@inproceedings{yin-etal-2026-dmretriever,
    title = "{DMR}etriever: A Family of Models for Improved Text Retrieval in Disaster Management",
    author = "Yin, Kai  and
      Dong, Xiangjue  and
      Liu, Chengkai  and
      Lin, Allen  and
      Shi, Lingfeng  and
      Mostafavi, Ali  and
      Caverlee, James",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.722/",
    pages = "15889--15909",
    ISBN = "979-8-89176-390-6",
    abstract = "Effective and efficient access to relevant information is essential for disaster management. However, no retrieval model is specialized for disaster management, and existing general-domain models fail to handle the varied search intents inherent to disaster management scenarios, resulting in inconsistent and unreliable performance. To this end, we introduce DMRetriever, the first series of dense retrieval models (33M to 7.6B) tailored for this domain. It is trained through a novel three-stage framework of bidirectional attention adaptation, unsupervised contrastive pre-training, and difficulty-aware progressive instruction fine-tuning, using high-quality data generated through an advanced data refinement pipeline. Comprehensive experiments demonstrate that DMRetriever achieves state-of-the-art performance across all six search intents at every model scale. Moreover, DMRetriever is highly parameter-efficient, with 596M model outperforming baselines over 13.3 larger and 33M model exceeding baselines with only 7.6{\%} of their parameters. All codes, data, and checkpoints are available at https://github.com/KaiYin97/DMRETRIEVER."
}