  Continuous word representations learned separately on distinct languages can
be aligned so that their words become comparable in a common space. Existing
works typically solve a least-square regression problem to learn a rotation
aligning a small bilingual lexicon, and use a retrieval criterion for
inference. In this paper, we propose an unified formulation that directly
optimizes a retrieval criterion in an end-to-end fashion. Our experiments on
standard benchmarks show that our approach outperforms the state of the art on
word translation, with the biggest improvements observed for distant language
pairs such as English-Chinese.
