Machine Translation (MT) models are well-known to suffer from gender bias, especially for gender beyond a binary conception. Due to the multiplicity of language-specific strategies for gender representation beyond the binary, debiasing MT is extremely challenging. As an alternative, we propose a case study on gender-fair post-editing. In this study, six professional translators each post-edited three English to German machine translations. For each translation, participants were instructed to use a different gender-fair language strategy, that is, gender-neutral rewording, gender-inclusive characters, and a neosystem. The focus of this study is not on translation quality but rather on the ease of integrating gender-fair language into the post-editing process. Findings from non-participant observation and interviews show clear differences in temporal and cognitive effort between participants and strategy as well as in the success of using gender-fair language.

With an increasing visibility of non-binary individuals, a growing number of language-specific strategies to linguistically include all genders or neutralize any gender references can be observed. Due to this multiplicity of proposed strategies and gender-specific grammatical differences across languages, selecting the one option to translate gender-fair language is challenging for machines and humans alike. As a first step towards gender-fair translation, we conducted a survey with translators to compare four gender-fair translations from a notional gender language, English, to a grammatical gender language, German. Proposed translations were rated by means of best-worst scaling as well as regarding their readability and comprehensibility. Participants expressed a clear preference for strategies with gender-inclusive character, i.e., colon.

Recent years have seen a strongly increased visibility of non-binary people in public discourse. Accordingly, considerations of gender-fair language go beyond a binary conception of male/female. However, language technology, especially machine translation (MT), still suffers from binary gender bias. Proposing a solution for gender-fair MT beyond the binary from a purely technological perspective might fall short to accommodate different target user groups and in the worst case might lead to misgendering. To address this challenge, we propose a method and case study building on participatory action research to include experiential experts, i.e., queer and non-binary people, translators, and MT experts, in the MT design process. The case study focuses on German, where central findings are the importance of context dependency to avoid identity invalidation and a desire for customizable MT solutions.