Towards Understanding Gender Bias in Relation Extraction

Andrew Gaut, Tony Sun, Shirlyn Tang, Yuxin Huang, Jing Qian, Mai ElSherief, Jieyu Zhao, Diba Mirza, Elizabeth Belding, Kai-Wei Chang, and William Yang Wang, in ACL, 2020.

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Abstract

Recent developments in Neural Relation Extraction (NRE) have made significant strides towards automated knowledge base construction. While much attention has been dedicated towards improvements in accuracy, there have been no attempts in the literature to evaluate social biases exhibited in NRE systems. In this paper, we create WikiGenderBias, a distantly supervised dataset composed of over 45,000 sentences including a 10% human annotated test set for the purpose of analyzing gender bias in relation extraction systems. We find that when extracting spouse and hypernym (i.e., occupation) relations, an NRE system performs differently when the gender of the target entity is different. However, such disparity does not appear when extracting relations such as birth date or birth place. We also analyze two existing bias mitigation techniques, word embedding debiasing and data augmentation. Unfortunately, due to NRE models relying heavily on surface level cues, we find that existing bias mitigation approaches have a negative effect on NRE. Our analysis lays groundwork for future quantifying and mitigating bias in relation extraction.

Our work “Towards Understanding Gender Bias in Relation Extraction” was accepted to #acl2020. We release WikiGenderBias, the first dataset for gender bias evaluation in relation extraction! Shoutout to amazing undergraduate students, collaborators and mentors!
— Mai ElSherief (@mai_elsherief) April 4, 2020

Bib Entry

@inproceedings{gaut2020towards,
  author = {Gaut, Andrew and Sun, Tony and Tang, Shirlyn and Huang, Yuxin and Qian, Jing and ElSherief, Mai and Zhao, Jieyu and Mirza, Diba and Belding, Elizabeth and Chang, Kai-Wei and Wang, William Yang},
  title = {Towards Understanding Gender Bias in Relation Extraction},
  booktitle = {ACL},
  year = {2020},
  presentation_id = {https://virtual.acl2020.org/paper_main.265.html}
}

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