  We propose a geometric framework for learning meta-embeddings of words from
different embedding sources. Our framework transforms the embeddings into a
common latent space, where, for example, simple averaging of different
embeddings (of a given word) is more amenable. The proposed latent space arises
from two particular geometric transformations - the orthogonal rotations and
the Mahalanobis metric scaling. Empirical results on several word similarity
and word analogy benchmarks illustrate the efficacy of the proposed framework.
