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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.
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https://hal.archives-ouvertes.fr/hal-01299850
Contributor : Georgios Balikas <>
Submitted on : Wednesday, April 20, 2016 - 6:37:53 PM
Last modification on : Monday, April 20, 2020 - 11:24:01 AM
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Georgios Balikas, Massih-Reza Amini. Multi-label, Multi-class Classification Using Polylingual Embeddings. 38th European Conference on Information Retrieval ECIR 2016, Mar 2016, Padoue, Italy. ⟨10.1007/978-3-319-30671-1_59⟩. ⟨hal-01299850⟩

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