Semantic Parsing with Semi-Supervised Sequential Autoencoders
Computation and Language
2016-09-30 v1 Artificial Intelligence
Neural and Evolutionary Computing
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.
Cite
@article{arxiv.1609.09315,
title = {Semantic Parsing with Semi-Supervised Sequential Autoencoders},
author = {Tomáš Kočiský and Gábor Melis and Edward Grefenstette and Chris Dyer and Wang Ling and Phil Blunsom and Karl Moritz Hermann},
journal= {arXiv preprint arXiv:1609.09315},
year = {2016}
}