Evaluating Gender Bias in Machine Translation

@article{Stanovsky2019EvaluatingGB,
  title={Evaluating Gender Bias in Machine Translation},
  author={Gabriel Stanovsky and Noah A. Smith and Luke Zettlemoyer},
  journal={ArXiv},
  year={2019},
  volume={abs/1906.00591}
}
We present the first challenge set and evaluation protocol for the analysis of gender bias in machine translation (MT. [] Key Method We devise an automatic gender bias evaluation method for eight target languages with grammatical gender, based on morphological analysis (e.g., the use of female inflection for the word "doctor"). Our analyses show that four popular industrial MT systems and two recent state-of-the-art academic MT models are significantly prone to gender-biased translation errors for all…

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