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        <title>Assessing the Cross-linguistic Utility of Abstract Meaning Representation</title>
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        <namePart type="given">Shira</namePart>
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    <abstract>Semantic representations capture the meaning of a text. Abstract Meaning Representation (AMR), a type of semantic representation, focuses on predicate-argument structure and abstracts away from surface form. Though AMR was developed initially for English, it has now been adapted to a multitude of languages in the form of non-English annotation schemas, cross-lingual text-to-AMR parsing, and AMR-to-(non-English) text generation. We advance prior work on cross-lingual AMR by thoroughly investigating the amount, types, and causes of differences that appear in AMRs of different languages. Further, we compare how AMR captures meaning in cross-lingual pairs versus strings, and show that AMR graphs are able to draw out fine-grained differences between parallel sentences. We explore three primary research questions: (1) What are the types and causes of differences in parallel AMRs? (2) How can we measure the amount of difference between AMR pairs in different languages? (3) Given that AMR structure is affected by language and exhibits cross-lingual differences, how do cross-lingual AMR pairs compare to string-based representations of cross-lingual sentence pairs? We find that the source language itself does have a measurable impact on AMR structure, and that translation divergences and annotator choices also lead to differences in cross-lingual AMR pairs. We explore the implications of this finding throughout our study, concluding that, although AMR is useful to capture meaning across languages, evaluations need to take into account source language influences if they are to paint an accurate picture of system output, and meaning generally.</abstract>
    <identifier type="citekey">wein-schneider-2024-assessing</identifier>
    <identifier type="doi">10.1162/coli_a_00503</identifier>
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        <date>2024-06</date>
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%0 Journal Article
%T Assessing the Cross-linguistic Utility of Abstract Meaning Representation
%A Wein, Shira
%A Schneider, Nathan
%J Computational Linguistics
%D 2024
%8 June
%V 50
%N 2
%I MIT Press
%C Cambridge, MA
%F wein-schneider-2024-assessing
%X Semantic representations capture the meaning of a text. Abstract Meaning Representation (AMR), a type of semantic representation, focuses on predicate-argument structure and abstracts away from surface form. Though AMR was developed initially for English, it has now been adapted to a multitude of languages in the form of non-English annotation schemas, cross-lingual text-to-AMR parsing, and AMR-to-(non-English) text generation. We advance prior work on cross-lingual AMR by thoroughly investigating the amount, types, and causes of differences that appear in AMRs of different languages. Further, we compare how AMR captures meaning in cross-lingual pairs versus strings, and show that AMR graphs are able to draw out fine-grained differences between parallel sentences. We explore three primary research questions: (1) What are the types and causes of differences in parallel AMRs? (2) How can we measure the amount of difference between AMR pairs in different languages? (3) Given that AMR structure is affected by language and exhibits cross-lingual differences, how do cross-lingual AMR pairs compare to string-based representations of cross-lingual sentence pairs? We find that the source language itself does have a measurable impact on AMR structure, and that translation divergences and annotator choices also lead to differences in cross-lingual AMR pairs. We explore the implications of this finding throughout our study, concluding that, although AMR is useful to capture meaning across languages, evaluations need to take into account source language influences if they are to paint an accurate picture of system output, and meaning generally.
%R 10.1162/coli_a_00503
%U https://aclanthology.org/2024.cl-2.1
%U https://doi.org/10.1162/coli_a_00503
%P 419-473

Markdown (Informal)

[Assessing the Cross-linguistic Utility of Abstract Meaning Representation](https://aclanthology.org/2024.cl-2.1) (Wein & Schneider, CL 2024)

• Assessing the Cross-linguistic Utility of Abstract Meaning Representation (Wein & Schneider, CL 2024)

ACL

• Shira Wein and Nathan Schneider. 2024. Assessing the Cross-linguistic Utility of Abstract Meaning Representation. Computational Linguistics, 50(2):419–473.