{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/towards-understanding-gender-bias-in-relation","title":"Towards Understanding Gender Bias in Relation Extraction","arxiv_id":"1911.03642","date":"2019-11-09","proceeding":"ACL 2020 6","authors":["Andrew Gaut","Tony Sun","Shirlyn Tang","Yuxin Huang","Jing Qian","Mai ElSherief","Jieyu Zhao","Diba Mirza","Elizabeth Belding","Kai-Wei Chang","William Yang Wang"],"abstract":"Recent developments in Neural Relation Extraction (NRE) have made significant strides towards Automated Knowledge Base Construction (AKBC). While much attention has been dedicated towards improvements in accuracy, there have been no attempts in the literature to our knowledge to evaluate social biases in NRE systems. We create WikiGenderBias, a distantly supervised dataset with a human annotated test set. WikiGenderBias has sentences specifically curated to analyze gender bias in relation extraction systems. We use WikiGenderBias to evaluate systems for bias and find that NRE systems exhibit gender biased predictions and lay groundwork for future evaluation of bias in NRE. We also analyze how name anonymization, hard debiasing for word embeddings, and counterfactual data augmentation affect gender bias in predictions and performance.","url_abs":"https://arxiv.org/abs/1911.03642v3","url_pdf":"https://arxiv.org/pdf/1911.03642v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"towards-understanding-gender-bias-in-relation","repo_url":"https://github.com/AndrewJGaut/Towards-Understanding-Gender-Bias-in-Neural-Relation-Extraction","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"knowledge-base-construction","task_name":"Knowledge Base Construction"},{"task_slug":null,"task_name":"Relation"},{"task_slug":"relation-extraction","task_name":"Relation Extraction"},{"task_slug":"word-embeddings","task_name":"Word Embeddings"},{"task_slug":null,"task_name":"counterfactual"}],"methods":[{"method_slug":"test","method_name":"Test"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1911.03642","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.03642"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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