2025/06/18 by Hillary Dawkins, Dawkins, Hillary, Isar Nejadgholi +3
Arts and Humanities · #Ambiguity #Binary number #Computation and Language (cs.CL) #FOS: Computer and information sciences #Grammatical gender #Machine translation #Neutrality #Notional amount #Translation (biology) #Translation Studies and Practices
paper · pdf · doi:10.48550/arxiv.2506.15676
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2025/06/18 · openalex created_date 2025/10/19 · openalex updated_date 2026/08/05
Gender-inclusive machine translation (MT) should preserve gender ambiguity in the source to avoid misgendering and representational harms. While gender ambiguity often occurs naturally in notional gender languages such as English, maintaining that gender neutrality in grammatical gender languages is a challenge. Here we assess the sensitivity of 21 MT systems to the need for gender neutrality in response to gender ambiguity in three translation directions of varying difficulty. The specific gender-neutral strategies that are observed in practice are categorized and discussed. Additionally, we examine the effect of binary gender stereotypes on the use of gender-neutral translation. In general, we report a disappointing absence of gender-neutral translations in response to gender ambiguity. However, we observe a small handful of MT systems that switch to gender neutral translation using specific strategies, depending on the target language.