BilBOWA: Fast Bilingual Distributed Representations without Word Alignments

We introduce BilBOWA ("Bilingual Bag-of-Words without Alignments"), a simple and computationally-efficient model for learning bilingual distributed representations of words which can scale to large datasets and does not require word-aligned training data. Instead it trains directly on monolingual data and extracts a bilingual signal from a smaller set of raw text sentence-aligned data. This is achieved using a novel sampled bag-of-words cross-lingual objective, which is used to regularize two noise-contrastive language models for efficient cross-lingual feature learning. We show that bilingual embeddings learned using the proposed model outperforms state-of-the-art methods on a cross-lingual document classification task as well as a lexical translation task on the WMT11 data. Our code will be made available as part of the open-source word2vec toolkit.