Abstrakt
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.
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.
| Originalsprog | Engelsk |
|---|---|
| Titel | Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics |
| Antal sider | 7 |
| Udgivelsessted | Melbourne |
| Forlag | Association for Computational Linguistics |
| Publikationsdato | 2018 |
| Status | Udgivet - 2018 |