Recent Advances in SQL Query Generation: A Survey

• URL: http://arxiv.org/abs/2005.07667v1
• Date: Fri, 15 May 2020 17:31:29 GMT
• Title: Recent Advances in SQL Query Generation: A Survey
• Authors: Jovan Kalajdjieski, Martina Toshevska, Frosina Stojanovska
• Abstract summary: With the rise of deep learning techniques, there is extensive ongoing research in designing a suitable natural language interface to relational databases. We describe models with various architectures such as convolutional neural networks, recurrent neural networks, pointer networks, reinforcement learning, etc.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Natural language is hypothetically the best user interface for many domains. However, general models that provide an interface between natural language and any other domain still do not exist. Providing natural language interface to relational databases could possibly attract a vast majority of users that are or are not proficient with query languages. With the rise of deep learning techniques, there is extensive ongoing research in designing a suitable natural language interface to relational databases. This survey aims to overview some of the latest methods and models proposed in the area of SQL query generation from natural language. We describe models with various architectures such as convolutional neural networks, recurrent neural networks, pointer networks, reinforcement learning, etc. Several datasets intended to address the problem of SQL query generation are interpreted and briefly overviewed. In the end, evaluation metrics utilized in the field are presented mainly as a combination of execution accuracy and logical form accuracy.

Related papers

• Text2SQL is Not Enough: Unifying AI and Databases with TAG [47.45480855418987]
  Table-Augmented Generation (TAG) is a paradigm for answering natural language questions over databases. We develop benchmarks to study the TAG problem and find that standard methods answer no more than 20% of queries correctly.
  arXiv  Detail & Related papers  (2024-08-27T00:50:14Z)
• UQE: A Query Engine for Unstructured Databases [71.49289088592842]
  We investigate the potential of Large Language Models to enable unstructured data analytics. We propose a new Universal Query Engine (UQE) that directly interrogates and draws insights from unstructured data collections.
  arXiv  Detail & Related papers  (2024-06-23T06:58:55Z)
• Natural Language Interfaces for Tabular Data Querying and Visualization: A Survey [30.836162812277085]
  The rise of large language models (LLMs) has further advanced this field, opening new avenues for natural language processing techniques. We introduce the fundamental concepts and techniques underlying these interfaces with a particular emphasis on semantic parsing. This includes a deep dive into the influence of LLMs, highlighting their strengths, limitations, and potential for future improvements.
  arXiv  Detail & Related papers  (2023-10-27T05:01:20Z)
• xDBTagger: Explainable Natural Language Interface to Databases Using Keyword Mappings and Schema Graph [0.17188280334580192]
  Translating natural language queries into structured query language (NLQ) in interfaces to relational databases is a challenging task. We propose xDBTagger, an explainable hybrid translation pipeline that explains the decisions made along the way to the user both textually and visually. xDBTagger is effective in terms of accuracy and translates the queries more efficiently compared to other state-of-the-art pipeline-based systems up to 10000 times.
  arXiv  Detail & Related papers  (2022-10-07T18:17:09Z)
• A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directions [102.8606542189429]
  The goal of text-to-corpora parsing is to convert a natural language (NL) question to its corresponding structured query language () based on the evidences provided by databases. Deep neural networks have significantly advanced this task by neural generation models, which automatically learn a mapping function from an input NL question to an output query.
  arXiv  Detail & Related papers  (2022-08-29T14:24:13Z)
• Deep Learning Driven Natural Languages Text to SQL Query Conversion: A Survey [2.309914459672557]
  In this paper, we try to present a holistic overview of 24 recent neural network models studied in the last couple of years. We also give an overview of 11 datasets that are widely used to train models for TEXT2 technologies.
  arXiv  Detail & Related papers  (2022-08-08T20:54:34Z)
• Towards Zero-shot Language Modeling [90.80124496312274]
  We construct a neural model that is inductively biased towards learning human languages. We infer this distribution from a sample of typologically diverse training languages. We harness additional language-specific side information as distant supervision for held-out languages.
  arXiv  Detail & Related papers  (2021-08-06T23:49:18Z)
• "What Do You Mean by That?" A Parser-Independent Interactive Approach for Enhancing Text-to-SQL [49.85635994436742]
  We include human in the loop and present a novel-independent interactive approach (PIIA) that interacts with users using multi-choice questions. PIIA is capable of enhancing the text-to-domain performance with limited interaction turns by using both simulation and human evaluation.
  arXiv  Detail & Related papers  (2020-11-09T02:14:33Z)
• Data Agnostic RoBERTa-based Natural Language to SQL Query Generation [0.0]
  The NL2 task aims at finding deep learning approaches to solve the problem converting by natural language questions into valid queries. We have presented an approach with data privacy at its core. Although we have not achieved state of the art results, we have eliminated the need for the table right from the training of the model.
  arXiv  Detail & Related papers  (2020-10-11T13:18:46Z)
• Towards a Natural Language Query Processing System [0.0]
  This paper reports our study on the design and development of a natural language query interface to a backend relational database. The novelty in the study lies in defining a graph database as a middle layer to store necessary metadata needed to transform a natural language query into structured query language. The translation results for some sample queries yielded a 90% accuracy rate.
  arXiv  Detail & Related papers  (2020-09-25T19:52:20Z)
• Photon: A Robust Cross-Domain Text-to-SQL System [189.1405317853752]
  We present Photon, a robust, modular, cross-domain NLIDB that can flag natural language input to which a mapping cannot be immediately determined. The proposed method effectively improves the robustness of text-to-native system against untranslatable user input.
  arXiv  Detail & Related papers  (2020-07-30T07:44:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.