@inproceedings{ye-etal-2025-sequence,
    title = "Sequence Structure Aware Retriever for Procedural Document Retrieval: A New Dataset and Baseline",
    author = "Ye, Zhenqi  and
      Ren, HaoPeng  and
      Cai, Yi  and
      Huang, Qingbao  and
      Qin, Jing  and
      Zhu, Pinli  and
      Gong, Songwen",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.findings-emnlp.223/",
    doi = "10.18653/v1/2025.findings-emnlp.223",
    pages = "4180--4198",
    ISBN = "979-8-89176-335-7",
    abstract = "Execution failures are common in daily life when individuals perform procedural tasks, such as cooking or handicrafts making. Retrieving relevant procedural documents that align closely with both the content of steps and the overall execution sequence can help correct these failures with fewer modifications. However, existing retrieval methods, which primarily focus on declarative knowledge, often neglect the execution sequence structures inherent in procedural documents. To tackle this challenge, we introduce a new dataset Procedural Questions, and propose a retrieval model Graph-Fusion Procedural Document Retriever (GFPDR) which integrates procedural graphs with document representations. Extensive experiments demonstrate the effectiveness of GFPDR, highlighting its superior performance in procedural document retrieval compared to existing models."
}