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Text-to-SQL Oriented to the Process Mining Domain: A PT-EN Dataset for Query Translation

arXiv 2509.09684 ↗First posted 18 Aug 2025cs.IR cs.AI cs.CLcited by 0

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Abstract (arXiv)

This paper introduces text-2-SQL-4-PM, a bilingual (Portuguese-English) benchmark dataset designed for the text-to-SQL task in the process mining domain. Text-to-SQL conversion facilitates natural language querying of databases, increasing accessibility for users without SQL expertise and productivity for those that are experts. The text-2-SQL-4-PM dataset is customized to address the unique challenges of process mining, including specialized vocabularies and single-table relational structures derived from event logs. The dataset comprises 1,655 natural language utterances, including human-generated paraphrases, 205 SQL statements, and ten qualifiers. Methods include manual curation by experts, professional translations, and a detailed annotation process to enable nuanced analyses of task complexity. Additionally, a baseline study using GPT-3.5 Turbo demonstrates the feasibility and utility of the dataset for text-to-SQL applications. The results show that text-2-SQL-4-PM supports evaluation of text-to-SQL implementations, offering broader applicability for semantic parsing and other natural language processing tasks.

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References · 14

  1. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,234
  2. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,139
  3. 2023Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLscited by 688
  4. 2017Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learningcited by 687
  5. 2023DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correctioncited by 477
  6. 2019RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parserscited by 424
  7. 2023Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluationcited by 385
  8. 2023RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQLcited by 195
  9. 2023C3: Zero-shot Text-to-SQL with ChatGPTcited by 164
  10. 2020Semantic Evaluation for Text-to-SQL with Distilled Test Suitescited by 126
  11. 2017Learning a Neural Semantic Parser from User Feedbackcited by 99
  12. 2018TypeSQL: Knowledge-based Type-Aware Neural Text-to-SQL Generationcited by 81
2 more
  1. 2019Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsingcited by 74
  2. 2021mRAT-SQL+GAP:A Portuguese Text-to-SQL Transformercited by 15

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