Neutralizing Gender Bias in Word Embedding with Latent Disentanglement and Counterfactual Generation
This addresses gender bias in NLP systems, which can lead to discriminatory outcomes, but it is an incremental improvement over prior debiasing techniques.
The paper tackles gender bias in word embeddings by introducing a latent disentanglement method with counterfactual generation, achieving better debiasing performance than existing methods while preserving semantic information for downstream NLP tasks.
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 layer. Our structure enables the separation of the semantic latent information and gender latent information of given word into the disjoint latent dimensions. Afterwards, we introduce a \textit{Counterfactual Generation} to convert the gender information of words, so the original and the modified embeddings can produce a gender-neutralized word embedding after geometric alignment regularization, without loss of semantic information. From the various quantitative and qualitative debiasing experiments, our method shows to be better than existing debiasing methods in debiasing word embeddings. In addition, Our method shows the ability to preserve semantic information during debiasing by minimizing the semantic information losses for extrinsic NLP downstream tasks.