Existing deep models for code tend to be trained on syntactic program representations. We present an alternative, called Neural Attribute Grammars, that exposes the semantics of the target language to the training procedure using an attribute grammar. During training, our model learns to replicate the relationship between the syntactic rules used to construct a program, and the semantic attributes (for example, symbol tables) constructed from the context in which the rules are fired. We implement the approach as a system for conditional generation of Java programs modulo eleven natural requirements. Our experiments show that the system generates constraint-abiding programs with significantly higher frequency than a baseline model trained on syntactic program representations, and also in terms of generation accuracy.
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