@inproceedings{zhang-etal-2026-mto,
    title = "{MTO}: A Multi-turn Conversational Text-to-{O}verpass{QL} Dataset for Enhanced {O}pen{S}treet{M}ap Query Generation",
    author = "Zhang, Haodi  and
      Zhu, Xinrui  and
      Kong, Mingze  and
      Liu, Zhidan  and
      Fan, Tao  and
      Wu, Kaishun  and
      Song, Yuanfeng",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.36/",
    pages = "750--771",
    ISBN = "979-8-89176-395-1",
    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 map-based query tasks."
}