@inproceedings{gao-etal-2025-beyond,
    title = "Beyond Seen Data: Improving {KBQA} Generalization Through Schema-Guided Logical Form Generation",
    author = "Gao, Shengxiang  and
      Lau, Jey Han  and
      Qi, Jianzhong",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.442/",
    pages = "8764--8783",
    ISBN = "979-8-89176-332-6",
    abstract = "Knowledge base question answering (KBQA) aims to answer user questions in natural language using rich human knowledge stored in large KBs. As current KBQA methods struggle with unseen knowledge base elements and their novel compositions at test time, we introduce SG-KBQA {---} a novel model that injects schema contexts into entity retrieval and logical form generation to tackle this issue. It exploits information about the semantics and structure of the knowledge base provided by schema contexts to enhance generalizability. We show that achieves strong generalizability, outperforming state-of-the-art models on two commonly used benchmark datasets across a variety of test settings. Our source code is available at \url{https://github.com/gaosx2000/SG_KBQA}."
}