@inproceedings{chen-etal-2025-track,
    title = "Track-{SQL}: Enhancing Generative Language Models with Dual-Extractive Modules for Schema and Context Tracking in Multi-turn Text-to-{SQL}",
    author = "Chen, Bingfeng  and
      Shi, Shaobin  and
      Luo, Yongqi  and
      Xu, Boyan  and
      Cai, Ruichu  and
      Hao, Zhifeng",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.naacl-long.536/",
    pages = "10690--10708",
    ISBN = "979-8-89176-189-6",
    abstract = "Generative language models have shown significant potential in single-turn Text-to-SQL. However, their performance does not extend equivalently to multi-turn Text-to-SQL. This is primarily due to generative language models' inadequacy in handling the complexities of context information and dynamic schema linking in multi-turn interactions. In this paper, we propose a framework named Track-SQL, which enhances generative language models with dual-extractive modules designed to track schema and contextual changes in multi-turn Text-to-SQL. Specifically, Track-SQL incorporates a \textit{Semantic-enhanced Schema Extractor} and a \textit{Schema-aware Context Extractor}. Experimental results demonstrate that Track-SQL achieves state-of-the-art performance on the SparC and CoSQL datasets. Furthermore, detailed ablation studies reveal that Track-SQL significantly improves execution accuracy in multi-turn interactions by 7.1{\%} and 9.55{\%} on these datasets, respectively. Our implementation will be open-sourced at \url{https://github.com/DMIRLAB-Group/Track-SQL}."
}