@inproceedings{luo-etal-2025-diverse,
    title = "A Diverse and Effective Retrieval-Based Debt Collection System with Expert Knowledge",
    author = "Luo, Jiaming  and
      Luo, Weiyi  and
      Sun, Guoqing  and
      Zhu, Mengchen  and
      Tang, Haifeng  and
      Zhu, Kenny Q.  and
      Wu, Mengyue",
    editor = "Chen, Weizhu  and
      Yang, Yi  and
      Kachuee, Mohammad  and
      Fu, Xue-Yong",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.naacl-industry.11/",
    pages = "129--137",
    ISBN = "979-8-89176-194-0",
    abstract = "Designing effective debt collection systems is crucial for improving operational efficiency and reducing costs in the financial industry. However, the challenges of maintaining script diversity, contextual relevance, and coherence make this task particularly difficult. This paper presents a debt collection system based on real debtor-collector data from a major commercial bank. We construct a script library from real-world debt collection conversations, and propose a two-stage retrieval based response system for contextual relevance. Experimental results show that our system improves script diversity, enhances response relevance, and achieves practical deployment efficiency through knowledge distillation. This work offers a scalable and automated solution, providing valuable insights for advancing debt collection practices in real-world applications."
}