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    <titleInfo>
        <title>A Prompt Response to the Demand for Automatic Gender-Neutral Translation</title>
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    <name type="personal">
        <namePart type="given">Beatrice</namePart>
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        <namePart type="given">Matteo</namePart>
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    <name type="personal">
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            <namePart type="given">Matthew</namePart>
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    <abstract>Gender-neutral translation (GNT) that avoids biased and undue binary assumptions is a pivotal challenge for the creation of more inclusive translation technologies. Advancements for this task in Machine Translation (MT), however, are hindered by the lack of dedicated parallel data, which are necessary to adapt MT systems to satisfy neutral constraints. For such a scenario, large language models offer hitherto unforeseen possibilities, as they come with the distinct advantage of being versatile in various (sub)tasks when provided with explicit instructions. In this paper, we explore this potential to automate GNT by comparing MT with the popular GPT-4 model. Through extensive manual analyses, our study empirically reveals the inherent limitations of current MT systems in generating GNTs and provides valuable insights into the potential and challenges associated with prompting for neutrality.</abstract>
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        <url>https://aclanthology.org/2024.eacl-short.23</url>
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        <date>2024-03</date>
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            <start>256</start>
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%0 Conference Proceedings
%T A Prompt Response to the Demand for Automatic Gender-Neutral Translation
%A Savoldi, Beatrice
%A Piergentili, Andrea
%A Fucci, Dennis
%A Negri, Matteo
%A Bentivogli, Luisa
%Y Graham, Yvette
%Y Purver, Matthew
%S Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F savoldi-etal-2024-prompt
%X Gender-neutral translation (GNT) that avoids biased and undue binary assumptions is a pivotal challenge for the creation of more inclusive translation technologies. Advancements for this task in Machine Translation (MT), however, are hindered by the lack of dedicated parallel data, which are necessary to adapt MT systems to satisfy neutral constraints. For such a scenario, large language models offer hitherto unforeseen possibilities, as they come with the distinct advantage of being versatile in various (sub)tasks when provided with explicit instructions. In this paper, we explore this potential to automate GNT by comparing MT with the popular GPT-4 model. Through extensive manual analyses, our study empirically reveals the inherent limitations of current MT systems in generating GNTs and provides valuable insights into the potential and challenges associated with prompting for neutrality.
%U https://aclanthology.org/2024.eacl-short.23
%P 256-267

Markdown (Informal)

[A Prompt Response to the Demand for Automatic Gender-Neutral Translation](https://aclanthology.org/2024.eacl-short.23) (Savoldi et al., EACL 2024)

• A Prompt Response to the Demand for Automatic Gender-Neutral Translation (Savoldi et al., EACL 2024)

ACL

• Beatrice Savoldi, Andrea Piergentili, Dennis Fucci, Matteo Negri, and Luisa Bentivogli. 2024. A Prompt Response to the Demand for Automatic Gender-Neutral Translation. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers), pages 256–267, St. Julian’s, Malta. Association for Computational Linguistics.