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Text-To-Sql

8 papers with code · Computer Code

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SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning

ICLR 2018 naver/sqlova

Existing state-of-the-art approaches rely on reinforcement learning to reward the decoder when it generates any of the equivalent serializations.

TEXT-TO-SQL

Improving Text-to-SQL Evaluation Methodology

ACL 2018 jkkummerfeld/text2sql-data

Second, we show that the current division of data into training and test sets measures robustness to variations in the way questions are asked, but only partially tests how well systems generalize to new queries; therefore, we propose a complementary dataset split for evaluation of future work.

TEXT-TO-SQL

Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task

EMNLP 2018 taoyds/spider

We define a new complex and cross-domain semantic parsing and text-to-SQL task where different complex SQL queries and databases appear in train and test sets.

SEMANTIC PARSING TEXT-TO-SQL

SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-DomainText-to-SQL Task

11 Oct 2018taoyds/syntaxsql

In this paper we propose SyntaxSQLNet, a syntax tree network to address the complex and cross-domain text-to-SQL generation task.

SEMANTIC PARSING TEXT-TO-SQL

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

NAACL 2018 taoyds/typesql

Interacting with relational databases through natural language helps users of any background easily query and analyze a vast amount of data.

SLOT FILLING TEXT-TO-SQL

Robust Text-to-SQL Generation with Execution-Guided Decoding

9 Jul 2018Microsoft/PointerSQL

We consider the problem of neural semantic parsing, which translates natural language questions into executable SQL queries.

SEMANTIC PARSING TEXT-TO-SQL

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

20 May 2019zhanzecheng/IRNet

We present a neural approach called IRNet for complex and cross-domain Text-to-SQL.

TEXT-TO-SQL