Density Matching for Bilingual Word Embedding
North American Chapter of the Association for Computational Linguistics, pp. 1588-1598, 2019.
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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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