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

本文研究了元词向量嵌入（meta-embedding）的学习方法，提出了利用加权拼接来学习更准确和广泛覆盖面的词向量的两种无监督方法，并在多个基准数据集上进行试验，结果表明这些加权拼接的 meta-embedding 方法优于以前的 meta-embedding 学习方法。

通过非监督加权合并源嵌入学习元词嵌入