Participatory Research as a Path to Community-Informed, Gender-Fair Machine Translation
This addresses gender-fair language issues in machine translation for non-binary and queer communities, representing an incremental approach by integrating participatory methods into existing MT design.
The paper tackles the problem of binary gender bias in machine translation by proposing a participatory action research method that includes queer and non-binary people, translators, and MT experts in the design process, resulting in findings such as the importance of context dependency and customizable solutions.
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.