example?
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First you find word vector correspondences: http://nlp.csai.tsinghua.edu.cn/~ly/papers/acl2017_zm.pdf … & http://www.aclweb.org/anthology/W/W16/W16-1614.pdf … Once you have multilingual embeddings, you can use denoising and back-translation to make full translation models: https://arxiv.org/pdf/1710.11041.pdf … https://arxiv.org/pdf/1711.00043.pdf …
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