Given a Chinese word w n with an index i in the vocabulary, it is then straightforward to retrieve the word's vector representation via simple multiplication with a binary vector d that is equal to zero at all positions except that with index i: Because the word embeddings for the two languages (L ch and L en ) are learned separately and located in different vector spaces, we suppose that a transformation exists between these two semantic embedding spaces.
we collect all the One-to-Many phrase tuples < e 1 , c j 2 j 1 > that satisfy e 1 ∈ V en and c j 2 j 1 ∈ V ch from the entire corpus of bilingual data.