  Word translation is an integral part of language translation. In machine
translation, each language is considered a domain with its own word embedding.
The alignment between word embeddings allows linking semantically equivalent
words in multilingual contexts. Moreover, it offers a way to infer
cross-lingual meaning for words without a direct translation. Current methods
for word embedding alignment are either supervised, i.e. they require known
word pairs, or learn a cross-domain transformation on fixed embeddings in an
unsupervised way. Here we propose an end-to-end approach for word embedding
alignment that does not require known word pairs. Our method, termed Word
Alignment through MMD (WAM), learns embeddings that are aligned during sentence
translation training using a localized Maximum Mean Discrepancy (MMD)
constraint between the embeddings. We show that our method not only
out-performs unsupervised methods, but also supervised methods that train on
known word translations.
