  Programmers typically organize executable source code using high-level coding
patterns or idiomatic structures such as nested loops, exception handlers and
recursive blocks, rather than as individual code tokens. In contrast, state of
the art (SOTA) semantic parsers still map natural language instructions to
source code by building the code syntax tree one node at a time. In this paper,
we introduce an iterative method to extract code idioms from large source code
corpora by repeatedly collapsing most-frequent depth-2 subtrees of their syntax
trees, and train semantic parsers to apply these idioms during decoding.
Applying idiom-based decoding on a recent context-dependent semantic parsing
task improves the SOTA by 2.2\% BLEU score while reducing training time by more
than 50\%. This improved speed enables us to scale up the model by training on
an extended training set that is 5$\times$ larger, to further move up the SOTA
by an additional 2.3\% BLEU and 0.9\% exact match. Finally, idioms also
significantly improve accuracy of semantic parsing to SQL on the ATIS-SQL
dataset, when training data is limited.
