Abstract
On the WikiSQL benchmark, state-of-the-art text-to-SQL systems typically take a slot-filling approach by building several dedicated models for each type of slots. Such modularized systems are not only complex but also of limited capacity for capturing inter-dependencies among SQL clauses. To solve these problems, this paper proposes a novel extraction-linking approach, where a unified extractor recognizes all types of slot mentions appearing in the question sentence before a linker maps the recognized columns to the table schema to generate executable SQL queries. Trained with automatically generated annotations, the proposed method achieves the first place on the WikiSQL benchmark.
Cite
CITATION STYLE
Ma, J., Yan, Z., Pang, S., Zhang, Y., & Shen, J. (2020). Mention extraction and linking for SQL query generation. In EMNLP 2020 - 2020 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference (pp. 6936–6942). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2020.emnlp-main.563
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