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 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.
BibTeX
@inproceedings{sun-etal-2022-leveraging,
title = "Leveraging Explicit Lexico-logical Alignments in Text-to-{SQL} Parsing",
author = "Sun, Runxin and
He, Shizhu and
Zhu, Chong and
He, Yaohan and
Li, Jinlong and
Zhao, Jun and
Liu, Kang",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-short.31/",
doi = "10.18653/v1/2022.acl-short.31",
pages = "283--289"
}