  Semantic parsing aims at mapping natural language utterances into structured
meaning representations. In this work, we propose a structure-aware neural
architecture which decomposes the semantic parsing process into two stages.
Given an input utterance, we first generate a rough sketch of its meaning,
where low-level information (such as variable names and arguments) is glossed
over. Then, we fill in missing details by taking into account the natural
language input and the sketch itself. Experimental results on four datasets
characteristic of different domains and meaning representations show that our
approach consistently improves performance, achieving competitive results
despite the use of relatively simple decoders.
