@inproceedings{ma-etal-2020-mention,
title = "Mention Extraction and Linking for {SQL} Query Generation",
author = "Ma, Jianqiang and
Yan, Zeyu and
Pang, Shuai and
Zhang, Yang and
Shen, Jianping",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.563",
doi = "10.18653/v1/2020.emnlp-main.563",
pages = "6936--6942",
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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<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.</abstract>
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%0 Conference Proceedings
%T Mention Extraction and Linking for SQL Query Generation
%A Ma, Jianqiang
%A Yan, Zeyu
%A Pang, Shuai
%A Zhang, Yang
%A Shen, Jianping
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F ma-etal-2020-mention
%X 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.
%R 10.18653/v1/2020.emnlp-main.563
%U https://aclanthology.org/2020.emnlp-main.563
%U https://doi.org/10.18653/v1/2020.emnlp-main.563
%P 6936-6942
Markdown (Informal)
[Mention Extraction and Linking for SQL Query Generation](https://aclanthology.org/2020.emnlp-main.563) (Ma et al., EMNLP 2020)
ACL
- Jianqiang Ma, Zeyu Yan, Shuai Pang, Yang Zhang, and Jianping Shen. 2020. Mention Extraction and Linking for SQL Query Generation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6936–6942, Online. Association for Computational Linguistics.