Guided Dialogue Policy Learning without Adversarial Learning in the Loop (2020.findings-emnlp)
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Ziming Li, Sungjin Lee, Baolin Peng, Jinchao Li, Julia Kiseleva, Maarten de Rijke, Shahin Shayandeh, Jianfeng Gao
| Challenge: | Reinforcement learning methods suffer from sparse and unstable reward signals . alternating training of dialogue agent and reward model can get stuck in local optima . |
| Approach: | They propose to decompose adversarial training into two steps to improve dialogue policy learning. |
| Outcome: | The proposed method achieves remarkable task success rate using both on-policy and off-poly reinforcement learning methods. |
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