We consider the problem of parsing natural language descriptions into source code written in a general-purpose programming language like Python. Existing datadriven methods treat this problem as a language generation task without considering the underlying syntax of the target programming language. Informed by previous work in semantic parsing, in this paper we propose a novel neural architecture powered by a grammar model to explicitly capture the target syntax as prior knowledge. Experiments find this an effective way to scale up to generation of complex programs from natural language descriptions, achieving state-of-the-art results that well outperform previous code generation and semantic parsing approaches.
Another related code generation model REF parses natural language descriptions into source code considering the grammar and syntax in the target programming language (e.g., Python).
A Syntactic Neural Model for General-Purpose Code Generation
ArXiv
ArXiv
Computer Science