Cross-language learning allows one to use training data from one language to build models for a different language. Many approaches to bilingual learning require that we have word-level alignment of sentences from parallel corpora. In this work we explore the use of autoencoder-based methods for cross-language learning of vectorial word representations that are coherent between two languages, while not relying on word-level alignments. We show that by simply learning to reconstruct the bag-of-words representations of aligned sentences, within and between languages, we can in fact learn high-quality representations and do without word alignments. We empirically investigate the success of our approach on the problem of cross-language text classification, where a classifier trained on a given language (e.g., English) must learn to generalize to a different language (e.g., German). In experiments on 3 language pairs, we show that our approach achieves state-of-the-art performance, outperforming a method exploiting word alignments and a strong machine translation baseline.
An Autoencoder Approach to Learning Bilingual Word Representations
A. Chandar,Stanislas Lauly,H. Larochelle,Mitesh M. Khapra,Balaraman Ravindran,V. Raykar,Amrita Saha
Published 2014 in Neural Information Processing Systems
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- Publication year
2014
- Venue
Neural Information Processing Systems
- Publication date
2014-02-06
- Fields of study
Mathematics, Computer Science
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