  We present *-CFQ ("star-CFQ"): a suite of large-scale datasets of varying
scope based on the CFQ semantic parsing benchmark, designed for principled
investigation of the scalability of machine learning systems in a realistic
compositional task setting. Using this suite, we conduct a series of
experiments investigating the ability of Transformers to benefit from increased
training size under conditions of fixed computational cost. We show that
compositional generalization remains a challenge at all training sizes, and we
show that increasing the scope of natural language leads to consistently higher
error rates, which are only partially offset by increased training data. We
further show that while additional training data from a related domain improves
the accuracy in data-starved situations, this improvement is limited and
diminishes as the distance from the related domain to the target domain
increases.
