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We introduce a multilingual extension of the HOLISTICBIAS dataset, thelargest English template-based taxonomy of textual people references:MULTILINGUALHOLISTICBIAS. This extension consists of 20,459 sentences in 50languages distributed across all 13 demographic axes. Source sentences arebuilt from combinations of 118 demographic descriptors and three patterns,excluding nonsensical combinations. Multilingual translations includealternatives for gendered languages that cover gendered translations when thereis ambiguity in English. Our benchmark is intended to uncover demographicimbalances and be the tool to quantify mitigations towards them.  Our initial findings show that translation quality for EN-to-XX translationsis an average of 8 spBLEU better when evaluating with the masculine humanreference compared to feminine. In the opposite direction, XX-to-EN, we comparethe robustness of the model when the source input only differs in gender(masculine or feminine) and masculine translations are an average of almost 4spBLEU better than feminine. When embedding sentences to a joint multilingualsentence representations space, we find that for most languages masculinetranslations are significantly closer to the English neutral sentences whenembedded.

Multilingual Holistic Bias: Extending Descriptors and Patterns to Unveil Demographic Biases in Languages at Scale