Loss In Translation: Learning Bilingual Word Mapping With A Retrieval Criterion
Armand Joulin, Piotr Bojanowski, Tomas Mikolov, Herve Jegou, Edouard Grave . Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing 2018 – 339 citations
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.