Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation

Jiaqi Guo, Zecheng Zhan, Yan Gao, Yan Xiao, Jian-Guang Lou, Ting Liu, Dongmei Zhang

Abstract
We present a neural approach called IRNet for complex and cross-domain Text-to-SQL. IRNet aims to address two challenges: 1) the mismatch between intents expressed in natural language (NL) and the implementation details in SQL; 2) the challenge in predicting columns caused by the large number of out-of-domain words. Instead of end-to-end synthesizing a SQL query, IRNet decomposes the synthesis process into three phases. In the first phase, IRNet performs a schema linking over a question and a database schema. Then, IRNet adopts a grammar-based neural model to synthesize a SemQL query which is an intermediate representation that we design to bridge NL and SQL. Finally, IRNet deterministically infers a SQL query from the synthesized SemQL query with domain knowledge. On the challenging Text-to-SQL benchmark Spider, IRNet achieves 46.7% accuracy, obtaining 19.5% absolute improvement over previous state-of-the-art approaches. At the time of writing, IRNet achieves the first position on the Spider leaderboard.
Anthology ID:
P19-1444
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Editors:
Anna Korhonen, David Traum, Lluís Màrquez
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4524–4535
Language:
URL:
https://aclanthology.org/P19-1444
DOI:
10.18653/v1/P19-1444
Bibkey:
Cite (ACL):
Jiaqi Guo, Zecheng Zhan, Yan Gao, Yan Xiao, Jian-Guang Lou, Ting Liu, and Dongmei Zhang. 2019. Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4524–4535, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation (Guo et al., ACL 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/dois-2013-emnlp/P19-1444.pdf
Supplementary:
 P19-1444.Supplementary.pdf
Code
 zhanzecheng/IRNet +  additional community code
Data
WikiSQL
PDF Search Code Supplementary