  Machine learning models are trained to find patterns in data. NLP models can
inadvertently learn socially undesirable patterns when training on gender
biased text. In this work, we propose a general framework that decomposes
gender bias in text along several pragmatic and semantic dimensions: bias from
the gender of the person being spoken about, bias from the gender of the person
being spoken to, and bias from the gender of the speaker. Using this
fine-grained framework, we automatically annotate eight large scale datasets
with gender information. In addition, we collect a novel, crowdsourced
evaluation benchmark of utterance-level gender rewrites. Distinguishing between
gender bias along multiple dimensions is important, as it enables us to train
finer-grained gender bias classifiers. We show our classifiers prove valuable
for a variety of important applications, such as controlling for gender bias in
generative models, detecting gender bias in arbitrary text, and shed light on
offensive language in terms of genderedness.
