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Given multiple source word embeddings learnt using diverse algorithms andlexical resources, meta word embedding learning methods attempt to learn moreaccurate and wide-coverage word embeddings.  Prior work on meta-embedding has repeatedly discovered that simple vectorconcatenation of the source embeddings to be a competitive baseline.  However, it remains unclear as to why and when simple vector concatenationcan produce accurate meta-embeddings.  We show that weighted concatenation can be seen as a spectrum matchingoperation between each source embedding and the meta-embedding, minimising thepairwise inner-product loss.  Following this theoretical analysis, we propose two \emph{unsupervised}methods to learn the optimal concatenation weights for creating meta-embeddingsfrom a given set of source embeddings.  Experimental results on multiple benchmark datasets show that the proposedweighted concatenated meta-embedding methods outperform previously proposedmeta-embedding learning methods.

Learning Meta Word Embeddings by Unsupervised Weighted Concatenation of Source Embeddings