  Machine translation and other NLP systems often contain significant biases
regarding sensitive attributes, such as gender or race, that worsen system
performance and perpetuate harmful stereotypes. Recent preliminary research
suggests that adversarial learning can be used as part of a model-agnostic bias
mitigation method that requires no data modifications. However, adapting this
strategy for machine translation and other modern NLP domains requires (1)
restructuring training objectives in the context of fine-tuning pretrained
large language models and (2) developing measures for gender or other protected
variables for tasks in which these attributes must be deduced from the data
itself.
  We present an adversarial learning framework that addresses these challenges
to mitigate gender bias in seq2seq machine translation. Our framework improves
the disparity in translation quality for sentences with male vs. female
entities by 86% for English-German translation and 91% for English-French
translation, with minimal effect on translation quality. The results suggest
that adversarial learning is a promising technique for mitigating gender bias
in machine translation.
