  It is held as a truism that deep neural networks require large datasets to
train effective models. However, large datasets, especially with high-quality
labels, can be expensive to obtain. This study sets out to investigate (i) how
large a dataset must be to train well-performing models, and (ii) what impact
can be shown from fractional changes to the dataset size. A practical method to
investigate these questions is to train a collection of deep neural answer
selection models using fractional subsets of varying sizes of an initial
dataset. We observe that dataset size has a conspicuous lack of effect on the
training of some of these models, bringing the underlying algorithms into
question.
