Few-Shot Structured Policy Learning for Multi-Domain and Multi-Task Dialogues (2023.findings-eacl)
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| Challenge: | Reinforcement learning is widely adopted to model dialogue managers in task-oriented dialogues, but the user simulator provided by state-of-the-art dialogue frameworks are only rough approximations of human behaviour. |
| Approach: | They propose to use structured policies to improve sample efficiency when learning on multi-domain and multi-task environments. |
| Outcome: | The proposed policies improve sample efficiency and performance on multi-domain and multi-task environments. |
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