Learning Bilingual Projections of Embeddings for Vocabulary Expansion in Machine Translation
Pranava Madhyastha, Cristina España-Bonet · 2017
We propose a simple log-bilinear softmaxbased 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, wordto-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.