TableQA: a Large-Scale Chinese Text-to-SQL Dataset for Table-Aware SQL Generation

• URL: http://arxiv.org/abs/2006.06434v1
• Date: Wed, 10 Jun 2020 03:49:08 GMT
• Title: TableQA: a Large-Scale Chinese Text-to-SQL Dataset for Table-Aware SQL Generation
• Abstract summary: Existing NL2 datasets assume that condition values should appear exactly in natural language questions and the queries are answerable given the table. We present TableQA, a large-scale cross-domain Natural Language dataset in Chinese language consisting 64,891 questions and 20,311 uniquesql queries on 6,000 tables. Two table-aware approaches are proposed to alleviate the problem, the end-to-end approaches obtains 51.3% and 47.4% accuracy on the condition value and logic form tasks, with improvement of 4.7% and 3.4% respectively.
• Score: 3.4161707164978137
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Parsing natural language to corresponding SQL (NL2SQL) with data driven approaches like deep neural networks attracts much attention in recent years. Existing NL2SQL datasets assume that condition values should appear exactly in natural language questions and the queries are answerable given the table. However, these assumptions may fail in practical scenarios, because user may use different expressions for the same content in the table, and query information outside the table without the full picture of contents in table. Therefore we present TableQA, a large-scale cross-domain Natural Language to SQL dataset in Chinese language consisting 64,891 questions and 20,311 unique SQL queries on over 6,000 tables. Different from exisiting NL2SQL datasets, TableQA requires to generalize well not only to SQL skeletons of different questions and table schemas, but also to the various expressions for condition values. Experiment results show that the state-of-the-art model with 95.1% condition value accuracy on WikiSQL only gets 46.8% condition value accuracy and 43.0% logic form accuracy on TableQA, indicating the proposed dataset is challenging and necessary to handle. Two table-aware approaches are proposed to alleviate the problem, the end-to-end approaches obtains 51.3% and 47.4% accuracy on the condition value and logic form tasks, with improvement of 4.7% and 3.4% respectively.

Related papers

• Spider 2.0-AIFunc: Extending Real-World Text-to-SQL to AI-Native SQL Workflows [54.49251107006261]
  We introduce Spider 2.0-AIFunc, a benchmark of 465 verified instances across 125 real-world databases covering six types of AI functions on the Snowflake platform.<n>We find that the strongest proprietary models reach 67-70% execution accuracy while the best open-source model achieves 58.1%.
  arXiv  Detail & Related papers  (2026-07-07T12:54:08Z)
• ProSPy: A Profiling-Driven SQL-Python Agentic Framework for Enterprise Text-to-SQL [51.98164069124653]
  ProSPy is a Profiling-driven framework for enterprise-scale Text-to-gnostic analysis.<n>ProSPy structures the reasoning process into four stages.<n>It first extracts fine-grained data evidence through automatic profiling.<n>It prunes large schemas into task-relevant contexts.
  arXiv  Detail & Related papers  (2026-06-04T08:13:05Z)
• Retrieve Only Relevant Tables Whether Few or Many: Adaptive Table Retrieval Method [49.38787814788666]
  Existing table retrieval approaches select a pre-determined set of k tables with the highest similarity to the query.<n>The number of required tables varies across queries and cannot be known in advance.<n>We propose an adaptive table retrieval method that adjusts the number of tables retrieved according to the requirements of each query.
  arXiv  Detail & Related papers  (2026-04-12T14:53:56Z)
• Text-to-SQL as Dual-State Reasoning: Integrating Adaptive Context and Progressive Generation [54.53145282349042]
  We introduce DSR-sourced, a textbfDual-textbfS textbfReasoning framework that models Text-to-context as an interaction between an adaptive context state and a progressive generation state.<n>Without any post-training or in-context examples, DSR-sourced achieves competitive performance, reaching 35.28% execution accuracy on Spider 2.0-Snow and 68.32% on BIRD development set.
  arXiv  Detail & Related papers  (2025-11-26T13:52:50Z)
• Agentic LLMs for Question Answering over Tabular Data [6.310433217813068]
  Question Answering over Tabular Data (Table QA) presents unique challenges due to the diverse structure, size, and data types of real-world tables.<n>This paper details our methodology, experimental results, and alternative approaches, providing insights into the strengths and limitations of Table QA.
  arXiv  Detail & Related papers  (2025-09-11T08:12:38Z)
• Sigma: A dataset for text-to-code semantic parsing with statistical analysis [9.781031411221395]
  SIGMA comprises 6000 questions with corresponding Python code labels, spanning across 160 databases. Half of the questions involve query types, which return information in its original format. The Python code labels in our dataset cover 4 types of query types and 40 types of statistical analysis patterns.
  arXiv  Detail & Related papers  (2025-04-05T23:30:20Z)
• RSL-SQL: Robust Schema Linking in Text-to-SQL Generation [51.00761167842468]
  We propose a novel framework called RSL- that combines bidirectional schema linking, contextual information augmentation, binary selection strategy, and multi-turn self-correction. benchmarks demonstrate that our approach achieves SOTA execution accuracy among open-source solutions, with 67.2% on BIRD and 87.9% on GPT-4ocorrection. Our approach outperforms a series of GPT-4 based Text-to-Seek systems when adopting DeepSeek (much cheaper) with same intact prompts.
  arXiv  Detail & Related papers  (2024-10-31T16:22:26Z)
• DataGpt-SQL-7B: An Open-Source Language Model for Text-to-SQL [7.76068876576964]
  We propose a suite of compact, fine-tuned models and self-refine mechanisms to democratize data access and analysis for non-expert users. Our system, DataGpt-sql, achieved 87.2% accuracy on the spider-dev.
  arXiv  Detail & Related papers  (2024-09-24T11:38:08Z)
• SelECT-SQL: Self-correcting ensemble Chain-of-Thought for Text-to-SQL [3.422309388045878]
  We introduce SelECT-, a novel in-context learning solution that uses an algorithmic combination of chain-of-thought, self-correction, and ensemble methods. Specifically, when configured using GPT as the base LLM, SelECT-Turbo achieves 84.2% execution accuracy on the Spider leaderboard's development set.
  arXiv  Detail & Related papers  (2024-09-16T05:40:18Z)
• KET-QA: A Dataset for Knowledge Enhanced Table Question Answering [63.56707527868466]
  We propose to use a knowledge base (KB) as the external knowledge source for TableQA. Every question requires the integration of information from both the table and the sub-graph to be answered. We design a retriever-reasoner structured pipeline model to extract pertinent information from the vast knowledge sub-graph.
  arXiv  Detail & Related papers  (2024-05-13T18:26:32Z)
• Analyzing the Effectiveness of Large Language Models on Text-to-SQL Synthesis [4.412170175171256]
  This study investigates various approaches to using Large Language Models for Text-to- program synthesis. The goal was to input a natural language question along with the database schema and output the correct SELECT query.
  arXiv  Detail & Related papers  (2024-01-22T22:05:42Z)
• UNITE: A Unified Benchmark for Text-to-SQL Evaluation [72.72040379293718]
  We introduce a UNIfied benchmark for Text-to-domain systems. It is composed of publicly available text-to-domain datasets and 29K databases. Compared to the widely used Spider benchmark, we introduce a threefold increase in SQL patterns.
  arXiv  Detail & Related papers  (2023-05-25T17:19:52Z)
• Improving Text-to-SQL Semantic Parsing with Fine-grained Query Understanding [84.04706075621013]
  We present a general-purpose, modular neural semantic parsing framework based on token-level fine-grained query understanding. Our framework consists of three modules: named entity recognizer (NER), neural entity linker (NEL) and neural entity linker (NSP)
  arXiv  Detail & Related papers  (2022-09-28T21:00:30Z)
• OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering [106.73213656603453]
  We develop a simple table-based QA model with minimal annotation effort. We propose an omnivorous pretraining approach that consumes both natural and synthetic data.
  arXiv  Detail & Related papers  (2022-07-08T01:23:45Z)
• 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)
• GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing [117.98107557103877]
  We present GraPPa, an effective pre-training approach for table semantic parsing. We construct synthetic question-pairs over high-free tables via a synchronous context-free grammar. To maintain the model's ability to represent real-world data, we also include masked language modeling.
  arXiv  Detail & Related papers  (2020-09-29T08:17:58Z)

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.