3 papers
cs.CL2026
Explaining GAND: A Resource on Gender-Ambiguous Natural Data & Contrastive Attribution
Janiça Hackenbuchner, Jasper Degraeuwe, Arda Tezcan +1
Machine translation (MT) systems continue to produce gender-biased translations. In a time where self-expression is paramount, mistranslations based on default behaviour and stereo…
cs.CL2026
What Triggers my Model? Contrastive Explanations Inform Gender Choices by Translation Models
Janiça Hackenbuchner, Arda Tezcan, Joke Daems
Interpretability can be implemented to understand decisions taken by (black box) models, such as neural machine translation (NMT) or large language models (LLMs). Yet, research in…
cs.CL2025
Gender Bias in English-to-Greek Machine Translation
Eleni Gkovedarou, Joke Daems, Luna De Bruyne
As the demand for inclusive language increases, concern has grown over the susceptibility of machine translation (MT) systems to reinforce gender stereotypes. This study investigat…