Semantic Parsing with Semi-Supervised Sequential Autoencoders
Tomáš Kočiský†‡ Gábor Melis† Edward Grefenstette† Chris Dyer† Wang Ling† Phil Blunsom†‡ Karl Moritz Hermann† †Google DeepMind ‡University of Oxford
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.
原文 arXiv:1609.09315;中英对照 + 大白话阅读 https://aha.fim.ai/paper/1609.09315v1