Work

MTO: A Multi-turn Conversational Text-to-OverpassQL Dataset for Enhanced OpenStreetMap Query Generation

Year: 2026

Type: conference-paper

Abstract: We propose a comprehensive framework for constructing multi-turn Text-to-OverpassQL dialogue datasets.Under this framework, we introduce the first multi-turn Text-to-OverpassQL dataset built upon the OverpassNL corpus.Our dataset comprises over 7,800 dialogues, each containing 2 to 4 user utterances, resulting in more than 20,000 individual utterances aligned with executable Overpass queries.To generate high-quality multi-turn dialogues, we design a four-stage pipeline.First, we convert Overpass queries into syntax trees using a custom parser developed based on the official OverpassQL grammar.This enables structural manipulation while preserving syntactic and executable validity.Second, we apply a diverse set of tree-editing templates, including both simple keyword-level changes and complex structural decompositions, to produce multiple valid and diverse Overpass queries.Third, we leverage a prompt-based approach to guide large language models in generating context-aware natural language questions, ensuring increasing inter-turn dependency across the dialogue.Finally, we implement a hybrid filtering strategy that combines manual annotation with model-assisted selection to validate alignment and correctness at scale.In addition to presenting the dataset, we evaluate the performance of several mainstream large language models and demonstrate that our end-to-end baseline model achieves competitive results.This work offers a new benchmark for studying executable semantic parsing and contextual understanding in mapbased query tasks.

Source: Findings of the Association for Computational Linguistics: ACL (Findings of ACL)

DOI: 10.18653/v1/2026.findings-acl.36