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

EMNLP 2018 Armand Joulin • Piotr Bojanowski • Tomas Mikolov • Herve Jegou • Edouard Grave

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.

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