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• W2955270045 abstract "Program synthesis of general-purpose source code from natural language specifications is challenging due to the need to reason about high-level patterns in the target program and low-level implementation details at the same time. In this work, we present PATOIS, a system that allows a neural program synthesizer to explicitly interleave high-level and low-level reasoning at every generation step. It accomplishes this by automatically mining common code idioms from a given corpus, incorporating them into the underlying language for neural synthesis, and training a tree-based neural synthesizer to use these idioms during code generation. We evaluate PATOIS on two complex semantic parsing datasets and show that using learned code idioms improves the synthesizer's accuracy." @default.
• W2955270045 created "2019-07-12" @default.
• W2955270045 creator A5031637058 @default.
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• W2955270045 date "2019-06-26" @default.
• W2955270045 modified "2023-09-27" @default.
• W2955270045 title "Program Synthesis and Semantic Parsing with Learned Code Idioms" @default.
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• W2955270045 hasPublicationYear "2019" @default.
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