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        <title>A Chinese Challenge Set to Assess Gender Bias in Automated Translation</title>
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            <title>Proceedings of the 4th Workshop on Gender-Inclusive Translation Technologies (GITT 2026)</title>
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            <namePart type="given">Manuel</namePart>
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    <abstract>Gender bias in automated translation (AT) is well-documented for English-source language pairs, while source languages generally lacking grammatical gender remain largely understudied. This paper addresses the Chinese-Portuguese direction, a language pair that has received little attention in this context. We developed a Chinese challenge set of 495 sentences constructed from 45 occupations across an eleven-sentence template matrix, systematically varying the type and syntactic position of gender cues: no cue, explicit prenominal modifiers, and coreferential pronouns varying in position and syntactic complexity. We tested two commercial neural machine translation (NMT) systems and six large language models (LLMs) with this challenge set. Results show a clear hierarchy of cue effectiveness: explicit prenominal modifiers yield universal 100% accuracy; in the absence of gender cues, models predominantly default to masculine forms; and coreferential pronouns in complex sentences reveal a pronounced masculine-feminine asymmetry. Also, LLMs demonstrate more symmetric gender cue processing than NMT systems. Our data and code are available at https://github.com/xu-xiaolan/chinese-challenge-set-gender-bias.</abstract>
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%0 Conference Proceedings
%T A Chinese Challenge Set to Assess Gender Bias in Automated Translation
%A Xu, Xiaolan
%A Mendes, Sara
%A Hsu, Yu-Yin
%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 xu-etal-2026-chinese
%X Gender bias in automated translation (AT) is well-documented for English-source language pairs, while source languages generally lacking grammatical gender remain largely understudied. This paper addresses the Chinese-Portuguese direction, a language pair that has received little attention in this context. We developed a Chinese challenge set of 495 sentences constructed from 45 occupations across an eleven-sentence template matrix, systematically varying the type and syntactic position of gender cues: no cue, explicit prenominal modifiers, and coreferential pronouns varying in position and syntactic complexity. We tested two commercial neural machine translation (NMT) systems and six large language models (LLMs) with this challenge set. Results show a clear hierarchy of cue effectiveness: explicit prenominal modifiers yield universal 100% accuracy; in the absence of gender cues, models predominantly default to masculine forms; and coreferential pronouns in complex sentences reveal a pronounced masculine-feminine asymmetry. Also, LLMs demonstrate more symmetric gender cue processing than NMT systems. Our data and code are available at https://github.com/xu-xiaolan/chinese-challenge-set-gender-bias.
%U https://aclanthology.org/2026.gitt-1.8/
%P 87-99

Markdown (Informal)

[A Chinese Challenge Set to Assess Gender Bias in Automated Translation](https://aclanthology.org/2026.gitt-1.8/) (Xu et al., GITT 2026)

• A Chinese Challenge Set to Assess Gender Bias in Automated Translation (Xu et al., GITT 2026)

ACL

• Xiaolan Xu, Sara Mendes, and Yu-Yin Hsu. 2026. A Chinese Challenge Set to Assess Gender Bias in Automated Translation. In Proceedings of the 4th Workshop on Gender-Inclusive Translation Technologies (GITT 2026), pages 87–99, Tilburg, the Netherlands. European Association for Machine Translation.