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    <titleInfo>
        <title>A Rewriting Approach for Gender Inclusivity in Portuguese</title>
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    <name type="personal">
        <namePart type="given">Leonor</namePart>
        <namePart type="family">Veloso</namePart>
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        <namePart type="given">Luisa</namePart>
        <namePart type="family">Coheur</namePart>
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    <name type="personal">
        <namePart type="given">Rui</namePart>
        <namePart type="family">Ribeiro</namePart>
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        <dateIssued>2023-12</dateIssued>
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            <title>Findings of the Association for Computational Linguistics: EMNLP 2023</title>
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        <name type="personal">
            <namePart type="given">Houda</namePart>
            <namePart type="family">Bouamor</namePart>
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        <name type="personal">
            <namePart type="given">Juan</namePart>
            <namePart type="family">Pino</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Kalika</namePart>
            <namePart type="family">Bali</namePart>
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    <abstract>In recent years, there has been a notable rise in research interest regarding the integration of gender-inclusive and gender-neutral language in natural language processing models. A specific area of focus that has gained practical and academic significant interest is gender-neutral rewriting, which involves converting binary-gendered text to its gender-neutral counterpart. However, current approaches to gender-neutral rewriting for gendered languages tend to rely on large datasets, which may not be an option for languages with fewer resources, such as Portuguese. In this paper, we present a rule-based and a neural-based tool for gender-neutral rewriting for Portuguese, a heavily gendered Romance language whose morphology creates different challenges from the ones tackled by other gender-neutral rewriters. Our neural approach relies on fine-tuning large multilingual machine translation models on examples generated by the rule-based model. We evaluate both models on texts from different sources and contexts. We provide the first Portuguese dataset explicitly containing gender-neutral language and neopronouns, as well as a manually annotated golden collection of 500 sentences that allows for evaluation of future work.</abstract>
    <identifier type="citekey">veloso-etal-2023-rewriting</identifier>
    <identifier type="doi">10.18653/v1/2023.findings-emnlp.585</identifier>
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        <url>https://aclanthology.org/2023.findings-emnlp.585</url>
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    <part>
        <date>2023-12</date>
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            <start>8747</start>
            <end>8759</end>
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%0 Conference Proceedings
%T A Rewriting Approach for Gender Inclusivity in Portuguese
%A Veloso, Leonor
%A Coheur, Luisa
%A Ribeiro, Rui
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F veloso-etal-2023-rewriting
%X In recent years, there has been a notable rise in research interest regarding the integration of gender-inclusive and gender-neutral language in natural language processing models. A specific area of focus that has gained practical and academic significant interest is gender-neutral rewriting, which involves converting binary-gendered text to its gender-neutral counterpart. However, current approaches to gender-neutral rewriting for gendered languages tend to rely on large datasets, which may not be an option for languages with fewer resources, such as Portuguese. In this paper, we present a rule-based and a neural-based tool for gender-neutral rewriting for Portuguese, a heavily gendered Romance language whose morphology creates different challenges from the ones tackled by other gender-neutral rewriters. Our neural approach relies on fine-tuning large multilingual machine translation models on examples generated by the rule-based model. We evaluate both models on texts from different sources and contexts. We provide the first Portuguese dataset explicitly containing gender-neutral language and neopronouns, as well as a manually annotated golden collection of 500 sentences that allows for evaluation of future work.
%R 10.18653/v1/2023.findings-emnlp.585
%U https://aclanthology.org/2023.findings-emnlp.585
%U https://doi.org/10.18653/v1/2023.findings-emnlp.585
%P 8747-8759

Markdown (Informal)

[A Rewriting Approach for Gender Inclusivity in Portuguese](https://aclanthology.org/2023.findings-emnlp.585) (Veloso et al., Findings 2023)

• A Rewriting Approach for Gender Inclusivity in Portuguese (Veloso et al., Findings 2023)

ACL

• Leonor Veloso, Luisa Coheur, and Rui Ribeiro. 2023. A Rewriting Approach for Gender Inclusivity in Portuguese. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 8747–8759, Singapore. Association for Computational Linguistics.