让法律对话机器人主动提问，像大法官一样挖掘关键信息。
Dual Hierarchical Dialogue Policy Learning for Legal Inquisitive Conversational Agents
- 双层级强化学习框架，分工协作管理对话与生成语句。
- 在最高法院辩论数据集上超越多个基线模型，主动提问效果更优。
- 适合高风险领域如法律、医疗的智能问答系统研究者参考。
现有对话系统多为用户驱动，仅被动响应请求。但在许多现实关键场景中，对话代理需主动获取信息以达成自身目标。为此，我们提出询问型对话代理（ICAs），并构建了专用于美国最高法院口头辩论的ICA。提出一种双层级强化学习框架，包含两个协同工作的强化学习代理，分别负责策略性对话管理与细粒度话语生成。通过学习何时以及如何提出深入问题，该代理模仿法官质询模式，系统性地揭示关键信息以实现法律目标。在美最高法院数据集上的评估显示，该方法在多项指标上优于多个基线模型。这为高风险、领域特定应用提供了重要初步探索。
原文摘要 · Abstract (English)
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 that our method outperforms various baselines across multiple metrics. It represents an important first step toward broader high-stakes, domain-specific applications.
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