Abstract: International audience; Previous literature has shown that it is possible to align word embeddings from different languages with unsupervised methods based on a distance-preserving mapping, with the assumption that the embeddings are isometric. However, these methods seem to work only when both embeddings are trained on the same domain. Nonetheless, we hypothesize that the deviation from isometry might be reduced between relevant subsets of embeddings from different domains, which would allow to partially align them. To support our hypothesis, ...
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Topics: 
Artificial intelligence
Algorithm
Natural language processing