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Current approaches to learning vector representations of text that arecompatible between different languages usually require some amount of paralleltext, aligned at word, sentence or at least document level. We hypothesizehowever, that different natural languages share enough semantic structure thatit should be possible, in principle, to learn compatible vector representationsjust by analyzing the monolingual distribution of words.  In order to evaluate this hypothesis, we propose a scheme to map word vectorstrained on a source language to vectors semantically compatible with wordvectors trained on a target language using an adversarial autoencoder.  We present preliminary qualitative results and discuss possible futuredevelopments of this technique, such as applications to cross-lingual sentencerepresentations.

Towards cross-lingual distributed representations without parallel text trained with adversarial autoencoders