Multi-label, Multi-class Classification Using Polylingual Embeddings

Georgios Balikas, Massih-Reza Amini
Laboratoire d'Informatique de Grenoble
700, avenue Centrale
38058 Saint-Martin d'Hérès

We propose a Polylingual text Embedding (PE) strategy, that learns a language independent representation of texts using Neural Networks. We study the effects of bilingual representation learning for text classification and we empirically show that the learned representations achieve better classification performance compared to traditional bag-of-words and other monolingual distributed representations. The performance gains are more significant in the interesting case where only few labeled examples are available for training the classifiers.