66 - Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods, with Jieyu Zhao

NACL 2018 paper, by Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente
Ordonez, and Kai-Wei Chang. Jieyu comes on the podcast to talk
about bias in coreference resolution models. This bias makes models
rely disproportionately on gender when making decisions for whether
"she" refers to a noun like "secretary" or "physician". Jieyu and
her co-authors show that coreference systems do not actually
exhibit much bias in standard evaluation settings (OntoNotes),
perhaps because there is a broad document context to aid in making
coreference decisions. But they then construct a really nice
diagnostic dataset that isolates simple coreference decisions, and
evaluates whether the model is using common sense, grammar, or
gender bias to make those decisions. This dataset shows that
current models are quite biased, particularly when it comes to
common sense, using gender to make incorrect coreference decisions.
Jieyu then tells us about some simple methods to correct the bias
without much of a drop in overall accuracy.
https://www.semanticscholar.org/paper/Gender-Bias-in-Coreference-Resolution%3A-Evaluation-Zhao-Wang/e4a31322ed60479a6ae05d1f2580dd0fa2d77e50
Also, there was a very similar paper also published at NAACL 2018
that used similar methodology and constructed a similar dataset:
https://www.semanticscholar.org/paper/Gender-Bias-in-Coreference-Resolution-Rudinger-Naradowsky/be2c8b5ec0eee2f32da950db1b6cf8cc4a621f8f.