Bleaching Text: Abstract Features for Cross-lingual Gender Prediction

van der Goot, R., Ljubešic, N., Matroos, I., Nissim, M. & Plank, B., Jul-2018. 7 p.

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  • Bleaching Text

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  • Rob van der Goot
  • Nikola Ljubešic
  • Ian Matroos
  • Malvina Nissim
  • Barbara Plank
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.
Original languageEnglish
Number of pages7
Publication statusPublished - Jul-2018
Event56th Annual Meeting of the Association for Computational Linguistics - Melbourne Convention and Exhibition Centre, Melbourne, Australia
Duration: 16-Jul-201820-Jul-2018


Conference56th Annual Meeting of the Association for Computational Linguistics
Abbreviated titleACL 2018
Internet address


56th Annual Meeting of the Association for Computational Linguistics


Melbourne, Australia

Event: Conference

ID: 61170687