  Neural word representations are at the core of many state-of-the-art natural
language processing models. A widely used approach is to pre-train, store and
look up word or character embedding matrices. While useful, such
representations occupy huge memory making it hard to deploy on-device and often
do not generalize to unknown words due to vocabulary pruning.
  In this paper, we propose a skip-gram based architecture coupled with
Locality-Sensitive Hashing (LSH) projections to learn efficient dynamically
computable representations. Our model does not need to store lookup tables as
representations are computed on-the-fly and require low memory footprint. The
representations can be trained in an unsupervised fashion and can be easily
transferred to other NLP tasks. For qualitative evaluation, we analyze the
nearest neighbors of the word representations and discover semantically similar
words even with misspellings. For quantitative evaluation, we plug our
transferable projections into a simple LSTM and run it on multiple NLP tasks
and show how our transferable projections achieve better performance compared
to prior work.
