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
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.emnlp-main.563.pdf
- Data
- WikiSQL