Coarse-to-Fine Decoding for Neural Semantic Parsing

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Semantic parsing aims at mapping natural language utterances into structured meaning representations. In this work, we propose a structure-aware neural architecture which decomposes the semantic parsing process into two stages. Given an input utterance, we first generate a rough sketch of its meaning, where low-level information (such as variable names and arguments) is glossed over. Then, we fill in missing details by taking into account the natural language input and the sketch itself. Experimental results on four datasets characteristic of different domains and meaning representations show that our approach consistently improves performance, achieving competitive results despite the use of relatively simple decoders.
Original languageEnglish
Title of host publicationProceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Place of PublicationMelbourne, Australia
PublisherAssociation for Computational Linguistics
Pages731-742
Number of pages12
ISBN (Print)978-1-948087-32-2
DOIs
Publication statusPublished - 20 Jul 2018
Event56th Annual Meeting of the Association for Computational Linguistics - Melbourne Convention and Exhibition Centre, Melbourne, Australia
Duration: 15 Jul 201820 Jul 2018
http://acl2018.org/

Conference

Conference56th Annual Meeting of the Association for Computational Linguistics
Abbreviated titleACL 2018
CountryAustralia
CityMelbourne
Period15/07/1820/07/18
Internet address

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