Mention Extraction and Linking for SQL Query Generation

Jianqiang Ma, Zeyu Yan, Shuai Pang, Yang Zhang, Jianping Shen


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.
Anthology ID:
2020.emnlp-main.563
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6936–6942
Language:
URL:
https://aclanthology.org/2020.emnlp-main.563
DOI:
10.18653/v1/2020.emnlp-main.563
Bibkey:
Cite (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.
Cite (Informal):
Mention Extraction and Linking for SQL Query Generation (Ma et al., EMNLP 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/2020.emnlp-main.563.pdf
Video:
 https://slideslive.com/38939352
Data
WikiSQL