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Gender prediction has typically focused on lexical and social networkfeatures, yielding good performance, but making systems highly language-,topic-, and platform-dependent. Cross-lingual embeddings circumvent some ofthese limitations, but capture gender-specific style less. We propose analternative: bleaching text, i.e., transforming lexical strings into moreabstract features. This study provides evidence that such features allow forbetter transfer across languages. Moreover, we present a first study on theability of humans to perform cross-lingual gender prediction. We find thathuman predictive power proves similar to that of our bleached models, and bothperform better than lexical models.

Bleaching Text: Abstract Features for Cross-lingual Gender Prediction