  Word-embeddings are vital components of Natural Language Processing (NLP)
models and have been extensively explored. However, they consume a lot of
memory which poses a challenge for edge deployment. Embedding matrices,
typically, contain most of the parameters for language models and about a third
for machine translation systems. In this paper, we propose Distilled Embedding,
an (input/output) embedding compression method based on low-rank matrix
decomposition and knowledge distillation. First, we initialize the weights of
our decomposed matrices by learning to reconstruct the full pre-trained
word-embedding and then fine-tune end-to-end, employing knowledge distillation
on the factorized embedding. We conduct extensive experiments with various
compression rates on machine translation and language modeling, using different
data-sets with a shared word-embedding matrix for both embedding and vocabulary
projection matrices. We show that the proposed technique is simple to
replicate, with one fixed parameter controlling compression size, has higher
BLEU score on translation and lower perplexity on language modeling compared to
complex, difficult to tune state-of-the-art methods.
