| Challenge: | Existing discriminatory biases in training data can be amplified by models . text corpora exhibit socially problematic biase . |
| Approach: | They propose a metric to measure gender bias and a regularization loss term to minimize embeddings onto an embeddable subspace that encodes gender. |
| Outcome: | The proposed method reduces gender bias up to an optimal weight assigned to the loss term, and the model becomes unstable as the perplexity increases. |
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| Challenge: | Existing methods to reduce gender bias in natural language datasets are inadequate. |
| Approach: | They propose a loss function modification approach which equalizes the probabilities of male and female words in the output. |
| Outcome: | The proposed approach outperforms existing methods in several aspects, especially in reducing gender bias in occupation words. |
Mitigating Gender Bias in Natural Language Processing: Literature Review (P19-1)
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Tony Sun, Andrew Gaut, Shirlyn Tang, Yuxin Huang, Mai ElSherief, Jieyu Zhao, Diba Mirza, Elizabeth Belding, Kai-Wei Chang, William Yang Wang
| Challenge: | NLP models propagate and may even amplify gender bias found in text corpora . methods to mitigate gender bias in NLP are relatively nascent . |
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| Outcome: | The proposed methods are based on four forms of representation bias and have advantages and drawbacks. |
Examining Gender Bias in Languages with Grammatical Gender (D19-1)
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| Challenge: | Existing studies on gender bias in word embeddings focus on English . however, these studies cannot be extended to languages with morphological agreement on gender . |
| Approach: | They propose new metrics to evaluate gender bias in word embeddings of English and Spanish . they extend existing approaches to mitigate gender bias while preserving original embeddables . |
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Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them (N19-1)
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| Challenge: | Existing methods to remove gender bias from word embeddings are insufficient, we argue . existing methods for gender-neutral modeling are ineffective, we conclude . |
| Approach: | They propose methods to reduce gender bias in word embeddings by debiasing them using text corpora. |
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Leveraging Pre-trained Language Models for Gender Debiasing (2022.lrec-1)
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| Challenge: | Existing methods to reduce gender bias in natural language are costly and time-consuming. |
| Approach: | They propose a method to generate gender variants for a given text using pre-trained language models as the resource without any task-specific labelled data. |
| Outcome: | The proposed method can reduce gender bias in a language generation context without a task-specific labelled data. |
Multi-Dimensional Gender Bias Classification (2020.emnlp-main)
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| Challenge: | a novel framework decomposes gender bias in text along several pragmatic and semantic dimensions . language is a primary means by which people communicate, express identities and categorize themselves . unwanted gender biases can affect downstream applications, leading to poor user experiences . |
| Approach: | They propose a framework that decomposes gender bias in text along several dimensions . they annotate eight large scale datasets with gender information and collect a benchmark . |
| Outcome: | The proposed framework decomposes gender bias in text along several pragmatic and semantic dimensions. |
Reducing Gender Bias in Abusive Language Detection (D18-1)
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| Challenge: | Abusive language detection models tend to be biased toward identity words of a certain group of people . recent studies have raised concerns about the robustness of such systems . |
| Approach: | They propose to use debiased word embeddings, gender swap data augmentation to reduce model bias . they also propose to fine-tune models with a larger corpus to correct such bias if needed . |
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Gender-preserving Debiasing for Pre-trained Word Embeddings (P19-1)
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| Challenge: | Existing methods for debiasing word embeddings have shown discriminative biases . word embeds learnt from social media have shown to encode racist, offensive and discriminative language usage. |
| Approach: | They propose a method that preserves gender-related information while removing stereotypical gender biases from pre-trained word embeddings. |
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Double-Hard Debias: Tailoring Word Embeddings for Gender Bias Mitigation (2020.acl-main)
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| Challenge: | Existing methods to debias word embeddings from human-generated corpora inherit strong gender bias . prior work has suggested removing gender component from pre-trained word embeds or compressing gender information into a few dimensions of the embeddable space . |
| Approach: | They propose a technique that purifies word embeddings against inferred gender subspaces . they propose to preserve distributional semantics of pre-trained word embeds while reducing gender bias . |
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Neutralizing Gender Bias in Word Embeddings with Latent Disentanglement and Counterfactual Generation (2020.findings-emnlp)
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| Challenge: | Recent research shows word embeddings have strong gender biases in embeddable spaces . a proposed method can be used to debiase word embeds without loss of semantic information . |
| Approach: | They propose a latent disentanglement method with a siamese auto-encoder structure with an adapted gradient reversal layer to debiase word embeddings. |
| Outcome: | The proposed method can preserve semantic information during debiasing while minimizing loss of semantic information for extrinsic NLP tasks. |