Amazon Research: Evaluating Gender Accuracy in Translation
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.Click here to access the article.
Citation Currey, A.; N˘adejde, M.; Pappagari, R.; Mayer, M.; Lauly, S.; Niu, X.; Hsu, B.; Dinu, G. (2022). MT-GenEval: A Counterfactual and Contextual Dataset for Evaluating Gender Accuracy in Machine Translation. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics: 4287–4299.