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    <abstract>Interactive semantic parsing based on natural language (NL) feedback, where users provide feedback to correct the parser mistakes, has emerged as a more practical scenario than the traditional one-shot semantic parsing. However, prior work has heavily relied on human-annotated feedback data to train the interactive semantic parser, which is prohibitively expensive and not scalable. In this work, we propose a new task of simulating NL feedback for interactive semantic parsing. We accompany the task with a novel feedback evaluator. The evaluator is specifically designed to assess the quality of the simulated feedback, based on which we decide the best feedback simulator from our proposed variants. On a text-to-SQL dataset, we show that our feedback simulator can generate high-quality NL feedback to boost the error correction ability of a specific parser. In low-data settings, our feedback simulator can help achieve comparable error correction performance as trained using the costly, full set of human annotations.</abstract>
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%0 Conference Proceedings
%T Learning to Simulate Natural Language Feedback for Interactive Semantic Parsing
%A Yan, Hao
%A Srivastava, Saurabh
%A Tai, Yintao
%A Wang, Sida I.
%A Yih, Wen-tau
%A Yao, Ziyu
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F yan-etal-2023-learning
%X Interactive semantic parsing based on natural language (NL) feedback, where users provide feedback to correct the parser mistakes, has emerged as a more practical scenario than the traditional one-shot semantic parsing. However, prior work has heavily relied on human-annotated feedback data to train the interactive semantic parser, which is prohibitively expensive and not scalable. In this work, we propose a new task of simulating NL feedback for interactive semantic parsing. We accompany the task with a novel feedback evaluator. The evaluator is specifically designed to assess the quality of the simulated feedback, based on which we decide the best feedback simulator from our proposed variants. On a text-to-SQL dataset, we show that our feedback simulator can generate high-quality NL feedback to boost the error correction ability of a specific parser. In low-data settings, our feedback simulator can help achieve comparable error correction performance as trained using the costly, full set of human annotations.
%R 10.18653/v1/2023.acl-long.177
%U https://aclanthology.org/2023.acl-long.177
%U https://doi.org/10.18653/v1/2023.acl-long.177
%P 3149-3170

Markdown (Informal)

[Learning to Simulate Natural Language Feedback for Interactive Semantic Parsing](https://aclanthology.org/2023.acl-long.177) (Yan et al., ACL 2023)

• Learning to Simulate Natural Language Feedback for Interactive Semantic Parsing (Yan et al., ACL 2023)

ACL

• Hao Yan, Saurabh Srivastava, Yintao Tai, Sida I. Wang, Wen-tau Yih, and Ziyu Yao. 2023. Learning to Simulate Natural Language Feedback for Interactive Semantic Parsing. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3149–3170, Toronto, Canada. Association for Computational Linguistics.