Abstract
Context-dependent Text-to-SQL aims to translate multi-turn natural language questions into SQL queries. Despite various methods have exploited context-dependence information implicitly for contextual SQL parsing, there are few attempts to explicitly address the dependencies between current question and question context. This paper presents QURG, a novel QUestion Rewriting Guided approach to help the models achieve adequate contextual understanding. Specifically, we first train a question rewriting model to complete the current question based on question context, and convert them into a rewriting edit matrix. We further design a two-stream matrix encoder to jointly model the rewriting relations between question and context, and the schema linking relations between natural language and structured schema. Experimental results show that QURG significantly improves the performances on two large-scale context-dependent datasets SParC and CoSQL, especially for hard and long-turn questions.
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Acknowledgement
This work was supported in part by the National Natural Science Foundation of China (Grant Nos. 62276017, U1636211, 61672081), the 2022 Tencent Big Travel Rhino-Bird Special Research Program, the 2022 CCF-NSFOCUS Kun-Peng Scientific Research Fund and the Fund of the State Key Laboratory of Software Development Environment (Grant No. SKLSDE-2021ZX-18).
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Chai, L. et al. (2024). QURG: Question Rewriting Guided Context-Dependent Text-to-SQL Semantic Parsing. In: Liu, F., Sadanandan, A.A., Pham, D.N., Mursanto, P., Lukose, D. (eds) PRICAI 2023: Trends in Artificial Intelligence. PRICAI 2023. Lecture Notes in Computer Science(), vol 14326. Springer, Singapore. https://doi.org/10.1007/978-981-99-7022-3_24
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DOI: https://doi.org/10.1007/978-981-99-7022-3_24
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