Bilingual Word Embeddings for Phrase-Based Machine Translation

We introduce bilingual word embeddings: semantic embeddings associated across two languages in the context of neural language models.We propose a method to learn bilingual embeddings from a large unlabeled corpus, while utilizing MT word alignments to constrain translational equivalence.The new embeddings significantly out-perform baselines in word semantic similarity.A single semantic similarity feature induced with bilingual embeddings adds near half a BLEU point to the results of NIST08 Chinese-English machine translation task.