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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.
Synthesizing a program that realizes a logical specification is a classical problem in computer science. We examine a particular type of program synthesis, where the objective is to synthesize a strategy that reacts to a potentially adversarial envir
Synthesizing user-intended programs from a small number of input-output examples is a challenging problem with several important applications like spreadsheet manipulation, data wrangling and code refactoring. Existing synthesis systems either comple
A key challenge for reinforcement learning is solving long-horizon planning and control problems. Recent work has proposed leveraging programs to help guide the learning algorithm in these settings. However, these approaches impose a high manual burd
Code summarization and generation empower conversion between programming language (PL) and natural language (NL), while code translation avails the migration of legacy code from one PL to another. This paper introduces PLBART, a sequence-to-sequence
We are interested in attribute-guided face generation: given a low-res face input image, an attribute vector that can be extracted from a high-res image (attribute image), our new method generates a high-res face image for the low-res input that sati