  Embedding matrices are key components in neural natural language processing
(NLP) models that are responsible to provide numerical representations of input
tokens.\footnote{In this paper words and subwords are referred to as
\textit{tokens} and the term \textit{embedding} only refers to embeddings of
inputs.} In this paper, we analyze the impact and utility of such matrices in
the context of neural machine translation (NMT). We show that detracting
syntactic and semantic information from word embeddings and running NMT systems
with random embeddings is not as damaging as it initially sounds. We also show
how incorporating only a limited amount of task-specific knowledge from
fully-trained embeddings can boost the performance NMT systems. Our findings
demonstrate that in exchange for negligible deterioration in performance, any
NMT model can be run with partially random embeddings. Working with such
structures means a minimal memory requirement as there is no longer need to
store large embedding tables, which is a significant gain in industrial and
on-device settings. We evaluated our embeddings in translating {English} into
{German} and {French} and achieved a $5.3$x compression rate. Despite having a
considerably smaller architecture, our models in some cases are even able to
outperform state-of-the-art baselines.
