| 1 |
Bleaching Text: Abstract Features for Cross-lingual Gender Prediction ...
|
|
|
|
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. ... : Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics ...
|
|
Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
|
|
URL: https://dx.doi.org/10.48550/arxiv.1805.03122
https://arxiv.org/abs/1805.03122
|
|
BASE
|
|
Hide details
|
|
|
|
|
