  Semantic parsing is the task of transducing natural language (NL) utterances
into formal meaning representations (MRs), commonly represented as tree
structures. Annotating NL utterances with their corresponding MRs is expensive
and time-consuming, and thus the limited availability of labeled data often
becomes the bottleneck of data-driven, supervised models. We introduce
StructVAE, a variational auto-encoding model for semisupervised semantic
parsing, which learns both from limited amounts of parallel data, and
readily-available unlabeled NL utterances. StructVAE models latent MRs not
observed in the unlabeled data as tree-structured latent variables. Experiments
on semantic parsing on the ATIS domain and Python code generation show that
with extra unlabeled data, StructVAE outperforms strong supervised models.
