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 wordaligned 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 crosslingual 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.
REF trained directly on monolingual data and extracted a bilingual signal from a smaller set of parallel data.
BilBOWA: Fast Bilingual Distributed Representations without Word Alignments
ArXiv
ArXiv
Mathematics Computer Science