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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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 . |
| 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. |
| Outcome: | The proposed methods are based on four forms of representation bias and have advantages and drawbacks. |
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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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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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. |
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 . |
| Outcome: | The proposed methods reduce model bias by 90-98% and can be extended to correct model bias in other scenarios. |
How Far Can It Go? On Intrinsic Gender Bias Mitigation for Text Classification (2023.eacl-main)
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| Challenge: | a growing interest in exploring how gender bias pertains in contextualized language models has been generated . intrinsic mitigation strategies and bias metrics have been proposed to mitigate gender bias in contextualised language models . |
| Approach: | They propose to use different intrinsic bias mitigation strategies to mitigate gender bias in contextualized language models. |
| Outcome: | The proposed probe shows that some mitigation techniques can hide gender bias . the probe also shows that not all mitigation techniques fool extrinsic bias despite their use . |
End-to-End Bias Mitigation by Modelling Biases in Corpora (2020.acl-main)
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| Challenge: | Recent studies have shown that strong natural language understanding models are prone to relying on unwanted dataset biases without learning the underlying task. |
| Approach: | They propose two learning strategies to train neural models that are more robust to dataset biases and transfer better to out-of-domain datasets. |
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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. |
Applying Intrinsic Debiasing on Downstream Tasks: Challenges and Considerations for Machine Translation (2024.emnlp-main)
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| Challenge: | In this study, we examine three considerations for intrinsic debiasing in neural machine translation models. |
| Approach: | They propose to measure the extrinsic bias of neural machine translation models by embedding them in a neural embeddable space and using different tokens to debias them. |
| Outcome: | The proposed methods over-rely on gender stereotypes and over-represent them in their models. |
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. |