Abstract
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.
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The code is available at http://ama.liglab.fr/~balikas/ecir2015.zip.
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Acknowledgements
We would like to thank the anonymous reviewers for their valuable comments. This work is partially supported by the CIFRE N 28/2015 and by the LabEx PERSYVAL Lab ANR-11-LABX-0025.
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Balikas, G., Amini, MR. (2016). Multi-label, Multi-class Classification Using Polylingual Embeddings. In: Ferro, N., et al. Advances in Information Retrieval. ECIR 2016. Lecture Notes in Computer Science(), vol 9626. Springer, Cham. https://doi.org/10.1007/978-3-319-30671-1_59
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DOI: https://doi.org/10.1007/978-3-319-30671-1_59
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