Leveraging Explicit Lexico-logical Alignments in Text-to-SQL Parsing
Runxin Sun, Shizhu He, Chong Zhu, Yaohan He, Jinlong Li, Jun Zhao, Kang Liu
Abstract
Text-to-SQL aims to parse natural language questions into SQL queries, which is valuable in providing an easy interface to access large databases. Previous work has observed that leveraging lexico-logical alignments is very helpful to improve parsing performance. However, current attention-based approaches can only model such alignments at the token level and have unsatisfactory generalization capability. In this paper, we propose a new approach to leveraging explicit lexico-logical alignments. It first identifies possible phrase-level alignments and injects them as additional contexts to guide the parsing procedure. Experimental results on \textsc{Squall} show that our approach can make better use of such alignments and obtains an absolute improvement of 3.4% compared with the current state-of-the-art.- Anthology ID:
- 2022.acl-short.31
- Volume:
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 283–289
- Language:
- URL:
- https://aclanthology.org/2022.acl-short.31
- DOI:
- 10.18653/v1/2022.acl-short.31
- Cite (ACL):
- Runxin Sun, Shizhu He, Chong Zhu, Yaohan He, Jinlong Li, Jun Zhao, and Kang Liu. 2022. Leveraging Explicit Lexico-logical Alignments in Text-to-SQL Parsing. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 283–289, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- Leveraging Explicit Lexico-logical Alignments in Text-to-SQL Parsing (Sun et al., ACL 2022)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2022.acl-short.31.pdf