Learning to Simulate Natural Language Feedback for Interactive Semantic Parsing

• URL: http://arxiv.org/abs/2305.08195v2
• Date: Sun, 4 Jun 2023 21:05:26 GMT
• Title: Learning to Simulate Natural Language Feedback for Interactive Semantic Parsing
• Authors: Hao Yan, Saurabh Srivastava, Yintao Tai, Sida I. Wang, Wen-tau Yih, Ziyu Yao
• Abstract summary: We propose a new task simulating NL feedback for interactive semantic parsing. We accompany the task with a novel feedback evaluator. Our feedback simulator can help achieve comparable error correction performance as trained using the costly, full set of human annotations.
• Score: 30.609805601567178
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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