StructVAE: Tree-structured Latent Variable Models for Semi-supervised Semantic Parsing

Understand

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.