6 research outputs found
Semantic Parsing with Dual Learning
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.Comment: Accepted by ACL 2019 Long Pape