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        <title>How Conservative are Language Models? Adapting to the Introduction of Gender-Neutral Pronouns</title>
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    <name type="personal">
        <namePart type="given">Stephanie</namePart>
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        <namePart type="given">Ruixiang</namePart>
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            <title>Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies</title>
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            <namePart type="given">Marine</namePart>
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            <namePart type="given">Marie-Catherine</namePart>
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            <namePart type="given">Ivan</namePart>
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            <namePart type="family">Meza Ruiz</namePart>
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    <abstract>Gender-neutral pronouns have recently been introduced in many languages to a) include non-binary people and b) as a generic singular. Recent results from psycholinguistics suggest that gender-neutral pronouns (in Swedish) are not associated with human processing difficulties. This, we show, is in sharp contrast with automated processing. We show that gender-neutral pronouns in Danish, English, and Swedish are associated with higher perplexity, more dispersed attention patterns, and worse downstream performance. We argue that such conservativity in language models may limit widespread adoption of gender-neutral pronouns and must therefore be resolved.</abstract>
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%0 Conference Proceedings
%T How Conservative are Language Models? Adapting to the Introduction of Gender-Neutral Pronouns
%A Brandl, Stephanie
%A Cui, Ruixiang
%A Søgaard, Anders
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F brandl-etal-2022-conservative
%X Gender-neutral pronouns have recently been introduced in many languages to a) include non-binary people and b) as a generic singular. Recent results from psycholinguistics suggest that gender-neutral pronouns (in Swedish) are not associated with human processing difficulties. This, we show, is in sharp contrast with automated processing. We show that gender-neutral pronouns in Danish, English, and Swedish are associated with higher perplexity, more dispersed attention patterns, and worse downstream performance. We argue that such conservativity in language models may limit widespread adoption of gender-neutral pronouns and must therefore be resolved.
%R 10.18653/v1/2022.naacl-main.265
%U https://aclanthology.org/2022.naacl-main.265
%U https://doi.org/10.18653/v1/2022.naacl-main.265
%P 3624-3630

Markdown (Informal)

[How Conservative are Language Models? Adapting to the Introduction of Gender-Neutral Pronouns](https://aclanthology.org/2022.naacl-main.265) (Brandl et al., NAACL 2022)

• How Conservative are Language Models? Adapting to the Introduction of Gender-Neutral Pronouns (Brandl et al., NAACL 2022)

ACL

• Stephanie Brandl, Ruixiang Cui, and Anders Søgaard. 2022. How Conservative are Language Models? Adapting to the Introduction of Gender-Neutral Pronouns. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3624–3630, Seattle, United States. Association for Computational Linguistics.