TITLE: MT-GenEval: A Counterfactual and Contextual Dataset for Evaluating Gender Accuracy in Machine Translation 

ABSTRACT: As generic machine translation (MT) quality has improved, the need for
targeted benchmarks that explore fine-grained aspects of quality has increased.
In particular, gender accuracy in translation can have implications in terms of
output fluency, translation accuracy, and ethics. In this paper, we introduce
MT-GenEval, a benchmark for evaluating gender accuracy in translation from
English into eight widely-spoken languages. MT-GenEval complements existing
benchmarks by providing realistic, gender-balanced, counterfactual data in
eight language pairs where the gender of individuals is unambiguous in the
input segment, including multi-sentence segments requiring inter-sentential
gender agreement. Our data and code is publicly available under a CC BY SA 3.0
license.
As generic machine translation (MT) quality has improved, the need for
targeted benchmarks that explore fine-grained aspects of quality has increased.
In particular, gender accuracy in translation can have implications in terms of
output fluency, translation accuracy, and ethics. In this paper, we introduce
MT-GenEval, a benchmark for evaluating gender accuracy in translation from
English into eight widely-spoken languages. MT-GenEval complements existing
benchmarks by providing realistic, gender-balanced, counterfactual data in
eight language pairs where the gender of individuals is unambiguous in the
input segment, including multi-sentence segments requiring inter-sentential
gender agreement. Our data and code is publicly available under a CC BY SA 3.0
license.
MT-GenEval: A Counterfactual and Contextual Dataset for Evaluating Gender Accuracy in Machine Translation
http://arxiv.org/abs/2211.01355v1