This is an open source implementation of the Bilingual Distributed Representations without Word Alignments method described in the paper cited below.
BilBOWA: Fast Bilingual Distributed Representations without Word Alignments
Stephan Gouws, Yoshua Bengio, Greg Corrado
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 an open-source toolkit.
arXiv:1410.2455 Submitted on 9 Oct 2014
Compiling and using
To compile, do the following
cd bilbowa make all
bidist in the
bilbowa to see the list of command line options.
bidist is a modification of the distance module in the word2vec package. It receives 2 binary vectors (let's say English and French) as input, and for any given English word returns the list of the similar French words. Note that similar to the original distance module, it only works with the binary vectors (use
-binary 1 switch when calling
bilbowa to train the system.)