TITLE: Collecting a Large-Scale Gender Bias Dataset for Coreference Resolution and Machine Translation 

ABSTRACT: Recent works have found evidence of gender bias in models of machine
translation and coreference resolution using mostly synthetic diagnostic
datasets. While these quantify bias in a controlled experiment, they often do
so on a small scale and consist mostly of artificial, out-of-distribution
sentences. In this work, we find grammatical patterns indicating stereotypical
and non-stereotypical gender-role assignments (e.g., female nurses versus male
dancers) in corpora from three domains, resulting in a first large-scale gender
bias dataset of 108K diverse real-world English sentences. We manually verify
the quality of our corpus and use it to evaluate gender bias in various
coreference resolution and machine translation models. We find that all tested
models tend to over-rely on gender stereotypes when presented with natural
inputs, which may be especially harmful when deployed in commercial systems.
Finally, we show that our dataset lends itself to finetuning a coreference
resolution model, finding it mitigates bias on a held out set. Our dataset and
models are publicly available at www.github.com/SLAB-NLP/BUG. We hope they will
spur future research into gender bias evaluation mitigation techniques in
realistic settings.
Recent works have found evidence of gender bias in models of machine
translation and coreference resolution using mostly synthetic diagnostic
datasets. While these quantify bias in a controlled experiment, they often do
so on a small scale and consist mostly of artificial, out-of-distribution
sentences. In this work, we find grammatical patterns indicating stereotypical
and non-stereotypical gender-role assignments (e.g., female nurses versus male
dancers) in corpora from three domains, resulting in a first large-scale gender
bias dataset of 108K diverse real-world English sentences. We manually verify
the quality of our corpus and use it to evaluate gender bias in various
coreference resolution and machine translation models. We find that all tested
models tend to over-rely on gender stereotypes when presented with natural
inputs, which may be especially harmful when deployed in commercial systems.
Finally, we show that our dataset lends itself to finetuning a coreference
resolution model, finding it mitigates bias on a held out set. Our dataset and
models are publicly available at www.github.com/SLAB-NLP/BUG. We hope they will
spur future research into gender bias evaluation mitigation techniques in
realistic settings.
Collecting a Large-Scale Gender Bias Dataset for Coreference Resolution and Machine Translation
http://arxiv.org/abs/2109.03858v2