Neutralizing Gender Bias in Word Embedding with Latent Disentanglement and Counterfactual Generation

Published: Apr 7, 2020
Abstract
Recent research demonstrates that word embeddings, trained on the human-generated corpus, have strong gender biases in embedding spaces, and these biases can result in the discriminative results from the various downstream tasks. Whereas the previous methods project word embeddings into a linear subspace for debiasing, we introduce a \textit{Latent Disentanglement} method with a siamese auto-encoder structure with an adapted gradient reversal...
Paper Details
Title
Neutralizing Gender Bias in Word Embedding with Latent Disentanglement and Counterfactual Generation
Published Date
Apr 7, 2020
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