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Continuous word representations learned separately on distinct languages canbe aligned so that their words become comparable in a common space. Existingworks typically solve a least-square regression problem to learn a rotationaligning a small bilingual lexicon, and use a retrieval criterion forinference. In this paper, we propose an unified formulation that directlyoptimizes a retrieval criterion in an end-to-end fashion. Our experiments onstandard benchmarks show that our approach outperforms the state of the art onword translation, with the biggest improvements observed for distant languagepairs such as English-Chinese.

Loss in Translation: Learning Bilingual Word Mapping with a Retrieval Criterion