  Gender prediction has typically focused on lexical and social network
features, yielding good performance, but making systems highly language-,
topic-, and platform-dependent. Cross-lingual embeddings circumvent some of
these limitations, but capture gender-specific style less. We propose an
alternative: bleaching text, i.e., transforming lexical strings into more
abstract features. This study provides evidence that such features allow for
better transfer across languages. Moreover, we present a first study on the
ability of humans to perform cross-lingual gender prediction. We find that
human predictive power proves similar to that of our bleached models, and both
perform better than lexical models.
