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• W2892266277 abstract "Cross-lingual or cross-domain correspondences play key roles in tasks ranging from machine translation to transfer learning. Recently, purely unsupervised methods operating on monolingual embeddings have become effective alignment tools. Current state-of-the-art methods, however, involve multiple steps, including heuristic post-hoc refinement strategies. In this paper, we cast the correspondence problem directly as an optimal transport (OT) problem, building on the idea that word embeddings arise from metric recovery algorithms. Indeed, we exploit the Gromov-Wasserstein distance that measures how similarities between pairs of words relate across languages. We show that our OT objective can be estimated efficiently, requires little or no tuning, and results in performance comparable with the state-of-the-art in various unsupervised word translation tasks." @default.
• W2892266277 created "2018-09-27" @default.
• W2892266277 creator A5009860808 @default.
• W2892266277 creator A5048915657 @default.
• W2892266277 date "2018-08-31" @default.
• W2892266277 modified "2023-10-11" @default.
• W2892266277 title "Gromov-Wasserstein Alignment of Word Embedding Spaces." @default.
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