Density Matching for Bilingual Word Embedding

North American Chapter of the Association for Computational Linguistics, pp. 1588-1598, 2019.

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We propose a density matching based unsupervised method for learning bilingual word embedding mappings

Abstract:

Recent approaches to cross-lingual word embedding have generally been based on linear transformations between the sets of embedding vectors in the two languages. In this paper, we propose an approach that instead expresses the two monolingual embedding spaces as probability densities defined by a Gaussian mixture model, and matches the tw...More

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