Abstract Transformer-based models are the current state-of-the-art in machine translation (MT) research. These systems can generate fluent and contextually appropriate translations for many translation tasks and languages. These models are trained on large amounts of unlabelled data, often consisting of billions of examples, and naturally, this data can be imbalanced or contain stereotypes. As a result, the models may produce inaccurate gender assignments in translation and reinforce societal stereotypes. This survey investigates the nature and impact of gender bias in neural MT (NMT). It begins with an overview of how such bias emerges through different data and models. The survey then examines various evaluation frameworks proposed to systematically identify and measure gender disparities in translation outputs. It also categorises mitigation techniques based on the stage at which they operate within an NMT pipeline, from data preprocessing, model adaptation, to inference-level interventions. This survey compiles existing research and highlights challenges to facilitate the creation of more unbiased and gender-aware NMT systems.
Gajakos et al. (Fri,) studied this question.