_structvae-tree-structured-latent-variable-models-for-semi-supervised-semantic-parsing.jpg "StructVAE: Tree-structured Latent Variable Models for Semi-supervised Semantic Parsing from meaning of code in python Video Screenshot Preview")
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⏲ Duration: 14 min 29 sec
✓ Published: 20-Aug-2018
✓ Published: 20-Aug-2018
Description:
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, su-pervised models. We introduce StructVAE, a variational auto-encoding model for semi-supervised semantic parsing, which learns both from limited amounts of
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, su-pervised models. We introduce StructVAE, a variational auto-encoding model for semi-supervised semantic parsing, which learns both from limited amounts of
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