@inproceedings{lin-etal-2026-dual,
    title = "Dual Hierarchical Dialogue Policy Learning for Legal Inquisitive Conversational Agents",
    author = "Lin, Xubo  and
      Deng, Zezhi  and
      Wang, Shihao  and
      Yang, Grace Hui  and
      Deng, Yang",
    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.536/",
    pages = "11030--11047",
    ISBN = "979-8-89176-395-1",
    abstract = "Most existing dialogue systems are user-driven, primarily designed to fulfill user requests. However, in many critical real-world scenarios, a conversational agent must proactively extract information to achieve its own objectives rather than merely respond. To address this gap, we introduce Inquisitive Conversational Agents (ICAs) and develop an ICA specifically tailored to U.S. Supreme Court oral arguments. We propose a Dual Hierarchical Reinforcement Learning framework featuring two cooperating RL agents, each with its own policy, to coordinate strategic dialogue management and fine-grained utterance generation. By learning when and how to ask probing questions, the agent emulates judicial questioning patterns and systematically uncovers crucial information to fulfill its legal objectives. Evaluations on a U.S. Supreme Court dataset show our method outperforms single-agent RL baselines in multiple metrics. Although specialized to a single legal domain, it represents an important first step toward broader high-stakes, domain-specific applications. We attached a part of the code as supplementary material. All code will be released upon publication for reproducibility."
}