A Syntactic Neural Model for General-Purpose Code Generation
Pengcheng Yin, Graham Neubig · 2017
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.