  We present WOMBAT, a Python tool which supports NLP practitioners in
accessing word embeddings from code. WOMBAT addresses common research problems,
including unified access, scaling, and robust and reproducible preprocessing.
Code that uses WOMBAT for accessing word embeddings is not only cleaner, more
readable, and easier to reuse, but also much more efficient than code using
standard in-memory methods: a Python script using WOMBAT for evaluating seven
large word embedding collections (8.7M embedding vectors in total) on a simple
SemEval sentence similarity task involving 250 raw sentence pairs completes in
under ten seconds end-to-end on a standard notebook computer.
