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There are concerns that neural language models may preserve some of thestereotypes of the underlying societies that generate the large corpora neededto train these models. For example, gender bias is a significant problem whengenerating text, and its unintended memorization could impact the userexperience of many applications (e.g., the smart-compose feature in Gmail).  In this paper, we introduce a novel architecture that decouples therepresentation learning of a neural model from its memory management role. Thisarchitecture allows us to update a memory module with an equal ratio acrossgender types addressing biased correlations directly in the latent space. Weexperimentally show that our approach can mitigate the gender biasamplification in the automatic generation of articles news while providingsimilar perplexity values when extending the Sequence2Sequence architecture.

On the Unintended Social Bias of Training Language Generation Models with Data from Local Media