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        <title>Grounded Adaptation for Zero-shot Executable Semantic Parsing</title>
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    <abstract>We propose Grounded Adaptation for Zeroshot Executable Semantic Parsing (GAZP) to adapt an existing semantic parser to new environments (e.g. new database schemas). GAZP combines a forward semantic parser with a backward utterance generator to synthesize data (e.g. utterances and SQL queries) in the new environment, then selects cycle-consistent examples to adapt the parser. Unlike data-augmentation, which typically synthesizes unverified examples in the training environment, GAZP synthesizes examples in the new environment whose input-output consistency are verified through execution. On the Spider, Sparc, and CoSQL zero-shot semantic parsing tasks, GAZP improves logical form and execution accuracy of the baseline parser. Our analyses show that GAZP outperforms data-augmentation in the training environment, performance increases with the amount of GAZP-synthesized data, and cycle-consistency is central to successful adaptation.</abstract>
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%0 Conference Proceedings
%T Grounded Adaptation for Zero-shot Executable Semantic Parsing
%A Zhong, Victor
%A Lewis, Mike
%A Wang, Sida I.
%A Zettlemoyer, Luke
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F zhong-etal-2020-grounded
%X We propose Grounded Adaptation for Zeroshot Executable Semantic Parsing (GAZP) to adapt an existing semantic parser to new environments (e.g. new database schemas). GAZP combines a forward semantic parser with a backward utterance generator to synthesize data (e.g. utterances and SQL queries) in the new environment, then selects cycle-consistent examples to adapt the parser. Unlike data-augmentation, which typically synthesizes unverified examples in the training environment, GAZP synthesizes examples in the new environment whose input-output consistency are verified through execution. On the Spider, Sparc, and CoSQL zero-shot semantic parsing tasks, GAZP improves logical form and execution accuracy of the baseline parser. Our analyses show that GAZP outperforms data-augmentation in the training environment, performance increases with the amount of GAZP-synthesized data, and cycle-consistency is central to successful adaptation.
%R 10.18653/v1/2020.emnlp-main.558
%U https://aclanthology.org/2020.emnlp-main.558
%U https://doi.org/10.18653/v1/2020.emnlp-main.558
%P 6869-6882

Markdown (Informal)

[Grounded Adaptation for Zero-shot Executable Semantic Parsing](https://aclanthology.org/2020.emnlp-main.558) (Zhong et al., EMNLP 2020)

• Grounded Adaptation for Zero-shot Executable Semantic Parsing (Zhong et al., EMNLP 2020)

ACL

• Victor Zhong, Mike Lewis, Sida I. Wang, and Luke Zettlemoyer. 2020. Grounded Adaptation for Zero-shot Executable Semantic Parsing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6869–6882, Online. Association for Computational Linguistics.