Challenge: a cross-domain text-to-SQL task aims to parse user questions into SQL on complete unseen databases . a single-domain task evaluates the performance on identical databases based on the same domain .
Approach: They propose a cross-domain text-to-SQL task that parses user questions into SQL on unseen databases.
Outcome: The proposed system can parse user questions into SQL on complete unseen databases.

Similar Papers

DuSQL: A Large-Scale and Pragmatic Chinese Text-to-SQL Dataset (2020.emnlp-main)

Copied to clipboard

Challenge: Existing text-to-SQL parsing methods mainly focus on English, but there is no labeled data available for the language . a larges-scale and pragmatic Chinese dataset is used for cross-domain text- to-Sql task .
Approach: They propose a larges-scale Chinese dataset for a cross-domain text-to-SQL task . they analyze questions from several representative applications and use an effective data construction framework .
Outcome: The proposed dataset contains 200 databases, 813 tables, and 23,797 question/SQL pairs.
Chase: A Large-Scale and Pragmatic Chinese Dataset for Cross-Database Context-Dependent Text-to-SQL (2021.acl-long)

Copied to clipboard

Challenge: XDTS is a cross-database context-dependent text-to-sql problem with wide range of applications.
Approach: They present a large-scale Chinese dataset for cross-database context-dependent Text-to-SQL . they find that only 35% of questions are context-independent and 28% of SQL queries are easy .
Outcome: The proposed approach achieves an exact match accuracy of 40% over all questions and 16% over all question sequences.
Beyond Read-Only: Crafting a Comprehensive Chinese Text-to-SQL Dataset for Database Manipulation and Query (2024.findings-naacl)

Copied to clipboard

Challenge: Current research focuses mainly on read operations and ignores other aspects of database operations such as create, update, and delete operations.
Approach: They propose a large-scale cross-domain single-table CRUD operations Chinese Text-to-SQL dataset . the dataset contains 10,000 question/SQl pairs involving 625 tables from different domains .
Outcome: The proposed method achieves 67.08% and 83.8% exact set matching accuracy under read and delete operations, but only 49.6% and 61.8% under create and update operations.
A Review of Cross-Domain Text-to-SQL Models (2020.aacl-srw)

Copied to clipboard

Challenge: WikiSQL and Spider are cross-domain text-to-SQl datasets that have attracted much attention from the research community.
Approach: They propose to divide top models into two paradigms and evaluate their models for schema linking, pretrained word embeddings, reasoning assistance modules.
Outcome: The proposed models have over 90% execution accuracy, the authors show . the proposed models are more complex and more complex than the proposed ones .
Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL Parsing (2021.emnlp-main)

Copied to clipboard

Challenge: Existing approaches to parse text-to-SQL data are lacking labeled data for unseen evaluation databases.
Approach: They propose a framework for enhancing SQL queries by automatically producing large numbers of SQL queries based on an abstract syntax tree grammar.
Outcome: The proposed framework can produce high-quality natural language questions over strong baselines.
Bridging the Generalization Gap in Text-to-SQL Parsing with Schema Expansion (2022.acl-long)

Copied to clipboard

Challenge: Existing text-to-SQL parsers struggle with out-of-domain generalization problems, arguing that they lack the ability to match domain specific phrases to composite operations over columns.
Approach: They propose to use a synthetic dataset and a re-purposed train/test split to quantify out-of-domain generalization over column operations to address this problem.
Outcome: The proposed method outperforms baseline parsers on the domain generalization problem, while boosting the underlying parser’ overall performance by 13.8% relative accuracy gain (5.1% absolute).
Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expect (2022.coling-1)

Copied to clipboard

Challenge: text-to-SQL is a language processing and database-based language processing (NLP) task is to convert natural utterances into SQL queries and its practical application is to build natural language interfaces to database systems.
Approach: They propose to conduct a systematic survey of text-to-SQL to examine the challenges and potential future directions.
Outcome: The proposed system converts natural utterances into SQL queries and is a representative task in semantic parsing.
Dataset and Enhanced Model for Eligibility Criteria-to-SQL Semantic Parsing (2020.lrec-1)

Copied to clipboard

Challenge: Clinical trials require that patients meet eligibility criteria to ensure safety and effectiveness of studies.
Approach: They propose a dataset that includes the first-of-its-kind eligibility-criteria corpus and queries for criteria-to-sql . they propose 'neuro semantic parser' which can translate eligibility criteria to executable SQL queries .
Outcome: The proposed parser outperforms existing state-of-the-art general-purpose models while highlighting the challenges presented by the new dataset.
A Pilot Study for Chinese SQL Semantic Parsing (D19-1)

Copied to clipboard

Challenge: Existing datasets to map natural language text into SQL are limited in their use in question-to-sql mapping.
Approach: They propose to use a Chinese-based semantic parser to map natural language text into SQL.
Outcome: The proposed dataset compares a character-based parser with a word embedding scheme for Chinese . the results show that the parsers are subject to segmentation errors and cross-lingual embedders are useful for text-to-SQL mapping.
Exploring Schema Generalizability of Text-to-SQL (2023.findings-acl)

Copied to clipboard

Challenge: Existing text-to-SQL models are limited in their generalizability, despite their performance being over-estimated.
Approach: They propose a framework to generate novel text-to-SQL data via automatic and synchronous (DS, SQL) pair altering.
Outcome: The proposed framework generates text-to-SQL data via automatic and synchronous (DS, SQL) pair altering.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations