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Semantic Parsing with Dual Learning
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
doi:10.18653/v1/p19-1007
dblp:conf/acl/CaoZLLY19
fatcat:tbsrr24ij5exphjctkmwwwf4l4