  Many language generation tasks require the production of text conditioned on
both structured and unstructured inputs. We present a novel neural network
architecture which generates an output sequence conditioned on an arbitrary
number of input functions. Crucially, our approach allows both the choice of
conditioning context and the granularity of generation, for example characters
or tokens, to be marginalised, thus permitting scalable and effective training.
Using this framework, we address the problem of generating programming code
from a mixed natural language and structured specification. We create two new
data sets for this paradigm derived from the collectible trading card games
Magic the Gathering and Hearthstone. On these, and a third preexisting corpus,
we demonstrate that marginalising multiple predictors allows our model to
outperform strong benchmarks.
