Autonomous Sub-domain Modeling for Dialogue Policy with Hierarchical Deep Reinforcement Learning
Solving composites tasks, which consist of several inherent sub-tasks, remains a challenge in the research area of dialogue. Current studies have tackled this issue by manually decomposing the composite tasks into several sub-domains. However, much human effort is inevitable. This paper proposes a dialogue framework that autonomously models meaningful sub-domains and learns the policy over them. Our experiments show that our framework outperforms the baseline without subdomains by 11{\%} in terms of success rate, and is competitive with that with manually defined sub-domains.
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