Multi-label, Multi-class Classification Using Polylingual Embeddings
Abstract
We propose a Polylingual text Embedding (PE) strategy, that learns a language independent representation of texts using Neu-ral 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.
Domains
Artificial Intelligence [cs.AI]Origin | Files produced by the author(s) |
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