Bleaching Text: Abstract Features for Cross-lingual Gender Prediction

ACL 2018

Bleaching Text: Abstract Features for Cross-lingual Gender Prediction

Jan 28, 2021
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Abstract: Gender prediction has typically focused on lexical and social network features, yielding good performance, but mak-ing 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. Authors: Rob van der Goot, Nikola Ljubešić, Ian Matroos, Malvina Nissim, Barbara Plank (University of Groningen)

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