@inproceedings{daems-etal-2026-dutch,
title = "{D}utch audience perceptions of human and machine-translated pronouns for non-binary referent in subtitles",
author = "Daems, Joke and
Van Hee, Cynthia and
Muylem, Alicia Van",
editor = "Lardelli, Manuel and
Savoldi, Beatrice and
Hackenbuchner, Jani{\c{c}}a and
Bentivogli, Luisa and
Gkovedarou, Eleni and
Daems, Joke",
booktitle = "Proceedings of the 4th Workshop on Gender-Inclusive Translation Technologies ({GITT} 2026)",
month = jun,
year = "2026",
address = "Tilburg, the Netherlands",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2026.gitt-1.2/",
pages = "3--15",
abstract = "The increased representation of non-binary characters in audiovisual media is societally crucial, yet non-binary language (e.g., pronouns) offers new challenges for translators. In Dutch, the acceptability of pronoun strategies for non-binary reference is debated and evolving. Machine translation (MT) is increasingly being used in audiovisual translation, yet its potential for translation in gender-sensitive contexts is understudied. In this paper, we compare three pronoun translation strategies (two human-produced, one MT) for rendering the English non-binary pronoun they into Dutch in an audiovisual context, using a fragment from the Netflix show Sex Education. We explore general acceptability of these strategies and compare this to audience perceptions after viewing subtitled fragments employing a specific strategy. The results support findings from earlier work on non-binary pronouns in Dutch and indicate that the MT-generated translations were perceived as the least suitable."
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<abstract>The increased representation of non-binary characters in audiovisual media is societally crucial, yet non-binary language (e.g., pronouns) offers new challenges for translators. In Dutch, the acceptability of pronoun strategies for non-binary reference is debated and evolving. Machine translation (MT) is increasingly being used in audiovisual translation, yet its potential for translation in gender-sensitive contexts is understudied. In this paper, we compare three pronoun translation strategies (two human-produced, one MT) for rendering the English non-binary pronoun they into Dutch in an audiovisual context, using a fragment from the Netflix show Sex Education. We explore general acceptability of these strategies and compare this to audience perceptions after viewing subtitled fragments employing a specific strategy. The results support findings from earlier work on non-binary pronouns in Dutch and indicate that the MT-generated translations were perceived as the least suitable.</abstract>
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%0 Conference Proceedings
%T Dutch audience perceptions of human and machine-translated pronouns for non-binary referent in subtitles
%A Daems, Joke
%A Van Hee, Cynthia
%A Muylem, Alicia Van
%Y Lardelli, Manuel
%Y Savoldi, Beatrice
%Y Hackenbuchner, Janiça
%Y Bentivogli, Luisa
%Y Gkovedarou, Eleni
%Y Daems, Joke
%S Proceedings of the 4th Workshop on Gender-Inclusive Translation Technologies (GITT 2026)
%D 2026
%8 June
%I European Association for Machine Translation
%C Tilburg, the Netherlands
%F daems-etal-2026-dutch
%X The increased representation of non-binary characters in audiovisual media is societally crucial, yet non-binary language (e.g., pronouns) offers new challenges for translators. In Dutch, the acceptability of pronoun strategies for non-binary reference is debated and evolving. Machine translation (MT) is increasingly being used in audiovisual translation, yet its potential for translation in gender-sensitive contexts is understudied. In this paper, we compare three pronoun translation strategies (two human-produced, one MT) for rendering the English non-binary pronoun they into Dutch in an audiovisual context, using a fragment from the Netflix show Sex Education. We explore general acceptability of these strategies and compare this to audience perceptions after viewing subtitled fragments employing a specific strategy. The results support findings from earlier work on non-binary pronouns in Dutch and indicate that the MT-generated translations were perceived as the least suitable.
%U https://aclanthology.org/2026.gitt-1.2/
%P 3-15
Markdown (Informal)
[Dutch audience perceptions of human and machine-translated pronouns for non-binary referent in subtitles](https://aclanthology.org/2026.gitt-1.2/) (Daems et al., GITT 2026)
ACL