  Creating accurate meta-embeddings from pre-trained source embeddings has
received attention lately. Methods based on global and locally-linear
transformation and concatenation have shown to produce accurate
meta-embeddings. In this paper, we show that the arithmetic mean of two
distinct word embedding sets yields a performant meta-embedding that is
comparable or better than more complex meta-embedding learning methods. The
result seems counter-intuitive given that vector spaces in different source
embeddings are not comparable and cannot be simply averaged. We give insight
into why averaging can still produce accurate meta-embedding despite the
incomparability of the source vector spaces.
