publication . Article . Conference object . Preprint . 2016

Semantic Parsing with Semi-Supervised Sequential Autoencoders

Tomáš Kočiský; Gábor Melis; Edward Grefenstette; Chris Dyer; Wang Ling; Phil Blunsom; Karl Moritz Hermann;
Open Access
  • Published: 29 Sep 2016
  • Publisher: Association for Computational Linguistics
  • Country: United Kingdom
Abstract
We present a novel semi-supervised approach for sequence transduction and apply it to semantic parsing. The unsupervised component is based on a generative model in which latent sentences generate the unpaired logical forms. We apply this method to a number of semantic parsing tasks focusing on domains with limited access to labelled training data and extend those datasets with synthetically generated logical forms.
Fields of Science and Technology classification (FOS)
02 engineering and technology, 0202 electrical engineering, 03 medical and health sciences, 0305 other medical science, 030507 speech-language pathology & audiology, electronic engineering
Sustainable Development Goals (SDG)
16. Peace & justice
Subjects
arXiv: Computer Science::Computation and Language (Computational Linguistics and Natural Language and Speech Processing)Computer Science::Logic in Computer Science
ACM Computing Classification System: ComputingMethodologies_PATTERNRECOGNITIONTheoryofComputation_MATHEMATICALLOGICANDFORMALLANGUAGES
free text keywords: Computation and Language (cs.CL), Artificial Intelligence (cs.AI), Neural and Evolutionary Computing (cs.NE), FOS: Computer and information sciences, Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Neural and Evolutionary Computing, Natural language processing, computer.software_genre, computer, Computer science, Training set, Transduction (machine learning), Limited access, Generative model, Sequence, Artificial intelligence, business.industry, business, Component (UML), Parsing
Communities
  • Digital Humanities and Cultural Heritage
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