@inproceedings{yang-tan-2026-segdre,
    title = "{S}eg{DRE}: A Salient Entity Guided Approach to Document-Level Relation Extraction",
    author = "Yang, Xing  and
      Tan, Chengxiang",
    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.1192/",
    pages = "23800--23812",
    ISBN = "979-8-89176-395-1",
    abstract = "In Document-level Relation Extraction (DocRE), relational facts are typically organized around a few salient entities. Explicitly capturing this topological structure is pivotal to addressing the two critical bottlenecks of the task: the extreme class imbalance and the complexity of multi-hop reasoning. Based on this insight, we first introduce the concept of the salient entity and propose a novel approach that decouples the extraction space into dense and sparse scenarios. Specifically, our approach restricts the search space for dense pairs to mitigate the dominance of the negative samples, and innovatively injects the rich semantic knowledge of salient entities to explicitly reconstruct the document for bridging disjoint evidence in multi-hop reasoning. Extensive experiments demonstrate that our approach yields consistent improvements over various backbone models and achieves advanced performance compared to existing enhancement methods."
}