Latent Predictor Networks for Code Generation

Understand

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.