Bias and stereotyping in human and machine translation: the influence of gender and directionality Ghent University
The growing societal debate and increased awareness of gender-inclusive language and non-binary identities pose new challenges for translators. While machine translation (MT) is actively being used in the translation industry, current systems lack the training data or optimization to handle gender-inclusive language strategies as they arise. MT output has been shown to exacerbate biases and perpetuate harmful stereotypes. This project aims to ...