Mention extraction and linking for SQL query generation

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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.

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APA

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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