@inproceedings{887ba37eee2c4a639eaed5d6ab77a838,
title = "Gender Coreference and Bias Evaluation at WMT 2020",
abstract = "Gender bias in machine translation can manifest when choosing gender inflections based on spurious gender correlations. For example, always translating doctors as men and nurses as women. This can be particularly harmful as models become more popular and deployed within commercial systems. Our work presents the largest evidence for the phenomenon in more than 19 systems submitted to the WMT over four diverse target languages: Czech, German, Polish, and Russian. To achieve this, we use WinoMT, a recent automatic test suite which examines gender coreference and bias when translating from English to languages with grammatical gender. We extend WinoMT to handle two new languages tested in WMT: Polish and Czech. We find that all systems consistently use spurious correlations in the data rather than meaningful contextual information.",
author = "Tom Kocmi and Tomasz Limisiewicz and Gabriel Stanovsky",
note = "Publisher Copyright: {\textcopyright} 2020 Association for Computational Linguistics; 5th Conference on Machine Translation, WMT 2020 ; Conference date: 19-11-2020 Through 20-11-2020",
year = "2020",
language = "الإنجليزيّة",
series = "5th Conference on Machine Translation, WMT 2020 - Proceedings",
publisher = "Association for Computational Linguistics (ACL)",
pages = "357--364",
editor = "Loic Barrault and Ondrej Bojar and Fethi Bougares and Rajen Chatterjee and Costa-Jussa, {Marta R.} and Christian Federmann and Mark Fishel and Alexander Fraser and Yvette Graham and Paco Guzman and Barry Haddow and Matthias Huck and Yepes, {Antonio Jimeno} and Philipp Koehn and Andre Martins and Makoto Morishita and Christof Monz and Masaaki Nagata and Toshiaki Nakazawa and Matteo Negri",
booktitle = "5th Conference on Machine Translation, WMT 2020 - Proceedings",
address = "الولايات المتّحدة",
}