Tasks like code generation and semantic parsing require mapping unstructured (or partially structured) inputs to well-formed, executable outputs. We introduce abstract syntax networks, a modeling framework for these problems. The outputs are represented as abstract syntax trees (ASTs) and constructed by a decoder with a dynamically-determined modular structure paralleling the structure of the output tree. On the benchmark HEARTHSTONE dataset for code generation, our model obtains 79.2 BLEU and 22.7% exact match accuracy, compared to previous state-of-the-art values of 67.1 and 6.1%. Furthermore, we perform competitively on the ATIS, JOBS, and GEO semantic parsing datasets with no task-specific engineering.
Title
Abstract Syntax Networks for Code Generation and Semantic Parsing
Published
2019-12-17
Full Collection Name
Electrical Engineering & Computer Sciences Technical Reports
Other Identifiers
EECS-2019-172
Type
Text
Extent
16 p
Archive
The Engineering Library
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