Abstract
Semantic parsing converts natural language
queries into structured logical forms. The
paucity of annotated training samples is a
fundamental challenge in this field. In this
work, we develop a semantic parsing framework with the dual learning algorithm, which
enables a semantic parser to make full use
of data (labeled and even unlabeled) through
a dual-learning game. This game between a
primal model (semantic parsing) and a dual
model (logical form to query) forces them to
regularize each other, and can achieve feedback signals from some prior-knowledge. By
utilizing the prior-knowledge of logical form
structures, we propose a novel reward signal
at the surface and semantic levels which tends
to generate complete and reasonable logical
forms. Experimental results show that our
approach achieves new state-of-the-art performance on ATIS dataset and gets competitive
performance on OVERNIGHT dataset