Semantic Parsing with Dual Learning

By Ruisheng Cao, Su Zhu, Chen Liu, Jieyu Li, Kai Yu. 2019

65 citations recorded by the source corpus.

Semantic parsing converts natural language queries into structured logical forms. The lack of training data is still one of the most serious problems in this area. 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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Total Recall: a Customized Continual Learning Method for Neural Semantic Parsers (2021, background): “Diversifying entities do not necessarily lead to diversities of LF templates since the LFs with different entities may share similar templates.”
Schema-Guided Multi-Domain Dialogue State Tracking with Graph Attention Neural Networks (2020, methodology): “To overcome the data sparsity problem, we would like to try data augmentation methods (Zhao, Zhu, and Yu 2019a; Cao et al. 2019).”
Grounded Adaptation for Zero-shot Executable Semantic Parsing (2020, methodology): “A method related to data-augmentation is jointly supervising the model using the training data in the reverse direction, for example by generating ut-terance from query (Fried et al., 2018; Cao et al., 2019).”
Prefix-to-SQL: Text-to-SQL Generation from Incomplete User Questions (2021, background): “Our results show that both generative and retrieval models can predict SQL queries that match the user’s intention, but the scores are much lower than the original text-to-SQL task (Finegan-Dollak et al., 2018; Dong and Lapata, 2018a; Yin and Neubig, 2018; Cao et al., 2019; Rubin and Berant, 2021; H”
AutoQA: From Databases to Q&A Semantic Parsers with Only Synthetic Training Data (2020, methodology): “For reference, we also include two of the best-performing models that use in-domain human data (Cao et al., 2019; Chen et al., 2018)4.”