Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods

Abstract

We introduce a new benchmark, WinoBias, for coreference resolution focused on gender bias. Our corpus contains Winograd-schema style sentences with entities corresponding to people referred by their occupation (e.g. the nurse, the doctor, the carpenter). We demonstrate that a rule-based, a feature-rich, and a neural coreference system all link gendered pronouns to pro-stereotypical entities with higher accuracy than anti-stereotypical entities, by an average difference of 21.1 in F1 score. Finally, we demonstrate a data-augmentation approach that, in combination with existing word-embedding debiasing techniques, removes the bias demonstrated by these systems in WinoBias without significantly affecting their performance on existing coreference benchmark datasets. Our dataset and code are available at http://winobias.org.

Unsupervised Discovery of Gendered Language through Latent-Variable Modeling

“…Model versus data. Recent NLP research has focused on gender biases in word embeddings (Bolukbasi et al, 2016;Zhao et al, 2017) and co-reference systems (Zhao et al, 2018;Rudinger et al, 2018).…”

Analysing Neural Language Models: Contextual Decomposition Reveals Default Reasoning in Number and Gender Assignment

“…To create our corpus, we use the templates from the WinoBias corpus created by Zhao et al (2018). This corpus contains sentences with job titles that are gender neutral, yet contain a stereotypical bias towards one gender (doctors and CEOs are male, nurses and housekeepers female).…”

“…Stereotypical referents The intermediate conclusion that the language model performs successful anaphora resolution on our experiment also provides us the opportunity to probe the gender biases of the model. To do so, we repeat the pronoun preference test on an adapted version of the WinoBias corpus (Zhao et al, 2018), in which all referents are only stereotypically considered to be male or female (e.g., doctor and nurse).…”

Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology

“…This means that if the gender of one word changes, the others have to be updated to match. As a result, simple heuristics, such as augmenting a corpus with additional sentences in which he and she have been swapped (Zhao et al, 2018), will yield ungrammatical sentences. Consider the Spanish phrase el ingeniero experto (the skilled engineer).…”