Machine Translation (MT) continues to make significant strides in quality and
is increasingly adopted on a larger scale. Consequently, analyses have been
redirected to more nuanced aspects, intricate phenomena, as well as potential
risks that may arise from the widespread use of MT tools. Along this line, this
paper offers a meticulous assessment of three commercial MT systems - Google
Translate, DeepL, and Modern MT - with a specific focus on gender translation
and bias. For three language pairs (English/Spanish, English/Italian, and
English/French), we scrutinize the behavior of such systems at several levels
of granularity and on a variety of naturally occurring gender phenomena in
translation. Our study takes stock of the current state of online MT tools, by
revealing significant discrepancies in the gender translation of the three
systems, with each system displaying varying degrees of bias despite their
overall translation quality.

本研究评估了 Google Translate、DeepL 和 Modern MT 这三种在线翻译系统在性别翻译和偏见方面的表现，并发现这些系统在性别翻译方面存在不同程度的偏见。