  The recent (2019-02) demonstration of the power of huge language models such
as GPT-2 to memorise the answers to factoid questions raises questions about
the extent to which knowledge is being embedded directly within these large
models. This short paper describes an architecture through which much smaller
models can also answer such questions - by making use of 'raw' external
knowledge. The contribution of this work is that the methods presented here
rely on unsupervised learning techniques, complementing the unsupervised
training of the Language Model. The goal of this line of research is to be able
to add knowledge explicitly, without extensive training.
