Chase: A Large-Scale and Pragmatic Chinese Dataset for Cross-Database Context-Dependent Text-to-SQL

Jiaqi Guo, Ziliang Si, Yu Wang, Qian Liu, Ming Fan, Jian-Guang Lou, Zijiang Yang, Ting Liu


Abstract
The cross-database context-dependent Text-to-SQL (XDTS) problem has attracted considerable attention in recent years due to its wide range of potential applications. However, we identify two biases in existing datasets for XDTS: (1) a high proportion of context-independent questions and (2) a high proportion of easy SQL queries. These biases conceal the major challenges in XDTS to some extent. In this work, we present Chase, a large-scale and pragmatic Chinese dataset for XDTS. It consists of 5,459 coherent question sequences (17,940 questions with their SQL queries annotated) over 280 databases, in which only 35% of questions are context-independent, and 28% of SQL queries are easy. We experiment on Chase with three state-of-the-art XDTS approaches. The best approach only achieves an exact match accuracy of 40% over all questions and 16% over all question sequences, indicating that Chase highlights the challenging problems of XDTS. We believe that XDTS can provide fertile soil for addressing the problems.
Anthology ID:
2021.acl-long.180
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2316–2331
Language:
URL:
https://aclanthology.org/2021.acl-long.180
DOI:
10.18653/v1/2021.acl-long.180
Bibkey:
Cite (ACL):
Jiaqi Guo, Ziliang Si, Yu Wang, Qian Liu, Ming Fan, Jian-Guang Lou, Zijiang Yang, and Ting Liu. 2021. Chase: A Large-Scale and Pragmatic Chinese Dataset for Cross-Database Context-Dependent Text-to-SQL. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 2316–2331, Online. Association for Computational Linguistics.
Cite (Informal):
Chase: A Large-Scale and Pragmatic Chinese Dataset for Cross-Database Context-Dependent Text-to-SQL (Guo et al., ACL-IJCNLP 2021)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/2021.acl-long.180.pdf
Video:
 https://preview.aclanthology.org/ingestion-script-update/2021.acl-long.180.mp4
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