Buljan et al., 2022), we performed an error exploration of an Abstract Meaning Representation (AMR) parser. Our aim is to perform a diagnosis of the types of errors found in the output of the tool in order to implement adaptation and correction strategies to accommodate these errors. This article presents the exploration, its results, the strategies we implemented and the effect of these strategies on the performances of the tool. Though we did not observe a significative rise on average in the performances of the tool, we got much better results in some cases using our adaptation techniques.
%U https://aclanthology.org/2023.iwcs-1.25
%P 246-251

Markdown (Informal)

[Error Exploration for Automatic Abstract Meaning Representation Parsing](https://aclanthology.org/2023.iwcs-1.25) (Boritchev & Heinecke, IWCS 2023)

• Error Exploration for Automatic Abstract Meaning Representation Parsing (Boritchev & Heinecke, IWCS 2023)

ACL

• Maria Boritchev and Johannes Heinecke. 2023. Error Exploration for Automatic Abstract Meaning Representation Parsing. In Proceedings of the 15th International Conference on Computational Semantics, pages 246–251, Nancy, France. Association for Computational Linguistics.