Work

Coarse-to-Fine Decoding for Neural Semantic Parsing

Year: 2018

Type: conference-paper

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.

Source: Proceedings of the conference - Association for Computational Linguistics. Meeting

DOI: 10.18653/v1/p18-1068

Authors: Li Dong, Mirella Lapata

Institution: University of Edinburgh

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: H2020 European Research Council

Award: Translating from Multiple Modalities into Text