Dual Hierarchical Dialogue Policy Learning for Legal Inquisitive Conversational Agents
Quick Answer
The paper introduces Inquisitive Conversational Agents (ICAs) designed for U.S.
Quick Take
The paper introduces Inquisitive Conversational Agents (ICAs) designed for U.S. Supreme Court oral arguments, utilizing a Dual Hierarchical Reinforcement Learning framework with two cooperating agents. Evaluations demonstrate that this approach outperforms various baselines, marking a significant advancement in proactive dialogue systems for legal applications.
Key Points
- Introduces Inquisitive Conversational Agents (ICAs) for proactive information extraction.
- Utilizes a Dual Hierarchical Reinforcement Learning framework with two cooperating agents.
- Emulates judicial questioning patterns to uncover crucial legal information.
- Outperforms various baselines on a U.S. Supreme Court dataset across multiple metrics.
- Represents a significant step toward high-stakes, domain-specific conversational applications.
Paper Resources
Article Excerpt
From source RSS / original summaryarXiv:2605. 14057v1 Announce Type: new 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 \emph{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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