Good, but not always Fair: An Evaluation of Gender Bias for three commercial Machine Translation Systems
arxiv(2023)
摘要
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.
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