@inproceedings{shi-etal-2025-adaptive,
    title = "Adaptive and Robust Translation from Natural Language to Multi-model Query Languages",
    author = "Shi, Gengyuan  and
      Wang, Chaokun  and
      Yabin, Liu  and
      Ren, Jiawei",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.acl-long.776/",
    doi = "10.18653/v1/2025.acl-long.776",
    pages = "15950--15965",
    ISBN = "979-8-89176-251-0",
    abstract = "Multi-model databases and polystore systems are increasingly studied for managing multi-model data holistically. As their primary interface, multi-model query languages (MMQLs) often exhibit complex grammars, highlighting the need for effective Text-to-MMQL translation methods. Despite advances in natural language translation, no effective solutions for Text-to-MMQL exist. To address this gap, we formally define the Text-to-MMQL task and present the first Text-to-MMQL dataset involving three representative MMQLs. We propose an adaptive Text-to-MMQL framework that includes both a schema embedding module for capturing multi-model schema information and an MMQL representation strategy to generate concise intermediate query formats with error correction in generated queries. Experimental results show that the proposed framework achieves over a 9{\%} accuracy improvement over our adapted baseline methods."
}