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. |
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| Challenge: | Recent research has shown that static word embeddings can encode words’ frequencies, but little has been studied about this behavior. |
| Approach: | They propose to use static word embeddings to encode words' frequencies and to assess the impact of this relationship on embeddable bias metrics. |
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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. |
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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 . |
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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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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: | Word embeddings are often criticized for capturing undesirable word associations such as gender stereotypes. |
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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: | Word embeddings have been used to quantify biases in texts for years, but their statistical properties and advantages have not been studied. |
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