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    <titleInfo>
        <title>AMR Similarity Metrics from Principles</title>
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    <name type="personal">
        <namePart type="given">Juri</namePart>
        <namePart type="family">Opitz</namePart>
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        <namePart type="given">Letitia</namePart>
        <namePart type="family">Parcalabescu</namePart>
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    <name type="personal">
        <namePart type="given">Anette</namePart>
        <namePart type="family">Frank</namePart>
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    <abstract>Different metrics have been proposed to compare Abstract Meaning Representation (AMR) graphs. The canonical Smatch metric (Cai and Knight, 2013) aligns the variables of two graphs and assesses triple matches. The recent SemBleu metric (Song and Gildea, 2019) is based on the machine-translation metric Bleu (Papineni et al., 2002) and increases computational efficiency by ablating the variable-alignment. In this paper, i) we establish criteria that enable researchers to perform a principled assessment of metrics comparing meaning representations like AMR; ii) we undertake a thorough analysis of Smatch and SemBleu where we show that the latter exhibits some undesirable properties. For example, it does not conform to the identity of indiscernibles rule and introduces biases that are hard to control; and iii) we propose a novel metric S2 match that is more benevolent to only very slight meaning deviations and targets the fulfilment of all established criteria. We assess its suitability and show its advantages over Smatch and SemBleu.</abstract>
    <identifier type="citekey">opitz-etal-2020-amr</identifier>
    <identifier type="doi">10.1162/tacl_a_00329</identifier>
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        <url>https://aclanthology.org/2020.tacl-1.34</url>
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    <part>
        <date>2020</date>
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            <start>522</start>
            <end>538</end>
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%0 Journal Article
%T AMR Similarity Metrics from Principles
%A Opitz, Juri
%A Parcalabescu, Letitia
%A Frank, Anette
%J Transactions of the Association for Computational Linguistics
%D 2020
%V 8
%I MIT Press
%C Cambridge, MA
%F opitz-etal-2020-amr
%X Different metrics have been proposed to compare Abstract Meaning Representation (AMR) graphs. The canonical Smatch metric (Cai and Knight, 2013) aligns the variables of two graphs and assesses triple matches. The recent SemBleu metric (Song and Gildea, 2019) is based on the machine-translation metric Bleu (Papineni et al., 2002) and increases computational efficiency by ablating the variable-alignment. In this paper, i) we establish criteria that enable researchers to perform a principled assessment of metrics comparing meaning representations like AMR; ii) we undertake a thorough analysis of Smatch and SemBleu where we show that the latter exhibits some undesirable properties. For example, it does not conform to the identity of indiscernibles rule and introduces biases that are hard to control; and iii) we propose a novel metric S2 match that is more benevolent to only very slight meaning deviations and targets the fulfilment of all established criteria. We assess its suitability and show its advantages over Smatch and SemBleu.
%R 10.1162/tacl_a_00329
%U https://aclanthology.org/2020.tacl-1.34
%U https://doi.org/10.1162/tacl_a_00329
%P 522-538

Markdown (Informal)

[AMR Similarity Metrics from Principles](https://aclanthology.org/2020.tacl-1.34) (Opitz et al., TACL 2020)

• AMR Similarity Metrics from Principles (Opitz et al., TACL 2020)

ACL

• Juri Opitz, Letitia Parcalabescu, and Anette Frank. 2020. AMR Similarity Metrics from Principles. Transactions of the Association for Computational Linguistics, 8:522–538.