<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="sobrevilla-cabezudo-etal-2024-investigating-paraphrase">
    <titleInfo>
        <title>Investigating Paraphrase Generation as a Data Augmentation Strategy for Low-Resource AMR-to-Text Generation</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Marco</namePart>
        <namePart type="given">Antonio</namePart>
        <namePart type="family">Sobrevilla Cabezudo</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Marcio</namePart>
        <namePart type="given">Lima</namePart>
        <namePart type="family">Inacio</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Thiago</namePart>
        <namePart type="given">Alexandre</namePart>
        <namePart type="given">Salgueiro</namePart>
        <namePart type="family">Pardo</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2024-09</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 17th International Natural Language Generation Conference</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Saad</namePart>
            <namePart type="family">Mahamood</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Nguyen</namePart>
            <namePart type="given">Le</namePart>
            <namePart type="family">Minh</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Daphne</namePart>
            <namePart type="family">Ippolito</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Tokyo, Japan</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Abstract Meaning Representation (AMR) is a meaning representation (MR) designed to abstract away from syntax, allowing syntactically different sentences to share the same AMR graph. Unlike other MRs, existing AMR corpora typically link one AMR graph to a single reference. This paper investigates the value of paraphrase generation in low-resource AMR-to-Text generation by testing various paraphrase generation strategies and evaluating their impact. The findings show that paraphrase generation significantly outperforms the baseline and traditional data augmentation methods, even with fewer training instances. Human evaluations indicate that this strategy often produces syntactic-based paraphrases and can exceed the performance of previous approaches. Additionally, the paper releases a paraphrase-extended version of the AMR corpus.</abstract>
    <identifier type="citekey">sobrevilla-cabezudo-etal-2024-investigating-paraphrase</identifier>
    <location>
        <url>https://aclanthology.org/2024.inlg-main.51</url>
    </location>
    <part>
        <date>2024-09</date>
        <extent unit="page">
            <start>663</start>
            <end>675</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Investigating Paraphrase Generation as a Data Augmentation Strategy for Low-Resource AMR-to-Text Generation
%A Sobrevilla Cabezudo, Marco Antonio
%A Inacio, Marcio Lima
%A Pardo, Thiago Alexandre Salgueiro
%Y Mahamood, Saad
%Y Minh, Nguyen Le
%Y Ippolito, Daphne
%S Proceedings of the 17th International Natural Language Generation Conference
%D 2024
%8 September
%I Association for Computational Linguistics
%C Tokyo, Japan
%F sobrevilla-cabezudo-etal-2024-investigating-paraphrase
%X Abstract Meaning Representation (AMR) is a meaning representation (MR) designed to abstract away from syntax, allowing syntactically different sentences to share the same AMR graph. Unlike other MRs, existing AMR corpora typically link one AMR graph to a single reference. This paper investigates the value of paraphrase generation in low-resource AMR-to-Text generation by testing various paraphrase generation strategies and evaluating their impact. The findings show that paraphrase generation significantly outperforms the baseline and traditional data augmentation methods, even with fewer training instances. Human evaluations indicate that this strategy often produces syntactic-based paraphrases and can exceed the performance of previous approaches. Additionally, the paper releases a paraphrase-extended version of the AMR corpus.
%U https://aclanthology.org/2024.inlg-main.51
%P 663-675

Markdown (Informal)

[Investigating Paraphrase Generation as a Data Augmentation Strategy for Low-Resource AMR-to-Text Generation](https://aclanthology.org/2024.inlg-main.51) (Sobrevilla Cabezudo et al., INLG 2024)

• Investigating Paraphrase Generation as a Data Augmentation Strategy for Low-Resource AMR-to-Text Generation (Sobrevilla Cabezudo et al., INLG 2024)

ACL

• Marco Antonio Sobrevilla Cabezudo, Marcio Lima Inacio, and Thiago Alexandre Salgueiro Pardo. 2024. Investigating Paraphrase Generation as a Data Augmentation Strategy for Low-Resource AMR-to-Text Generation. In Proceedings of the 17th International Natural Language Generation Conference, pages 663–675, Tokyo, Japan. Association for Computational Linguistics.