@inproceedings{zhang-etal-2025-enhancing-goal-oriented,
    title = "Enhancing Goal-oriented Proactive Dialogue Systems via Dynamic Multi-dimensional Consistency Optimization",
    author = "Zhang, Didi  and
      Fan, Yaxin  and
      Li, Peifeng  and
      Zhu, Qiaoming",
    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/ingest-luhme/2025.findings-emnlp.1378/",
    doi = "10.18653/v1/2025.findings-emnlp.1378",
    pages = "25283--25296",
    ISBN = "979-8-89176-335-7",
    abstract = "Previous work on goal-oriented proactive dialogue systems frequently failed to address the multi-dimensional consistency issue between generated responses and key contextual elements (e.g., user profile, dialogue history, domain knowledge, and subgoal). To address this issue, we propose a novel Dynamic Multi-dimensional Consistency Reinforcement Learning (DMCRL) framework, which adaptively measures the impact of each consistency dimension on overall dialogue quality and provides targeted feedback to improve response quality. Experimental results on two datasets demonstrate that our DMCRL significantly improves the consistency of generated responses."
}