Papers with debiasing word embeddings
Nurse is Closer to Woman than Surgeon? Mitigating Gender-Biased Proximities in Word Embeddings (2020.tacl-1)
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| Challenge: | Existing methods for debiasing word embeddings lack gender-based debiases . Existing approaches only reduce gender-related proximity biases by at least 42.02% . |
| Approach: | They propose a gender debiasing methodology that eliminates bias in word vectors and alters spatial distribution of neighboring vectors, achieving a bias-free setting while maintaining minimal semantic offset. |
| Outcome: | The proposed method outperforms the state-of-the-art in reducing proximity bias by at least 42.02% and reduces direct bias, adding minimal semantic disturbance, and achieves the best performance in a downstream application task. |
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. |