We propose a novel geometric approach for learning bilingual mappings given monolingual embeddings and a bilingual dictionary.Our approach decouples the source-to-target language transformation into (a) language-specific rotations on the original embeddings to align them in a Trends in observed surface solar radiation and their causes in Brazil in the first 2 decades of the 21st century common, latent space, and (b) a language-independent similarity metric in this common space to better model the similarity between the embeddings.Overall, we pose the bilingual mapping problem as a classification problem on smooth Riemannian manifolds.Empirically, our approach outperforms previous approaches on the bilingual lexicon induction and cross-lingual word similarity tasks.
We next generalize our framework to Family management risk and protective factors for adolescent substance use in South Africa represent multiple languages in a common latent space.Language-specific rotations for all the languages and a common similarity metric in the latent space are learned jointly from bilingual dictionaries for multiple language pairs.We illustrate the effectiveness of joint learning for multiple languages in an indirect word translation setting.