| Challenge: | Word embeddings are often criticized for capturing undesirable word associations such as gender stereotypes. |
| Approach: | They propose to use subspace projection to debias vectors post hoc using a model that implicitly does matrix factorization to debunk gender bias. |
| Outcome: | The proposed test overestimates gender bias in word embeddings by using subspace projection, a method that is widely used in training. |
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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 . |
| Outcome: | The proposed technique preserves distributional semantics of pre-trained word embeddings while reducing gender bias to a larger degree than prior approaches. |
Querying Word Embeddings for Similarity and Relatedness (N18-1)
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| Challenge: | Word2Vec embeddings have become popular representations of word meaning . similarity between two words is often assumed to be a direction-less measure, whereas relatedness is inherently directional. |
| Approach: | They propose to use word embeddings to predict asymmetric association between words from a dataset of production norms to generate thematically related words. |
| Outcome: | The proposed model predicts asymmetric association between words from a recently published dataset of production norms. |
Exploring Human Gender Stereotypes with Word Association Test (D19-1)
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| Challenge: | Existing word embeddings have been used to study gender stereotypes in texts . however, evaluating their validities is still an open problem . et al.: this study investigates gender bias using the lens of language, especially, the words . |
| Approach: | They use word association test to derive bias scores for large amount of words . they find that these bias scores correlate well with bias in the real world . |
| Outcome: | The proposed method correlates well with bias in the real world, and with census data, it provides a different perspective on gender stereotypes in words. |
The Undesirable Dependence on Frequency of Gender Bias Metrics Based on Word Embeddings (2022.findings-emnlp)
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| Challenge: | Recent studies have found word embeddings can capture semantic similarity but may be affected by word frequency. |
| Approach: | They find that word embeddings can capture semantic similarity but may be affected by word frequency . they compare this effect with an alternative metric based on Pointwise Mutual Information . |
| Outcome: | The proposed method does not depend on word frequency, but it does return female bias in low frequency words. |
A Transparent Framework for Evaluating Unintended Demographic Bias in Word Embeddings (P19-1)
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| Challenge: | Word embeddings suffer from unintended demographic biases, a new study shows . word embedders can cause downstream NLP systems to be unfair, the authors argue . |
| Approach: | They propose a metric to evaluate the fairness of word embeddings via the relative negative sentiment associated with demographic identity terms from various protected groups. |
| Outcome: | The proposed metric measures fairness in word embeddings via the relative negative sentiment associated with demographic identity terms from various protected groups. |
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. |
OSCaR: Orthogonal Subspace Correction and Rectification of Biases in Word Embeddings (2021.emnlp-main)
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| Challenge: | Existing methods to mitigate stereotypical biases by linear projection are too aggressive . existing methods remove bias, but they also erase valuable information from word embeddings . |
| Approach: | They propose a bias-mitigating method that disentangles biased associations between concepts instead of removing concepts wholesale. |
| Outcome: | The proposed method disentangles biased associations between concepts rather than eliminating concepts wholesale. |
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. |
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. |
| 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 . |