  Ensembling word embeddings to improve distributed word representations has
shown good success for natural language processing tasks in recent years. These
approaches either carry out straightforward mathematical operations over a set
of vectors or use unsupervised learning to find a lower-dimensional
representation. This work compares meta-embeddings trained for different
losses, namely loss functions that account for angular distance between the
reconstructed embedding and the target and those that account normalized
distances based on the vector length. We argue that meta-embeddings are better
to treat the ensemble set equally in unsupervised learning as the respective
quality of each embedding is unknown for upstream tasks prior to
meta-embedding. We show that normalization methods that account for this such
as cosine and KL-divergence objectives outperform meta-embedding trained on
standard $\ell_1$ and $\ell_2$ loss on \textit{defacto} word similarity and
relatedness datasets and find it outperforms existing meta-learning strategies.
