Abstract
We investigate systematic biases in neural machine translation (NMT) systems when translating text between languages with different cultural contexts. Our analysis reveals that NMT systems often produce translations that reflect the dominant cultural perspectives present in their training data, leading to subtle but significant meaning shifts. We propose a framework for measuring and mitigating these translation biases, introducing metrics that capture semantic drift across cultural dimensions. Experiments on 15 language pairs demonstrate the prevalence of these biases and the effectiveness of our debiasing approaches.