Semantic Parsing with Dual Learning

Ruisheng Cao, Su Zhu, Chen Liu, Jieyu Li, Kai Yu
2019 Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics  
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
more » ... 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. * Ruisheng Cao and Su Zhu are co-first authors and contribute equally to this work. † The corresponding author is Kai Yu.
doi:10.18653/v1/p19-1007 dblp:conf/acl/CaoZLLY19 fatcat:tbsrr24ij5exphjctkmwwwf4l4