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This paper presents a new technique for creating monolingual andcross-lingual meta-embeddings. Our method integrates multiple word embeddingscreated from complementary techniques, textual sources, knowledge bases andlanguages. Existing word vectors are projected to a common semantic space usinglinear transformations and averaging. With our method the resultingmeta-embeddings maintain the dimensionality of the original embeddings withoutlosing information while dealing with the out-of-vocabulary problem. Anextensive empirical evaluation demonstrates the effectiveness of our techniquewith respect to previous work on various intrinsic and extrinsic multilingualevaluations, obtaining competitive results for Semantic Textual Similarity andstate-of-the-art performance for word similarity and POS tagging (English andSpanish). The resulting cross-lingual meta-embeddings also exhibit excellentcross-lingual transfer learning capabilities. In other words, we can leveragepre-trained source embeddings from a resource-rich language in order to improvethe word representations for under-resourced languages.

A Common Semantic Space for Monolingual and Cross-Lingual Meta-Embeddings