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Neural Semantic Parsing for Syntax-Aware Code Generation

Baranowski, Artur and Hochgeschwender, Nico (2020) Neural Semantic Parsing for Syntax-Aware Code Generation. Master's, TH Köln.

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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.

Item URL in elib:https://elib.dlr.de/138954/
Document Type:Thesis (Master's)
Title:Neural Semantic Parsing for Syntax-Aware Code Generation
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Baranowski, ArturArtur.Baranowski (at) dlr.deUNSPECIFIED
Hochgeschwender, NicoNico.Hochgeschwender (at) dlr.deUNSPECIFIED
Date:23 September 2020
Refereed publication:No
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:No
Number of Pages:64
Keywords:Grammar, NLP, Query
Institution:TH Köln
Department:Fakultät für Informations-, Medien- und Elektrotechnik
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space System Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Space System Technology
DLR - Research theme (Project):R - Vorhaben SISTEC (old)
Location: Köln-Porz
Institutes and Institutions:Institute for Software Technology > Intelligent and Distributed Systems
Institute for Software Technology
Deposited By: Hochgeschwender, Nico
Deposited On:07 Dec 2020 09:59
Last Modified:07 Dec 2020 09:59

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