Papers by Runze Liang
Multi-Agent Task-Oriented Dialog Policy Learning with Role-Aware Reward Decomposition (2020.acl-main)
Copied to clipboard
| Challenge: | Many studies have applied reinforcement learning to train a dialog policy . but modeling a real-world user simulator is challenging and requires domain expertise . |
| Approach: | They propose to build dialog policies with two agents as dialog agents to avoid building a user simulator beforehand. |
| Outcome: | The proposed method can build a system policy and a user policy simultaneously . it can achieve high task success rate through conversational interaction . |