SOTAVerified

Bleaching Text: Abstract Features for Cross-lingual Gender Prediction

2018-05-08ACL 2018Code Available0· sign in to hype

Rob van der Goot, Nikola Ljubešić, Ian Matroos, Malvina Nissim, Barbara Plank

Code Available — Be the first to reproduce this paper.

Reproduce

Code

Abstract

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.

Tasks

Reproductions