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.
Outcome: The proposed methods show that they can reduce gender bias in word embeddings . the proposed methods are insufficient and should not be trusted, the authors argue .

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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.
Outcome: The proposed method preserves gender-related information while removing stereotypical discriminative gender biases from pre-trained word embeddings.
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.
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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Robustness and Reliability of Gender Bias Assessment in Word Embeddings: The Role of Base Pairs (2020.aacl-main)

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Challenge: Existing methods to quantify gender bias in word embeddings are not robust and cannot identify common types of bias.
Approach: They propose to quantify gender bias by using cosine similarity to a pair of gender words and using analogies.
Outcome: The proposed methods are not robust and cannot identify common types of bias, while analogies are unsuitable indicators.
Identifying and Reducing Gender Bias in Word-Level Language Models (N19-3)

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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.
Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings (N19-1)

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Challenge: Existing methods to debias word embeddings in binary settings such as gender and religion are limited to binary labels, whereas word2vec embedders can be used to propagate biases.
Approach: They propose a method to debias word embeddings in multiclass settings such as gender and religion, extending the work of Bolukbasi et al. (2016).
Outcome: The proposed method maintains the efficacy in standard NLP tasks while maintaining the utility of embeddings.
Mitigating Gender Bias in Natural Language Processing: Literature Review (P19-1)

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Challenge: NLP models propagate and may even amplify gender bias found in text corpora . methods to mitigate gender bias in NLP are relatively nascent .
Approach: They propose to analyze gender bias based on four forms of representation bias and discuss the advantages and drawbacks of existing gender debiasing methods.
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Reducing Gender Bias in Word-Level Language Models with a Gender-Equalizing Loss Function (P19-2)

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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.
Exploring the Linear Subspace Hypothesis in Gender Bias Mitigation (2020.emnlp-main)

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Challenge: Existing methods for gender bias mitigation for word embeddings are based on pre-trained word embeds . however, the assumption that the bias subspace is linear is untested .
Approach: They propose a method to isolate gender bias in word embeddings using pre-trained word embeds.
Outcome: The proposed method eliminates gender bias in word embeddings but assumes bias subspace is linear . the proposed method has some drawbacks, but it is a good one for a non-linear analysis.
Projective Methods for Mitigating Gender Bias in Pre-trained Language Models (2024.lrec-main)

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Challenge: Mitigating gender bias in NLP has a long history tied to debiasing static word embeddings.
Approach: They propose a masked language modelling task where content is developed around known social stereotypes and a projective debiasing method is used to reduce bias.
Outcome: The proposed methods reduce intrinsic bias and mitigat observed bias in a downstream setting, but the two outcomes are not necessarily correlated.

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