Learning Bilingual Projections of Embeddings for Vocabulary Expansion in Machine Translation
We propose a simple log-bilinear softmax-based model to deal with vocabulary expansion in machine translation. Our model uses word embeddings trained on significantly large unlabelled monolingual corpora and learns over a fairly small, word-to-word bilingual dictionary. Given an out-of-vocabulary source word, the model generates a probabilistic list of possible translations in the target language using the trained bilingual embeddings. We integrate these translation options into a standard phrase-based statistical machine translation system and obtain consistent improvements in translation quality on the English{--}Spanish language pair. When tested over an out-of-domain testset, we get a significant improvement of 3.9 BLEU points.
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