Semantic Parsing with Dual Learning
Ruisheng Cao, Su Zhu, Chen Liu, Jieyu Li, Kai Yu · 2019
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.