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. |
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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 . |
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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. |
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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 . |
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| Challenge: | Existing discriminatory biases in training data can be amplified by models . text corpora exhibit socially problematic biase . |
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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 . |
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Gender Bias in Contextualized Word Embeddings (N19-1)
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| Challenge: | Existing studies show that training word embeddings in large corpora could lead to encoding societal biases present in these human-produced data. |
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Dictionary-based Debiasing of Pre-trained Word Embeddings (2021.eacl-main)
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| Challenge: | Existing methods for learning word embeddings using dictionaries do not require access to training resources or knowledge regarding the word embeds used. |
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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. |
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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. |