Latent Predictor Networks for Code Generation
This addresses code generation for developers, but it is incremental as it builds on existing neural methods with marginalization techniques.
The paper tackles the problem of generating programming code from mixed natural language and structured specifications by introducing a neural network architecture that marginalizes over conditioning contexts and generation granularities. It demonstrates improved performance over strong benchmarks on two new datasets from trading card games and a preexisting corpus.
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.