Evaluating Gender Bias in Machine Translation

@inproceedings{Stanovsky2019EvaluatingGB,
  title={Evaluating Gender Bias in Machine Translation},
  author={Gabriel Stanovsky and Noah A. Smith and Luke Zettlemoyer},
  booktitle={ACL},
  year={2019}
}
We present the first challenge set and evaluation protocol for the analysis of gender bias in machine translation (MT). Our approach uses two recent coreference resolution datasets composed of English sentences which cast participants into non-stereotypical gender roles (e.g., "The doctor asked the nurse to help her in the operation"). 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… CONTINUE READING

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