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

• URL: http://arxiv.org/abs/2509.09684v1
• Date: Mon, 18 Aug 2025 01:25:41 GMT
• Title: Text-to-SQL Oriented to the Process Mining Domain: A PT-EN Dataset for Query Translation
• Authors: Bruno Yui Yamate, Thais Rodrigues Neubauer, Marcelo Fantinato, Sarajane Marques Peres,
• Abstract summary: This paper introduces text-2--4-PM, a benchmark dataset for the text-to-four task in the process mining domain.<n>The dataset comprises 1,655 natural language utterances, including human-generated paraphrases, 205sql statements, and ten qualifiers.<n>The results show that text-2--4-PM supports evaluation of text-to-four implementations, offering broader applicability for semantic parsing and other natural language processing tasks.
• Score: 0.10499611180329804
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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