Translating Questions to SQL Queries with Generative Parsers Discriminatively Reranked

By Alessandra Giordani, Alessandro Moschitti. 2012

76 citations recorded by the source corpus.

Subfields: Natural Language Processing, Code Generation & Program Synthesis

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Citations in the corpus

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning (2017, background): “Giordani & Moschitti (2012) translate questions to SQL by ﬁrst generating candidate queries from a grammar then ranking them using tree kernels.”
Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task (2018, background): “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”
Summarizing Source Code using a Neural Attention Model (2016, methodology): “…n s(c, n) (1) RET We also use the scoring function s to retrieve the highest scoring code snippet c∗j from our training corpus, given a NL question n ∈ UN : c∗j = argmax cj s(cj , n), 1 ≤ j ≤ J (2) In this work, s is computed using an LSTM neural attention model, to be described in Section 5.”
Learning a Neural Semantic Parser from User Feedback (2017, background): “A few systems have been developed to directly generate SQL queries from natural language (Popescu et al., 2003; Giordani and Moschitti, 2012; Poon, 2013).”
SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-Domain Text-to-SQL Task (2018, methodology): “As a sub-task of semantic parsing, the text-to-SQL problem has been studied for decades (War-ren and Pereira, 1982; Popescu et al., 2003a, 2004; Li et al., 2006; Giordani and Moschitti, 2012; Wang et al., 2017b).”
TypeSQL: Knowledge-Based Type-Aware Neural Text-to-SQL Generation (2018, methodology): “As a sub-task of semantic parsing, the text-to-SQL problem has been studied for decades (War-ren and Pereira, 1982; Popescu et al., 2003, 2004; Li et al., 2006; Giordani and Moschitti, 2012; Wang et al., 2017b).”
Natural language to SQL: Where are we today? (2020, methodology): “ATIS/GeoQuery: ATIS [33, 12, 22, 51] and GeoQuery [49, 50, 32, 17, 22] are widely used for semantic parsing which is the task of translating q nl into a formal meaning representation.”
Improving Text-to-SQL Evaluation Methodology (2018, methodology): “tactic parser (Giordani and Moschitti, 2012) and applying techniques from logical parsing research (Poon, 2013).”
SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning (2017, background): “The study of translating natural language into SQL queries has a long history (Warren & Pereira, 1982; Androutsopoulos et al., 1993; 1995; Popescu et al., 2003; 2004; Li et al., 2006; Giordani & Moschitti, 2012; Zhang & Sun, 2013; Li & Jagadish, 2014; Wang et al., 2017).”