TypeSQL: Knowledge-Based Type-Aware Neural Text-to-SQL Generation

By Tao Yu, Zifan Li, Zilin Zhang, Rui Zhang, Dragomir R. Radev. 2018

308 citations recorded by the source corpus.

Interacting with relational databases through natural language helps users with 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 formats the problem as a slot filling task in a more reasonable way. In addition, TypeSQL utilizes type information to better understand rare entities and numbers in the questions. We experiment this idea on the WikiSQL dataset and outperform the prior art by 6% in much shorter time. We also show that accessing the content of databases can significantly improve the performance when users’ queries are not well-formed. TypeSQL can reach 82.6% accuracy, a 17.5% absolute improvement compared to the previous content-sensitive model.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

The Natural Language Decathlon: Multitask Learning as Question Answering (2018, methodology): “For WikiSQL, this model nearly matches the prior state-of-the-art validation results of 72.4% without using a structured approach [Dong and Lapata, 2018, Huang et al., 2018, Yu et al., 2018b].”
A Discrete Hard EM Approach for Weakly Supervised Question Answering (2019, methodology): “Large-scale reading comprehension (RC) tasks that provide full super-vision for answer spans (Rajpurkar et al., 2016) have seen signiﬁcant progress recently (Seo et al., 2017; Xiong et al., 2018; Yu et al., 2018a; De-vlin et al., 2019).”
Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task (2018, methodology): “These datasets have been studied for decades in both the NLP community (Warren and Pereira, 1982; Popescu et al., 2003b, 2004; Li et al., 2006; Giordani and Moschitti, 2012; Wang et al., 2017; Iyer et al., 2017; Zhong et al., 2017; Xu et al., 2017; Yu et al., 2018; Huang et al., 2018; Wang et al., 2”
Structural Language Models of Code (2019, background): “In related tasks such as semantic parsing (Dong & Lapata, 2018; Yu et al., 2018; Iyer et al., 2019), natural-languageto-code (Allamanis et al.”
SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-Domain Text-to-SQL Task (2018, methodology): “We build each module based on the TypeSQL (Yu et al., 2018a) implementation.”
A Comprehensive Exploration on WikiSQL with Table-Aware Word Contextualization (2019, methodology): “0 TypeSQL (Yu et al., 2018) 68.”
Model-based Interactive Semantic Parsing: A Unified Framework and A Text-to-SQL Case Study (2019, methodology): “3 In our experiments, we follow the same database split as in (Yu et al., 2018c) and report the Exact Matching accuracy (“ Acc em ”) on Dev set.”
GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing (2020, methodology): “Recent pre-trained language models (LMs) such as BERT (Devlin et al., 2019) and RoBERTa (Liu et al., 2019) achieve tremendous success on a spectrum of natural language processing tasks, including semantic parsing (Zettlemoyer & Collins, 2005; Zhong et al., 2017; Yu et al., 2018b).”
Structural Language Models for Any-Code Generation (2019, background): “Other work used datasets that are either small (Ling et al., 2016), containing highly aligned examples (Oda et al., 2015; Chen et al., 2018), limited-purpose languages like SQL (Yu et al., 2018), or general-purpose but containing eminently templated programs (Ling et al., 2016).”
Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation (2019, methodology): “The large number of OOD words poses another steep challenge in predicting columns in SQL queries (Yu et al., 2018b), because the OOD words usually lack of accurate representations in neural models.”
TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation (2018, unknown): “…A CC EM A CC EX Seq2Seq (Zhong et al., 2017) 23.4 35.9 S EQ 2T REE (Dong and Lapata, 2016) 23.4 35.9 Seq2SQL (Zhong et al., 2017) 48.3 59.4 SQLNet (Xu et al., 2017) – 68.0 PT-MAML (Huang et al., 2018) 62.8 68.0 TypeSQL (Yu et al., 2018 two conﬁgurations, with or without parent feeding ( § 2.3).”
X-SQL: reinforce schema representation with context (2019, methodology): “Future work includes experimenting with more complex dataset such as Spider (Yu et al., 2018b).”
SParC: Cross-Domain Semantic Parsing in Context (2019, methodology): “…community by virtue of a continuous effort of dataset creation (Zelle and Mooney, 1996; Iyyer et al., 2017; Zhong et al., 2017; Finegan-Dollak et al., 2018; Yu et al., 2018a) and the modeling innovation that follows it (Xu et al., 2017; Wang et al., 2018; Yu et al., 2018b; Shi et al., 2018).”
RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL (2023, methodology): “Because there is a huge gap between natural language questions and their corresponding SQL queries, some works have focused on how to design an efﬁcient intermediate representation (IR) to bridge the aforementioned gap (Yu et al. 2018b; Guo et al. 2019; Gan et al. 2021b).”
Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions (2019, methodology): “(2) SyntaxSQL-con: This is adapted from the original context-agnostic SyntaxSQLNet (Yu et al., 2018b) by using bi-LSTMs to encode the interaction history including the utterance and the associated SQL query response.”
Natural language to SQL: Where are we today? (2020, methodology): “For example, the studies in the natural language community [22, 44, 53, 46, 21, 24] exclude comparisons with [25, 35] from the database community.”