Dual Hierarchical Dialogue Policy Learning for Legal Inquisitive Conversational Agents

This research introduces Inquisitive Conversational Agents (ICAs) designed to proactively extract information, specifically tailored for U.S. Supreme Court oral arguments. It proposes a Dual Hierarchical Reinforcement Learning framework to coordinate strategic dialogue management and fine-grained utterance generation, significantly outperforming baselines.