Abstract
NL2SQL is a translation task that converts natural language queries to SQL. We revisit the popular NL2SQL models and find that the accuracy of aggregation operator prediction remains a bottleneck of current NL2SQL models. We present a novel statistics-based approach called AOED, which stands for Aggregation Operator Enhanced Decoding, to help predict aggregation operator. AOED is a carefully designed mechanism that takes full advantage of the statistical information of the aggregation keywords in the natural language query to help improve the prediction accuracy of the aggregation operator. Experiments on the WikiSQL dataset show that our model outperforms the state-of-the-art model SQLova and NL2SQL-RULE by 3.4% and 0.7% on overall SQL results in the logical form accuracy and by 0.2% and 0.7% on aggregation operator result.
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Acknowledgement
This research is supported by Chinese Scientific and Technical Innovation Project 2030 (No. 2018AAA0102100), National Natural Science Foundation of China (No. 62077031). We thank the reviewers for their constructive comments.
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Li, Y., Pan, X., Zhao, D., Wang, M., Wen, Y. (2022). AOED: Generating SQL with the Aggregation Operator Enhanced Decoding. In: Zhao, X., Yang, S., Wang, X., Li, J. (eds) Web Information Systems and Applications. WISA 2022. Lecture Notes in Computer Science, vol 13579. Springer, Cham. https://doi.org/10.1007/978-3-031-20309-1_18
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DOI: https://doi.org/10.1007/978-3-031-20309-1_18
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