Reducing Word Embedding Bias Using Learned Latent Structure

Abstract:

Word embeddings learned from collections of data have demonstrated a significant level of biases. When these embeddings are used in machine learn- ing tasks it often amplifies the bias. We propose a debiasing method that uses (Figure 1) a hybrid classification - variational autoencoder network. In this work, we developed a semi-supervised classification algorithm based on variational autoencoders which learns the latent structure within the dataset and then based on learned latent structure adaptively re-weights the importance of certain data points while training. Experimental results have shown that the proposed approach works better than existing SoTA methods for debiasing word embeddings.

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