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Toward Code Generation: A Survey and Lessons from Semantic Parsing

2021-04-26 22:05:22
Celine Lee (1 and 2), Justin Gottschlich (1 and 2), Dan Roth (2) ((1) Intel Labs, (2) University of Pennsylvania)
arXiv_CL
     
Abstract

With the growth of natural language processing techniques and demand for improved software engineering efficiency, there is an emerging interest in translating intention from human languages to programming languages. In this survey paper, we attempt to provide an overview of the growing body of research in this space. We begin by reviewing natural language semantic parsing techniques and draw parallels with program synthesis efforts. We then consider semantic parsing works from an evolutionary perspective, with specific analyses on neuro-symbolic methods, architecture, and supervision. We then analyze advancements in frameworks for semantic parsing for code generation. In closing, we present what we believe are some of the emerging open challenges in this domain.

Abstract (translated)

URL

https://arxiv.org/abs/2105.03317

PDF

https://arxiv.org/pdf/2105.03317.pdf

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