Towards Understanding Gender Bias in Relation Extraction

By Andrew Gaut, Tony Sun, Shirlyn Tang, Yuxin Huang, Jing Qian, Mai Elsherief, Jieyu Zhao, Diba Mirza, E. Belding, Kai-Wei Chang and 1 more. 2019

44 citations recorded by the source corpus.

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-of 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 birthDate or birthPlace. We also analyze how existing bias mitigation techniques, such as name anonymization, word embedding debiasing, and data augmentation affect the NRE system in terms of maintaining the test performance and reducing biases. Unfortunately, due to NRE models rely 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 NRE.

Subfields: Artificial Intelligence, Natural Language Processing, Safety, Robustness & Interpretability

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Citations in the corpus

Language (Technology) is Power: A Critical Survey of “Bias” in NLP (2020, neutral): “…Nozza et al. (2019); Sap et al. (2019); Sheng et al. (2019); Stanovsky et al. (2019); Vaidya et al. (2019); Webster et al. (2019); Ethayarajh (2020); Gaut et al. (2020); Gencoglu (2020); Hovy et al. (2020); Huang et al. (2020); Kim et al. (2020); Peng et al. (2020); Ravfogel et al. (2020); Rios…”
CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models (2020, background): “One line of work explores evaluation grounded to specific downstream tasks, such as coreference resolution (Rudinger et al., 2018; Webster et al., 2018; Dinan et al., 2020) and relation extraction (Gaut et al., 2019).”
Queens Are Powerful Too: Mitigating Gender Bias in Dialogue Generation (2019, background): “2019), uncovering many gender disparities and harmful biases in algorithms and text (Cao and Daumé III 2020; Chang et al. 2019; Chang and McKeown 2019; Costa-jussà 2019; Du et al. 2019; Emami et al. 2019; Garimella et al. 2019; Gaut et al. 2020; Habash et al. 2019; Hashempour 2019; Hoyle et al. 2019”
Multi-Dimensional Gender Bias Classification (2020, background): “propriate turn-taking classification (Lepp, 2019), relation extraction (Gaut et al., 2019), identification of offensive content (Sharifirad and Matwin, 2019; Sharifirad et al.”
Large Language Model Alignment: A Survey (2023, background): “Similarly, WikiGenderBias (Gaut et al., 2020) is a dataset aimed at analyzing gender bias in the task of relation extraction.”
OSCaR: Orthogonal Subspace Correction and Rectification of Biases in Word Embeddings (2020, background): “Most work focuses on gender bias, and in particular, the stereotypical associations of occupations to males and females (Rudinger et al., 2018; DeArteaga et al., 2019; Gaut et al., 2019; Dev et al., 2020).”
Quantifying Social Biases in NLP: A Generalization and Empirical Comparison of Extrinsic Fairness Metrics (2021, background): “, 2019), relation extraction (Gaut et al., 2020), text generation (Huang et al.”
What do Bias Measures Measure? (2021, background): “There are other related surveys on existing bias mitigation methods in natural language understanding (NLU) (Sun et al., 2019), biases in natural language generation (NLG) (Sheng et al., 2021), adapted deﬁnitions of biases (Blodgett et al., 2020), and the relation between large language models and h”