Inspired by related techniques aimed at making synthesis more efficient (Devlin et al., 2017), we use learning to help us develop an effective optimization procedure for natural language parameters.
we simply use the languagelearning datasets, consisting of pairs (x where q provides a (suitably normalized) approximation to the distribution of descriptions given task data.