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Text-To-Sql

22 papers with code · Computer Code

( Image credit: SyntaxSQLNet )

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SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning

ICLR 2018 salesforce/WikiSQL

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

TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data

ACL 2020 facebookresearch/tabert

Recent years have witnessed the burgeoning of pretrained language models (LMs) for text-based natural language (NL) understanding tasks.

SEMANTIC PARSING 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

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

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

ACL 2019 microsoft/IRNet

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

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

RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers

ICLR 2020 Microsoft/rat-sql

In addition, we observe qualitative improvements in the model’s understanding of schema linking and alignment.

SEMANTIC PARSING TEXT-TO-SQL

RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers

ACL 2020 Microsoft/rat-sql

The generalization challenge lies in (a) encoding the database relations in an accessible way for the semantic parser, and (b) modeling alignment between database columns and their mentions in a given query.

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