Text-To-SQL
138 papers with code • 6 benchmarks • 14 datasets
Text-to-SQL is a task in natural language processing (NLP) where the goal is to automatically generate SQL queries from natural language text. The task involves converting the text input into a structured representation and then using this representation to generate a semantically correct SQL query that can be executed on a database.
( Image credit: SyntaxSQLNet )
Benchmarks
These leaderboards are used to track progress in Text-To-SQL
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Libraries
Use these libraries to find Text-To-SQL models and implementationsDatasets
Spider-Realistic
SParC
CoSQL
KaggleDBQA
SEDE
MultiSpider
Latest papers
T5-SR: A Unified Seq-to-Seq Decoding Strategy for Semantic Parsing
JuruoMP/T5-SR
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Translating natural language queries into SQLs in a seq2seq manner has attracted much attention recently.
Correcting Semantic Parses with Natural Language through Dynamic Schema Encoding
In addressing the task of converting natural language to SQL queries, there are several semantic and syntactic challenges.
Improving Generalization in Language Model-Based Text-to-SQL Semantic Parsing: Two Simple Semantic Boundary-Based Techniques
dakingrai/ood-generalization-semantic-boundary-techniques
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Compositional and domain generalization present significant challenges in semantic parsing, even for state-of-the-art semantic parsers based on pre-trained language models (LMs).
Federated Learning for Semantic Parsing: Task Formulation, Evaluation Setup, New Algorithms
osu-nlp-group/fl4semanticparsing
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By leveraging data from multiple clients, the FL paradigm can be especially beneficial for clients that have little training data to develop a data-hungry neural semantic parser on their own.
UNITE: A Unified Benchmark for Text-to-SQL Evaluation
A practical text-to-SQL system should generalize well on a wide variety of natural language questions, unseen database schemas, and novel SQL query structures.
CSS: A Large-scale Cross-schema Chinese Text-to-SQL Medical Dataset
Furthermore, we present CSS, a large-scale CrosS-Schema Chinese text-to-SQL dataset, to carry on corresponding studies.
Uncovering and Categorizing Social Biases in Text-to-SQL
In this work, we aim to uncover and categorize social biases in Text-to-SQL models.
Error Detection for Text-to-SQL Semantic Parsing
osu-nlp-group/text2sql-error-detection
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Despite remarkable progress in text-to-SQL semantic parsing in recent years, the performance of existing parsers is still far from perfect.
Text-to-SQL Error Correction with Language Models of Code
Thus, we propose a novel representation for SQL queries and their edits that adheres more closely to the pre-training corpora of language models of code.
How to Prompt LLMs for Text-to-SQL: A Study in Zero-shot, Single-domain, and Cross-domain Settings
Large language models (LLMs) with in-context learning have demonstrated remarkable capability in the text-to-SQL task.
