Neural Semantic Parsing for Syntax-Aware Code Generation

Abstract

The task of mapping natural language expressions to logical forms is referred to as semantic parsing. The syntax of logical forms that are based on programming or query languages, such as Python or SQL, is defined by a formal grammar. In this thesis, we present an efficient neural semantic parser that exploits the underlying grammar of logical forms to enforce well-formed expressions. We use an encoder-decoder model for sequence prediction. Syntactically valid programs are guaranteed by means of a bottom-up shift-reduce parser, that keeps track of the set of viable tokens at each decoding step. We show that the proposed model outperforms the standard encoder-decoder model across datasets and is competitive with comparable grammar-guided semantic parsing approaches.