@inproceedings{aec63f7082a2418ba2df7a991e607372,
title = "TypeSQL: Knowledge-based type-aware neural text-to-SQL generation",
abstract = "Interacting with relational databases through natural language helps users of any background easily query and analyze a vast amount of data. This requires a system that understands users' questions and converts them to SQL queries automatically. In this paper we present a novel approach, TYPESQL, which views this problem as a slot filling task. Additionally, TYPESQL utilizes type information to better understand rare entities and numbers in natural language questions. We test this idea on the WikiSQL dataset and outperform the prior state-of-the-art by 5.5% in much less time. We also show that accessing the content of databases can significantly improve the performance when users' queries are not wellformed. TYPESQL gets 82.6% accuracy, a 17.5% absolute improvement compared to the previous content-sensitive model.",
author = "Tao Yu and Zifan Li and Zilin Zhang and Rui Zhang and Dragomir Radev",
note = "Publisher Copyright: {\textcopyright} 2018 Association for Computational Linguistics. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018 ; Conference date: 01-06-2018 Through 06-06-2018",
year = "2018",
language = "English (US)",
series = "NAACL HLT 2018 - 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference",
publisher = "Association for Computational Linguistics (ACL)",
pages = "588--594",
booktitle = "Short Papers",
}